paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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... | ['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 -3.97953957... | [8.238405227661133, -3.3135690689086914] |
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 ... | ['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 2.82210588... | [5.877018451690674, 6.928746700286865] |
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 ... | ['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 1.18059143... | [8.689888954162598, 9.786558151245117] |
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 t... | ['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 3.83662544... | [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
regi... | ['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 -2.02754185... | [10.640932083129883, 0.5680288076400757] |
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
(Neutros... | ['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 -1.95187166... | [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, an... | ['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 -5.25627792... | [9.701550483703613, 7.463779449462891] |
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/cla... | ['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 1.77138716... | [15.131364822387695, -2.5137906074523926] |
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 contributio... | ['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 -1.17111731... | [4.150547027587891, 1.2443562746047974] |
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 a... | ['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 -5.09315073... | [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 ... | ['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 -2.68200874... | [9.087141990661621, 2.367063283920288] |
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... | ['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 -5.17444670... | [6.978875160217285, 2.9492392539978027] |
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 ... | ['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 -6.78453267... | [8.72119426727295, -2.3052637577056885] |
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 a... | ['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 -2.92156368... | [7.058113098144531, 7.784421443939209] |
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 mod... | ['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 1.15676746... | [11.614005088806152, -2.4007880687713623] |
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 proble... | ['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 -3.48331422... | [14.49184513092041, 7.167129993438721] |
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 thi... | ['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 3.72764990... | [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 ... | ['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 -5.87330818... | [4.360353469848633, 0.9651219248771667] |
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 m... | ['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 -2.42065534... | [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 r... | ['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 -1.43004715... | [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 reco... | ['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 4.95448351... | [14.346586227416992, -3.158238410949707] |
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 differe... | ['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 -8.64630938... | [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 p... | ['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 -4.40985477... | [8.488656997680664, -0.08404632657766342] |
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... | ['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 2.77787626... | [15.182940483093262, -1.9137710332870483] |
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 evaluat... | ['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 -5.44918895... | [9.950339317321777, 7.4943318367004395] |
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 d... | ['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 -5.61177790... | [12.236205101013184, 8.926209449768066] |
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 surfac... | ['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 -2.86963820... | [6.124297618865967, 0.9067937731742859] |
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 ... | ['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 -4.58954066... | [14.338390350341797, 6.210703372955322] |
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 clas... | ['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 -2.26902843... | [12.716659545898438, 0.07164353132247925] |
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 ... | ['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 -2.39198864... | [7.813746452331543, -2.5835182666778564] |
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... | ['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 -1.73980340... | [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. ... | ['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 1.82308435... | [7.573177814483643, 2.7071735858917236] |
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 l... | ['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 -2.80888468... | [11.555742263793945, -0.6157540082931519] |
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. Detec... | ['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 -1.81127042... | [8.815744400024414, -2.2116034030914307] |
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 pr... | ['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 -5.40264070... | [10.547330856323242, -1.9198615550994873] |
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 -6.93831444... | [-7.368320941925049, 3.60109806060791] |
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, Oo... | ['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 -1.26336604... | [7.919038772583008, 2.419149160385132] |
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-designe... | ['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 -1.54780000... | [11.994202613830566, 2.156076431274414] |
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 Ed... | ['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 2.02366039... | [13.554004669189453, -2.4875917434692383] |
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 alignmen... | ['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 -1.47078097... | [13.61241340637207, 1.378199577331543] |
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 tra... | ['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 -9.61890519... | [15.035351753234863, 5.961259365081787] |
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... | ['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 3.75794321... | [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 feature... | ['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 -6.32850587... | [9.762187004089355, -0.7687188982963562] |
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 ... | ['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 6.45112470... | [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 CD... | ['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 -3.96807849... | [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 dist... | ['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 2.20503807... | [13.340803146362305, 0.6761196255683899] |
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 reconstru... | ['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 -4.44947034... | [10.022547721862793, -1.4192255735397339] |
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 predicat... | ['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 -6.18119121... | [10.315616607666016, 9.327171325683594] |
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... | ['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 -4.62570161... | [14.824930191040039, 6.416823387145996] |
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 mi... | ['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 -4.82814461... | [10.498250961303711, -1.9733096361160278] |
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 Agen... | ['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 -3.79353791... | [9.866613388061523, 7.897465229034424] |
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 perf... | ['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 -1.52569264... | [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-optim... | ['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 3.26694638... | [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 sc... | ['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 3.52658659... | [8.637335777282715, 4.351678371429443] |
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... | ['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 -4.25794512... | [10.6524076461792, 9.365711212158203] |
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 n... | ['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... | [10.21589183807373, 8.136409759521484] |
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... | ['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 -5.77803075... | [9.731157302856445, -0.7271175980567932] |
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 t... | ['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 8.18443671... | [8.485574722290039, 0.5783730149269104] |
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 develop... | ['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 -7.66999125... | [10.458714485168457, 10.082316398620605] |
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. dist... | ['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 -4.65684742... | [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 pape... | ['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 -4.42694902... | [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 mod... | ['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 -5.28777182... | [8.020852088928223, 10.051492691040039] |
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 mode... | ['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 -4.10961688... | [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 20... | ['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 -4.68935043... | [10.276265144348145, 9.171575546264648] |
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... | ['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 3.12015831... | [12.941341400146484, 0.8071940541267395] |
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 convol... | ['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 -1.07450761... | [7.955019950866699, -3.631420850753784] |
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 tria... | ['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 -1.61594182... | [13.647253036499023, -2.765930414199829] |
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 ... | ['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 -2.50963479... | [6.5487542152404785, 3.433741569519043] |
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. Wher... | ['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 -1.97350472... | [9.801745414733887, 3.5030386447906494] |
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 ... | ['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 1.83118239... | [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... | ['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 -2.14267179... | [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 eac... | ['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 -9.91959572... | [15.088369369506836, -2.9627561569213867] |
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, wher... | ['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 -2.53808260... | [7.110718250274658, 4.71491003036499] |
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, co... | ['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 -2.41366133... | [7.6359076499938965, -2.9792635440826416] |
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-... | ['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 3.36948723... | [7.152989387512207, 3.541124105453491] |
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 extens... | ['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 7.01156482... | [9.775654792785645, 3.448092460632324] |
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 ... | ['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 -3.82186830... | [10.285940170288086, 9.652315139770508] |
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 -6.93831444... | [-7.240747928619385, 3.7852773666381836] |
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 rel... | ['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 -6.56186283... | [12.222145080566406, 9.476752281188965] |
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... | ['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 1.65303528... | [8.002121925354004, 6.820231914520264] |
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... | ['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 5.81138372... | [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 deri... | ['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 -2.06150904... | [8.127664566040039, -3.0092532634735107] |
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 estim... | ['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 -2.85931855... | [7.171926498413086, -2.29722261428833] |
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 f... | ['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 1.73460431... | [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 no... | ['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 3.07959646... | [7.860119342803955, 5.4037628173828125] |
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 ... | ['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 -2.76818961... | [10.39882755279541, 8.076268196105957] |
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 complex... | ['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 1.06561929... | [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, ... | ['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 -4.95522857... | [3.7466695308685303, 2.0258424282073975] |
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 inva... | ['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 -3.78218740... | [9.092916488647461, 2.314565896987915] |
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) re... | ['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 -5.51879883... | [13.63783073425293, 1.7509963512420654] |
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 i... | ['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 -3.25948834... | [8.720647811889648, 10.08929443359375] |
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 p... | ['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 -3.20787758... | [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 o... | ['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 -1.72151159... | [6.2925262451171875, 5.03359842300415] |
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 ... | ['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 -4.38706219... | [6.230605602264404, 2.744316816329956] |
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 came... | ['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 -4.82562557... | [8.488580703735352, -2.041262626647949] |
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 attac... | ['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 -1.57613218... | [5.420957088470459, 7.928060054779053] |
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 t... | ['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 2.62072355... | [8.019225120544434, 5.824923038482666] |
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 tr... | ['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 -1.47692591... | [9.547409057617188, 1.7396856546401978] |
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 decod... | ['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 -2.07613148... | [10.443697929382324, 0.6672310829162598] |
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 alignme... | ['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 -7.64757633... | [9.243729591369629, 8.177421569824219] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.