Split (2)

train · 44.6k rows

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b6c75284-ff66-4e86-b988-2bb393e69058	ulip-2-towards-scalable-multimodal-pre	2305.08275	null	https://arxiv.org/abs/2305.08275v2	https://arxiv.org/pdf/2305.08275v2.pdf	ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understanding	Recent advancements in multimodal pre-training methods have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes, their 2D counterparts, and language descriptions. However, the methods used by existing multimodal pre-training frameworks to gather multimodal data for 3D applications lack scalability and comprehensiveness, potentially constraining the full potential of multimodal learning. The main bottleneck lies in the language modality's scalability and comprehensiveness. To address this, we introduce ULIP-2, a tri-modal pre-training framework that leverages state-of-the-art large multimodal models to automatically generate holistic language counterparts for 3D objects. It does not require any 3D annotations, and is therefore scalable to large datasets. We conduct experiments on two large-scale 3D datasets, Objaverse and ShapeNet, and augment them with tri-modal datasets of 3D point clouds, images, and language for training ULIP-2. ULIP-2 achieves significant improvements on downstream zero-shot classification on ModelNet40 (74.0% in top-1 accuracy); on the real-world ScanObjectNN benchmark, it obtains 91.5% in overall accuracy with only 1.4 million parameters, signifying a breakthrough in scalable multimodal 3D representation learning without human 3D annotations. The code, along with the generated tri-modal datasets, can be found at https://github.com/salesforce/ULIP.	['Silvio Savarese', 'Juan Carlos Niebles', 'ran Xu', 'Caiming Xiong', 'Jiajun Wu', 'Roberto Martín-Martín', 'Junnan Li', 'Shu Zhang', 'Ning Yu', 'Le Xue']	2023-05-14	null	null	null	null	['3d-point-cloud-classification']	['computer-vision']	[-1.01192981e-01 -3.97245027e-02 -2.90150583e-01 -3.81612033e-01 -1.43571401e+00 -8.82122934e-01 7.33198404e-01 1.17776342e-01 -9.19461846e-02 1.43215835e-01 4.48040098e-01 -3.68064076e-01 6.71386495e-02 -5.93048811e-01 -7.48395860e-01 -4.45494086e-01 1.87636260e-02 7.91542470e-01 -1.05982319e-01 -3.97953957e-01 -9.42448992e-03 5.46040893e-01 -1.66204464e+00 5.78019977e-01 3.37410718e-01 1.13130188e+00 -1.29918054e-01 7.40827799e-01 -4.87213224e-01 3.24206412e-01 -1.44180432e-01 -4.74791914e-01 1.66000262e-01 1.81230143e-01 -6.90663636e-01 1.17754117e-01 8.57617915e-01 -3.71152848e-01 -3.50112051e-01 4.94454980e-01 1.02676916e+00 3.96392420e-02 9.47490156e-01 -1.43127990e+00 -9.08076763e-01 2.74719745e-01 -5.06269038e-01 -3.53309721e-01 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5b2e546f-5c95-4bbd-8b71-9a615536c200	disguised-nets-image-disguising-for-privacy	1902.01878	null	http://arxiv.org/abs/1902.01878v2	http://arxiv.org/pdf/1902.01878v2.pdf	Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning	Deep learning model developers often use cloud GPU resources to experiment with large data and models that need expensive setups. However, this practice raises privacy concerns. Adversaries may be interested in: 1) personally identifiable information or objects encoded in the training images, and 2) the models trained with sensitive data to launch model-based attacks. Learning deep neural networks (DNN) from encrypted data is still impractical due to the large training data and the expensive learning process. A few recent studies have tried to provide efficient, practical solutions to protect data privacy in outsourced deep-learning. However, we find out that they are vulnerable under certain attacks. In this paper, we specifically identify two types of unique attacks on outsourced deep-learning: 1) the visual re-identification attack on the training data, and 2) the class membership attack on the learned models, which can break existing privacy-preserving solutions. We develop an image disguising approach to address these attacks and design a suite of methods to evaluate the levels of attack resilience for a privacy-preserving solution for outsourced deep learning. The experimental results show that our image-disguising mechanisms can provide a high level of protection against the two attacks while still generating high-quality DNN models for image classification.	['Sagar Sharma', 'Keke Chen']	2019-02-05	null	null	null	null	['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning']	['methodology', 'natural-language-processing']	[ 1.25822723e-01 5.33694169e-04 1.00393914e-01 -5.85508108e-01 -8.01886559e-01 -1.08345509e+00 4.08701748e-01 -1.21446498e-01 -6.95516884e-01 3.76457423e-01 -3.08381796e-01 -4.83813137e-01 3.10506254e-01 -8.57112110e-01 -1.07002890e+00 -9.59698737e-01 -8.33864138e-02 1.57010313e-02 5.15874662e-02 2.82210588e-01 9.86409262e-02 8.65478992e-01 -1.52361846e+00 4.43973929e-01 4.93847668e-01 1.19887519e+00 -1.58680990e-01 5.70037067e-01 2.16714710e-01 7.91054666e-01 -8.60077024e-01 -1.13184309e+00 9.66935635e-01 7.50368387e-02 -7.46222019e-01 -3.20203125e-01 7.45075703e-01 -9.76306856e-01 -4.55532402e-01 1.62555575e+00 3.42394531e-01 -5.85974216e-01 -3.80086862e-02 -2.00578737e+00 -4.15238678e-01 2.93379188e-01 -2.32204273e-01 1.90551970e-02 -3.74235094e-01 3.03416669e-01 4.08355832e-01 -4.16263878e-01 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7c7a3ed1-97b4-4d76-a04d-3ca9112bc4c4	scientific-fact-checking-a-survey-of	2305.16859	null	https://arxiv.org/abs/2305.16859v1	https://arxiv.org/pdf/2305.16859v1.pdf	Scientific Fact-Checking: A Survey of Resources and Approaches	The task of fact-checking deals with assessing the veracity of factual claims based on credible evidence and background knowledge. In particular, scientific fact-checking is the variation of the task concerned with verifying claims rooted in scientific knowledge. This task has received significant attention due to the growing importance of scientific and health discussions on online platforms. Automated scientific fact-checking methods based on NLP can help combat the spread of misinformation, assist researchers in knowledge discovery, and help individuals understand new scientific breakthroughs. In this paper, we present a comprehensive survey of existing research in this emerging field and its related tasks. We provide a task description, discuss the construction process of existing datasets, and analyze proposed models and approaches. Based on our findings, we identify intriguing challenges and outline potential future directions to advance the field.	['Florian Matthes', 'Juraj Vladika']	2023-05-26	null	null	null	null	['misinformation']	['miscellaneous']	[ 1.59757026e-02 3.76753777e-01 -8.35361481e-01 -1.24427781e-01 -1.16704106e+00 -7.56244540e-01 6.79753244e-01 9.80191112e-01 -2.15148032e-01 1.13670802e+00 5.19776165e-01 -6.42010570e-01 -8.27322826e-02 -6.72063291e-01 -8.93780470e-01 -2.05877572e-01 3.38542312e-01 1.75343186e-01 -4.72261682e-02 1.18059143e-01 1.01002693e+00 1.32043108e-01 -9.66888249e-01 4.62467730e-01 1.08970428e+00 8.93864572e-01 -3.98703724e-01 9.03630257e-03 -3.10575366e-01 1.17102277e+00 -8.62421632e-01 -1.28656971e+00 -2.29706109e-01 -2.37737969e-01 -8.67809832e-01 -4.59570199e-01 4.65152860e-01 1.62971526e-01 -3.27703618e-02 1.41022313e+00 2.94017017e-01 -3.24951231e-01 2.21714601e-01 -1.17554307e+00 -1.22035158e+00 8.59889567e-01 -5.82568467e-01 5.45889258e-01 4.87250477e-01 -2.15355173e-01 1.01623774e+00 -6.10917389e-01 9.82487619e-01 8.45819950e-01 7.16412902e-01 4.47815269e-01 -6.44346893e-01 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9df3a76d-5e49-40e5-b787-57217bad5bc9	conditionally-strongly-log-concave-generative	2306.00181	null	https://arxiv.org/abs/2306.00181v1	https://arxiv.org/pdf/2306.00181v1.pdf	Conditionally Strongly Log-Concave Generative Models	There is a growing gap between the impressive results of deep image generative models and classical algorithms that offer theoretical guarantees. The former suffer from mode collapse or memorization issues, limiting their application to scientific data. The latter require restrictive assumptions such as log-concavity to escape the curse of dimensionality. We partially bridge this gap by introducing conditionally strongly log-concave (CSLC) models, which factorize the data distribution into a product of conditional probability distributions that are strongly log-concave. This factorization is obtained with orthogonal projectors adapted to the data distribution. It leads to efficient parameter estimation and sampling algorithms, with theoretical guarantees, although the data distribution is not globally log-concave. We show that several challenging multiscale processes are conditionally log-concave using wavelet packet orthogonal projectors. Numerical results are shown for physical fields such as the $\varphi^4$ model and weak lensing convergence maps with higher resolution than in previous works.	['Stéphane Mallat', 'Joan Bruna', 'Etienne Lempereur', 'Florentin Guth']	2023-05-31	null	null	null	null	['memorization']	['natural-language-processing']	[ 1.00279851e-02 -7.10991677e-04 2.83570826e-01 -8.68980810e-02 -5.52002192e-01 -5.24950922e-01 6.11169934e-01 -4.13925767e-01 -2.67861605e-01 7.54436314e-01 1.63798392e-01 -5.04185446e-02 -3.11651230e-01 -7.51164377e-01 -7.59055018e-01 -1.34173822e+00 -1.17742941e-01 6.36569262e-01 4.43372410e-03 3.83662544e-02 2.71133751e-01 2.72792399e-01 -1.48384261e+00 -3.38624418e-02 8.68791521e-01 8.60646665e-01 3.77402335e-01 7.55734205e-01 1.49254605e-01 3.12752843e-01 -5.71537055e-02 -3.67249012e-01 3.16500962e-01 -5.49916685e-01 -3.56334716e-01 1.09452315e-01 2.24191949e-01 2.02391725e-02 -1.79295689e-01 1.36534512e+00 2.78884619e-01 -1.52745843e-01 8.98211896e-01 -1.02191722e+00 -9.88904059e-01 2.19608441e-01 -7.14250505e-01 9.37759653e-02 -1.55520767e-01 -2.47048303e-01 8.75093937e-01 -1.06382620e+00 6.94762230e-01 1.10834157e+00 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-1.02900708e+00 -6.69790447e-01 -1.02259862e+00 -7.96469808e-01 1.68049231e-01 3.24741811e-01 -6.63652301e-01 -6.89025700e-01 4.04888719e-01]	[6.980778217315674, 3.8556089401245117]
27b2ea49-88db-4ab1-bf8a-d2b089df12d4	bags-of-local-convolutional-features-for	1604.04653	null	http://arxiv.org/abs/1604.04653v1	http://arxiv.org/pdf/1604.04653v1.pdf	Bags of Local Convolutional Features for Scalable Instance Search	This work proposes a simple instance retrieval pipeline based on encoding the convolutional features of CNN using the bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an \textit{assignment map}, a compact representation that relates regions of an image with a visual word. We use the assignment map for fast spatial reranking, obtaining object localizations that are used for query expansion. We demonstrate the suitability of the BoW representation based on local CNN features for instance retrieval, achieving competitive performance on the Oxford and Paris buildings benchmarks. We show that our proposed system for CNN feature aggregation with BoW outperforms state-of-the-art techniques using sum pooling at a subset of the challenging TRECVid INS benchmark.	['Xavier Giro-i-Nieto', "Noel E. O'Connor", 'Kevin McGuinness', 'Ferran Marques', 'Eva Mohedano', 'Amaia Salvador']	2016-04-15	null	null	null	null	['instance-search']	['computer-vision']	[-6.04952723e-02 -3.33365887e-01 -2.03803301e-01 -4.44192767e-01 -1.09813452e+00 -7.71549284e-01 9.48877513e-01 8.75753403e-01 -7.54158616e-01 3.90293956e-01 5.86485803e-01 1.73793323e-02 -5.77266812e-01 -9.06735480e-01 -1.01211798e+00 -5.78859925e-01 -1.69566929e-01 3.62067491e-01 5.82245886e-01 -2.02754185e-01 3.97625327e-01 9.21572328e-01 -1.95327199e+00 9.44754243e-01 1.06292497e-03 1.76865494e+00 1.64646178e-01 7.49810457e-01 -1.90282449e-01 7.25968719e-01 -7.62836099e-01 -1.17545068e-01 3.24990422e-01 2.53250360e-01 -1.06310356e+00 -5.36277771e-01 1.30400944e+00 -6.61694884e-01 -6.12385809e-01 6.01105630e-01 3.49042773e-01 3.66854787e-01 9.54801559e-01 -8.15882027e-01 -1.03420508e+00 3.21841091e-01 -2.89552510e-01 7.10679471e-01 4.39424574e-01 -3.67834896e-01 1.72217071e+00 -1.05968189e+00 7.81131864e-01 1.30922806e+00 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01008f8b-a0ae-44f9-8788-148343980776	a-convolutional-neural-network-model-based-on	1901.10629	null	http://arxiv.org/abs/1901.10629v2	http://arxiv.org/pdf/1901.10629v2.pdf	A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition	Convolutional neural networks are sensitive to unknown noisy condition in the test phase and so their performance degrades for the noisy data classification task including noisy speech recognition. In this research, a new convolutional neural network (CNN) model with data uncertainty handling; referred as NCNN (Neutrosophic Convolutional Neural Network); is proposed for classification task. Here, speech signals are used as input data and their noise is modeled as uncertainty. In this task, using speech spectrogram, a definition of uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed for each Time-frequency point of speech spectrogram as like a pixel. Therefore, uncertainty matrix with the same size of spectrogram is created in NS domain. In the next step, a two parallel paths CNN classification model is proposed. Speech spectrogram is used as input of the first path and uncertainty matrix for the second path. The outputs of two paths are combined to compute the final output of the classifier. To show the effectiveness of the proposed method, it has been compared with conventional CNN on the isolated words of Aurora2 dataset. The proposed method achieves the average accuracy of 85.96 in noisy train data. It is more robust against Car, Airport and Subway noises with accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show that the proposed method outperforms conventional CNN with the improvement of 6, 5 and 2 percentage in test set A, test set B and test sets C, respectively. It means that the proposed method is more robust against noisy data and handle these data effectively.	['Ahmad Akbari', 'Elyas Rashno', 'Babak Nasersharif']	2019-01-27	null	null	null	null	['noisy-speech-recognition']	['speech']	[-2.17819169e-01 -2.47455075e-01 5.01570523e-01 -4.67287153e-01 -4.35997099e-01 -4.42286581e-01 3.00945103e-01 1.91513568e-01 -5.12000918e-01 1.04611278e+00 2.04514340e-01 -3.34553897e-01 -2.97203660e-01 -1.04780412e+00 -5.59257805e-01 -6.31044269e-01 2.59629283e-02 1.84411146e-02 5.91375753e-02 -1.95187166e-01 1.46662325e-01 5.50165296e-01 -1.84688437e+00 5.80662370e-01 7.15748191e-01 1.75854397e+00 -2.23009139e-01 7.06281483e-01 -3.66639763e-01 7.44299412e-01 -1.18141103e+00 -6.46433681e-02 1.65600851e-01 -6.03060350e-02 -5.99824250e-01 -9.54368114e-02 -1.41695440e-01 -1.51664346e-01 -3.29617351e-01 1.37184775e+00 8.49068999e-01 7.91869521e-01 7.28998125e-01 -1.09907293e+00 -4.17482615e-01 1.06241024e+00 -1.20032765e-01 4.66200978e-01 -1.42940432e-01 -2.43257001e-01 4.52048779e-01 -9.41354394e-01 8.21759403e-02 1.36968851e+00 7.62294173e-01 3.18734825e-01 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-6.30642891e-01 7.04972595e-02 3.92428130e-01 -7.33641744e-01 -6.50875688e-01 -2.44500920e-01]	[14.52983570098877, 5.699688911437988]
e9797a39-a43a-4d4c-b266-b2828881f57f	rcot-detecting-and-rectifying-factual	2305.11499	null	https://arxiv.org/abs/2305.11499v1	https://arxiv.org/pdf/2305.11499v1.pdf	RCOT: Detecting and Rectifying Factual Inconsistency in Reasoning by Reversing Chain-of-Thought	Large language Models (LLMs) have achieved promising performance on arithmetic reasoning tasks by incorporating step-by-step chain-of-thought (CoT) prompting. However, LLMs face challenges in maintaining factual consistency during reasoning, exhibiting tendencies to condition overlooking, question misinterpretation, and condition hallucination over given problems. Existing methods use coarse-grained feedback (e.g., whether the answer is correct) to improve factual consistency. In this work, we propose RCoT (Reversing Chain-of-Thought), a novel method to improve LLMs' reasoning abilities by automatically detecting and rectifying factual inconsistency in LLMs' generated solutions. To detect factual inconsistency, RCoT first asks LLMs to reconstruct the problem based on generated solutions. Then fine-grained comparisons between the original problem and the reconstructed problem expose the factual inconsistency in the original solutions. To rectify the solution, RCoT formulates detected factual inconsistency into fine-grained feedback to guide LLMs in revising solutions. Experimental results demonstrate consistent improvements of RCoT over standard CoT across seven arithmetic datasets. Moreover, we find that manually written fine-grained feedback can dramatically improve LLMs' reasoning abilities (e.g., ChatGPT reaches 94.6% accuracy on GSM8K), encouraging the community to further explore the fine-grained feedback generation methods.	['Heng Ji', 'Pengfei Yu', 'Chi Han', 'Zhenhailong Wang', 'Ziqi Wang', 'Tianci Xue']	2023-05-19	null	null	null	null	['gsm8k', 'arithmetic-reasoning']	['natural-language-processing', 'reasoning']	[ 1.21067464e-01 3.44801605e-01 -9.67203155e-02 -5.62944353e-01 -1.03968060e+00 -4.91159648e-01 4.14245754e-01 6.11952424e-01 -1.62613019e-01 7.71325111e-01 7.60944009e-01 -5.44671237e-01 -4.73220497e-01 -8.58307660e-01 -6.12985849e-01 -8.72881562e-02 5.39709210e-01 5.50186396e-01 -4.81265225e-03 -5.25627792e-01 1.10563087e+00 -1.96688008e-02 -1.31047845e+00 9.68399107e-01 1.43043256e+00 4.60155934e-01 2.06509963e-01 6.01707816e-01 -3.33430707e-01 1.77858078e+00 -9.44948852e-01 -5.98306835e-01 -1.07728444e-01 -6.49869263e-01 -1.46219075e+00 -6.97544292e-02 6.23848379e-01 -5.34440875e-01 -2.22580321e-02 1.28851640e+00 2.97595292e-01 3.01143467e-01 1.71415150e-01 -1.08298516e+00 -9.62891042e-01 9.48835969e-01 -2.58367300e-01 5.78996062e-01 9.78328645e-01 2.99508393e-01 9.15057063e-01 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690200e4-df68-4e4a-8191-761af49db4f8	explainable-disease-classification-via-weakly	2008.10268	null	https://arxiv.org/abs/2008.10268v1	https://arxiv.org/pdf/2008.10268v1.pdf	Explainable Disease Classification via weakly-supervised segmentation	Deep learning based approaches to Computer Aided Diagnosis (CAD) typically pose the problem as an image classification (Normal or Abnormal) problem. These systems achieve high to very high accuracy in specific disease detection for which they are trained but lack in terms of an explanation for the provided decision/classification result. The activation maps which correspond to decisions do not correlate well with regions of interest for specific diseases. This paper examines this problem and proposes an approach which mimics the clinical practice of looking for an evidence prior to diagnosis. A CAD model is learnt using a mixed set of information: class labels for the entire training set of images plus a rough localisation of suspect regions as an extra input for a smaller subset of training images for guiding the learning. The proposed approach is illustrated with detection of diabetic macular edema (DME) from OCT slices. Results of testing on on a large public dataset show that with just a third of images with roughly segmented fluid filled regions, the classification accuracy is on par with state of the art methods while providing a good explanation in the form of anatomically accurate heatmap /region of interest. The proposed solution is then adapted to Breast Cancer detection from mammographic images. Good evaluation results on public datasets underscores the generalisability of the proposed solution.	['Gaurav Mishra', 'Jayanthi Sivaswamy', 'Aniket Joshi']	2020-08-24	null	null	null	null	['breast-cancer-detection', 'breast-cancer-detection']	['knowledge-base', 'medical']	[ 4.64773655e-01 6.31476343e-01 -1.36745036e-01 -7.96925068e-01 -8.54320347e-01 -6.14511482e-02 5.87005973e-01 5.27742863e-01 -1.37138650e-01 7.09299684e-01 1.01929270e-02 -6.43735945e-01 -3.03677529e-01 -6.95927680e-01 -5.42195082e-01 -7.51380026e-01 -2.89625302e-02 9.16077495e-01 3.09637368e-01 1.77138716e-01 2.62018532e-01 5.42795897e-01 -1.51136076e+00 9.14865434e-01 8.02075565e-01 1.25076580e+00 2.32475191e-01 5.99018872e-01 -1.58518210e-01 8.73005688e-01 -2.74372429e-01 -1.44956127e-01 2.12826803e-01 -5.92712760e-01 -1.13727081e+00 3.99765551e-01 8.32259893e-01 -7.13642359e-01 3.61690700e-01 8.65863144e-01 4.33693916e-01 -6.52751565e-01 1.17100728e+00 -8.26549888e-01 -2.67644197e-01 -1.71670504e-02 -5.89348078e-01 3.65001112e-01 4.57904711e-02 7.34618679e-02 6.03632510e-01 -8.25576901e-01 5.91050029e-01 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3ceff6ef-5951-41b6-b43f-0026db743608	vusfavariational-universal-successor-features	1908.06376	null	https://arxiv.org/abs/1908.06376v1	https://arxiv.org/pdf/1908.06376v1.pdf	VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation	In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulator. Specifically, we build on the concept of Universal Successor Features with an A3C agent. We introduce the novel architectural contribution of a Successor Feature Dependant Policy (SFDP) and adopt the concept of Variational Information Bottlenecks to achieve state of the art performance. VUSFA, our final architecture, is a straightforward approach that can be implemented using our open source repository. Our approach is generalizable, showed greater stability in training, and outperformed recent approaches in terms of transfer learning ability.	['Suranga Nanayakkara', 'Shamane Siriwardhana', 'Rivindu Weerasakera', 'Denys J. C. Matthies']	2019-08-18	null	null	null	null	['transfer-reinforcement-learning']	['methodology']	[-1.08694904e-01 1.63694441e-01 -4.48877998e-02 -1.33375097e-02 -6.48301423e-01 -6.60351992e-01 1.12841868e+00 -5.58784068e-01 -9.44734871e-01 1.05918431e+00 1.72391022e-03 -5.53491414e-01 -3.97016138e-01 -3.84610802e-01 -8.36954355e-01 -6.84003651e-01 -5.03039539e-01 6.06097460e-01 8.12966168e-01 -1.17111731e+00 3.86230111e-01 5.04355192e-01 -1.73713052e+00 -4.52090427e-02 5.28320789e-01 7.85892367e-01 2.23382086e-01 8.92116070e-01 5.80645382e-01 1.06206131e+00 -3.99339408e-01 1.26673505e-01 5.81618011e-01 -3.12270820e-01 -1.06531000e+00 -5.14644384e-01 3.14020991e-01 -5.72328389e-01 -6.13614738e-01 3.96247715e-01 7.10396767e-01 6.48679256e-01 8.14882755e-01 -1.37939596e+00 -3.98166507e-01 2.49657482e-01 -2.04602093e-01 5.24540305e-01 2.92496771e-01 6.66144788e-01 1.00102639e+00 -5.28872132e-01 8.19398463e-01 1.32485819e+00 4.54852253e-01 1.29207611e+00 -1.29553699e+00 -4.98180360e-01 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2de4eacc-8619-44a5-8db0-169f9722d268	a-study-on-a-q-learning-algorithm-application	2304.08375	null	https://arxiv.org/abs/2304.08375v1	https://arxiv.org/pdf/2304.08375v1.pdf	A study on a Q-Learning algorithm application to a manufacturing assembly problem	The development of machine learning algorithms has been gathering relevance to address the increasing modelling complexity of manufacturing decision-making problems. Reinforcement learning is a methodology with great potential due to the reduced need for previous training data, i.e., the system learns along time with actual operation. This study focuses on the implementation of a reinforcement learning algorithm in an assembly problem of a given object, aiming to identify the effectiveness of the proposed approach in the optimisation of the assembly process time. A model-free Q-Learning algorithm is applied, considering the learning of a matrix of Q-values (Q-table) from the successive interactions with the environment to suggest an assembly sequence solution. This implementation explores three scenarios with increasing complexity so that the impact of the Q-Learning\textsc's parameters and rewards is assessed to improve the reinforcement learning agent performance. The optimisation approach achieved very promising results by learning the optimal assembly sequence 98.3% of the times.	['Pedro Neto', 'Miguel Vieira', 'Miguel Neves']	2023-04-17	null	null	null	null	['q-learning']	['methodology']	[ 1.40694976e-01 3.08825135e-01 7.64963552e-02 -1.64649919e-01 -1.21520840e-01 -1.90957218e-01 5.38743317e-01 6.07886672e-01 -6.20543778e-01 9.93447840e-01 -3.30107242e-01 -3.52840908e-02 -8.63482714e-01 -8.26703608e-01 -6.23607814e-01 -7.56800950e-01 -3.87858152e-01 8.79922926e-01 -1.63353205e-01 -5.09315073e-01 7.30485499e-01 5.93225300e-01 -1.81364119e+00 1.18112214e-01 8.16183746e-01 6.41071618e-01 7.60685921e-01 7.79055119e-01 5.20623587e-02 7.59427667e-01 -8.45670521e-01 9.97316614e-02 2.88398445e-01 -5.08597672e-01 -6.78372264e-01 2.52976358e-01 -6.91909254e-01 2.04058597e-03 2.96431422e-01 8.09753895e-01 4.53190923e-01 2.93421328e-01 6.63514674e-01 -1.26050723e+00 1.04335658e-01 5.31477988e-01 1.38728619e-01 1.40994921e-01 4.92880464e-01 4.24487561e-01 5.08578837e-01 -3.49434376e-01 6.29124105e-01 1.21307743e+00 4.72729355e-02 4.77678888e-02 -1.12824523e+00 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2.37423018e-01 -1.71229735e-01 -7.95534313e-01 1.89267308e-01 -8.31888393e-02]	[4.6210784912109375, 1.9729557037353516]
e0fbabb9-85cf-4e15-afef-b9488e470739	a-deeper-look-at-power-normalizations	1806.09183	null	http://arxiv.org/abs/1806.09183v1	http://arxiv.org/pdf/1806.09183v1.pdf	A Deeper Look at Power Normalizations	Power Normalizations (PN) are very useful non-linear operators in the context of Bag-of-Words data representations as they tackle problems such as feature imbalance. In this paper, we reconsider these operators in the deep learning setup by introducing a novel layer that implements PN for non-linear pooling of feature maps. Specifically, by using a kernel formulation, our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN. Linearization of such a kernel results in a positive definite matrix capturing the second-order statistics of the feature vectors, to which PN operators are applied. We study two types of PN functions, namely (i) MaxExp and (ii) Gamma, addressing their role and meaning in the context of nonlinear pooling. We also provide a probabilistic interpretation of these operators and derive their surrogates with well-behaved gradients for end-to-end CNN learning. We apply our theory to practice by implementing the PN layer on a ResNet-50 model and showcase experiments on four benchmarks for fine-grained recognition, scene recognition, and material classification. Our results demonstrate state-of-the-art performance across all these tasks.	['Piotr Koniusz', 'Hongguang Zhang', 'Fatih Porikli']	2018-06-24	a-deeper-look-at-power-normalizations-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Koniusz_A_Deeper_Look_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Koniusz_A_Deeper_Look_CVPR_2018_paper.pdf	cvpr-2018-6	['material-classification']	['computer-vision']	[ 1.71140417e-01 -1.81817979e-01 -8.16399753e-02 -5.23494244e-01 -5.08798182e-01 -4.94078547e-01 8.06632280e-01 4.29484248e-01 -9.17868674e-01 2.96224266e-01 3.42738599e-01 -1.08295657e-01 -3.28244239e-01 -8.18172514e-01 -1.07648754e+00 -9.05444264e-01 -2.07709178e-01 -1.08864814e-01 1.86660379e-01 -2.68200874e-01 2.40418419e-01 7.71847546e-01 -1.53055096e+00 4.32919204e-01 3.54712099e-01 1.44125772e+00 -2.57174850e-01 6.03650272e-01 7.68841356e-02 8.46368194e-01 -6.38944685e-01 -3.22148144e-01 3.57504398e-01 4.89259735e-02 -7.89738595e-01 -1.31163403e-01 6.17486894e-01 -2.43702084e-02 -4.25305218e-01 9.24971282e-01 4.44452614e-01 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5aec97e8-9303-49d7-97e0-4f53720e741d	put-attention-to-temporal-saliency-patterns	2212.07771	null	https://arxiv.org/abs/2212.07771v1	https://arxiv.org/pdf/2212.07771v1.pdf	Put Attention to Temporal Saliency Patterns of Multi-Horizon Time Series	Time series, sets of sequences in chronological order, are essential data in statistical research with many forecasting applications. Although recent performance in many Transformer-based models has been noticeable, long multi-horizon time series forecasting remains a very challenging task. Going beyond transformers in sequence translation and transduction research, we observe the effects of down-and-up samplings that can nudge temporal saliency patterns to emerge in time sequences. Motivated by the mentioned observation, in this paper, we propose a novel architecture, Temporal Saliency Detection (TSD), on top of the attention mechanism and apply it to multi-horizon time series prediction. We renovate the traditional encoder-decoder architecture by making as a series of deep convolutional blocks to work in tandem with the multi-head self-attention. The proposed TSD approach facilitates the multiresolution of saliency patterns upon condensed multi-heads, thus progressively enhancing complex time series forecasting. Experimental results illustrate that our proposed approach has significantly outperformed existing state-of-the-art methods across multiple standard benchmark datasets in many far-horizon forecasting settings. Overall, TSD achieves 31% and 46% relative improvement over the current state-of-the-art models in multivariate and univariate time series forecasting scenarios on standard benchmarks. The Git repository is available at https://github.com/duongtrung/time-series-temporal-saliency-patterns.	['Lars Schmidt-Thieme', 'Danh Le-Phuoc', 'Johannes Burchert', 'Randolf Scholz', 'Kiran Madhusudhanan', 'Stefan Born', 'Nghia Duong-Trung']	2022-12-15	null	null	null	null	['saliency-detection', 'time-series-prediction', 'univariate-time-series-forecasting']	['computer-vision', 'time-series', 'time-series']	[ 5.60903728e-01 -3.92962694e-01 -2.20899463e-01 -3.43455970e-01 -7.16334522e-01 -3.48432273e-01 7.49510109e-01 3.67013924e-02 -1.52231771e-02 5.85860789e-01 6.26359880e-01 -5.46955705e-01 1.85435824e-02 -2.18720615e-01 -9.35290217e-01 -5.92474699e-01 -3.75669301e-01 -1.30671307e-01 3.32071036e-01 -5.17444670e-01 3.62973601e-01 2.30008010e-02 -1.59253335e+00 4.01816219e-01 8.38873267e-01 1.13859260e+00 4.74287719e-01 6.49636686e-01 1.36278734e-01 9.54745770e-01 -3.73976111e-01 -1.59396768e-01 1.23259671e-01 -4.24108297e-01 -4.28048909e-01 -1.31471455e-01 4.73758485e-03 -1.37363791e-01 -3.68559867e-01 6.94318473e-01 5.38591027e-01 2.36859933e-01 1.74797267e-01 -1.36996830e+00 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0c8c1595-f5dc-43df-b8ee-5780515477c9	read-large-scale-neural-scene-rendering-for	2205.05509	null	https://arxiv.org/abs/2205.05509v1	https://arxiv.org/pdf/2205.05509v1.pdf	READ: Large-Scale Neural Scene Rendering for Autonomous Driving	Synthesizing free-view photo-realistic images is an important task in multimedia. With the development of advanced driver assistance systems~(ADAS) and their applications in autonomous vehicles, experimenting with different scenarios becomes a challenge. Although the photo-realistic street scenes can be synthesized by image-to-image translation methods, which cannot produce coherent scenes due to the lack of 3D information. In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to synthesize large-scale driving scenarios on a PC through a variety of sampling schemes. In order to represent driving scenarios, we propose an {\omega} rendering network to learn neural descriptors from sparse point clouds. Our model can not only synthesize realistic driving scenes but also stitch and edit driving scenes. Experiments show that our model performs well in large-scale driving scenarios.	['Jianke Zhu', 'Junbo Chen', 'Ping Zhang', 'Zeyu Ma', 'Lu Li', 'Zhuopeng Li']	2022-05-11	null	null	null	null	['3d-scene-reconstruction']	['computer-vision']	[ 4.73455042e-01 -1.15390420e-01 3.59807044e-01 -8.06048095e-01 -5.42508066e-01 -2.65324146e-01 6.03866160e-01 -7.46452451e-01 -4.23283800e-02 5.02299845e-01 -1.21192195e-01 -4.85352814e-01 4.31697667e-01 -1.01613593e+00 -9.63140607e-01 -5.54455996e-01 4.92320925e-01 4.61993486e-01 3.82838488e-01 -6.78453267e-01 1.98038116e-01 8.77593100e-01 -2.26646471e+00 4.58829999e-01 9.56120491e-01 6.40428901e-01 7.97669291e-01 7.04586029e-01 -4.64213461e-01 5.70626438e-01 -6.74158335e-01 -3.59602004e-01 5.31510293e-01 -2.64886707e-01 -9.52481478e-02 3.66301745e-01 1.01518452e+00 -7.40985870e-01 -8.10360253e-01 1.21313930e+00 3.76732916e-01 -3.17116966e-03 4.83042270e-01 -1.59679806e+00 -3.42358649e-01 -2.00587027e-02 -6.26291454e-01 -2.02688470e-01 3.11769694e-01 5.19704938e-01 2.31329113e-01 -8.05993080e-01 6.68111980e-01 1.72459757e+00 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f17a4785-f265-4305-84ad-c0a072ca6100	vulcnn-an-image-inspired-scalable	null	null	https://ieeexplore.ieee.org/document/9793871	http://youngwei.com/pdf/VulCNN.pdf	VulCNN: An Image-inspired Scalable Vulnerability Detection System	Since deep learning (DL) can automatically learn features from source code, it has been widely used to detect source code vulnerability. To achieve scalable vulnerability scanning, some prior studies intent to process the source code directly by treating them as text.To achieve accurate vulnerability detection, other approaches consider distilling the program semantics into graph representations and use them to detect vulnerability. In practice, text-based techniques are scalable but not accurate due to the lack of program semantics. Graph-based methods are accurate but not scalable since graph analysis is typically time-consuming. In this paper, we aim to achieve both scalability and accuracy on scanning large-scale source code vulnerabilities. Inspired by existing DL-based image classification which has the ability to analyze millions of images accurately, we prefer to use these techniques to accomplish our purpose. Specifically, we propose a novel idea that can efficiently convert the source code of a function into an image while preserving the program details. We implement VulCNN and evaluate it on a dataset of 13,687 vulnerable functions and 26,970 non-vulnerable functions. Experimental results report that VulCNN performs better than eight state-of-the-art vulnerability detectors (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, VulDeePecker, SySeVR, VulDeeLocator, and Devign). As for scalability, VulCNN is about four times faster than VulDeePecker and SySeVR, about 15 times faster than VulDeeLocator, and about six times faster than Devign. Furthermore, we conduct a case study on more than 25 million lines of code and the result indicates that VulCNN has the ability to detect large-scale vulnerability. Through the scanning reports, we finally discover 73 vulnerabilities that are not reported in NVD.	['Hai Jin', 'Duo Xu', 'Wei Yang', 'Shihan Dou', 'Deqing Zou', 'Yueming Wu']	2022-06-20	null	null	null	international-conference-on-software-1	['vulnerability-detection']	['miscellaneous']	[-2.66727924e-01 -2.88464874e-01 -4.84415531e-01 -1.15538679e-01 -7.04743087e-01 -9.17544663e-01 1.48268551e-01 4.90299135e-01 -3.51918451e-02 1.32410973e-01 6.27521500e-02 -1.01296246e+00 3.61572176e-01 -1.27688754e+00 -6.92295134e-01 8.04598257e-02 -3.50943714e-01 -3.11784208e-01 5.47878683e-01 -2.92156368e-01 6.04873538e-01 1.74520731e-01 -1.25984883e+00 3.66187692e-01 8.53808701e-01 5.80378473e-01 -2.59006083e-01 5.91793776e-01 -4.51332331e-01 9.47093427e-01 -8.34043026e-01 -6.16167068e-01 2.00782955e-01 -2.08922476e-01 -9.38289881e-01 -5.84368646e-01 5.10188401e-01 -5.74078918e-01 -3.87506247e-01 1.58220124e+00 1.56605005e-01 -4.68938440e-01 2.74329454e-01 -1.38951051e+00 -7.96039522e-01 9.92272258e-01 -1.13934410e+00 3.45781296e-01 6.46221519e-01 2.00286776e-01 1.09587502e+00 -4.58948553e-01 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e29a04cb-9838-4532-a51a-fabb5b15b646	explicit-diffusion-of-gaussian-mixture-model	2302.08411	null	https://arxiv.org/abs/2302.08411v1	https://arxiv.org/pdf/2302.08411v1.pdf	Explicit Diffusion of Gaussian Mixture Model Based Image Priors	In this work we tackle the problem of estimating the density $f_X$ of a random variable $X$ by successive smoothing, such that the smoothed random variable $Y$ fulfills $(\partial_t - \Delta_1)f_Y(\,\cdot\,, t) = 0$, $f_Y(\,\cdot\,, 0) = f_X$. With a focus on image processing, we propose a product/fields of experts model with Gaussian mixture experts that admits an analytic expression for $f_Y (\,\cdot\,, t)$ under an orthogonality constraint on the filters. This construction naturally allows the model to be trained simultaneously over the entire diffusion horizon using empirical Bayes. We show preliminary results on image denoising where our model leads to competitive results while being tractable, interpretable, and having only a small number of learnable parameters. As a byproduct, our model can be used for reliable noise estimation, allowing blind denoising of images corrupted by heteroscedastic noise.	['Antonin Chambolle', 'Erich Kobler', 'Thomas Pock', 'Martin Zach']	2023-02-16	null	null	null	null	['noise-estimation']	['medical']	[-3.52264047e-02 1.36327624e-01 4.03091460e-01 -1.95572495e-01 -9.82259393e-01 -3.10678124e-01 1.19291104e-01 -5.46570718e-01 -4.50785309e-01 6.97302401e-01 -1.30388677e-01 -7.08825290e-02 -4.58014607e-01 -7.21548438e-01 -5.28688073e-01 -1.14320993e+00 -2.70915151e-01 2.41145538e-03 -2.00761288e-01 1.15676746e-02 1.31270826e-01 2.76947170e-01 -1.25394094e+00 -4.18287367e-01 8.07419360e-01 1.49227512e+00 3.19230437e-01 8.86330485e-01 9.17854831e-02 8.50409925e-01 -4.98295754e-01 -6.08251870e-01 6.24350250e-01 -6.00780666e-01 -5.14928102e-01 3.56203586e-01 2.09866777e-01 -1.74860045e-01 -4.34000880e-01 1.64003658e+00 3.98401290e-01 3.06864560e-01 8.93224776e-01 -7.97554314e-01 -7.34567583e-01 4.42924291e-01 -7.00252593e-01 1.37708977e-01 -2.29781851e-01 1.06770866e-01 8.12070191e-01 -7.46971667e-01 6.31006718e-01 1.29419112e+00 9.30464447e-01 4.65322673e-01 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82b48e36-495f-4968-9621-08df4370cf5e	end-to-end-speech-translation-via-cross-modal	2104.10380	null	https://arxiv.org/abs/2104.10380v2	https://arxiv.org/pdf/2104.10380v2.pdf	End-to-end Speech Translation via Cross-modal Progressive Training	End-to-end speech translation models have become a new trend in research due to their potential of reducing error propagation. However, these models still suffer from the challenge of data scarcity. How to effectively use unlabeled or other parallel corpora from machine translation is promising but still an open problem. In this paper, we propose Cross Speech-Text Network (XSTNet), an end-to-end model for speech-to-text translation. XSTNet takes both speech and text as input and outputs both transcription and translation text. The model benefits from its three key design aspects: a self-supervised pre-trained sub-network as the audio encoder, a multi-task training objective to exploit additional parallel bilingual text, and a progressive training procedure. We evaluate the performance of XSTNet and baselines on the MuST-C En-X and LibriSpeech En-Fr datasets. In particular, XSTNet achieves state-of-the-art results on all language directions with an average BLEU of 28.8, outperforming the previous best method by 3.2 BLEU. Code, models, cases, and more detailed analysis are available at https://github.com/ReneeYe/XSTNet.	['Lei LI', 'Mingxuan Wang', 'Rong Ye']	2021-04-21	null	null	null	null	['speech-to-text-translation']	['natural-language-processing']	[ 2.18647078e-01 -1.54099483e-02 -4.10253495e-01 -4.36582595e-01 -1.63775361e+00 -5.24087131e-01 7.27755427e-01 -3.90952140e-01 -3.63145411e-01 7.41475582e-01 5.24422586e-01 -6.29944086e-01 4.45667893e-01 -9.92556438e-02 -8.60784113e-01 -4.49754596e-01 4.46177930e-01 7.42081463e-01 -1.76407337e-01 -3.48331422e-01 -1.90893576e-01 -6.33265972e-02 -7.09492981e-01 6.16347373e-01 1.06725216e+00 7.09199429e-01 2.92725742e-01 6.04073346e-01 2.27647573e-02 5.37332475e-01 -5.05136430e-01 -7.08089173e-01 1.23570040e-01 -7.80077457e-01 -7.89123833e-01 -1.47497654e-01 2.36057401e-01 -2.71161079e-01 -4.89383847e-01 8.30173194e-01 1.05813873e+00 -3.43731903e-02 3.81036967e-01 -9.38517153e-01 -8.29923034e-01 1.06612325e+00 -3.38784933e-01 1.45018667e-01 1.80476531e-01 1.54288024e-01 9.44129407e-01 -1.39646971e+00 5.33343136e-01 1.16996372e+00 3.99529070e-01 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fe95febc-e695-4db2-9545-eb5b5f1f3a08	using-multiple-instance-learning-to-build	2212.05561	null	https://arxiv.org/abs/2212.05561v2	https://arxiv.org/pdf/2212.05561v2.pdf	Using Multiple Instance Learning to Build Multimodal Representations	Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-modal retrieval. In this work, we establish a connection between multimodal representation learning and multiple instance learning. Based on this connection, we propose a generic framework for constructing permutation-invariant score functions with many existing multimodal representation learning approaches as special cases. Furthermore, we use the framework to derive a novel contrastive learning approach and demonstrate that our method achieves state-of-the-art results in several downstream tasks.	['Polina Golland', 'Steven Horng', 'Seth Berkowitz', 'William M. Wells', 'Peiqi Wang']	2022-12-11	null	null	null	null	['multiple-instance-learning']	['methodology']	[ 7.46085584e-01 -7.03566819e-02 -7.31465161e-01 -4.65863556e-01 -1.57273698e+00 -5.15912712e-01 7.43263900e-01 5.52043080e-01 -1.83225200e-01 4.32279378e-01 4.33498323e-01 -6.49343431e-02 -5.51685274e-01 -4.41039145e-01 -6.65820181e-01 -7.11842358e-01 1.70726385e-02 3.56618315e-01 -1.66301429e-01 3.72764990e-02 2.45035276e-01 1.63893193e-01 -1.40124059e+00 8.75154316e-01 5.45672536e-01 7.76193798e-01 -1.06228657e-01 5.43189108e-01 -8.26317444e-02 6.23057604e-01 -1.57356799e-01 -5.96132338e-01 -1.66724324e-01 -5.26817799e-01 -1.07484698e+00 2.00604379e-01 4.96293932e-01 1.05226882e-01 -3.93413037e-01 8.72111022e-01 5.70329309e-01 2.64932394e-01 8.83488774e-01 -1.29508388e+00 -7.88334072e-01 3.51229906e-01 -9.62802529e-01 -1.37278885e-01 5.71665347e-01 -3.83266568e-01 1.16990507e+00 -8.83346021e-01 7.59500504e-01 1.24924076e+00 3.83479983e-01 6.50818348e-01 -1.35810816e+00 -2.44499937e-01 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3.56194526e-01 -5.91447115e-01 1.30855739e-01 8.86445940e-02]	[10.738899230957031, 1.541377067565918]
35bb11dd-1a0f-44b7-95c3-440a88026881	decentralized-distributed-ppo-solving	1911.00357	null	https://arxiv.org/abs/1911.00357v2	https://arxiv.org/pdf/1911.00357v2.pdf	DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames	We present Decentralized Distributed Proximal Policy Optimization (DD-PPO), a method for distributed reinforcement learning in resource-intensive simulated environments. DD-PPO is distributed (uses multiple machines), decentralized (lacks a centralized server), and synchronous (no computation is ever stale), making it conceptually simple and easy to implement. In our experiments on training virtual robots to navigate in Habitat-Sim, DD-PPO exhibits near-linear scaling -- achieving a speedup of 107x on 128 GPUs over a serial implementation. We leverage this scaling to train an agent for 2.5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs. This massive-scale training not only sets the state of art on Habitat Autonomous Navigation Challenge 2019, but essentially solves the task --near-perfect autonomous navigation in an unseen environment without access to a map, directly from an RGB-D camera and a GPS+Compass sensor. Fortuitously, error vs computation exhibits a power-law-like distribution; thus, 90% of peak performance is obtained relatively early (at 100 million steps) and relatively cheaply (under 1 day with 8 GPUs). Finally, we show that the scene understanding and navigation policies learned can be transferred to other navigation tasks -- the analog of ImageNet pre-training + task-specific fine-tuning for embodied AI. Our model outperforms ImageNet pre-trained CNNs on these transfer tasks and can serve as a universal resource (all models and code are publicly available).	['Erik Wijmans', 'Abhishek Kadian', 'Stefan Lee', 'Manolis Savva', 'Dhruv Batra', 'Irfan Essa', 'Devi Parikh', 'Ari Morcos']	2019-11-01	null	https://openreview.net/forum?id=H1gX8C4YPr	https://openreview.net/pdf?id=H1gX8C4YPr	iclr-2020-1	['pointgoal-navigation']	['robots']	[-3.66589159e-01 7.33846650e-02 1.26331985e-01 -1.06385879e-01 -6.12466037e-01 -6.34623349e-01 6.07363641e-01 -6.37457669e-02 -1.25432789e+00 9.77724552e-01 7.56265819e-02 -5.93532622e-01 9.10499319e-02 -9.67215121e-01 -1.31699896e+00 -7.51964927e-01 -7.65359461e-01 7.65191495e-01 2.29563683e-01 -5.87330818e-01 -4.69136126e-02 2.51457870e-01 -1.57307315e+00 -2.83140182e-01 4.84087259e-01 8.69165421e-01 6.53671861e-01 1.07789230e+00 4.51796293e-01 9.11155701e-01 -3.91949773e-01 1.67698443e-01 6.60001099e-01 -1.79350749e-01 -6.48493588e-01 -4.78383452e-01 2.12607086e-01 -9.34281528e-01 -6.51182890e-01 8.63026023e-01 8.10862601e-01 3.00480485e-01 3.22318494e-01 -1.28445005e+00 -2.26467922e-01 3.20782512e-01 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d48189cd-ac7b-49ae-985c-f6c0b0b46583	automatic-multi-label-prompting-simple-and-1	2204.06305	null	https://arxiv.org/abs/2204.06305v2	https://arxiv.org/pdf/2204.06305v2.pdf	Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification	Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method to automatically select label mappings for few-shot text classification with prompting. Our method exploits one-to-many label mappings and a statistics-based algorithm to select label mappings given a prompt template. Our experiments demonstrate that AMuLaP achieves competitive performance on the GLUE benchmark without human effort or external resources.	['Julian McAuley', 'Canwen Xu', 'Han Wang']	2022-04-13	null	https://aclanthology.org/2022.naacl-main.401	https://aclanthology.org/2022.naacl-main.401.pdf	naacl-2022-7	['few-shot-text-classification']	['natural-language-processing']	[ 3.71892214e-01 -2.41006061e-01 -5.99665999e-01 -6.98149145e-01 -1.39297533e+00 -6.70431554e-01 8.88085604e-01 5.01375377e-01 -7.64758945e-01 5.10852456e-01 3.01801383e-01 -8.56313796e-04 -2.54423678e-01 -2.99985707e-01 -1.44670159e-01 -2.77404815e-01 4.39496785e-01 6.39712155e-01 4.60756391e-01 -2.42065534e-01 5.34636438e-01 8.25730488e-02 -1.56936502e+00 7.24255085e-01 5.89736164e-01 8.06614816e-01 1.95211038e-01 6.94851995e-01 -7.55109072e-01 1.25995982e+00 -3.26567739e-01 -3.57127517e-01 -1.72999203e-02 -3.65535468e-01 -1.18952787e+00 -2.17691883e-01 6.29541576e-01 4.42479886e-02 -1.11083619e-01 9.12520468e-01 4.55697149e-01 7.03626812e-01 8.10469151e-01 -1.31259847e+00 -3.64923924e-01 9.66572523e-01 -2.50301719e-01 4.50184643e-01 5.56090236e-01 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1.29040575e+00 3.83916736e-01 3.42039436e-01 8.29010963e-01 -2.05290288e-01 -7.42114842e-01 -1.18841839e+00 -4.38444793e-01 5.73815048e-01 -1.83201939e-01 -9.07842815e-01 -1.98781714e-01 2.15668723e-01]	[10.75040340423584, 7.743224143981934]
6bff2804-83a2-46de-87ff-2c9ea6ea8705	a-fast-dictionary-learning-method-for-coupled	1904.06968	null	http://arxiv.org/abs/1904.06968v1	http://arxiv.org/pdf/1904.06968v1.pdf	A Fast Dictionary Learning Method for Coupled Feature Space Learning	In this letter, we propose a novel computationally efficient coupled dictionary learning method that enforces pairwise correlation between the atoms of dictionaries learned to represent the underlying feature spaces of two different representations of the same signals, e.g., representations in different modalities or representations of the same signals measured with different qualities. The jointly learned correlated feature spaces represented by coupled dictionaries are used in sparse representation based classification, recognition and reconstruction tasks. The presented experimental results show that the proposed coupled dictionary learning method has a significantly lower computational cost. Moreover, the visual presentation of jointly learned dictionaries shows that the pairwise correlations between the corresponding atoms are ensured.	['F. G. Veshki', 'S. A. Vorobyov']	2019-04-15	null	null	null	null	['sparse-representation-based-classification']	['computer-vision']	[ 1.48929298e-01 -2.52225846e-01 -1.29813492e-01 -2.72070080e-01 -5.50562263e-01 -4.07232791e-01 4.71314460e-01 6.97264001e-02 -1.24189883e-01 6.23171806e-01 4.50491101e-01 4.28555638e-01 -4.73201275e-01 -6.15520775e-01 -3.71703207e-01 -1.13477206e+00 1.15562543e-01 2.69722432e-01 -4.74772602e-01 -1.43004715e-01 1.15030125e-01 5.33394158e-01 -1.67064786e+00 2.80078739e-01 3.05794746e-01 1.19794965e+00 2.05443114e-01 1.55954257e-01 1.93457440e-01 6.92642748e-01 -3.02113414e-01 -1.33501396e-01 2.93284923e-01 -6.83437586e-01 -1.62572619e-02 1.44458592e-01 5.60783744e-01 1.28984883e-01 -8.40716720e-01 1.11447978e+00 3.49196970e-01 3.39873224e-01 6.98148370e-01 -9.23390985e-01 -8.77836764e-01 6.00928187e-01 -3.69312137e-01 2.34777078e-01 3.48513603e-01 -4.73487258e-01 1.09828258e+00 -1.17359030e+00 4.82482553e-01 9.57483411e-01 1.78405344e-01 2.29286999e-01 -1.35353351e+00 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6.07551098e-01 5.32583773e-01 -1.06109416e+00 -3.39202464e-01 9.27852392e-02]	[12.38134765625, 0.3950346112251282]
d8fd50d2-f7c3-477d-a073-2deb45306184	an-iterative-convolutional-neural-network	1506.05849	null	http://arxiv.org/abs/1506.05849v1	http://arxiv.org/pdf/1506.05849v1.pdf	An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation	To build the connectomics map of the brain, we developed a new algorithm that can automatically refine the Membrane Detection Probability Maps (MDPM) generated to perform automatic segmentation of electron microscopy (EM) images. To achieve this, we executed supervised training of a convolutional neural network to recover the removed center pixel label of patches sampled from a MDPM. MDPM can be generated from other machine learning based algorithms recognizing whether a pixel in an image corresponds to the cell membrane. By iteratively applying this network over MDPM for multiple rounds, we were able to significantly improve membrane segmentation results.	['Xundong Wu']	2015-06-18	null	null	null	null	['electron-microscopy-image-segmentation']	['computer-vision']	[ 6.51506364e-01 5.52301764e-01 4.56385970e-01 -2.63776630e-01 -6.21467113e-01 -4.19542044e-01 3.55953366e-01 3.28950286e-01 -8.60483646e-01 9.11967456e-01 -4.22862351e-01 -2.50493854e-01 3.36274356e-01 -8.83586049e-01 -9.54676688e-01 -7.14564502e-01 -1.18030585e-01 8.17215800e-01 6.32874966e-01 4.95448351e-01 5.40538311e-01 1.06054318e+00 -1.10421705e+00 6.23893023e-01 6.56376064e-01 6.49983227e-01 7.57260144e-01 6.48102105e-01 -3.35154891e-01 2.47736499e-01 -3.44755143e-01 -4.59278971e-02 1.81296825e-01 -2.61980385e-01 -1.20065117e+00 1.84203889e-02 2.99994260e-01 -1.21374719e-01 1.15880206e-01 1.31873155e+00 1.49998501e-01 -3.37460220e-01 1.03013480e+00 -6.52914047e-01 -2.85801709e-01 7.83264697e-01 -5.02749383e-01 5.04870176e-01 -1.25459731e-01 -8.19095224e-02 4.51214075e-01 -9.39050913e-01 1.08305371e+00 1.02950549e+00 4.06311750e-01 5.31487167e-01 -1.82639670e+00 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526ff06e-9be2-45a6-b69f-7085dba6bf8a	pseudo-trilateral-adversarial-training-for	2306.14370	null	https://arxiv.org/abs/2306.14370v1	https://arxiv.org/pdf/2306.14370v1.pdf	Pseudo-Trilateral Adversarial Training for Domain Adaptive Traversability Prediction	Traversability prediction is a fundamental perception capability for autonomous navigation. Deep neural networks (DNNs) have been widely used to predict traversability during the last decade. The performance of DNNs is significantly boosted by exploiting a large amount of data. However, the diversity of data in different domains imposes significant gaps in the prediction performance. In this work, we make efforts to reduce the gaps by proposing a novel pseudo-trilateral adversarial model that adopts a coarse-to-fine alignment (CALI) to perform unsupervised domain adaptation (UDA). Our aim is to transfer the perception model with high data efficiency, eliminate the prohibitively expensive data labeling, and improve the generalization capability during the adaptation from easy-to-access source domains to various challenging target domains. Existing UDA methods usually adopt a bilateral zero-sum game structure. We prove that our CALI model -- a pseudo-trilateral game structure is advantageous over existing bilateral game structures. This proposed work bridges theoretical analyses and algorithm designs, leading to an efficient UDA model with easy and stable training. We further develop a variant of CALI -- Informed CALI (ICALI), which is inspired by the recent success of mixup data augmentation techniques and mixes informative regions based on the results of CALI. This mixture step provides an explicit bridging between the two domains and exposes underperforming classes more during training. We show the superiorities of our proposed models over multiple baselines in several challenging domain adaptation setups. To further validate the effectiveness of our proposed models, we then combine our perception model with a visual planner to build a navigation system and show the high reliability of our model in complex natural environments.	['Lantao Liu', 'Jason M. Gregory', 'Durgakant Pushp', 'Zheng Chen']	2023-06-26	null	null	null	null	['autonomous-navigation', 'unsupervised-domain-adaptation']	['computer-vision', 'methodology']	[ 3.09159011e-01 1.92865476e-01 -1.36936530e-01 -2.46756360e-01 -5.22537589e-01 -7.97347307e-01 6.02654338e-01 -2.55130887e-01 -6.11875415e-01 7.72674918e-01 6.60027266e-02 -3.92498195e-01 -3.21734101e-01 -1.14426231e+00 -1.00705004e+00 -6.36698425e-01 -5.59596112e-04 5.24514735e-01 4.82920229e-01 -8.64630938e-01 3.48307006e-02 4.65600073e-01 -1.73664606e+00 -1.56070337e-01 1.38903594e+00 9.34860826e-01 3.28288049e-01 3.68012637e-01 1.11362860e-01 5.31420529e-01 -3.30138624e-01 -3.39201808e-01 7.74827540e-01 -2.03458026e-01 -6.47668958e-01 -3.70013118e-01 2.88351625e-01 -2.69049168e-01 -3.55166495e-01 1.04877687e+00 5.43469012e-01 4.26384181e-01 6.53095663e-01 -1.43962014e+00 -4.26212192e-01 2.56783694e-01 -3.83098513e-01 -1.73453435e-01 1.40888989e-01 1.91524643e-02 8.60773861e-01 -5.58822632e-01 7.10014522e-01 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-1.15447426e+00 -4.61127609e-01 -7.86574543e-01 -3.71121019e-01 5.40361881e-01 3.17202538e-01 -1.24522316e+00 -2.66855806e-01 -8.74326155e-02]	[9.678011894226074, 1.1820260286331177]
1df4b5c7-e1d0-4623-9b2c-d49c0b014710	learning-compositional-neural-information-1	2001.06804	null	https://arxiv.org/abs/2001.06804v1	https://arxiv.org/pdf/2001.06804v1.pdf	Learning Compositional Neural Information Fusion for Human Parsing	This work proposes to combine neural networks with the compositional hierarchy of human bodies for efficient and complete human parsing. We formulate the approach as a neural information fusion framework. Our model assembles the information from three inference processes over the hierarchy: direct inference (directly predicting each part of a human body using image information), bottom-up inference (assembling knowledge from constituent parts), and top-down inference (leveraging context from parent nodes). The bottom-up and top-down inferences explicitly model the compositional and decompositional relations in human bodies, respectively. In addition, the fusion of multi-source information is conditioned on the inputs, i.e., by estimating and considering the confidence of the sources. The whole model is end-to-end differentiable, explicitly modeling information flows and structures. Our approach is extensively evaluated on four popular datasets, outperforming the state-of-the-arts in all cases, with a fast processing speed of 23fps. Our code and results have been released to help ease future research in this direction.	['Ling Shao', 'Jianbing Shen', 'Siyuan Qi', 'Zhijie Zhang', 'Wenguan Wang', 'Yanwei Pang']	2020-01-19	learning-compositional-neural-information	http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Learning_Compositional_Neural_Information_Fusion_for_Human_Parsing_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Learning_Compositional_Neural_Information_Fusion_for_Human_Parsing_ICCV_2019_paper.pdf	iccv-2019-10	['human-parsing']	['computer-vision']	[ 3.05268645e-01 7.16672719e-01 -1.92911714e-01 -6.30450130e-01 -5.07121027e-01 -2.66461790e-01 4.38682377e-01 3.73413235e-01 -2.63640583e-01 5.46405911e-01 4.39161807e-01 -1.32551864e-01 1.65224031e-01 -9.92079675e-01 -1.15573072e+00 -1.58732161e-01 8.94395635e-03 7.72587359e-01 5.35550117e-01 -4.40985477e-03 -1.33157507e-01 9.06487778e-02 -1.80234098e+00 7.22697139e-01 6.69094741e-01 1.33021498e+00 8.13340619e-02 9.01954412e-01 -1.94625318e-01 1.21261811e+00 -2.84662396e-01 -1.18728650e+00 9.96371582e-02 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6b229cbb-84a5-452a-a4f6-01e6ccfea03c	optimising-chest-x-rays-for-image-analysis-by	2208.10320	null	https://arxiv.org/abs/2208.10320v1	https://arxiv.org/pdf/2208.10320v1.pdf	Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors	During the COVID-19 pandemic, the sheer volume of imaging performed in an emergency setting for COVID-19 diagnosis has resulted in a wide variability of clinical CXR acquisitions. This variation is seen in the CXR projections used, image annotations added and in the inspiratory effort and degree of rotation of clinical images. The image analysis community has attempted to ease the burden on overstretched radiology departments during the pandemic by developing automated COVID-19 diagnostic algorithms, the input for which has been CXR imaging. Large publicly available CXR datasets have been leveraged to improve deep learning algorithms for COVID-19 diagnosis. Yet the variable quality of clinically-acquired CXRs within publicly available datasets could have a profound effect on algorithm performance. COVID-19 diagnosis may be inferred by an algorithm from non-anatomical features on an image such as image labels. These imaging shortcuts may be dataset-specific and limit the generalisability of AI systems. Understanding and correcting key potential biases in CXR images is therefore an essential first step prior to CXR image analysis. In this study, we propose a simple and effective step-wise approach to pre-processing a COVID-19 chest X-ray dataset to remove undesired biases. We perform ablation studies to show the impact of each individual step. The results suggest that using our proposed pipeline could increase accuracy of the baseline COVID-19 detection algorithm by up to 13%.	['Joseph Jacob', 'Daniel C Alexander', 'Paul Taylor', 'Yipeng Hu', 'Alexandra L Young', 'Bojidar Rangelov', 'Divya Raj', 'Vaishnavi Gnanananthan', 'Watjana Lilaonitkul', 'Shahab Aslani']	2022-08-22	null	null	null	null	['covid-19-detection']	['medical']	[ 3.20805132e-01 -9.44307894e-02 6.43697530e-02 -4.70504135e-01 -8.24721158e-01 -9.45613265e-01 3.48288387e-01 3.19371730e-01 -5.80583930e-01 3.04406166e-01 3.14163983e-01 -8.98949385e-01 -3.40035111e-01 -4.92498785e-01 -6.66825414e-01 -4.05486554e-01 -8.83088112e-02 7.96147108e-01 -1.12516694e-01 2.77787626e-01 1.62831768e-01 7.03951001e-01 -6.63228035e-01 5.04414618e-01 6.02665544e-01 7.30682194e-01 4.91950154e-01 1.15928352e+00 1.93304211e-01 8.91816735e-01 -6.54953480e-01 -5.51378950e-02 4.84098762e-01 -5.42059481e-01 -7.51813829e-01 -1.78600758e-01 4.85346437e-01 -6.85541868e-01 -3.65443349e-01 5.39613426e-01 8.29274535e-01 -2.19332546e-01 8.17401648e-01 -8.19740534e-01 -2.74115771e-01 5.98025799e-01 -5.31260788e-01 8.89632940e-01 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14c7cd1d-7b5e-482a-91dd-22fd52b3d583	precog-exploring-the-relation-between	2305.04673	null	https://arxiv.org/abs/2305.04673v2	https://arxiv.org/pdf/2305.04673v2.pdf	PreCog: Exploring the Relation between Memorization and Performance in Pre-trained Language Models	Pre-trained Language Models such as BERT are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution to the analysis of the interplay between memorization and performance of BERT in downstream tasks. We propose PreCog, a measure for evaluating memorization from pre-training, and we analyze its correlation with the BERT's performance. Our experiments show that highly memorized examples are better classified, suggesting memorization is an essential key to success for BERT.	['Fabio Massimo Zanzotto', 'Elena Sofia Ruzzetti', 'Leonardo Ranaldi']	2023-05-08	null	null	null	null	['memorization']	['natural-language-processing']	[-3.17745596e-01 2.14159474e-01 -2.97481995e-02 -3.42503071e-01 -5.70389330e-01 -3.06328118e-01 8.80730748e-01 7.70385981e-01 -9.23048079e-01 8.04755270e-01 2.57034957e-01 -5.63306987e-01 -4.55565423e-01 -8.64175618e-01 -8.30280483e-01 -3.61753911e-01 -4.97074425e-01 5.76482415e-01 2.15772822e-01 -5.44918895e-01 4.92987722e-01 3.89683813e-01 -1.13289273e+00 4.26657140e-01 6.86096132e-01 4.35395569e-01 6.00889921e-01 1.07218003e+00 1.09514810e-01 1.13652742e+00 -9.00493920e-01 -4.29599196e-01 -1.28715619e-01 -4.69587296e-01 -1.09682798e+00 -5.28373420e-01 1.54201999e-01 -1.83177382e-01 -2.25335166e-01 4.98652786e-01 3.81978363e-01 4.94361788e-01 7.62015760e-01 -7.21736073e-01 -8.08691382e-01 1.18417990e+00 -6.39211014e-02 1.14707088e+00 1.90640897e-01 5.16787767e-01 1.22303665e+00 -1.02306974e+00 5.77651739e-01 1.07923007e+00 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8e3ea3a3-8beb-4afd-af60-c9d2cb4c42f2	redefining-absent-keyphrases-and-their-effect	2103.12440	null	https://arxiv.org/abs/2103.12440v2	https://arxiv.org/pdf/2103.12440v2.pdf	Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness	Neural keyphrase generation models have recently attracted much interest due to their ability to output absent keyphrases, that is, keyphrases that do not appear in the source text. In this paper, we discuss the usefulness of absent keyphrases from an Information Retrieval (IR) perspective, and show that the commonly drawn distinction between present and absent keyphrases is not made explicit enough. We introduce a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval. Under this scheme, we find that only a fraction (around 20%) of the words that make up keyphrases actually serves as document expansion, but that this small fraction of words is behind much of the gains observed in retrieval effectiveness. We also discuss how the proposed scheme can offer a new angle to evaluate the output of neural keyphrase generation models.	['Ygor Gallina', 'Florian Boudin']	2021-03-23	null	https://aclanthology.org/2021.naacl-main.330	https://aclanthology.org/2021.naacl-main.330.pdf	naacl-2021-4	['keyphrase-generation']	['natural-language-processing']	[ 2.78735638e-01 5.62798977e-02 -3.21429044e-01 2.14694291e-01 -7.14301109e-01 -9.75429118e-01 1.20421576e+00 1.04996133e+00 -7.05106556e-01 8.59410286e-01 6.38866246e-01 -5.25042653e-01 -3.37889522e-01 -9.60768282e-01 -7.55899787e-01 -6.92317724e-01 2.54285219e-03 3.61560807e-02 6.46017790e-02 -5.61177790e-01 8.57444525e-01 7.67526329e-01 -1.64493060e+00 3.64325732e-01 6.88428998e-01 5.92154503e-01 3.20559770e-01 6.43067896e-01 -6.16937816e-01 6.70392454e-01 -1.05231357e+00 -1.56641871e-01 1.36745349e-01 -3.30631286e-01 -7.77883947e-01 -6.28775358e-01 6.47273600e-01 -4.08856362e-01 -6.74650788e-01 9.73143876e-01 4.10722584e-01 1.96069956e-01 8.60402524e-01 -9.77634490e-01 -9.93589222e-01 1.07100236e+00 -4.11999226e-01 6.76514566e-01 3.39571655e-01 -3.70876908e-01 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55c8ff38-f7a2-4e64-8f2d-d4cdf338e068	data-fusion-for-multipath-based-slam	2211.09241	null	https://arxiv.org/abs/2211.09241v3	https://arxiv.org/pdf/2211.09241v3.pdf	Data Fusion for Multipath-Based SLAM: Combining Information from Multiple Propagation Paths	Multipath-based simultaneous localization and mapping (SLAM) is an emerging paradigm for accurate indoor localization with limited resources. The goal of multipath-based SLAM is to detect and localize radio reflective surfaces to support the estimation of time-varying positions of mobile agents. Radio reflective surfaces are typically represented by so-called virtual anchors (VAs), which are mirror images of base stations at the actual surfaces. In existing multipath-based SLAM methods, a VA is introduced for each propagation path, even if the goal is to map the reflective surfaces. The fact that not every reflective surface but every propagation path is modeled by a VA, complicates a consistent combination "fusion" of statistical information across multiple paths and base stations and thus limits the accuracy and mapping speed of existing multipath-based SLAM methods. In this paper, we introduce an improved statistical model and estimation method that enables data fusion for multipath-based SLAM by representing each surface by a single master virtual anchor (MVA). We further develop a particle-based sum-product algorithm (SPA) that performs probabilistic data association to compute marginal posterior distributions of MVA and agent positions efficiently. A key aspect of the proposed estimation method based on MVAs is to check the availability of singlebounce and double-bounce propagation paths at a specific agent position by means of ray-launching. The availability check is directly integrated into the statistical model by providing detection probabilities for probabilistic data association. Numerical results based on simulated and real data demonstrate significant improvements in estimation accuracy compared to state-of-theart multipath-based SLAM methods.	['Florian Meyer', 'Bryan Teague', 'Alexander Venus', 'Erik Leitinger']	2022-11-16	null	null	null	null	['simultaneous-localization-and-mapping', 'indoor-localization']	['computer-vision', 'computer-vision']	[-9.83948447e-03 -2.65334219e-01 3.14144462e-01 -1.66006207e-01 -8.92296970e-01 -4.78798360e-01 8.37208271e-01 4.47582811e-01 -6.52126074e-01 1.14355755e+00 -5.05347073e-01 -1.05955623e-01 -3.36841643e-01 -1.09371924e+00 -9.47558999e-01 -8.75013888e-01 -7.28059113e-01 8.03084791e-01 5.56525111e-01 -2.86963820e-01 1.25741720e-01 6.28177583e-01 -1.47741365e+00 -3.83040488e-01 9.40117955e-01 8.41982067e-01 6.86281860e-01 8.93602788e-01 -9.65202078e-02 1.48185521e-01 -8.49305511e-01 1.66129187e-01 -3.40396888e-03 -9.49033797e-02 2.59168744e-02 -6.08336985e-01 4.92334999e-02 -5.32688126e-02 1.22082025e-01 8.17402422e-01 3.23620230e-01 2.38329589e-01 5.78232169e-01 -1.67045784e+00 3.01960498e-01 6.32322654e-02 -4.21995103e-01 -5.01075350e-02 7.69302011e-01 -3.69365752e-01 3.95184338e-01 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e0367054-f938-4088-b3b2-2b8d7ab2e465	towards-a-unified-conformer-structure-from	2211.07201	null	https://arxiv.org/abs/2211.07201v2	https://arxiv.org/pdf/2211.07201v2.pdf	Towards A Unified Conformer Structure: from ASR to ASV Task	Transformer has achieved extraordinary performance in Natural Language Processing and Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant Conformer has become a state-of-the-art architecture in the field of Automatic Speech Recognition (ASR). However, the main-stream architecture for Automatic Speaker Verification (ASV) is convolutional Neural Networks, and there is still much room for research on the Conformer based ASV. In this paper, firstly, we modify the Conformer architecture from ASR to ASV with very minor changes. Length-Scaled Attention (LSA) method and Sharpness-Aware Minimizationis (SAM) are adopted to improve model generalization. Experiments conducted on VoxCeleb and CN-Celeb show that our Conformer based ASV achieves competitive performance compared with the popular ECAPA-TDNN. Secondly, inspired by the transfer learning strategy, ASV Conformer is natural to be initialized from the pretrained ASR model. Via parameter transferring, self-attention mechanism could better focus on the relationship between sequence features, brings about 11% relative improvement in EER on test set of VoxCeleb and CN-Celeb, which reveals the potential of Conformer to unify ASV and ASR task. Finally, we provide a runtime in ASV-Subtools to evaluate its inference speed in production scenario. Our code is released at https://github.com/Snowdar/asv-subtools/tree/master/doc/papers/conformer.md.	['Qingyang Hong', 'Lin Li', 'Feng Wang', 'Tao Jiang', 'Dexin Liao']	2022-11-14	null	null	null	null	['speaker-verification']	['speech']	[-6.51264796e-04 -1.02375425e-01 1.58052847e-01 -5.30796468e-01 -1.01292944e+00 -5.25699735e-01 6.01255059e-01 -3.49907130e-01 -3.68865669e-01 3.83944631e-01 1.98936880e-01 -6.26836479e-01 2.57267803e-01 -4.21649784e-01 -7.57740557e-01 -6.05955780e-01 3.98590267e-01 3.76799911e-01 -2.40806397e-02 -4.58954066e-01 -5.89754879e-02 4.09375012e-01 -1.26855278e+00 2.35020071e-01 7.44141936e-01 1.04646361e+00 4.20256972e-01 6.53846622e-01 -2.93280371e-03 3.48508060e-01 -5.94343066e-01 -6.42117500e-01 1.35997742e-01 -2.58266062e-01 -9.01257813e-01 -1.89739451e-01 5.25755942e-01 3.16753867e-03 -7.69946516e-01 1.01610398e+00 8.94591808e-01 1.23441987e-01 3.78128707e-01 -1.34333444e+00 -9.72886682e-01 1.05438447e+00 -3.37860793e-01 5.31118512e-01 1.58050805e-01 4.24386352e-01 9.55118895e-01 -1.10610425e+00 1.28686085e-01 1.46015418e+00 5.84627986e-01 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f60911e1-fed4-403e-a80c-2b0338eaeb93	open-set-classification-of-gan-based-image	2304.05212	null	https://arxiv.org/abs/2304.05212v1	https://arxiv.org/pdf/2304.05212v1.pdf	Open Set Classification of GAN-based Image Manipulations via a ViT-based Hybrid Architecture	Classification of AI-manipulated content is receiving great attention, for distinguishing different types of manipulations. Most of the methods developed so far fail in the open-set scenario, that is when the algorithm used for the manipulation is not represented by the training set. In this paper, we focus on the classification of synthetic face generation and manipulation in open-set scenarios, and propose a method for classification with a rejection option. The proposed method combines the use of Vision Transformers (ViT) with a hybrid approach for simultaneous classification and localization. Feature map correlation is exploited by the ViT module, while a localization branch is employed as an attention mechanism to force the model to learn per-class discriminative features associated with the forgery when the manipulation is performed locally in the image. Rejection is performed by considering several strategies and analyzing the model output layers. The effectiveness of the proposed method is assessed for the task of classification of facial attribute editing and GAN attribution.	['Mauro Barni', 'Benedetta Tondi', 'Omran Alamayreh', 'Jun Wang']	2023-04-11	null	null	null	null	['face-generation']	['computer-vision']	[ 6.03803039e-01 2.08802089e-01 1.80798277e-01 -1.55683428e-01 -3.33171427e-01 -5.93439639e-01 8.72529685e-01 -1.69539198e-01 -2.42120042e-01 4.15504426e-01 -3.45162451e-01 2.75687099e-01 -3.00658166e-01 -7.47291028e-01 -6.85949743e-01 -1.02707303e+00 4.76458877e-01 6.19509876e-01 -2.01911598e-01 -2.26902843e-01 4.05202448e-01 1.07462120e+00 -2.05022931e+00 4.16251928e-01 5.84153295e-01 1.28949666e+00 2.75904208e-01 5.50021231e-01 -1.81272969e-01 6.50344670e-01 -9.70156670e-01 -4.81899232e-01 3.21735919e-01 -7.17632413e-01 -4.22909409e-01 3.33998829e-01 5.84911644e-01 -1.62677355e-02 2.62365133e-01 9.39299881e-01 6.31791532e-01 -1.02928802e-02 1.22444916e+00 -1.41910398e+00 -2.38458559e-01 4.02287006e-01 -2.85576880e-01 -1.44600764e-01 4.37575221e-01 1.57751217e-01 3.82222533e-01 -9.75825667e-01 6.89045489e-01 9.61644113e-01 4.76907670e-01 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47603393-ea9e-4f43-ad5f-1cb8ee55b5b8	accurate-and-real-time-pseudo-lidar-detection	2206.13858	null	https://arxiv.org/abs/2206.13858v1	https://arxiv.org/pdf/2206.13858v1.pdf	Accurate and Real-time Pseudo Lidar Detection: Is Stereo Neural Network Really Necessary?	The proposal of Pseudo-Lidar representation has significantly narrowed the gap between visual-based and active Lidar-based 3D object detection. However, current researches exclusively focus on pushing the accuracy improvement of Pseudo-Lidar by taking the advantage of complex and time-consuming neural networks. Seldom explore the profound characteristics of Pseudo-Lidar representation to obtain the promoting opportunities. In this paper, we dive deep into the pseudo Lidar representation and argue that the performance of 3D object detection is not fully dependent on the high precision stereo depth estimation. We demonstrate that even for the unreliable depth estimation, with proper data processing and refining, it can achieve comparable 3D object detection accuracy. With this finding, we further show the possibility that utilizing fast but inaccurate stereo matching algorithms in the Pseudo-Lidar system to achieve low latency responsiveness. In the experiments, we develop a system with a less powerful stereo matching predictor and adopt the proposed refinement schemes to improve the accuracy. The evaluation on the KITTI benchmark shows that the presented system achieves competitive accuracy to the state-of-the-art approaches with only 23 ms computing, showing it is a suitable candidate for deploying to real car-hold applications.	['Alois Knoll', 'Gang Chen', 'Changcai Li', 'Haitao Meng']	2022-06-28	null	null	null	null	['stereo-depth-estimation', 'stereo-matching-1']	['computer-vision', 'computer-vision']	[ 2.99376845e-01 -1.25593722e-01 1.02893747e-02 -3.77193838e-01 -6.23884082e-01 -7.01021180e-02 4.68381286e-01 8.19679059e-04 -7.28118420e-01 4.42328811e-01 -4.50349003e-01 -5.68176806e-01 -2.20069028e-02 -9.13646877e-01 -5.91974914e-01 -6.48620725e-01 1.07866161e-01 7.13091314e-01 8.17928612e-01 -2.39198864e-01 6.22165084e-01 1.05241072e+00 -2.22365355e+00 -3.97840105e-02 7.31440663e-01 1.22377896e+00 4.43145156e-01 4.80849445e-01 -3.61033499e-01 3.16067040e-01 -3.60927135e-01 -6.62683249e-02 5.99755764e-01 3.94472986e-01 -1.88180119e-01 -1.08499430e-01 7.82061696e-01 -7.46304870e-01 -9.37205404e-02 9.07883465e-01 7.41153479e-01 -1.57318994e-01 4.15598035e-01 -1.29245901e+00 -1.49626151e-01 1.59085199e-01 -8.50816369e-01 2.44025394e-01 2.13489026e-01 1.57902420e-01 5.56307554e-01 -1.32099557e+00 4.09347951e-01 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4196c879-3871-4409-9ce2-d895d94ac570	maskgan-better-text-generation-via-filling-in-1	null	null	https://openreview.net/forum?id=ByOExmWAb	https://openreview.net/pdf?id=ByOExmWAb	MaskGAN: Better Text Generation via Filling in the _______	Neural text generation models are often autoregressive language models or seq2seq models. Neural autoregressive and seq2seq models that generate text by sampling words sequentially, with each word conditioned on the previous model, are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of sample quality. Language models are typically trained via maximum likelihood and most often with teacher forcing. Teacher forcing is well-suited to optimizing perplexity but can result in poor sample quality because generating text requires conditioning on sequences of words that were never observed at training time. We propose to improve sample quality using Generative Adversarial Network (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally to designed to output differentiable values, so discrete language generation is challenging for them. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic text samples compared to a maximum likelihood trained model.	['William Fedus', 'Ian Goodfellow', 'Andrew M. Dai']	2018-01-01	null	null	null	iclr-2018-1	['multivariate-time-series-imputation']	['time-series']	[ 7.16446400e-01 6.91186070e-01 -3.88713554e-02 -1.28521338e-01 -1.40126467e+00 -6.09624028e-01 1.22439182e+00 -4.84360993e-01 -2.25080505e-01 1.30729377e+00 7.27463484e-01 -2.06390679e-01 6.35976315e-01 -9.42572594e-01 -1.05697250e+00 -7.02518106e-01 4.09513324e-01 1.01037991e+00 -6.11836970e-01 -1.73980340e-01 -1.14360347e-01 1.69224199e-02 -1.01861942e+00 2.30164826e-01 9.40060437e-01 2.02384457e-01 1.91152617e-01 1.20400834e+00 -3.17073792e-01 8.91035736e-01 -1.17783296e+00 -5.15553772e-01 1.67481899e-01 -1.35373497e+00 -7.17343569e-01 4.09955345e-02 3.92066747e-01 -4.86937314e-01 -2.10579813e-01 7.51530766e-01 6.83417916e-01 -9.79467556e-02 1.09139383e+00 -9.12408948e-01 -9.94532883e-01 9.44648981e-01 -3.60925853e-01 -1.59713283e-01 4.15851355e-01 5.29202819e-01 9.78304088e-01 -7.74612546e-01 7.81664968e-01 1.38054526e+00 3.43393862e-01 1.13944530e+00 -1.63676143e+00 -4.57896650e-01 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-1.38088465e-01 4.49040592e-01 4.49050307e-01 2.02813849e-01 -2.10938558e-01 4.69751954e-01]	[11.893450736999512, 9.277470588684082]
f0eba5d0-f2e3-43f9-ad15-844769ef6597	probabilistic-distance-based-outlier	2305.09446	null	https://arxiv.org/abs/2305.09446v1	https://arxiv.org/pdf/2305.09446v1.pdf	Probabilistic Distance-Based Outlier Detection	The scores of distance-based outlier detection methods are difficult to interpret, making it challenging to determine a cut-off threshold between normal and outlier data points without additional context. We describe a generic transformation of distance-based outlier scores into interpretable, probabilistic estimates. The transformation is ranking-stable and increases the contrast between normal and outlier data points. Determining distance relationships between data points is necessary to identify the nearest-neighbor relationships in the data, yet, most of the computed distances are typically discarded. We show that the distances to other data points can be used to model distance probability distributions and, subsequently, use the distributions to turn distance-based outlier scores into outlier probabilities. Our experiments show that the probabilistic transformation does not impact detection performance over numerous tabular and image benchmark datasets but results in interpretable outlier scores with increased contrast between normal and outlier samples. Our work generalizes to a wide range of distance-based outlier detection methods, and because existing distance computations are used, it adds no significant computational overhead.	['Josef Küng', 'Michael Affenzeller', 'David Muhr']	2023-05-16	null	null	null	null	['outlier-detection']	['methodology']	[-1.44072622e-01 -4.50313836e-01 -2.16178149e-02 -5.00195086e-01 -9.33448732e-01 -7.59141445e-01 4.04615730e-01 1.09116852e+00 -2.86059976e-01 2.75137395e-01 1.50191203e-01 -2.77920872e-01 -4.47225630e-01 -6.27881646e-01 -3.89889419e-01 -5.03483236e-01 -6.50483608e-01 5.21574855e-01 4.64682788e-01 1.82308435e-01 6.90140426e-01 8.34810317e-01 -1.62130666e+00 2.55239576e-01 1.00794327e+00 8.91490459e-01 -6.80103123e-01 6.93792403e-01 -5.80866858e-02 3.71708393e-01 -1.08119273e+00 2.87678614e-02 5.62610567e-01 -3.85063797e-01 -4.02413577e-01 -2.45918721e-01 6.03426635e-01 -3.16431135e-01 3.38473246e-02 8.54190469e-01 2.65633076e-01 2.45996729e-01 8.12291324e-01 -1.75918818e+00 -4.61712003e-01 2.90300936e-01 -7.36875236e-01 4.55194980e-01 7.21073091e-01 4.19646315e-03 1.05634415e+00 -1.04775691e+00 4.85298723e-01 1.05199170e+00 9.78162944e-01 -1.61763117e-01 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cbee8184-af73-4735-8042-2552cec544cb	deflocnet-deep-image-editing-via-flexible-low	2103.12723	null	https://arxiv.org/abs/2103.12723v1	https://arxiv.org/pdf/2103.12723v1.pdf	DeFLOCNet: Deep Image Editing via Flexible Low-level Controls	User-intended visual content fills the hole regions of an input image in the image editing scenario. The coarse low-level inputs, which typically consist of sparse sketch lines and color dots, convey user intentions for content creation (\ie, free-form editing). While existing methods combine an input image and these low-level controls for CNN inputs, the corresponding feature representations are not sufficient to convey user intentions, leading to unfaithfully generated content. In this paper, we propose DeFLOCNet which relies on a deep encoder-decoder CNN to retain the guidance of these controls in the deep feature representations. In each skip-connection layer, we design a structure generation block. Instead of attaching low-level controls to an input image, we inject these controls directly into each structure generation block for sketch line refinement and color propagation in the CNN feature space. We then concatenate the modulated features with the original decoder features for structure generation. Meanwhile, DeFLOCNet involves another decoder branch for texture generation and detail enhancement. Both structures and textures are rendered in the decoder, leading to user-intended editing results. Experiments on benchmarks demonstrate that DeFLOCNet effectively transforms different user intentions to create visually pleasing content.	['Wei Liu', 'Bing Jiang', 'Jing Liao', 'Xintong Han', 'Yibing Song', 'Wei Huang', 'Ziyu Wan', 'Hongyu Liu']	2021-03-23	null	http://openaccess.thecvf.com//content/CVPR2021/html/Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_DeFLOCNet_Deep_Image_Editing_via_Flexible_Low-Level_Controls_CVPR_2021_paper.pdf	cvpr-2021-1	['texture-synthesis']	['computer-vision']	[ 4.72617894e-01 1.45781133e-02 -7.88667127e-02 -3.45801204e-01 -4.09163497e-02 -4.93894249e-01 5.87767124e-01 -1.29738495e-01 -6.39384761e-02 5.29267371e-01 3.19323301e-01 -7.35210255e-02 4.69425350e-01 -1.14226472e+00 -9.11795855e-01 -3.75497967e-01 5.44435203e-01 -2.33733252e-01 2.54447162e-01 -2.80888468e-01 3.71372610e-01 4.74367738e-01 -1.42629671e+00 6.52766943e-01 8.12471747e-01 1.19801378e+00 3.84416759e-01 6.71624660e-01 -6.59202278e-01 9.58945990e-01 -3.89032692e-01 -4.90961939e-01 2.27684557e-01 -6.12177014e-01 -3.39845687e-01 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87d07e8a-8519-466f-b692-b741b0b20dee	symmetry-detection-and-classification-in	1907.01004	null	https://arxiv.org/abs/1907.01004v3	https://arxiv.org/pdf/1907.01004v3.pdf	Symmetry Detection and Classification in Drawings of Graphs	Symmetry is a key feature observed in nature (from flowers and leaves, to butterflies and birds) and in human-made objects (from paintings and sculptures, to manufactured objects and architectural design). Rotational, translational, and especially reflectional symmetries, are also important in drawings of graphs. Detecting and classifying symmetries can be very useful in algorithms that aim to create symmetric graph drawings and in this paper we present a machine learning approach for these tasks. Specifically, we show that deep neural networks can be used to detect reflectional symmetries with 92% accuracy. We also build a multi-class classifier to distinguish between reflectional horizontal, reflectional vertical, rotational, and translational symmetries. Finally, we make available a collection of images of graph drawings with specific symmetric features that can be used in machine learning systems for training, testing and validation purposes. Our datasets, best trained ML models, source code are available online.	['Md Iqbal Hossain', 'Stephen Kobourov', 'Felice De Luca']	2019-07-01	null	null	null	null	['symmetry-detection']	['computer-vision']	[ 3.72806519e-01 -4.92914170e-02 -1.27464443e-01 -3.63844216e-01 1.19144253e-01 -7.35286295e-01 8.30886543e-01 1.12794824e-01 4.10521537e-01 4.53348041e-01 5.32867350e-02 -2.69342512e-01 -5.01337111e-01 -1.10438669e+00 -5.46331525e-01 -4.53185230e-01 -3.91259938e-01 6.80471122e-01 9.00563151e-02 -1.81127042e-01 6.85479999e-01 1.41418672e+00 -1.62463439e+00 7.69551516e-01 2.23552302e-01 9.83570516e-01 -1.94058299e-01 7.76159883e-01 -7.78168887e-02 7.14941144e-01 -4.29439783e-01 -6.26901686e-01 2.00794905e-01 -2.88341314e-01 -9.35612023e-01 1.49825484e-01 6.85215950e-01 -2.01076895e-01 1.28023922e-01 7.95828164e-01 2.35164076e-01 -1.54009715e-01 1.04259074e+00 -1.29830945e+00 -8.51755559e-01 5.82341373e-01 -8.52457881e-01 -3.71453345e-01 2.59445339e-01 -4.39310849e-01 1.14130926e+00 -4.80573356e-01 8.34947169e-01 1.08679605e+00 6.34079337e-01 3.32987845e-01 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ecdf39f5-c901-4761-a534-e5d37b92e850	unsupervised-misaligned-infrared-and-visible	2205.11876	null	https://arxiv.org/abs/2205.11876v1	https://arxiv.org/pdf/2205.11876v1.pdf	Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration	Recent learning-based image fusion methods have marked numerous progress in pre-registered multi-modality data, but suffered serious ghosts dealing with misaligned multi-modality data, due to the spatial deformation and the difficulty narrowing cross-modality discrepancy. To overcome the obstacles, in this paper, we present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion (IVIF). Specifically, we propose a Cross-modality Perceptual Style Transfer Network (CPSTN) to generate a pseudo infrared image taking a visible image as input. Benefiting from the favorable geometry preservation ability of the CPSTN, the generated pseudo infrared image embraces a sharp structure, which is more conducive to transforming cross-modality image alignment into mono-modality registration coupled with the structure-sensitive of the infrared image. In this case, we introduce a Multi-level Refinement Registration Network (MRRN) to predict the displacement vector field between distorted and pseudo infrared images and reconstruct registered infrared image under the mono-modality setting. Moreover, to better fuse the registered infrared images and visible images, we present a feature Interaction Fusion Module (IFM) to adaptively select more meaningful features for fusion in the Dual-path Interaction Fusion Network (DIFN). Extensive experimental results suggest that the proposed method performs superior capability on misaligned cross-modality image fusion.	['Risheng Liu', 'Xin Fan', 'JinYuan Liu', 'Di Wang']	2022-05-24	null	null	null	null	['infrared-and-visible-image-fusion']	['computer-vision']	[ 6.43229246e-01 -3.53535384e-01 8.82972479e-02 -5.07276878e-02 -9.77735221e-01 -3.51455957e-01 4.98848617e-01 -4.09727573e-01 -2.60294020e-01 4.78881389e-01 3.96958351e-01 -1.02175921e-01 -4.95020390e-01 -5.98042428e-01 -5.05202651e-01 -1.15532541e+00 4.10818607e-01 -2.65749604e-01 -2.76003271e-01 -5.40264070e-01 1.10174239e-01 4.32193935e-01 -1.50881100e+00 1.91748679e-01 1.14731777e+00 7.96768725e-01 3.25209439e-01 5.07839739e-01 1.82389230e-01 3.43356580e-01 -7.84836635e-02 -7.55886510e-02 6.26588345e-01 -5.16750574e-01 -9.34839308e-01 1.27609193e-01 6.69714749e-01 -1.03864692e-01 -3.21814775e-01 1.29711938e+00 8.33236158e-01 3.48735690e-01 5.16311824e-01 -1.30548573e+00 -8.44115555e-01 2.10919067e-01 -1.00599205e+00 7.24314451e-02 7.89127946e-01 9.90425646e-02 4.78353351e-01 -8.90966415e-01 4.76399094e-01 1.15827703e+00 6.68406487e-01 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6219acd2-ed1b-45e6-b558-b1e1ed97b529	generative-incremental-dependency-parsing	null	null	https://aclanthology.org/P15-2142	https://aclanthology.org/P15-2142.pdf	Generative Incremental Dependency Parsing with Neural Networks	null	['Jan Buys', 'Phil Blunsom']	2015-07-01	generative-incremental-dependency-parsing-1	https://aclanthology.org/P15-2142	https://aclanthology.org/P15-2142.pdf	ijcnlp-2015-7	['transition-based-dependency-parsing']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 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c432c171-16b1-4228-b058-6c2fe59ec56f	frob-few-shot-robust-model-for-classification-1	2111.15487	null	https://arxiv.org/abs/2111.15487v2	https://arxiv.org/pdf/2111.15487v2.pdf	FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection	Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims due to rarity and the limited samples in the few-shot setting, and because of adversarial attacks. Accomplishing these aims is important for critical systems in safety, security, and defence. In parallel, OoD detection is challenging since deep neural network classifiers set high confidence to OoD samples away from the training data. To address such limitations, we propose the Few-shot ROBust (FROB) model for classification and few-shot OoD detection. We devise FROB for improved robustness and reliable confidence prediction for few-shot OoD detection. We generate the support boundary of the normal class distribution and combine it with few-shot Outlier Exposure (OE). We propose a self-supervised learning few-shot confidence boundary methodology based on generative and discriminative models. The contribution of FROB is the combination of the generated boundary in a self-supervised learning manner and the imposition of low confidence at this learned boundary. FROB implicitly generates strong adversarial samples on the boundary and forces samples from OoD, including our boundary, to be less confident by the classifier. FROB achieves generalization to unseen OoD with applicability to unknown, in the wild, test sets that do not correlate to the training datasets. To improve robustness, FROB redesigns OE to work even for zero-shots. By including our boundary, FROB reduces the threshold linked to the model's few-shot robustness; it maintains the OoD performance approximately independent of the number of few-shots. The few-shot robustness analysis evaluation of FROB on different sets and on One-Class Classification (OCC) data shows that FROB achieves competitive performance and outperforms benchmarks in terms of robustness to the outlier few-shot sample population and variability.	['Sotirios A. Tsaftaris', 'Mehrdad Yaghoobi', 'Nikolaos Dionelis']	2021-11-30	null	null	null	null	['one-class-classification']	['miscellaneous']	[ 9.61534902e-02 1.03609778e-01 -1.37142569e-01 -2.64952723e-02 -9.96665895e-01 -2.97143489e-01 6.21151686e-01 2.06511036e-01 -5.74797876e-02 5.07765234e-01 -2.63322383e-01 5.82622774e-02 -1.61566958e-01 -8.29999804e-01 -8.72051656e-01 -7.47373164e-01 -1.36009037e-01 3.16853464e-01 5.59482098e-01 -1.26336604e-01 8.03609192e-02 4.79268670e-01 -1.70079172e+00 2.53474683e-01 7.97925591e-01 1.04772937e+00 -4.58577752e-01 7.29635298e-01 2.50712961e-01 4.06734377e-01 -1.20385563e+00 -2.29471087e-01 5.65611601e-01 -4.86712039e-01 8.64493400e-02 -3.34516652e-02 5.16081572e-01 -4.35733944e-01 -2.47517511e-01 1.10785270e+00 7.27358282e-01 2.72192031e-01 1.16617799e+00 -1.73162520e+00 -5.81074953e-01 -1.71823502e-02 -4.40136850e-01 3.46574754e-01 9.59692746e-02 4.44214106e-01 5.54126680e-01 -8.87618780e-01 4.94825244e-01 1.00134134e+00 8.45060170e-01 7.71818936e-01 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65a718e9-ec05-48cb-b7f1-a88adca5e6da	toward-understanding-wordart-corner-guided	2208.00438	null	https://arxiv.org/abs/2208.00438v1	https://arxiv.org/pdf/2208.00438v1.pdf	Toward Understanding WordArt: Corner-Guided Transformer for Scene Text Recognition	Artistic text recognition is an extremely challenging task with a wide range of applications. However, current scene text recognition methods mainly focus on irregular text while have not explored artistic text specifically. The challenges of artistic text recognition include the various appearance with special-designed fonts and effects, the complex connections and overlaps between characters, and the severe interference from background patterns. To alleviate these problems, we propose to recognize the artistic text at three levels. Firstly, corner points are applied to guide the extraction of local features inside characters, considering the robustness of corner structures to appearance and shape. In this way, the discreteness of the corner points cuts off the connection between characters, and the sparsity of them improves the robustness for background interference. Secondly, we design a character contrastive loss to model the character-level feature, improving the feature representation for character classification. Thirdly, we utilize Transformer to learn the global feature on image-level and model the global relationship of the corner points, with the assistance of a corner-query cross-attention mechanism. Besides, we provide an artistic text dataset to benchmark the performance. Experimental results verify the significant superiority of our proposed method on artistic text recognition and also achieve state-of-the-art performance on several blurred and perspective datasets.	['Xiang Bai', 'Zhaowen Wang', 'Zhifei Zhang', 'Ling Fu', 'Xudong Xie']	2022-07-31	null	null	null	null	['scene-text-recognition']	['computer-vision']	[ 1.92031488e-01 -8.45138848e-01 6.03171848e-02 -5.96796870e-02 4.50409912e-02 -3.82396907e-01 6.46498680e-01 -4.39245343e-01 2.89937425e-02 2.35903263e-01 1.90159917e-01 1.83238268e-01 1.58042181e-02 -4.38893497e-01 -4.89611298e-01 -9.67446089e-01 7.89754629e-01 3.70673686e-02 4.10899043e-01 -1.54780000e-01 6.90163732e-01 6.75632477e-01 -1.23462856e+00 5.09443104e-01 9.62139785e-01 1.19324565e+00 3.12967747e-01 2.66026735e-01 -4.78438854e-01 7.48258591e-01 -7.45770693e-01 -4.31183070e-01 5.60487211e-02 -2.39007488e-01 7.14185163e-02 7.60840714e-01 6.30827427e-01 -4.34746951e-01 -7.80306458e-01 1.16120183e+00 5.44018269e-01 2.16172040e-02 7.65072048e-01 -9.88764703e-01 -1.19066119e+00 2.80418843e-01 -1.08921671e+00 8.86287615e-02 2.50522524e-01 2.57430136e-01 7.60642827e-01 -1.31138325e+00 3.33723098e-01 1.20970452e+00 5.38205564e-01 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e2cc322c-9740-4895-bc42-0563198dee2d	fast-mri-reconstruction-via-edge-attention	2304.11400	null	https://arxiv.org/abs/2304.11400v1	https://arxiv.org/pdf/2304.11400v1.pdf	Fast MRI Reconstruction via Edge Attention	Fast and accurate MRI reconstruction is a key concern in modern clinical practice. Recently, numerous Deep-Learning methods have been proposed for MRI reconstruction, however, they usually fail to reconstruct sharp details from the subsampled k-space data. To solve this problem, we propose a lightweight and accurate Edge Attention MRI Reconstruction Network (EAMRI) to reconstruct images with edge guidance. Specifically, we design an efficient Edge Prediction Network to directly predict accurate edges from the blurred image. Meanwhile, we propose a novel Edge Attention Module (EAM) to guide the image reconstruction utilizing the extracted edge priors, as inspired by the popular self-attention mechanism. EAM first projects the input image and edges into Q_image, K_edge, and V_image, respectively. Then EAM pairs the Q_image with K_edge along the channel dimension, such that 1) it can search globally for the high-frequency image features that are activated by the edge priors; 2) the overall computation burdens are largely reduced compared with the traditional spatial-wise attention. With the help of EAM, the predicted edge priors can effectively guide the model to reconstruct high-quality MR images with accurate edges. Extensive experiments show that our proposed EAMRI outperforms other methods with fewer parameters and can recover more accurate edges.	['Tieyong Zeng', 'Jun Shi', 'Shihui Ying', 'Lok Ming Lui', 'Juncheng Li', 'Hanhui Yang']	2023-04-22	null	null	null	null	['image-reconstruction', 'mri-reconstruction']	['computer-vision', 'computer-vision']	[ 6.24199659e-02 -8.59455541e-02 -4.52831797e-02 -2.41299093e-01 -7.31038630e-01 2.11791098e-01 -6.15278743e-02 -2.99015284e-01 -2.62564033e-01 5.86509943e-01 6.35576367e-01 7.75193647e-02 -5.23168921e-01 -5.94009459e-01 -6.44870102e-01 -7.58567810e-01 1.38444752e-01 2.74932804e-03 2.19148040e-01 2.02366039e-01 1.14638217e-01 2.86900878e-01 -8.06341887e-01 4.09466736e-02 1.00548995e+00 1.00633359e+00 7.48666108e-01 2.52694845e-01 1.52395340e-02 7.67509162e-01 7.26452693e-02 -2.88002845e-02 -1.61253177e-02 -3.13156158e-01 -8.18581402e-01 7.50246570e-02 -8.75987560e-02 -7.15843618e-01 -9.42187190e-01 1.35953391e+00 7.47542918e-01 2.31754810e-01 4.37457383e-01 -6.97543085e-01 -1.09238946e+00 5.48681855e-01 -8.93362582e-01 5.81907451e-01 -1.55259371e-01 1.07796177e-01 4.48559821e-01 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8d567cdb-52f7-4b00-a024-599d35ffa98c	jaa-net-joint-facial-action-unit-detection	2003.08834	null	https://arxiv.org/abs/2003.08834v3	https://arxiv.org/pdf/2003.08834v3.pdf	J$\hat{\text{A}}$A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention	Facial action unit (AU) detection and face alignment are two highly correlated tasks, since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU detection. However, most existing AU detection works handle the two tasks independently by treating face alignment as a preprocessing, and often use landmarks to predefine a fixed region or attention for each AU. In this paper, we propose a novel end-to-end deep learning framework for joint AU detection and face alignment, which has not been explored before. In particular, multi-scale shared feature is learned firstly, and high-level feature of face alignment is fed into AU detection. Moreover, to extract precise local features, we propose an adaptive attention learning module to refine the attention map of each AU adaptively. Finally, the assembled local features are integrated with face alignment feature and global feature for AU detection. Extensive experiments demonstrate that our framework (i) significantly outperforms the state-of-the-art AU detection methods on the challenging BP4D, DISFA, GFT and BP4D+ benchmarks, (ii) can adaptively capture the irregular region of each AU, (iii) achieves competitive performance for face alignment, and (iv) also works well under partial occlusions and non-frontal poses. The code for our method is available at https://github.com/ZhiwenShao/PyTorch-JAANet.	['Zhilei Liu', 'Jianfei Cai', 'Zhiwen Shao', 'Lizhuang Ma']	2020-03-18	null	null	null	null	['action-unit-detection', 'facial-action-unit-detection']	['computer-vision', 'computer-vision']	[-1.90930963e-01 -5.74305914e-02 -1.50006607e-01 -1.95811450e-01 -8.78599882e-01 -2.65010238e-01 3.19444299e-01 -2.00730830e-01 -1.63909808e-01 2.09463537e-02 2.02546194e-01 3.26195419e-01 3.65623116e-01 -7.46576071e-01 -5.55800259e-01 -8.68224978e-01 1.31165460e-01 2.59062171e-01 1.43169224e-01 -1.47078097e-01 8.39228276e-03 5.26924431e-01 -1.41980684e+00 -5.85571714e-02 5.25811613e-01 1.43337190e+00 2.20668521e-02 2.05500498e-01 2.90371150e-01 1.48249313e-01 -2.54319847e-01 -3.37490916e-01 4.96685773e-01 -4.45298523e-01 -4.92586195e-01 2.50932544e-01 6.55058324e-01 -7.09862173e-01 -3.80777895e-01 1.09951341e+00 8.24290037e-01 -6.97364956e-02 6.47245884e-01 -1.01514411e+00 -6.00740373e-01 5.49575947e-02 -1.29555464e+00 6.59872368e-02 4.34564948e-01 3.03336024e-01 9.75240469e-01 -1.33417463e+00 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1a82abf4-bcb2-4dff-9016-01bdc4f4064d	evaluation-of-deep-learning-based-voice	2106.13511	null	https://arxiv.org/abs/2106.13511v1	https://arxiv.org/pdf/2106.13511v1.pdf	Evaluation of Deep-Learning-Based Voice Activity Detectors and Room Impulse Response Models in Reverberant Environments	State-of-the-art deep-learning-based voice activity detectors (VADs) are often trained with anechoic data. However, real acoustic environments are generally reverberant, which causes the performance to significantly deteriorate. To mitigate this mismatch between training data and real data, we simulate an augmented training set that contains nearly five million utterances. This extension comprises of anechoic utterances and their reverberant modifications, generated by convolutions of the anechoic utterances with a variety of room impulse responses (RIRs). We consider five different models to generate RIRs, and five different VADs that are trained with the augmented training set. We test all trained systems in three different real reverberant environments. Experimental results show $20\%$ increase on average in accuracy, precision and recall for all detectors and response models, compared to anechoic training. Furthermore, one of the RIR models consistently yields better performance than the other models, for all the tested VADs. Additionally, one of the VADs consistently outperformed the other VADs in all experiments.	['Baruch Berdugo', 'Israel Cohen', 'Amir Ivry']	2021-06-25	null	null	null	null	['room-impulse-response']	['audio']	[-2.65915662e-01 -4.91521925e-01 1.01300335e+00 -5.98950163e-02 -8.84185374e-01 -6.40102029e-01 4.55603361e-01 -3.72912824e-01 -4.18888897e-01 5.45899272e-01 5.00134766e-01 -3.09492379e-01 4.32474643e-01 -4.32219923e-01 -5.41827381e-01 -8.01947773e-01 -2.17390925e-01 -1.18855141e-01 5.32204211e-02 -9.61890519e-02 -4.18453246e-01 1.84681058e-01 -1.46625721e+00 2.56467879e-01 5.51835120e-01 9.29227054e-01 4.50056881e-01 1.18655610e+00 1.66909531e-01 6.01481855e-01 -1.43552983e+00 -5.12265526e-02 2.17252195e-01 -4.02675390e-01 -1.53437212e-01 -1.71796575e-01 2.77322799e-01 -1.76872209e-01 -6.87569082e-01 7.11149633e-01 1.13861561e+00 5.65227091e-01 4.25872862e-01 -6.30937457e-01 -3.42452049e-01 4.50255513e-01 7.79712107e-03 4.80391800e-01 4.82488364e-01 3.14775527e-01 6.50907993e-01 -1.24949861e+00 4.44282126e-03 1.27791703e+00 7.90895164e-01 6.41264200e-01 -9.30237353e-01 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e7688d6c-dcff-4024-890b-c195173dc25e	supervised-multiview-learning-based-on	1601.02098	null	http://arxiv.org/abs/1601.02098v1	http://arxiv.org/pdf/1601.02098v1.pdf	Supervised multiview learning based on simultaneous learning of multiview intact and single view classifier	Multiview learning problem refers to the problem of learning a classifier from multiple view data. In this data set, each data points is presented by multiple different views. In this paper, we propose a novel method for this problem. This method is based on two assumptions. The first assumption is that each data point has an intact feature vector, and each view is obtained by a linear transformation from the intact vector. The second assumption is that the intact vectors are discriminative, and in the intact space, we have a linear classifier to separate the positive class from the negative class. We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices. Moreover, we also propose a linear classifier in the intact space, and learn it jointly with the intact vectors. The learning problem is modeled by a minimization problem, and the objective function is composed of a Cauchy error estimator-based view-conditional reconstruction term over all data points and views, and a classification error term measured by hinge loss over all the intact vectors of all the data points. Some regularization terms are also imposed to different variables in the objective function. The minimization problem is solve by an iterative algorithm using alternate optimization strategy and gradient descent algorithm. The proposed algorithm shows it advantage in the compression to other multiview learning algorithms on benchmark data sets.	['Haiyan Lv', 'Eugene Mitchell', 'Qingjun Wang', 'Jun Yue']	2016-01-09	null	null	null	null	['multiview-learning']	['computer-vision']	[-3.75725850e-02 -9.27697122e-02 -3.51576835e-01 -5.40189505e-01 -8.73077571e-01 -4.49564576e-01 3.04851234e-01 -6.81459904e-02 -1.43774211e-01 3.68960053e-01 4.37301286e-02 4.60324734e-01 -1.79499865e-01 -5.93059838e-01 -7.72588789e-01 -9.73505855e-01 2.72725880e-01 4.30442423e-01 -5.86822294e-02 3.75794321e-02 1.59923077e-01 2.04558253e-01 -1.56067479e+00 3.04312974e-01 5.16330242e-01 1.21406782e+00 1.57835975e-01 3.73604625e-01 1.99013025e-01 6.29914522e-01 -2.42040217e-01 -1.30882874e-01 4.40066040e-01 -3.70460927e-01 -4.81293470e-01 5.56637824e-01 6.16675317e-01 -2.86814272e-01 -1.00342467e-01 1.19877613e+00 2.47104302e-01 2.51798239e-02 9.97008443e-01 -1.45402265e+00 -4.22539443e-01 -5.39728142e-02 -8.61401618e-01 -3.20477873e-01 3.77464831e-01 -5.72012603e-01 1.08093214e+00 -1.38671923e+00 7.85145819e-01 1.03539574e+00 3.98151696e-01 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-1.14049923e+00 5.83590984e-01 4.61349636e-01 -6.58898056e-01 -6.23554230e-01 3.14190894e-01]	[8.351173400878906, 4.546793460845947]
3e9fe912-79a6-40ea-8933-eec9b4f748ec	dynamic-cross-feature-fusion-for-remote	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Wu_Dynamic_Cross_Feature_Fusion_for_Remote_Sensing_Pansharpening_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Wu_Dynamic_Cross_Feature_Fusion_for_Remote_Sensing_Pansharpening_ICCV_2021_paper.pdf	Dynamic Cross Feature Fusion for Remote Sensing Pansharpening	Deep Convolution Neural Networks have been adopted for pansharpening and achieved state-of-the-art performance. However, most of the existing works mainly focus on single-scale feature fusion, which leads to failure in fully considering relationships of information between high-level semantics and low-level features, despite the network is deep enough. In this paper, we propose a dynamic cross feature fusion network (DCFNet) for pansharpening. Specifically, DCFNet contains multiple parallel branches, including a high-resolution branch served as the backbone, and the low-resolution branches progressively supplemented into the backbone. Thus our DCFNet can represent the overall information well. In order to enhance the relationships of inter-branches, dynamic cross feature transfers are embedded into multiple branches to obtain high-resolution representations. Then contextualized features will be learned to improve the fusion of information. Experimental results indicate that DCFNet significantly outperforms the prior arts in both quantitative indicators and visual qualities.	['Tian-Jing Zhang', 'Liang-Jian Deng', 'Ting-Zhu Huang', 'Xiao Wu']	2021-01-01	null	null	null	iccv-2021-1	['pansharpening']	['computer-vision']	[ 2.88751543e-01 -3.17239285e-01 -3.03151697e-01 -2.23794669e-01 -4.39313024e-01 -1.34332851e-01 4.24943238e-01 6.48332164e-02 -6.49683103e-02 3.47658992e-01 4.65541214e-01 3.41461331e-01 -2.59885460e-01 -1.10970640e+00 -6.29280329e-01 -6.22244716e-01 7.53719732e-02 -3.34545642e-01 6.72127903e-01 -6.32850587e-01 -9.60740149e-02 4.53296185e-01 -1.52139771e+00 5.30069351e-01 1.04345798e+00 1.31799352e+00 2.90255547e-01 1.80431217e-01 -2.54524380e-01 5.41182458e-01 -2.77642310e-01 -3.89519274e-01 3.71110946e-01 -1.43514633e-01 -4.02802140e-01 5.73231541e-02 4.30855989e-01 -6.08493149e-01 -6.39510393e-01 1.25832093e+00 2.93860614e-01 4.82627004e-02 1.25587255e-01 -1.25680482e+00 -8.13468993e-01 6.40757382e-01 -8.80779564e-01 1.41846061e-01 1.08109087e-01 7.81332180e-02 1.03663993e+00 -7.35970676e-01 3.47066015e-01 1.43709445e+00 7.10211873e-01 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c727cedb-cdde-4801-a118-ce9d2c66585a	hierarchical-deep-temporal-models-for-group	1607.02643	null	http://arxiv.org/abs/1607.02643v1	http://arxiv.org/pdf/1607.02643v1.pdf	Hierarchical Deep Temporal Models for Group Activity Recognition	In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can be developed to capture these dynamics at both person-level and group-level detail. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. In order to model both person-level and group-level dynamics, we present a 2-stage deep temporal model for the group activity recognition problem. In our approach, one LSTM model is designed to represent action dynamics of individual people in a video sequence and another LSTM model is designed to aggregate person-level information for group activity recognition. We collected a new dataset consisting of volleyball videos labeled with individual and group activities in order to evaluate our method. Experimental results on this new Volleyball Dataset and the standard benchmark Collective Activity Dataset demonstrate the efficacy of the proposed models.	['Mostafa S. Ibrahim', 'Zhiwei Deng', 'Greg Mori', 'Arash Vahdat', 'Srikanth Muralidharan']	2016-07-09	null	null	null	null	['group-activity-recognition']	['computer-vision']	[ 2.58930057e-01 -4.77020770e-01 -3.66661400e-01 -3.00545245e-01 -4.63962071e-02 -2.46886566e-01 1.03197181e+00 1.07398197e-01 -5.22872448e-01 4.74254787e-01 7.00435817e-01 2.48382390e-01 -1.87259912e-01 -8.31035674e-01 -6.09440327e-01 -7.44575620e-01 -6.02429092e-01 1.21030107e-01 1.72546655e-01 6.45112470e-02 1.37436569e-01 2.70014197e-01 -1.55719292e+00 7.56824493e-01 3.10138226e-01 8.90974939e-01 1.22031234e-02 9.47554231e-01 -2.96913018e-03 1.97187376e+00 -7.74225473e-01 1.77598238e-01 1.98221341e-01 -8.35829794e-01 -1.01631534e+00 6.66246295e-01 4.57560331e-01 -3.93747896e-01 -7.81774044e-01 5.16077280e-01 1.14085719e-01 6.33023977e-01 4.64389205e-01 -1.24237323e+00 -1.39626175e-01 5.07171988e-01 -2.17334449e-01 7.22688735e-01 7.32624531e-01 1.67379424e-01 8.13243926e-01 -3.53525698e-01 6.65069342e-01 1.09984052e+00 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-1.09977925e+00 4.48312312e-02 -1.07864988e+00 -7.03407288e-01 8.84730518e-01 3.93474489e-01 -6.06531978e-01 -1.64146677e-01 4.64982778e-01]	[8.086458206176758, 0.6097585558891296]
e4be9e6f-b08c-437f-b386-6dec9507667a	neural-cdes-for-long-time-series-via-the-log	2009.08295	null	https://arxiv.org/abs/2009.08295v4	https://arxiv.org/pdf/2009.08295v4.pdf	Neural Rough Differential Equations for Long Time Series	Neural controlled differential equations (CDEs) are the continuous-time analogue of recurrent neural networks, as Neural ODEs are to residual networks, and offer a memory-efficient continuous-time way to model functions of potentially irregular time series. Existing methods for computing the forward pass of a Neural CDE involve embedding the incoming time series into path space, often via interpolation, and using evaluations of this path to drive the hidden state. Here, we use rough path theory to extend this formulation. Instead of directly embedding into path space, we instead represent the input signal over small time intervals through its \textit{log-signature}, which are statistics describing how the signal drives a CDE. This is the approach for solving \textit{rough differential equations} (RDEs), and correspondingly we describe our main contribution as the introduction of Neural RDEs. This extension has a purpose: by generalising the Neural CDE approach to a broader class of driving signals, we demonstrate particular advantages for tackling long time series. In this regime, we demonstrate efficacy on problems of length up to 17k observations and observe significant training speed-ups, improvements in model performance, and reduced memory requirements compared to existing approaches.	['Patrick Kidger', 'Cristopher Salvi', 'Terry Lyons', 'James Morrill', 'James Foster']	2020-09-17	null	null	null	null	['irregular-time-series']	['time-series']	[ 2.16461539e-01 4.67083324e-03 2.76099760e-02 -1.72963105e-02 -4.57317561e-01 -3.79629046e-01 7.41096377e-01 -1.97235212e-01 -4.52181429e-01 7.69550562e-01 -8.09735507e-02 -5.75014412e-01 -3.61423135e-01 -6.93759441e-01 -8.10450256e-01 -7.20685363e-01 -6.93036735e-01 1.09293960e-01 -4.91052726e-03 -3.96807849e-01 3.45501900e-02 8.37848365e-01 -1.25688350e+00 -1.61798820e-01 4.00242567e-01 1.22513556e+00 -3.85946453e-01 9.95456636e-01 2.51912892e-01 9.62634504e-01 -3.96710366e-01 1.49488509e-01 1.96745917e-01 -4.45244461e-01 -6.36638880e-01 -3.56126457e-01 -2.10363433e-01 -2.49616787e-01 -7.89333820e-01 6.63136184e-01 3.81787866e-01 5.35194874e-01 7.95366347e-01 -1.18887651e+00 -5.89002132e-01 3.13324779e-01 2.04860438e-02 5.27138650e-01 3.52778123e-03 5.23717031e-02 7.07690716e-01 -9.88962293e-01 6.25397086e-01 9.89287198e-01 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-9.20555353e-01 -7.24240124e-01 -4.75466728e-01 -1.49422631e-01 1.21774757e+00 -3.33646834e-01 -5.82602322e-01 8.63025859e-02]	[6.993121147155762, 3.326075792312622]
dae31c2b-1159-4e77-a210-a41046730e09	age-invariant-face-embedding-using-the	2305.02745	null	https://arxiv.org/abs/2305.02745v1	https://arxiv.org/pdf/2305.02745v1.pdf	Age-Invariant Face Embedding using the Wasserstein Distance	In this work, we study face verification in datasets where images of the same individuals exhibit significant age differences. This poses a major challenge for current face recognition and verification techniques. To address this issue, we propose a novel approach that utilizes multitask learning and a Wasserstein distance discriminator to disentangle age and identity embeddings of facial images. Our approach employs multitask learning with a Wasserstein distance discriminator that minimizes the mutual information between the age and identity embeddings by minimizing the Jensen-Shannon divergence. This improves the encoding of age and identity information in face images and enhances the performance of face verification in age-variant datasets. We evaluate the effectiveness of our approach using multiple age-variant face datasets and demonstrate its superiority over state-of-the-art methods in terms of face verification accuracy.	['Yosi Keller', 'Eran Dahan']	2023-05-04	null	null	null	null	['face-recognition', 'face-verification']	['computer-vision', 'computer-vision']	[ 6.07234575e-02 -2.84506083e-01 -1.53592685e-02 -8.32335830e-01 -7.30877817e-01 -4.03557539e-01 7.02118337e-01 1.08570658e-01 -6.76850617e-01 5.45775115e-01 -3.23553104e-03 -2.08753739e-02 -3.35293770e-01 -4.29915905e-01 -2.76145875e-01 -9.29587603e-01 -1.22366175e-01 2.03630656e-01 -5.41220546e-01 2.20503807e-01 3.31387788e-01 3.95448685e-01 -1.58586037e+00 2.42333021e-02 7.76759446e-01 1.31399441e+00 -4.66884345e-01 4.63776588e-01 2.72018492e-01 -9.61724371e-02 -4.96139705e-01 -7.07448661e-01 5.16640484e-01 -2.14533031e-01 -5.58963835e-01 -1.11713022e-01 1.23049700e+00 -4.44399357e-01 -1.78276837e-01 9.90743876e-01 8.04352403e-01 -7.38263428e-02 9.59693074e-01 -1.68859613e+00 -1.05497909e+00 4.00674194e-02 -7.19288111e-01 2.29139060e-01 2.45315060e-01 -2.07072765e-01 8.74608457e-01 -9.07890201e-01 1.91880781e-02 1.52942538e+00 9.74000752e-01 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da79ae4a-98b6-4708-9211-eef130fc7536	small-object-detection-in-remote-sensing	2003.09085	null	https://arxiv.org/abs/2003.09085v5	https://arxiv.org/pdf/2003.09085v5.pdf	Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network	The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.	['Nilanjan Ray', 'Matthias Schubert', 'Dennis Chao', 'Jakaria Rabbi', 'Subir Chowdhury']	2020-03-20	null	null	null	null	['small-object-detection', 'satellite-image-super-resolution', 'remote-sensing-image-classification']	['computer-vision', 'computer-vision', 'miscellaneous']	[ 5.71372390e-01 -9.95680019e-02 3.81549358e-01 4.43514176e-02 -9.18236911e-01 -3.22831571e-01 3.69743913e-01 -9.61411059e-01 -3.10699701e-01 5.97687066e-01 -1.35625969e-03 -6.49190843e-02 1.33630872e-01 -1.35056686e+00 -6.97640419e-01 -9.69938636e-01 -6.09893650e-02 -4.31287214e-02 6.23893142e-01 -4.44947034e-01 -2.32884690e-01 7.17538238e-01 -1.43399572e+00 3.14306229e-01 8.23248506e-01 8.71635675e-01 5.32381713e-01 8.93391252e-01 4.04926062e-01 1.04657471e+00 -4.62174207e-01 -9.78788361e-02 7.53035128e-01 -6.81253552e-01 -3.11507821e-01 1.11684836e-01 1.36280850e-01 -8.50491285e-01 -5.94186425e-01 1.31329620e+00 9.10293162e-01 5.03710322e-02 5.30373812e-01 -9.20515835e-01 -1.18822169e+00 5.91420591e-01 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1c50428d-d9b3-4fa4-bbce-959feae46f58	transition-based-semantic-role-labeling-with	2205.10023	null	https://arxiv.org/abs/2205.10023v2	https://arxiv.org/pdf/2205.10023v2.pdf	Transition-based Semantic Role Labeling with Pointer Networks	Semantic role labeling (SRL) focuses on recognizing the predicate-argument structure of a sentence and plays a critical role in many natural language processing tasks such as machine translation and question answering. Practically all available methods do not perform full SRL, since they rely on pre-identified predicates, and most of them follow a pipeline strategy, using specific models for undertaking one or several SRL subtasks. In addition, previous approaches have a strong dependence on syntactic information to achieve state-of-the-art performance, despite being syntactic trees equally hard to produce. These simplifications and requirements make the majority of SRL systems impractical for real-world applications. In this article, we propose the first transition-based SRL approach that is capable of completely processing an input sentence in a single left-to-right pass, with neither leveraging syntactic information nor resorting to additional modules. Thanks to our implementation based on Pointer Networks, full SRL can be accurately and efficiently done in $O(n^2)$, achieving the best performance to date on the majority of languages from the CoNLL-2009 shared task.	['Daniel Fernández-González']	2022-05-20	null	null	null	null	['semantic-role-labeling']	['natural-language-processing']	[ 6.98273778e-01 4.24664527e-01 -2.73754746e-01 -4.83893126e-01 -8.30024421e-01 -9.47618723e-01 8.34141612e-01 6.27590477e-01 -7.35537112e-01 6.27244115e-01 2.90551901e-01 -7.76830375e-01 -7.29417726e-02 -7.65182793e-01 -6.09453976e-01 -1.08766690e-01 8.58026668e-02 6.19243443e-01 7.97286987e-01 -6.18119121e-01 2.65508831e-01 3.33340198e-01 -1.71918237e+00 6.27200484e-01 5.18562198e-01 7.69349277e-01 1.23447813e-01 6.42128170e-01 -5.98059535e-01 1.10079896e+00 -4.56243545e-01 -3.00434828e-01 7.12947622e-02 -2.12808654e-01 -1.33293927e+00 -4.12502557e-01 2.55693495e-01 1.69866577e-01 5.47175147e-02 8.56956005e-01 3.88537854e-01 1.66125178e-01 8.07544217e-02 -1.01272416e+00 -1.37475416e-01 9.96296525e-01 -9.41383988e-02 2.08797455e-01 7.84427404e-01 -1.20078819e-02 1.54472649e+00 -6.09704137e-01 7.00326860e-01 1.43379831e+00 3.83791029e-01 6.34257555e-01 -1.15688252e+00 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4e9fa51f-206c-4d7b-a422-c26aa3fe7757	why-can-big-bi-be-changed-to-bi-gbi-a	2307.02299	null	https://arxiv.org/abs/2307.02299v1	https://arxiv.org/pdf/2307.02299v1.pdf	Why can big.bi be changed to bi.gbi? A mathematical model of syllabification and articulatory synthesis	A simplified model of articulatory synthesis involving four stages is presented. The planning of articulatory gestures is based on syllable graphs with arcs and nodes that are implemented in a complex representation. This was first motivated by a reduction in the many-to-one relationship between articulatory parameters and formant space. This allows for consistent trajectory planning and computation of articulation dynamics with coordination and selection operators. The flow of articulatory parameters is derived from these graphs with four equations. Many assertions of Articulatory Phonology have been abandoned. This framework is adapted to synthesis using VLAM (a Maeda's model) and simulations are performed with syllables including main vowels and the plosives /b,d,g/ only. The model is able to describe consonant-vowel coarticulation, articulation of consonant clusters, and verbal transformations are seen as transitions of the syllable graph structure.	['Frédéric Berthommier']	2023-07-05	null	null	null	null	['trajectory-planning']	['robots']	[-1.66030273e-01 2.44386122e-01 -3.32418084e-01 2.07468480e-01 1.20318264e-01 -9.17470813e-01 1.13578713e+00 -1.69080347e-01 -2.46124923e-01 6.26667023e-01 3.83176804e-01 -3.18884492e-01 -2.23735526e-01 -5.24713874e-01 -1.12497747e-01 -7.05870986e-01 -1.58980295e-01 7.85779238e-01 3.23574573e-01 -4.62570161e-01 3.29398900e-01 8.04495394e-01 -1.84783518e+00 -9.13892090e-02 4.29397404e-01 -6.51703775e-02 4.26254570e-01 1.12025237e+00 -5.19693792e-01 1.54714331e-01 -5.26829720e-01 -6.24984577e-02 -6.91272467e-02 -7.76706874e-01 -5.57637513e-01 2.47296378e-01 -4.65778768e-01 8.95547569e-02 1.27864540e-01 7.60553300e-01 2.52387911e-01 3.96799535e-01 1.32305312e+00 -8.62683177e-01 -5.77404499e-01 8.30225646e-01 1.91496864e-01 -2.02668115e-01 3.66744548e-01 1.21495806e-01 8.16070795e-01 -1.19017828e+00 9.29063022e-01 1.57798040e+00 3.00591379e-01 7.73406625e-01 -8.95402730e-01 2.85084844e-01 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1533ac08-b527-4650-8b53-467cc71a2d11	self-fusenet-data-free-unsupervised-remote	null	null	https://ieeexplore.ieee.org/abstract/document/10025676	https://ieeexplore.ieee.org/abstract/document/10025676	Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution	Real-world degradations deviate from ideal degradations, as most deep learning-based scenarios involve the ideal synthesis of low-resolution (LR) counterpart images by popularly used bicubic interpolation. Moreover, supervised learning approaches rely on many high-resolution (HR) and LR image pairings to reconstruct missing information based on their association, developed by complex long hours of deep neural network training. Additionally, the trained model's generalizability on various image datasets with various distributions is not guaranteed. To overcome this challenge, we proposed our novel Self-FuseNet, particularly for extremely poor-resolution satellite images. Also, the network exhibits strong generalization performance on additional datasets (both “ideal” and “nonideal” scenarios). The network is especially for those image datasets suffering from the following two significant limitations: 1) nonavailability of ground truth HR images; 2) limitation of a large count of the unpaired dataset for deep neural network training. The benefit of the proposed model is threefold: 1) it does not require any significant extensive training data, either paired or unpaired but only a single LR image without prior knowledge of its distribution; 2) it is a simple and effective model for super-resolving very poor-resolution images, saving computational resources and time; 3) using UNet, the processing of data are accelerated by the network's wide skip connections, allowing image reconstruction with fewer parameters. Rather than using an inverse approach, as common in most deep learning scenarios, we introduced a forward approach to super-resolve exceptionally LR remote sensing images. This demonstrates its supremacy over recently proposed state-of-the-art methods for unsupervised single real-world image blind super-resolution.	['Ofer Hadar', 'Divya Mishra']	2023-01-23	null	null	null	ieee-journal-2023-1	['multi-exposure-image-fusion', 'image-enhancement', 'unsupervised-image-to-image-translation', 'satellite-image-super-resolution', 'unsupervised-pre-training', 'feature-engineering']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'methodology', 'methodology']	[ 4.04690117e-01 -2.44386867e-01 1.13357522e-01 -2.62964606e-01 -1.02898419e+00 -2.93973207e-01 4.10623372e-01 -4.94092792e-01 -4.20326144e-01 1.25731432e+00 -1.86940394e-02 -1.51335686e-01 -3.98431480e-01 -1.02298093e+00 -7.51198292e-01 -1.08086038e+00 -7.73627907e-02 3.11341703e-01 -1.61407292e-02 -4.82814461e-01 -9.86211821e-02 8.07843387e-01 -2.00529718e+00 1.49620593e-01 1.25398159e+00 1.19188845e+00 4.97849405e-01 4.23817903e-01 8.43456909e-02 6.66932762e-01 -4.29860532e-01 -1.95260458e-02 5.66808939e-01 -3.24448466e-01 -3.92520249e-01 -1.53885573e-01 6.73108220e-01 -7.73755074e-01 -3.79796267e-01 1.12484086e+00 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01403c47-393d-4f65-ad7a-84a3e47fb3e1	knowledge-base-question-answering-for-space	2305.19734	null	https://arxiv.org/abs/2305.19734v1	https://arxiv.org/pdf/2305.19734v1.pdf	Knowledge Base Question Answering for Space Debris Queries	Space agencies execute complex satellite operations that need to be supported by the technical knowledge contained in their extensive information systems. Knowledge bases (KB) are an effective way of storing and accessing such information at scale. In this work we present a system, developed for the European Space Agency (ESA), that can answer complex natural language queries, to support engineers in accessing the information contained in a KB that models the orbital space debris environment. Our system is based on a pipeline which first generates a sequence of basic database operations, called a %program sketch, from a natural language question, then specializes the sketch into a concrete query program with mentions of entities, attributes and relations, and finally executes the program against the database. This pipeline decomposition approach enables us to train the system by leveraging out-of-domain data and semi-synthetic data generated by GPT-3, thus reducing overfitting and shortcut learning even with limited amount of in-domain training data. Our code can be found at \url{https://github.com/PaulDrm/DISCOSQA}.	['Annalisa Riccardi', 'Shay B. Cohen', 'Antonio Valerio Miceli-Barone', 'Paul Darm']	2023-05-31	null	null	null	null	['knowledge-base-question-answering']	['natural-language-processing']	[-3.71347696e-01 3.81918043e-01 -1.64351195e-01 -3.61974508e-01 -1.00932682e+00 -9.11856592e-01 5.68536341e-01 3.08382362e-01 -3.58539969e-01 8.50496292e-01 1.77733645e-01 -7.26749539e-01 -2.37245247e-01 -1.16580331e+00 -7.39690900e-01 -5.71150668e-02 5.49253151e-02 1.11749017e+00 6.13821685e-01 -3.79353791e-01 2.78282642e-01 7.06473470e-01 -1.57253516e+00 4.30378586e-01 9.47610319e-01 1.16699791e+00 3.46729338e-01 4.79881138e-01 -5.39025486e-01 9.72763717e-01 -6.03686750e-01 -4.88192886e-01 1.66177511e-01 2.34412104e-01 -1.16561627e+00 -6.20038986e-01 -1.82387829e-02 -1.70144781e-01 -3.18625867e-01 8.74999166e-01 4.12302285e-01 4.68598530e-02 3.93376559e-01 -1.29967773e+00 -2.92250454e-01 4.74109918e-01 4.46455538e-01 1.16689943e-01 6.16391301e-01 3.50815266e-01 1.26132405e+00 -1.01490676e+00 8.48126292e-01 9.26979184e-01 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7de44e18-3b01-43d1-bfb4-ce7fa6da0046	conditional-denoising-diffusion-for	2304.11433	null	https://arxiv.org/abs/2304.11433v1	https://arxiv.org/pdf/2304.11433v1.pdf	Conditional Denoising Diffusion for Sequential Recommendation	Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions. However, two prominent generative models, namely Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs), exhibit challenges that impede achieving optimal performance in sequential recommendation tasks. Specifically, GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. The sparse and noisy nature of sequential recommendation further exacerbates these issues. In response to these limitations, we present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser. This approach streamlines the optimization and generation process by dividing it into easier and tractable steps in a conditional autoregressive manner. Furthermore, we introduce a novel optimization schema that incorporates both cross-divergence loss and contrastive loss. This novel training schema enables the model to generate high-quality sequence/item representations and meanwhile precluding collapse. We conducted comprehensive experiments on four benchmark datasets, and the superior performance achieved by our model attests to its efficacy.	['Philip S. Yu', 'Liangwei Yang', 'Zhiwei Liu', 'Yu Wang']	2023-04-22	null	null	null	null	['sequential-recommendation']	['miscellaneous']	[ 1.74414441e-01 -1.73981965e-01 8.68005380e-02 -2.34240204e-01 -8.06242406e-01 -4.55863446e-01 7.44579256e-01 -5.78067005e-01 -3.21054533e-02 9.01863396e-01 5.36500573e-01 -5.53174466e-02 5.95426001e-02 -8.94009590e-01 -9.01658595e-01 -1.03588879e+00 4.44536448e-01 2.12704003e-01 -1.46426722e-01 -1.52569264e-01 -8.44254494e-02 4.09236103e-02 -1.25439608e+00 1.27444565e-01 1.48818421e+00 9.31969464e-01 2.95774490e-01 3.14391106e-01 -1.01325944e-01 9.02135491e-01 -6.89006865e-01 -7.59170055e-01 7.75093064e-02 -6.97375178e-01 -1.43771812e-01 8.43439177e-02 -8.20317790e-02 -5.57080030e-01 -5.28489530e-01 9.75018263e-01 6.84860289e-01 1.95265591e-01 8.78494203e-01 -1.14386773e+00 -1.37898195e+00 7.42448270e-01 -4.60748434e-01 -1.49209723e-01 5.12518268e-03 2.28181913e-01 1.04759240e+00 -8.82678032e-01 2.04181656e-01 1.19610691e+00 6.96889281e-01 6.40013218e-01 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-6.70807004e-01 3.49385679e-01 1.13344304e-01 -5.18516898e-01 -4.91800755e-01 3.87600181e-03]	[10.486137390136719, 5.396844387054443]
3b23a6c8-2ffb-48d9-b1c9-e6dbf0cf34bf	flexibo-cost-aware-multi-objective	2001.06588	null	https://arxiv.org/abs/2001.06588v3	https://arxiv.org/pdf/2001.06588v3.pdf	FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks	The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optimal designs involves evaluating designs in an iterative process, and the measurements are used to evaluate an acquisition function that guides the search process. However, measuring different objectives incurs different costs. For example, the cost of measuring the prediction error of DNNs is orders of magnitude higher than that of measuring the energy consumption of a pre-trained DNN, as it requires re-training the DNN. Current state-of-the-art methods do not consider this difference in objective evaluation cost, potentially incurring expensive evaluations of objective functions in the optimization process. In this paper, we develop a novel decoupled and cost-aware multi-objective optimization algorithm, we call Flexible Multi-Objective Bayesian Optimization (FlexiBO) to address this issue. FlexiBO weights the improvement of the hypervolume of the Pareto region by the measurement cost of each objective to balance the expense of collecting new information with the knowledge gained through objective evaluations, preventing us from performing expensive measurements for little to no gain. We evaluate FlexiBO on seven state-of-the-art DNNs for image recognition, natural language processing (NLP), and speech-to-text translation. Our results indicate that, given the same total experimental budget, FlexiBO discovers designs with 4.8$\%$ to 12.4$\%$ lower hypervolume error than the best method in state-of-the-art multi-objective optimization.	['Lars Kotthoff', 'Jianhai Su', 'Pooyan Jamshidi', 'Md Shahriar Iqbal']	2020-01-18	null	null	null	null	['speech-to-text-translation']	['natural-language-processing']	[ 3.77614498e-02 -1.39823973e-01 -2.64579564e-01 -4.08486575e-01 -6.82844341e-01 -4.98023033e-01 2.98448838e-02 3.28231789e-02 -7.74911046e-01 8.52928758e-01 -1.75930724e-01 -3.20095807e-01 -3.45710278e-01 -6.92282677e-01 -8.46389294e-01 -8.40844929e-01 1.61884621e-01 6.15089178e-01 -1.02599144e-01 3.26694638e-01 6.88122660e-02 5.12550354e-01 -1.27733362e+00 -2.16789305e-01 7.98548341e-01 1.40967834e+00 3.68906289e-01 2.73421317e-01 -4.71803956e-02 1.33466929e-01 -7.25702047e-01 -3.88584644e-01 2.26413503e-01 -4.08937663e-01 -4.85074699e-01 -3.48901600e-01 1.40115172e-01 -3.36371511e-01 -1.29918844e-01 1.18987513e+00 6.67250872e-01 1.69575036e-01 5.54044724e-01 -1.19537377e+00 -3.06947023e-01 7.06582606e-01 -2.70378470e-01 -5.95510788e-02 -5.87178409e-01 1.40011087e-01 1.19439733e+00 -5.05019307e-01 1.26684025e-01 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-7.16383636e-01 -7.26333499e-01 -6.48975670e-01 -2.10461333e-01 -4.51171488e-01 2.73596525e-01 -7.41194606e-01 -2.87638903e-01 -3.95834178e-01]	[8.391984939575195, 3.265502452850342]
8a6d4697-8ddc-4d82-a091-87c764bb175b	calibration-with-bias-corrected-temperature	1901.06852	null	https://arxiv.org/abs/1901.06852v5	https://arxiv.org/pdf/1901.06852v5.pdf	Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation	Label shift refers to the phenomenon where the prior class probability p(y) changes between the training and test distributions, while the conditional probability p(x\|y) stays fixed. Label shift arises in settings like medical diagnosis, where a classifier trained to predict disease given symptoms must be adapted to scenarios where the baseline prevalence of the disease is different. Given estimates of p(y\|x) from a predictive model, Saerens et al. proposed an efficient maximum likelihood algorithm to correct for label shift that does not require model retraining, but a limiting assumption of this algorithm is that p(y\|x) is calibrated, which is not true of modern neural networks. Recently, Black Box Shift Learning (BBSL) and Regularized Learning under Label Shifts (RLLS) have emerged as state-of-the-art techniques to cope with label shift when a classifier does not output calibrated probabilities, but both methods require model retraining with importance weights and neither has been benchmarked against maximum likelihood. Here we (1) show that combining maximum likelihood with a type of calibration we call bias-corrected calibration outperforms both BBSL and RLLS across diverse datasets and distribution shifts, (2) prove that the maximum likelihood objective is concave, and (3) introduce a principled strategy for estimating source-domain priors that improves robustness to poor calibration. This work demonstrates that the maximum likelihood with appropriate calibration is a formidable and efficient baseline for label shift adaptation; notebooks reproducing experiments available at https://github.com/kundajelab/labelshiftexperiments	['Avanti Shrikumar', 'Amr Alexandari', 'Anshul Kundaje']	2019-01-21	null	null	null	null	['diabetic-retinopathy-detection']	['medical']	[ 7.00092435e-01 3.13877821e-01 -7.68848002e-01 -6.84523344e-01 -1.01873052e+00 -5.43120563e-01 5.41580260e-01 2.34077170e-01 -5.37659645e-01 1.00842607e+00 -1.04606546e-01 -4.15621668e-01 -3.40101123e-01 -4.84707117e-01 -9.34291899e-01 -8.79535377e-01 2.14247361e-01 8.26344371e-01 1.37219593e-01 3.52658659e-01 -5.48695549e-02 3.71806085e-01 -1.21889734e+00 3.71172540e-02 8.69426072e-01 5.09130180e-01 -2.85711139e-02 7.40611434e-01 1.49843991e-01 2.75237203e-01 -6.09945178e-01 -3.52578014e-01 2.78974473e-01 -4.25540090e-01 -7.42737293e-01 7.14190304e-02 4.76747274e-01 -1.10382751e-01 1.55314412e-02 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45b632cc-1c46-46e0-a93a-e3329a86df28	acceptability-judgements-via-examining-the	2205.09630	null	https://arxiv.org/abs/2205.09630v2	https://arxiv.org/pdf/2205.09630v2.pdf	Acceptability Judgements via Examining the Topology of Attention Maps	The role of the attention mechanism in encoding linguistic knowledge has received special interest in NLP. However, the ability of the attention heads to judge the grammatical acceptability of a sentence has been underexplored. This paper approaches the paradigm of acceptability judgments with topological data analysis (TDA), showing that the geometric properties of the attention graph can be efficiently exploited for two standard practices in linguistics: binary judgments and linguistic minimal pairs. Topological features enhance the BERT-based acceptability classifier scores by $8$%-$24$% on CoLA in three languages (English, Italian, and Swedish). By revealing the topological discrepancy between attention maps of minimal pairs, we achieve the human-level performance on the BLiMP benchmark, outperforming nine statistical and Transformer LM baselines. At the same time, TDA provides the foundation for analyzing the linguistic functions of attention heads and interpreting the correspondence between the graph features and grammatical phenomena.	['Evgeny Burnaev', 'Dmitri Piontkovski', 'Irina Piontkovskaya', 'Serguei Barannikov', 'Ekaterina Artemova', 'Laida Kushnareva', 'Irina Proskurina', 'Vladislav Mikhailov', 'Eduard Tulchinskii', 'Daniil Cherniavskii']	2022-05-19	null	null	null	null	['linguistic-acceptability']	['natural-language-processing']	[-1.02832690e-01 5.22889853e-01 1.22340493e-01 -5.81620991e-01 -7.64712155e-01 -7.88241982e-01 7.24253237e-01 8.71733785e-01 -3.36632073e-01 1.07865356e-01 4.35689688e-01 -8.55721414e-01 -2.59292245e-01 -9.11969185e-01 -7.15790510e-01 -4.34041798e-01 -2.93095142e-01 5.44560850e-01 -1.61788329e-01 -4.25794512e-01 6.58960640e-01 3.10576171e-01 -1.24584937e+00 5.07529855e-01 1.22937608e+00 1.21128416e+00 1.55055553e-01 3.85104448e-01 -2.77375311e-01 4.22333866e-01 -2.40959480e-01 -7.52150774e-01 -1.16771489e-01 -3.04175436e-01 -1.20204043e+00 -5.74212611e-01 9.16577816e-01 2.78308302e-01 7.84386881e-03 1.02638519e+00 3.29146594e-01 9.89013314e-02 9.40903544e-01 -7.54397988e-01 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4bfea0b4-b353-46f9-907e-e2461b682d8a	a-semantics-aware-transformer-model-of	null	null	https://aclanthology.org/2021.acl-short.34	https://aclanthology.org/2021.acl-short.34.pdf	A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering	Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.	['Alexander Gray', 'Alfio Gliozzo', 'Salim Roukos', 'Pavan Kapanipathi', 'Young-suk Lee', 'Ibrahim Abdelaziz', 'Nandana Mihindukulasooriya', 'Srinivas Ravishankar', 'Tahira Naseem']	2021-08-01	null	null	null	acl-2021-5	['knowledge-base-question-answering']	['natural-language-processing']	[-5.6992702e-02 5.7351065e-01 -4.8463500e-01 -2.7743757e-01 -1.0155407e+00 -6.3105917e-01 5.3650379e-01 6.7070520e-01 -3.4829724e-01 8.9996266e-01 4.2176637e-01 -6.5667075e-01 -2.6359242e-01 -1.3912466e+00 -9.0340453e-01 1.8107696e-01 1.4749417e-01 8.8029259e-01 8.7436223e-01 -1.0435551e+00 -1.8275334e-01 -9.6019834e-02 -1.2489773e+00 4.4976002e-01 8.6351687e-01 1.1042714e+00 -1.5235583e-01 4.2275208e-01 -8.3376795e-01 1.2703638e+00 -4.7605562e-01 -9.9550003e-01 7.2255954e-02 -1.5221439e-01 -1.4322879e+00 -7.6672989e-01 6.1712205e-01 1.0765289e-02 -5.4312342e-01 9.4392198e-01 2.7064961e-01 1.9584270e-03 2.5342867e-01 -9.1944695e-01 -1.0051378e+00 9.1875994e-01 -9.2563026e-02 4.8293057e-01 6.7212719e-01 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3930d028-fbab-4a22-a781-5ec7053b6464	rgb-d-salient-object-detection-with-cross	2007.07051	null	https://arxiv.org/abs/2007.07051v1	https://arxiv.org/pdf/2007.07051v1.pdf	RGB-D Salient Object Detection with Cross-Modality Modulation and Selection	We present an effective method to progressively integrate and refine the cross-modality complementarities for RGB-D salient object detection (SOD). The proposed network mainly solves two challenging issues: 1) how to effectively integrate the complementary information from RGB image and its corresponding depth map, and 2) how to adaptively select more saliency-related features. First, we propose a cross-modality feature modulation (cmFM) module to enhance feature representations by taking the depth features as prior, which models the complementary relations of RGB-D data. Second, we propose an adaptive feature selection (AFS) module to select saliency-related features and suppress the inferior ones. The AFS module exploits multi-modality spatial feature fusion with the self-modality and cross-modality interdependencies of channel features are considered. Third, we employ a saliency-guided position-edge attention (sg-PEA) module to encourage our network to focus more on saliency-related regions. The above modules as a whole, called cmMS block, facilitates the refinement of saliency features in a coarse-to-fine fashion. Coupled with a bottom-up inference, the refined saliency features enable accurate and edge-preserving SOD. Extensive experiments demonstrate that our network outperforms state-of-the-art saliency detectors on six popular RGB-D SOD benchmarks.	['Runmin Cong', 'Yongri Piao', 'Qianqian Xu', 'Chen Change Loy', 'Chongyi Li']	2020-07-14	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/521_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530222.pdf	eccv-2020-8	['rgb-d-salient-object-detection']	['computer-vision']	[ 3.11086446e-01 -1.25628784e-01 -3.79463375e-01 -2.61006206e-01 -6.93441808e-01 -1.87023841e-02 2.64783651e-01 3.30216661e-02 -3.46258849e-01 3.97786349e-01 4.25305396e-01 2.66918540e-01 -1.77001819e-01 -7.04751015e-01 -6.51005507e-01 -7.63634324e-01 2.06681922e-01 -5.26417732e-01 9.08810318e-01 -5.77803075e-01 4.00442600e-01 4.79608417e-01 -1.98103845e+00 2.86980599e-01 1.03676879e+00 1.47062016e+00 6.31851077e-01 2.04010963e-01 1.14978319e-02 6.60007834e-01 3.49906087e-02 1.02392189e-01 3.03030252e-01 -3.14579636e-01 -6.01934850e-01 2.67310053e-01 2.69533634e-01 -1.96016073e-01 -3.77463847e-01 1.25431573e+00 5.17510831e-01 1.53591961e-01 2.15470314e-01 -1.38447452e+00 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96606157-e957-4fdc-85d1-43c48e25cfbf	autoloc-weakly-supervised-temporal-action-1	null	null	http://openaccess.thecvf.com/content_ECCV_2018/html/Zheng_Shou_AutoLoc_Weakly-supervised_Temporal_ECCV_2018_paper.html	http://openaccess.thecvf.com/content_ECCV_2018/papers/Zheng_Shou_AutoLoc_Weakly-supervised_Temporal_ECCV_2018_paper.pdf	AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos	Temporal Action Localization (TAL) in untrimmed video is important for many applications. But it is very expensive to annotate the segment-level ground truth (action class and temporal boundary). This raises the interest of addressing TAL with weak supervision, namely only video-level annotations are available during training). However, the state-of-the-art weakly-supervised TAL methods only focus on generating good Class Activation Sequence (CAS) over time but conduct simple thresholding on CAS to localize actions. In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance. We propose a novel Outer-Inner-Contrastive (OIC) loss to automatically discover the needed segment-level supervision for training such a boundary predictor. Our method achieves dramatically improved performance: under the IoU threshold 0.5, our method improves mAP on THUMOS'14 from 13.7% to 21.2% and mAP on ActivityNet from 7.4% to 27.3%. It is also very encouraging to see that our weakly-supervised method achieves comparable results with some fully-supervised methods.	['Shih-Fu Chang', 'Kazuyuki Miyazawa', 'Hang Gao', 'Zheng Shou', 'Lei Zhang']	2018-09-01	null	null	null	eccv-2018-9	['weakly-supervised-action-localization', 'weakly-supervised-temporal-action']	['computer-vision', 'computer-vision']	[ 5.26366115e-01 9.99517143e-02 -6.87408030e-01 -4.12672728e-01 -1.06910872e+00 -4.24761832e-01 5.34843802e-01 -1.96854278e-01 -4.82400745e-01 7.32216716e-01 2.46574983e-01 5.04334923e-03 3.31970215e-01 -2.86982983e-01 -9.76590216e-01 -5.20915031e-01 -3.76942515e-01 1.11664899e-01 8.19930136e-01 8.18443671e-02 1.28549173e-01 6.04957016e-03 -1.38036764e+00 7.69671321e-01 9.41766441e-01 1.05961573e+00 5.24260178e-02 5.58558941e-01 1.78289384e-01 1.33485436e+00 -4.95916903e-01 -7.65476525e-02 1.66178688e-01 -8.24914694e-01 -1.10736728e+00 2.30591536e-01 5.19040585e-01 -2.41237700e-01 -2.03715891e-01 8.86259258e-01 2.05427065e-01 1.00703172e-01 4.31851596e-01 -1.26923800e+00 -1.58917028e-02 6.00945890e-01 -7.59424686e-01 5.41970015e-01 2.82530844e-01 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fefb4f4c-9f7f-4af3-b8e6-ed919df6b5cd	sikugpt-a-generative-pre-trained-model-for	2304.07778	null	https://arxiv.org/abs/2304.07778v1	https://arxiv.org/pdf/2304.07778v1.pdf	SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities	The rapid advance in artificial intelligence technology has facilitated the prosperity of digital humanities research. Against such backdrop, research methods need to be transformed in the intelligent processing of ancient texts, which is a crucial component of digital humanities research, so as to adapt to new development trends in the wave of AIGC. In this study, we propose a GPT model called SikuGPT based on the corpus of Siku Quanshu. The model's performance in tasks such as intralingual translation and text classification exceeds that of other GPT-type models aimed at processing ancient texts. SikuGPT's ability to process traditional Chinese ancient texts can help promote the organization of ancient information and knowledge services, as well as the international dissemination of Chinese ancient culture.	['Zhao Lianzheng', 'Zhang Hai', 'Liu Jiangfeng', 'Li Bin', 'Shen Si', 'Lin Litao', 'Wu Mengcheng', 'Hu Die', 'Zhao Zhixiao', 'Wang Dongbo', 'Liu Chang']	2023-04-16	null	null	null	null	['culture']	['speech']	[-9.93981734e-02 4.47206050e-02 -1.66113630e-01 -8.32216069e-02 -3.41446966e-01 -4.36051816e-01 8.47571433e-01 8.87104645e-02 -6.74733639e-01 6.57044530e-01 6.26946509e-01 -7.16205478e-01 -9.74736288e-02 -9.95702207e-01 -1.42710939e-01 -4.99165386e-01 3.00584674e-01 9.02064502e-01 -7.21928254e-02 -7.66999125e-01 8.26501608e-01 1.66215375e-01 -1.22056627e+00 3.59838545e-01 1.24382436e+00 3.29644889e-01 3.18841100e-01 3.20787460e-01 -4.84636515e-01 4.16445076e-01 -5.85530877e-01 -5.85834026e-01 1.29556000e-01 -5.54573655e-01 -1.00372148e+00 -4.67415571e-01 -2.06524178e-01 -6.74192682e-02 -4.17630464e-01 1.01115501e+00 6.53614044e-01 -7.93461800e-02 7.05162942e-01 -3.74778092e-01 -1.38313937e+00 1.15814519e+00 -3.63016635e-01 4.37869519e-01 4.02693838e-01 -1.64078102e-01 1.11227787e+00 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77448b01-76fc-4151-8b49-dc006c765bba	approximately-stationary-bandits-with	2302.14686	null	https://arxiv.org/abs/2302.14686v2	https://arxiv.org/pdf/2302.14686v2.pdf	Approximately Stationary Bandits with Knapsacks	Bandits with Knapsacks (BwK), the generalization of the Bandits problem under global budget constraints, has received a lot of attention in recent years. Previous work has focused on one of the two extremes: Stochastic BwK where the rewards and consumptions of the resources of each round are sampled from an i.i.d. distribution, and Adversarial BwK where these parameters are picked by an adversary. Achievable guarantees in the two cases exhibit a massive gap: No-regret learning is achievable in the stochastic case, but in the adversarial case only competitive ratio style guarantees are achievable, where the competitive ratio depends either on the budget or on both the time and the number of resources. What makes this gap so vast is that in Adversarial BwK the guarantees get worse in the typical case when the budget is more binding. While ``best-of-both-worlds'' type algorithms are known (single algorithms that provide the best achievable guarantee in each extreme case), their bounds degrade to the adversarial case as soon as the environment is not fully stochastic. Our work aims to bridge this gap, offering guarantees for a workload that is not exactly stochastic but is also not worst-case. We define a condition, Approximately Stationary BwK, that parameterizes how close to stochastic or adversarial an instance is. Based on these parameters, we explore what is the best competitive ratio attainable in BwK. We explore two algorithms that are oblivious to the values of the parameters but guarantee competitive ratios that smoothly transition between the best possible guarantees in the two extreme cases, depending on the values of the parameters. Our guarantees offer great improvement over the adversarial guarantee, especially when the available budget is small. We also prove bounds on the achievable guarantee, showing that our results are approximately tight when the budget is small.	['Éva Tardos', 'Giannis Fikioris']	2023-02-28	null	null	null	null	['multi-armed-bandits']	['miscellaneous']	[ 6.55390471e-02 2.62954086e-01 -5.60512066e-01 2.96410620e-02 -9.07389522e-01 -1.03868556e+00 1.84090316e-01 -6.04630401e-03 -4.46673363e-01 9.60657358e-01 1.00922100e-01 -5.06861448e-01 -7.40388215e-01 -9.43454683e-01 -1.14796650e+00 -1.11498892e+00 -2.26293519e-01 8.06437433e-01 3.79991233e-01 -4.65684742e-01 -3.71565181e-03 3.29721689e-01 -1.19454682e+00 4.26176749e-02 5.20977557e-01 1.15997171e+00 -2.25465477e-01 7.96616077e-01 -2.89114118e-01 2.45069236e-01 -4.96566087e-01 -5.35191357e-01 8.81222844e-01 -4.82009768e-01 -8.47234905e-01 -9.07970592e-03 -1.83174536e-02 -2.78012782e-01 -1.98746338e-01 1.04338086e+00 1.83384463e-01 -1.21489011e-01 2.79826611e-01 -1.43899560e+00 -2.80279040e-01 1.32427442e+00 -6.54387534e-01 1.61065921e-01 1.12330534e-01 7.31699169e-02 1.23716927e+00 4.25620735e-01 3.77420336e-01 8.68580282e-01 4.09051657e-01 5.83444893e-01 -1.46777809e+00 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2.56099790e-01 5.63552260e-01 -2.42821544e-01 -6.38588786e-01 -4.56528068e-01]	[4.567148208618164, 3.371987819671631]
56c00ced-fb24-4ecf-9414-404093b114dc	performance-evaluation-of-two-layer-lossless	1907.10889	null	https://arxiv.org/abs/1907.10889v1	https://arxiv.org/pdf/1907.10889v1.pdf	Performance Evaluation of Two-layer lossless HDR Coding using Histogram Packing Technique under Various Tone-mapping Operators	We proposed a lossless two-layer HDR coding method using a histogram packing technique. The proposed method was demonstrated to outperform the normative JPEG XT encoder, under the use of the default tone-mapping operator. However, the performance under various tone-mapping operators has not been discussed. In this paper, we aim to compare the performance of the proposed method with that of the JPEG XT encoder under the use of various tone-mapping operators to clearly show the characteristic difference between them.	['Hitoshi Kiya', 'Hiroyuki Kobayashi']	2019-07-25	null	null	null	null	['tone-mapping']	['computer-vision']	[ 4.69689667e-01 -6.62854910e-02 1.08477540e-01 -2.56012045e-02 -3.73209208e-01 1.77074283e-01 3.20343256e-01 2.35112637e-01 -4.11587059e-01 9.03604388e-01 -2.80052759e-02 -1.35747075e-01 9.34377089e-02 -7.42463112e-01 -3.70331705e-01 -5.75051904e-01 -2.39413396e-01 8.45016725e-03 7.19253957e-01 -4.42694902e-01 4.21860248e-01 3.43838543e-01 -1.89521503e+00 2.05239594e-01 7.31180608e-01 1.32266891e+00 5.03868520e-01 7.39812016e-01 4.97015892e-03 9.53834355e-01 -7.23421037e-01 -3.39417547e-01 5.13360023e-01 -4.79370534e-01 -3.10034722e-01 1.73495978e-01 4.00240839e-01 -6.64892077e-01 -6.06623709e-01 9.31151628e-01 6.04057729e-01 1.04373775e-01 2.25728214e-01 -7.62183070e-01 -2.86055833e-01 1.83377534e-01 -4.60345477e-01 3.26132625e-01 4.99293506e-01 -4.13039893e-01 8.29467297e-01 -2.30498761e-01 6.01956666e-01 9.15481389e-01 5.10133922e-01 3.99984010e-02 -9.99030828e-01 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2.38182053e-01 6.25461757e-01 -4.95296091e-01 -4.15694416e-01 1.69312522e-01]	[11.143908500671387, -2.3180103302001953]
920a2927-9214-4a57-938c-c4016fc4e5db	traditional-and-context-specific-spam	null	null	https://link.springer.com/article/10.1007/s10994-022-06176-x	https://rdcu.be/cP1SU	Traditional and context-specific spam detection in low resource settings	Social media data has a mix of high and low-quality content. One form of commonly studied low-quality content is spam. Most studies assume that spam is context-neutral. We show on different Twitter data sets that context-specific spam exists and is identifiable. We then compare multiple traditional machine learning models and a neural network model that uses a pre-trained BERT language model to capture contextual features for identifying spam, both traditional and context-specific, using only content-based features. The neural network model outperforms the traditional models with an F1 score of 0.91. Because spam training data sets are notoriously imbalanced, we also investigate the impact of this imbalance and show that simple Bag-of-Words models are best with extreme imbalance, but a neural model that fine-tunes using language models from other domains significantly improves the F1 score, but not to the levels of domain-specific neural models. This suggests that the strategy employed may vary depending upon the level of imbalance in the data set, the amount of data available in a low resource setting, and the prevalence of context-specific spam vs. traditional spam. Finally, we make our data sets available for use by the research community.	['Lisa Singh', 'Kornraphop Kawintiranon']	2022-06-09	null	null	null	machine-learning-2022-6	['context-specific-spam-detection', 'traditional-spam-detection', 'spam-detection']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-1.14815138e-01 -4.22152311e-01 -3.86895448e-01 -3.62903625e-01 -7.18588591e-01 -5.88087082e-01 8.74024987e-01 5.03236055e-01 -6.02045774e-01 6.58928216e-01 6.44230425e-01 -5.50067246e-01 -2.15214282e-01 -9.07258749e-01 -5.09234011e-01 -4.10311878e-01 2.56756470e-02 5.73473036e-01 2.84517556e-01 -5.28777182e-01 4.38690782e-01 1.35528281e-01 -1.57492602e+00 4.47942585e-01 8.86760116e-01 5.93255579e-01 -2.94656754e-01 4.16718513e-01 -3.73762339e-01 8.47083271e-01 -9.99897122e-01 -3.10144037e-01 3.31107318e-01 -2.29315028e-01 -5.85446954e-01 -8.84919688e-02 7.80129373e-01 -1.87647268e-01 -4.14297372e-01 8.81774127e-01 2.67422050e-01 -6.57696128e-02 6.74367666e-01 -1.17886591e+00 -5.28734982e-01 5.09898722e-01 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e12bd964-63a7-47c3-98cf-57c322614e7c	llm-grounded-diffusion-enhancing-prompt	2305.13655	null	https://arxiv.org/abs/2305.13655v1	https://arxiv.org/pdf/2305.13655v1.pdf	LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models	Recent advancements in text-to-image generation with diffusion models have yielded remarkable results synthesizing highly realistic and diverse images. However, these models still encounter difficulties when generating images from prompts that demand spatial or common sense reasoning. We propose to equip diffusion models with enhanced reasoning capabilities by using off-the-shelf pretrained large language models (LLMs) in a novel two-stage generation process. First, we adapt an LLM to be a text-guided layout generator through in-context learning. When provided with an image prompt, an LLM outputs a scene layout in the form of bounding boxes along with corresponding individual descriptions. Second, we steer a diffusion model with a novel controller to generate images conditioned on the layout. Both stages utilize frozen pretrained models without any LLM or diffusion model parameter optimization. We validate the superiority of our design by demonstrating its ability to outperform the base diffusion model in accurately generating images according to prompts that necessitate both language and spatial reasoning. Additionally, our method naturally allows dialog-based scene specification and is able to handle prompts in a language that is not well-supported by the underlying diffusion model.	['Trevor Darrell', 'Adam Yala', 'Boyi Li', 'Long Lian']	2023-05-23	null	null	null	null	['common-sense-reasoning']	['reasoning']	[ 2.93191820e-01 4.88966078e-01 2.86127150e-01 -4.34058100e-01 -5.20640731e-01 -8.60647142e-01 9.73690629e-01 -1.41702831e-01 -1.14750504e-01 4.62701529e-01 2.49638647e-01 -6.03750229e-01 1.23707369e-01 -9.28493977e-01 -7.62788296e-01 -1.83562264e-01 3.76865298e-01 6.64798677e-01 2.92409480e-01 -4.10961688e-01 3.84647220e-01 7.13176250e-01 -1.50153446e+00 6.89818263e-01 8.78984571e-01 7.56870866e-01 8.71094465e-01 1.09790993e+00 -5.30993402e-01 1.20008826e+00 -8.59678090e-01 -5.60739934e-02 2.29688123e-01 -4.85506713e-01 -6.78891003e-01 4.19890761e-01 5.34024060e-01 -5.11022389e-01 -3.05225607e-02 4.48171645e-01 5.43661654e-01 1.16222585e-02 6.22058272e-01 -1.37237573e+00 -9.76415455e-01 5.23117304e-01 -3.06834608e-01 -2.44550005e-01 7.24372804e-01 7.56806791e-01 7.25469828e-01 -8.46035063e-01 1.02590680e+00 1.42674530e+00 3.17401946e-01 8.23634982e-01 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-6.19067371e-01 -2.58444220e-01 2.29206249e-01 1.87256247e-01 -2.57898062e-01 2.37018183e-01 3.75645965e-01]	[11.264503479003906, -0.17985454201698303]
3283df02-2fb9-4c06-8775-8d496c6c0684	cogalex-2-0-impact-of-data-quality-on-lexical	null	null	https://datacentricai.org/papers/164_CameraReady_CogALex_2_0.pdf	https://datacentricai.org/papers/164_CameraReady_CogALex_2_0.pdf	CogALex 2.0: Impact of Data Quality on Lexical-Semantic Relation Prediction	Predicting lexical-semantic relations between word pairs has successfully been accomplished by pre-trained neural language models. An XLM-RoBERTa-based approach, for instance, achieved the best performance differentiating between hypernymy, synonymy, antonymy, and random relations in four languages in the CogALex-VI 2020 shared task. However, the results also revealed strong performance divergences between languages and confusions of specific relations, especially hypernymy and synonymy. Upon inspection, a difference in data quality across languages and relations could be observed. Thus, we provide a manually improved dataset for lexical-semantic relation prediction and evaluate its impact across three pre-trained neural language models.	['Dagmar Gromann', 'Barbara Heinisch', 'Lennart Wachowiak', 'Christian Lang']	2021-12-14	null	null	null	neurips-data-centric-ai-workshop-2021-12	['relation-classification', 'hypernym-discovery']	['natural-language-processing', 'natural-language-processing']	[ 3.10359839e-02 2.61985242e-01 -4.18868959e-01 -3.67547810e-01 -2.13404462e-01 -5.58901191e-01 1.09464526e+00 7.76984692e-01 -6.85430408e-01 9.09571528e-01 4.95787919e-01 -4.84511405e-01 -6.12190545e-01 -8.91127586e-01 -1.83515012e-01 -3.35100964e-02 -5.76783866e-02 9.55135584e-01 -1.73325017e-01 -4.68935043e-01 5.21621620e-03 2.07743540e-01 -1.25708163e+00 4.00304198e-01 8.06036651e-01 6.94851041e-01 -5.33191413e-02 -3.72997783e-02 -5.68881989e-01 6.90087140e-01 -5.56828141e-01 -9.13234413e-01 -1.00916490e-01 -1.76101610e-01 -1.28714168e+00 -6.40358984e-01 2.32485473e-01 5.57949245e-01 -1.68411478e-01 9.46185946e-01 3.55393261e-01 6.25496656e-02 7.88203537e-01 -1.18511021e+00 -9.34237242e-01 1.23687351e+00 -1.83966503e-01 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f59d24a1-1adf-4394-84ec-1923d99acb42	data-driven-but-privacy-conscious-pedestrian	2306.11710	null	https://arxiv.org/abs/2306.11710v2	https://arxiv.org/pdf/2306.11710v2.pdf	Data-Driven but Privacy-Conscious: Pedestrian Dataset De-identification via Full-Body Person Synthesis	The advent of data-driven technology solutions is accompanied by an increasing concern with data privacy. This is of particular importance for human-centered image recognition tasks, such as pedestrian detection, re-identification, and tracking. To highlight the importance of privacy issues and motivate future research, we motivate and introduce the Pedestrian Dataset De-Identification (PDI) task. PDI evaluates the degree of de-identification and downstream task training performance for a given de-identification method. As a first baseline, we propose IncogniMOT, a two-stage full-body de-identification pipeline based on image synthesis via generative adversarial networks. The first stage replaces target pedestrians with synthetic identities. To improve downstream task performance, we then apply stage two, which blends and adapts the synthetic image parts into the data. To demonstrate the effectiveness of IncogniMOT, we generate a fully de-identified version of the MOT17 pedestrian tracking dataset and analyze its application as training data for pedestrian re-identification, detection, and tracking models. Furthermore, we show how our data is able to narrow the synthetic-to-real performance gap in a privacy-conscious manner.	['Cristian Canton Ferrer', 'Laura Leal-Taixé', 'Caner Hazirbas', 'Zoe Papakipos', 'Ismail Elezi', 'Tim Meinhardt', 'Maxim Maximov']	2023-06-20	null	null	null	null	['pedestrian-detection', 'de-identification']	['computer-vision', 'natural-language-processing']	[ 2.57448047e-01 -6.98463619e-02 1.24057062e-01 -3.99567693e-01 -6.01266265e-01 -7.82644570e-01 7.54168153e-01 2.77083945e-02 -8.51027727e-01 6.75292432e-01 1.84838608e-01 -2.27059841e-01 5.50626516e-01 -5.97860157e-01 -8.62426221e-01 -5.19491076e-01 3.35097939e-01 3.05653602e-01 1.65028647e-01 3.12015831e-01 -1.66000023e-01 5.26502490e-01 -1.49611449e+00 3.60285342e-01 5.99649310e-01 7.69286156e-01 -5.59668005e-01 8.16010416e-01 5.11213660e-01 2.14814112e-01 -7.40504503e-01 -1.31808066e+00 6.79259360e-01 -9.87913236e-02 -4.02781785e-01 3.88354547e-02 8.89452755e-01 -6.07752621e-01 -4.54262018e-01 1.02471495e+00 8.13943803e-01 -6.71760663e-02 4.05286938e-01 -1.71686780e+00 -5.12741566e-01 1.58134490e-01 -4.69005704e-01 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ff8f2411-2523-42bc-8800-e1d5c905bebc	pointcnn-convolution-on-x-transformed-points	null	null	http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points	http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points.pdf	PointCNN: Convolution On X-Transformed Points	We present a simple and general framework for feature learning from point cloud. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point cloud are irregular and unordered, thus a direct convolving of kernels against the features associated with the points will result in deserting the shape information while being variant to the orders. To address these problems, we propose to learn a X-transformation from the input points, which is used for simultaneously weighting the input features associated with the points and permuting them into latent potentially canonical order. Then element-wise product and sum operations of typical convolution operator are applied on the X-transformed features. The proposed method is a generalization of typical CNNs into learning features from point cloud, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.	['Rui Bu', 'Yangyan Li', 'Xinhan Di', 'Wei Wu', 'Mingchao Sun', 'Baoquan Chen']	2018-12-01	null	null	null	neurips-2018-12	['3d-part-segmentation', 'few-shot-3d-point-cloud-classification']	['computer-vision', 'computer-vision']	[-7.22405165e-02 -4.72291142e-01 1.94161311e-01 -4.08508301e-01 -2.70396382e-01 -4.44976449e-01 7.17286646e-01 1.81174710e-01 -4.96746927e-01 4.25380826e-01 -1.93704620e-01 3.04708015e-02 -4.70759988e-01 -1.03429592e+00 -1.04011548e+00 -8.46077204e-01 -3.55716050e-01 3.04807961e-01 1.53885573e-01 -1.07450761e-01 5.15761077e-01 1.14292383e+00 -1.78846419e+00 2.57891476e-01 6.48502648e-01 1.33568418e+00 3.44792843e-01 3.67300570e-01 -3.35136831e-01 2.24602610e-01 -1.91065907e-01 1.32775065e-02 4.96076524e-01 2.16085970e-01 -6.56891465e-01 9.25510600e-02 5.97699583e-01 5.86841106e-02 -3.23595345e-01 1.12725163e+00 1.59924895e-01 4.05111402e-01 8.06972504e-01 -1.24711502e+00 -9.13853586e-01 4.99622710e-02 -6.15075946e-01 4.78426926e-02 -1.22554459e-01 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dad2d185-4f2a-4041-b055-f1a383cc80d0	shape-based-pose-estimation-for-automatic	2305.16717	null	https://arxiv.org/abs/2305.16717v1	https://arxiv.org/pdf/2305.16717v1.pdf	Shape-based pose estimation for automatic standard views of the knee	Surgical treatment of complicated knee fractures is guided by real-time imaging using a mobile C-arm. Immediate and continuous control is achieved via 2D anatomy-specific standard views that correspond to a specific C-arm pose relative to the patient positioning, which is currently determined manually, following a trial-and-error approach at the cost of time and radiation dose. The characteristics of the standard views of the knee suggests that the shape information of individual bones could guide an automatic positioning procedure, reducing time and the amount of unnecessary radiation during C-arm positioning. To fully automate the C-arm positioning task during knee surgeries, we propose a complete framework that enables (1) automatic laterality and standard view classification and (2) automatic shape-based pose regression toward the desired standard view based on a single initial X-ray. A suitable shape representation is proposed to incorporate semantic information into the pose regression pipeline. The pipeline is designed to handle two distinct standard views simultaneously. Experiments were conducted to assess the performance of the proposed system on 3528 synthetic and 1386 real X-rays for the a.-p. and lateral standard. The view/laterality classificator resulted in an accuracy of 100\%/98\% on the simulated and 99\%/98\% on the real X-rays. The pose regression performance was $d\theta_{a.-p}=5.8\pm3.3\degree,\,d\theta_{lateral}=3.7\pm2.0\degree$ on the simulated data and $d\theta_{a.-p}=7.4\pm5.0\degree,\,d\theta_{lateral}=8.4\pm5.4\degree$ on the real data outperforming intensity-based pose regression.	['Klaus Maier-Hein', 'Jan Siad El Barbari', 'Holger Kunze', 'Sarina Thomas', 'Lisa Kausch']	2023-05-26	null	null	null	null	['pose-estimation', 'anatomy', 'continuous-control']	['computer-vision', 'miscellaneous', 'playing-games']	[ 1.35805812e-02 2.46686250e-01 -7.11728781e-02 -6.60647228e-02 -1.17170703e+00 -3.93780172e-01 2.94847012e-01 2.27296382e-01 -5.72840333e-01 3.86615664e-01 -3.48320091e-03 -2.59163648e-01 -6.47491753e-01 -5.56585252e-01 -4.81283545e-01 -4.97367322e-01 -2.29228482e-01 8.59550893e-01 4.02919173e-01 -1.61594182e-01 2.53518254e-01 6.49578989e-01 -1.33009934e+00 9.86258462e-02 3.18398535e-01 9.90721285e-01 3.25704724e-01 5.39658189e-01 2.53439069e-01 -6.01511030e-03 -4.70638037e-01 -2.67754178e-02 4.34032768e-01 -3.20296556e-01 -3.06005746e-01 2.50381052e-01 2.88597614e-01 -6.72223493e-02 1.45406216e-01 7.55895317e-01 7.62049913e-01 -2.11483523e-01 6.26541436e-01 -7.62389004e-01 4.74231720e-01 2.46830136e-02 -8.89459014e-01 -7.76143968e-02 4.80639875e-01 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9160f5c6-7f3d-49b9-9c50-80ae93a4fc85	extending-classical-surrogate-modelling-to	1812.06309	null	https://arxiv.org/abs/1812.06309v3	https://arxiv.org/pdf/1812.06309v3.pdf	Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach	Thanks to their versatility, ease of deployment and high-performance, surrogate models have become staple tools in the arsenal of uncertainty quantification (UQ). From local interpolants to global spectral decompositions, surrogates are characterised by their ability to efficiently emulate complex computational models based on a small set of model runs used for training. An inherent limitation of many surrogate models is their susceptibility to the curse of dimensionality, which traditionally limits their applicability to a maximum of $\mathcal{O}(10^2)$ input dimensions. We present a novel approach at high-dimensional surrogate modelling that is model-, dimensionality reduction- and surrogate model- agnostic (black box), and can enable the solution of high dimensional (i.e. up to $\mathcal{O}(10^4)$) problems. After introducing the general algorithm, we demonstrate its performance by combining Kriging and polynomial chaos expansions surrogates and kernel principal component analysis. In particular, we compare the generalisation performance that the resulting surrogates achieve to the classical sequential application of dimensionality reduction followed by surrogate modelling on several benchmark applications, comprising an analytical function and two engineering applications of increasing dimensionality and complexity.	['S. Marelli', 'C. Lataniotis', 'B. Sudret']	2018-12-15	null	null	null	null	['supervised-dimensionality-reduction']	['computer-vision']	[-1.84318215e-01 -3.53895873e-01 5.65472841e-01 1.01144582e-01 -9.46419954e-01 -5.02934933e-01 7.30677426e-01 2.23212510e-01 -2.57564873e-01 8.46891701e-01 -2.55755484e-01 -4.58509624e-01 -8.82251799e-01 -7.74090230e-01 -2.68168241e-01 -8.11458468e-01 -4.66599375e-01 7.72527516e-01 -4.15824614e-02 -2.50963479e-01 1.70624048e-01 8.46597493e-01 -1.84250915e+00 -3.34229201e-01 9.47745740e-01 1.36722553e+00 -1.22498482e-01 5.34453332e-01 7.90995210e-02 9.63136628e-02 -4.59546715e-01 5.37597537e-02 2.27483839e-01 -1.55458793e-01 -3.19559336e-01 -4.03744906e-01 -2.71126032e-01 -1.49913589e-02 7.07109123e-02 6.66551113e-01 6.93791926e-01 2.83806741e-01 1.01650238e+00 -1.00143802e+00 -2.97032058e-01 -2.84605086e-01 -2.65419185e-01 3.45521905e-02 3.20173264e-01 1.59833699e-01 3.37052286e-01 -1.09828115e+00 2.23039195e-01 9.81193244e-01 1.02280021e+00 7.45485500e-02 -1.86593306e+00 -2.15793088e-01 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70424631-4279-4e22-b2e5-4efa080fe968	exploring-the-joint-use-of-rehearsal-and	2211.08161	null	https://arxiv.org/abs/2211.08161v2	https://arxiv.org/pdf/2211.08161v2.pdf	An Investigation of the Combination of Rehearsal and Knowledge Distillation in Continual Learning for Spoken Language Understanding	Continual learning refers to a dynamical framework in which a model receives a stream of non-stationary data over time and must adapt to new data while preserving previously acquired knowledge. Unluckily, neural networks fail to meet these two desiderata, incurring the so-called catastrophic forgetting phenomenon. Whereas a vast array of strategies have been proposed to attenuate forgetting in the computer vision domain, for speech-related tasks, on the other hand, there is a dearth of works. In this paper, we consider the joint use of rehearsal and knowledge distillation (KD) approaches for spoken language understanding under a class-incremental learning scenario. We report on multiple KD combinations at different levels in the network, showing that combining feature-level and predictions-level KDs leads to the best results. Finally, we provide an ablation study on the effect of the size of the rehearsal memory that corroborates the efficacy of our approach for low-resource devices.	['Alessio Brutti', 'Daniele Falavigna', 'Umberto Cappellazzo']	2022-11-15	null	null	null	null	['spoken-language-understanding', 'spoken-language-understanding']	['natural-language-processing', 'speech']	[ 2.71915942e-01 1.96127385e-01 1.55392885e-01 -1.72056615e-01 -6.61921725e-02 -3.56219292e-01 7.12098956e-01 1.77053675e-01 -6.76624596e-01 7.43267894e-01 1.59752980e-01 -3.06503445e-01 -3.48757744e-01 -6.65654480e-01 -7.68701732e-01 -6.93334043e-01 -1.06143646e-01 3.01599860e-01 6.77252889e-01 -1.97350472e-01 2.51568437e-01 5.83022177e-01 -2.14959073e+00 7.77039081e-02 6.49987817e-01 6.94725454e-01 4.02444929e-01 7.36720741e-01 -6.40382320e-02 9.24961030e-01 -4.73018974e-01 -1.09370127e-01 5.56810275e-02 -4.48711574e-01 -9.16466951e-01 -1.10771984e-01 4.56422091e-01 -3.08656961e-01 -5.65329015e-01 7.53889561e-01 6.77034318e-01 3.22953790e-01 2.18516678e-01 -1.06880629e+00 -7.08987415e-01 6.69220269e-01 -1.07341021e-01 5.86741745e-01 1.21583275e-01 1.27480313e-01 5.89869320e-01 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d391e13d-59a2-46c0-94c1-7dc2a22f72c2	pristi-a-conditional-diffusion-framework-for	2302.09746	null	https://arxiv.org/abs/2302.09746v1	https://arxiv.org/pdf/2302.09746v1.pdf	PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation	Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow modeling, and climate forecasting. However, the originally collected spatiotemporal data in real-world scenarios is usually incomplete due to sensor failures or transmission loss. Spatiotemporal imputation aims to fill the missing values according to the observed values and the underlying spatiotemporal dependence of them. The previous dominant models impute missing values autoregressively and suffer from the problem of error accumulation. As emerging powerful generative models, the diffusion probabilistic models can be adopted to impute missing values conditioned by observations and avoid inferring missing values from inaccurate historical imputation. However, the construction and utilization of conditional information are inevitable challenges when applying diffusion models to spatiotemporal imputation. To address above issues, we propose a conditional diffusion framework for spatiotemporal imputation with enhanced prior modeling, named PriSTI. Our proposed framework provides a conditional feature extraction module first to extract the coarse yet effective spatiotemporal dependencies from conditional information as the global context prior. Then, a noise estimation module transforms random noise to realistic values, with the spatiotemporal attention weights calculated by the conditional feature, as well as the consideration of geographic relationships. PriSTI outperforms existing imputation methods in various missing patterns of different real-world spatiotemporal data, and effectively handles scenarios such as high missing rates and sensor failure. The implementation code is available at https://github.com/LMZZML/PriSTI.	['Yanjie Fu', 'Bowen Du', 'Leilei Sun', 'Hao Feng', 'Han Huang', 'Mingzhe Liu']	2023-02-20	null	null	null	null	['noise-estimation']	['medical']	[ 1.38620317e-01 -4.31723297e-01 -3.04741591e-01 -4.92047101e-01 -7.62863994e-01 -6.60268366e-02 4.80826676e-01 3.91920358e-02 -1.44550428e-01 1.13611925e+00 7.47546077e-01 -1.22100510e-01 -4.55501050e-01 -1.21338391e+00 -7.32865214e-01 -7.93005347e-01 2.72013366e-01 3.17996919e-01 1.68496873e-02 1.83118239e-01 -1.80224150e-01 5.30384369e-02 -1.43675077e+00 2.18887523e-01 1.35068560e+00 6.15079522e-01 3.48009974e-01 3.47105294e-01 -3.88182223e-01 8.50450695e-01 -5.58550358e-01 -1.35006070e-01 -3.67118746e-01 -1.34213969e-01 -3.81450243e-02 -2.76564777e-01 -4.45806980e-01 -2.39813060e-01 -2.72392273e-01 8.34353387e-01 3.78277332e-01 4.07078974e-02 6.17657244e-01 -1.47983611e+00 -7.09921002e-01 6.57139957e-01 -6.83241427e-01 2.37454206e-01 2.21977562e-01 2.80054450e-01 1.15281776e-01 -9.27975953e-01 1.28461763e-01 1.05357742e+00 8.21609974e-01 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5.72874881e-02 -9.70501661e-01 -4.10673827e-01 3.89523447e-01 5.00100553e-01 -5.46861827e-01 -1.90848053e-01 1.22528777e-01]	[6.625133037567139, 2.1461431980133057]
2ee78fc8-0d66-464f-b538-2d9e6bb251c3	sign-language-recognition-using-temporal	1701.01875	null	http://arxiv.org/abs/1701.01875v1	http://arxiv.org/pdf/1701.01875v1.pdf	Sign Language Recognition Using Temporal Classification	Devices like the Myo armband available in the market today enable us to collect data about the position of a user's hands and fingers over time. We can use these technologies for sign language translation since each sign is roughly a combination of gestures across time. In this work, we utilize a dataset collected by a group at the University of South Wales, which contains parameters, such as hand position, hand rotation, and finger bend, for 95 unique signs. For each input stream representing a sign, we predict which sign class this stream falls into. We begin by implementing baseline SVM and logistic regression models, which perform reasonably well on high quality data. Lower quality data requires a more sophisticated approach, so we explore different methods in temporal classification, including long short term memory architectures and sequential pattern mining methods.	['Zeshan Hussain', 'Hardie Cate', 'Fahim Dalvi']	2017-01-07	null	null	null	null	['sign-language-translation', 'sequential-pattern-mining']	['computer-vision', 'natural-language-processing']	[ 1.98358461e-01 -7.92571723e-01 -7.43314505e-01 -4.54758734e-01 -4.72563088e-01 -8.63727450e-01 5.83519340e-01 -5.01781940e-01 -3.94542187e-01 4.66948807e-01 6.60465479e-01 -3.78716588e-01 -7.39990994e-02 -3.45862210e-01 -2.93285549e-01 -3.80212635e-01 -2.54068077e-01 4.41547275e-01 4.13011342e-01 -2.14267179e-01 5.16478002e-01 6.43145859e-01 -1.60830879e+00 7.36930013e-01 -4.29738965e-03 9.35387552e-01 -3.84470463e-01 1.07778549e+00 -3.23590972e-02 6.26576602e-01 -6.57330155e-01 4.32865731e-02 3.46912593e-01 -4.13239032e-01 -7.54599154e-01 -3.82819027e-01 7.03109920e-01 -6.57593608e-01 -4.51622337e-01 3.21401328e-01 6.92422450e-01 2.13873491e-01 6.83942258e-01 -1.22192216e+00 5.45212179e-02 4.50037330e-01 -3.33067149e-01 3.18427473e-01 6.75793886e-01 5.06339014e-01 8.85016680e-01 -5.58944106e-01 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1.97478637e-01 -9.34112906e-01 -7.64976859e-01 -1.11527789e+00 -3.48364562e-01 4.83180642e-01 5.10022640e-01 -5.51595151e-01 -2.90018559e-01 3.64685059e-01]	[9.112025260925293, -6.400548458099365]
e348a146-2a70-4eff-9bb5-0dcb27cd82fa	evaluating-histopathology-transfer-learning	2206.06862	null	https://arxiv.org/abs/2206.06862v1	https://arxiv.org/pdf/2206.06862v1.pdf	Evaluating histopathology transfer learning with ChampKit	Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for various tasks, including immune cell detection and microsatellite instability classification. The state-of-the-art for each task often employs base architectures that have been pretrained for image classification on ImageNet. The standard approach to develop classifiers in histopathology tends to focus narrowly on optimizing models for a single task, not considering the aspects of modeling innovations that improve generalization across tasks. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible benchmarking toolkit that consists of a broad collection of patch-level image classification tasks across different cancers. ChampKit enables a way to systematically document the performance impact of proposed improvements in models and methodology. ChampKit source code and data are freely accessible at https://github.com/kaczmarj/champkit .	['Peter K. Koo', 'Joel H. Saltz', 'Rajarsi Gupta', 'Shahira Abousamra', 'Tahsin M. Kurc', 'Jakub R. Kaczmarzyk']	2022-06-14	null	null	null	null	['cell-detection', 'classification']	['computer-vision', 'methodology']	[ 1.88052952e-01 -1.84066415e-01 -4.01574820e-01 -3.84812027e-01 -1.39050853e+00 -4.69764024e-01 4.59768713e-01 6.01496279e-01 -6.01010323e-01 5.16077101e-01 1.07387900e-01 -5.74086130e-01 -4.72109616e-02 -4.31256920e-01 -4.37486351e-01 -1.09066832e+00 -4.35924418e-02 3.93606603e-01 -2.06714254e-02 -9.91959572e-02 4.93676849e-02 5.87774396e-01 -8.74454498e-01 6.34147584e-01 3.77722025e-01 8.43930304e-01 9.95905623e-02 1.20834923e+00 1.61127627e-01 6.13476634e-01 -1.84395954e-01 -4.61890697e-01 -2.30435669e-01 -7.61290044e-02 -9.84125614e-01 -2.40845770e-01 3.62923265e-01 7.12575205e-03 -4.76160467e-01 8.23888004e-01 7.14978039e-01 -6.67543471e-01 8.85852098e-01 -9.12028670e-01 -5.02693951e-01 3.59950364e-01 -5.25751829e-01 6.29561424e-01 -2.79641926e-01 5.41243017e-01 9.19920146e-01 -6.05081618e-01 8.80267859e-01 6.01425469e-01 1.25718153e+00 7.57759273e-01 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fb6fd45a-1f1c-463b-9003-f6adf51670a2	many-body-approximation-for-tensors	2209.15338	null	https://arxiv.org/abs/2209.15338v2	https://arxiv.org/pdf/2209.15338v2.pdf	Many-body Approximation for Non-negative Tensors	We present an alternative approach to decompose non-negative tensors, called many-body approximation. Traditional decomposition methods assume low-rankness in the representation, resulting in difficulties in global optimization and target rank selection. We avoid these problems by energy-based modeling of tensors, where a tensor and its mode correspond to a probability distribution and a random variable, respectively. Our model can be globally optimized in terms of the KL divergence minimization by taking the interaction between variables, i.e. modes, into account that can be tuned more intuitively than ranks. Furthermore, we visualize interactions between modes as tensor networks and reveal a nontrivial relationship between many-body approximation and low-rank approximation. We demonstrate the effectiveness of our approach in tensor completion and approximation.	['Yoshinobu Kawahara', 'Mahito Sugiyama', 'Kazu Ghalamkari']	2022-09-30	null	null	null	null	['tensor-networks']	['methodology']	[-1.71621710e-01 5.12091117e-03 -2.66379956e-03 -1.96859553e-01 -5.47385037e-01 -7.24826396e-01 3.94565344e-01 2.13460624e-03 -4.33916487e-02 3.43251526e-01 7.78820753e-01 -9.62661505e-02 -4.83282566e-01 -6.96060181e-01 -6.29079521e-01 -8.53029966e-01 -3.68386090e-01 7.01137364e-01 -1.13810621e-01 -2.53808260e-01 -1.06324323e-01 3.72659445e-01 -9.63361084e-01 4.42798853e-01 8.57777119e-01 7.20852613e-01 -2.37809554e-01 6.34799659e-01 -4.03194781e-03 6.46616518e-01 -3.39723021e-01 -6.82298243e-01 2.80781537e-01 -2.20630109e-01 -1.12610590e+00 -2.02325117e-02 4.59567755e-01 -9.97578818e-03 -2.82331467e-01 1.17463064e+00 1.27188355e-01 3.64661038e-01 7.90881693e-01 -1.10317886e+00 -6.34187400e-01 7.12944150e-01 -6.06702864e-01 -2.19041348e-01 3.66710037e-01 -3.36659104e-02 1.57025766e+00 -1.03093827e+00 5.59670329e-01 1.50330532e+00 7.25762129e-01 3.57092619e-01 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54e31a31-572c-4580-bafe-f6283c16395e	robust-multiview-point-cloud-registration	2304.00467	null	https://arxiv.org/abs/2304.00467v1	https://arxiv.org/pdf/2304.00467v1.pdf	Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting	In this paper, we present a new method for the multiview registration of point cloud. Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses. However, constructing a densely-connected graph is time-consuming and contains lots of outlier edges, which makes the subsequent IRLS struggle to find correct poses. To address the above problems, we first propose to use a neural network to estimate the overlap between scan pairs, which enables us to construct a sparse but reliable pose graph. Then, we design a novel history reweighting function in the IRLS scheme, which has strong robustness to outlier edges on the graph. In comparison with existing multiview registration methods, our method achieves 11% higher registration recall on the 3DMatch dataset and ~13% lower registration errors on the ScanNet dataset while reducing ~70% required pairwise registrations. Comprehensive ablation studies are conducted to demonstrate the effectiveness of our designs.	['Bisheng Yang', 'Wenping Wang', 'Yu-Shen Liu', 'Yulan Guo', 'Zhen Dong', 'YuAn Liu', 'Haiping Wang']	2023-04-02	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Robust_Multiview_Point_Cloud_Registration_With_Reliable_Pose_Graph_Initialization_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Robust_Multiview_Point_Cloud_Registration_With_Reliable_Pose_Graph_Initialization_CVPR_2023_paper.pdf	cvpr-2023-1	['point-cloud-registration']	['computer-vision']	[ 4.48145382e-02 1.02426499e-01 -1.60468742e-01 -6.13454640e-01 -1.00515783e+00 -2.71728903e-01 1.77311286e-01 1.72636673e-01 -3.71282279e-01 1.30399063e-01 1.29460171e-01 1.62048832e-01 -1.48333490e-01 -7.57600665e-01 -7.10889637e-01 -2.87046432e-01 -2.89232612e-01 5.47778904e-01 3.25065315e-01 -2.41366133e-01 1.78161621e-01 5.64716578e-01 -1.27499008e+00 -4.77741480e-01 8.66295278e-01 7.36693978e-01 1.32179633e-01 1.38136610e-01 1.60690501e-01 5.20900711e-02 -2.25830138e-01 -1.15130782e-01 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0ab15823-7657-4fc8-9108-ba9382cd9be4	time-series-clustering-with-an-em-algorithm	2208.11907	null	https://arxiv.org/abs/2208.11907v3	https://arxiv.org/pdf/2208.11907v3.pdf	Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models	In this paper, we consider the task of clustering a set of individual time series while modeling each cluster, that is, model-based time series clustering. The task requires a parametric model with sufficient flexibility to describe the dynamics in various time series. To address this problem, we propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models, which have high flexibility. The proposed method uses a new expectation-maximization algorithm for the mixture model to estimate the model parameters, and determines the number of clusters using the Bayesian information criterion. Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection. The method is applied to real datasets commonly used to evaluate time series clustering methods. Results showed that the proposed method produces clustering results that are as accurate or more accurate than those obtained using previous methods.	['Shutaro Kunimasa', 'Kaoru Kawamoto', 'Takashi Imai', 'Ryohei Umatani']	2022-08-25	null	null	null	null	['time-series-clustering']	['time-series']	[-2.34321728e-01 -7.67156601e-01 -1.43643826e-01 -1.51301414e-01 -5.12349486e-01 -5.18692613e-01 3.88871461e-01 -5.67820482e-02 -1.96032226e-01 2.47285262e-01 -2.88392961e-01 -1.74227789e-01 -5.98772585e-01 -4.44587588e-01 7.33024478e-02 -1.07499993e+00 -4.63971585e-01 6.23910069e-01 1.49561599e-01 3.36948723e-01 3.21059197e-01 5.04420400e-01 -1.49061430e+00 -2.74661124e-01 8.79117608e-01 9.26952183e-01 3.80079508e-01 5.68940818e-01 -1.00403734e-01 5.12455642e-01 -8.43350172e-01 5.47529519e-01 1.90813541e-01 -3.93189937e-01 -1.53741121e-01 5.21341860e-01 -7.75067985e-01 2.12104052e-01 2.26172674e-02 9.04828787e-01 2.36982077e-01 7.85964191e-01 9.38875675e-01 -1.72519994e+00 5.53292446e-02 5.89773893e-01 -6.92089677e-01 2.41742149e-01 -1.55239895e-01 -1.73363104e-01 1.71840429e-01 -2.50110447e-01 1.30074412e-01 1.16756713e+00 6.52376711e-01 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c7c94537-fbdd-434a-aad6-59e434d608b9	exemplar-free-class-incremental-learning-via	2201.01488	null	https://arxiv.org/abs/2201.01488v1	https://arxiv.org/pdf/2201.01488v1.pdf	Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers	The exemplar-free class incremental learning requires classification models to learn new class knowledge incrementally without retaining any old samples. Recently, the framework based on parallel one-class classifiers (POC), which trains a one-class classifier (OCC) independently for each category, has attracted extensive attention, since it can naturally avoid catastrophic forgetting. POC, however, suffers from weak discriminability and comparability due to its independent training strategy for different OOCs. To meet this challenge, we propose a new framework, named Discriminative and Comparable One-class classifiers for Incremental Learning (DisCOIL). DisCOIL follows the basic principle of POC, but it adopts variational auto-encoders (VAE) instead of other well-established one-class classifiers (e.g. deep SVDD), because a trained VAE can not only identify the probability of an input sample belonging to a class but also generate pseudo samples of the class to assist in learning new tasks. With this advantage, DisCOIL trains a new-class VAE in contrast with the old-class VAEs, which forces the new-class VAE to reconstruct better for new-class samples but worse for the old-class pseudo samples, thus enhancing the comparability. Furthermore, DisCOIL introduces a hinge reconstruction loss to ensure the discriminability. We evaluate our method extensively on MNIST, CIFAR10, and Tiny-ImageNet. The experimental results show that DisCOIL achieves state-of-the-art performance.	['Yangli-ao Geng', 'Wen Wang', 'Danyu Wang', 'Jing Zhang', 'Qingyong Li', 'Wenju Sun']	2022-01-05	null	null	null	null	['one-class-classifier']	['methodology']	[ 1.00128166e-01 -1.31493853e-02 -2.46747091e-01 -3.38915616e-01 -3.69228780e-01 -2.64646709e-01 4.99837577e-01 4.98787723e-02 -4.59208548e-01 9.26393211e-01 -1.58474699e-01 -2.85314955e-02 -2.90544145e-02 -1.03376877e+00 -1.00406456e+00 -1.03143489e+00 1.99427709e-01 4.56011504e-01 6.41838133e-01 7.01156482e-02 -2.45829821e-01 3.12172085e-01 -1.90875995e+00 5.73786259e-01 1.23354709e+00 1.16395283e+00 4.63259310e-01 1.24689639e-01 -5.94320521e-02 7.90364325e-01 -4.78872150e-01 -5.44096053e-01 1.22654796e-01 -4.39914942e-01 -5.03925085e-01 -2.73821741e-01 3.59092623e-01 -3.82813215e-01 -6.30399168e-01 9.80088770e-01 4.05099571e-01 1.83048010e-01 8.57435048e-01 -1.34802103e+00 -9.91908669e-01 7.70551205e-01 -2.33576015e-01 9.22853202e-02 -2.80825227e-01 2.70961821e-01 8.72397363e-01 -1.40848935e+00 4.90529567e-01 1.07978630e+00 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a75c6af5-5d71-4ef4-bc13-e3d55c48f551	graph-to-graph-transformer-for-transition	1911.03561	null	https://arxiv.org/abs/1911.03561v4	https://arxiv.org/pdf/1911.03561v4.pdf	Graph-to-Graph Transformer for Transition-based Dependency Parsing	We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing. After proposing two novel Transformer models of transition-based dependency parsing as strong baselines, we show that adding the proposed mechanisms for conditioning on and predicting graphs of Graph2Graph Transformer results in significant improvements, both with and without BERT pre-training. The novel baselines and their integration with Graph2Graph Transformer significantly outperform the state-of-the-art in traditional transition-based dependency parsing on both English Penn Treebank, and 13 languages of Universal Dependencies Treebanks. Graph2Graph Transformer can be integrated with many previous structured prediction methods, making it easy to apply to a wide range of NLP tasks.	['Alireza Mohammadshahi', 'James Henderson']	2019-11-08	null	https://aclanthology.org/2020.findings-emnlp.294	https://aclanthology.org/2020.findings-emnlp.294.pdf	findings-of-the-association-for-computational	['transition-based-dependency-parsing']	['natural-language-processing']	[-4.43557464e-03 8.41608047e-01 -3.85126799e-01 -6.16096079e-01 -1.20869303e+00 -8.60435128e-01 3.22464913e-01 4.24624830e-01 1.31359957e-02 7.51132965e-01 3.75331104e-01 -1.02558768e+00 2.86953628e-01 -9.80895221e-01 -7.09504724e-01 -3.47266555e-01 -4.19104934e-01 1.09968424e+00 8.28068912e-01 -3.82186830e-01 -2.40311429e-01 2.49285892e-01 -5.68514585e-01 2.96306252e-01 6.34465694e-01 6.39692962e-01 2.60644913e-01 7.04176128e-01 -3.10236335e-01 1.01540613e+00 -3.37094635e-01 -1.08560514e+00 -1.65555552e-01 -2.38023773e-01 -1.13442552e+00 -4.74331170e-01 4.38060731e-01 1.44453431e-02 -3.82641375e-01 7.81074166e-01 2.12029412e-01 -3.16832103e-02 2.63929516e-01 -7.49025643e-01 -6.82636797e-01 1.38392973e+00 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df6713e1-520c-4185-af04-7b437a710f08	generating-stories-using-role-playing-games	null	null	https://aclanthology.org/W18-6606	https://aclanthology.org/W18-6606.pdf	Generating Stories Using Role-playing Games and Simulated Human-like Conversations	null	["Pablo Gerv{\\'a}s", "Carlos Le{\\'o}n", 'Alan Tapscott']	2018-11-01	null	null	null	ws-2018-11	['human-dynamics']	['computer-vision']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 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5d812c83-acaf-4a21-ab31-dd1732cb5a83	incorporating-distributions-of-discourse	2305.16784	null	https://arxiv.org/abs/2305.16784v1	https://arxiv.org/pdf/2305.16784v1.pdf	Incorporating Distributions of Discourse Structure for Long Document Abstractive Summarization	For text summarization, the role of discourse structure is pivotal in discerning the core content of a text. Regrettably, prior studies on incorporating Rhetorical Structure Theory (RST) into transformer-based summarization models only consider the nuclearity annotation, thereby overlooking the variety of discourse relation types. This paper introduces the 'RSTformer', a novel summarization model that comprehensively incorporates both the types and uncertainty of rhetorical relations. Our RST-attention mechanism, rooted in document-level rhetorical structure, is an extension of the recently devised Longformer framework. Through rigorous evaluation, the model proposed herein exhibits significant superiority over state-of-the-art models, as evidenced by its notable performance on several automatic metrics and human evaluation.	['Vera Demberg', 'Yifan Wang', 'Dongqi Pu']	2023-05-26	null	null	null	null	['abstractive-text-summarization', 'text-summarization']	['natural-language-processing', 'natural-language-processing']	[ 4.12138581e-01 9.02291954e-01 -6.26745701e-01 6.37715589e-03 -9.85596001e-01 -7.57028520e-01 1.29336429e+00 7.35100210e-01 -1.55695021e-01 8.82256687e-01 1.43911016e+00 -5.82296789e-01 -1.76167160e-01 -3.79537970e-01 -3.22094381e-01 -2.44274169e-01 1.67368799e-01 4.56912100e-01 7.96820000e-02 -6.56186283e-01 7.21451640e-01 -1.13392442e-01 -1.16426647e+00 4.33650583e-01 1.18019688e+00 6.34239256e-01 -1.55389503e-01 5.55538833e-01 -3.35145652e-01 1.58025599e+00 -9.82511222e-01 -6.93272829e-01 -4.52980131e-01 -7.46758103e-01 -1.40747809e+00 -7.37023950e-02 5.89406013e-01 -1.28672048e-01 -3.88401628e-01 8.18462908e-01 4.14704382e-01 4.49212594e-03 7.98735559e-01 -5.83032429e-01 -6.80401504e-01 1.31601202e+00 -3.98503721e-01 7.53052831e-01 9.03911769e-01 -3.17883879e-01 1.80040455e+00 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cc41b474-0017-447b-aace-6db173c1435b	multitask-recalibrated-aggregation-network	2104.00952	null	https://arxiv.org/abs/2104.00952v3	https://arxiv.org/pdf/2104.00952v3.pdf	Multitask Recalibrated Aggregation Network for Medical Code Prediction	Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.	['Pekka Marttinen', 'Erik Cambria', 'Shaoxiong Ji', 'Wei Sun']	2021-04-02	null	null	null	null	['medical-code-prediction']	['medical']	[ 2.82097429e-01 -5.55254370e-02 -2.89630800e-01 -5.98509669e-01 -1.26861978e+00 -3.58190745e-01 -2.28725150e-01 6.53483331e-01 -1.64950520e-01 5.70634961e-01 5.52149475e-01 -4.11119878e-01 -2.66055942e-01 -4.47112739e-01 -1.47232845e-01 -3.00877631e-01 -1.40334547e-01 5.91463864e-01 -5.30744851e-01 1.65303528e-01 1.92759521e-02 -8.97346810e-02 -1.01740932e+00 1.15202701e+00 1.24734890e+00 1.16554487e+00 1.26816526e-01 6.82837546e-01 -4.47325051e-01 1.19664562e+00 -4.25384820e-01 -6.04701400e-01 -1.46180000e-02 -3.75252038e-01 -7.02224135e-01 1.67596534e-01 2.01737583e-01 -5.12782075e-02 -9.01394933e-02 1.17381179e+00 4.07853544e-01 -3.33054841e-01 5.47580600e-01 -7.35615611e-01 -9.85306680e-01 8.52564096e-01 -2.45185599e-01 7.32211843e-02 4.12402987e-01 -7.23792091e-02 1.16847908e+00 -8.07223916e-01 2.34529212e-01 7.76835799e-01 1.13524842e+00 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0160d946-d1f1-45ae-b49f-595bd62ef7f3	phenotype-detection-in-real-world-data-via	2211.07549	null	https://arxiv.org/abs/2211.07549v2	https://arxiv.org/pdf/2211.07549v2.pdf	Phenotype Detection in Real World Data via Online MixEHR Algorithm	Understanding patterns of diagnoses, medications, procedures, and laboratory tests from electronic health records (EHRs) and health insurer claims is important for understanding disease risk and for efficient clinical development, which often require rules-based curation in collaboration with clinicians. We extended an unsupervised phenotyping algorithm, mixEHR, to an online version allowing us to use it on order of magnitude larger datasets including a large, US-based claims dataset and a rich regional EHR dataset. In addition to recapitulating previously observed disease groups, we discovered clinically meaningful disease subtypes and comorbidities. This work scaled up an effective unsupervised learning method, reinforced existing clinical knowledge, and is a promising approach for efficient collaboration with clinicians.	['Jacob Oppenheim', 'Anna Decker', 'Romane Gauriau', 'Ying Xu']	2022-11-14	null	null	null	null	['clinical-knowledge']	['miscellaneous']	[ 9.95879024e-02 2.79313326e-01 -8.29257965e-01 -4.17100042e-01 -7.44319677e-01 -6.04564011e-01 -2.02319831e-01 1.18247676e+00 -1.91979278e-02 7.72839606e-01 7.04715133e-01 -9.04790878e-01 -5.39877355e-01 -8.66143167e-01 -3.92758399e-01 6.34573102e-02 -3.02228391e-01 8.51171672e-01 -3.13232362e-01 5.81138372e-01 -1.42040581e-01 4.59783942e-01 -8.81576478e-01 4.14299011e-01 9.84025240e-01 6.06405497e-01 -4.71948177e-01 3.89004558e-01 1.68932185e-01 1.24680960e+00 -4.18367945e-02 -4.20355082e-01 3.53459179e-01 -2.59658307e-01 -3.86888534e-01 -6.29565865e-02 1.60012156e-01 -4.29573685e-01 -2.18974441e-01 4.56689775e-01 3.61645877e-01 -3.01872253e-01 7.17838049e-01 -7.54632533e-01 -7.78731108e-01 8.79289150e-01 1.90263987e-02 -3.47068049e-02 3.65249902e-01 4.26026225e-01 9.30824101e-01 -9.16896835e-02 9.64345098e-01 5.26164412e-01 1.11671138e+00 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4.43409592e-01 -4.19737637e-01 -5.40480673e-01 -6.98333442e-01 7.25100279e-01 3.48819755e-02 -1.99253872e-01 2.71449275e-02 4.29405212e-01]	[7.83674955368042, 6.225624084472656]
8d28c96f-a49a-44ab-ad66-dbfd6dbf74e3	abo-dataset-and-benchmarks-for-real-world-3d	2110.06199	null	https://arxiv.org/abs/2110.06199v2	https://arxiv.org/pdf/2110.06199v2.pdf	ABO: Dataset and Benchmarks for Real-World 3D Object Understanding	We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval.	['Erhan Gundogdu', 'Achleshwar Luthra', 'Jitendra Malik', 'Matthieu Guillaumin', 'Thomas Dideriksen', 'Himanshu Arora', 'Tomas F. Yago Vicente', 'Xi Zhang', 'Kenan Deng', 'Leon Xu', 'Shubham Goel', 'Jasmine Collins']	2021-10-12	null	http://openaccess.thecvf.com//content/CVPR2022/html/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Collins_ABO_Dataset_and_Benchmarks_for_Real-World_3D_Object_Understanding_CVPR_2022_paper.pdf	cvpr-2022-1	['single-view-3d-reconstruction']	['computer-vision']	[-3.67449224e-01 -4.46115255e-01 6.72725663e-02 -2.73026854e-01 -7.70065904e-01 -8.81956518e-01 6.00758016e-01 4.36799452e-02 4.46082264e-01 -1.67542398e-01 4.62928824e-02 1.70471221e-01 -2.30402410e-01 -7.74360061e-01 -8.95327926e-01 -1.78299472e-01 -1.35305896e-01 1.23532629e+00 2.66012877e-01 -2.06150904e-01 2.79848695e-01 1.03386962e+00 -1.75907362e+00 4.58983392e-01 3.04278582e-01 1.63411736e+00 1.26606047e-01 4.49529529e-01 -1.87637329e-01 8.89498889e-02 -2.44556576e-01 -6.75382912e-01 7.56309271e-01 5.96690893e-01 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65b3fab7-a230-4746-9d8e-341f921bef1e	mpf6d-masked-pyramid-fusion-6d-pose	2111.09378	null	https://arxiv.org/abs/2111.09378v2	https://arxiv.org/pdf/2111.09378v2.pdf	MPF6D: Masked Pyramid Fusion 6D Pose Estimation	Object pose estimation has multiple important applications, such as robotic grasping and augmented reality. We present a new method to estimate the 6D pose of objects that improves upon the accuracy of current proposals and can still be used in real-time. Our method uses RGB-D data as input to segment objects and estimate their pose. It uses a neural network with multiple heads to identify the objects in the scene, generate the appropriate masks and estimate the values of the translation vectors and the quaternion that represents the objects' rotation. These heads leverage a pyramid architecture used during feature extraction and feature fusion. We conduct an empirical evaluation using the two most common datasets in the area, and compare against state-of-the-art approaches, illustrating the capabilities of MPF6D. Our method can be used in real-time with its low inference time and high accuracy.	['Luís A. Alexandre', 'Nuno Pereira']	2021-11-17	null	null	null	null	['robotic-grasping']	['robots']	[ 1.55704126e-01 -1.96396410e-01 -1.14175804e-01 -2.41943166e-01 -2.78606206e-01 -4.86506999e-01 4.02220309e-01 5.17118163e-03 -4.47166085e-01 1.10071190e-01 -2.73122877e-01 -1.94781780e-04 -2.25051656e-01 -7.32702136e-01 -8.42852116e-01 -4.28483605e-01 -2.13660970e-01 8.58659506e-01 6.15246534e-01 -2.85931855e-01 5.72704971e-01 1.05167711e+00 -1.76782000e+00 5.77203184e-02 2.60337710e-01 1.35511220e+00 3.29766303e-01 4.51028287e-01 1.29192412e-01 3.77707243e-01 -5.98305583e-01 2.37265900e-02 6.87010765e-01 4.09914136e-01 -5.01875877e-01 2.27973476e-01 3.72096539e-01 -7.23784685e-01 -4.21546042e-01 6.69218421e-01 5.45628786e-01 -6.59743398e-02 5.05936384e-01 -1.29691935e+00 -1.73001453e-01 2.94381946e-01 -5.54839492e-01 -4.27851737e-01 6.76987648e-01 1.14891209e-01 4.63298202e-01 -1.08076191e+00 7.92129457e-01 1.42992246e+00 6.30031884e-01 2.22581103e-01 -7.90263116e-01 -5.83018243e-01 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16c9bbab-288f-4fc4-9706-60bbb920e896	revisit-visual-representation-in-analytics	2106.08512	null	https://arxiv.org/abs/2106.08512v1	https://arxiv.org/pdf/2106.08512v1.pdf	Revisit Visual Representation in Analytics Taxonomy: A Compression Perspective	Visual analytics have played an increasingly critical role in the Internet of Things, where massive visual signals have to be compressed and fed into machines. But facing such big data and constrained bandwidth capacity, existing image/video compression methods lead to very low-quality representations, while existing feature compression techniques fail to support diversified visual analytics applications/tasks with low-bit-rate representations. In this paper, we raise and study the novel problem of supporting multiple machine vision analytics tasks with the compressed visual representation, namely, the information compression problem in analytics taxonomy. By utilizing the intrinsic transferability among different tasks, our framework successfully constructs compact and expressive representations at low bit-rates to support a diversified set of machine vision tasks, including both high-level semantic-related tasks and mid-level geometry analytic tasks. In order to impose compactness in the representations, we propose a codebook-based hyperprior, which helps map the representation into a low-dimensional manifold. As it well fits the signal structure of the deep visual feature, it facilitates more accurate entropy estimation, and results in higher compression efficiency. With the proposed framework and the codebook-based hyperprior, we further investigate the relationship of different task features owning different levels of abstraction granularity. Experimental results demonstrate that with the proposed scheme, a set of diversified tasks can be supported at a significantly lower bit-rate, compared with existing compression schemes.	['Jiaying Liu', 'Haofeng Huang', 'Wenhan Yang', 'Yueyu Hu']	2021-06-16	null	null	null	null	['feature-compression']	['computer-vision']	[ 1.47405013e-01 -8.69947746e-02 -2.08382815e-01 -1.88648835e-01 -2.06414223e-01 -5.77891506e-02 4.65169042e-01 2.65506059e-01 -1.84318900e-01 2.17029259e-01 2.92279392e-01 7.01240227e-02 -4.72664237e-01 -6.86843395e-01 -3.06164384e-01 -4.35904562e-01 -1.49335966e-01 1.64191023e-01 1.15520522e-01 1.73460431e-02 2.94729412e-01 4.13321227e-01 -2.03469062e+00 3.78344268e-01 9.33369100e-01 1.62552392e+00 7.19992876e-01 4.35929179e-01 -1.95009187e-01 6.63506269e-01 -2.60251939e-01 -4.76222873e-01 3.70619178e-01 -6.25722110e-02 -6.06251955e-01 3.68743807e-01 2.96333164e-01 -4.62909013e-01 -5.62670410e-01 1.08354306e+00 2.53240138e-01 4.52283770e-02 5.77620089e-01 -1.48007941e+00 -5.29356480e-01 3.25683534e-01 -6.74261749e-01 3.72581631e-01 2.00889036e-01 1.30359009e-01 1.02548063e+00 -9.37641978e-01 4.38103735e-01 1.40663564e+00 3.69616807e-01 -1.09440060e-02 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8.08840215e-01 3.24061036e-01 -8.58168721e-01 -2.25370944e-01 1.58574909e-01]	[11.282999992370605, -1.5840625762939453]
5930264f-46f7-4820-91b7-0f55de27bdcc	causal-discovery-and-optimal-experimental	2304.03210	null	https://arxiv.org/abs/2304.03210v1	https://arxiv.org/pdf/2304.03210v1.pdf	Causal Discovery and Optimal Experimental Design for Genome-Scale Biological Network Recovery	Causal discovery of genome-scale networks is important for identifying pathways from genes to observable traits - e.g. differences in cell function, disease, drug resistance and others. Causal learners based on graphical models rely on interventional samples to orient edges in the network. However, these models have not been shown to scale up the size of the genome, which are on the order of 1e3-1e4 genes. We introduce a new learner, SP-GIES, that jointly learns from interventional and observational datasets and achieves almost 4x speedup against an existing learner for 1,000 node networks. SP-GIES achieves an AUC-PR score of 0.91 on 1,000 node networks, and scales up to 2,000 node networks - this is 4x larger than existing works. We also show how SP-GIES improves downstream optimal experimental design strategies for selecting interventional experiments to perform on the system. This is an important step forward in realizing causal discovery at scale via autonomous experimental design.	['Rick Stevens', 'Valerie Hayot-Sasson', 'Arvind Ramanathan', 'Ashka Shah']	2023-04-06	null	null	null	null	['causal-discovery', 'experimental-design']	['knowledge-base', 'methodology']	[ 3.84621531e-01 7.44227469e-01 -6.68971896e-01 -1.18543968e-01 -5.38800299e-01 -6.01817310e-01 3.17929715e-01 4.29140270e-01 -2.85717044e-02 1.18180490e+00 1.24821797e-01 -8.90559733e-01 -9.70590174e-01 -9.07720804e-01 -1.26902938e+00 -4.95921969e-01 -1.12906432e+00 5.60551345e-01 2.39087060e-01 3.07959646e-01 3.69216092e-02 2.79525787e-01 -1.07274258e+00 9.49521959e-02 7.82855570e-01 1.57533839e-01 -2.85249323e-01 1.06838918e+00 3.88639778e-01 3.00350875e-01 -2.42008999e-01 -2.33803317e-02 -7.49096796e-02 -6.57845974e-01 -6.15297198e-01 -6.22661233e-01 2.95097798e-01 -4.88735437e-02 -2.93708920e-01 7.91439533e-01 6.66352212e-01 -4.76440847e-01 5.92443168e-01 -1.41193414e+00 -3.31896573e-01 1.33195114e+00 -6.42126858e-01 1.58699483e-01 1.34580627e-01 1.84835643e-01 1.34190655e+00 -2.71677554e-01 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3e9d94d6-d61e-4600-b6d1-bde701298cb8	text-classification-in-shipping-industry	2212.12407	null	https://arxiv.org/abs/2212.12407v1	https://arxiv.org/pdf/2212.12407v1.pdf	Text classification in shipping industry using unsupervised models and Transformer based supervised models	Obtaining labelled data in a particular context could be expensive and time consuming. Although different algorithms, including unsupervised learning, semi-supervised learning, self-learning have been adopted, the performance of text classification varies with context. Given the lack of labelled dataset, we proposed a novel and simple unsupervised text classification model to classify cargo content in international shipping industry using the Standard International Trade Classification (SITC) codes. Our method stems from representing words using pretrained Glove Word Embeddings and finding the most likely label using Cosine Similarity. To compare unsupervised text classification model with supervised classification, we also applied several Transformer models to classify cargo content. Due to lack of training data, the SITC numerical codes and the corresponding textual descriptions were used as training data. A small number of manually labelled cargo content data was used to evaluate the classification performances of the unsupervised classification and the Transformer based supervised classification. The comparison reveals that unsupervised classification significantly outperforms Transformer based supervised classification even after increasing the size of the training dataset by 30%. Lacking training data is a key bottleneck that prohibits deep learning models (such as Transformers) from successful practical applications. Unsupervised classification can provide an alternative efficient and effective method to classify text when there is scarce training data.	['Dongping Song', 'Ying Xie']	2022-12-21	null	null	null	null	['self-learning', 'unsupervised-text-classification']	['natural-language-processing', 'natural-language-processing']	[ 1.33127242e-01 1.24739727e-03 -3.42948377e-01 -5.91910481e-01 -4.60107028e-01 -8.42127264e-01 6.72710896e-01 5.99744081e-01 -6.37762725e-01 4.07193124e-01 3.37565184e-01 -4.82391983e-01 -1.86822906e-01 -1.00566018e+00 -1.90582737e-01 -6.86329842e-01 1.93228334e-01 4.84753788e-01 -8.08277428e-02 -2.76818961e-01 4.63200688e-01 2.62914449e-01 -1.66935360e+00 1.75168470e-01 8.28504562e-01 1.32502520e+00 3.17069203e-01 3.35153639e-01 -9.29768980e-01 9.68102276e-01 -7.01177895e-01 -5.40648818e-01 3.33346426e-01 -2.84353018e-01 -8.16918910e-01 3.30683440e-01 -7.18408376e-02 6.84052184e-02 -2.32852802e-01 8.50908816e-01 2.67991781e-01 3.78498107e-01 9.34876502e-01 -1.23480046e+00 -5.77883720e-01 5.96069455e-01 2.35904902e-01 -3.36627327e-02 1.29926533e-01 -3.54441971e-01 1.22974968e+00 -7.62365699e-01 4.35614347e-01 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30a65788-e156-444f-a325-0c6400ba7656	video-mobile-former-video-recognition-with	2208.12257	null	https://arxiv.org/abs/2208.12257v1	https://arxiv.org/pdf/2208.12257v1.pdf	Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling	Transformer-based models have achieved top performance on major video recognition benchmarks. Benefiting from the self-attention mechanism, these models show stronger ability of modeling long-range dependencies compared to CNN-based models. However, significant computation overheads, resulted from the quadratic complexity of self-attention on top of a tremendous number of tokens, limit the use of existing video transformers in applications with limited resources like mobile devices. In this paper, we extend Mobile-Former to Video Mobile-Former, which decouples the video architecture into a lightweight 3D-CNNs for local context modeling and a Transformer modules for global interaction modeling in a parallel fashion. To avoid significant computational cost incurred by computing self-attention between the large number of local patches in videos, we propose to use very few global tokens (e.g., 6) for a whole video in Transformers to exchange information with 3D-CNNs with a cross-attention mechanism. Through efficient global spatial-temporal modeling, Video Mobile-Former significantly improves the video recognition performance of alternative lightweight baselines, and outperforms other efficient CNN-based models at the low FLOP regime from 500M to 6G total FLOPs on various video recognition tasks. It is worth noting that Video Mobile-Former is the first Transformer-based video model which constrains the computational budget within 1G FLOPs.	['Yu-Gang Jiang', 'Lu Yuan', 'Luowei Zhou', 'Mengchen Liu', 'Xiyang Dai', 'Yinpeng Chen', 'Dongdong Chen', 'Zuxuan Wu', 'Rui Wang']	2022-08-25	null	null	null	null	['video-recognition']	['computer-vision']	[-1.38865680e-01 -3.45569789e-01 -5.30583501e-01 -1.91747233e-01 -6.15121543e-01 -1.92618564e-01 4.51636523e-01 -4.05327916e-01 -4.19442981e-01 2.22203031e-01 2.02067986e-01 -6.06139660e-01 2.91996270e-01 -7.19510972e-01 -1.24807847e+00 -5.62404394e-01 -1.21673360e-01 9.38551575e-02 5.50195515e-01 1.06561929e-01 5.15698344e-02 2.09172145e-01 -1.58585835e+00 6.53989613e-01 5.78641295e-01 1.40513957e+00 3.92172068e-01 7.20081925e-01 -1.50486067e-01 1.29797816e+00 -3.38256568e-01 -3.94433081e-01 1.40863851e-01 -4.33820626e-03 -7.48494446e-01 2.34687235e-03 8.86505902e-01 -8.60481083e-01 -8.56780887e-01 6.59338295e-01 3.90590936e-01 -3.34496014e-02 1.23273864e-01 -1.18725336e+00 -5.75321436e-01 6.23010695e-01 -5.46157956e-01 6.68540180e-01 2.20571104e-02 2.05512851e-01 1.09011936e+00 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2.81126738e-01 -9.34486210e-01 -4.81397547e-02 -8.12759399e-01 -1.95364594e-01 -1.38162613e-01 1.75328329e-01 -5.90115666e-01 -6.33748248e-02 1.06581420e-01]	[9.058188438415527, 0.6130639910697937]
8ef05f1d-430d-4e4c-9855-e86892e27822	deep-multi-agent-reinforcement-learning-for	2003.06709	null	https://arxiv.org/abs/2003.06709v5	https://arxiv.org/pdf/2003.06709v5.pdf	FACMAC: Factored Multi-Agent Centralised Policy Gradients	We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces. Like MADDPG, a popular multi-agent actor-critic method, our approach uses deep deterministic policy gradients to learn policies. However, FACMAC learns a centralised but factored critic, which combines per-agent utilities into the joint action-value function via a non-linear monotonic function, as in QMIX, a popular multi-agent Q-learning algorithm. However, unlike QMIX, there are no inherent constraints on factoring the critic. We thus also employ a nonmonotonic factorisation and empirically demonstrate that its increased representational capacity allows it to solve some tasks that cannot be solved with monolithic, or monotonically factored critics. In addition, FACMAC uses a centralised policy gradient estimator that optimises over the entire joint action space, rather than optimising over each agent's action space separately as in MADDPG. This allows for more coordinated policy changes and fully reaps the benefits of a centralised critic. We evaluate FACMAC on variants of the multi-agent particle environments, a novel multi-agent MuJoCo benchmark, and a challenging set of StarCraft II micromanagement tasks. Empirical results demonstrate FACMAC's superior performance over MADDPG and other baselines on all three domains.	['Shimon Whiteson', 'Philip H. S. Torr', 'Pierre-Alexandre Kamienny', 'Christian A. Schroeder de Witt', 'Tabish Rashid', 'Wendelin Böhmer', 'Bei Peng']	2020-03-14	null	http://proceedings.neurips.cc/paper/2021/hash/65b9eea6e1cc6bb9f0cd2a47751a186f-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/65b9eea6e1cc6bb9f0cd2a47751a186f-Paper.pdf	neurips-2021-12	['smac']	['playing-games']	[-3.88864458e-01 -3.70367966e-03 -3.03627640e-01 3.07027996e-01 -1.06387365e+00 -6.06394649e-01 8.64343524e-01 5.07207923e-02 -1.05224013e+00 1.45206547e+00 3.62420738e-01 -1.95887357e-01 -3.71639729e-01 -5.40757835e-01 -7.29666829e-01 -8.93700898e-01 -3.57166052e-01 8.12054336e-01 2.52148628e-01 -4.95522857e-01 7.20412061e-02 2.12616399e-01 -1.00479662e+00 -1.26368746e-01 9.12220597e-01 5.90009391e-01 9.16670561e-02 1.23848414e+00 4.76838857e-01 1.27793896e+00 -8.12246084e-01 -1.03069777e-02 4.89570916e-01 -5.13238251e-01 -6.97499275e-01 6.63527101e-02 1.35089844e-01 -6.26426518e-01 -1.35746300e-01 7.86111236e-01 6.95590377e-01 6.82158828e-01 5.64789653e-01 -1.48448515e+00 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d28a4ed8-32ca-42bc-aeee-ff1de2c24397	improving-the-sample-complexity-of-deep	2202.03967	null	https://arxiv.org/abs/2202.03967v1	https://arxiv.org/pdf/2202.03967v1.pdf	Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration	Leveraging prior knowledge on intraclass variance due to transformations is a powerful method to improve the sample complexity of deep neural networks. This makes them applicable to practically important use-cases where training data is scarce. Rather than being learned, this knowledge can be embedded by enforcing invariance to those transformations. Invariance can be imposed using group-equivariant convolutions followed by a pooling operation. For rotation-invariance, previous work investigated replacing the spatial pooling operation with invariant integration which explicitly constructs invariant representations. Invariant integration uses monomials which are selected using an iterative approach requiring expensive pre-training. We propose a novel monomial selection algorithm based on pruning methods to allow an application to more complex problems. Additionally, we replace monomials with different functions such as weighted sums, multi-layer perceptrons and self-attention, thereby streamlining the training of invariant-integration-based architectures. We demonstrate the improved sample complexity on the Rotated-MNIST, SVHN and CIFAR-10 datasets where rotation-invariant-integration-based Wide-ResNet architectures using monomials and weighted sums outperform the respective baselines in the limited sample regime. We achieve state-of-the-art results using full data on Rotated-MNIST and SVHN where rotation is a main source of intraclass variation. On STL-10 we outperform a standard and a rotation-equivariant convolutional neural network using pooling.	['Alexandru Paul Condurache', 'Matthias Rath']	2022-02-08	null	null	null	null	['rotated-mnist']	['computer-vision']	[ 3.38577867e-01 -4.50482741e-02 -5.13200946e-02 -6.99813545e-01 -4.24626827e-01 -6.39855385e-01 7.25947559e-01 -1.17980875e-01 -1.03565395e+00 6.35630906e-01 9.45330262e-02 -3.22373241e-01 -4.05575335e-01 -7.09820747e-01 -1.05856299e+00 -6.94155872e-01 -1.14068210e-01 2.87343442e-01 2.73408026e-01 -3.78218740e-01 -1.86279025e-02 1.03024054e+00 -1.57707989e+00 5.08258522e-01 4.97623563e-01 1.00353539e+00 4.47806381e-02 4.77561831e-01 3.11133742e-01 5.89363277e-01 -6.04373515e-01 -2.71322280e-01 7.27501929e-01 -1.92631991e-03 -7.56000936e-01 -2.04991385e-01 8.56039882e-01 7.66248023e-03 -3.20497453e-01 8.36624026e-01 5.64323425e-01 7.66008914e-01 6.23677909e-01 -6.55102015e-01 -7.44196892e-01 6.96550846e-01 -2.29468971e-01 4.34789211e-01 -3.18538040e-01 -1.29362762e-01 1.16219079e+00 -8.90366614e-01 6.41228557e-01 1.38645744e+00 7.52875865e-01 2.69418508e-01 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e0d17154-7706-4537-9a08-e757f7da5975	feature-representation-learning-with-adaptive	2304.04420	null	https://arxiv.org/abs/2304.04420v1	https://arxiv.org/pdf/2304.04420v1.pdf	Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition	Micro-expressions are spontaneous, rapid and subtle facial movements that can neither be forged nor suppressed. They are very important nonverbal communication clues, but are transient and of low intensity thus difficult to recognize. Recently deep learning based methods have been developed for micro-expression (ME) recognition using feature extraction and fusion techniques, however, targeted feature learning and efficient feature fusion still lack further study according to the ME characteristics. To address these issues, we propose a novel framework Feature Representation Learning with adaptive Displacement Generation and Transformer fusion (FRL-DGT), in which a convolutional Displacement Generation Module (DGM) with self-supervised learning is used to extract dynamic features from onset/apex frames targeted to the subsequent ME recognition task, and a well-designed Transformer Fusion mechanism composed of three Transformer-based fusion modules (local, global fusions based on AU regions and full-face fusion) is applied to extract the multi-level informative features after DGM for the final ME prediction. The extensive experiments with solid leave-one-subject-out (LOSO) evaluation results have demonstrated the superiority of our proposed FRL-DGT to state-of-the-art methods.	['Huijuan Zhao', 'Shuangjiang He', 'Wenju Xu', 'Chengjiang Long', 'Jianhui Zhao', 'Zhijun Zhai']	2023-04-10	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhai_Feature_Representation_Learning_With_Adaptive_Displacement_Generation_and_Transformer_Fusion_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhai_Feature_Representation_Learning_With_Adaptive_Displacement_Generation_and_Transformer_Fusion_CVPR_2023_paper.pdf	cvpr-2023-1	['micro-expression-recognition']	['computer-vision']	[ 5.78722060e-02 -2.27687597e-01 -2.49948099e-01 -6.63350105e-01 -6.52643442e-01 8.96600857e-02 6.33962214e-01 -3.84635776e-01 -1.36116058e-01 4.55249727e-01 3.40040058e-01 4.80820417e-01 -4.22803536e-02 -4.41662908e-01 -3.27846229e-01 -1.03522098e+00 -2.34762818e-01 -3.20123173e-02 2.09738761e-02 -5.51879883e-01 7.99528882e-03 6.93141043e-01 -2.00378537e+00 5.67278385e-01 8.15632224e-01 1.46009600e+00 -3.76686305e-01 3.26973587e-01 -3.11792105e-01 9.53397632e-01 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beb93b87-3f21-4643-a507-462494400561	the-challenges-of-studying-misinformation-on	2303.14309	null	https://arxiv.org/abs/2303.14309v1	https://arxiv.org/pdf/2303.14309v1.pdf	The Challenges of Studying Misinformation on Video-Sharing Platforms During Crises and Mass-Convergence Events	Mis- and disinformation can spread rapidly on video-sharing platforms (VSPs). Despite the growing use of VSPs, there has not been a proportional increase in our ability to understand this medium and the messages conveyed through it. In this work, we draw on our prior experiences to outline three core challenges faced in studying VSPs in high-stakes and fast-paced settings: (1) navigating the unique affordances of VSPs, (2) understanding VSP content and determining its authenticity, and (3) novel user behaviors on VSPs for spreading misinformation. By highlighting these challenges, we hope that researchers can reflect on how to adapt existing research methods and tools to these new contexts, or develop entirely new ones.	['Stephen Prochaska', 'Joseph S. Schafer', 'Sukrit Venkatagiri']	2023-03-25	null	null	null	null	['misinformation']	['miscellaneous']	[ 1.70501515e-01 4.09970031e-04 -5.75944304e-01 7.95961842e-02 -3.08041960e-01 -1.01920247e+00 6.45972192e-01 3.39784473e-01 -5.08197784e-01 6.77318454e-01 8.68782043e-01 -5.03622174e-01 3.36332470e-02 -4.81193691e-01 -2.33688608e-01 2.02078044e-01 -1.55481890e-01 -2.52828509e-01 5.05902946e-01 -3.25948834e-01 6.84296191e-01 1.83073640e-01 -1.51947308e+00 3.93323541e-01 4.71823543e-01 5.77332258e-01 1.86249256e-01 6.06221855e-01 -2.13461891e-01 1.12937593e+00 -6.72314405e-01 -7.41595328e-01 1.07361125e-02 -3.03909123e-01 -9.44561362e-01 -1.31315440e-01 2.25759655e-01 -9.86989498e-01 -4.52022910e-01 8.95724237e-01 1.90312400e-01 7.24843564e-03 1.46569714e-01 -1.32709968e+00 -9.26460862e-01 6.86425030e-01 -4.69526619e-01 8.25376511e-01 1.09108686e+00 1.02546491e-01 6.51364326e-01 -3.12854111e-01 1.31813025e+00 1.10656238e+00 7.96047926e-01 1.93248674e-01 -9.45166469e-01 -9.29997444e-01 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0895b312-4357-4ddb-aa33-c1b71636823c	daily-peak-electrical-load-forecasting-with-a	2112.04492	null	https://arxiv.org/abs/2112.04492v1	https://arxiv.org/pdf/2112.04492v1.pdf	Daily peak electrical load forecasting with a multi-resolution approach	In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are a) a general and formal introduction to the multi-resolution modelling approach, b) a discussion on modelling approaches at different resolutions implemented via Generalised Additive Models and Neural Networks and c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.	['Hui Yan', 'Yannig Goude', 'Matteo Fasiolo', 'Yvenn Amara-Ouali']	2021-12-08	null	null	null	null	['additive-models']	['methodology']	[-4.54171225e-02 -2.66175836e-01 2.56541610e-01 -2.83843637e-01 -5.87738216e-01 -4.38423872e-01 1.15916193e+00 3.33577663e-01 2.38436982e-01 9.04883981e-01 4.93187755e-01 -2.20513985e-01 -8.16853583e-01 -1.12608862e+00 2.38991126e-01 -1.00027788e+00 -5.91025293e-01 4.35316116e-01 -2.82869935e-01 -3.20787758e-01 1.35799259e-01 6.50342822e-01 -1.30641007e+00 1.69017166e-01 9.41643059e-01 9.44873393e-01 3.90178621e-01 4.72047329e-01 2.66976655e-01 6.23585045e-01 -3.93544376e-01 5.62838733e-01 3.08048695e-01 -4.16772962e-01 -3.57709825e-01 -2.79147513e-02 -6.86170220e-01 -2.06036329e-01 3.53616595e-01 7.69305587e-01 7.63230562e-01 1.94822535e-01 5.83972752e-01 -1.07914579e+00 6.72355220e-02 6.58193231e-01 -4.98008013e-01 6.78582609e-01 2.65666366e-01 1.54169220e-02 7.79535472e-01 -5.20966351e-01 -1.79276317e-01 6.55857444e-01 6.64348483e-01 -6.00691736e-01 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-1.21843302e+00 -4.80959490e-02 1.83108985e-01 -4.48056042e-01 -4.80882674e-01 3.02123814e-03]	[6.09458065032959, 2.8255789279937744]
8fc8b542-91ad-4937-991f-64e3f3bbe614	fast-training-method-for-stochastic-1	null	null	https://openreview.net/forum?id=Mobm1AGs64v	https://openreview.net/pdf?id=Mobm1AGs64v	Fast Training Method for Stochastic Compositional Optimization Problems	The stochastic compositional optimization problem covers a wide range of machine learning models, such as sparse additive models and model-agnostic meta-learning. Thus, it is necessary to develop efficient methods for its optimization. Existing methods for the stochastic compositional optimization problem only focus on the single machine scenario, which is far from satisfactory when data are distributed on different devices. To address this problem, we propose novel decentralized stochastic compositional gradient descent methods to efficiently train the large-scale stochastic compositional optimization problem. To the best of our knowledge, our work is the first one facilitating decentralized training for this kind of problem. Furthermore, we provide the convergence analysis for our methods, which shows that the convergence rate of our methods can achieve linear speedup with respect to the number of devices. At last, we apply our decentralized training methods to the model-agnostic meta-learning problem, and the experimental results confirm the superior performance of our methods.	['Heng Huang', 'Hongchang Gao']	2021-05-21	null	http://proceedings.neurips.cc/paper/2021/hash/d5397f1497b5cdaad7253fdc92db610b-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/d5397f1497b5cdaad7253fdc92db610b-Paper.pdf	neurips-2021-12	['additive-models']	['methodology']	[-6.33398667e-02 -3.23861897e-01 -6.24868810e-01 -2.58765340e-01 -9.95876312e-01 -1.48671582e-01 2.45433912e-01 -1.57323942e-01 -8.87854844e-02 7.52894342e-01 6.38781637e-02 -3.74831975e-01 -3.41493301e-02 -5.56585312e-01 -1.10242891e+00 -8.07695150e-01 8.17226395e-02 6.29785061e-01 4.22443449e-02 -1.72151159e-02 1.01779088e-01 1.33612037e-01 -1.31629956e+00 2.96404392e-01 8.45898032e-01 1.10810137e+00 3.98568034e-01 5.95789433e-01 -1.94083631e-01 9.46616113e-01 -4.63718414e-01 -3.86087656e-01 5.08775041e-02 -4.05466467e-01 -6.33303404e-01 -1.12137266e-01 3.17809016e-01 -3.76791716e-01 -1.55312762e-01 1.03858054e+00 6.77912235e-01 -1.17643006e-01 4.66380775e-01 -1.32507789e+00 -2.95587093e-01 1.25764334e+00 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419bcdf9-6f6c-4213-8c5f-117f4759d51d	predicting-power-system-dynamics-and	2111.01103	null	https://arxiv.org/abs/2111.01103v3	https://arxiv.org/pdf/2111.01103v3.pdf	A Frequency Domain Approach to Predict Power System Transients	The dynamics of power grids are governed by a large number of nonlinear differential and algebraic equations (DAEs). To safely operate the system, operators need to check that the states described by these DAEs stay within prescribed limits after various potential faults. However, current numerical solvers of DAEs are often too slow for real-time system operations. In addition, detailed system parameters are often not exactly known. Machine learning approaches have been proposed to reduce computational efforts, but existing methods generally suffer from overfitting and failures to predict unstable behaviors. This paper proposes a novel framework to predict power system transients by learning in the frequency domain. The intuition is that although the system behavior is complex in the time domain, there are relatively few dominant modes in the frequency domain. Therefore, we learn to predict by constructing neural networks with Fourier transform and filtering layers. System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies. We show that the proposed approach does not need detailed system parameters, greatly speeds up prediction computations and is highly accurate for different fault types.	['Baosen Zhang', 'Weiwei Yang', 'Wenqi Cui']	2021-11-01	null	null	null	null	['numerical-integration']	['miscellaneous']	[-3.23543310e-01 -5.06842136e-01 -2.69327499e-02 1.25983104e-01 -1.77919701e-01 -7.21087694e-01 1.65194914e-01 6.51121885e-02 5.06463826e-01 8.94716918e-01 -3.95307034e-01 -2.83769399e-01 -5.18769264e-01 -7.62333810e-01 -4.12582010e-01 -9.70320106e-01 -6.45185173e-01 2.83790529e-01 -2.32145026e-01 -4.38706219e-01 4.94120903e-02 8.01750004e-01 -1.15254986e+00 -9.74898413e-02 1.02019835e+00 1.17384458e+00 -2.93649495e-01 5.53348303e-01 4.63568568e-01 1.00789952e+00 -8.11088204e-01 5.88698626e-01 2.40882710e-01 -4.24674928e-01 -7.02593267e-01 2.16776744e-01 -3.15036446e-01 -3.69674146e-01 -8.41547430e-01 1.27168739e+00 3.84115338e-01 2.57769912e-01 5.11365950e-01 -1.47128499e+00 -3.79212946e-01 2.86393970e-01 -2.13490844e-01 4.67542291e-01 2.48695523e-01 2.70601600e-01 6.86312735e-01 -8.43498588e-01 9.47805122e-02 5.90552807e-01 1.15361369e+00 4.96846288e-02 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09dfd59d-05fa-438b-b2dd-75fd736ccd97	pointflownet-learning-representations-for	1806.02170	null	http://arxiv.org/abs/1806.02170v3	http://arxiv.org/pdf/1806.02170v3.pdf	PointFlowNet: Learning Representations for Rigid Motion Estimation from Point Clouds	Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D motion from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.	['Simon Donné', 'Despoina Paschalidou', 'Aseem Behl', 'Andreas Geiger']	2018-06-06	pointflownet-learning-representations-for-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Behl_PointFlowNet_Learning_Representations_for_Rigid_Motion_Estimation_From_Point_Clouds_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Behl_PointFlowNet_Learning_Representations_for_Rigid_Motion_Estimation_From_Point_Clouds_CVPR_2019_paper.pdf	cvpr-2019-6	['scene-flow-estimation']	['computer-vision']	[ 1.60269644e-02 -1.08271360e-01 -6.05663322e-02 -3.15715253e-01 -4.52252656e-01 -6.16293669e-01 6.77936375e-01 -2.81537831e-01 -4.10104781e-01 3.33917081e-01 -1.40509129e-01 -3.35066319e-01 2.29277343e-01 -7.43491948e-01 -1.05578876e+00 -5.12126565e-01 2.78995126e-01 9.38792586e-01 3.35983992e-01 -4.82562557e-02 2.77130991e-01 1.05603671e+00 -1.45372391e+00 -3.64093095e-01 5.63625515e-01 9.38982844e-01 1.90972015e-01 7.94593930e-01 -3.95334572e-01 6.75451815e-01 -1.68341830e-01 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d5c81751-cbf4-4505-a5ed-edc5c0aacc33	fast-adversarial-cnn-based-perturbation	null	null	https://openreview.net/forum?id=xKf-LSD2-Jg	https://openreview.net/pdf?id=xKf-LSD2-Jg	Fast Adversarial CNN-based Perturbation Attack of No-Reference Image Quality Metrics	Modern neural-network-based no-reference image- and video-quality metrics exhibit performance as high as full-reference metrics. These metrics are widely used to improve visual quality in computer vision methods and compare video processing methods. However, these metrics are not stable to traditional adversarial attacks, which can cause incorrect results. Our goal is to investigate the boundaries of no-reference metrics applicability, and in this paper, we propose a fast adversarial perturbation attack of no-reference quality metrics. The proposed attack (FACPA) can be exploited as a preprocessing step in real-time video processing and compression algorithms. This research can yield insights to further aid in designing of stable neural-network-based no-reference quality metrics.	['Dmitriy S. Vatolin', 'Anastasia Antsiferova', 'Ekaterina Shumitskaya']	2023-04-11	null	null	null	iclr-tiny-papers-2023-4	['no-reference-image-quality-assessment']	['computer-vision']	[ 5.69233358e-01 -4.34195042e-01 -2.07488108e-02 -8.37995857e-02 -5.37587702e-01 -4.94202793e-01 5.92011154e-01 -2.60535359e-01 -4.31428552e-01 5.64702690e-01 -1.64406583e-01 -4.50689554e-01 -7.84377381e-02 -7.43203163e-01 -7.47446299e-01 -8.12369049e-01 -3.93131018e-01 -5.74216843e-01 3.10790032e-01 -1.57613218e-01 3.50572139e-01 7.60059595e-01 -1.29575431e+00 1.26241103e-01 5.78785181e-01 1.07869637e+00 -3.48001450e-01 1.07644796e+00 4.21817392e-01 7.41105735e-01 -1.01726377e+00 -7.38976955e-01 8.25265706e-01 -4.89573121e-01 -4.58553195e-01 -3.21724832e-01 8.11625123e-01 -6.87513292e-01 -8.76722872e-01 1.61681211e+00 6.11708403e-01 6.81127012e-02 6.36703551e-01 -1.49413228e+00 -7.66730905e-01 5.40114760e-01 -3.77504081e-01 5.90253890e-01 3.07209253e-01 4.71485794e-01 4.15822983e-01 -4.45021868e-01 3.47028732e-01 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59389761-4b78-4bee-beae-ce991d74a902	prediction-of-new-onset-diabetes-after-liver	1812.00506	null	https://arxiv.org/abs/1812.00506v2	https://arxiv.org/pdf/1812.00506v2.pdf	Prediction of New Onset Diabetes after Liver Transplant	25% of people who received a liver transplant will go on to develop diabetes within the next 5 years. These thousands of individuals are at 2-fold higher risk of cardiovascular events, graft loss, infections, as well as lower long-term survival. This is partly due to the medication used during and/or after transplant that significantly impacts metabolic balance. To assess which medication best suits the patient's condition, clinicians need an accurate estimate of diabetes risk. Both patient's historical data and observations at the current visit are informative in predicting whether the patient will develop diabetes within the following year. In this work we compared a variety of time-to-event prediction models as well as classifiers predicting the likelihood of the event within a year from the current checkup. We are particularly interested in comparing two types of models: 1) standard time-to-event predictors where the historical measurements are merely concatenated, 2) incorporating Deep Markov Model to first obtain low-dimensional embedding of historical data and then using this embedding as an additional input into the model. We compared a variety of algorithms including standard and regularized Cox proportional-hazards model (CPH), mixed effect random forests, survival-forests and Weibull Time-To-Event Recurrent Neural Network (WTTE-RNN). The results show that although all methods' performances varied from year to year and there was no clear winner across all the time points, regularized CPH model that used 1 to 3 years of historical visits data on average achieved a high, clinically relevant Concordance Index of .863. We thus recommend this model for further prospective clinical validation and hopefully, an eventual use in the clinic to improve clinicians' ability to personalize post-operative care and reduce the incidence of new-onset diabetes post liver transplant.	['Angeline Yasodhara', 'Mamatha Bhat', 'Anna Goldenberg']	2018-12-03	null	null	null	null	['time-to-event-prediction']	['time-series']	[-8.46588165e-02 -3.71076673e-01 -5.62327266e-01 -3.97313088e-01 -6.02095664e-01 -1.50909543e-01 4.59550440e-01 8.08819950e-01 -3.88358325e-01 8.72954071e-01 7.19669223e-01 -6.05490983e-01 -3.86053026e-01 -1.07479799e+00 -1.82277486e-01 -6.44471645e-01 -6.22982681e-01 7.85263777e-01 -4.35905308e-01 2.62072355e-01 -1.08058706e-01 6.21234477e-01 -8.65992844e-01 3.58953215e-02 6.72778547e-01 8.94217432e-01 -2.19512448e-01 5.20680487e-01 -1.97689399e-01 5.03479242e-01 -2.52704531e-01 -9.45333317e-02 8.54335502e-02 -5.47406971e-01 -2.65414208e-01 -3.62416774e-01 -6.99274391e-02 -4.00642216e-01 -1.97457209e-01 2.56357729e-01 7.57184923e-01 -2.25066990e-01 7.49985814e-01 -9.42809701e-01 -4.05037671e-01 5.78988552e-01 2.01174542e-01 2.66280007e-02 1.97509900e-01 3.51650685e-01 1.53188840e-01 -4.91602689e-01 5.27960956e-01 9.15529132e-01 1.09977901e+00 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036ddb9d-66a1-4b10-bac2-6918aa92bcf6	convnets-vs-transformers-whose-visual	2108.05305	null	https://arxiv.org/abs/2108.05305v2	https://arxiv.org/pdf/2108.05305v2.pdf	ConvNets vs. Transformers: Whose Visual Representations are More Transferable?	Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets. However, although Transformer-based backbones have achieved much progress on ImageNet classification, it is still unclear whether the learned representations are as transferable as or even more transferable than ConvNets' features. To address this point, we systematically investigate the transfer learning ability of ConvNets and vision transformers in 15 single-task and multi-task performance evaluations. Given the strong correlation between the performance of pre-trained models and transfer learning, we include 2 residual ConvNets (i.e., R-101x3 and R-152x4) and 3 Transformer-based visual backbones (i.e., ViT-B, ViT-L and Swin-B), which have close error rates on ImageNet, that indicate similar transfer learning performance on downstream datasets. We observe consistent advantages of Transformer-based backbones on 13 downstream tasks (out of 15), including but not limited to fine-grained classification, scene recognition (classification, segmentation and depth estimation), open-domain classification, face recognition, etc. More specifically, we find that two ViT models heavily rely on whole network fine-tuning to achieve performance gains while Swin Transformer does not have such a requirement. Moreover, vision transformers behave more robustly in multi-task learning, i.e., bringing more improvements when managing mutually beneficial tasks and reducing performance losses when tackling irrelevant tasks. We hope our discoveries can facilitate the exploration and exploitation of vision transformers in the future.	['Yizhou Yu', 'Sibei Yang', 'Chixiang Lu', 'Hong-Yu Zhou']	2021-08-11	null	null	null	null	['scene-recognition']	['computer-vision']	[ 1.41518280e-01 6.95349798e-02 -2.42230054e-02 -4.34827477e-01 -4.21036184e-01 -6.39560461e-01 6.38782501e-01 -3.46889883e-01 -4.88842875e-01 6.50171041e-01 -4.41772081e-02 -4.53920454e-01 -2.81715482e-01 -8.42190385e-01 -9.11946356e-01 -6.68459594e-01 1.29716724e-01 3.08838546e-01 4.66998309e-01 -1.47692591e-01 1.71886653e-01 4.83970553e-01 -1.29111695e+00 2.78171092e-01 6.61345840e-01 1.32297349e+00 1.59956440e-01 3.69684368e-01 3.12945731e-02 8.89300227e-01 -3.96971315e-01 -5.29420257e-01 2.63951659e-01 -9.45162997e-02 -9.29086268e-01 4.74993959e-02 5.90680480e-01 -2.33985543e-01 -4.21994269e-01 8.14646304e-01 3.82891685e-01 -1.30754516e-01 8.07962716e-01 -1.28208852e+00 -8.38075042e-01 3.25714529e-01 -7.42167056e-01 3.45830083e-01 -9.88796577e-02 5.25864542e-01 9.57043946e-01 -8.76043737e-01 3.99789751e-01 1.26975417e+00 8.13881278e-01 5.23212552e-01 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7ea4cf9e-a8f4-4fa2-ba36-f707fb3c0535	bidirectional-attentive-fusion-with-context	1804.00100	null	http://arxiv.org/abs/1804.00100v2	http://arxiv.org/pdf/1804.00100v2.pdf	Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning	Dense video captioning is a newly emerging task that aims at both localizing and describing all events in a video. We identify and tackle two challenges on this task, namely, (1) how to utilize both past and future contexts for accurate event proposal predictions, and (2) how to construct informative input to the decoder for generating natural event descriptions. First, previous works predominantly generate temporal event proposals in the forward direction, which neglects future video context. We propose a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions. Second, different events ending at (nearly) the same time are indistinguishable in the previous works, resulting in the same captions. We solve this problem by representing each event with an attentive fusion of hidden states from the proposal module and video contents (e.g., C3D features). We further propose a novel context gating mechanism to balance the contributions from the current event and its surrounding contexts dynamically. We empirically show that our attentively fused event representation is superior to the proposal hidden states or video contents alone. By coupling proposal and captioning modules into one unified framework, our model outperforms the state-of-the-arts on the ActivityNet Captions dataset with a relative gain of over 100% (Meteor score increases from 4.82 to 9.65).	['Lin Ma', 'Jingwen Wang', 'Wei Liu', 'Yong Xu', 'Wenhao Jiang']	2018-03-31	bidirectional-attentive-fusion-with-context-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Bidirectional_Attentive_Fusion_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Bidirectional_Attentive_Fusion_CVPR_2018_paper.pdf	cvpr-2018-6	['dense-video-captioning']	['computer-vision']	[ 3.05439502e-01 5.23470044e-02 -3.28458846e-01 -4.50204879e-01 -9.24174309e-01 -3.79937887e-01 8.54562163e-01 1.05911419e-01 -4.66864884e-01 7.40575731e-01 8.49784553e-01 2.07126498e-01 5.09573221e-01 -4.78587836e-01 -1.01730096e+00 -4.48349118e-01 -1.50410952e-02 2.59163290e-01 5.06198049e-01 -2.07613148e-02 -7.56629780e-02 6.49599433e-02 -1.54846191e+00 6.59868658e-01 4.65881467e-01 1.06972158e+00 4.87327337e-01 5.60375750e-01 -1.18117079e-01 9.35871542e-01 -5.05065560e-01 -2.87703127e-01 -2.06303019e-02 -6.64088190e-01 -6.31898224e-01 1.31328940e-01 2.60933697e-01 -6.28554940e-01 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0d6d0181-3790-48e8-8ef8-0a9e64c7c2dd	deepalignment-unsupervised-ontology-matching	null	null	https://aclanthology.org/N18-1072	https://aclanthology.org/N18-1072.pdf	DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors	Ontologies compartmentalize types and relations in a target domain and provide the semantic backbone needed for a plethora of practical applications. Very often different ontologies are developed independently for the same domain. Such {``}parallel{''} ontologies raise the need for a process that will establish alignments between their entities in order to unify and extend the existing knowledge. In this work, we present a novel entity alignment method which we dub DeepAlignment. DeepAlignment refines pre-trained word vectors aiming at deriving ontological entity descriptions which are tailored to the ontology matching task. The absence of explicit information relevant to the ontology matching task during the refinement process makes DeepAlignment completely unsupervised. We empirically evaluate our method using standard ontology matching benchmarks. We present significant performance improvements over the current state-of-the-art, demonstrating the advantages that representation learning techniques bring to ontology matching.	['Dimitris Kiritsis', 'ros', 'Prodromos Kolyvakis', 'Alex Kalousis']	2018-06-01	null	null	null	naacl-2018-6	['ontology-matching']	['knowledge-base']	[ 3.57286572e-01 3.11839193e-01 -1.62921831e-01 -5.90768337e-01 -3.15439075e-01 -5.24154723e-01 6.34179473e-01 7.70730972e-01 -5.30035794e-01 5.38298786e-01 5.08434415e-01 -2.34060049e-01 -5.16197920e-01 -1.11041820e+00 -4.65829134e-01 -1.44468054e-01 1.00221656e-01 9.76825356e-01 1.34775013e-01 -7.64757633e-01 3.56309488e-02 3.32946241e-01 -1.84402847e+00 5.42486131e-01 7.81365454e-01 9.12932158e-01 1.01344541e-01 1.08433500e-01 -7.80943871e-01 6.24045968e-01 -3.46301436e-01 -8.42180252e-01 1.87843904e-01 -1.09662730e-02 -1.36684155e+00 -2.94738859e-01 5.81779540e-01 2.17362881e-01 -4.19331580e-01 1.22331345e+00 3.47598761e-01 2.30429396e-01 5.91910601e-01 -1.39345813e+00 -6.85036898e-01 8.01043391e-01 1.59162506e-02 2.99168050e-01 4.33538705e-01 -4.91758019e-01 1.62434149e+00 -5.03014803e-01 9.80845571e-01 1.09832919e+00 8.19855452e-01 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