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aded5510-56ac-4e25-89d5-d6cfce0edf5f | exploring-the-relationship-between-center-and | null | null | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | http://dx.doi.org/10.1109/tcsvt.2022.3218284 | Exploring the Relationship between Center and Neighborhoods: Central Vector oriented Self-Similarity Network for Hyperspectral Image Classification | To mine the spectral-spatial information of target pixel in hyperspectral image classification (HSIC), convolutional neural network (CNN)-based models widely adopt patch-based input pattern, where a patch represents its central pixel and the neighbor pixels play auxiliary roles in the classification process. However, c... | ['and Gongping Yang', 'Yuwen Huang', 'Guangkuo Xue', 'Yikun Liu', 'Mingsong Li'] | 2022-10-31 | exploring-the-relationship-between-center-and-1 | https://ieeexplore.ieee.org/document/9933425?arnumber=9933425&tag=1 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9933425 | ieee-transactions-on-circuits-and-systems-for-7 | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 5.19361496e-01 -5.62227130e-01 -5.00368662e-02 -2.52849281e-01
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-3.75598818e-01 4.22306806e-01 -1.75913900e-01 -1.56760037e-01
-5.59235871e-01 -1.08352983e+00 -3.57385457e-01 -1.06885505e+00
1.67366937e-02 -4.31391567e-01 2.35377192e-01 -2.10957751... | [10.047791481018066, -1.6403043270111084] |
9875d6c6-0589-4211-b87e-5986ccc172f8 | no-gestures-left-behind-learning | null | null | https://aclanthology.org/2020.findings-emnlp.170 | https://aclanthology.org/2020.findings-emnlp.170.pdf | No Gestures Left Behind: Learning Relationships between Spoken Language and Freeform Gestures | We study relationships between spoken language and co-speech gestures in context of two key challenges. First, distributions of text and gestures are inherently skewed making it important to model the long tail. Second, gesture predictions are made at a subword level, making it important to learn relationships between ... | ['Louis-Philippe Morency', 'Ryo Ishii', 'Dong Won Lee', 'Chaitanya Ahuja'] | 2020-10-01 | null | null | null | findings-of-the-association-for-computational | ['gesture-generation'] | ['robots'] | [ 3.86497676e-01 7.66844749e-02 -3.61231565e-01 -4.30825680e-01
-1.28009880e+00 -7.61894226e-01 8.60137284e-01 -1.82076976e-01
-5.45987785e-01 7.47911751e-01 8.93993616e-01 -8.51881579e-02
1.33310691e-01 -5.27517378e-01 -8.62027228e-01 -4.58420873e-01
-2.04444140e-01 4.29623067e-01 9.67197865e-02 -2.66296506... | [5.629237174987793, -0.1108575388789177] |
3f457c05-c7ae-45a3-999b-a7a89d1b43a6 | deep-fast-vision-accelerated-deep-transfer | null | null | https://doi.org/10.5281/zenodo.7865289 | https://doi.org/10.5281/zenodo.7865289 | Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and Beyond | Deep Fast Vision is a versatile Python library for rapid prototyping of deep transfer learning vision models. It caters to users of various levels, offering different levels of abstraction from high-level configurations for beginners to mid and low-level customization for professional data scientists and developers. Bu... | ['Fabi Prezja'] | 2023-04-26 | null | null | null | zenodo-github-2023-4 | ['automl'] | ['methodology'] | [-9.34719980e-01 -4.33663189e-01 -1.75718442e-01 -4.82849002e-01
-2.17617273e-01 -6.98438168e-01 5.19892573e-01 -1.59664020e-01
-5.43178260e-01 1.52132198e-01 -2.03654110e-01 -6.81092501e-01
2.79333085e-01 -4.86132592e-01 -2.19169721e-01 -3.81795913e-01
-2.63788730e-01 3.08927476e-01 5.47602177e-01 -4.11131717... | [8.680054664611816, 2.7875308990478516] |
70961c30-261c-494b-99a8-ce786a84ff14 | lossy-compression-for-lossless-prediction | 2106.10800 | null | https://arxiv.org/abs/2106.10800v5 | https://arxiv.org/pdf/2106.10800v5.pdf | Lossy Compression for Lossless Prediction | Most data is automatically collected and only ever "seen" by algorithms. Yet, data compressors preserve perceptual fidelity rather than just the information needed by algorithms performing downstream tasks. In this paper, we characterize the bit-rate required to ensure high performance on all predictive tasks that are ... | ['Chris J. Maddison', 'Karen Ullrich', 'Benjamin Bloem-Reddy', 'Yann Dubois'] | 2021-06-21 | null | http://proceedings.neurips.cc/paper/2021/hash/7535bbb91c8fde347ad861f293126633-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/7535bbb91c8fde347ad861f293126633-Paper.pdf | neurips-2021-12 | ['feature-compression'] | ['computer-vision'] | [ 7.19168544e-01 1.79059461e-01 -4.98157382e-01 -5.14337957e-01
-4.73274887e-01 -1.42062396e-01 4.24567521e-01 3.74090582e-01
-7.58328557e-01 3.77976686e-01 4.83739018e-01 -4.81067717e-01
1.16265118e-01 -6.79407179e-01 -1.07450473e+00 -4.28446919e-01
-4.95358169e-01 -2.44801827e-02 -1.65387079e-01 3.03413719... | [11.400357246398926, -1.5583937168121338] |
f7c07ed8-fa81-44ea-84b4-c290135121af | graph-based-long-term-and-short-term-interest | 2306.10028 | null | https://arxiv.org/abs/2306.10028v1 | https://arxiv.org/pdf/2306.10028v1.pdf | Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction | Click-through rate (CTR) prediction aims to predict the probability that the user will click an item, which has been one of the key tasks in online recommender and advertising systems. In such systems, rich user behavior (viz. long- and short-term) has been proved to be of great value in capturing user interests. Both ... | ['Dong Wang', 'Xingxing Wang', 'Bo Zhang', 'Pengye Zhang', 'Guangliang Yu', 'Huinan Sun'] | 2023-06-05 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-6.21936172e-02 -5.40252626e-01 -5.46184897e-01 -4.21891779e-01
-3.43853235e-01 -3.42237592e-01 3.76033902e-01 1.39307871e-01
-6.41051456e-02 4.98512477e-01 2.47008398e-01 -4.41217959e-01
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-2.62523085e-01 1.04137614e-01 4.23867643e-01 -6.91141307... | [10.111244201660156, 5.602616786956787] |
952f230f-e92b-420c-a364-a21af08ea795 | toward-qualitative-evaluation-of-embeddings | null | null | https://aclanthology.org/2020.lrec-1.610 | https://aclanthology.org/2020.lrec-1.610.pdf | Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis | In this paper, we propose several protocols to evaluate specific embeddings for Arabic sentiment analysis (SA) task. In fact, Arabic language is characterized by its agglutination and morphological richness contributing to great sparsity that could affect embedding quality. This work presents a study that compares embe... | ['lamia hadrich belguith', 'Yannick Est{\\`e}ve', 'Amira Barhoumi', 'Chafik Aloulou', 'Nathalie Camelin'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-3.88978422e-01 -6.53991401e-02 -8.91862810e-02 -4.12250876e-01
-2.82492459e-01 -8.53870273e-01 6.36970460e-01 6.88776433e-01
-7.13306308e-01 5.22764325e-01 3.26475173e-01 -3.55862886e-01
5.72944619e-02 -9.12160754e-01 -4.06778306e-01 -6.62885368e-01
-4.19561267e-01 4.30052549e-01 2.17033029e-02 -1.04616380... | [11.102437019348145, 7.168318748474121] |
a8e29e16-5b80-4f37-b6a1-6581162ecfdb | disentangled-generation-network-for-enlarged | 2206.00859 | null | https://arxiv.org/abs/2206.00859v2 | https://arxiv.org/pdf/2206.00859v2.pdf | Disentangled Generation Network for Enlarged License Plate Recognition and A Unified Dataset | License plate recognition plays a critical role in many practical applications, but license plates of large vehicles are difficult to be recognized due to the factors of low resolution, contamination, low illumination, and occlusion, to name a few. To overcome the above factors, the transportation management department... | ['Jin Tang', 'Ruoran Jia', 'Chang Tan', 'Aihua Zheng', 'Guohao Wang', 'Xiaobin Yang', 'Chenglong Li'] | 2022-06-02 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 2.34461308e-01 -7.44804502e-01 7.55768940e-02 -1.04357481e-01
-7.69049466e-01 -6.73409462e-01 5.25930464e-01 -7.80022562e-01
-6.46953210e-02 6.84713721e-01 4.36188020e-02 -3.50564718e-02
1.80143178e-01 -7.23982275e-01 -5.47044516e-01 -1.08703351e+00
5.63182652e-01 1.93752557e-01 2.88903296e-01 -1.49973467... | [9.856074333190918, -4.917244911193848] |
47676900-f4fd-4d1d-a82d-a923c4ea2e5f | don-t-discard-fixed-window-audio-segmentation | 2210.13363 | null | https://arxiv.org/abs/2210.13363v1 | https://arxiv.org/pdf/2210.13363v1.pdf | Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation | For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation. For online spoken language translation, where models need to start translating before the full utterance is spoken, most previous work has ignore... | ['Barry Haddow', 'Chantal Amrhein'] | 2022-10-24 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 2.22552791e-01 -2.78369011e-03 -2.30676606e-01 -5.66536427e-01
-1.66172862e+00 -8.80452394e-01 4.60187376e-01 5.62100336e-02
-3.83033395e-01 5.98514438e-01 2.33200967e-01 -7.50406921e-01
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1.86303243e-01 7.31761217e-01 3.93493772e-01 -3.22213858... | [14.598464012145996, 6.995684623718262] |
accb72df-001d-4fb0-910f-24a570b12634 | self-supervised-learning-for-biologically | 2303.02370 | null | https://arxiv.org/abs/2303.02370v2 | https://arxiv.org/pdf/2303.02370v2.pdf | Self-Supervised Learning for Place Representation Generalization across Appearance Changes | Visual place recognition is a key to unlocking spatial navigation for animals, humans and robots. While state-of-the-art approaches are trained in a supervised manner and therefore hardly capture the information needed for generalizing to unusual conditions, we argue that self-supervised learning may help abstracting t... | ['Djamila Aouada', 'Vincent Gaudillière', 'Mohamed Adel Musallam'] | 2023-03-04 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 4.51063454e-01 8.00367165e-03 -3.17655683e-01 -6.05809510e-01
-2.20376655e-01 -8.25819075e-01 9.85445976e-01 3.32641572e-01
-5.02163768e-01 7.28797674e-01 -1.54720142e-01 -1.70976356e-01
-2.17892915e-01 -5.21699190e-01 -9.55979943e-01 -8.29307377e-01
-2.79462636e-01 3.99718046e-01 3.02262187e-01 -3.95195127... | [7.744194030761719, -1.9312554597854614] |
85a2d29a-e012-4529-84d1-1c28fa3d2973 | investigating-audio-visual-and-text-fusion | 1805.00705 | null | http://arxiv.org/abs/1805.00705v2 | http://arxiv.org/pdf/1805.00705v2.pdf | Investigating Audio, Visual, and Text Fusion Methods for End-to-End Automatic Personality Prediction | We propose a tri-modal architecture to predict Big Five personality trait
scores from video clips with different channels for audio, text, and video
data. For each channel, stacked Convolutional Neural Networks are employed. The
channels are fused both on decision-level and by concatenating their respective
fully conne... | ['Pascale Fung', 'Dario Bertero', 'Onno Kampman', 'Elham J. Barezi'] | 2018-05-02 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [ 1.56500623e-01 3.78546596e-01 5.31859696e-02 -5.68628848e-01
-5.43631732e-01 -2.97954798e-01 5.85273921e-01 1.30054966e-01
-3.44204634e-01 5.70788145e-01 3.76937628e-01 3.06977183e-01
-2.21282154e-01 -4.77950513e-01 -6.44976377e-01 -5.78792095e-01
-2.39197776e-01 1.05155539e-02 -2.76485056e-01 -3.67967159... | [13.26174259185791, 5.143737316131592] |
f6dd11fa-ce5b-4326-b91a-fb307bedf07f | deepgum-learning-deep-robust-regression-with | 1808.09211 | null | http://arxiv.org/abs/1808.09211v1 | http://arxiv.org/pdf/1808.09211v1.pdf | DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model | In this paper, we address the problem of how to robustly train a ConvNet for
regression, or deep robust regression. Traditionally, deep regression employs
the L2 loss function, known to be sensitive to outliers, i.e. samples that
either lie at an abnormal distance away from the majority of the training
samples, or that... | ['Xavier Alameda-Pineda', 'Stéphane Lathuilière', 'Radu Horaud', 'Pablo Mesejo'] | 2018-08-28 | deepgum-learning-deep-robust-regression-with-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Stephane_Lathuiliere_DeepGUM_Learning_Deep_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Stephane_Lathuiliere_DeepGUM_Learning_Deep_ECCV_2018_paper.pdf | eccv-2018-9 | ['head-pose-estimation'] | ['computer-vision'] | [-1.31188065e-01 6.29329830e-02 2.77612478e-01 -6.68967664e-01
-7.02533185e-01 1.73240583e-02 4.93473470e-01 2.16766074e-01
-5.70581675e-01 5.84157646e-01 -3.87243293e-02 2.23485619e-01
9.41616148e-02 -3.77712905e-01 -9.38843191e-01 -9.52103853e-01
-6.83073476e-02 5.33525229e-01 1.02680475e-01 -8.69244058... | [13.263623237609863, 0.496972918510437] |
53bcd4ee-907b-4bb9-bdc8-eddf43331e9f | multi-modal-wireless-flexible-gel-free | 2305.17629 | null | https://arxiv.org/abs/2305.17629v1 | https://arxiv.org/pdf/2305.17629v1.pdf | Multi-Modal Wireless Flexible Gel-Free Sensors with Edge Deep Learning for Detecting and Alerting Freezing of Gait in Parkinson's Patients | Freezing of gait (FoG) is a debilitating symptom of Parkinson's disease (PD). This work develops flexible wearable sensors that can detect FoG and alert patients and companions to help prevent falls. FoG is detected on the sensors using a deep learning (DL) model with multi-modal sensory inputs collected from distribut... | ['Xilin Liu', 'Thomas Dell', 'Yi Zhu', 'Jack Ji', 'Yuhan Hou'] | 2023-05-28 | null | null | null | null | ['electromyography-emg', 'eeg', 'specificity', 'eeg'] | ['medical', 'methodology', 'natural-language-processing', 'time-series'] | [ 2.62442559e-01 -7.23012760e-02 -8.14332366e-02 -2.74073668e-02
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1.41714156e-01 -7.77332544e-01 -4.03519392e-01 -7.97116399e-01
-4.57788438e-01 -8.32895711e-02 4.57929164e-01 -1.92520134... | [13.569940567016602, 3.259120464324951] |
3c5df030-b887-4926-adb5-02123825543b | sleep-model-a-sequence-model-for-predicting | 2302.12709 | null | https://arxiv.org/abs/2302.12709v1 | https://arxiv.org/pdf/2302.12709v1.pdf | Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage | As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way.In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has... | ['Wonyong Sung', 'Iksoo Choi'] | 2023-02-17 | null | null | null | null | ['electromyography-emg', 'electrocardiography-ecg', 'eeg', 'eeg'] | ['medical', 'methodology', 'methodology', 'time-series'] | [ 3.00883591e-01 -4.83576775e-01 2.45946366e-03 -6.63977504e-01
-4.59195614e-01 -7.18801692e-02 -4.54547778e-02 -5.31706512e-02
-7.81640291e-01 8.51297379e-01 2.02318460e-01 -5.21764040e-01
-9.58245695e-02 -3.93553078e-01 -1.24867246e-01 -6.23889685e-01
-1.12048782e-01 1.52745452e-02 -9.29982439e-02 -4.44009118... | [13.536579132080078, 3.4910805225372314] |
982dee6d-4a9d-4d36-a116-443deaa1a18d | weakly-supervised-segmentation-of-referring | 2205.04725 | null | https://arxiv.org/abs/2205.04725v2 | https://arxiv.org/pdf/2205.04725v2.pdf | Weakly-supervised segmentation of referring expressions | Visual grounding localizes regions (boxes or segments) in the image corresponding to given referring expressions. In this work we address image segmentation from referring expressions, a problem that has so far only been addressed in a fully-supervised setting. A fully-supervised setup, however, requires pixel-wise sup... | ['Cordelia Schmid', 'Ivan Laptev', 'Robin Strudel'] | 2022-05-10 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 8.22399139e-01 5.33142686e-01 -3.49217236e-01 -7.41687179e-01
-1.30130696e+00 -6.86833441e-01 3.91894132e-01 8.57993364e-02
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3.16145390e-01 -5.66493869e-01 -1.13467669e+00 -6.13038301e-01
6.92147553e-01 7.73633838e-01 5.65039456e-01 -2.03323826... | [9.964070320129395, 1.0950195789337158] |
934b055e-d3bd-4f43-ad72-65b64405307b | contrastive-learning-for-unsupervised-video | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Badamdorj_Contrastive_Learning_for_Unsupervised_Video_Highlight_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Badamdorj_Contrastive_Learning_for_Unsupervised_Video_Highlight_Detection_CVPR_2022_paper.pdf | Contrastive Learning for Unsupervised Video Highlight Detection | Video highlight detection can greatly simplify video browsing, potentially paving the way for a wide range of applications. Existing efforts are mostly fully-supervised, requiring humans to manually identify and label the interesting moments (called highlights) in a video. Recent weakly supervised methods forgo the... | ['Li Cheng', 'Yang Wang', 'Mrigank Rochan', 'Taivanbat Badamdorj'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['highlight-detection'] | ['computer-vision'] | [ 4.57087398e-01 -3.52525771e-01 -5.39247334e-01 -2.82506526e-01
-8.81090164e-01 -7.84575164e-01 5.79717398e-01 4.69890743e-01
-4.54698980e-01 3.95795196e-01 2.42133200e-01 4.45342362e-02
2.07983568e-01 -3.91339570e-01 -9.08834517e-01 -7.69735217e-01
-6.02721393e-01 -3.20396572e-01 5.13927281e-01 2.30317950... | [10.093179702758789, 0.434783935546875] |
52250e2a-05b6-45d3-83c5-2a55fba69d01 | a-unified-benchmark-for-the-unknown-detection | 2112.00337 | null | https://arxiv.org/abs/2112.00337v1 | https://arxiv.org/pdf/2112.00337v1.pdf | A Unified Benchmark for the Unknown Detection Capability of Deep Neural Networks | Deep neural networks have achieved outstanding performance over various tasks, but they have a critical issue: over-confident predictions even for completely unknown samples. Many studies have been proposed to successfully filter out these unknown samples, but they only considered narrow and specific tasks, referred to... | ['Sangheum Hwang', 'Jiin Koo', 'Jihyo Kim'] | 2021-12-01 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 6.22003153e-03 -2.50149578e-01 9.78094265e-02 -6.44585907e-01
-9.08905029e-01 -6.92426980e-01 6.22087777e-01 -1.87504128e-01
-1.88789740e-01 1.08832073e+00 -4.70258534e-01 -2.40478769e-01
-1.80194512e-01 -6.16306841e-01 -6.38059616e-01 -6.59640074e-01
1.10031091e-01 5.90196609e-01 2.09775135e-01 1.98811311... | [9.406068801879883, 2.9661829471588135] |
74888053-663f-494f-acb8-d6a7c7adc725 | semi-supervised-intent-discovery-with | null | null | https://aclanthology.org/2021.nlp4convai-1.12 | https://aclanthology.org/2021.nlp4convai-1.12.pdf | Semi-supervised Intent Discovery with Contrastive Learning | User intent discovery is a key step in developing a Natural Language Understanding (NLU) module at the core of any modern Conversational AI system. Typically, human experts review a representative sample of user input data to discover new intents, which is subjective, costly, and error-prone. In this work, we aim to as... | ['Mani Najmabadi', 'Yao Zhang', 'Yinge Sun', 'Xiang Shen'] | null | null | null | null | emnlp-nlp4convai-2021-11 | ['intent-discovery'] | ['natural-language-processing'] | [ 3.41167212e-01 2.39605784e-01 -2.75156885e-01 -6.21052980e-01
-8.03720713e-01 -6.94185734e-01 6.84192777e-01 2.90197462e-01
-4.00445074e-01 4.24644709e-01 4.67133492e-01 -1.33806333e-01
2.47722208e-01 -3.80043000e-01 -3.49509478e-01 -5.18994443e-02
1.28393890e-02 7.39222586e-01 5.86701781e-02 -4.04738218... | [12.488569259643555, 7.5520339012146] |
9d4ffa26-2a83-4a0f-b60b-dd37b3e76a50 | spae-semantic-pyramid-autoencoder-for | 2306.17842 | null | https://arxiv.org/abs/2306.17842v2 | https://arxiv.org/pdf/2306.17842v2.pdf | SPAE: Semantic Pyramid AutoEncoder for Multimodal Generation with Frozen LLMs | In this work, we introduce Semantic Pyramid AutoEncoder (SPAE) for enabling frozen LLMs to perform both understanding and generation tasks involving non-linguistic modalities such as images or videos. SPAE converts between raw pixels and interpretable lexical tokens (or words) extracted from the LLM's vocabulary. The r... | ['Lu Jiang', 'Alexander G. Hauptmann', 'Kevin Murphy', 'Ming-Hsuan Yang', 'Yonatan Bisk', 'Irfan Essa', 'David A. Ross', 'Yanping Huang', 'Wolfgang Macherey', 'Vivek Kumar', 'Zhiruo Wang', 'Yong Cheng', 'Lijun Yu'] | 2023-06-30 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 7.42054164e-01 5.05807579e-01 3.41574065e-02 -2.36047223e-01
-8.99081647e-01 -6.70258582e-01 1.01048017e+00 -1.40399888e-01
-2.15360835e-01 5.69649220e-01 4.02893186e-01 -3.13174576e-01
4.06930000e-01 -9.43743229e-01 -1.16917253e+00 -4.48271722e-01
2.93184191e-01 2.78107256e-01 -2.88079321e-01 -2.23466560... | [10.991865158081055, 1.2394617795944214] |
8b0ec59b-0eb1-4eb1-83e6-b2f86b2fb95b | tiny-always-on-and-fragile-bias-propagation | 2201.07677 | null | https://arxiv.org/abs/2201.07677v4 | https://arxiv.org/pdf/2201.07677v4.pdf | Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows | Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data. On-device ML is highly context dependent, and sensitive to user, usage, hardware and environment attributes. This sensitivity and the propensity to... | ['Akhil Mathur', 'Aaron Yi Ding', 'Fahim Kawsar', 'Wiebke Toussaint'] | 2022-01-19 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 2.64008254e-01 4.69156951e-02 -7.04938054e-01 -6.38395250e-01
-4.67806607e-01 -5.98053575e-01 2.34694883e-01 2.46356443e-01
-2.31905162e-01 3.30700755e-01 3.63600463e-01 -8.68676543e-01
-2.26528525e-01 -5.94270289e-01 -5.90927064e-01 -7.34989643e-02
2.65708804e-01 1.87415347e-01 -5.06707668e-01 2.07105771... | [8.790160179138184, 5.882959365844727] |
fe4e8b72-e6fe-4a4c-b5d1-c4a2e7410a30 | design-and-comparison-of-two-linear | 2203.01409 | null | https://arxiv.org/abs/2203.01409v1 | https://arxiv.org/pdf/2203.01409v1.pdf | Design and comparison of two linear controllers with precompensation gain for the Quadruple inverted pendulum | In this work we present a workflow for designing two linear control techniques applied to the dynamic system quadruple inverted pendulum mounted on a cart (QIP) where the steady state error on cart position is eliminated through a precompensation gain. The first control law designed was based on LQR, technique that sta... | ['Franklin Josue Ticona Coaquira'] | 2022-02-28 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 5.03490604e-02 5.88473260e-01 1.57563627e-01 5.12389362e-01
4.73852634e-01 -8.31135452e-01 4.52761382e-01 2.20938772e-01
-2.78623074e-01 9.87371802e-01 -4.35265005e-01 -3.47892314e-01
-5.03347993e-01 -5.08720577e-01 -6.46766663e-01 -5.75901151e-01
3.17797363e-01 3.70134145e-01 1.45524502e-01 -6.81728303... | [5.441054821014404, 2.559298038482666] |
d615b6fb-121b-4284-9afd-4ad62264f9aa | distance-aware-occlusion-detection-with | 2208.11122 | null | https://arxiv.org/abs/2208.11122v1 | https://arxiv.org/pdf/2208.11122v1.pdf | Distance-Aware Occlusion Detection with Focused Attention | For humans, understanding the relationships between objects using visual signals is intuitive. For artificial intelligence, however, this task remains challenging. Researchers have made significant progress studying semantic relationship detection, such as human-object interaction detection and visual relationship dete... | ['Guyue Zhou', 'Hao Zhao', 'Xiaoxue Chen', 'Yucheng Tu', 'Yang Li'] | 2022-08-23 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.67743230e-01 -4.38957736e-02 -2.41098046e-01 -7.55650520e-01
-6.28281653e-01 -3.99128467e-01 4.98741060e-01 4.25728083e-01
-3.49284142e-01 2.38249823e-01 2.87210166e-01 -1.19755268e-01
5.11159152e-02 -5.87784290e-01 -7.89569020e-01 -9.75028053e-02
-1.51387021e-01 4.64400828e-01 6.29478157e-01 -2.58050382... | [10.207860946655273, 1.5828046798706055] |
f3aad835-03a3-425e-81ea-d13ee7cfdd69 | delidata-a-dataset-for-deliberation-in-multi | 2108.05271 | null | https://arxiv.org/abs/2108.05271v3 | https://arxiv.org/pdf/2108.05271v3.pdf | DeliData: A dataset for deliberation in multi-party problem solving | Group deliberation enables people to collaborate and solve problems, however, it is understudied due to a lack of resources. To this end, we introduce the first publicly available dataset containing collaborative conversations on solving a well-established cognitive task, consisting of 500 group dialogues and 14k utter... | ['Andreas Vlachos', 'Tom Stafford', 'Georgi Karadzhov'] | 2021-08-11 | null | null | null | null | ['problem-solving-deliberation'] | ['natural-language-processing'] | [ 1.43334493e-02 8.76261413e-01 2.97893137e-01 -5.76828480e-01
-8.41475248e-01 -6.75015032e-01 7.88697958e-01 2.54109830e-01
-3.63208473e-01 1.12959898e+00 1.08871353e+00 7.98436180e-02
-1.03426754e-01 -5.12493610e-01 -5.34300469e-02 -4.65216696e-01
4.56284851e-01 6.98695898e-01 -3.35215002e-01 -2.55818427... | [12.623285293579102, 8.037618637084961] |
3d0649e9-2176-4f5e-9bbd-7376b4555239 | demonstrating-the-feasibility-of-automatic | 1603.03795 | null | http://arxiv.org/abs/1603.03795v1 | http://arxiv.org/pdf/1603.03795v1.pdf | Demonstrating the Feasibility of Automatic Game Balancing | Game balancing is an important part of the (computer) game design process, in
which designers adapt a game prototype so that the resulting gameplay is as
entertaining as possible. In industry, the evaluation of a game is often based
on costly playtests with human players. It suggests itself to automate this
process usi... | ['Günter Rudolph', 'Boris Naujoks', 'Vanessa Volz'] | 2016-03-11 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.13017583e-01 -6.27946779e-02 5.32418609e-01 4.85322252e-02
5.08264499e-03 -4.46170777e-01 4.14672345e-01 4.25703049e-01
-8.64717126e-01 9.77092505e-01 -4.22725022e-01 -4.42448616e-01
-6.78066373e-01 -1.00287294e+00 -3.43197674e-01 -5.90376616e-01
-1.57636274e-02 8.73809576e-01 2.52744555e-01 -7.46903360... | [3.4809441566467285, 1.5828652381896973] |
b0c10160-505f-4aa8-8cc3-c23801d807e9 | source-free-adaptation-to-measurement-shift | 2107.05446 | null | https://arxiv.org/abs/2107.05446v3 | https://arxiv.org/pdf/2107.05446v3.pdf | Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration | Source-free domain adaptation (SFDA) aims to adapt a model trained on labelled data in a source domain to unlabelled data in a target domain without access to the source-domain data during adaptation. Existing methods for SFDA leverage entropy-minimization techniques which: (i) apply only to classification; (ii) destro... | ['Bernhard Schölkopf', 'Christopher K. I. Williams', 'Ian Mason', 'Cian Eastwood'] | 2021-07-12 | source-free-adaptation-to-measurement-shift-1 | https://openreview.net/forum?id=1JDiK_TbV4S | https://openreview.net/pdf?id=1JDiK_TbV4S | iclr-2022-4 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 5.53899109e-01 -4.25018929e-03 -9.39306617e-02 -3.73679608e-01
-1.01302409e+00 -6.31760955e-01 6.12027526e-01 2.22186431e-01
-2.89474249e-01 8.24276805e-01 7.28825256e-02 4.54848334e-02
-9.06907097e-02 -6.56863689e-01 -8.67841661e-01 -7.95582592e-01
4.02870625e-01 4.40893799e-01 1.68165594e-01 -1.19151570... | [10.326803207397461, 3.171558380126953] |
27efc540-619a-450e-8c17-9ada3e116b0c | universal-domain-adaptation | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/You_Universal_Domain_Adaptation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/You_Universal_Domain_Adaptation_CVPR_2019_paper.pdf | Universal Domain Adaptation | Domain adaptation aims to transfer knowledge in the presence of the domain gap. Existing domain adaptation methods rely on rich prior knowledge about the relationship between the label sets of source and target domains, which greatly limits their application in the wild. This paper introduces Universal Domain Adaptatio... | [' Michael I. Jordan', ' Jianmin Wang', ' Zhangjie Cao', ' Mingsheng Long', 'Kaichao You'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['universal-domain-adaptation'] | ['computer-vision'] | [ 4.04572606e-01 -3.25743929e-02 -5.72064757e-01 -4.32705015e-01
-6.15248144e-01 -8.39900494e-01 3.02137464e-01 1.13646790e-01
-1.98462978e-01 9.73475039e-01 -2.93774098e-01 3.17390710e-02
-7.31119663e-02 -9.81533349e-01 -6.79370880e-01 -8.18184614e-01
3.48530531e-01 8.34452331e-01 3.24411690e-01 -1.82287529... | [10.358174324035645, 3.158475160598755] |
147e217d-ccbd-47b7-bc65-0ace969913c4 | corenet-coherent-3d-scene-reconstruction-from | 2004.12989 | null | https://arxiv.org/abs/2004.12989v2 | https://arxiv.org/pdf/2004.12989v2.pdf | CoReNet: Coherent 3D scene reconstruction from a single RGB image | Advances in deep learning techniques have allowed recent work to reconstruct the shape of a single object given only one RBG image as input. Building on common encoder-decoder architectures for this task, we propose three extensions: (1) ray-traced skip connections that propagate local 2D information to the output 3D v... | ['Pablo Bauszat', 'Vittorio Ferrari', 'Stefan Popov'] | 2020-04-27 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3439_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470358.pdf | eccv-2020-8 | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 2.65023224e-02 2.74967372e-01 3.48925859e-01 -3.01330030e-01
-8.98285687e-01 -6.04438663e-01 7.29343593e-01 -1.32577851e-01
-1.34691015e-01 6.58768475e-01 2.88772583e-01 1.25575513e-01
1.00920193e-01 -9.49318409e-01 -1.33789814e+00 -4.67350602e-01
2.45328799e-01 1.14762533e+00 4.01070505e-01 1.26619518... | [8.848993301391602, -3.4218969345092773] |
77233f36-4c8a-485b-85a2-085b723a45f0 | multi-stream-3d-fcn-with-multi-scale-deep | 1711.10212 | null | http://arxiv.org/abs/1711.10212v2 | http://arxiv.org/pdf/1711.10212v2.pdf | Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality Isointense Infant Brain MR Image Segmentation | We present a method to address the challenging problem of segmentation of
multi-modality isointense infant brain MR images into white matter (WM), gray
matter (GM), and cerebrospinal fluid (CSF). Our method is based on
context-guided, multi-stream fully convolutional networks (FCN), which after
training, can directly m... | ['Guodong Zeng', 'Guoyan Zheng'] | 2017-11-28 | null | null | null | null | ['infant-brain-mri-segmentation'] | ['medical'] | [ 2.72872239e-01 -7.93571398e-03 2.59302914e-01 -7.13594973e-01
-3.59252959e-01 -3.08847427e-01 2.10809797e-01 3.59410644e-01
-6.72750890e-01 6.54375255e-01 1.46193147e-01 -4.33698565e-01
1.62050068e-01 -4.28929061e-01 -8.74581814e-01 -4.33097363e-01
-5.43654799e-01 5.06251693e-01 2.69171655e-01 2.39466771... | [14.188393592834473, -2.363065004348755] |
188a360e-30a2-4910-95ba-c5b0c04eee06 | ldmic-learning-based-distributed-multi-view | 2301.09799 | null | https://arxiv.org/abs/2301.09799v3 | https://arxiv.org/pdf/2301.09799v3.pdf | LDMIC: Learning-based Distributed Multi-view Image Coding | Multi-view image compression plays a critical role in 3D-related applications. Existing methods adopt a predictive coding architecture, which requires joint encoding to compress the corresponding disparity as well as residual information. This demands collaboration among cameras and enforces the epipolar geometric cons... | ['Jun Zhang', 'Jiawei Shao', 'Xinjie Zhang'] | 2023-01-24 | null | null | null | null | ['data-compression'] | ['time-series'] | [-7.43743926e-02 -3.27054977e-01 -2.42289722e-01 -2.62528211e-01
-6.62460446e-01 -3.12724352e-01 3.05891007e-01 -1.73884168e-01
1.12712048e-01 2.57391661e-01 4.27558333e-01 -2.01350097e-02
-1.05991788e-01 -7.09496200e-01 -7.80854404e-01 -7.00590193e-01
7.67703429e-02 -1.28462417e-02 1.88473001e-01 3.04183085... | [11.01176643371582, -1.7584235668182373] |
52ce978e-4ed6-45f6-a71d-d101476e0b6a | a-permutation-free-kernel-two-sample-test | 2211.14908 | null | https://arxiv.org/abs/2211.14908v2 | https://arxiv.org/pdf/2211.14908v2.pdf | A Permutation-free Kernel Two-Sample Test | The kernel Maximum Mean Discrepancy~(MMD) is a popular multivariate distance metric between distributions that has found utility in two-sample testing. The usual kernel-MMD test statistic is a degenerate U-statistic under the null, and thus it has an intractable limiting distribution. Hence, to design a level-$\alpha$ ... | ['Aaditya Ramdas', 'Ilmun Kim', 'Shubhanshu Shekhar'] | 2022-11-27 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 7.03617185e-02 -1.09449692e-01 -4.78810966e-01 -3.66885543e-01
-1.08393836e+00 -6.99736357e-01 6.91204369e-02 2.44487941e-01
-4.64241832e-01 1.15327573e+00 -5.48184216e-01 -8.41822743e-01
-5.11822462e-01 -9.30757642e-01 -7.32776284e-01 -8.28993082e-01
-3.89700174e-01 5.20348728e-01 3.03646743e-01 4.78580266... | [7.476391315460205, 4.376537799835205] |
5acadc90-1ca6-4cc6-9ba9-a65e96163024 | image-cropping-with-composition-and-saliency | 1911.10492 | null | https://arxiv.org/abs/1911.10492v1 | https://arxiv.org/pdf/1911.10492v1.pdf | Image Cropping with Composition and Saliency Aware Aesthetic Score Map | Aesthetic image cropping is a practical but challenging task which aims at finding the best crops with the highest aesthetic quality in an image. Recently, many deep learning methods have been proposed to address this problem, but they did not reveal the intrinsic mechanism of aesthetic evaluation. In this paper, we pr... | ['Weijie Zhao', 'Yi Tu', 'Li Niu', 'Dawei Cheng', 'Liqing Zhang'] | 2019-11-24 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 3.22055608e-01 1.40866667e-01 -1.18498348e-01 -3.78014982e-01
-2.69413054e-01 -4.39249694e-01 1.23301759e-01 3.65737438e-01
1.54345647e-01 1.88394353e-01 2.39565045e-01 -4.27032337e-02
2.65402999e-02 -9.24598455e-01 -8.61595333e-01 -7.64178634e-01
1.78139478e-01 -1.00836396e-01 3.20286244e-01 -4.43170965... | [11.496810913085938, -0.9444612860679626] |
6ab175a7-1e76-48cb-a979-1c2a74fb84b6 | self-supervised-optimization-of-hand-pose | 2307.03007 | null | https://arxiv.org/abs/2307.03007v1 | https://arxiv.org/pdf/2307.03007v1.pdf | Self-supervised Optimization of Hand Pose Estimation using Anatomical Features and Iterative Learning | Manual assembly workers face increasing complexity in their work. Human-centered assistance systems could help, but object recognition as an enabling technology hinders sophisticated human-centered design of these systems. At the same time, activity recognition based on hand poses suffers from poor pose estimation in c... | ['Marco F. Huber', 'Timo Leitritz', 'Christian Jauch'] | 2023-07-06 | null | null | null | null | ['pose-estimation', 'activity-recognition', 'object-recognition', 'hand-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.75032037e-01 9.12148505e-02 -2.80502707e-01 -2.15358049e-01
-6.36553645e-01 -6.70803428e-01 1.72741473e-01 -2.27654606e-01
-6.72970355e-01 4.52433079e-01 2.38515303e-01 -8.51036906e-02
-3.18350732e-01 1.12466224e-01 -2.87382185e-01 -6.33307993e-01
2.02272236e-01 8.52176547e-01 3.29597592e-01 -4.08467166... | [6.548125267028809, -0.7172561287879944] |
cfa8a915-e680-4a60-b6a3-3fcb916ef910 | medical-image-segmentation-using-squeeze-and | 2105.09511 | null | https://arxiv.org/abs/2105.09511v3 | https://arxiv.org/pdf/2105.09511v3.pdf | Medical Image Segmentation Using Squeeze-and-Expansion Transformers | Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net... | ['Rick Goh', 'Yong liu', 'Xinxing Xu', 'Xiangde Luo', 'Xiuchao Sui', 'Shaohua Li'] | 2021-05-20 | null | null | null | null | ['optic-cup-segmentation'] | ['medical'] | [ 2.91905910e-01 2.83425450e-01 -3.34457666e-01 -3.83733451e-01
-9.04362500e-01 -3.05478722e-01 7.60654211e-02 8.96228030e-02
-3.17198217e-01 4.83707964e-01 1.95672169e-01 -3.32805127e-01
-2.04332843e-01 -6.85464382e-01 -6.30439460e-01 -8.72464180e-01
-1.02294860e-02 1.57030925e-01 3.95808160e-01 -9.91721600... | [14.665172576904297, -2.5833194255828857] |
33cc0e1c-4cf9-4f60-a2ce-d805544f92dc | neuspike-net-high-speed-video-reconstruction | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Zhu_NeuSpike-Net_High_Speed_Video_Reconstruction_via_Bio-Inspired_Neuromorphic_Cameras_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Zhu_NeuSpike-Net_High_Speed_Video_Reconstruction_via_Bio-Inspired_Neuromorphic_Cameras_ICCV_2021_paper.pdf | NeuSpike-Net: High Speed Video Reconstruction via Bio-Inspired Neuromorphic Cameras | Neuromorphic vision sensor is a new bio-inspired imaging paradigm that emerged in recent years, which continuously sensing luminance intensity and firing asynchronous spikes (events) with high temporal resolution. Typically, there are two types of neuromorphic vision sensors, namely dynamic vision sensor (DVS) and ... | ['Yonghong Tian', 'Tiejun Huang', 'Xiao Wang', 'Jianing Li', 'Lin Zhu'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['video-reconstruction'] | ['computer-vision'] | [ 5.31980574e-01 -7.99946189e-01 6.38638139e-01 -1.93281248e-01
-6.68905228e-02 -4.71552312e-01 5.93027651e-01 -5.31178832e-01
-6.84894919e-01 6.41335845e-01 -1.82912499e-01 3.79874825e-01
-3.87453772e-02 -6.18792176e-01 -7.92084038e-01 -1.18051219e+00
4.15651590e-01 -3.95928293e-01 8.29005122e-01 1.22363754... | [8.274908065795898, 2.2212183475494385] |
62c9cb1a-9398-4940-8744-916c7a81d130 | monotdp-twin-depth-perception-for-monocular | 2305.10974 | null | https://arxiv.org/abs/2305.10974v2 | https://arxiv.org/pdf/2305.10974v2.pdf | MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes | 3D object detection plays a crucial role in numerous intelligent vision systems. Detection in the open world inevitably encounters various adverse scenes, such as dense fog, heavy rain, and low light conditions. Although existing efforts primarily focus on diversifying network architecture or training schemes, resultin... | ['JinYuan Liu', 'Risheng Liu', 'Xin Fan', 'Long Ma', 'Yixin Lei', 'Xingyuan Li'] | 2023-05-18 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 2.50610203e-01 -5.72477341e-01 3.42649817e-01 -3.47969979e-01
-1.74523637e-01 -4.86538947e-01 2.23251313e-01 -2.84978420e-01
-4.49538976e-01 3.28817248e-01 -2.60704964e-01 -1.55663908e-01
2.48007894e-01 -7.71934271e-01 -5.49889028e-01 -9.36748624e-01
3.12596336e-02 -1.31297246e-01 6.27225816e-01 -1.04129218... | [8.183815002441406, -2.369802713394165] |
c2519f61-f9aa-48dc-91f7-a823185e18da | recursive-metropolis-hastings-naming-game | 2305.19761 | null | https://arxiv.org/abs/2305.19761v1 | https://arxiv.org/pdf/2305.19761v1.pdf | Recursive Metropolis-Hastings Naming Game: Symbol Emergence in a Multi-agent System based on Probabilistic Generative Models | In the studies on symbol emergence and emergent communication in a population of agents, a computational model was employed in which agents participate in various language games. Among these, the Metropolis-Hastings naming game (MHNG) possesses a notable mathematical property: symbol emergence through MHNG is proven to... | ['Yoshinobu Hagiwara', 'Akira Taniguchi', 'Tadahiro Taniguchi', 'Jun Inukai'] | 2023-05-31 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-2.03601018e-01 4.67694908e-01 3.23866338e-01 3.43759775e-01
6.21505417e-02 -2.12704748e-01 1.09653366e+00 -2.71005780e-01
-2.51992106e-01 9.11753774e-01 -1.12679370e-01 -3.13959390e-01
-4.11503702e-01 -1.00939381e+00 -5.99729538e-01 -9.67299163e-01
-6.96169436e-01 7.63767183e-01 5.52591991e-05 -1.20763376... | [4.375793933868408, 2.77817964553833] |
027576fe-619c-4344-85f0-f8981db1a1a6 | adaptive-energy-management-for-real-driving | 2007.12560 | null | https://arxiv.org/abs/2007.12560v1 | https://arxiv.org/pdf/2007.12560v1.pdf | Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning | This article proposes a transfer reinforcement learning (RL) based adaptive energy managing approach for a hybrid electric vehicle (HEV) with parallel topology. This approach is bi-level. The up-level characterizes how to transform the Q-value tables in the RL framework via driving cycle transformation (DCT). Especiall... | ['Wenhao Tan', 'Xiaolin Tang', 'Teng Liu', 'Jiaxin Chen', 'Dongpu Cao'] | 2020-07-24 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 9.12894960e-03 9.01076943e-02 -4.78532732e-01 1.95641086e-01
-4.02472645e-01 -4.92298603e-01 6.35717988e-01 1.53273651e-02
-2.74843305e-01 1.12910116e+00 -3.97580355e-01 -2.17043847e-01
-6.88484132e-01 -9.64165568e-01 -5.17679393e-01 -1.01712132e+00
-1.28952460e-02 2.49442443e-01 8.48394409e-02 -4.85218763... | [5.544878005981445, 2.3183176517486572] |
04f60fab-84d8-4489-a42b-20074c64cb55 | tstarbots-defeating-the-cheating-level | 1809.07193 | null | http://arxiv.org/abs/1809.07193v3 | http://arxiv.org/pdf/1809.07193v3.pdf | TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game | Starcraft II (SC2) is widely considered as the most challenging Real Time
Strategy (RTS) game. The underlying challenges include a large observation
space, a huge (continuous and infinite) action space, partial observations,
simultaneous move for all players, and long horizon delayed rewards for local
decisions. To pus... | ['Bo Li', 'Yongsheng Liu', 'Peng Sun', 'Xinghai Sun', 'Ji Liu', 'Jiechao Xiong', 'Han Liu', 'Yang Zheng', 'Tong Zhang', 'Qing Wang', 'Lei Han'] | 2018-09-19 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-3.85368675e-01 4.94325012e-01 -3.47990021e-02 2.42621034e-01
-5.98444879e-01 -6.32725835e-01 5.18832982e-01 -4.03057396e-01
-7.40802348e-01 9.57543969e-01 -3.36924404e-01 -3.79652798e-01
-5.00187099e-01 -7.17722178e-01 -4.28906620e-01 -6.82145417e-01
-2.93113142e-01 7.86593199e-01 6.77474022e-01 -1.17562938... | [3.54858660697937, 1.5538793802261353] |
4a89c6e5-27fc-45ad-8c66-9141b87cb176 | 3dteethseg-22-3d-teeth-scan-segmentation-and | 2305.18277 | null | https://arxiv.org/abs/2305.18277v1 | https://arxiv.org/pdf/2305.18277v1.pdf | 3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge | Teeth localization, segmentation, and labeling from intra-oral 3D scans are essential tasks in modern dentistry to enhance dental diagnostics, treatment planning, and population-based studies on oral health. However, developing automated algorithms for teeth analysis presents significant challenges due to variations in... | ['Edouard Ladroit', 'Cyril Trosset', 'Hugo Setbon', 'Aurélien Thollot', 'Julien Strippoli', 'Shankeeth Vinayahalingam', 'Steven Kempers', 'Niels van Nistelrooij', 'Byungsun Choi', 'Jae-Hwan Han', 'Wan Kim', 'Hong-Gi Ahn', 'Tae-Hoon Yong', 'Bulat Ibragimov', 'Tudor Dascalu', 'Yuanfeng Zhou', 'Zhiming Cui', 'Guangshun We... | 2023-05-29 | null | null | null | null | ['anatomy'] | ['miscellaneous'] | [ 1.55768663e-01 7.42645919e-01 -4.23373014e-01 -4.42393482e-01
-1.47838736e+00 -8.39385465e-02 1.24545179e-01 4.66240257e-01
-2.58394659e-01 1.96545303e-01 1.62078083e-01 -4.20964330e-01
3.39728631e-02 -4.45767671e-01 -3.63561451e-01 -7.21468508e-01
-1.75803900e-01 9.22267258e-01 2.31685609e-01 2.25479558... | [13.740005493164062, -2.2246017456054688] |
b1a20084-279e-41aa-b27b-aa7e33773690 | temporal-context-mining-for-learned-video | 2111.13850 | null | https://arxiv.org/abs/2111.13850v2 | https://arxiv.org/pdf/2111.13850v2.pdf | Temporal Context Mining for Learned Video Compression | We address end-to-end learned video compression with a special focus on better learning and utilizing temporal contexts. For temporal context mining, we propose to store not only the previously reconstructed frames, but also the propagated features into the generalized decoded picture buffer. From the stored propagated... | ['Yan Lu', 'Dong Liu', 'Li Li', 'Bin Li', 'Jiahao Li', 'Xihua Sheng'] | 2021-11-27 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.16531658e-01 -1.87472492e-01 -2.57982492e-01 -3.39943349e-01
-7.67923892e-01 -2.82449811e-03 6.11950085e-02 -8.18674453e-03
-4.23691124e-01 6.56341553e-01 2.91750580e-01 -3.61136258e-01
4.87310477e-02 -5.17446399e-01 -6.15141451e-01 -7.66869426e-01
-7.81150043e-01 -6.27855897e-01 2.48436734e-01 2.18759239... | [11.332518577575684, -1.6449073553085327] |
65520799-d12b-4443-a06c-46814587afb9 | humor-generation-and-detection-in-code-mixed | null | null | https://aclanthology.org/2021.ranlp-srw.1 | https://aclanthology.org/2021.ranlp-srw.1.pdf | Humor Generation and Detection in Code-Mixed Hindi-English | Computational humor generation is one of the hardest tasks in natural language generation, especially in code-mixed languages. Existing research has shown that humor generation in English is a promising avenue. However, studies have shown that bilingual speakers often appreciate humor more in code-mixed languages with ... | ['Rhythm Narula', 'Kaustubh Agarwal'] | null | null | null | null | ranlp-2021-9 | ['humor-detection'] | ['natural-language-processing'] | [-7.75177479e-01 3.46479230e-02 -2.57838294e-02 1.68471217e-01
-3.20490330e-01 -3.06462377e-01 5.73157847e-01 -1.32480502e-01
8.35398491e-03 8.39749694e-01 9.36705172e-01 -4.74298060e-01
5.21049380e-01 -6.67186022e-01 -2.48627111e-01 -2.22977161e-01
3.52145523e-01 7.90278390e-02 -3.59460622e-01 -9.03839707... | [8.886561393737793, 11.037076950073242] |
eb591c93-0f20-42bb-8cb5-efd925f2fae1 | realistic-pointgoal-navigation-via-auxiliary | 2109.08677 | null | https://arxiv.org/abs/2109.08677v1 | https://arxiv.org/pdf/2109.08677v1.pdf | Realistic PointGoal Navigation via Auxiliary Losses and Information Bottleneck | We propose a novel architecture and training paradigm for training realistic PointGoal Navigation -- navigating to a target coordinate in an unseen environment under actuation and sensor noise without access to ground-truth localization. Specifically, we find that the primary challenge under this setting is learning lo... | ['Erik Wijmans', 'Dhruv Batra', 'Guillermo Grande'] | 2021-09-17 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-5.15855066e-02 1.76389903e-01 -1.08657360e-01 -1.89621091e-01
-9.67031419e-01 -1.01845479e+00 3.70998204e-01 1.49997830e-01
-1.11031163e+00 1.10613847e+00 -3.34260076e-01 -5.02566576e-01
-1.84691295e-01 -8.59951675e-01 -1.41482413e+00 -8.46531451e-01
-7.81740904e-01 4.21141297e-01 4.65279957e-03 -1.94885224... | [4.583775520324707, 0.7603144645690918] |
ba9687d1-907d-4407-93e1-093c7a9e99ec | visual-spatio-temporal-relation-enhanced | 2110.15609 | null | https://arxiv.org/abs/2110.15609v3 | https://arxiv.org/pdf/2110.15609v3.pdf | BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval | The task of text-video retrieval aims to understand the correspondence between language and vision, has gained increasing attention in recent years. Previous studies either adopt off-the-shelf 2D/3D-CNN and then use average/max pooling to directly capture spatial features with aggregated temporal information as global ... | ['Hao Chen', 'Chuhao Shi', 'Yawen Zeng', 'Guangyi Xiao', 'Jingjing Chen', 'Ning Han'] | 2021-10-29 | null | null | null | null | ['video-similarity'] | ['computer-vision'] | [-1.64278626e-01 -6.70450449e-01 -5.22668123e-01 -1.81686208e-01
-5.76772511e-01 -5.12598634e-01 8.36708546e-01 3.09309419e-02
-3.34614515e-01 2.12158635e-01 3.44573557e-01 1.87933445e-02
-4.84639913e-01 -6.98496103e-01 -4.42047209e-01 -5.60892463e-01
-1.75325453e-01 -5.11059947e-02 4.52217132e-01 -1.74548224... | [10.337059020996094, 0.9699962735176086] |
bde7c416-b369-486b-8141-c058a2a2ec39 | generating-3d-adversarial-point-clouds | 1809.07016 | null | https://arxiv.org/abs/1809.07016v4 | https://arxiv.org/pdf/1809.07016v4.pdf | Generating 3D Adversarial Point Clouds | Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions. While adversarial examples for 2D images and CNNs have been extensively studied, less attention has been paid to 3D data such as point clouds. Given many safety-cr... | ['Chong Xiang', 'Bo Li', 'Charles R. Qi'] | 2018-09-19 | generating-3d-adversarial-point-clouds-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Xiang_Generating_3D_Adversarial_Point_Clouds_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xiang_Generating_3D_Adversarial_Point_Clouds_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 2.58664563e-02 1.97026789e-01 4.26927388e-01 -2.14947239e-01
-4.72330213e-01 -9.83463049e-01 7.19882607e-01 2.23516688e-01
-1.81982875e-01 3.35930169e-01 -4.90646452e-01 -5.70127547e-01
2.35624090e-01 -1.06657541e+00 -1.38420665e+00 -7.79705286e-01
-1.59624875e-01 5.08442044e-01 3.58542681e-01 -4.83755171... | [7.7050323486328125, -4.4678239822387695] |
c6758ee9-abc3-4e82-bc40-88cfb8eafff1 | exploring-hierarchy-aware-inverse | 1807.05037 | null | http://arxiv.org/abs/1807.05037v1 | http://arxiv.org/pdf/1807.05037v1.pdf | Exploring Hierarchy-Aware Inverse Reinforcement Learning | We introduce a new generative model for human planning under the Bayesian
Inverse Reinforcement Learning (BIRL) framework which takes into account the
fact that humans often plan using hierarchical strategies. We describe the
Bayesian Inverse Hierarchical RL (BIHRL) algorithm for inferring the values of
hierarchical pl... | ['Daniel Filan', 'Chris Cundy'] | 2018-07-13 | null | null | null | null | ['birl-cima'] | ['medical'] | [ 1.02103293e-01 9.15366292e-01 1.00411676e-01 -1.47742769e-02
-8.37148011e-01 -5.76125383e-01 8.66811156e-01 -1.27917469e-01
-4.93463159e-01 1.08268511e+00 7.70627677e-01 -1.84148803e-01
-6.07671320e-01 -8.07572901e-01 -7.07669020e-01 -5.30449808e-01
-2.06995040e-01 1.15381789e+00 3.76591593e-01 -4.20531660... | [4.003285884857178, 1.4393506050109863] |
58369241-3c1d-43e6-8bf8-3a07a5e96dce | image-forgery-detection-based-on-the-fusion | 1311.6934 | null | http://arxiv.org/abs/1311.6934v1 | http://arxiv.org/pdf/1311.6934v1.pdf | Image forgery detection based on the fusion of machine learning and block-matching methods | Dense local descriptors and machine learning have been used with success in
several applications, like classification of textures, steganalysis, and
forgery detection. We develop a new image forgery detector building upon some
descriptors recently proposed in the steganalysis field suitably merging some
of such descrip... | ['Luisa Verdoliva', 'Diego Gragnaniello', 'Davide Cozzolino'] | 2013-11-27 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 5.66310525e-01 -2.23696113e-01 4.32350766e-03 -1.63584054e-01
-6.06634080e-01 -1.73034415e-01 8.86051059e-01 1.54187799e-01
-3.23649108e-01 5.93534946e-01 -1.11576036e-01 -1.98450759e-01
1.98658362e-01 -8.22679102e-01 -3.43379706e-01 -1.06078196e+00
-2.47673005e-01 -4.28208485e-02 6.44442260e-01 -4.15471286... | [4.331357479095459, 8.033811569213867] |
1bf3efac-16ee-4635-abf4-717c4141ebc6 | cross-lingual-prosody-transfer-for-expressive | 2306.11658 | null | https://arxiv.org/abs/2306.11658v1 | https://arxiv.org/pdf/2306.11658v1.pdf | Cross-lingual Prosody Transfer for Expressive Machine Dubbing | Prosody transfer is well-studied in the context of expressive speech synthesis. Cross-lingual prosody transfer, however, is challenging and has been under-explored to date. In this paper, we present a novel solution to learn prosody representations that are transferable across languages and speakers for machine dubbing... | ['Vincent Pollet', 'Ravichander Vipperla', 'Patrick Lumban Tobing', 'Mikolaj Babianski', 'Duo Wang', 'Jakub Swiatkowski'] | 2023-06-20 | null | null | null | null | ['expressive-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 2.00470254e-01 1.10293940e-01 -2.71541979e-02 -2.49113962e-01
-1.60266280e+00 -7.00975895e-01 4.28344369e-01 -2.96363860e-01
-1.65330172e-01 7.13047624e-01 8.54361236e-01 1.19160734e-01
4.39777166e-01 -5.48730254e-01 -8.37153435e-01 -5.63653231e-01
4.25473630e-01 1.89369202e-01 9.34515893e-02 -5.09018302... | [15.017630577087402, 6.531139373779297] |
2ef5abf3-d3ec-4ec9-a252-bf9d399389e6 | investor-s-sentiment-in-multi-agent-model-of | 1208.3083 | null | http://arxiv.org/abs/1208.3083v4 | http://arxiv.org/pdf/1208.3083v4.pdf | Investor's sentiment in multi-agent model of the continuous double auction | We introduce and treat rigorously a new multi-agent model of the continuous
double auction or in other words the order book (OB). It is designed to explain
collective behaviour of the market when new information affecting the market
arrives. The novel feature of the model is two additional slow changing
parameters, the... | [] | 2016-02-17 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-5.54606676e-01 -5.60529344e-02 1.59668356e-01 7.33585656e-02
1.36160076e-01 -8.92627180e-01 6.45132840e-01 2.43480444e-01
-7.98465610e-01 9.25707102e-01 -1.04144119e-01 3.30368360e-03
-3.51969510e-01 -9.43778157e-01 -5.37198484e-01 -1.01909685e+00
-2.87430137e-01 7.41417587e-01 7.16148838e-02 -6.54893100... | [4.951384544372559, 3.9483392238616943] |
bb3e09ff-03a4-4113-9960-3b4f379fef7e | a-french-fairy-tale-corpus-syntactically-and | null | null | https://aclanthology.org/L12-1076 | https://aclanthology.org/L12-1076.pdf | A French Fairy Tale Corpus syntactically and semantically annotated | Fairy tales, folktales and more generally children stories have lately attracted the Natural Language Processing (NLP) community. As such, very few corpora exist and linguistic resources are lacking. The work presented in this paper aims at filling this gap by presenting a syntactically and semantically annotated corpu... | ['Isma{\\"\\i}l El Maarouf', 'Jeanne Villaneau'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['ontology-matching'] | ['knowledge-base'] | [ 7.17578158e-02 6.71203673e-01 -3.25476557e-01 -4.67993766e-01
-3.21738183e-01 -5.38078725e-01 6.62794709e-01 9.60550845e-01
-6.05204046e-01 1.00445080e+00 8.05934370e-01 6.43885210e-02
-5.37742853e-01 -7.76493430e-01 -1.50369346e-01 -3.00100058e-01
2.00225264e-01 8.89892995e-01 7.05755174e-01 -5.60937703... | [10.063883781433105, 9.40151596069336] |
4fcb7ecd-a34b-40fe-9713-e5df1d727f67 | coverage-of-information-extraction-from | null | null | https://aclanthology.org/D19-1583 | https://aclanthology.org/D19-1583.pdf | Coverage of Information Extraction from Sentences and Paragraphs | Scalar implicatures are language features that imply the negation of stronger statements, e.g., {``}She was married twice{''} typically implicates that she was not married thrice. In this paper we discuss the importance of scalar implicatures in the context of textual information extraction. We investigate how textual ... | ['Paramita Mirza', 'Nitisha Jain', 'Simon Razniewski', 'Gerhard Weikum'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['implicatures'] | ['natural-language-processing'] | [ 1.48330435e-01 8.59365642e-01 -5.85184455e-01 -9.03491020e-01
-4.19372082e-01 -7.34656572e-01 9.02118742e-01 9.28400636e-01
-4.28214222e-01 1.33290470e+00 6.49055541e-01 -5.79074144e-01
-2.66101241e-01 -8.35932851e-01 -7.40851820e-01 -1.50975659e-01
-1.89518794e-01 4.32743132e-01 1.10256799e-01 -4.92339134... | [9.980610847473145, 8.92947006225586] |
19b79a59-5f87-4702-aff6-ae2a1b3613b6 | cross-domain-text-classification-with | null | null | https://aclanthology.org/P16-1155 | https://aclanthology.org/P16-1155.pdf | Cross-domain Text Classification with Multiple Domains and Disparate Label Sets | null | ['Shourya Roy', 'Himanshu Sharad Bhatt', 'Manjira Sinha'] | 2016-08-01 | null | null | null | acl-2016-8 | ['cross-domain-text-classification'] | ['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... | [-7.463138580322266, 3.6788830757141113] |
6ff54e4d-4b33-4ab6-a66d-e28ddce5c439 | solving-mixed-modal-jigsaw-puzzle-for-fine | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Pang_Solving_Mixed-Modal_Jigsaw_Puzzle_for_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Pang_Solving_Mixed-Modal_Jigsaw_Puzzle_for_Fine-Grained_Sketch-Based_Image_Retrieval_CVPR_2020_paper.pdf | Solving Mixed-Modal Jigsaw Puzzle for Fine-Grained Sketch-Based Image Retrieval | ImageNet pre-training has long been considered crucial by the fine-grained sketch-based image retrieval (FG-SBIR) community due to the lack of large sketch-photo paired datasets for FG-SBIR training. In this paper, we propose a self-supervised alternative for representation pre-training. Specifically, we consider the j... | [' Yi-Zhe Song', ' Tao Xiang', ' Timothy M. Hospedales', ' Yongxin Yang', 'Kaiyue Pang'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 5.27857482e-01 -3.07001434e-02 -3.19075793e-01 -2.94189513e-01
-1.04700124e+00 -8.93631399e-01 9.91893768e-01 -1.95312560e-01
-2.73045838e-01 3.96601528e-01 3.70083153e-01 -2.08203509e-01
-5.52926064e-01 -6.75094604e-01 -9.64285970e-01 -3.93234670e-01
3.08433235e-01 8.03043246e-01 2.93284655e-02 -3.25673282... | [11.614635467529297, 0.5508106350898743] |
da21b9a7-14c5-452f-8446-d3acad8687be | pre-and-post-counting-for-scalable | 2110.09767 | null | https://arxiv.org/abs/2110.09767v1 | https://arxiv.org/pdf/2110.09767v1.pdf | Pre and Post Counting for Scalable Statistical-Relational Model Discovery | Statistical-Relational Model Discovery aims to find statistically relevant patterns in relational data. For example, a relational dependency pattern may stipulate that a user's gender is associated with the gender of their friends. As with propositional (non-relational) graphical models, the major scalability bottlenec... | ['Oliver Schulte', 'Richard Mar'] | 2021-10-19 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-3.30951577e-03 3.92567039e-01 -5.68146169e-01 -5.13198197e-01
-5.68389535e-01 -2.74947196e-01 7.57239103e-01 1.02990139e+00
-1.83403343e-01 7.20791280e-01 -2.71680772e-01 -6.31733119e-01
-4.73411441e-01 -1.58677137e+00 -7.60023594e-01 -1.15074933e-01
-7.75369763e-01 1.43630147e+00 6.22533858e-01 2.72482574... | [9.024761199951172, 7.270232200622559] |
aa5d89b6-0b19-4175-8c3d-36db12379797 | unsupervised-domain-adaptation-on-question | null | null | https://aclanthology.org/2022.sigdial-1.42 | https://aclanthology.org/2022.sigdial-1.42.pdf | Unsupervised Domain Adaptation on Question-Answering System with Conversation Data | Machine reading comprehension (MRC) is a task for question answering that finds answers to questions from documents of knowledge. Most studies on the domain adaptation of MRC require documents describing knowledge of the target domain. However, it is sometimes difficult to prepare such documents. The goal of this study... | ['Yasuhiro Sogawa', 'Takeshi Homma', 'Amalia Adiba'] | null | null | null | null | sigdial-acl-2022-9 | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 7.35299110e-01 6.00688815e-01 3.65585864e-01 -5.09478211e-01
-9.99051690e-01 -7.76014328e-01 7.97457099e-01 3.24157387e-01
-3.60273689e-01 8.84597421e-01 5.17660141e-01 -6.39429927e-01
-1.30676493e-01 -8.99503767e-01 -7.15984106e-01 -1.81089491e-01
4.54472721e-01 8.81375134e-01 6.47874832e-01 -5.71294188... | [11.732295036315918, 8.015398979187012] |
9611b7ad-db53-4f2c-b856-f7476eba5a48 | real-time-mapping-of-tissue-properties-for | 2107.08120 | null | https://arxiv.org/abs/2107.08120v1 | https://arxiv.org/pdf/2107.08120v1.pdf | Real-Time Mapping of Tissue Properties for Magnetic Resonance Fingerprinting | Magnetic resonance Fingerprinting (MRF) is a relatively new multi-parametric quantitative imaging method that involves a two-step process: (i) reconstructing a series of time frames from highly-undersampled non-Cartesian spiral k-space data and (ii) pattern matching using the time frames to infer tissue properties (e.g... | ['Pew-Thian Yap', 'Yong Chen', 'Yilin Liu'] | 2021-07-16 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.74580592e-01 -2.81312346e-01 -5.23933023e-02 -4.89739925e-01
-1.16881156e+00 -3.22352767e-01 2.72050470e-01 1.21184587e-01
-6.84090197e-01 6.20239973e-01 1.50697947e-01 -1.60375968e-01
-4.67358321e-01 -4.06996459e-01 -5.98112404e-01 -9.69672263e-01
-4.30377275e-01 6.91947281e-01 4.10574377e-01 4.92070287... | [13.497953414916992, -2.3965036869049072] |
c6a7e447-e6d2-4b19-a220-8c603e8c58db | tweetsumm-a-dialog-summarization-dataset-for-1 | 2111.11894 | null | https://arxiv.org/abs/2111.11894v1 | https://arxiv.org/pdf/2111.11894v1.pdf | TWEETSUMM -- A Dialog Summarization Dataset for Customer Service | In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benef... | ['Ranit Aharonov', 'David Konopnicki', 'Sachindra Joshi', 'Benjamin Sznajder', 'Chulaka Gunasekara', 'Guy Feigenblat'] | 2021-11-23 | null | null | null | null | ['unsupervised-extractive-summarization', 'extractive-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.05776560e-01 7.93674648e-01 6.18869513e-02 -4.84547883e-01
-1.07144630e+00 -6.48230553e-01 5.17336130e-01 9.90495265e-01
-3.62259269e-01 1.16774011e+00 8.95592511e-01 1.50150478e-01
-1.98790938e-01 -3.08353841e-01 3.35473031e-01 -3.97875667e-01
6.93842351e-01 1.48907661e+00 5.65364864e-03 -4.76399988... | [12.676711082458496, 8.757143020629883] |
3aa5b967-1f4b-4350-9384-63acf538bc8b | agile-the-first-lemmatizer-for-ancient-greek | null | null | https://aclanthology.org/2022.lrec-1.571 | https://aclanthology.org/2022.lrec-1.571.pdf | AGILe: The First Lemmatizer for Ancient Greek Inscriptions | To facilitate corpus searches by classicists as well as to reduce data sparsity when training models, we focus on the automatic lemmatization of ancient Greek inscriptions, which have not received as much attention in this sense as literary text data has. We show that existing lemmatizers for ancient Greek, trained on ... | ['Malvina Nissim', 'Saskia Peels-Matthey', 'Jasper K. Bos', 'Silvia Stopponi', 'Evelien de Graaf'] | null | null | null | null | lrec-2022-6 | ['lemmatization'] | ['natural-language-processing'] | [ 1.05822548e-01 1.68695197e-01 -9.83135700e-02 -7.30436742e-02
-9.52076614e-01 -7.98159361e-01 9.11133349e-01 3.24933767e-01
-9.94285047e-01 6.44850492e-01 7.42933273e-01 -5.75908899e-01
-1.35351539e-01 -8.49959016e-01 -4.01395649e-01 -3.11598957e-01
3.49095672e-01 8.97840381e-01 -6.32385835e-02 -4.08890933... | [10.654107093811035, 9.987006187438965] |
1f8bd7a7-038f-4e32-aeec-02a2f1be9c66 | bilingual-word-embeddings-from-parallel-and | null | null | https://aclanthology.org/N16-1083 | https://aclanthology.org/N16-1083.pdf | Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification | null | ['Achim Rettinger', 'Aditya Mogadala'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['multilingual-word-embeddings'] | ['methodology'] | [-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... | [-7.3765082359313965, 3.5638673305511475] |
69855cf6-1a4c-407b-a1b4-155ac53012fc | bootstrapping-apprenticeship-learning | null | null | http://papers.nips.cc/paper/4160-bootstrapping-apprenticeship-learning | http://papers.nips.cc/paper/4160-bootstrapping-apprenticeship-learning.pdf | Bootstrapping Apprenticeship Learning | We consider the problem of apprenticeship learning where the examples, demonstrated by an expert, cover only a small part of a large state space. Inverse Reinforcement Learning (IRL) provides an efficient tool for generalizing the demonstration, based on the assumption that the expert is maximizing a utility function t... | ['Brahim Chaib-Draa', 'Abdeslam Boularias'] | 2010-12-01 | null | null | null | neurips-2010-12 | ['carracing-v0'] | ['playing-games'] | [-2.40633860e-01 1.19721323e-01 -3.45187277e-01 6.93408586e-03
-7.74481297e-01 -7.14547098e-01 5.82999945e-01 3.36790197e-02
-7.59876668e-01 1.31705368e+00 -4.28664386e-01 -3.83863777e-01
-2.32257128e-01 -6.12192273e-01 -1.22015989e+00 -6.30414844e-01
-5.31352162e-01 6.02287650e-01 3.30713212e-01 -1.47792384... | [4.257894515991211, 2.0640571117401123] |
4eb19b71-07c4-42e8-8a27-54a25454eeaa | structure-inference-machines-recurrent-neural | 1511.04196 | null | http://arxiv.org/abs/1511.04196v2 | http://arxiv.org/pdf/1511.04196v2.pdf | Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition | Rich semantic relations are important in a variety of visual recognition
problems. As a concrete example, group activity recognition involves the
interactions and relative spatial relations of a set of people in a scene.
State of the art recognition methods center on deep learning approaches for
training highly effecti... | ['Zhiwei Deng', 'Greg Mori', 'Arash Vahdat', 'Hexiang Hu'] | 2015-11-13 | structure-inference-machines-recurrent-neural-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Deng_Structure_Inference_Machines_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Deng_Structure_Inference_Machines_CVPR_2016_paper.pdf | cvpr-2016-6 | ['group-activity-recognition'] | ['computer-vision'] | [ 3.98429692e-01 -1.03394119e-02 -1.73947036e-01 -7.02646613e-01
-7.68207908e-02 -3.99880677e-01 1.06286156e+00 1.52537838e-01
-1.71040580e-01 5.06223202e-01 3.37266356e-01 -4.36106712e-01
-1.78737789e-01 -9.06102479e-01 -6.07258081e-01 -5.50466359e-01
5.66792451e-02 3.91455084e-01 7.72764683e-02 1.69925429... | [10.01307487487793, 1.127840518951416] |
c0e834fe-6415-4e70-b52a-c689614a74dc | molo-motion-augmented-long-short-contrastive | 2304.00946 | null | https://arxiv.org/abs/2304.00946v1 | https://arxiv.org/pdf/2304.00946v1.pdf | MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition | Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force... | ['Nong Sang', 'Deli Zhao', 'Yingya Zhang', 'Changxin Gao', 'Zhiwu Qing', 'Shiwei Zhang', 'Xiang Wang'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_MoLo_Motion-Augmented_Long-Short_Contrastive_Learning_for_Few-Shot_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_MoLo_Motion-Augmented_Long-Short_Contrastive_Learning_for_Few-Shot_Action_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['few-shot-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [-8.75184219e-03 -6.12924695e-01 -6.30130947e-01 -2.83384204e-01
-7.38415122e-01 5.28170392e-02 5.49470544e-01 -4.62296784e-01
-3.16537291e-01 4.52659518e-01 5.81265628e-01 3.45045000e-01
-3.83288227e-02 -4.09406632e-01 -5.44090152e-01 -9.01400387e-01
6.56670406e-02 -1.46030977e-01 4.36585009e-01 -1.82191983... | [8.635452270507812, 0.5151599645614624] |
ab5fa383-157b-4ea8-a52d-0241670475c0 | neural-document-summarization-by-jointly | 1807.02305 | null | http://arxiv.org/abs/1807.02305v1 | http://arxiv.org/pdf/1807.02305v1.pdf | Neural Document Summarization by Jointly Learning to Score and Select Sentences | Sentence scoring and sentence selection are two main steps in extractive
document summarization systems. However, previous works treat them as two
separated subtasks. In this paper, we present a novel end-to-end neural network
framework for extractive document summarization by jointly learning to score
and select sente... | ['Nan Yang', 'Furu Wei', 'Shaohan Huang', 'Qingyu Zhou', 'Ming Zhou', 'Tiejun Zhao'] | 2018-07-06 | neural-document-summarization-by-jointly-1 | https://aclanthology.org/P18-1061 | https://aclanthology.org/P18-1061.pdf | acl-2018-7 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 5.71395397e-01 3.81273746e-01 -3.83155167e-01 -5.74653208e-01
-1.26468515e+00 -3.60710442e-01 3.78857017e-01 4.57491696e-01
-6.14382446e-01 9.35254574e-01 1.12522209e+00 1.47673100e-01
1.76185861e-01 -5.63588440e-01 -5.31538188e-01 -2.20059261e-01
3.96603405e-01 3.39253426e-01 1.52926311e-01 -1.83018297... | [12.603411674499512, 9.394997596740723] |
975a02be-ef56-4cda-b151-a56fe538b24f | sleep-stage-classification-from-heart-rate | null | null | https://doi.org/10.1038/s41598-019-49703-y | https://www.nature.com/articles/s41598-019-49703-y.pdf | Sleep stage classification from heart-rate variability using long short-term memory neural networks | Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep archit... | ['Arnaud Moreau', 'Mustafa Radha', 'Ronald M. Aarts', 'Peter Anderer', 'Pedro Fonseca', 'Xi Long', 'Marco Ross', 'Andreas Cerny'] | 2019-10-02 | null | null | null | scientific-reports-2019-10 | ['sleep-stage-detection', 'heart-rate-variability', 'electrocardiography-ecg'] | ['medical', 'medical', 'methodology'] | [-1.23061694e-01 -1.94103226e-01 -2.77818471e-01 -3.77043784e-01
-2.81225801e-01 -9.68575701e-02 5.65722696e-02 -1.76557805e-02
-6.86681509e-01 7.89447784e-01 2.13043302e-01 -1.78866580e-01
-1.20959453e-01 -4.46771294e-01 2.94737160e-01 -5.78862309e-01
-4.19467390e-01 1.56457007e-01 -2.55544960e-01 -2.24281728... | [13.554085731506348, 3.462825059890747] |
3b059bc6-54d7-4a04-8119-064f92f00a57 | a-pilot-study-on-dialogue-level-dependency | 2305.12441 | null | https://arxiv.org/abs/2305.12441v2 | https://arxiv.org/pdf/2305.12441v2.pdf | A Pilot Study on Dialogue-Level Dependency Parsing for Chinese | Dialogue-level dependency parsing has received insufficient attention, especially for Chinese. To this end, we draw on ideas from syntactic dependency and rhetorical structure theory (RST), developing a high-quality human-annotated corpus, which contains 850 dialogues and 199,803 dependencies. Considering that such tas... | ['Min Zhang', 'Meishan Zhang', 'Shuang Liu', 'Gongyao Jiang'] | 2023-05-21 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [ 1.22368664e-01 5.18666804e-01 -2.80921757e-01 -6.03583395e-01
-1.22473896e+00 -8.12882304e-01 6.96047246e-01 -1.19675025e-01
-3.72812629e-01 1.02786720e+00 7.59633839e-01 -1.99894175e-01
5.47957242e-01 -4.54825073e-01 -4.51114655e-01 -4.75436926e-01
-2.64807176e-02 2.36896396e-01 3.63511294e-01 -3.50431383... | [10.671801567077637, 9.38416862487793] |
1dafb61e-b344-4691-ac33-1609103c266d | distributionally-robust-learning-for-1 | 2010.05784 | null | https://arxiv.org/abs/2010.05784v3 | https://arxiv.org/pdf/2010.05784v3.pdf | Distributionally Robust Learning for Uncertainty Calibration under Domain Shift | We propose a framework for learning calibrated uncertainties under domain shifts. We consider the case where the source (training) distribution differs from the target (test) distribution. We detect such domain shifts through the use of a binary domain classifier and integrate it with the task network and train them jo... | ['Anima Anandkumar', 'Junchi Yan', 'Yisong Yue', 'Zhiding Yu', 'Anqi Liu', 'Haoxuan Wang'] | 2020-10-08 | distributionally-robust-learning-for | https://openreview.net/forum?id=FZyZiRYbdK8 | https://openreview.net/pdf?id=FZyZiRYbdK8 | null | ['density-ratio-estimation'] | ['methodology'] | [ 4.64489609e-01 4.61038470e-01 -2.61021465e-01 -8.43654692e-01
-1.30599010e+00 -1.04474366e+00 8.15629482e-01 -8.92147608e-03
-6.18859947e-01 1.09490418e+00 6.05089888e-02 -1.59620821e-01
-1.75098836e-01 -5.36390543e-01 -1.13279915e+00 -6.08921766e-01
2.76103765e-01 8.00350666e-01 2.30594561e-01 1.58973217... | [10.245031356811523, 3.2294323444366455] |
a741a564-7b90-426e-a990-ad427828df0d | composite-learning-for-robust-and-effective | 2210.07239 | null | https://arxiv.org/abs/2210.07239v1 | https://arxiv.org/pdf/2210.07239v1.pdf | Composite Learning for Robust and Effective Dense Predictions | Multi-task learning promises better model generalization on a target task by jointly optimizing it with an auxiliary task. However, the current practice requires additional labeling efforts for the auxiliary task, while not guaranteeing better model performance. In this paper, we find that jointly training a dense pred... | ['Luc van Gool', 'Fisher Yu', 'David Bruggemann', 'Thomas E. Huang', 'Menelaos Kanakis'] | 2022-10-13 | null | null | null | null | ['boundary-detection'] | ['computer-vision'] | [ 4.30840343e-01 2.56756097e-01 -5.62653482e-01 -8.03346038e-01
-1.26575971e+00 -5.03916383e-01 5.11001706e-01 -1.56464159e-01
-4.83854979e-01 5.86095214e-01 2.02885002e-01 -8.95161703e-02
3.37475747e-01 -3.10086012e-01 -8.05382311e-01 -7.06972957e-01
3.92996043e-01 6.54160142e-01 3.11921746e-01 5.03831089... | [9.524868965148926, 1.4347320795059204] |
805c8b36-45d7-44ef-a7b1-a9bf9fc09c9d | from-recognition-to-cognition-visual | 1811.10830 | null | http://arxiv.org/abs/1811.10830v2 | http://arxiv.org/pdf/1811.10830v2.pdf | From Recognition to Cognition: Visual Commonsense Reasoning | Visual understanding goes well beyond object recognition. With one glance at
an image, we can effortlessly imagine the world beyond the pixels: for
instance, we can infer people's actions, goals, and mental states. While this
task is easy for humans, it is tremendously difficult for today's vision
systems, requiring hi... | ['Yejin Choi', 'Rowan Zellers', 'Ali Farhadi', 'Yonatan Bisk'] | 2018-11-27 | from-recognition-to-cognition-visual-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zellers_From_Recognition_to_Cognition_Visual_Commonsense_Reasoning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zellers_From_Recognition_to_Cognition_Visual_Commonsense_Reasoning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['multiple-choice-qa', 'visual-commonsense-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 6.23503685e-01 5.54768503e-01 1.57157630e-01 -5.32748103e-01
-7.29462385e-01 -7.76045263e-01 6.93909645e-01 3.42999399e-03
-3.92556548e-01 6.10321939e-01 3.33824277e-01 -7.35975027e-01
6.17294759e-02 -6.76800132e-01 -7.79247463e-01 -9.35834795e-02
6.54960096e-01 4.42822129e-01 4.82443646e-02 -5.08823633... | [10.786489486694336, 1.937195897102356] |
6744747c-897b-4825-a602-f26734e39be5 | cross-domain-aspect-extraction-using | 2210.10144 | null | https://arxiv.org/abs/2210.10144v1 | https://arxiv.org/pdf/2210.10144v1.pdf | Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs | The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training and testing data are from the same domain. However, these methods show a drastic decrease in performance when applied to cross-domain settings... | ['Gadi Singer', 'Moshe Wasserblat', 'Oren Pereg', 'Daniel Korat', 'Ana Paula Simoes', 'Vasudev Lal', 'Arden Ma', 'Phillip Howard'] | 2022-10-18 | null | null | null | null | ['term-extraction', 'aspect-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.62392932e-01 1.84892997e-01 -4.22249556e-01 -4.67140973e-01
-1.08677185e+00 -9.95773315e-01 8.04366410e-01 4.25299197e-01
-2.93981791e-01 5.13391554e-01 8.09227526e-02 -4.84047830e-01
-1.06953070e-01 -1.03573501e+00 -7.63159692e-01 -1.42087445e-01
3.26262325e-01 7.11809099e-01 4.20537740e-01 -4.83438075... | [11.327338218688965, 6.852653980255127] |
221f3f41-da56-4a23-8551-dea0d9a004df | 4dac-learning-attribute-compression-for | 2204.11723 | null | https://arxiv.org/abs/2204.11723v1 | https://arxiv.org/pdf/2204.11723v1.pdf | 4DAC: Learning Attribute Compression for Dynamic Point Clouds | With the development of the 3D data acquisition facilities, the increasing scale of acquired 3D point clouds poses a challenge to the existing data compression techniques. Although promising performance has been achieved in static point cloud compression, it remains under-explored and challenging to leverage temporal c... | ['Yulan Guo', 'Yiling Xu', 'Qingyong Hu', 'Guangchi Fang'] | 2022-04-25 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.38690302e-01 -4.60472435e-01 -1.63364097e-01 -2.77983010e-01
-6.08799875e-01 -1.34090930e-01 3.48021686e-01 1.18453734e-01
-1.52932301e-01 2.10429952e-01 1.62347242e-01 1.46309147e-02
-1.71360195e-01 -8.59266698e-01 -8.81924272e-01 -7.63494551e-01
-3.90682518e-01 3.89241457e-01 2.23226458e-01 2.11805001... | [8.48956298828125, -3.002126455307007] |
742434b4-b602-4a14-b04f-a506261b8224 | investigating-non-local-features-for-neural | 2109.12814 | null | https://arxiv.org/abs/2109.12814v2 | https://arxiv.org/pdf/2109.12814v2.pdf | Investigating Non-local Features for Neural Constituency Parsing | Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the t... | ['Yue Zhang', 'Sen yang', 'Leyang Cui'] | 2021-09-27 | null | https://aclanthology.org/2022.acl-long.146 | https://aclanthology.org/2022.acl-long.146.pdf | acl-2022-5 | ['constituency-parsing'] | ['natural-language-processing'] | [-1.91670761e-01 2.96841472e-01 -2.70971119e-01 -7.88666368e-01
-1.49911368e+00 -5.69589496e-01 5.81989706e-01 2.68650353e-01
-5.71851790e-01 8.58562887e-01 5.67999005e-01 -6.06181920e-01
-8.52701720e-04 -7.52429187e-01 -8.52660298e-01 -4.08232868e-01
-2.30409056e-01 5.70981741e-01 2.32603133e-01 -3.80506396... | [10.341704368591309, 9.727469444274902] |
05edb334-8fd1-4291-8d11-2954f350a934 | dialogue-act-recognition-using-reweighted | null | null | https://aclanthology.org/W12-1616 | https://aclanthology.org/W12-1616.pdf | Dialogue Act Recognition using Reweighted Speaker Adaptation | null | ['Louis-Philippe Morency', 'Congkai Sun'] | 2012-07-01 | null | null | null | ws-2012-7 | ['dialogue-understanding'] | ['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... | [-7.429410934448242, 3.570490598678589] |
66ff8435-7bdb-4804-bdb8-c73eb5daeae3 | conformal-predictors-for-compound-activity | 1603.04506 | null | http://arxiv.org/abs/1603.04506v1 | http://arxiv.org/pdf/1603.04506v1.pdf | Conformal Predictors for Compound Activity Prediction | The paper presents an application of Conformal Predictors to a
chemoinformatics problem of identifying activities of chemical compounds. The
paper addresses some specific challenges of this domain: a large number of
compounds (training examples), high-dimensionality of feature space, sparseness
and a strong class imbal... | ['Alexander Gammerman', 'Paolo Toccacheli', 'Ilia Nouretdinov'] | 2016-03-14 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 4.18680280e-01 -2.56225377e-01 -1.50150880e-01 -4.31452543e-01
-6.85069621e-01 -7.94867039e-01 9.07989502e-01 4.98614788e-01
-5.58413416e-02 1.19006395e+00 -4.69398238e-02 -4.65655744e-01
-7.06221163e-01 -5.71254969e-01 -3.04519206e-01 -9.86371577e-01
-4.80570227e-01 6.77280486e-01 1.71526119e-01 -3.05352602... | [5.132468223571777, 5.586060047149658] |
74f203fa-0ec3-48de-8f04-cfc41951b23f | dual-tasks-siamese-transformer-framework-for | 2201.10953 | null | https://arxiv.org/abs/2201.10953v2 | https://arxiv.org/pdf/2201.10953v2.pdf | Dual-Tasks Siamese Transformer Framework for Building Damage Assessment | Accurate and fine-grained information about the extent of damage to buildings is essential for humanitarian relief and disaster response. However, as the most commonly used architecture in remote sensing interpretation tasks, Convolutional Neural Networks (CNNs) have limited ability to model the non-local relationship ... | ['Lars Bromley', 'Chen Wu', 'Xi Li', 'Sofia Vallecorsa', 'Edoardo Nemni', 'Hongruixuan Chen'] | 2022-01-26 | null | null | null | null | ['extracting-buildings-in-remote-sensing-images'] | ['miscellaneous'] | [ 4.74554658e-01 -1.50937065e-01 3.06922883e-01 -5.84886134e-01
-1.05604744e+00 -4.83736657e-02 5.60568869e-01 3.17423493e-01
-5.01202226e-01 5.71934581e-01 5.21011293e-01 -2.92064011e-01
-2.21325070e-01 -1.06649339e+00 -5.01887798e-01 -7.03593612e-01
-2.84289032e-01 1.75077334e-01 9.94843617e-02 -4.50697631... | [9.69180679321289, -1.2826253175735474] |
426a2276-9025-4825-ae05-97cf5b7dfbea | searching-for-a-search-method-benchmarking | 2009.06368 | null | https://arxiv.org/abs/2009.06368v2 | https://arxiv.org/pdf/2009.06368v2.pdf | Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples | We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks. We perform a fine-grained analysis of three elements relevant to search: search algorithm, search space, and search budget. When new search algorithms are proposed in past w... | ['Yanjun Qi', 'John X. Morris', 'Eli Lifland', 'Jin Yong Yoo'] | 2020-09-09 | null | https://aclanthology.org/2020.blackboxnlp-1.30 | https://aclanthology.org/2020.blackboxnlp-1.30.pdf | emnlp-blackboxnlp-2020-11 | ['adversarial-text'] | ['adversarial'] | [ 4.81626302e-01 -1.32483646e-01 -2.51675040e-01 -8.29427838e-02
-1.09065676e+00 -1.29180741e+00 8.79247010e-01 1.48323685e-01
-6.52060926e-01 7.03007400e-01 4.30653960e-01 -8.38945746e-01
-2.10813418e-01 -8.28352749e-01 -4.59164143e-01 -5.87732673e-01
2.32949257e-01 7.53334939e-01 2.01641992e-01 -4.02407050... | [6.034277439117432, 8.134843826293945] |
6d7b4ff4-7968-4656-9ba4-15c545b710d0 | hierarchical-task-network-planning-for | 2306.08359 | null | https://arxiv.org/abs/2306.08359v1 | https://arxiv.org/pdf/2306.08359v1.pdf | Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning | Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task. Agents typically fail to reach the goal state and fall into traps, which affects the overall performance of the system. To overcome this issue, we present SOMARL, a framework that uses ... | ['Jianye Hao', 'Chao Yu', 'Kai Zhang', 'Chen Chen', 'Hankz Hankui Zhuo', 'Xuechen Mu'] | 2023-06-14 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-3.09132665e-01 1.96049497e-01 -4.98316407e-01 2.89446209e-02
-8.29383075e-01 -6.94573104e-01 5.01846433e-01 3.06672275e-01
-6.73308372e-01 9.84835386e-01 1.72348782e-01 -3.01141977e-01
-3.20812374e-01 -1.04170740e+00 -5.86945951e-01 -6.51528299e-01
-5.92305481e-01 9.29794848e-01 5.76647937e-01 -5.54728866... | [3.894062042236328, 1.7269234657287598] |
78d65f38-62d7-4716-b86f-e24a6cadf939 | instructpix2pix-learning-to-follow-image | 2211.09800 | null | https://arxiv.org/abs/2211.09800v2 | https://arxiv.org/pdf/2211.09800v2.pdf | InstructPix2Pix: Learning to Follow Image Editing Instructions | We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (G... | ['Alexei A. Efros', 'Aleksander Holynski', 'Tim Brooks'] | 2022-11-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Brooks_InstructPix2Pix_Learning_To_Follow_Image_Editing_Instructions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Brooks_InstructPix2Pix_Learning_To_Follow_Image_Editing_Instructions_CVPR_2023_paper.pdf | cvpr-2023-1 | ['text-based-image-editing'] | ['computer-vision'] | [ 8.05705667e-01 3.95107418e-01 1.82150602e-01 -7.13628531e-01
-4.59358811e-01 -6.64715230e-01 8.14128160e-01 -9.26050022e-02
-5.84790051e-01 5.16990244e-01 -1.54440468e-02 -5.82028985e-01
4.00020182e-01 -7.18010604e-01 -1.25242949e+00 -1.79727495e-01
3.67173195e-01 6.18305027e-01 2.18645498e-01 8.09222162... | [11.312725067138672, -0.19570960104465485] |
9696f7ca-507a-42b8-ad64-8accacdd2392 | federated-distillation-based-indoor | 2205.11440 | null | https://arxiv.org/abs/2205.11440v2 | https://arxiv.org/pdf/2205.11440v2.pdf | Federated Distillation based Indoor Localization for IoT Networks | Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithms are designed for only classification tasks and less attention has been given to r... | ['El Mehdi Amhoud', 'Marwa Chafii', 'Yaya Etiabi'] | 2022-05-23 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [ 7.86368083e-03 4.30620760e-01 -1.59583598e-01 -3.13889205e-01
-8.10050011e-01 -2.41482899e-01 6.34648561e-01 2.62702674e-01
-6.04443550e-01 1.25642896e+00 -5.68123311e-02 -5.40315151e-01
-4.49666470e-01 -1.11185288e+00 -7.43695199e-01 -8.15075397e-01
-5.86839497e-01 1.12733305e-01 9.64818448e-02 -6.21025227... | [6.402238845825195, 0.9739000201225281] |
97b8fec4-615f-4e9a-9b7a-369d1797408f | ctran-cnn-transformer-based-network-for | 2303.10606 | null | https://arxiv.org/abs/2303.10606v1 | https://arxiv.org/pdf/2303.10606v1.pdf | CTRAN: CNN-Transformer-based Network for Natural Language Understanding | Intent-detection and slot-filling are the two main tasks in natural language understanding. In this study, we propose CTRAN, a novel encoder-decoder CNN-Transformer-based architecture for intent-detection and slot-filling. In the encoder, we use BERT, followed by several convolutional layers, and rearrange the output u... | ['Javad Salimi Sartakhti', 'Mehrdad Rafiepour'] | 2023-03-19 | null | null | null | null | ['intent-detection', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.19132560e-01 3.94509882e-01 -3.32562298e-01 -3.34108800e-01
-6.23785794e-01 -3.52138132e-01 5.83640337e-01 2.04771638e-01
-9.36452925e-01 2.62932628e-01 5.95663488e-01 -7.76382744e-01
6.22272253e-01 -6.53151333e-01 -6.53255403e-01 -1.49553539e-02
2.03524277e-01 4.72883731e-01 2.62502491e-01 -2.57872462... | [12.446806907653809, 7.389571189880371] |
bb26bd01-ecb4-4d39-8ba6-f745ab01b79b | neural-execution-engines-learning-to-execute | 2006.08084 | null | https://arxiv.org/abs/2006.08084v3 | https://arxiv.org/pdf/2006.08084v3.pdf | Neural Execution Engines: Learning to Execute Subroutines | A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms. This is evidenced by their inability to generalize to data distributions that are outside of their restricted training sets, namely larger inp... | ['Danai Koutra', 'Yujun Yan', 'Kevin Swersky', 'Parthasarathy Ranganathan', 'Milad Hashemi'] | 2020-06-15 | null | http://proceedings.neurips.cc/paper/2020/hash/c8b9abffb45bf79a630fb613dcd23449-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/c8b9abffb45bf79a630fb613dcd23449-Paper.pdf | neurips-2020-12 | ['learning-to-execute'] | ['computer-code'] | [ 4.66993302e-01 -1.70066312e-01 -1.64293796e-01 -3.14394265e-01
-3.73626560e-01 -9.39115942e-01 4.50005561e-01 5.39926648e-01
-4.94168282e-01 6.10038280e-01 2.26527572e-01 -7.75175631e-01
1.29222438e-01 -1.13517189e+00 -1.03721428e+00 -3.68417591e-01
-1.67941168e-01 3.59230846e-01 4.36482951e-02 -3.03691208... | [9.480319023132324, 7.181978225708008] |
cb7a3382-700a-4db1-96d8-e3d0e93b1fe0 | deep-dynamic-scene-deblurring-from-optical | 2301.07329 | null | https://arxiv.org/abs/2301.07329v1 | https://arxiv.org/pdf/2301.07329v1.pdf | Deep Dynamic Scene Deblurring from Optical Flow | Deblurring can not only provide visually more pleasant pictures and make photography more convenient, but also can improve the performance of objection detection as well as tracking. However, removing dynamic scene blur from images is a non-trivial task as it is difficult to model the non-uniform blur mathematically. S... | ['Jimmy Ren', 'Jianbo Liu', 'Furong Zhao', 'Xing Wei', 'Shangchen Zhou', 'Daoye Wang', 'Jinshan Pan', 'Jiawei Zhang'] | 2023-01-18 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 2.29765326e-02 -8.79355788e-01 1.85103431e-01 -1.81065515e-01
-2.12179706e-01 -4.46061760e-01 2.83262640e-01 -8.05612445e-01
-3.65426481e-01 7.26034164e-01 6.47940755e-01 -2.95550078e-02
-7.78180137e-02 -2.80533671e-01 -6.51743352e-01 -7.56228626e-01
3.74497563e-01 -4.63645488e-01 2.36590996e-01 6.56831861... | [11.477450370788574, -2.6012463569641113] |
f131d5c0-ade9-42ff-b725-d7ed8f0c374f | experimental-demonstration-of-neuromorphic | 2112.04749 | null | https://arxiv.org/abs/2112.04749v1 | https://arxiv.org/pdf/2112.04749v1.pdf | Experimental Demonstration of Neuromorphic Network with STT MTJ Synapses | We present the first experimental demonstration of a neuromorphic network with magnetic tunnel junction (MTJ) synapses, which performs image recognition via vector-matrix multiplication. We also simulate a large MTJ network performing MNIST handwritten digit recognition, demonstrating that MTJ crossbars can match memri... | ['Joseph S. Friedman', 'Sanjeev Aggarwal', 'Dimitri Houssameddine', 'Fred B. Mancoff', 'Alexander J. Edwards', 'Peng Zhou'] | 2021-12-09 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.99471071e-01 -4.69834954e-01 -6.11461848e-02 1.09848261e-01
3.90112668e-01 -1.84738114e-01 2.56709158e-01 -3.53199929e-01
-6.90025210e-01 9.06059921e-01 -8.15448523e-01 -6.64674997e-01
-8.85663852e-02 -6.58921540e-01 -1.11926925e+00 -6.08229101e-01
-2.66882420e-01 2.85451412e-01 8.08935404e-01 -4.63475972... | [8.218255996704102, 2.514755964279175] |
ef86ea61-741d-4311-9c76-dfcbb6d028be | period-vits-variational-inference-with | 2210.15964 | null | https://arxiv.org/abs/2210.15964v2 | https://arxiv.org/pdf/2210.15964v2.pdf | Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis | Several fully end-to-end text-to-speech (TTS) models have been proposed that have shown better performance compared to cascade models (i.e., training acoustic and vocoder models separately). However, they often generate unstable pitch contour with audible artifacts when the dataset contains emotional attributes, i.e., ... | ['Kentaro Tachibana', 'Jae-Min Kim', 'Ryo Terashima', 'Eunwoo Song', 'Ryuichi Yamamoto', 'Yuma Shirahata'] | 2022-10-28 | null | null | null | null | ['emotional-speech-synthesis'] | ['speech'] | [-1.79263443e-01 2.12460116e-01 1.57724231e-01 -2.28325188e-01
-1.10340858e+00 -5.67108393e-01 3.15371484e-01 -2.74587393e-01
9.68415141e-02 7.13921428e-01 4.27904755e-01 1.11239962e-01
6.20184064e-01 -4.84809011e-01 -6.88172162e-01 -7.89167404e-01
3.89074981e-01 -4.68968637e-02 -1.96913220e-02 -2.05246657... | [15.290383338928223, 6.273355007171631] |
3ff40401-d3e6-4263-8ed8-e659bf189697 | privacy-preserving-community-detection-for | 2306.15709 | null | https://arxiv.org/abs/2306.15709v1 | https://arxiv.org/pdf/2306.15709v1.pdf | Privacy-Preserving Community Detection for Locally Distributed Multiple Networks | Modern multi-layer networks are commonly stored and analyzed in a local and distributed fashion because of the privacy, ownership, and communication costs. The literature on the model-based statistical methods for community detection based on these data is still limited. This paper proposes a new method for consensus c... | ['Shujie Ma', 'Xiangyu Chang', 'Xiang Li', 'Xiao Guo'] | 2023-06-27 | null | null | null | null | ['stochastic-block-model', 'community-detection', 'clustering'] | ['graphs', 'graphs', 'methodology'] | [ 2.10371405e-01 -4.14662719e-01 4.86166887e-02 -1.33116126e-01
-6.37993097e-01 -8.06055725e-01 1.35273412e-01 7.37070367e-02
-2.90158272e-01 6.39137805e-01 2.27254763e-01 -2.12917373e-01
-5.39415240e-01 -6.53273880e-01 -4.70121115e-01 -1.26856518e+00
-5.39748013e-01 8.11657533e-02 1.45667031e-01 2.97864854... | [6.860136985778809, 5.296004295349121] |
c6965e70-1a38-43c5-965b-b249ecb145ef | few-shot-action-recognition-with-compromised | 2104.03737 | null | https://arxiv.org/abs/2104.03737v1 | https://arxiv.org/pdf/2104.03737v1.pdf | Few-Shot Action Recognition with Compromised Metric via Optimal Transport | Although vital to computer vision systems, few-shot action recognition is still not mature despite the wide research of few-shot image classification. Popular few-shot learning algorithms extract a transferable embedding from seen classes and reuse it on unseen classes by constructing a metric-based classifier. One mai... | ['De-Chuan Zhan', 'Han-Jia Ye', 'Su Lu'] | 2021-04-08 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 4.60689098e-01 -4.08388108e-01 -5.68424463e-01 -4.56005126e-01
-5.31804323e-01 -4.38555449e-01 4.88969445e-01 1.06335253e-01
-5.73615432e-01 4.34450418e-01 2.64407277e-01 3.46422493e-01
-4.68382686e-01 -6.09264612e-01 -5.02094388e-01 -8.24753284e-01
-2.08174273e-01 8.34001824e-02 6.48594797e-01 6.55207485... | [8.48694133758545, 0.7433567643165588] |
c6d94925-70a7-4903-a694-7175ba3e4cd4 | joint-aspect-extraction-and-sentiment | null | null | https://aclanthology.org/2020.coling-main.24 | https://aclanthology.org/2020.coling-main.24.pdf | Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks | End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first extracts the aspect terms in a sentence and the second predicts the sentiment polarities for such terms. For EASA, compared to pipeline and multi-task approaches, joint aspect extraction and sentiment analysis provides a one-step sol... | ['Yan Song', 'Yuanhe Tian', 'Guimin Chen'] | 2020-12-01 | null | null | null | coling-2020-8 | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.21781656e-01 3.74132633e-01 -1.49421141e-01 -7.58787453e-01
-6.08987451e-01 -6.16339743e-01 6.38286471e-01 2.26535544e-01
-2.98326463e-01 1.88210398e-01 5.48509240e-01 -5.12453139e-01
3.00594866e-01 -9.47951615e-01 -6.14975870e-01 -4.17270899e-01
5.66349700e-02 2.82133728e-01 -1.67502135e-01 -5.82443058... | [11.47685718536377, 6.670983791351318] |
f6a1293f-cc55-49ea-995f-6756c6331076 | robust-submodular-maximization-a-non-uniform | 1706.04918 | null | http://arxiv.org/abs/1706.04918v1 | http://arxiv.org/pdf/1706.04918v1.pdf | Robust Submodular Maximization: A Non-Uniform Partitioning Approach | We study the problem of maximizing a monotone submodular function subject to
a cardinality constraint $k$, with the added twist that a number of items
$\tau$ from the returned set may be removed. We focus on the worst-case setting
considered in (Orlin et al., 2016), in which a constant-factor approximation
guarantee wa... | ['Volkan Cevher', 'Slobodan Mitrović', 'Jonathan Scarlett', 'Ilija Bogunovic'] | 2017-06-15 | robust-submodular-maximization-a-non-uniform-1 | https://icml.cc/Conferences/2017/Schedule?showEvent=674 | http://proceedings.mlr.press/v70/bogunovic17a/bogunovic17a.pdf | icml-2017-8 | ['data-summarization'] | ['miscellaneous'] | [ 2.55367488e-01 4.90745425e-01 -4.50411677e-01 -1.51555955e-01
-9.58090007e-01 -1.00276828e+00 -3.30735356e-01 5.17303526e-01
-3.89294028e-01 9.46023941e-01 4.30462286e-02 -5.69506697e-02
-7.02235460e-01 -8.67053390e-01 -9.70418334e-01 -8.60230207e-01
-5.66135764e-01 7.14975893e-01 -1.08565725e-01 -1.35141656... | [6.587095737457275, 4.895382881164551] |
e8a50cbc-6332-45ac-9944-e3693ba2a001 | adversarial-generation-of-training-examples | 1707.03124 | null | http://arxiv.org/abs/1707.03124v3 | http://arxiv.org/pdf/1707.03124v3.pdf | Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition | Generative Adversarial Networks (GAN) have attracted much research attention
recently, leading to impressive results for natural image generation. However,
to date little success was observed in using GAN generated images for improving
classification tasks. Here we attempt to explore, in the context of car license
plat... | ['Chunhua Shen', 'Zhipeng Man', 'Xinlong Wang', 'Mingyu You'] | 2017-07-11 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [ 8.08886468e-01 1.46463886e-01 1.29604653e-01 -2.38497853e-01
-1.12758660e+00 -5.46082437e-01 7.73638546e-01 -8.54896724e-01
-3.48527968e-01 8.41351807e-01 1.69391483e-02 -5.35672069e-01
7.25265026e-01 -8.98810148e-01 -1.07913601e+00 -8.76306415e-01
2.81980336e-01 1.68346226e-01 -1.99608691e-02 5.95950112... | [11.648541450500488, -0.507445216178894] |
1b2d7f5a-f864-4895-bbb3-a1d53bffe1ac | adversarial-autoencoders | 1511.05644 | null | http://arxiv.org/abs/1511.05644v2 | http://arxiv.org/pdf/1511.05644v2.pdf | Adversarial Autoencoders | In this paper, we propose the "adversarial autoencoder" (AAE), which is a
probabilistic autoencoder that uses the recently proposed generative
adversarial networks (GAN) to perform variational inference by matching the
aggregated posterior of the hidden code vector of the autoencoder with an
arbitrary prior distributio... | ['Brendan Frey', 'Navdeep Jaitly', 'Jonathon Shlens', 'Alireza Makhzani', 'Ian Goodfellow'] | 2015-11-18 | null | null | null | null | ['unsupervised-image-classification', 'unsupervised-mnist'] | ['computer-vision', 'methodology'] | [ 5.15548959e-02 5.64532459e-01 5.85874438e-01 -4.22627032e-01
-4.28123683e-01 -5.87726772e-01 8.87894452e-01 -7.06902087e-01
1.11923642e-01 6.89970791e-01 3.80020469e-01 1.11732192e-01
3.52485068e-02 -1.03349125e+00 -1.06711912e+00 -9.47710872e-01
1.79079220e-01 1.06741929e+00 -3.24188292e-01 1.57500070... | [11.566797256469727, -0.07775796949863434] |
3a3123d2-8455-4d86-9f10-f95d2936c342 | case-aligning-coarse-to-fine-cognition-and | 2208.08845 | null | https://arxiv.org/abs/2208.08845v2 | https://arxiv.org/pdf/2208.08845v2.pdf | CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation | Empathetic conversation is psychologically supposed to be the result of conscious alignment and interaction between the cognition and affection of empathy. However, existing empathetic dialogue models usually consider only the affective aspect or treat cognition and affection in isolation, which limits the capability o... | ['Minlie Huang', 'Zheng Zhang', 'Bo wang', 'Chujie Zheng', 'Jinfeng Zhou'] | 2022-08-18 | null | null | null | null | ['empathetic-response-generation'] | ['natural-language-processing'] | [-3.39794487e-01 7.30246544e-01 2.99537033e-02 -5.17615080e-01
-1.55643240e-01 -5.41417956e-01 8.31782639e-01 1.54409915e-01
-1.15794621e-01 7.98034549e-01 9.86656010e-01 2.34812260e-01
1.86980233e-01 -8.33511472e-01 3.67443323e-01 -2.60079205e-01
4.29943532e-01 6.25637174e-01 -5.58880270e-01 -9.31463659... | [13.137838363647461, 7.656383514404297] |
45d7ed43-444e-4a61-9b36-46d6cb9f9fe4 | show-me-what-i-like-detecting-user-specific | 2207.08352 | null | https://arxiv.org/abs/2207.08352v2 | https://arxiv.org/pdf/2207.08352v2.pdf | Show Me What I Like: Detecting User-Specific Video Highlights Using Content-Based Multi-Head Attention | We propose a method to detect individualized highlights for users on given target videos based on their preferred highlight clips marked on previous videos they have watched. Our method explicitly leverages the contents of both the preferred clips and the target videos using pre-trained features for the objects and the... | ['Dinesh Manocha', 'Viswanathan Swaminathan', 'Stefano Petrangeli', 'Gang Wu', 'Uttaran Bhattacharya'] | 2022-07-18 | null | null | null | null | ['highlight-detection'] | ['computer-vision'] | [ 1.97560400e-01 -3.70562494e-01 -2.77477890e-01 -2.85722464e-01
-1.21915758e+00 -8.01072538e-01 5.46916425e-01 3.04437906e-01
-4.77828920e-01 1.06418744e-01 7.57689238e-01 6.31263316e-01
1.96074381e-01 -1.39664516e-01 -5.49017727e-01 -3.45474571e-01
-5.49799323e-01 -3.37434709e-01 4.60279256e-01 2.26598263... | [10.152823448181152, 0.4962579309940338] |
1f3f6538-0133-4003-b2ba-06c41c96f1e7 | analyzing-scrna-seq-data-by-ccp-assisted-umap | 2306.13750 | null | https://arxiv.org/abs/2306.13750v1 | https://arxiv.org/pdf/2306.13750v1.pdf | Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNE | Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell-cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, di... | ['Gu-Wei Wei', 'Yuta Hozumi'] | 2023-06-23 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.74150169e-01 -6.86303079e-01 -7.26079866e-02 1.71894401e-01
-7.82265306e-01 -7.84803152e-01 4.26895231e-01 1.66125476e-01
-1.50117785e-01 6.83658719e-01 5.22603393e-01 -1.50909841e-01
-3.43435258e-01 -5.94911337e-01 -1.77398786e-01 -1.34245837e+00
1.11129113e-01 4.23027366e-01 -1.19212225e-01 1.70394238... | [6.640946865081787, 5.201248645782471] |
14c3d7c5-f6e2-49e8-b85a-a0eca888bc90 | hispidin-and-lepidine-e-two-natural-compounds | 2004.08920 | null | https://arxiv.org/abs/2004.08920v1 | https://arxiv.org/pdf/2004.08920v1.pdf | Hispidin and Lepidine E: two Natural Compounds and Folic acid as Potential Inhibitors of 2019-novel coronavirus Main Protease (2019-nCoVMpro), molecular docking and SAR study | 2019-nCoV is a novel coronavirus was isolated and identified in 2019 in Wuhan, China. On 17th February and according to world health organization, a number of 71 429 confirmed cases worldwide, among them 2162 new cases recorded in the last 24 hours. There is no drug or vaccine for human and animal coronavirus. The inhi... | ['Mohamed Yousfi', 'Khedidja Benarous', 'Talia Serseg'] | 2020-04-19 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 5.07313618e-03 -3.72814685e-01 -3.48855942e-01 2.90283393e-02
6.18200712e-02 -7.64629960e-01 1.66884974e-01 4.45299774e-01
-3.81097049e-01 1.18398154e+00 1.20077856e-01 -4.13397014e-01
2.60003597e-01 -4.59365875e-01 -4.35831070e-01 -7.92763412e-01
-2.66774327e-01 4.33834255e-01 -1.20574236e-01 2.08200160... | [4.6884541511535645, 5.090554714202881] |
8458458f-6531-4ef2-960a-e60e1f34f91d | learning-occupancy-for-monocular-3d-object | 2305.15694 | null | https://arxiv.org/abs/2305.15694v1 | https://arxiv.org/pdf/2305.15694v1.pdf | Learning Occupancy for Monocular 3D Object Detection | Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to facilitate the learning, but often fail to fully exploit the benefits of three-dimensional feature extraction in frustum and 3D space. In this ... | ['Deng Cai', 'Boxi Wu', 'Wenxiao Wang', 'Wei Qian', 'Xiaopei Wu', 'Zheng Yang', 'Haoran Cheng', 'Junkai Xu', 'Liang Peng'] | 2023-05-25 | null | null | null | null | ['monocular-3d-object-detection'] | ['computer-vision'] | [ 1.98772866e-02 -4.64750886e-01 -4.92386878e-01 -3.48514855e-01
-7.40973055e-01 -4.26969856e-01 5.64375937e-01 1.35383755e-01
-3.74550223e-01 8.98384750e-01 1.53176472e-01 -1.62363067e-01
-2.15730667e-01 -6.92936063e-01 -8.03357601e-01 -7.33040452e-01
-2.73234010e-01 6.71427488e-01 1.25303671e-01 2.38162994... | [7.8998003005981445, -2.604307174682617] |
92794a8d-cf1c-44d4-863c-921846fa44ae | support-vector-machine-based-arrhythmia | null | null | http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.463.8764 | https://pdfs.semanticscholar.org/f170/a2137b48720a5f83c55ef204419f4b7551cc.pdf | Support vector machine based arrhythmia classification using reduced features | In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, ... | ['Sun Kook Yoo', 'Kyoung Joung Lee', 'Sung Pil Cho', 'Mi Hye Song', 'Jeon Lee'] | 2005-01-01 | null | null | null | international-journal-of-control-automation | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 4.61911932e-02 -2.12783292e-01 -7.08379820e-02 1.88614726e-02
-1.43336803e-01 -6.59819365e-01 3.09870243e-01 1.34138003e-01
-1.14304043e-01 1.13865435e+00 -2.11798280e-01 -5.49728751e-01
-2.05981910e-01 -5.47472179e-01 1.60328925e-01 -8.93270254e-01
-1.93347991e-01 3.51002187e-01 -1.66302487e-01 1.06345415... | [14.137404441833496, 3.2195916175842285] |
0a486359-5d28-43e3-97c6-f6eef158bea3 | deep-bayesian-experimental-design-for-quantum | 2306.14510 | null | https://arxiv.org/abs/2306.14510v1 | https://arxiv.org/pdf/2306.14510v1.pdf | Deep Bayesian Experimental Design for Quantum Many-Body Systems | Bayesian experimental design is a technique that allows to efficiently select measurements to characterize a physical system by maximizing the expected information gain. Recent developments in deep neural networks and normalizing flows allow for a more efficient approximation of the posterior and thus the extension of ... | ['Florian Marquardt', 'Leopoldo Sarra'] | 2023-06-26 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 2.25195870e-01 -2.94159025e-01 4.26265001e-02 -3.35114717e-01
-5.70458233e-01 -5.56029856e-01 7.27016211e-01 -3.48240579e-03
-7.86612928e-01 9.89836812e-01 -1.86783001e-01 -4.25052255e-01
-2.78698713e-01 -8.53315651e-01 -3.96969527e-01 -1.21640980e+00
-1.56561315e-01 6.99359775e-01 -1.65665850e-01 -2.13691920... | [5.6091508865356445, 4.914169788360596] |
5bd2403a-06f8-48cf-95b2-c4ef3fc41bc3 | learning-towards-abstractive-timeline | null | null | https://www.ijcai.org/proceedings/2019/686 | https://www.ijcai.org/proceedings/2019/0686.pdf | Learning towards Abstractive Timeline Summarization | Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods.In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time... | ['Meng-Hsuan Yu', 'Zhangming Chan', 'Shen Gao', 'Xiuying Chen', 'Rui Yan', 'Dongyan Zhao'] | 2019-08-11 | null | null | null | ijcai-2019-2019-8 | ['timeline-summarization'] | ['natural-language-processing'] | [ 4.54616696e-01 -1.10497892e-01 -3.51498663e-01 -1.90151662e-01
-1.05200756e+00 -3.87702286e-01 8.52986157e-01 9.93989050e-01
-3.21861923e-01 7.98810840e-01 1.32123232e+00 -6.39997050e-02
-7.78426081e-02 -6.18694246e-01 -5.67547083e-01 -1.56975329e-01
-1.17527865e-01 2.36353278e-01 1.01451106e-01 -5.90731064... | [12.56629467010498, 9.490503311157227] |
f23c8539-039b-480d-ac9a-571d35a340a2 | text-adaptive-multiple-visual-prototype | 2209.13307 | null | https://arxiv.org/abs/2209.13307v1 | https://arxiv.org/pdf/2209.13307v1.pdf | Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval | Cross-modal retrieval between videos and texts has gained increasing research interest due to the rapid emergence of videos on the web. Generally, a video contains rich instance and event information and the query text only describes a part of the information. Thus, a video can correspond to multiple different text des... | ['Chunhua Shen', 'Wei-Shi Zheng', 'Wenhang Ge', 'Jun Zhang', 'Junwei Liang', 'AnCong Wu', 'Chengzhi Lin'] | 2022-09-27 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.63772076e-01 -7.25697875e-01 -5.00941634e-01 -4.12596703e-01
-8.05456161e-01 -6.67544305e-01 5.87358415e-01 4.79510397e-01
-3.15655857e-01 1.89762875e-01 4.32693064e-01 5.51309288e-01
-1.85110912e-01 -3.20441872e-01 -6.54647171e-01 -4.92218345e-01
1.14299871e-01 3.94223332e-01 5.20899653e-01 1.16358921... | [10.166189193725586, 0.7826024889945984] |
7dd9166b-f4a2-469e-b93d-d44e8cb7efa3 | collaborative-agent-gameplay-in-the-pandemic | 2103.11388 | null | https://arxiv.org/abs/2103.11388v1 | https://arxiv.org/pdf/2103.11388v1.pdf | Collaborative Agent Gameplay in the Pandemic Board Game | While artificial intelligence has been applied to control players' decisions in board games for over half a century, little attention is given to games with no player competition. Pandemic is an exemplar collaborative board game where all players coordinate to overcome challenges posed by events occurring during the ga... | ['Antonios Liapis', 'Konstantinos Sfikas'] | 2021-03-21 | null | null | null | null | ['board-games'] | ['playing-games'] | [-4.78406921e-02 3.75012100e-01 8.98216963e-02 2.07117960e-01
6.64928481e-02 -6.29807115e-01 4.57382858e-01 3.16970080e-01
-8.08688641e-01 1.00958955e+00 -2.09068507e-02 -4.37331885e-01
-5.26114345e-01 -1.14463603e+00 2.28109777e-01 -4.94121730e-01
-1.48891658e-01 7.47944713e-01 7.34905899e-01 -8.51576447... | [3.4585366249084473, 1.5494972467422485] |
670eb1e6-287b-4aa4-a904-3f00d48cee13 | alexsis-pt-a-new-resource-for-portuguese | 2209.09034 | null | https://arxiv.org/abs/2209.09034v1 | https://arxiv.org/pdf/2209.09034v1.pdf | ALEXSIS-PT: A New Resource for Portuguese Lexical Simplification | Lexical simplification (LS) is the task of automatically replacing complex words for easier ones making texts more accessible to various target populations (e.g. individuals with low literacy, individuals with learning disabilities, second language learners). To train and test models, LS systems usually require corpora... | ['Tharindu Ranasinghe', 'Marcos Zampieri', 'Kai North'] | 2022-09-19 | null | https://aclanthology.org/2022.coling-1.529 | https://aclanthology.org/2022.coling-1.529.pdf | coling-2022-10 | ['lexical-simplification', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing'] | [-9.00574308e-03 3.90895218e-01 -3.80932748e-01 -1.64849356e-01
-1.12041259e+00 -3.66663009e-01 4.60676670e-01 4.20150369e-01
-8.75801504e-01 1.25664628e+00 6.03473485e-01 -2.34790757e-01
3.11118633e-01 -6.27157748e-01 -7.53259778e-01 9.71491113e-02
7.22788632e-01 8.91551197e-01 9.31480229e-02 -8.21750045... | [10.934338569641113, 10.416213989257812] |
2501d1ec-5144-498c-8b89-512860c109da | your-attention-deserves-attention-a-self | 2203.12570 | null | https://arxiv.org/abs/2203.12570v1 | https://arxiv.org/pdf/2203.12570v1.pdf | Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis | Visual attention has been extensively studied for learning fine-grained features in both facial expression recognition (FER) and Action Unit (AU) detection. A broad range of previous research has explored how to use attention modules to localize detailed facial parts (e,g. facial action units), learn discriminative fea... | ['Lijun Yin', 'Geran Zhao', 'Huiyuan Yang', 'Zhihua Li', 'Xiaotian Li'] | 2022-03-23 | null | null | null | null | ['action-analysis', 'facial-expression-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.43857608e-02 -1.71019658e-01 -1.85095802e-01 -4.56544042e-01
-4.32993114e-01 7.95686096e-02 2.31017694e-01 -5.28541088e-01
-1.78202629e-01 5.11397779e-01 1.61232427e-01 4.69639897e-01
2.27214880e-02 -4.65604484e-01 -5.22519290e-01 -8.75874460e-01
-5.85673414e-02 -2.00011536e-01 -1.11996621e-01 -3.80119562... | [13.610480308532715, 1.659234881401062] |
7e8ad3fc-8cb3-462b-9c70-a1f10de83bac | verbal-valency-frame-detection-and-selection | null | null | https://aclanthology.org/W14-2902 | https://aclanthology.org/W14-2902.pdf | Verbal Valency Frame Detection and Selection in Czech and English | null | ["Zde{\\v{n}}ka Ure{\\v{s}}ov{\\'a}", 'Jan Haji{\\v{c}}', 'Ond{\\v{r}}ej Du{\\v{s}}ek'] | 2014-06-01 | null | null | null | ws-2014-6 | ['predicate-detection'] | ['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... | [-7.397832870483398, 3.8665268421173096] |
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