paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4f802da1-9151-4031-92cd-3411e8e08a47 | active-stacking-for-heart-rate-estimation | 1903.10862 | null | http://arxiv.org/abs/1903.10862v1 | http://arxiv.org/pdf/1903.10862v1.pdf | Active Stacking for Heart Rate Estimation | Heart rate estimation from electrocardiogram signals is very important for
the early detection of cardiovascular diseases. However, due to large
individual differences and varying electrocardiogram signal quality, there does
not exist a single reliable estimation algorithm that works well on all
subjects. Every algorit... | ['Chengyu Liu', 'Feifei Liu', 'Dongrui Wu'] | 2019-03-26 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 3.06838661e-01 -2.33330384e-01 -4.44546402e-01 -5.36448956e-01
-9.73249912e-01 -2.38237053e-01 -1.42270371e-01 3.78942400e-01
-1.96145386e-01 9.32173669e-01 -4.09274846e-01 -2.08007723e-01
-1.80446461e-01 -4.63302940e-01 5.81413023e-02 -9.51314926e-01
-2.02174738e-01 3.25789452e-01 -8.07011500e-02 2.55303532... | [14.283777236938477, 3.296741485595703] |
f30fb240-fae5-4b7e-8a3b-5fdcab4f2e7c | conditional-temporal-variational-autoencoder | 2108.05658 | null | https://arxiv.org/abs/2108.05658v1 | https://arxiv.org/pdf/2108.05658v1.pdf | Conditional Temporal Variational AutoEncoder for Action Video Prediction | To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video. This paper proposes an Action Conditional Temporal Variational AutoEncoder (ACT-VAE) to improve motion prediction accuracy and capture movement diversity. ACT-VAE predi... | ['Jiaya Jia', 'Bei Yu', 'LiWei Wang', 'Yi Wang', 'Xiaogang Xu'] | 2021-08-12 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.19841778e-01 8.42986032e-02 -2.26029024e-01 -5.51374704e-02
-5.78083932e-01 -1.51180789e-01 7.72029638e-01 -8.84168744e-01
1.54978465e-02 5.89242637e-01 6.33475184e-01 1.45456240e-01
2.90252000e-01 -7.07149267e-01 -9.99723077e-01 -5.72028995e-01
1.65962160e-01 4.02728587e-01 4.77903336e-01 -2.75975645... | [7.3759236335754395, -0.07743725180625916] |
e6897efb-9fab-4524-aeae-735267de6e4c | leveraging-unsupervised-and-weakly-supervised | 2203.13339 | null | https://arxiv.org/abs/2203.13339v2 | https://arxiv.org/pdf/2203.13339v2.pdf | Leveraging unsupervised and weakly-supervised data to improve direct speech-to-speech translation | End-to-end speech-to-speech translation (S2ST) without relying on intermediate text representations is a rapidly emerging frontier of research. Recent works have demonstrated that the performance of such direct S2ST systems is approaching that of conventional cascade S2ST when trained on comparable datasets. However, i... | ['Nobuyuki Morioka', 'Alexis Conneau', 'Yu Zhang', 'Colin Cherry', 'Ankur Bapna', 'Yifan Ding', 'Ye Jia'] | 2022-03-24 | null | null | null | null | ['speech-to-speech-translation'] | ['speech'] | [ 2.73363650e-01 2.02143028e-01 -4.08301055e-01 -4.03477311e-01
-1.69314790e+00 -5.72020233e-01 7.70006001e-01 -2.20447391e-01
-3.58110040e-01 8.20033073e-01 5.17370582e-01 -7.55467892e-01
6.47291541e-01 -1.73954576e-01 -7.99348474e-01 -4.17382240e-01
4.43362594e-01 7.12685287e-01 1.01278849e-01 -7.94273496... | [14.52054500579834, 7.1479902267456055] |
0dfe5a17-348a-4b10-94e2-c37a9b4815ce | supervised-hierarchical-clustering-using | 2302.12716 | null | https://arxiv.org/abs/2302.12716v1 | https://arxiv.org/pdf/2302.12716v1.pdf | Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization | Conventional methods for speaker diarization involve windowing an audio file into short segments to extract speaker embeddings, followed by an unsupervised clustering of the embeddings. This multi-step approach generates speaker assignments for each segment. In this paper, we propose a novel Supervised HierArchical gRa... | ['Sriram Ganapathy', 'Amrit Kaul', 'Prachi Singh'] | 2023-02-24 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [ 1.38209239e-01 3.62869084e-01 1.15667075e-01 -6.36500895e-01
-8.36086988e-01 -4.06252980e-01 2.67174274e-01 3.20792675e-01
-2.92230517e-01 -4.32947204e-02 4.91151839e-01 -1.55169874e-01
-3.59215848e-02 -5.76699376e-01 -3.68138969e-01 -9.04417694e-01
-4.48098660e-01 5.67047358e-01 2.29226068e-01 2.04218999... | [14.420991897583008, 6.150274276733398] |
a787af7d-4dd5-411e-8f03-29cb551d7518 | multimodal-across-domains-gaze-target | 2208.10822 | null | https://arxiv.org/abs/2208.10822v1 | https://arxiv.org/pdf/2208.10822v1.pdf | Multimodal Across Domains Gaze Target Detection | This paper addresses the gaze target detection problem in single images captured from the third-person perspective. We present a multimodal deep architecture to infer where a person in a scene is looking. This spatial model is trained on the head images of the person-of- interest, scene and depth maps representing rich... | ['Elisa Ricci', 'Cigdem Beyan', 'Francesco Tonini'] | 2022-08-23 | null | null | null | null | ['gaze-target-estimation', 'gaze-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.26073346e-01 -1.68863870e-02 1.51473470e-02 -6.12886965e-01
-6.80873334e-01 -7.77963817e-01 6.13565147e-01 -2.99421638e-01
-4.70049232e-01 4.59836543e-01 2.52109617e-01 1.98243663e-01
1.48025915e-01 -1.55336648e-01 -8.47065091e-01 -7.47157395e-01
3.34747136e-01 1.03171706e-01 1.13659292e-01 -1.47660166... | [14.094762802124023, 0.05817781016230583] |
6af27df4-4045-4c0a-a4ba-bd5d1e2c3978 | contour-detection-in-unstructured-3d-point | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Hackel_Contour_Detection_in_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Hackel_Contour_Detection_in_CVPR_2016_paper.pdf | Contour Detection in Unstructured 3D Point Clouds | We describe a method to automatically detect contours, i.e. lines along which the surface orientation sharply changes, in large-scale outdoor point clouds. Contours are important intermediate features for structuring point clouds and converting them into high-quality surface or solid models, and are extensively used in... | ['Jan D. Wegner', 'Timo Hackel', 'Konrad Schindler'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['line-detection', 'contour-detection'] | ['computer-vision', 'computer-vision'] | [ 3.41748714e-01 3.69131193e-02 -1.28874451e-01 -3.45072776e-01
-6.18373632e-01 -8.15147340e-01 4.19572592e-01 5.70961833e-01
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-3.94038618e-01 7.59627759e-01 8.39724183e-01 -1.10959940... | [7.998084545135498, -3.0423715114593506] |
39b5d65f-2052-4d45-a3d1-5ffe2ac596d9 | mindgames-targeting-theory-of-mind-in-large | 2305.03353 | null | https://arxiv.org/abs/2305.03353v1 | https://arxiv.org/pdf/2305.03353v1.pdf | MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic | Theory of Mind (ToM) is a critical component of intelligence, yet accurately measuring it continues to be a subject of debate. Prior research has attempted to apply human ToM assessments to natural language processing models using either human-created standardized tests or rule-based templates. However, these methods p... | ['Antoine Lernould', 'Damien Sileo'] | 2023-05-05 | null | null | null | null | ['epistemic-reasoning'] | ['miscellaneous'] | [-1.44569904e-01 5.97861886e-01 -1.96157977e-01 -3.10749590e-01
-6.02336287e-01 -4.43205267e-01 6.55716836e-01 2.19693363e-01
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-3.62138361e-01 -8.43143225e-01 -3.89345378e-01 -1.37906373e-02
3.51877958e-01 8.10928285e-01 -2.42752172e-02 -2.72113532... | [9.655898094177246, 7.449678897857666] |
b347b789-6fca-464e-942a-a70b5682b335 | chae-fine-grained-controllable-story | 2210.05221 | null | https://arxiv.org/abs/2210.05221v1 | https://arxiv.org/pdf/2210.05221v1.pdf | CHAE: Fine-Grained Controllable Story Generation with Characters, Actions and Emotions | Story generation has emerged as an interesting yet challenging NLP task in recent years. Some existing studies aim at generating fluent and coherent stories from keywords and outlines; while others attempt to control the global features of the story, such as emotion, style and topic. However, these works focus on coars... | ['Shanlin Zhou', 'Zhihua Wei', 'Han Jiang', 'Xinpeng Wang'] | 2022-10-11 | null | https://aclanthology.org/2022.coling-1.559 | https://aclanthology.org/2022.coling-1.559.pdf | coling-2022-10 | ['story-generation'] | ['natural-language-processing'] | [-4.75008339e-02 2.47288078e-01 -2.82175034e-01 -3.01716417e-01
-2.05818862e-01 -7.31132567e-01 9.28221703e-01 -1.35678556e-02
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8.54492188e-02 -9.30619776e-01 -4.16885495e-01 -4.20641601e-01
4.65680540e-01 3.28558743e-01 1.55259714e-01 -5.43662071... | [11.757243156433105, 8.843435287475586] |
26298307-01d0-4e08-ac3a-bdfa5c3346b6 | logicllm-exploring-self-supervised-logic | 2305.13718 | null | https://arxiv.org/abs/2305.13718v2 | https://arxiv.org/pdf/2305.13718v2.pdf | LogicLLM: Exploring Self-supervised Logic-enhanced Training for Large Language Models | Existing efforts to improve logical reasoning ability of language models have predominantly relied on supervised fine-tuning, hindering generalization to new domains and/or tasks. The development of Large Langauge Models (LLMs) has demonstrated the capacity of compressing abundant knowledge into a single proxy, enablin... | ['Nancy F. Chen', 'Zhengyuan Liu', 'Aixin Sun', 'Bosheng Ding', 'Shafiq Joty', 'Zhiyang Teng', 'Fangkai Jiao'] | 2023-05-23 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [-7.02133775e-02 3.47205222e-01 -3.68597090e-01 -5.75020254e-01
-7.80137062e-01 -4.85001922e-01 8.05547416e-01 -1.93655461e-01
-3.73679429e-01 7.76233077e-01 2.94238567e-01 -8.16658497e-01
1.21335862e-02 -7.47428417e-01 -9.35470819e-01 5.56788258e-02
-3.61435451e-02 4.23265278e-01 1.38074681e-01 -3.10131282... | [9.647929191589355, 7.405823230743408] |
f618d48a-5925-42b7-a8c3-3e7a27a21d5e | promptonomyvit-multi-task-prompt-learning | 2212.04821 | null | https://arxiv.org/abs/2212.04821v2 | https://arxiv.org/pdf/2212.04821v2.pdf | PromptonomyViT: Multi-Task Prompt Learning Improves Video Transformers using Synthetic Scene Data | Action recognition models have achieved impressive results by incorporating scene-level annotations, such as objects, their relations, 3D structure, and more. However, obtaining annotations of scene structure for videos requires a significant amount of effort to gather and annotate, making these methods expensive to tr... | ['Amir Globerson', 'Trevor Darrell', 'Ariel Shamir', 'Leonid Karlinsky', 'Assaf Arbelle', 'Elad Ben-Avraham', 'Ofir Abramovich', 'Roei Herzig'] | 2022-12-08 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 5.50193548e-01 1.21849090e-01 5.56616522e-02 -5.22762716e-01
-8.15624833e-01 -6.30795479e-01 6.88850105e-01 -1.31598324e-01
-1.60578102e-01 4.61459219e-01 3.28372627e-01 7.74000399e-03
1.03287056e-01 -5.16817093e-01 -1.09003687e+00 -4.05945271e-01
1.89698651e-01 4.51702476e-01 6.80063665e-01 -2.76924518... | [8.90475082397461, 0.5838494896888733] |
a9bd6fd8-4eaf-40ac-91c7-7423c59183e8 | glad-group-anomaly-detection-in-social-media | 1410.1940 | null | http://arxiv.org/abs/1410.1940v1 | http://arxiv.org/pdf/1410.1940v1.pdf | GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract | Traditional anomaly detection on social media mostly focuses on individual
point anomalies while anomalous phenomena usually occur in groups. Therefore it
is valuable to study the collective behavior of individuals and detect group
anomalies. Existing group anomaly detection approaches rely on the assumption
that the g... | ['Yu', 'QI', 'Xinran He', 'Yan Liu'] | 2014-10-07 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [-7.41356611e-02 -1.38212740e-01 2.40875646e-01 -3.25297177e-01
-4.20583151e-02 -3.36549997e-01 8.10265064e-01 8.29615235e-01
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-2.16277391e-01 -1.04102695e+00 -3.41413498e-01 -6.04231238e-01
-7.65759706e-01 4.67898458e-01 5.54292023e-01 6.41470961... | [7.452335834503174, 2.7296440601348877] |
5af453b0-ef2a-42e9-bbf5-b5e408d3636c | prior-induced-information-alignment-for-image | 2106.14439 | null | https://arxiv.org/abs/2106.14439v1 | https://arxiv.org/pdf/2106.14439v1.pdf | Prior-Induced Information Alignment for Image Matting | Image matting is an ill-posed problem that aims to estimate the opacity of foreground pixels in an image. However, most existing deep learning-based methods still suffer from the coarse-grained details. In general, these algorithms are incapable of felicitously distinguishing the degree of exploration between determini... | ['Xin Yang', 'Yong Tang and', 'Yu Qiao', 'Jiake Xie', 'Yuhao Liu'] | 2021-06-28 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.79365265e-01 -2.07626432e-01 3.01774353e-01 -3.06935549e-01
-5.01132786e-01 -5.92432544e-02 6.36367738e-01 -1.74480006e-01
-2.66827703e-01 6.09428465e-01 -2.43407279e-01 -1.20441224e-02
-1.44164205e-01 -9.46069539e-01 -8.12461555e-01 -1.16615248e+00
1.92849874e-01 4.94974494e-01 7.23134816e-01 2.09613889... | [10.664796829223633, -1.0590382814407349] |
2c0f1535-6756-433e-9f5f-d59bd00bddac | does-the-order-of-training-samples-matter | 2102.03554 | null | https://arxiv.org/abs/2102.03554v1 | https://arxiv.org/pdf/2102.03554v1.pdf | Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning | Recent advancements in data-to-text generation largely take on the form of neural end-to-end systems. Efforts have been dedicated to improving text generation systems by changing the order of training samples in a process known as curriculum learning. Past research on sequence-to-sequence learning showed that curriculu... | ['Vera Demberg', 'Hui-Syuan Yeh', 'Ernie Chang'] | 2021-02-06 | null | https://aclanthology.org/2021.eacl-main.61 | https://aclanthology.org/2021.eacl-main.61.pdf | eacl-2021-2 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 5.52064836e-01 1.76583961e-01 -1.50544420e-01 -4.87694949e-01
-8.12681437e-01 -6.10336483e-01 8.28967929e-01 3.37501675e-01
-6.41311347e-01 9.27908897e-01 4.75564361e-01 -4.69555140e-01
1.75498366e-01 -7.61790216e-01 -6.19448304e-01 -3.17591608e-01
2.80427605e-01 7.70200849e-01 5.76051027e-02 -5.74350655... | [11.858701705932617, 9.026329040527344] |
555f5d50-b415-48c4-9dc3-455c8a4eabc3 | image-augmentation-with-conformal-mappings | 2212.05258 | null | https://arxiv.org/abs/2212.05258v1 | https://arxiv.org/pdf/2212.05258v1.pdf | Image augmentation with conformal mappings for a convolutional neural network | For augmentation of the square-shaped image data of a convolutional neural network (CNN), we introduce a new method, in which the original images are mapped onto a disk with a conformal mapping, rotated around the center of this disk and mapped under such a M\"obius transformation that preserves the disk, and then mapp... | ['Riku Klén', 'Matti Vuorinen', 'Mohamed M. S. Nasser', 'Oona Rainio'] | 2022-12-10 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 3.86078745e-01 9.06656206e-01 3.68784547e-01 -3.16458344e-01
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3.36812027e-02 4.59868610e-01 4.80465293e-01 -2.48026401... | [9.232810020446777, 2.312486410140991] |
0142572a-70a0-4aad-a962-42b5c6ec0628 | dynamics-aware-adversarial-attack-of-3d | 2112.09428 | null | https://arxiv.org/abs/2112.09428v2 | https://arxiv.org/pdf/2112.09428v2.pdf | Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network | In this paper, we investigate the dynamics-aware adversarial attack problem in deep neural networks. Most existing adversarial attack algorithms are designed under a basic assumption -- the network architecture is fixed throughout the attack process. However, this assumption does not hold for many recently proposed net... | ['Jiwen Lu', 'Jie zhou', 'Haowen Sun', 'Pengliang Ji', 'Ziyi Wu', 'He Wang', 'Yueqi Duan', 'An Tao'] | 2021-12-17 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-1.66227624e-01 -1.40046597e-01 2.58815251e-02 -2.28607282e-01
-2.46441327e-02 -9.10232484e-01 4.04548049e-01 -4.75532889e-01
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-2.75725216e-01 3.53411138e-01 8.69939566e-01 -4.01860654... | [5.567728519439697, 7.886961936950684] |
e989c4c9-cea0-41c3-ab39-35127cc3e4d1 | insertionnet-2-0-minimal-contact-multi-step | 2203.01153 | null | https://arxiv.org/abs/2203.01153v1 | https://arxiv.org/pdf/2203.01153v1.pdf | InsertionNet 2.0: Minimal Contact Multi-Step Insertion Using Multimodal Multiview Sensory Input | We address the problem of devising the means for a robot to rapidly and safely learn insertion skills with just a few human interventions and without hand-crafted rewards or demonstrations. Our InsertionNet version 2.0 provides an improved technique to robustly cope with a wide range of use-cases featuring different sh... | ['Dotan Di Castro', 'Vladimir Tchuiev', 'Oren Spector'] | 2022-03-02 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 0.21884915 0.02903462 -0.06221942 -0.10276025 -0.7377117 -0.48379192
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0.4431591 -0.79125196 0.43466803 0.45906407 -1.8359853 0.06966661
0.993759 0.94593513 0.7... | [4.62382173538208, 0.8026793599128723] |
fc6d591f-8f65-47f4-b6d1-7c059bbb54cd | time-to-green-predictions-for-fully-actuated | 2208.11344 | null | https://arxiv.org/abs/2208.11344v1 | https://arxiv.org/pdf/2208.11344v1.pdf | Time-to-Green predictions for fully-actuated signal control systems with supervised learning | Recently, efforts have been made to standardize signal phase and timing (SPaT) messages. These messages contain signal phase timings of all signalized intersection approaches. This information can thus be used for efficient motion planning, resulting in more homogeneous traffic flows and uniform speed profiles. Despite... | ['Anastasios Kouvelas', 'Monica Menendez', 'Lukas Ambühl', 'Kaidi Yang', 'Michail A. Makridis', 'Alexander Genser'] | 2022-08-24 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 3.91601920e-01 -2.95100689e-01 -9.02534187e-01 -7.66694784e-01
-9.76695836e-01 5.36211953e-02 5.74946642e-01 -5.44726476e-02
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-1.96324632e-01 -8.73799205e-01 -4.65865433e-01 -3.65660906e-01
-3.95385712e-01 6.25366151e-01 6.29569650e-01 -4.92319077... | [5.753003120422363, 1.0844441652297974] |
a72034cd-2016-4f5b-8d2e-3f6a6618987e | nicts-unsupervised-neural-and-statistical | null | null | https://aclanthology.org/W19-5330 | https://aclanthology.org/W19-5330.pdf | NICT's Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task | This paper presents the NICT{'}s participation in the WMT19 unsupervised news translation task. We participated in the unsupervised translation direction: German-Czech. Our primary submission to the task is the result of a simple combination of our unsupervised neural and statistical machine translation systems. Our sy... | ['Rui Wang', 'Masao Utiyama', 'Kehai Chen', 'Atsushi Fujita', 'Haipeng Sun', 'Benjamin Marie', 'Eiichiro Sumita'] | 2019-08-01 | null | null | null | ws-2019-8 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 1.46885708e-01 2.50273287e-01 -2.92038649e-01 -4.70536560e-01
-1.46854937e+00 -7.72402763e-01 9.60359454e-01 2.14800891e-02
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1.41173610e-02 -3.45101744e-01 -5.40456414e-01 -3.49216729e-01
4.65364605e-01 1.24538255e+00 -3.10025960e-01 -5.71365058... | [11.556175231933594, 10.392269134521484] |
7b783119-370d-446b-83bf-11a2587895aa | se-ornet-self-ensembling-orientation-aware | 2304.05395 | null | https://arxiv.org/abs/2304.05395v1 | https://arxiv.org/pdf/2304.05395v1.pdf | SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence | Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some animals have bilateral symmetry and various orientations, which lead to severe mispredictions of symmetrical parts. Besides, point cloud noise... | ['Zhe Zhang', 'Jiyang Yu', 'Tianzhu Zhang', 'Jianfeng He', 'Jiahao Lu', 'Chuxin Wang', 'Jiacheng Deng'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Deng_SE-ORNet_Self-Ensembling_Orientation-Aware_Network_for_Unsupervised_Point_Cloud_Shape_Correspondence_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Deng_SE-ORNet_Self-Ensembling_Orientation-Aware_Network_for_Unsupervised_Point_Cloud_Shape_Correspondence_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-dense-shape-correspondence'] | ['computer-vision'] | [-2.40132123e-01 1.46163687e-01 4.63059247e-02 -6.15891635e-01
-1.75091714e-01 -5.33070624e-01 4.76193666e-01 2.54096538e-01
-5.64059056e-02 2.35081285e-01 -3.23065728e-01 5.26093468e-02
-8.90959799e-02 -7.92735577e-01 -9.66289103e-01 -4.92280453e-01
2.72616625e-01 9.64696527e-01 3.67605180e-01 -2.34867662... | [8.046256065368652, -3.363560199737549] |
f82eb974-dd46-48ec-ab8d-0fbd216d2f6d | transformer-based-visual-segmentation-a | 2304.09854 | null | https://arxiv.org/abs/2304.09854v2 | https://arxiv.org/pdf/2304.09854v2.pdf | Transformer-Based Visual Segmentation: A Survey | Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strid... | ['Chen Change Loy', 'Ziwei Liu', 'Kai Chen', 'Guangliang Cheng', 'Jiangmiao Pang', 'Haobo Yuan', 'Wenwei Zhang', 'Henghui Ding', 'Xiangtai Li'] | 2023-04-19 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 3.76853466e-01 -4.47952263e-02 -2.92477757e-01 -3.97762299e-01
-7.32058227e-01 -5.42143881e-01 2.14142099e-01 -2.75854170e-02
-2.70459890e-01 1.20517910e-01 -2.45697215e-01 -3.95668596e-01
1.76407397e-01 -5.73965132e-01 -6.56634629e-01 -6.03401721e-01
1.16886690e-01 5.04667163e-01 4.28482920e-01 2.12582462... | [9.470132827758789, 0.09020749479532242] |
f4d26d3f-1492-46bf-9ef0-6880f7454bf2 | efficient-eigen-updating-for-spectral-graph | 1301.1318 | null | http://arxiv.org/abs/1301.1318v4 | http://arxiv.org/pdf/1301.1318v4.pdf | Efficient Eigen-updating for Spectral Graph Clustering | Partitioning a graph into groups of vertices such that those within each
group are more densely connected than vertices assigned to different groups,
known as graph clustering, is often used to gain insight into the organisation
of large scale networks and for visualisation purposes. Whereas a large number
of dedicated... | ['Stéphan Clémençon', 'Romaric Gaudel', 'Charanpal Dhanjal'] | 2013-01-07 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 2.02065676e-01 2.08337218e-01 6.91211596e-02 2.71890372e-01
1.54880121e-01 -8.56401801e-01 5.12661338e-01 4.69965130e-01
-9.42587033e-02 4.38680023e-01 -4.64744791e-02 -4.94359285e-01
-8.21118534e-01 -7.23611832e-01 -3.27277005e-01 -7.34248340e-01
-6.74025834e-01 6.35050654e-01 3.71048331e-01 -5.52992858... | [7.057108402252197, 5.250741958618164] |
f30e6af8-b9fd-4cca-adca-eab4e9edd098 | linking-garment-with-person-via-semantically | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yan_Linking_Garment_With_Person_via_Semantically_Associated_Landmarks_for_Virtual_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yan_Linking_Garment_With_Person_via_Semantically_Associated_Landmarks_for_Virtual_CVPR_2023_paper.pdf | Linking Garment With Person via Semantically Associated Landmarks for Virtual Try-On | In this paper, a novel virtual try-on algorithm, dubbed SAL-VTON, is proposed, which links the garment with the person via semantically associated landmarks to alleviate misalignment. The semantically associated landmarks are a series of landmark pairs with the same local semantics on the in-shop garment image and ... | ['Chengjun Xie', 'HUI ZHANG', 'Tingwei Gao', 'Keyu Yan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['virtual-try-on'] | ['computer-vision'] | [-3.52029830e-01 -4.25427184e-02 -2.36921757e-01 -2.51592398e-01
-5.11285365e-01 -4.47398096e-01 5.35649538e-01 -2.03550726e-01
6.33962974e-02 2.36342147e-01 4.92838353e-01 3.88162613e-01
-1.40915841e-01 -6.85137272e-01 -5.55332720e-01 -6.62447512e-01
2.98303012e-02 5.97518623e-01 1.05367221e-01 -3.21903020... | [11.876921653747559, -0.8745721578598022] |
e8d9ab76-f6a0-461e-894c-56f417b5a59b | structural-stability-of-infinite-order | 1911.08637 | null | https://arxiv.org/abs/1911.08637v4 | https://arxiv.org/pdf/1911.08637v4.pdf | Robust Inference on Infinite and Growing Dimensional Time Series Regression | We develop a class of tests for time series models such as multiple regression with growing dimension, infinite-order autoregression and nonparametric sieve regression. Examples include the Chow test and general linear restriction tests of growing rank $p$. Employing such increasing $p$ asymptotics, we introduce a new ... | ['Myung Hwan Seo', 'Abhimanyu Gupta'] | 2019-11-20 | null | null | null | null | ['time-series-regression'] | ['time-series'] | [-2.40860984e-01 -2.75574446e-01 -5.94748974e-01 -3.00851703e-01
-7.67107844e-01 -6.99857116e-01 5.14839053e-01 -2.15023607e-01
-4.15635407e-01 1.09749007e+00 -1.31505728e-03 -1.07356000e+00
-6.43853724e-01 -9.33140635e-01 -6.59279525e-01 -5.29741228e-01
-7.07762778e-01 5.16015440e-02 8.53364915e-02 1.32618830... | [6.3974289894104, 4.217240810394287] |
649c89cf-d62a-4951-b667-4f25e9de6fc3 | table-to-text-generation-by-structure-aware | 1711.09724 | null | http://arxiv.org/abs/1711.09724v1 | http://arxiv.org/pdf/1711.09724v1.pdf | Table-to-text Generation by Structure-aware Seq2seq Learning | Table-to-text generation aims to generate a description for a factual table
which can be viewed as a set of field-value records. To encode both the content
and the structure of a table, we propose a novel structure-aware seq2seq
architecture which consists of field-gating encoder and description generator
with dual att... | ['Lei Sha', 'Baobao Chang', 'Zhifang Sui', 'Tianyu Liu', 'Kexiang Wang'] | 2017-11-27 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 3.02357674e-01 4.32438672e-01 -1.97919935e-01 -4.16679978e-01
-9.70195472e-01 -6.33374751e-01 5.71669579e-01 4.47582334e-01
-4.65610325e-02 1.34806526e+00 9.39086854e-01 -2.30383910e-02
2.48646840e-01 -1.08825529e+00 -1.02813804e+00 -4.61436898e-01
2.20868662e-01 5.62648058e-01 -1.54362589e-01 -4.92948413... | [11.608440399169922, 8.776432037353516] |
b0b79a40-40c2-49f0-99cc-1567572366fa | bunji-at-semeval-2017-task-3-combination-of | null | null | https://aclanthology.org/S17-2058 | https://aclanthology.org/S17-2058.pdf | bunji at SemEval-2017 Task 3: Combination of Neural Similarity Features and Comment Plausibility Features | This paper describes a text-ranking system developed by bunji team in SemEval-2017 Task 3: Community Question Answering, Subtask A and C. The goal of the task is to re-rank the comments in a question-and-answer forum such that useful comments for answering the question are ranked high. We proposed a method that combine... | ['Yuta Koreeda', 'Takuya Hashito', 'Yoshiki Niwa', 'Misa Sato', 'Kohsuke Yanai', 'Toshihiko Yanase', 'Kenzo Kurotsuchi'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [-2.26177365e-01 5.49308062e-01 3.60754952e-02 -5.75038373e-01
-1.17820263e+00 -5.00288129e-01 9.56063151e-01 9.80946600e-01
-5.34941077e-01 9.08814609e-01 9.91742551e-01 -3.22402447e-01
-2.95175523e-01 -3.76654148e-01 -4.17083859e-01 1.47687152e-01
1.85443938e-01 6.70018017e-01 6.98265016e-01 -3.81129086... | [11.4056396484375, 8.004109382629395] |
52d048b4-02ae-40be-a437-3f9c03d22a57 | a-free-lunch-from-vit-adaptive-attention | 2110.01240 | null | https://arxiv.org/abs/2110.01240v2 | https://arxiv.org/pdf/2110.01240v2.pdf | A free lunch from ViT:Adaptive Attention Multi-scale Fusion Transformer for Fine-grained Visual Recognition | Learning subtle representation about object parts plays a vital role in fine-grained visual recognition (FGVR) field. The vision transformer (ViT) achieves promising results on computer vision due to its attention mechanism. Nonetheless, with the fixed size of patches in ViT, the class token in deep layer focuses on th... | ['Weiqian Chen', 'Feng Ling', 'Zhiyi Wang', 'Xiangcheng Liu', 'Ling Zhang', 'Jian Cao', 'Yuan Zhang'] | 2021-10-04 | null | null | null | null | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-5.49191516e-03 -1.70072690e-02 -1.78126171e-01 -4.74405438e-01
-7.84387827e-01 -3.74365866e-01 6.82387054e-01 -3.07000488e-01
-3.31200838e-01 4.13327605e-01 5.08060753e-01 1.50160640e-01
1.98401794e-01 -6.94862068e-01 -1.02568543e+00 -4.99996752e-01
3.57646108e-01 2.28477135e-01 5.45354903e-01 -7.34754056... | [9.580404281616211, 2.0093352794647217] |
c34e6b10-4546-45c5-8b55-17f2f77faffc | adaptive-ensemble-q-learning-minimizing-1 | 2306.11918 | null | https://arxiv.org/abs/2306.11918v1 | https://arxiv.org/pdf/2306.11918v1.pdf | Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback | The ensemble method is a promising way to mitigate the overestimation issue in Q-learning, where multiple function approximators are used to estimate the action values. It is known that the estimation bias hinges heavily on the ensemble size (i.e., the number of Q-function approximators used in the target), and that de... | ['Junshan Zhang', 'Sen Lin', 'Hang Wang'] | 2023-06-20 | adaptive-ensemble-q-learning-minimizing | http://proceedings.neurips.cc/paper/2021/hash/cfa45151ccad6bf11ea146ed563f2119-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/cfa45151ccad6bf11ea146ed563f2119-Paper.pdf | neurips-2021-12 | ['q-learning'] | ['methodology'] | [ 3.58436368e-02 -2.90916353e-01 -1.75572038e-01 -4.11466882e-02
-7.74345875e-01 -5.90866268e-01 1.99530214e-01 1.09112076e-01
-3.96653801e-01 9.52031374e-01 -1.61785722e-01 -4.38640326e-01
-4.11066473e-01 -6.78209186e-01 -6.04267955e-01 -1.10839593e+00
8.33560079e-02 1.99145768e-02 -2.78909337e-02 -4.88309175... | [4.214423656463623, 2.408907651901245] |
419870f9-cb77-4219-ae38-c69a67eb0d58 | chatgpt-vs-human-authored-text-insights-into | 2306.07799 | null | https://arxiv.org/abs/2306.07799v1 | https://arxiv.org/pdf/2306.07799v1.pdf | ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer | Large-scale language models, like ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short natural language prompts. In this paper, we aim to conduct a systematic inspection of ChatGPT's performance in two controllable generation ta... | ['Vera Demberg', 'Dongqi Pu'] | 2023-06-13 | null | null | null | null | ['style-transfer', 'text-summarization'] | ['computer-vision', 'natural-language-processing'] | [-8.60466883e-02 4.35383022e-01 1.72208428e-01 -1.85173392e-01
-7.69177556e-01 -8.53061438e-01 1.03896582e+00 2.31003195e-01
-2.68154472e-01 7.32367754e-01 7.68029332e-01 -3.21539372e-01
2.63054729e-01 -4.34098452e-01 -1.70453370e-01 -2.15893552e-01
5.32974064e-01 7.63713717e-01 -8.76634642e-02 -5.45764446... | [11.768999099731445, 8.947772026062012] |
cba50433-edab-47ba-abae-a6991030c8af | multidimensional-evaluation-for-text-style | 2304.13462 | null | https://arxiv.org/abs/2304.13462v1 | https://arxiv.org/pdf/2304.13462v1.pdf | Multidimensional Evaluation for Text Style Transfer Using ChatGPT | We investigate the potential of ChatGPT as a multidimensional evaluator for the task of \emph{Text Style Transfer}, alongside, and in comparison to, existing automatic metrics as well as human judgements. We focus on a zero-shot setting, i.e. prompting ChatGPT with specific task instructions, and test its performance o... | ['Malvina Nissim', 'Antonio Toral', 'Huiyuan Lai'] | 2023-04-26 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 2.11283088e-01 5.20529523e-02 -2.46859007e-02 -3.14479321e-01
-9.82322633e-01 -9.24877107e-01 9.00300622e-01 1.54598475e-01
-5.91864228e-01 5.24006009e-01 7.87891448e-01 -2.66863227e-01
-2.00462788e-02 -3.20654064e-01 1.69211984e-01 -2.17040092e-01
5.63079715e-01 8.42822433e-01 -5.54255843e-02 -4.20733899... | [11.481846809387207, 9.649511337280273] |
17164e0e-2366-4de0-9a1b-a7991613f6be | overflow-putting-flows-on-top-of-neural | 2211.06892 | null | https://arxiv.org/abs/2211.06892v2 | https://arxiv.org/pdf/2211.06892v2.pdf | OverFlow: Putting flows on top of neural transducers for better TTS | Neural HMMs are a type of neural transducer recently proposed for sequence-to-sequence modelling in text-to-speech. They combine the best features of classic statistical speech synthesis and modern neural TTS, requiring less data and fewer training updates, and are less prone to gibberish output caused by neural attent... | ['Gustav Eje Henter', 'Éva Székely', 'Jonas Beskow', 'Harm Lameris', 'Ambika Kirkland', 'Shivam Mehta'] | 2022-11-13 | null | null | null | null | ['normalising-flows', 'text-to-speech-synthesis'] | ['methodology', 'speech'] | [-2.54145619e-02 1.69158012e-01 2.49816738e-02 -5.06346285e-01
-1.04249001e+00 -4.45211619e-01 3.42493504e-01 -2.04306111e-01
-1.83636874e-01 6.92898750e-01 3.03155899e-01 -8.55485678e-01
3.52876902e-01 -1.20373711e-01 -6.47037983e-01 -7.07009912e-01
1.10568486e-01 4.36793566e-01 4.54328567e-01 4.50539626... | [14.807901382446289, 6.598591327667236] |
b9884956-e6f2-4f5d-8bf6-5b0b39f66231 | benchmarking-person-re-identification-1 | 2212.09981 | null | https://arxiv.org/abs/2212.09981v1 | https://arxiv.org/pdf/2212.09981v1.pdf | Benchmarking person re-identification datasets and approaches for practical real-world implementations | Recently, Person Re-Identification (Re-ID) has received a lot of attention. Large datasets containing labeled images of various individuals have been released, allowing researchers to develop and test many successful approaches. However, when such Re-ID models are deployed in new cities or environments, the task of sea... | ['Joris Guerin', 'Esteban Clua', 'Luigy Machaca', 'Felix O. Sumari', 'Jose Huaman'] | 2022-12-20 | null | null | null | null | ['person-re-identification', 'pedestrian-detection'] | ['computer-vision', 'computer-vision'] | [-9.07071307e-02 -3.33425939e-01 1.21960059e-01 -6.49846852e-01
-1.10137247e-01 -6.88979506e-01 6.76687539e-01 3.06438297e-01
-6.84008420e-01 6.27224565e-01 2.49082536e-01 2.57045388e-01
1.64900482e-01 -6.90027297e-01 -4.09599394e-01 -4.94232178e-01
1.15870712e-02 6.83332205e-01 2.24618852e-01 -5.36175929... | [14.526824951171875, 1.039085030555725] |
e9d16df4-ddad-4db8-bf23-1e96c4625c2e | stance-detection-and-open-research-avenues | 2210.12383 | null | https://arxiv.org/abs/2210.12383v1 | https://arxiv.org/pdf/2210.12383v1.pdf | Stance Detection and Open Research Avenues | This tutorial aims to cover the state-of-the-art on stance detection and address open research avenues for interested researchers and practitioners. Stance detection is a recent research topic where the stance towards a given target or target set is determined based on the given content and there are significant applic... | ['Fazli Can', 'Dilek Küçük'] | 2022-10-22 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 2.63600081e-01 3.40626776e-01 -1.00471973e+00 -2.55538464e-01
-4.31865990e-01 -6.58952415e-01 7.19243884e-01 3.82874429e-01
-6.50418503e-03 8.72903287e-01 4.37526345e-01 -9.11612883e-02
-7.48314783e-02 -9.76153135e-01 1.81947544e-01 -6.12275064e-01
-9.04761031e-02 5.54461718e-01 3.58518749e-01 -7.24795341... | [8.932502746582031, 9.986995697021484] |
68fb76da-dcd6-4ee9-89de-baa4e407c4c4 | sleep-stage-classification-based-on-multi | 1711.00629 | null | http://arxiv.org/abs/1711.00629v1 | http://arxiv.org/pdf/1711.00629v1.pdf | Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device | This paper proposes a practical approach for automatic sleep stage
classification based on a multi-level feature learning framework and Recurrent
Neural Network (RNN) classifier using heart rate and wrist actigraphy derived
from a wearable device. The feature learning framework is designed to extract
low- and mid-level... | ['Eric I-Chao Chang', 'Yan Xu', 'Yubo Fan', 'Xin Zhang', 'Weixuan Kou', 'He Gao'] | 2017-11-02 | null | null | null | null | ['sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical'] | [ 3.39249641e-01 -2.95311213e-01 -3.04277092e-01 -4.35940385e-01
-2.35622421e-01 2.29713656e-02 5.82687892e-02 -1.91594839e-01
-6.78158700e-01 8.78810048e-01 2.99560815e-01 -9.23679769e-02
-1.63065448e-01 -5.35756409e-01 2.66100336e-02 -7.31158197e-01
-3.97364289e-01 -2.15220064e-01 -4.39712480e-02 -1.27388105... | [13.550385475158691, 3.469374895095825] |
3290ec5d-688e-4651-b3a0-406f047ee29a | a-commonsense-reasoning-framework-for | 2101.04017 | null | https://arxiv.org/abs/2101.04017v5 | https://arxiv.org/pdf/2101.04017v5.pdf | A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification | We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an explainable system for emotion attribution and recommendation. This system relies on a recently introduced commonsense reasoning framework, the TCL logic, which is based on a human-like procedure for the automatic generation of novel concepts in a Descr... | ['Rossana Damiano', 'Viviana Patti', 'Stefano Zoia', 'Gian Luca Pozzato', 'Antonio Lieto'] | 2021-01-11 | null | null | null | null | ['commonsense-knowledge-base-construction', 'causal-emotion-entailment', 'novel-concepts'] | ['knowledge-base', 'natural-language-processing', 'reasoning'] | [ 2.22074613e-01 8.48241448e-01 -1.69828549e-01 -5.55835426e-01
1.72346517e-01 -5.82127392e-01 9.24008667e-01 2.67488480e-01
1.24235637e-01 7.64910817e-01 3.25793028e-01 1.23997994e-01
-5.44532478e-01 -8.79091918e-01 -4.69164252e-01 -2.22432941e-01
2.07930394e-02 5.50978243e-01 -2.27664798e-01 -7.34225750... | [9.924309730529785, 7.4331207275390625] |
81e096d2-9607-456f-89a7-41e39ac21976 | structured-domain-adaptation-for-unsupervised | 2003.06650 | null | https://arxiv.org/abs/2003.06650v3 | https://arxiv.org/pdf/2003.06650v3.pdf | Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID | Unsupervised domain adaptation (UDA) aims at adapting the model trained on a labeled source-domain dataset to an unlabeled target-domain dataset. The task of UDA on open-set person re-identification (re-ID) is even more challenging as the identities (classes) do not have overlap between the two domains. One major resea... | ['Hongsheng Li', 'Xiaogang Wang', 'Rui Zhao', 'Dapeng Chen', 'Yixiao Ge', 'Feng Zhu'] | 2020-03-14 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 4.45313156e-01 1.17916808e-01 -2.63469577e-01 -7.00705171e-01
-8.12431455e-01 -5.19407988e-01 8.68950188e-01 -6.24079645e-01
-4.07508254e-01 1.01268554e+00 3.83472234e-01 3.65007967e-01
3.48936558e-01 -4.19757485e-01 -7.75729299e-01 -4.98246372e-01
5.53313673e-01 1.08620322e+00 -1.60314336e-01 -1.48100361... | [14.726445198059082, 1.0641043186187744] |
ca1ef739-c8a2-42db-980e-90d553db38ef | mathsf-g-2retro-two-step-graph-generative | 2206.04882 | null | https://arxiv.org/abs/2206.04882v3 | https://arxiv.org/pdf/2206.04882v3.pdf | $\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction | Retrosynthesis is a procedure where a target molecule is transformed into potential reactants and thus the synthesis routes can be identified. Recently, computational approaches have been developed to accelerate the design of synthesis routes. In this paper, we develop a generative framework $\mathsf{G^2Retro}$ for one... | ['Xia Ning', 'Huan Sun', 'James R. Fuchs', 'Oluwatosin R. Ayinde', 'Ziqi Chen'] | 2022-06-10 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 4.28300649e-01 1.61619306e-01 -3.80045772e-01 -3.65588404e-02
-4.18785930e-01 -1.05784726e+00 5.70188344e-01 3.29721212e-01
2.63574898e-01 8.54732275e-01 8.80013183e-02 -6.45617187e-01
2.55645383e-02 -1.13102794e+00 -8.78884375e-01 -9.16026890e-01
-1.96835831e-01 4.95195717e-01 2.60698736e-01 -4.07200247... | [4.501950740814209, 6.105088710784912] |
0187cbdf-182f-49d2-b019-5796e580d8be | meshnet-mesh-neural-network-for-3d-shape | 1811.11424 | null | http://arxiv.org/abs/1811.11424v1 | http://arxiv.org/pdf/1811.11424v1.pdf | MeshNet: Mesh Neural Network for 3D Shape Representation | Mesh is an important and powerful type of data for 3D shapes and widely
studied in the field of computer vision and computer graphics. Regarding the
task of 3D shape representation, there have been extensive research efforts
concentrating on how to represent 3D shapes well using volumetric grid,
multi-view and point cl... | ['Yutong Feng', 'Yue Gao', 'Yifan Feng', 'Xibin Zhao', 'Haoxuan You'] | 2018-11-28 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-representation'] | ['computer-vision', 'computer-vision'] | [-3.50410491e-01 -4.25235420e-01 -3.17593925e-02 -1.72775820e-01
-2.11971164e-01 -1.65033773e-01 4.05945003e-01 9.01070461e-02
9.17459205e-02 2.77769119e-01 -1.15305245e-01 -1.48362905e-01
-3.60318780e-01 -1.21456468e+00 -3.72645408e-01 -4.92346346e-01
9.41948369e-02 8.04949224e-01 2.78155506e-01 -2.96728998... | [8.118633270263672, -3.79814076423645] |
00bf78c4-88fd-407d-9bdf-55d8d3556f9c | spliceradar-a-learned-method-for-blind-image | 1906.11663 | null | https://arxiv.org/abs/1906.11663v1 | https://arxiv.org/pdf/1906.11663v1.pdf | SpliceRadar: A Learned Method For Blind Image Forensics | Detection and localization of image manipulations like splices are gaining in importance with the easy accessibility of image editing softwares. While detection generates a verdict for an image it provides no insight into the manipulation. Localization helps explain a positive detection by identifying the pixels of the... | ['Terrance E. Boult', 'Aurobrata Ghosh', 'Zheng Zhong', 'Maneesh Singh'] | 2019-06-27 | null | null | null | null | ['image-manipulation-detection', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 4.93707210e-01 -1.21519931e-01 -1.46198481e-01 -1.83792233e-01
-1.21643484e+00 -8.13690484e-01 4.85780686e-01 -7.39187449e-02
-3.67619127e-01 3.05191517e-01 -2.01839566e-01 -3.50279808e-01
4.09185678e-01 -4.07341599e-01 -1.33628631e+00 -7.46325970e-01
2.43765160e-01 1.87630594e-01 2.47914404e-01 4.25563395... | [12.32193374633789, 1.0213125944137573] |
549ad968-e600-4dea-8183-16430605e3d9 | pairwise-sequence-alignment-at-arbitrarily | 2207.12543 | null | https://arxiv.org/abs/2207.12543v1 | https://arxiv.org/pdf/2207.12543v1.pdf | Pairwise sequence alignment at arbitrarily large evolutionary distance | Ancestral sequence reconstruction is a key task in computational biology. It consists in inferring a molecular sequence at an ancestral species of a known phylogeny, given descendant sequences at the tip of the tree. In addition to its many biological applications, it has played a key role in elucidating the statistica... | ['Sebastien Roch', 'Brandon Legried'] | 2022-07-25 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 8.02523732e-01 -1.92205846e-01 -1.81122750e-01 -2.23493174e-01
-6.80871010e-01 -9.76090193e-01 2.18493044e-01 5.89373887e-01
-6.74229205e-01 1.09915352e+00 -4.61732633e-02 -6.71916485e-01
-1.30504638e-01 -4.65038478e-01 -8.33895802e-01 -1.13269281e+00
-3.03948671e-01 7.49709129e-01 3.65471214e-01 -1.45688191... | [4.843857288360596, 5.15723180770874] |
209a5163-d009-4974-9b85-8d6b2957fd3c | multimodality-and-dialogue-act-classification | null | null | https://aclanthology.org/W13-4031 | https://aclanthology.org/W13-4031.pdf | Multimodality and Dialogue Act Classification in the RoboHelper Project | null | ['Lin Chen', 'Barbara Di Eugenio'] | 2013-08-01 | null | null | null | ws-2013-8 | ['dialogue-act-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... | [-7.292131423950195, 3.794543504714966] |
8e24dead-f0f8-4836-b733-c593ef7e67b3 | referring-segmentation-in-images-and-videos | 2102.04762 | null | https://arxiv.org/abs/2102.04762v1 | https://arxiv.org/pdf/2102.04762v1.pdf | Referring Segmentation in Images and Videos with Cross-Modal Self-Attention Network | We consider the problem of referring segmentation in images and videos with natural language. Given an input image (or video) and a referring expression, the goal is to segment the entity referred by the expression in the image or video. In this paper, we propose a cross-modal self-attention (CMSA) module to utilize fi... | ['Yang Wang', 'Xiaoqin Zhang', 'Zhi Liu', 'Mrigank Rochan', 'Linwei Ye'] | 2021-02-09 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 3.28150272e-01 -1.07101195e-01 -4.06038791e-01 -4.73946214e-01
-8.36712956e-01 -2.42700607e-01 3.73014957e-01 -9.23779905e-02
-4.89756167e-01 4.74790305e-01 5.45574427e-01 3.15196842e-01
6.85053319e-02 -5.58095634e-01 -8.34076822e-01 -5.88100374e-01
1.93071678e-01 -2.28486121e-01 5.81340551e-01 -1.62793949... | [10.004650115966797, 0.8482334613800049] |
6196f4a2-f506-4157-9031-f5bc0f2ef522 | discriminative-models-still-outperform | null | null | https://openreview.net/forum?id=BB1pmTFXqOS | https://openreview.net/pdf?id=BB1pmTFXqOS | Discriminative Models Still Outperform Generative Models in Aspect Based Sentiment Analysis In Cross-Domain and Cross-Lingual Settings | Aspect-based Sentiment Analysis (ABSA) helps to explain customers' opinions towards products and services. In the past, ABSA models were discriminative, but more recently generative models have been used to generate aspects and polarities directly from text. In contrast, discriminative models first select aspects from... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.11595690e-01 1.12294026e-01 -4.22798991e-01 -9.31778312e-01
-1.01943243e+00 -1.08140934e+00 8.59358728e-01 6.35989606e-02
-1.55402794e-01 4.71854150e-01 6.08897865e-01 -4.42205340e-01
8.76108408e-02 -8.82974863e-01 -3.83437604e-01 -4.15589124e-01
5.96526980e-01 8.06239426e-01 -3.57285321e-01 -5.71802676... | [11.42342758178711, 6.722738742828369] |
9c9dad5d-91cb-4552-ad2e-3e68fc8bee43 | prompt-learning-to-mitigate-catastrophic | 2305.07393 | null | https://arxiv.org/abs/2305.07393v2 | https://arxiv.org/pdf/2305.07393v2.pdf | Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation | Dialogue systems for non-English languages have long been under-explored. In this paper, we take the first step to investigate few-shot cross-lingual transfer learning (FS-XLT) and multitask learning (MTL) in the context of open-domain dialogue generation for non-English languages with limited data. We observed catastr... | ['Jimmy Xiangji Huang', 'Lei Liu'] | 2023-05-12 | null | null | null | null | ['dialogue-generation', 'cross-lingual-transfer', 'dialogue-generation'] | ['natural-language-processing', 'natural-language-processing', 'speech'] | [-8.83100554e-02 3.68316650e-01 1.64583027e-02 -4.20103937e-01
-1.42111325e+00 -7.84974337e-01 8.47366333e-01 -1.37958914e-01
-6.86463058e-01 1.35721445e+00 4.32316273e-01 -7.02271104e-01
2.44074866e-01 -3.16721499e-01 -5.77706695e-01 -2.61054933e-01
1.05438791e-01 6.94792688e-01 1.92154035e-01 -7.10725069... | [12.382258415222168, 8.553850173950195] |
e795e292-77a5-4d93-a98a-df18ea0206b6 | passage-ranking-with-weak-supervsion | 1905.05910 | null | https://arxiv.org/abs/1905.05910v2 | https://arxiv.org/pdf/1905.05910v2.pdf | Passage Ranking with Weak Supervision | In this paper, we propose a \textit{weak supervision} framework for neural ranking tasks based on the data programming paradigm \citep{Ratner2016}, which enables us to leverage multiple weak supervision signals from different sources. Empirically, we consider two sources of weak supervision signals, unsupervised rankin... | ['Xiaofei Ma', 'Peng Xu', 'Bing Xiang', 'Ramesh Nallapati'] | 2019-05-15 | null | https://openreview.net/forum?id=S1ltj47xdE | https://openreview.net/pdf?id=S1ltj47xdE | iclr-workshop-lld-2019 | ['passage-ranking'] | ['natural-language-processing'] | [ 1.72669902e-01 -1.58878416e-02 -5.87113678e-01 -6.09636605e-01
-1.08167958e+00 -4.30168897e-01 1.12208366e+00 2.21981093e-01
-7.62684286e-01 7.70364702e-01 4.90205735e-01 -1.74189404e-01
-4.87382084e-01 -5.81626236e-01 -9.25364137e-01 -3.19375128e-01
-2.43719518e-02 8.49616051e-01 6.99855089e-01 -5.98401129... | [11.411267280578613, 7.530455589294434] |
1bf987c8-cd98-480a-82aa-7a7eef1cf225 | diffgrad-an-optimization-method-for | 1909.11015 | null | https://arxiv.org/abs/1909.11015v4 | https://arxiv.org/pdf/1909.11015v4.pdf | diffGrad: An Optimization Method for Convolutional Neural Networks | Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which a function has the steepest rate of change. The main problem with basic SGD is to change by equal sized steps for all parameters, irrespective of gradien... | ['Bidyut Baran Chaudhuri', 'Swalpa Kumar Roy', 'Snehasis Mukherjee', 'Soumendu Chakraborty', 'Satish Kumar Singh', 'Shiv Ram Dubey'] | 2019-09-12 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [-5.88741243e-01 -3.10973525e-01 1.45051211e-01 -6.41427100e-01
-2.28671581e-01 -4.49720562e-01 3.71422708e-01 1.34784114e-02
-9.79459107e-01 1.03102160e+00 -3.06055814e-01 -3.59038264e-01
2.15950739e-02 -7.05035329e-01 -7.47891963e-01 -9.76826191e-01
-5.45249842e-02 4.71101478e-02 1.29602551e-01 -4.03361768... | [7.7345685958862305, 3.634995222091675] |
4fe4c629-6d43-4406-8b83-131b5f5427bb | deep-movement-primitives-toward-breast-cancer | 2202.09265 | null | https://arxiv.org/abs/2202.09265v1 | https://arxiv.org/pdf/2202.09265v1.pdf | Deep Movement Primitives: toward Breast Cancer Examination Robot | Breast cancer is the most common type of cancer worldwide. A robotic system performing autonomous breast palpation can make a significant impact on the related health sector worldwide. However, robot programming for breast palpating with different geometries is very complex and unsolved. Robot learning from demonstrati... | ['Amir M. Ghalamzan E.', 'Kiyanoush Nazari', 'Pablo C. Lopez-Custodio', 'Muhammad Arshad Khan', 'Giorgio Bonvicini', 'Oluwatoyin Sanni'] | 2022-02-14 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 6.34688661e-02 4.95256603e-01 -2.64835775e-01 -2.43455067e-01
-4.59321707e-01 -4.16889995e-01 1.74074635e-01 1.82411686e-01
-2.23439083e-01 4.86333728e-01 -5.23980021e-01 -5.87315500e-01
-4.70926642e-01 -4.67159420e-01 -1.06473482e+00 -6.56441748e-01
-2.86135286e-01 7.84885228e-01 4.24337611e-02 -2.56773263... | [5.895063400268555, -0.7316150069236755] |
67c8bb47-1c6b-4548-886c-8c37b1be25c4 | lambdabeam-neural-program-search-with-higher | 2306.02049 | null | https://arxiv.org/abs/2306.02049v1 | https://arxiv.org/pdf/2306.02049v1.pdf | LambdaBeam: Neural Program Search with Higher-Order Functions and Lambdas | Search is an important technique in program synthesis that allows for adaptive strategies such as focusing on particular search directions based on execution results. Several prior works have demonstrated that neural models are effective at guiding program synthesis searches. However, a common drawback of those approac... | ['Charles Sutton', 'Kevin Ellis', 'Wen-Ding Li', 'Hanjun Dai', 'Kensen Shi'] | 2023-06-03 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 8.57765302e-02 1.46620711e-02 -8.42090905e-01 -4.18245465e-01
-3.99088264e-01 -7.06460476e-01 4.52813655e-01 2.10895672e-01
-1.46581873e-01 5.24271190e-01 2.79670507e-01 -1.04987466e+00
-2.84913424e-02 -1.26785564e+00 -7.41225481e-01 -6.44838810e-02
-2.50561625e-01 3.12330216e-01 3.63515705e-01 -3.66708815... | [8.279458045959473, 7.3221845626831055] |
75b77fca-6f4a-4685-80e1-225cfa0addf8 | interpreting-arabic-transformer-models | 2201.07434 | null | https://arxiv.org/abs/2201.07434v2 | https://arxiv.org/pdf/2201.07434v2.pdf | Interpreting Arabic Transformer Models | Arabic is a Semitic language which is widely spoken with many dialects. Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects have surfaced. While these models have been compared with respect to downstream NLP tasks, no evaluation has been carried out to directly c... | ['Hassan Sajjad', 'Fahim Dalvi', 'Nadir Durrani', 'Ahmed Abdelali'] | 2022-01-19 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [-1.78739712e-01 -1.21351937e-02 1.48676470e-01 -3.91799122e-01
-1.72152996e-01 -1.07496452e+00 6.64937139e-01 4.08165127e-01
-6.50483251e-01 1.53425708e-01 4.75931793e-01 -4.46663588e-01
1.38065219e-01 -9.01017606e-01 -4.59600329e-01 -7.72712290e-01
-2.66604275e-01 5.89969456e-01 4.36250605e-02 -7.85788894... | [10.567975044250488, 10.090438842773438] |
efdf1269-5c46-4628-92fe-a26f1421ca70 | predicting-above-sentence-discourse-structure | 2112.06196 | null | https://arxiv.org/abs/2112.06196v1 | https://arxiv.org/pdf/2112.06196v1.pdf | Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation | RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its importance, one of the most prevailing limitations in modern day discourse parsing is the lack of large-scale datasets. To overcome the data s... | ['Giuseppe Carenini', 'Linzi Xing', 'Patrick Huber'] | 2021-12-12 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.38047236e-01 7.35871732e-01 -5.33309639e-01 -4.30129230e-01
-1.35448015e+00 -8.96593273e-01 1.03903902e+00 7.35933304e-01
-2.87715018e-01 1.22771454e+00 1.00581014e+00 -4.10049349e-01
-1.08641982e-02 -3.74111861e-01 -5.08038044e-01 -5.31336486e-01
1.27204165e-01 4.67493385e-01 4.99975026e-01 -4.14177090... | [10.87222957611084, 9.386451721191406] |
f8a7eadc-70e3-4052-a251-8641aae2c39e | data-augmentation-for-skin-lesion-analysis | 1809.01442 | null | http://arxiv.org/abs/1809.01442v1 | http://arxiv.org/pdf/1809.01442v1.pdf | Data Augmentation for Skin Lesion Analysis | Deep learning models show remarkable results in automated skin lesion
analysis. However, these models demand considerable amounts of data, while the
availability of annotated skin lesion images is often limited. Data
augmentation can expand the training dataset by transforming input images. In
this work, we investigate... | ['Fábio Perez', 'Sandra Avila', 'Eduardo Valle', 'Cristina Vasconcelos'] | 2018-09-05 | null | null | null | null | ['skin-cancer-classification', 'skin-lesion-classification'] | ['medical', 'medical'] | [ 6.22987747e-01 1.86318874e-01 4.94052842e-02 -1.12458199e-01
-6.57563329e-01 -4.78646189e-01 7.47577548e-01 2.38799199e-01
-9.73500192e-01 7.20166087e-01 2.00476244e-01 -4.48613048e-01
1.70280889e-01 -6.82502031e-01 -5.27166486e-01 -8.26516569e-01
2.03471594e-02 4.83752191e-02 2.23489150e-01 -1.71741158... | [15.549935340881348, -2.8469510078430176] |
cb80a615-560e-4726-87b2-48a152b74a22 | why-we-should-report-the-details-in | 2306.02044 | null | https://arxiv.org/abs/2306.02044v1 | https://arxiv.org/pdf/2306.02044v1.pdf | Why We Should Report the Details in Subjective Evaluation of TTS More Rigorously | This paper emphasizes the importance of reporting experiment details in subjective evaluations and demonstrates how such details can significantly impact evaluation results in the field of speech synthesis. Through an analysis of 80 papers presented at INTERSPEECH 2022, we find a lack of thorough reporting on critical ... | ['Hung-Yi Lee', 'Wei-Ping Huang', 'Cheng-Han Chiang'] | 2023-06-03 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 6.10176735e-02 -1.67774707e-01 -4.93669920e-02 -6.28500760e-01
-1.31004226e+00 -9.84773517e-01 4.67522442e-01 2.88735423e-03
-4.98409599e-01 7.67775536e-01 5.19759476e-01 -7.37337649e-01
1.80458948e-02 2.10766256e-01 -2.58204639e-01 -2.57929295e-01
-3.74121740e-02 1.30592659e-01 1.99745938e-01 -3.44782442... | [14.775620460510254, 6.507643699645996] |
90b675bf-c33b-48c7-9d42-818606e9dedd | dependency-grammar-induction-with-a-neural | 1811.05889 | null | http://arxiv.org/abs/1811.05889v1 | http://arxiv.org/pdf/1811.05889v1.pdf | Dependency Grammar Induction with a Neural Variational Transition-based Parser | Dependency grammar induction is the task of learning dependency syntax
without annotated training data. Traditional graph-based models with global
inference achieve state-of-the-art results on this task but they require
$O(n^3)$ run time. Transition-based models enable faster inference with $O(n)$
time complexity, but ... | ['Frank Keller', 'Yang Liu', 'Jianpeng Cheng', 'Bowen Li'] | 2018-11-14 | null | null | null | null | ['dependency-grammar-induction'] | ['natural-language-processing'] | [ 8.63995254e-02 7.91216969e-01 -3.47704321e-01 -6.09695494e-01
-1.41464019e+00 -4.96968716e-01 8.88673514e-02 3.87584180e-01
-5.25134981e-01 7.77467132e-01 3.81175918e-03 -1.00606847e+00
9.89247635e-02 -9.33707774e-01 -8.63479376e-01 -4.22911823e-01
-4.97217000e-01 8.82633150e-01 1.83999151e-01 -7.33201578... | [10.369688034057617, 9.639059066772461] |
940fa26e-265b-4c85-85f6-973222348f18 | transformer-based-language-models-for | 2204.03214 | null | https://arxiv.org/abs/2204.03214v2 | https://arxiv.org/pdf/2204.03214v2.pdf | Transformer-Based Language Models for Software Vulnerability Detection | The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the transferability of the knowledge gained by these models in one domain to other related domains, and the closeness of natural languages to high-level programming languages, such as C/C++, this... | ['Surya Nepal', 'Josef Pieprzyk', 'Seyit Camtepe', 'Muhammad Ejaz Ahmed', 'Seung Ick Jang', 'Chandra Thapa'] | 2022-04-07 | null | null | null | null | ['code-translation', 'vulnerability-detection'] | ['computer-code', 'miscellaneous'] | [-1.72680646e-01 -4.02522057e-01 -3.95007402e-01 -8.18333775e-02
-8.20362210e-01 -7.27428436e-01 1.86789826e-01 3.30184132e-01
-2.05866415e-02 7.37064630e-02 1.99225917e-01 -1.02328467e+00
8.86436254e-02 -8.62141550e-01 -5.43152332e-01 -1.45389736e-01
-4.98362690e-01 -4.38614368e-01 4.20618057e-01 -4.49373573... | [7.081683158874512, 7.7787766456604] |
cbdd53fd-e0b2-43db-a2ea-8bd3892d9f54 | controlling-keywords-and-their-positions-in | 2304.09516 | null | https://arxiv.org/abs/2304.09516v1 | https://arxiv.org/pdf/2304.09516v1.pdf | Controlling keywords and their positions in text generation | One of the challenges in text generation is to control generation as intended by a user. Previous studies have proposed to specify the keywords that should be included in the generated text. However, this is insufficient to generate text which reflect the user intent. For example, placing the important keyword beginnin... | ['Yasuhiro Sogawa', 'Osamu Imaichi', 'Hiroaki Ozaki', 'Terufumi Morishita', 'Yuichi Sasazawa'] | 2023-04-19 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 4.50289041e-01 2.72308230e-01 -2.97654122e-01 -7.15066940e-02
-6.73564911e-01 -7.47786105e-01 7.80951142e-01 5.16599536e-01
-3.84631127e-01 7.45417833e-01 8.49059999e-01 -2.40423903e-01
2.18892992e-01 -7.01323509e-01 -5.05146742e-01 -3.58187526e-01
4.42385316e-01 3.26294988e-01 3.82218003e-01 -4.39323604... | [11.926097869873047, 8.985147476196289] |
52f4a5d4-13da-48b7-a9a7-e9fcfae4ed66 | annotating-targets-of-opinions-in-arabic | null | null | https://aclanthology.org/W15-3210 | https://aclanthology.org/W15-3210.pdf | Annotating Targets of Opinions in Arabic using Crowdsourcing | null | ['Kathy Mckeown', 'Noura Farra', 'Nizar Habash'] | 2015-07-01 | null | null | null | ws-2015-7 | ['fine-grained-opinion-analysis', 'subjectivity-analysis'] | ['natural-language-processing', '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... | [-7.411048889160156, 3.7760651111602783] |
9edea27b-8487-4b2a-a2a7-61f2ba027d3f | machine-learning-in-sports-a-case-study-on | 2206.09258 | null | https://arxiv.org/abs/2206.09258v1 | https://arxiv.org/pdf/2206.09258v1.pdf | Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches | Machine Learning has become an integral part of engineering design and decision making in several domains, including sports. Deep Neural Networks (DNNs) have been the state-of-the-art methods for predicting outcomes of professional sports events. However, apart from getting highly accurate predictions on these sports e... | ['Tirtharaj Dash', 'Aditya Jain', 'Apoorv Singh', 'Aman Saraiya', 'Abhinav Lalwani'] | 2022-06-18 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 7.04236748e-03 4.97107804e-01 -5.78913033e-01 -4.91182208e-01
-2.50974298e-01 -2.69343853e-01 1.32478386e-01 2.08716124e-01
2.15225205e-01 8.40262949e-01 2.48571917e-01 -5.86721599e-01
-7.64064312e-01 -1.11482048e+00 -9.85099375e-01 -2.99584270e-01
-2.13752031e-01 8.21998298e-01 -7.93434978e-02 -4.74860936... | [8.852115631103516, 5.9402384757995605] |
7ef910d9-4773-4e6d-96fc-d1a401c40521 | knowledgebra-an-algebraic-learning-framework | 2204.07328 | null | https://arxiv.org/abs/2204.07328v1 | https://arxiv.org/pdf/2204.07328v1.pdf | Knowledgebra: An Algebraic Learning Framework for Knowledge Graph | Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented. Dense embeddings trained from KG datasets benefit a variety of downstream tasks such as KG completion and link prediction. ... | ['Pengyu Hong', 'Jan Engelbrecht', 'Long Sha', 'Yifei Wang', 'Tong Yang'] | 2022-04-15 | null | null | null | null | ['general-knowledge', 'abstract-algebra'] | ['miscellaneous', 'reasoning'] | [-3.51254284e-01 6.17585957e-01 -4.00803477e-01 -3.20098668e-01
7.01033371e-03 -4.54844415e-01 5.65876663e-01 4.17998701e-01
-3.79737318e-02 5.05376935e-01 1.77768350e-01 -4.76040423e-01
-6.57619298e-01 -1.23604941e+00 -9.61840749e-01 -5.15267372e-01
-4.42347765e-01 5.27706206e-01 -9.88717079e-02 -3.82836133... | [8.817604064941406, 7.706940174102783] |
af26ab36-9a59-421d-a01b-2b02ae59d32f | distributed-maximization-of-submodular-plus | 1903.08351 | null | http://arxiv.org/abs/1903.08351v2 | http://arxiv.org/pdf/1903.08351v2.pdf | Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets | There are many problems in machine learning and data mining which are
equivalent to selecting a non-redundant, high "quality" set of objects.
Recommender systems, feature selection, and data summarization are among many
applications of this. In this paper, we consider this problem as an
optimization problem that seeks ... | ['Mehrdad Ghadiri', 'Mark Schmidt'] | 2019-03-20 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.96531996e-01 4.07959335e-02 -4.18352157e-01 -4.20928687e-01
-8.37281704e-01 -4.52516109e-01 -6.63684160e-02 6.15316868e-01
-2.20348179e-01 7.46646345e-01 1.50048554e-01 2.20034644e-01
-8.35491359e-01 -7.86158502e-01 -4.43704456e-01 -9.11819339e-01
-4.20126691e-02 8.67615104e-01 -1.38775548e-02 -1.24383807... | [6.6453094482421875, 4.9015326499938965] |
129e8a42-adcb-43e5-9b6e-76f4cb411720 | physics-based-motion-retargeting-from-sparse | 2307.01938 | null | https://arxiv.org/abs/2307.01938v1 | https://arxiv.org/pdf/2307.01938v1.pdf | Physics-based Motion Retargeting from Sparse Inputs | Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an a... | ['Alexander Winkler', 'Michiel Van de Panne', 'Yuting Ye', 'Jungdam Won', 'Daniele Reda'] | 2023-07-04 | null | null | null | null | ['motion-retargeting'] | ['computer-vision'] | [ 1.19403312e-02 2.79749423e-01 1.20199122e-01 3.17149252e-01
-3.68816555e-01 -8.48174751e-01 4.93109286e-01 -4.36118811e-01
-4.87247795e-01 6.59649014e-01 -1.38450623e-01 6.58421144e-02
3.43665689e-01 -4.40055966e-01 -7.13674724e-01 -4.51287150e-01
-1.05666161e-01 7.07116365e-01 6.35819614e-01 -6.54766798... | [5.106180667877197, 0.7020900249481201] |
2f8c8587-233b-4df6-bcd5-f57ac23fce82 | tgrl-an-algorithm-for-teacher-guided | 2307.03186 | null | https://arxiv.org/abs/2307.03186v1 | https://arxiv.org/pdf/2307.03186v1.pdf | TGRL: An Algorithm for Teacher Guided Reinforcement Learning | Learning from rewards (i.e., reinforcement learning or RL) and learning to imitate a teacher (i.e., teacher-student learning) are two established approaches for solving sequential decision-making problems. To combine the benefits of these different forms of learning, it is common to train a policy to maximize a combina... | ['Pulkit Agrawal', 'Aviv Tamar', 'Zhang-Wei Hong', 'Idan Shenfeld'] | 2023-07-06 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 2.83448786e-01 5.44170439e-01 -5.07943094e-01 -4.23888564e-01
-7.80929506e-01 -5.16794384e-01 6.41495109e-01 2.20005006e-01
-9.76729870e-01 1.15599608e+00 -1.01340488e-01 -6.42469764e-01
-4.75935578e-01 -7.45780051e-01 -7.11711407e-01 -9.57676411e-01
2.39356279e-01 6.90524578e-01 1.52693629e-01 -1.69983774... | [4.088977813720703, 1.9883264303207397] |
816207e4-922e-4014-a2e9-28e6d7cff65a | bindsnet-a-machine-learning-oriented-spiking | 1806.01423 | null | http://arxiv.org/abs/1806.01423v2 | http://arxiv.org/pdf/1806.01423v2.pdf | BindsNET: A machine learning-oriented spiking neural networks library in Python | The development of spiking neural network simulation software is a critical
component enabling the modeling of neural systems and the development of
biologically inspired algorithms. Existing software frameworks support a wide
range of neural functionality, software abstraction levels, and hardware
devices, yet are typ... | ['Robert Kozma', 'Hava T. Siegelmann', 'Hananel Hazan', 'Daniel J. Saunders', 'Hassaan Khan', 'Darpan T. Sanghavi'] | 2018-06-04 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-3.10896814e-01 -3.58995408e-01 4.65347946e-01 -2.31885254e-01
1.36874497e-01 -6.40732050e-01 3.61239940e-01 -1.29712075e-01
-6.67331755e-01 6.17064953e-01 -2.53867716e-01 -3.81546825e-01
2.72804260e-01 -8.10793281e-01 -6.18972898e-01 -8.72231185e-01
-2.57394016e-01 1.33457735e-01 6.16984427e-01 -4.48518574... | [8.151185035705566, 2.6167449951171875] |
bb68862e-bc0c-48aa-acc8-416f9b82268a | behaviour-discriminator-a-simple-data | 2301.11734 | null | https://arxiv.org/abs/2301.11734v1 | https://arxiv.org/pdf/2301.11734v1.pdf | Behaviour Discriminator: A Simple Data Filtering Method to Improve Offline Policy Learning | This paper studies the problem of learning a control policy without the need for interactions with the environment; instead, learning purely from an existing dataset. Prior work has demonstrated that offline learning algorithms (e.g., behavioural cloning and offline reinforcement learning) are more likely to discover a... | ['Stephen J. Redmond', 'Francisco Roldan Sanchez', "Noel E. O'Connor", 'Kevin McGuinness', 'David Cordova Bulens', 'Robert McCarthy', 'Qiang Wang'] | 2023-01-27 | null | null | null | null | ['d4rl'] | ['robots'] | [ 2.37283587e-01 -3.04098092e-02 -2.15305164e-01 -2.20164269e-01
-7.46786892e-01 -8.95838857e-01 6.55941904e-01 2.62799174e-01
-8.96052778e-01 9.32081640e-01 -2.93999583e-01 -3.61063004e-01
-5.97719252e-01 -3.54751140e-01 -9.72088516e-01 -6.10702872e-01
-4.36830223e-01 8.71377051e-01 3.55579019e-01 -4.22136307... | [4.2136454582214355, 1.5421786308288574] |
c4358ac4-c66f-4e52-921f-8ac72322ba57 | qasc-a-dataset-for-question-answering-via | 1910.11473 | null | https://arxiv.org/abs/1910.11473v2 | https://arxiv.org/pdf/1910.11473v2.pdf | QASC: A Dataset for Question Answering via Sentence Composition | Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first... | ['Ashish Sabharwal', 'Tushar Khot', 'Peter Jansen', 'Peter Clark', 'Michal Guerquin'] | 2019-10-25 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.46813238e-01 6.07507527e-01 -1.07076801e-01 -1.03370063e-01
-1.59624350e+00 -8.67855430e-01 6.72546983e-01 7.44004369e-01
-3.27190995e-01 1.02834558e+00 4.97209638e-01 -4.37970221e-01
-4.51328158e-01 -7.65420675e-01 -8.19119632e-01 -9.33063924e-02
2.92332411e-01 9.31304812e-01 8.86438072e-01 -8.58838618... | [10.684418678283691, 7.942838191986084] |
e538348e-5cff-4279-9310-616314ef1215 | jigsawgan-self-supervised-learning-for | 2101.07555 | null | https://arxiv.org/abs/2101.07555v3 | https://arxiv.org/pdf/2101.07555v3.pdf | JigsawGAN: Auxiliary Learning for Solving Jigsaw Puzzles with Generative Adversarial Networks | The paper proposes a solution based on Generative Adversarial Network (GAN) for solving jigsaw puzzles. The problem assumes that an image is divided into equal square pieces, and asks to recover the image according to information provided by the pieces. Conventional jigsaw puzzle solvers often determine the relationshi... | ['Bing Zeng', 'Guanghui Liu', 'Guangfu Wang', 'Shuaicheng Liu', 'Ru Li'] | 2021-01-19 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 5.01185119e-01 1.24903254e-01 -4.58773747e-02 2.71435887e-01
-7.60864377e-01 -1.05470741e+00 3.69195670e-01 -7.37013340e-01
1.81238294e-01 7.63379157e-01 5.97449318e-02 -8.65132883e-02
-1.21750042e-01 -1.18781292e+00 -7.61274934e-01 -1.04167664e+00
5.23114800e-01 6.89333975e-01 6.06007501e-02 -3.70490342... | [11.557024955749512, -0.5976397395133972] |
fcd018b0-f156-4054-9425-6a3b253d1b79 | evaluation-of-output-embeddings-for-fine | 1409.8403 | null | http://arxiv.org/abs/1409.8403v2 | http://arxiv.org/pdf/1409.8403v2.pdf | Evaluation of Output Embeddings for Fine-Grained Image Classification | Image classification has advanced significantly in recent years with the
availability of large-scale image sets. However, fine-grained classification
remains a major challenge due to the annotation cost of large numbers of
fine-grained categories. This project shows that compelling classification
performance can be ach... | ['Bernt Schiele', 'Scott Reed', 'Zeynep Akata', 'Honglak Lee', 'Daniel Walter'] | 2014-09-30 | evaluation-of-output-embeddings-for-fine-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Akata_Evaluation_of_Output_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Akata_Evaluation_of_Output_2015_CVPR_paper.pdf | cvpr-2015-6 | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 3.83803211e-02 -6.29256666e-02 -2.97684610e-01 -8.07138503e-01
-6.29515707e-01 -6.45864308e-01 9.15014505e-01 4.00326461e-01
-7.65421808e-01 5.36885202e-01 3.65698785e-01 3.15825939e-01
-8.77030864e-02 -7.63448417e-01 -5.69411218e-01 -4.99986142e-01
-1.45837024e-01 6.29963696e-01 1.74506754e-01 -7.84750357... | [9.83305549621582, 2.15319561958313] |
a185b880-2f12-436f-bfd7-675c1c298f5d | temporally-aware-feature-pooling-for-action | 2104.06779 | null | https://arxiv.org/abs/2104.06779v1 | https://arxiv.org/pdf/2104.06779v1.pdf | Temporally-Aware Feature Pooling for Action Spotting in Soccer Broadcasts | Toward the goal of automatic production for sports broadcasts, a paramount task consists in understanding the high-level semantic information of the game in play. For instance, recognizing and localizing the main actions of the game would allow producers to adapt and automatize the broadcast production, focusing on the... | ['Bernard Ghanem', 'Silvio Giancola'] | 2021-04-14 | null | null | null | null | ['action-spotting'] | ['computer-vision'] | [ 5.04575903e-03 -7.89771527e-02 -2.91422158e-01 -2.71301478e-01
-8.00354660e-01 -6.30738735e-01 6.44224286e-01 1.81814596e-01
-7.05806434e-01 4.54033107e-01 9.48354483e-01 5.48218369e-01
-1.02078840e-01 -8.13066900e-01 -7.66704798e-01 -6.68113172e-01
-1.12190172e-02 9.74150822e-02 6.89033806e-01 -5.14824450... | [8.033080101013184, 0.2271987646818161] |
9cf6da07-ff55-4ec6-9a60-13958d3261c7 | combining-word-embeddings-with-bilingual | null | null | https://aclanthology.org/2020.coling-main.531 | https://aclanthology.org/2020.coling-main.531.pdf | Combining Word Embeddings with Bilingual Orthography Embeddings for Bilingual Dictionary Induction | Bilingual dictionary induction (BDI) is the task of accurately translating words to the target language. It is of great importance in many low-resource scenarios where cross-lingual training data is not available. To perform BDI, bilingual word embeddings (BWEs) are often used due to their low bilingual training signal... | ['Hinrich Sch{\\"u}tze', 'Alexander Fraser', 'Viktor Hangya', 'Silvia Severini'] | 2020-12-01 | null | null | null | coling-2020-8 | ['transliteration'] | ['natural-language-processing'] | [-1.42541528e-01 -5.79776287e-01 -5.18753707e-01 -3.67456853e-01
-5.49941301e-01 -6.54462099e-01 7.81965137e-01 2.76118428e-01
-7.11619854e-01 7.77417541e-01 5.00888526e-01 -8.84945214e-01
1.16492687e-02 -8.68714690e-01 -5.37118852e-01 -3.95534843e-01
4.21410888e-01 8.40971172e-01 -1.87323213e-01 -6.58472478... | [11.04175853729248, 10.025375366210938] |
5df4db35-558c-4d00-9bae-8eca3cc45718 | fast-underwater-image-enhancement-for | 1903.09766 | null | https://arxiv.org/abs/1903.09766v3 | https://arxiv.org/pdf/1903.09766v3.pdf | Fast Underwater Image Enhancement for Improved Visual Perception | In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. W... | ['Md Jahidul Islam', 'Youya Xia', 'Junaed Sattar'] | 2019-03-23 | null | null | null | null | ['underwater-image-restoration'] | ['computer-vision'] | [ 3.01695198e-01 3.54336321e-01 9.32911992e-01 -5.75617015e-01
-7.72683680e-01 -6.28323734e-01 2.90298164e-01 -1.84104949e-01
-9.28155839e-01 5.44992447e-01 5.48954271e-02 1.17621504e-01
7.19671398e-02 -9.53855097e-01 -1.16697311e+00 -8.37047577e-01
-4.62472230e-01 8.11602250e-02 2.74839699e-01 -5.15562236... | [10.689448356628418, -3.5447585582733154] |
eb62696e-076c-4974-940f-60fdab205c08 | cross-modal-progressive-comprehension-for | 2105.07175 | null | https://arxiv.org/abs/2105.07175v1 | https://arxiv.org/pdf/2105.07175v1.pdf | Cross-Modal Progressive Comprehension for Referring Segmentation | Given a natural language expression and an image/video, the goal of referring segmentation is to produce the pixel-level masks of the entities described by the subject of the expression. Previous approaches tackle this problem by implicit feature interaction and fusion between visual and linguistic modalities in a one-... | ['Guanbin Li', 'Bo Li', 'Yunchao Wei', 'Shaofei Huang', 'Tianrui Hui', 'Si Liu'] | 2021-05-15 | null | null | null | null | ['referring-expression-segmentation'] | ['computer-vision'] | [ 4.07391310e-01 2.14917898e-01 -2.04765081e-01 -2.90031314e-01
-6.87815607e-01 -4.89912152e-01 6.16469085e-01 1.46679476e-01
-3.83723706e-01 3.15702736e-01 1.89443842e-01 -6.73256069e-03
-5.40825315e-02 -7.53700614e-01 -6.22328758e-01 -6.63161576e-01
3.97024989e-01 1.52471349e-01 6.29052043e-01 -3.02321762... | [10.312397956848145, 1.2337366342544556] |
7f37725d-c408-4416-a9fa-332470bd2cda | evaluation-and-optimization-of-gradient | 2306.08881 | null | https://arxiv.org/abs/2306.08881v1 | https://arxiv.org/pdf/2306.08881v1.pdf | Evaluation and Optimization of Gradient Compression for Distributed Deep Learning | To accelerate distributed training, many gradient compression methods have been proposed to alleviate the communication bottleneck in synchronous stochastic gradient descent (S-SGD), but their efficacy in real-world applications still remains unclear. In this work, we first evaluate the efficiency of three representati... | ['Bo Li', 'Xiaowen Chu', 'Shaohuai Shi', 'Longteng Zhang', 'Lin Zhang'] | 2023-06-15 | null | null | null | null | ['quantization'] | ['methodology'] | [-3.12419444e-01 -7.66898096e-01 -9.41089541e-02 -3.55348170e-01
-8.21933985e-01 -2.20132858e-01 4.98661876e-01 2.67291695e-01
-4.98632371e-01 3.91780496e-01 4.65164840e-01 -9.50340986e-01
4.36329171e-02 -8.60505521e-01 -6.19678020e-01 -5.68289399e-01
-4.36723888e-01 3.90746772e-01 4.10060167e-01 -2.18830600... | [8.52370834350586, 3.421203136444092] |
93710241-16c8-4cab-ba60-f45b1f4a13a6 | learning-long-range-spatial-dependencies-with-1 | 1805.08315 | null | https://arxiv.org/abs/1805.08315v4 | https://arxiv.org/pdf/1805.08315v4.pdf | Learning long-range spatial dependencies with horizontal gated-recurrent units | Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks. Here, however, we show that t... | ['Junkyung Kim', 'Drew Linsley', 'Thomas Serre', 'Vijay Veerabadran'] | 2018-05-21 | null | null | null | neurips-2018 | ['contour-detection'] | ['computer-vision'] | [ 5.29170036e-01 1.50989905e-01 7.84687102e-02 -4.24505889e-01
-5.62074661e-01 -3.80673289e-01 6.81837082e-01 -1.80589175e-03
-6.40203357e-01 4.75465596e-01 2.87508905e-01 -3.55139226e-01
6.11221306e-02 -4.35051650e-01 -6.93479538e-01 -8.44015658e-01
-4.69088614e-01 -1.14670448e-01 5.24605215e-01 -2.00909674... | [9.573237419128418, 2.4122469425201416] |
1f0762ce-9043-4d36-ab81-89ae5224959b | sentube-a-corpus-for-sentiment-analysis-on | null | null | https://aclanthology.org/L14-1188 | https://aclanthology.org/L14-1188.pdf | SenTube: A Corpus for Sentiment Analysis on YouTube Social Media | In this paper we present SenTube -- a dataset of user-generated comments on YouTube videos annotated for information content and sentiment polarity. It contains annotations that allow to develop classifiers for several important NLP tasks: (i) sentiment analysis, (ii) text categorization (relatedness of a comment to vi... | ['ro', 'Agata Rotondi', 'Olga Uryupina', 'Barbara Plank', 'Aliaksei Severyn', 'Aless Moschitti'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['spam-detection'] | ['natural-language-processing'] | [-1.33732632e-01 -5.16499244e-02 -6.84257865e-01 -4.94903147e-01
-6.80604935e-01 -1.01735306e+00 8.01693082e-01 5.95754325e-01
-4.17644262e-01 4.88328218e-01 6.74461246e-01 -5.28398342e-03
2.90622473e-01 -9.91592184e-02 -2.68029511e-01 -4.76801157e-01
-1.10546267e-02 1.21544562e-01 3.84621292e-01 8.86325762... | [12.93708324432373, 5.261788845062256] |
bd0cb3aa-757f-456d-b9f5-4b047376668a | damo-nlp-at-semeval-2023-task-2-a-unified | 2305.03688 | null | https://arxiv.org/abs/2305.03688v3 | https://arxiv.org/pdf/2305.03688v3.pdf | DAMO-NLP at SemEval-2023 Task 2: A Unified Retrieval-augmented System for Multilingual Named Entity Recognition | The MultiCoNER \RNum{2} shared task aims to tackle multilingual named entity recognition (NER) in fine-grained and noisy scenarios, and it inherits the semantic ambiguity and low-context setting of the MultiCoNER \RNum{1} task. To cope with these problems, the previous top systems in the MultiCoNER \RNum{1} either inco... | ['Yong Jiang', 'Fei Huang', 'Pengjun Xie', 'Kewei Tu', 'Yueting Zhuang', 'Weiming Lu', 'Yinghui Li', 'Jiong Cai', 'Zixia Jia', 'Shen Huang', 'Zeqi Tan'] | 2023-05-05 | null | null | null | null | ['named-entity-recognition-ner', 'multilingual-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [-4.76772219e-01 -1.35393351e-01 -8.65060985e-02 -7.27487653e-02
-1.40208614e+00 -9.96452808e-01 4.51031417e-01 -1.83043584e-01
-8.90285134e-01 1.03146684e+00 3.63163531e-01 -5.25600672e-01
-2.92105377e-01 -4.43198234e-01 -5.68278253e-01 -3.31584185e-01
2.79220611e-01 6.43411279e-01 3.55285376e-01 -6.84982657... | [9.708683013916016, 9.554638862609863] |
54113c34-3613-451b-b1b4-f3b7e8017f68 | t-leap-occlusion-robust-pose-estimation-of | 2104.08029 | null | https://arxiv.org/abs/2104.08029v2 | https://arxiv.org/pdf/2104.08029v2.pdf | T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information | As herd size on dairy farms continues to increase, automatic health monitoring of cows is gaining in interest. Lameness, a prevalent health disorder in dairy cows, is commonly detected by analyzing the gait of cows. A cow's gait can be tracked in videos using pose estimation models because models learn to automatically... | ['Gert Kootstra', 'Rik van der Tol', 'Helena Russello'] | 2021-04-16 | null | null | null | null | ['animal-pose-estimation'] | ['computer-vision'] | [-8.77805725e-02 2.30088860e-01 -1.38728330e-02 -6.05414152e-01
-5.78520931e-02 -3.85528922e-01 -4.76895049e-02 4.68026608e-01
-3.77772629e-01 4.14333373e-01 -4.46894884e-01 1.45740539e-01
-1.14971861e-01 -6.67629778e-01 -1.32864821e+00 -6.64190948e-01
-9.77780044e-01 4.32309568e-01 5.96134961e-01 -3.12468648... | [7.678747177124023, -0.9594832062721252] |
ff1e56be-5bae-4056-8cd9-afb93ed124b2 | kest-kernel-distance-based-efficient-self | 2306.10414 | null | https://arxiv.org/abs/2306.10414v1 | https://arxiv.org/pdf/2306.10414v1.pdf | KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation | Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheless, in facilitating semi-supervised controllable language generation, ST faces two key challenges. First, augmented by self-generated pseudo ... | ['Xing Xie', 'Laks V. S. Lakshmanan', 'Xiaoyuan Yi', 'Yuxi Feng'] | 2023-06-17 | null | null | null | null | ['text-generation'] | ['natural-language-processing'] | [ 3.57497662e-01 3.76815110e-01 -2.70790756e-01 -1.83714807e-01
-9.18826878e-01 -6.35046601e-01 7.78398693e-01 -7.64911398e-02
-2.40935862e-01 1.13959110e+00 4.79090720e-01 -7.33974949e-02
1.99624047e-01 -7.11067617e-01 -6.04807734e-01 -6.23309731e-01
3.06376755e-01 7.64564812e-01 -2.81193256e-01 -3.56148094... | [11.831191062927246, 9.187782287597656] |
dfed8df9-6dcc-494e-a49d-9594f9e83b77 | knowledge-aware-bayesian-deep-topic-model | 2209.14228 | null | https://arxiv.org/abs/2209.14228v1 | https://arxiv.org/pdf/2209.14228v1.pdf | Knowledge-Aware Bayesian Deep Topic Model | We propose a Bayesian generative model for incorporating prior domain knowledge into hierarchical topic modeling. Although embedded topic models (ETMs) and its variants have gained promising performance in text analysis, they mainly focus on mining word co-occurrence patterns, ignoring potentially easy-to-obtain prior ... | ['Mingyuan Zhou', 'Bo Chen', 'Chaojie Wang', 'Zhibin Duan', 'Miaoge Li', 'Yishi Xu', 'Dongsheng Wang'] | 2022-09-20 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-7.86655396e-02 5.56540430e-01 -4.89913791e-01 -4.71041024e-01
-5.85123956e-01 -3.23807508e-01 7.59045720e-01 4.02107418e-01
2.26566680e-02 4.90649968e-01 5.51537573e-01 -5.10197692e-02
-4.25194800e-01 -1.16042554e+00 -3.42010140e-01 -4.66988564e-01
-5.51530123e-02 9.81300890e-01 5.06341875e-01 -1.97621454... | [10.3707857131958, 6.966981410980225] |
4505d6b8-a47b-4636-a57b-8aba052cf8a7 | query-adaptive-late-fusion-for-image | 1810.13103 | null | http://arxiv.org/abs/1810.13103v1 | http://arxiv.org/pdf/1810.13103v1.pdf | Query Adaptive Late Fusion for Image Retrieval | Feature fusion is a commonly used strategy in image retrieval tasks, which
aggregates the matching responses of multiple visual features. Feasible sets of
features can be either descriptors (SIFT, HSV) for an entire image or the same
descriptor for different local parts (face, body). Ideally, the to-be-fused
heterogene... | ['Liang Zheng', 'Shengjin Wang', 'Zhongdao Wang'] | 2018-10-31 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 6.49123117e-02 -5.42313099e-01 -1.38875201e-01 -4.68477458e-01
-8.17291975e-01 -5.25564253e-01 7.05926597e-01 4.27864164e-01
-4.32788491e-01 5.34068346e-01 3.16824764e-02 3.21822792e-01
-3.46273482e-01 -6.92962408e-01 -3.37676048e-01 -9.54588056e-01
1.02486968e-01 3.00688148e-01 3.84499937e-01 -2.66118914... | [10.846480369567871, 0.6857770681381226] |
f8e66df5-6ed0-408e-b793-5f40e132d929 | rego-reference-guided-outpainting-for-scenery | 2106.10601 | null | https://arxiv.org/abs/2106.10601v4 | https://arxiv.org/pdf/2106.10601v4.pdf | ReGO: Reference-Guided Outpainting for Scenery Image | We aim to tackle the challenging yet practical scenery image outpainting task in this work. Recently, generative adversarial learning has significantly advanced the image outpainting by producing semantic consistent content for the given image. However, the existing methods always suffer from the blurry texture and the... | ['Yi Yang', 'Li Zhu', 'Xueming Qian', 'Yunchao Wei', 'Yaxiong Wang'] | 2021-06-20 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 5.31967282e-01 8.34665000e-02 7.80752599e-02 -1.48691565e-01
-6.70059919e-01 -3.48396063e-01 4.01914060e-01 -5.55770040e-01
7.60786161e-02 1.03488219e+00 7.35054538e-02 8.79527181e-02
1.16562702e-01 -7.62336791e-01 -1.05555236e+00 -1.00144887e+00
5.73600590e-01 -1.55104786e-01 2.56363694e-02 -3.05166274... | [11.391535758972168, -1.1937392950057983] |
9f650975-151b-4072-bd2d-3b4f7eacfa71 | viser-visual-self-regularization | 1802.02568 | null | http://arxiv.org/abs/1802.02568v1 | http://arxiv.org/pdf/1802.02568v1.pdf | VISER: Visual Self-Regularization | In this work, we propose the use of large set of unlabeled images as a source
of regularization data for learning robust visual representation. Given a
visual model trained by a labeled dataset in a supervised fashion, we augment
our training samples by incorporating large number of unlabeled data and train
a semi-supe... | ['Hamid Izadinia', 'Pierre Garrigues'] | 2018-02-07 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.77942351e-01 -2.37342007e-02 -5.49730003e-01 -6.04065895e-01
-9.73888397e-01 -1.08243525e+00 4.79222089e-01 -6.55547082e-02
-4.38232690e-01 3.22212130e-01 1.76289484e-01 -2.51343977e-02
1.49800643e-01 -3.82058948e-01 -1.16030788e+00 -5.94670832e-01
2.27759212e-01 4.39517081e-01 4.34373803e-02 3.58565375... | [10.041766166687012, 1.9345341920852661] |
3911df60-409b-466d-a1cb-a759be858506 | functional-regularisation-for-continual | 1901.11356 | null | https://arxiv.org/abs/1901.11356v4 | https://arxiv.org/pdf/1901.11356v4.pdf | Functional Regularisation for Continual Learning with Gaussian Processes | We introduce a framework for Continual Learning (CL) based on Bayesian inference over the function space rather than the parameters of a deep neural network. This method, referred to as functional regularisation for Continual Learning, avoids forgetting a previous task by constructing and memorising an approximate post... | ['Yee Whye Teh', 'Jonathan Schwarz', 'Michalis K. Titsias', 'Alexander G. de G. Matthews', 'Razvan Pascanu'] | 2019-01-31 | null | https://openreview.net/forum?id=HkxCzeHFDB | https://openreview.net/pdf?id=HkxCzeHFDB | iclr-2020-1 | ['sequential-bayesian-inference'] | ['time-series'] | [ 3.82532984e-01 3.12757492e-01 2.04141080e-01 -3.75204295e-01
-6.77177668e-01 -1.46536097e-01 1.09524751e+00 2.27078289e-01
-8.45504165e-01 8.89922321e-01 3.12799066e-01 7.35124126e-02
-4.05641288e-01 -7.08507955e-01 -1.12568891e+00 -1.07486463e+00
7.98910204e-03 9.29846823e-01 1.44177929e-01 3.56588989... | [7.233018398284912, 3.7820887565612793] |
d64d9696-f536-4ed8-8da0-a651b1d35494 | in-silico-identification-of-potential-natural | 2006.00652 | null | https://arxiv.org/abs/2006.00652v1 | https://arxiv.org/pdf/2006.00652v1.pdf | In silico identification of potential natural product inhibitors of human proteases key to SARS-CoV-2 infection | Presently, there are no approved drugs or vaccines to treat COVID-19 which has spread to over 200 countries and is responsible for over 3,65,000 deaths worldwide. Recent studies have shown that two human proteases, TMPRSS2 and cathepsin L, play a key role in host cell entry of SARS-CoV-2. Importantly, inhibitors of the... | ['Areejit Samal', 'Himansu S. Biswal', 'Nithin Rajan', 'Abhijit Rana', 'R. P. Vivek-Ananth'] | 2020-06-01 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.90273598e-01 -3.85955483e-01 -4.54088032e-01 -5.24860248e-02
-6.92299664e-01 -9.48005736e-01 6.82511702e-02 8.32695365e-01
-5.05497456e-01 1.12913430e+00 1.96815711e-02 -5.57066202e-01
3.49922121e-01 -4.25944746e-01 -5.80465317e-01 -6.72443032e-01
-3.68979424e-01 4.15723294e-01 6.91665933e-02 -2.60680288... | [4.647504806518555, 5.0618414878845215] |
9a804d52-b485-4c8a-bba1-609264429ca6 | data-free-sketch-based-image-retrieval | 2303.07775 | null | https://arxiv.org/abs/2303.07775v1 | https://arxiv.org/pdf/2303.07775v1.pdf | Data-Free Sketch-Based Image Retrieval | Rising concerns about privacy and anonymity preservation of deep learning models have facilitated research in data-free learning (DFL). For the first time, we identify that for data-scarce tasks like Sketch-Based Image Retrieval (SBIR), where the difficulty in acquiring paired photos and hand-drawn sketches limits data... | ['Anjan Dutta', 'Yi-Zhe Song', 'Ayan Kumar Bhunia', 'Abhra Chaudhuri'] | 2023-03-14 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.33373484e-01 -2.82613546e-01 -4.11779225e-01 -4.26889598e-01
-1.39542961e+00 -1.10342550e+00 1.02678585e+00 -5.72887585e-02
-6.29568219e-01 5.74019492e-01 7.54417032e-02 -2.51479805e-01
-2.93340445e-01 -3.91893506e-01 -8.79441023e-01 -5.03158391e-01
3.59958336e-02 2.51803607e-01 -2.71909535e-01 -1.14211254... | [11.55176830291748, 0.6575353145599365] |
4492ead1-b119-4703-b055-b3fe495aaa36 | a-large-scale-english-multi-label-twitter | null | null | https://aclanthology.org/2021.woah-1.16 | https://aclanthology.org/2021.woah-1.16.pdf | A Large-Scale English Multi-Label Twitter Dataset for Cyberbullying and Online Abuse Detection | In this paper, we introduce a new English Twitter-based dataset for cyberbullying detection and online abuse. Comprising 62,587 tweets, this dataset was sourced from Twitter using specific query terms designed to retrieve tweets with high probabilities of various forms of bullying and offensive content, including insul... | ['Yulan He', 'Jo Lumsden', 'Semiu Salawu'] | null | null | null | null | acl-woah-2021-8 | ['abuse-detection'] | ['natural-language-processing'] | [-3.74203980e-01 4.18531537e-01 -2.09928498e-01 -3.08055609e-01
-8.38384926e-01 -5.70513010e-01 4.35327888e-01 9.00051177e-01
-6.31285012e-01 7.00905621e-01 5.77587128e-01 -3.65933366e-02
1.11563072e-01 -6.31680012e-01 -3.05655032e-01 -3.29499394e-01
-6.58674166e-02 2.14022934e-01 7.86821842e-02 -5.07494628... | [8.707881927490234, 10.501469612121582] |
5c7ec035-f896-42ec-842b-b7da034e07a1 | multimodal-material-classification-for-robots | 2004.01160 | null | https://arxiv.org/abs/2004.01160v2 | https://arxiv.org/pdf/2004.01160v2.pdf | Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging | Material recognition can help inform robots about how to properly interact with and manipulate real-world objects. In this paper, we present a multimodal sensing technique, leveraging near-infrared spectroscopy and close-range high resolution texture imaging, that enables robots to estimate the materials of household o... | ['Sonia Chernova', 'Eliot Xing', 'Zackory Erickson', 'Charles C. Kemp', 'Bharat Srirangam'] | 2020-04-02 | null | null | null | null | ['material-classification', 'material-recognition'] | ['computer-vision', 'computer-vision'] | [ 8.92921388e-01 -1.96034104e-01 4.40345071e-02 -4.67540175e-01
-7.42653131e-01 -4.05871302e-01 2.57158250e-01 -3.98104578e-01
-2.61566401e-01 4.59714055e-01 -3.06203514e-01 1.90152720e-01
-4.27557945e-01 -8.17764044e-01 -9.33930576e-01 -8.03111315e-01
1.03325985e-01 7.98663557e-01 -1.20879142e-02 -1.45668313... | [5.920146465301514, -0.9078185558319092] |
5c94b21e-19b4-42d3-b883-e765a9202f0e | blocking-bandits | 1907.11975 | null | https://arxiv.org/abs/1907.11975v1 | https://arxiv.org/pdf/1907.11975v1.pdf | Blocking Bandits | We consider a novel stochastic multi-armed bandit setting, where playing an arm makes it unavailable for a fixed number of time slots thereafter. This models situations where reusing an arm too often is undesirable (e.g. making the same product recommendation repeatedly) or infeasible (e.g. compute job scheduling on ma... | ['Rajat Sen', 'Sujay Sanghavi', 'Soumya Basu', 'Sanjay Shakkottai'] | 2019-07-27 | blocking-bandits-1 | http://papers.nips.cc/paper/8725-blocking-bandits | http://papers.nips.cc/paper/8725-blocking-bandits.pdf | neurips-2019-12 | ['product-recommendation'] | ['miscellaneous'] | [ 2.09494397e-01 5.10270357e-01 -6.63755059e-01 2.75437981e-02
-8.96673262e-01 -1.07990789e+00 -2.40407035e-01 -2.55394708e-02
-6.20717406e-01 9.94679749e-01 -4.62072730e-01 -1.11438549e+00
-1.00144625e+00 -8.21985543e-01 -1.18988335e+00 -9.06820357e-01
-4.64158803e-01 1.07494628e+00 -1.31087914e-01 -1.07970931... | [4.556836128234863, 3.345365524291992] |
79a5ddeb-9cdb-4f59-b5a0-0590a2ecf593 | extracting-opinion-expressions-with-semi | null | null | https://aclanthology.org/D12-1122 | https://aclanthology.org/D12-1122.pdf | Extracting Opinion Expressions with semi-Markov Conditional Random Fields | null | ['Bishan Yang', 'Claire Cardie'] | 2012-07-01 | null | null | null | emnlp-2012-7 | ['fine-grained-opinion-analysis'] | ['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... | [-7.303305149078369, 3.7040557861328125] |
4e405e8a-35a0-4a09-94a3-c963e8b25709 | face-image-reflection-removal | 1903.00865 | null | http://arxiv.org/abs/1903.00865v1 | http://arxiv.org/pdf/1903.00865v1.pdf | Face Image Reflection Removal | Face images captured through the glass are usually contaminated by
reflections. The non-transmitted reflections make the reflection removal more
challenging than for general scenes, because important facial features are
completely occluded. In this paper, we propose and solve the face image
reflection removal problem. ... | ['Ling-Yu Duan', 'Boxin Shi', 'Alex C. Kot', 'Haoliang Li', 'Renjie Wan'] | 2019-03-03 | null | null | null | null | ['reflection-removal'] | ['computer-vision'] | [ 7.47560441e-01 9.71128717e-02 4.63026196e-01 -7.35420167e-01
-6.98384106e-01 7.58381411e-02 4.58251417e-01 -1.28929079e+00
-2.07666792e-02 5.03033102e-01 2.81240612e-01 3.63729954e-01
1.49548769e-01 -6.03683889e-01 -6.85307860e-01 -1.03245747e+00
4.95124280e-01 -7.15130987e-03 -3.58114749e-01 -3.17610204... | [12.909052848815918, -0.05766501650214195] |
22b9b74c-109d-4a67-aa0f-463239ad4492 | signals-to-spikes-for-neuromorphic-regulated | 2106.11169 | null | https://arxiv.org/abs/2106.11169v4 | https://arxiv.org/pdf/2106.11169v4.pdf | Signals to Spikes for Neuromorphic Regulated Reservoir Computing and EMG Hand Gesture Recognition | Surface electromyogram (sEMG) signals result from muscle movement and hence they are an ideal candidate for benchmarking event-driven sensing and computing. We propose a simple yet novel approach for optimizing the spike encoding algorithm's hyper-parameters inspired by the readout layer concept in reservoir computing.... | ['Jean Rouat', 'Fabien Alibart', 'Dominique Drouin', 'Yann Beilliard', 'Ismael Balafrej', 'Nikhil Garg'] | 2021-06-09 | null | null | null | null | ['emg-gesture-recognition'] | ['medical'] | [ 8.36315036e-01 -2.33449385e-01 2.71051198e-01 1.79641008e-01
-3.25006783e-01 -1.85320422e-01 5.37473619e-01 -6.94895685e-02
-7.16517627e-01 9.21099305e-01 3.34671955e-03 2.52538621e-01
-1.44815862e-01 -5.94227433e-01 -9.08049941e-01 -1.28111184e+00
-4.61566180e-01 2.23173663e-01 2.85309911e-01 -3.35973889... | [8.264547348022461, 2.454026699066162] |
4a914cae-6c52-4a75-bd06-8689e6a92e7b | scaling-semantic-parsers-with-on-the-fly | null | null | https://aclanthology.org/D13-1161 | https://aclanthology.org/D13-1161.pdf | Scaling Semantic Parsers with On-the-Fly Ontology Matching | null | ['Luke Zettlemoyer', 'Tom Kwiatkowski', 'Eunsol Choi', 'Yoav Artzi'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['ontology-matching'] | ['knowledge-base'] | [-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... | [-7.342721939086914, 3.7508883476257324] |
d08ee114-6a72-4be3-a13b-66a499f90827 | understanding-dynamic-scenes-using-graph | 2005.04437 | null | https://arxiv.org/abs/2005.04437v5 | https://arxiv.org/pdf/2005.04437v5.pdf | Understanding Dynamic Scenes using Graph Convolution Networks | We present a novel Multi-Relational Graph Convolutional Network (MRGCN) based framework to model on-road vehicle behaviors from a sequence of temporally ordered frames as grabbed by a moving monocular camera. The input to MRGCN is a multi-relational graph where the graph's nodes represent the active and passive agents/... | ['K. Madhava Krishna', 'Anoop Namboodiri', 'Priyesh Vijayan', 'Mahtab Sandhu', 'Balaraman Ravindran', 'Sravan Mylavarapu'] | 2020-05-09 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [-4.52051908e-02 8.21871236e-02 -4.01647955e-01 -5.70936322e-01
-3.73872370e-01 -2.71730810e-01 6.97285950e-01 -2.28992347e-02
-4.08195376e-01 3.05343419e-01 1.90261409e-01 -5.62624037e-01
-2.65792668e-01 -8.63063097e-01 -1.14609051e+00 -4.99774843e-01
-4.81641203e-01 4.84996378e-01 8.06606233e-01 -3.62335622... | [6.074680805206299, 0.7894836068153381] |
b72effc7-7de9-4c08-b816-507d6351238a | adaptive-and-personalized-exercise-generation | 2306.02457 | null | https://arxiv.org/abs/2306.02457v1 | https://arxiv.org/pdf/2306.02457v1.pdf | Adaptive and Personalized Exercise Generation for Online Language Learning | Adaptive learning aims to provide customized educational activities (e.g., exercises) to address individual learning needs. However, manual construction and delivery of such activities is a laborious process. Thus, in this paper, we study a novel task of adaptive and personalized exercise generation for online language... | ['Mrinmaya Sachan', 'Peng Cui'] | 2023-06-04 | null | null | null | null | ['knowledge-tracing'] | ['miscellaneous'] | [ 1.35548666e-01 2.75142789e-01 -2.09989905e-01 -3.82608384e-01
-4.14986044e-01 -9.13085938e-01 2.00359508e-01 3.69367301e-01
-9.32649821e-02 7.83060789e-01 9.69443992e-02 -5.28450310e-01
-2.94852138e-01 -1.02529204e+00 -4.45286453e-01 -1.02398992e-01
1.61587983e-01 2.41851315e-01 5.73780477e-01 -4.85312551... | [10.168664932250977, 7.192203521728516] |
7e311d68-c8a9-48a6-8197-b02d378616a9 | l2-constrained-remnet-for-camera-model | 2009.05379 | null | https://arxiv.org/abs/2009.05379v2 | https://arxiv.org/pdf/2009.05379v2.pdf | L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection | Source camera model identification (CMI) and image manipulation detection are of paramount importance in image forensics. In this paper, we propose an L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet) for performing these two crucial tasks. The proposed network architecture consists of a dynam... | ['Md. Kamrul Hasan', 'Jonathan Wu', 'Abdul Muntakim Rafi'] | 2020-09-10 | null | null | null | null | ['image-manipulation-detection', 'image-forensics'] | ['computer-vision', 'computer-vision'] | [ 4.63355333e-01 -1.76437899e-01 6.34798855e-02 -8.84637982e-02
-5.39923072e-01 -2.41229445e-01 3.00649881e-01 4.84145880e-02
-8.58092666e-01 1.36841461e-01 -5.72432935e-01 -2.79983759e-01
-1.03922170e-02 -6.22360885e-01 -9.08452690e-01 -9.07251835e-01
-1.60683692e-01 -8.76811668e-02 9.01599228e-02 4.53721255... | [12.403790473937988, 0.9979224801063538] |
6675b952-adf9-4c74-92c9-6560c4835ca4 | physically-guided-disentangled-implicit | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhang_Physically-Guided_Disentangled_Implicit_Rendering_for_3D_Face_Modeling_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhang_Physically-Guided_Disentangled_Implicit_Rendering_for_3D_Face_Modeling_CVPR_2022_paper.pdf | Physically-Guided Disentangled Implicit Rendering for 3D Face Modeling | This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for high-fidelity 3D face modeling. The motivation comes from two observations: widely-used graphics renderers yield excessive approximations against photo-realistic imaging, while neural rendering methods are highly en... | ['Dongjin Huang', 'Zhifeng Xie', 'Chengjie Wang', 'Xiaoming Huang', 'Hao Tang', 'Kunlin Liu', 'Renwang Chen', 'Weijian Cao', 'Ying Tai', 'Yanhao Ge', 'Zhenyu Zhang'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['3d-face-modeling'] | ['computer-vision'] | [-3.78959402e-02 2.35046551e-01 6.43973723e-02 -2.55989224e-01
-4.65991527e-01 -4.63986397e-01 7.05686033e-01 -9.43783045e-01
3.17023695e-01 3.87345701e-01 2.36990675e-01 -2.18678609e-01
-1.06349535e-01 -8.27507913e-01 -7.95627058e-01 -1.08524346e+00
2.52760381e-01 3.63988757e-01 -4.55858707e-01 -1.81475013... | [12.865384101867676, -0.24175941944122314] |
bca18210-1832-4b6f-83d9-b78a074dc9c7 | unsupervised-sampling-promoting-for | 2304.04298 | null | https://arxiv.org/abs/2304.04298v1 | https://arxiv.org/pdf/2304.04298v1.pdf | Unsupervised Sampling Promoting for Stochastic Human Trajectory Prediction | The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to c... | ['Kun Zhang', 'Shunxing Fan', 'Zhenhao Chen', 'Guangyi Chen'] | 2023-04-09 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Unsupervised_Sampling_Promoting_for_Stochastic_Human_Trajectory_Prediction_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Unsupervised_Sampling_Promoting_for_Stochastic_Human_Trajectory_Prediction_CVPR_2023_paper.pdf | cvpr-2023-1 | ['trajectory-prediction'] | ['computer-vision'] | [ 1.82764485e-01 -5.75491972e-02 -5.22425950e-01 -2.50856251e-01
-7.24496663e-01 -4.04543519e-01 7.09999323e-01 -4.63936716e-01
-1.81422442e-01 9.48539734e-01 5.60717881e-01 -3.08190167e-01
-1.98391467e-01 -1.03122723e+00 -7.59825468e-01 -7.71955848e-01
1.80000931e-01 5.40361881e-01 7.24152088e-01 2.09213331... | [6.5062408447265625, 0.8591596484184265] |
e38d2acb-4ced-46fc-9b71-a2fadaaf7366 | carla-a-python-library-to-benchmark | 2108.00783 | null | https://arxiv.org/abs/2108.00783v1 | https://arxiv.org/pdf/2108.00783v1.pdf | CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms | Counterfactual explanations provide means for prescriptive model explanations by suggesting actionable feature changes (e.g., increase income) that allow individuals to achieve favorable outcomes in the future (e.g., insurance approval). Choosing an appropriate method is a crucial aspect for meaningful counterfactual e... | ['Gjergji Kasneci', 'Tobias Richter', 'Johannes van den Heuvel', 'Sascha Bielawski', 'Martin Pawelczyk'] | 2021-08-02 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 6.35951981e-02 5.70007145e-01 -1.07364583e+00 -4.95027095e-01
-5.81050038e-01 -3.95674318e-01 1.01205111e+00 2.31520981e-01
-3.77896726e-02 1.33013344e+00 8.86149883e-01 -9.38918650e-01
-2.62974143e-01 -5.62609017e-01 -6.84545100e-01 -1.17089137e-01
-7.93137401e-03 3.71096402e-01 -5.82215488e-01 -9.22335777... | [8.680935859680176, 5.662644863128662] |
847982b1-6a1d-4485-a6ae-97e3f273f9d4 | an-efficient-framework-for-zero-shot-sketch | 2102.04016 | null | https://arxiv.org/abs/2102.04016v1 | https://arxiv.org/pdf/2102.04016v1.pdf | An Efficient Framework for Zero-Shot Sketch-Based Image Retrieval | Recently, Zero-shot Sketch-based Image Retrieval (ZS-SBIR) has attracted the attention of the computer vision community due to it's real-world applications, and the more realistic and challenging setting than found in SBIR. ZS-SBIR inherits the main challenges of multiple computer vision problems including content-base... | ['Clinton Fookes', 'Ethan Goan', 'Sridha Sridharan', 'Simon Denman', 'Osman Tursun'] | 2021-02-08 | null | null | null | null | ['sketch-based-image-retrieval', 'content-based-image-retrieval'] | ['computer-vision', 'computer-vision'] | [ 3.71705681e-01 -4.71924841e-01 -4.80258197e-01 -2.87717193e-01
-7.90071666e-01 -3.76577139e-01 6.84805036e-01 -5.29429391e-02
-6.11012220e-01 4.99524713e-01 -1.93550661e-01 1.29309535e-01
-5.33779263e-01 -1.00299895e+00 -5.09515047e-01 -5.17386913e-01
4.20332372e-01 3.61574680e-01 4.87608910e-01 -2.88894296... | [11.528831481933594, 0.7692456841468811] |
06edc7e2-8bb2-435f-8ce0-7f469b525c36 | adversarial-representation-learning-for-3 | 2305.00011 | null | https://arxiv.org/abs/2305.00011v1 | https://arxiv.org/pdf/2305.00011v1.pdf | Adversarial Representation Learning for Robust Privacy Preservation in Audio | Sound event detection systems are widely used in various applications such as surveillance and environmental monitoring where data is automatically collected, processed, and sent to a cloud for sound recognition. However, this process may inadvertently reveal sensitive information about users or their surroundings, hen... | ['Tuomas Virtanen', 'Konstantinos Drossos', 'Diep Luong', 'Minh Tran', 'Shayan Gharib'] | 2023-04-29 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 9.08704758e-01 3.54906440e-01 4.17553037e-01 -1.56918690e-01
-7.16221571e-01 -8.41598630e-01 4.53467190e-01 2.44084448e-01
-4.33117777e-01 4.43807721e-01 7.63598979e-02 -7.74661079e-02
2.08644181e-01 -8.98123026e-01 -7.17343748e-01 -9.83480573e-01
1.22163016e-02 -1.87601432e-01 1.31278858e-01 1.75433800... | [13.98108196258545, 5.822329044342041] |
649eca70-954d-4415-9fde-5a5b3f8669f4 | an-entity-based-claim-extraction-pipeline-for | 2304.05268 | null | https://arxiv.org/abs/2304.05268v1 | https://arxiv.org/pdf/2304.05268v1.pdf | An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking | Existing fact-checking models for biomedical claims are typically trained on synthetic or well-worded data and hardly transfer to social media content. This mismatch can be mitigated by adapting the social media input to mimic the focused nature of common training claims. To do so, Wuehrl & Klinger (2022) propose to ex... | ['Roman Klinger', 'Lara Grimminger', 'Amelie Wührl'] | 2023-04-11 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [ 4.21449721e-01 6.89918637e-01 -1.83121413e-01 6.89677373e-02
-1.18992281e+00 -6.07080698e-01 6.40144229e-01 9.49811697e-01
-7.79364824e-01 9.83891904e-01 3.70120734e-01 -4.31713670e-01
6.62187040e-02 -7.90325701e-01 -5.03769040e-01 -7.68148974e-02
3.84886891e-01 4.68827307e-01 5.12888670e-01 -2.80585557... | [8.694701194763184, 8.9251070022583] |
87bb69ff-4d97-455b-a1d3-b732d0d0a484 | multi-frame-super-resolution-reconstruction | 1812.09375 | null | http://arxiv.org/abs/1812.09375v1 | http://arxiv.org/pdf/1812.09375v1.pdf | Multi-Frame Super-Resolution Reconstruction with Applications to Medical Imaging | The optical resolution of a digital camera is one of its most crucial
parameters with broad relevance for consumer electronics, surveillance systems,
remote sensing, or medical imaging. However, resolution is physically limited
by the optics and sensor characteristics. In addition, practical and economic
reasons often ... | ['Thomas Köhler'] | 2018-12-21 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 8.51981521e-01 -4.58186448e-01 3.42501774e-02 -1.37003556e-01
-7.51108050e-01 -2.07590923e-01 2.69574344e-01 -1.17402755e-01
-4.85560745e-01 1.06111550e+00 -1.87792897e-01 1.63694888e-01
-2.96933353e-01 -7.30611682e-01 -1.61451161e-01 -9.35907602e-01
1.57557517e-01 4.32194993e-02 4.75851953e-01 -1.12000823... | [11.110040664672852, -2.3406996726989746] |
e4de7791-6f25-4a3a-8be0-5710811c92f8 | large-age-gap-face-verification-by-feature | 1602.06149 | null | http://arxiv.org/abs/1602.06149v1 | http://arxiv.org/pdf/1602.06149v1.pdf | Large age-gap face verification by feature injection in deep networks | This paper introduces a new method for face verification across large age
gaps and also a dataset containing variations of age in the wild, the Large
Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The
proposed method exploits a deep convolutional neural network (DCNN) pre-trained
for the face... | ['Simone Bianco'] | 2016-02-19 | null | null | null | null | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 8.92817825e-02 -1.44495564e-02 4.89789620e-02 -6.00865960e-01
-5.28368831e-01 -2.52238810e-01 7.98926592e-01 -1.46862283e-01
-5.38355291e-01 6.27046168e-01 -1.59781575e-01 6.00617416e-02
-1.90300629e-01 -6.37116432e-01 -6.71609700e-01 -7.27998435e-01
-3.75370800e-01 5.06055057e-01 -3.04167092e-01 -3.33550088... | [13.32540225982666, 0.6297290921211243] |
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