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f2d1a241-15dc-476d-9c87-4033771974fe | rest-retrieve-self-train-for-generative | 2209.15000 | null | https://arxiv.org/abs/2209.15000v1 | https://arxiv.org/pdf/2209.15000v1.pdf | REST: REtrieve & Self-Train for generative action recognition | This work is on training a generative action/video recognition model whose output is a free-form action-specific caption describing the video (rather than an action class label). A generative approach has practical advantages like producing more fine-grained and human-readable output, and being naturally open-world. To... | ['Georgios Tzimiropoulos', 'Brais Martinez', 'Enrique Sanchez', 'Adrian Bulat'] | 2022-09-29 | null | null | null | null | ['zero-shot-action-recognition', 'video-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.05423176e-01 1.65324256e-01 -3.70762721e-02 -3.44363511e-01
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1.07079387e-01 6.87475562e-01 3.65687519e-01 -1.26492754... | [8.751005172729492, 0.7382466197013855] |
a3e93e8c-bad8-4c69-9d34-5817093e017a | autoencoder-based-time-series-clustering-with | 2002.03624 | null | https://arxiv.org/abs/2002.03624v1 | https://arxiv.org/pdf/2002.03624v1.pdf | Autoencoder-based time series clustering with energy applications | Time series clustering is a challenging task due to the specific nature of the data. Classical approaches do not perform well and need to be adapted either through a new distance measure or a data transformation. In this paper we investigate the combination of a convolutional autoencoder and a k-medoids algorithm to pe... | ['Georges Hébrail', 'Benoît Grossin', 'Anne de Moliner', 'Guillaume Richard', 'Guillaume Germaine'] | 2020-02-10 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.65751863e-01 -4.21252191e-01 5.14922619e-01 -3.40325147e-01
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-5.06298304e-01 5.44068933e-01 1.18479632e-01 -2.09577024... | [7.247866630554199, 3.2769339084625244] |
18a911da-c52d-4a2a-a959-f0fe42908e9b | one-size-fits-all-hypernetwork-for-tunable | 2206.05970 | null | https://arxiv.org/abs/2206.05970v3 | https://arxiv.org/pdf/2206.05970v3.pdf | Hypernetwork-Based Adaptive Image Restoration | Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant reduction in the number of parameters. In contrast to existing methods, our approach can restore images ... | ['Gil Ben-Artzi', 'Shai Aharon'] | 2022-06-13 | null | null | null | null | ['color-image-denoising', 'jpeg-artifact-correction', 'jpeg-artifact-removal'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.25588453e-01 -5.06068170e-01 3.86919707e-01 -2.32001230e-01
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1.63004503e-01 -6.37572408e-02 3.15301478e-01 -4.10089344... | [11.156813621520996, -2.2280805110931396] |
21c1efba-7804-48d0-b4dc-51ccc8ff7265 | patient-contrastive-learning-a-performant | 2104.04569 | null | https://arxiv.org/abs/2104.04569v1 | https://arxiv.org/pdf/2104.04569v1.pdf | Patient Contrastive Learning: a Performant, Expressive, and Practical Approach to ECG Modeling | Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate this effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of ECGs from a large numb... | ['Puneet Batra', 'Collin Stultz', 'Aaron Aguirre', 'Steven Song', 'Erik Reinertsen', 'Nathaniel Diamant'] | 2021-04-09 | null | null | null | null | ['electrocardiography-ecg', 'unsupervised-pre-training'] | ['methodology', 'methodology'] | [ 4.81579214e-01 2.02459097e-01 -5.32436728e-01 -5.58401465e-01
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-1.90025315e-01 5.85265279e-01 -5.87432325e-01 4.38942134... | [8.024843215942383, 6.51099157333374] |
be4e516c-ed5f-49c4-bb1d-118a3032c8db | laxity-aware-scalable-reinforcement-learning | 2306.16619 | null | https://arxiv.org/abs/2306.16619v1 | https://arxiv.org/pdf/2306.16619v1.pdf | Laxity-Aware Scalable Reinforcement Learning for HVAC Control | Demand flexibility plays a vital role in maintaining grid balance, reducing peak demand, and saving customers' energy bills. Given their highly shiftable load and significant contribution to a building's energy consumption, Heating, Ventilation, and Air Conditioning (HVAC) systems can provide valuable demand flexibilit... | ['Yize Chen', 'Yuxin Pan', 'Ruohong Liu'] | 2023-06-29 | null | null | null | null | ['reinforcement-learning-1'] | ['methodology'] | [-3.44645113e-01 -7.00545162e-02 -1.09071657e-01 8.76877010e-02
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-1.48373097e-01 7.62939215e-01 -3.51078063e-01 -1.69760048... | [5.639039993286133, 2.472252130508423] |
269e6093-4f2c-44ee-82b1-b370b683a4c9 | on-influence-functions-classification | 2305.16094 | null | https://arxiv.org/abs/2305.16094v1 | https://arxiv.org/pdf/2305.16094v1.pdf | On Influence Functions, Classification Influence, Relative Influence, Memorization and Generalization | Machine learning systems such as large scale recommendation systems or natural language processing systems are usually trained on billions of training points and are associated with hundreds of billions or trillions of parameters. Improving the learning process in such a way that both the training load is reduced and t... | ['Ilqar Ramazanli', 'Ousmane Dia', 'Michael Kounavis'] | 2023-05-25 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 2.58440316e-01 1.30495295e-01 -7.20994025e-02 -3.93583238e-01
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-1.07850187e-01 6.20773077e-01 9.91582796e-02 -6.39505610... | [8.38067626953125, 4.30801248550415] |
a15b10ea-da39-4428-ac44-a1caa49758b9 | variational-quantum-soft-actor-critic-for | 2212.11681 | null | https://arxiv.org/abs/2212.11681v1 | https://arxiv.org/pdf/2212.11681v1.pdf | Variational Quantum Soft Actor-Critic for Robotic Arm Control | Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning parad... | ['Antonio Policicchio', 'Mattia Pavese', 'Matteo Conterno', 'Ludovico Bozzolo', 'Paola Barillà', 'Alberto Acuto'] | 2022-12-20 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [ 2.09876567e-01 2.14862570e-01 -2.02405974e-01 1.64540157e-01
-5.58180273e-01 -2.12632373e-01 8.27677727e-01 8.91726464e-02
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3.17686871e-02 4.21639472e-01 -3.07204090e-02 -7.01503873... | [5.592259407043457, 4.856123924255371] |
02c0cd07-40dc-42a6-8814-b3f70de2acfd | dp-ssl-towards-robust-semi-supervised | 2110.13740 | null | https://arxiv.org/abs/2110.13740v1 | https://arxiv.org/pdf/2110.13740v1.pdf | DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples | The scarcity of labeled data is a critical obstacle to deep learning. Semi-supervised learning (SSL) provides a promising way to leverage unlabeled data by pseudo labels. However, when the size of labeled data is very small (say a few labeled samples per class), SSL performs poorly and unstably, possibly due to the low... | ['Shuigeng Zhou', 'Lu Zhang', 'Jiandong Ding', 'Yi Xu'] | 2021-10-26 | null | http://proceedings.neurips.cc/paper/2021/hash/854d6fae5ee42911677c739ee1734486-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/854d6fae5ee42911677c739ee1734486-Paper.pdf | neurips-2021-12 | ['semi-supervised-image-classification'] | ['computer-vision'] | [-2.94146650e-02 3.37508082e-01 -5.41662157e-01 -9.68758583e-01
-1.37763774e+00 -7.84807384e-01 3.79200667e-01 -6.28959686e-02
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4.80944514e-01 7.24677801e-01 -1.24801114e-01 3.38854223... | [9.503409385681152, 3.821488380432129] |
1c6194e8-a676-4ab1-8039-90a3151a6e7b | camouflaged-object-detection | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Camouflaged_Object_Detection_CVPR_2020_paper.pdf | Camouflaged Object Detection | We present a comprehensive study on a new task named camouflaged object detection (COD), which aims to identify objects that are "seamlessly" embedded in their surroundings. The high intrinsic similarities between the target object and the background make COD far more challenging than the traditional object detection t... | [' Ling Shao', ' Jianbing Shen', ' Ming-Ming Cheng', ' Guolei Sun', ' Ge-Peng Ji', 'Deng-Ping Fan'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['camouflaged-object-segmentation'] | ['computer-vision'] | [ 2.17209503e-01 -4.58184749e-01 -3.57960165e-01 6.44253567e-02
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1.60208255e-01 6.80301636e-02 7.62752712e-01 1.75174475... | [9.653617858886719, -0.19412729144096375] |
7150c21f-dab0-4d1c-8910-cc8667d20dbb | androdet-an-adaptive-android-obfuscation | null | null | https://0m1d.com/assets/pdf/J5.pdf | https://0m1d.com/assets/pdf/J5.pdf | AndrODet: An Adaptive Android Obfuscation Detector | Obfuscation techniques modify an app’s source (or machine) code in order to make it more difficult to analyze. This is typically applied to protect intellectual property in benign apps, or to hinder the process of extracting actionable information in the case malware. Since malware analysis often requires considerable ... | ['Lorena Gonzáles-Manzano', 'Juan Tapiador', 'Jose Maria de Fuentes', 'Omid Mirzaei'] | 2019-01-01 | null | null | null | future-generation-computer-systems-2019-1 | ['android-malware-detection'] | ['miscellaneous'] | [ 2.44234726e-01 -3.03095520e-01 -8.62134516e-01 2.19579428e-01
-6.42211556e-01 -8.84738803e-01 3.94975543e-01 2.30176613e-01
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-3.42443287e-01 -9.84307304e-02 3.08372736e-01 2.41239205... | [14.417659759521484, 9.678044319152832] |
2d94aea9-ca92-4318-9ff1-80be7ecf4254 | pushing-one-pair-of-labels-apart-each-time-in | 2302.14695 | null | https://arxiv.org/abs/2302.14695v1 | https://arxiv.org/pdf/2302.14695v1.pdf | Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels | In Multi-Label Learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be Single Positive Multi-Label Learning (SPMLL), where only one positive label needs to be ... | ['Songcan Chen', 'Xinrui Wang', 'Xiang Li'] | 2023-02-28 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 4.10544097e-01 -1.17130987e-01 -2.73254752e-01 -5.71764827e-01
-1.19465911e+00 -6.66332662e-01 2.71449327e-01 2.90610760e-01
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-1.40097380e-01 -5.15421689e-01 -5.44268787e-01 -1.07172704e+00
3.39776933e-01 2.97243178e-01 1.22420117e-01 2.25479245... | [9.439359664916992, 4.006559371948242] |
1dc04d6f-783f-460f-9802-f89d582eb774 | extracting-label-specific-key-input-features | 2202.06474 | null | https://arxiv.org/abs/2202.06474v1 | https://arxiv.org/pdf/2202.06474v1.pdf | Extracting Label-specific Key Input Features for Neural Code Intelligence Models | The code intelligence (CI) models are often black-box and do not offer any insights on the input features that they learn for making correct predictions. This opacity may lead to distrust in their prediction and hamper their wider adoption in safety-critical applications. In recent, the program reduction technique is w... | ['Md Rafiqul Islam Rabin'] | 2022-02-14 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 3.61966908e-01 2.86209822e-01 -5.70213258e-01 -3.92659456e-01
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1.66169256e-01 -1.11019969e-01 5.73367536e-01 -1.94512844... | [7.338871955871582, 7.741972923278809] |
da7906ba-efc1-4dd2-887f-e1e04f4d1176 | recognition-of-they-them-as-singular-personal | null | null | https://aclanthology.org/2022.naacl-main.250 | https://aclanthology.org/2022.naacl-main.250.pdf | Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution | As using they/them as personal pronouns becomes increasingly common in English, it is important that coreference resolution systems work as well for individuals who use personal “they” as they do for those who use gendered personal pronouns. We introduce a new benchmark for coreference resolution systems which evaluate... | ['Rachel Rudinger', 'Connor Baumler'] | null | null | null | null | naacl-2022-7 | ['coreference-resolution'] | ['natural-language-processing'] | [-7.16138780e-02 3.97038460e-01 -6.36989415e-01 -3.10043871e-01
-9.08531964e-01 -9.94937420e-01 8.58030856e-01 4.17992741e-01
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5.43453395e-01 1.38645732e+00 1.89085126e-01 -8.59856963... | [9.305306434631348, 9.541956901550293] |
1d1a1615-7099-4e08-8d5a-5ee3d18ff6f9 | setsum-summarization-and-visualization-of-1 | 2207.03640 | null | https://arxiv.org/abs/2207.03640v1 | https://arxiv.org/pdf/2207.03640v1.pdf | SETSum: Summarization and Visualization of Student Evaluations of Teaching | Student Evaluations of Teaching (SETs) are widely used in colleges and universities. Typically SET results are summarized for instructors in a static PDF report. The report often includes summary statistics for quantitative ratings and an unsorted list of open-ended student comments. The lack of organization and summar... | ['Mohit Bansal', 'A. T. Panter', 'Viji Sathy', 'Shiyue Zhang', 'Yinuo Hu'] | 2022-07-08 | setsum-summarization-and-visualization-of | https://aclanthology.org/2022.naacl-demo.9 | https://aclanthology.org/2022.naacl-demo.9.pdf | naacl-acl-2022-7 | ['aspect-extraction'] | ['natural-language-processing'] | [-3.64078283e-01 2.16093257e-01 -3.98676306e-01 -6.47711396e-01
-1.22397184e+00 -1.26798093e+00 4.76067923e-02 9.69185412e-01
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-2.88932979e-01 -5.76570868e-01 -5.42976022e-01 -1.55571193e-01
4.06632245e-01 2.39293519e-02 9.59909260e-02 -1.57722786... | [11.18919563293457, 9.262994766235352] |
ebc66325-f652-448e-8d53-f65519f601ba | reinforced-axial-refinement-network-for | 2008.13748 | null | https://arxiv.org/abs/2008.13748v1 | https://arxiv.org/pdf/2008.13748v1.pdf | Reinforced Axial Refinement Network for Monocular 3D Object Detection | Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. This is an ill-posed problem with a major difficulty lying in the information loss by depth-agnostic cameras. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between ... | ['Jie zhou', 'Jiwen Lu', 'Lijie Liu', 'Chufan Wu', 'Qi Tian', 'Lingxi Xie'] | 2020-08-31 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2822_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620528.pdf | eccv-2020-8 | ['vehicle-pose-estimation'] | ['computer-vision'] | [ 1.20610058e-01 7.41790235e-02 -9.61359888e-02 -1.50580823e-01
-4.91771162e-01 -4.17222649e-01 4.28246230e-01 -3.07996184e-01
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1.95462093e-01 8.28882337e-01 6.88152850e-01 4.72064108... | [7.794862747192383, -2.525606155395508] |
50132051-c75a-4144-ad5e-c4d751daad2c | masked-trajectory-models-for-prediction | 2305.02968 | null | https://arxiv.org/abs/2305.02968v1 | https://arxiv.org/pdf/2305.02968v1.pdf | Masked Trajectory Models for Prediction, Representation, and Control | We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making. MTM takes a trajectory, such as a state-action sequence, and aims to reconstruct the trajectory conditioned on random subsets of the same trajectory. By training with a highly randomized masking pattern, MTM learns vers... | ['Aravind Rajeswaran', 'Pieter Abbeel', 'Igor Mordatch', 'Yixin Lin', 'Kevin Stone', 'Arjun Majumdar', 'Philipp Wu'] | 2023-05-04 | null | null | null | null | ['offline-rl', 'continuous-control'] | ['playing-games', 'playing-games'] | [ 1.59249291e-01 3.14398617e-01 -9.32182431e-01 -5.11659384e-02
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-3.40970188e-01 5.83611965e-01 1.20917857e-01 -3.00786525... | [4.12246561050415, 1.7543448209762573] |
167da35e-192c-4092-be5d-761c3e1cc4c5 | a-multi-gate-encoder-for-joint-entity-and | null | null | https://aclanthology.org/2022.ccl-1.75 | https://aclanthology.org/2022.ccl-1.75.pdf | A Multi-Gate Encoder for Joint Entity and Relation Extraction | “Named entity recognition and relation extraction are core sub-tasks of relational triple extraction. Recent studies have used parameter sharing or joint decoding to create interaction between these two tasks. However, ensuring the specificity of task-specific traits while the two tasks interact properly is a huge diff... | ['Li Shengyang', 'Gong Shuai', 'Liu Anqi', 'Liu Yunfei', 'Xiong Xiong'] | null | null | null | null | ccl-2022-10 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 8.08354188e-03 5.59269726e-01 -3.75253737e-01 -6.87806964e-01
-8.12251508e-01 -4.84746873e-01 5.89749813e-01 3.89363281e-02
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-4.11822723e-04 4.55279440e-01 1.49158269e-01 -3.38055909... | [9.277892112731934, 8.716504096984863] |
ba00e79b-8822-4b8f-8dd0-45d74256a765 | simplifying-models-with-unlabeled-output-data-1 | null | null | https://openreview.net/forum?id=GXJPLbB5P-y | https://openreview.net/pdf?id=GXJPLbB5P-y | Simplifying Models with Unlabeled Output Data | We focus on prediction problems with high-dimensional outputs that are subject to output validity constraints, e.g. a pseudocode-to-code translation task where the code must compile. For these problems, labeled input-output pairs are expensive to obtain, but "unlabeled" outputs, i.e. outputs without corresponding input... | ['Percy Liang', 'Tengyu Ma', 'Sang Michael Xie'] | 2020-09-28 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 5.52007139e-01 2.83701777e-01 -1.36532605e-01 -5.88680625e-01
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4.44647312e-01 -5.73311210e-01 -1.52440453e+00 -5.29996872e-01
2.40317345e-01 4.30772781e-01 -2.84182787e-01 1.44503102... | [7.894435882568359, 7.722781181335449] |
70d81846-2aac-41d5-a5fb-04808dc1cf8e | rapid-and-robust-endoscopic-content-area | 2210.14771 | null | https://arxiv.org/abs/2210.14771v1 | https://arxiv.org/pdf/2210.14771v1.pdf | Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset | Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several facto... | ['Tom Vercauteren', 'Sebastien Ourselin', 'Martin Huber', 'Luis C. Garcia-Peraza-Herrera', 'Charlie Budd'] | 2022-10-26 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 1.26744658e-01 2.72601813e-01 -1.02511505e-02 -1.09214978e-02
-9.51979160e-01 -6.37084126e-01 3.40250522e-01 6.14458263e-01
-7.72826731e-01 4.73022968e-01 1.67851925e-01 -3.15583467e-01
-1.29318118e-01 -4.37004298e-01 -5.93976557e-01 -6.20516777e-01
-3.15564007e-01 2.30692953e-01 4.39217985e-01 1.40783247... | [14.049025535583496, -3.1598422527313232] |
80ccd3d4-000a-4e50-a926-b2ecf4709803 | comma-deer-common-sense-aware-multimodal | null | null | https://aclanthology.org/2022.coling-1.608 | https://aclanthology.org/2022.coling-1.608.pdf | COMMA-DEER: COmmon-sense Aware Multimodal Multitask Approach for Detection of Emotion and Emotional Reasoning in Conversations | Mental health is a critical component of the United Nations’ Sustainable Development Goals (SDGs), particularly Goal 3, which aims to provide “good health and well-being”. The present mental health treatment gap is exacerbated by stigma, lack of human resources, and lack of research capability for implementation and po... | ['Pushpak Bhattacharyya', 'Asif Ekbal', 'Gopendra Vikram Singh', 'Soumitra Ghosh'] | null | null | null | null | coling-2022-10 | ['common-sense-reasoning'] | ['reasoning'] | [ 2.55833179e-01 5.87747276e-01 -1.97044104e-01 -5.29559791e-01
-1.29591584e+00 3.46653499e-02 3.42220962e-01 4.28599536e-01
-3.43218416e-01 5.58108807e-01 6.54824615e-01 2.14562014e-01
5.57349548e-02 -4.00153875e-01 6.88832104e-02 -4.18882221e-01
1.51451260e-01 2.51423031e-01 -5.66909432e-01 -5.08131742... | [13.125397682189941, 5.66584587097168] |
94858475-4bf6-4a43-b973-3451289b3e09 | meta-variational-monte-carlo | 2011.10614 | null | https://arxiv.org/abs/2011.10614v1 | https://arxiv.org/pdf/2011.10614v1.pdf | Meta Variational Monte Carlo | An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble. A model-agnostic meta-learning approach is proposed to solve the associated learning problem and a preliminary experimental study of random Max-Cut problems i... | ['Shravan Veerapaneni', 'Brian Chen', 'Oliver Knitter', 'James Stokes', 'Tianchen Zhao'] | 2020-11-20 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.54915035e-01 9.84966084e-02 -5.32758355e-01 -1.56045347e-01
-1.37957752e+00 -2.83823639e-01 5.38392305e-01 -1.73628598e-01
-5.84004104e-01 1.43088853e+00 -3.91167045e-01 -2.25297585e-01
-4.72246587e-01 -9.03095543e-01 -7.14937747e-01 -1.08807898e+00
2.42228545e-02 9.09693182e-01 -4.40899372e-01 -2.42565736... | [5.609994888305664, 4.857828617095947] |
23cde788-4385-4810-94ca-702c00ae2ba0 | co-learning-of-word-representations-and | null | null | https://aclanthology.org/C14-1015 | https://aclanthology.org/C14-1015.pdf | Co-learning of Word Representations and Morpheme Representations | null | ['Tie-Yan Liu', 'Bin Gao', 'Siyu Qiu', 'Qing Cui', 'Jiang Bian'] | 2014-08-01 | co-learning-of-word-representations-and-1 | https://aclanthology.org/C14-1015 | https://aclanthology.org/C14-1015.pdf | coling-2014-8 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.416479587554932, 3.5920908451080322] |
1c9bced2-5170-4a91-97f8-9d00222d1b98 | learning-to-caricature-via-semantic-shape | 2008.05090 | null | https://arxiv.org/abs/2008.05090v2 | https://arxiv.org/pdf/2008.05090v2.pdf | Learning to Caricature via Semantic Shape Transform | Caricature is an artistic drawing created to abstract or exaggerate facial features of a person. Rendering visually pleasing caricatures is a difficult task that requires professional skills, and thus it is of great interest to design a method to automatically generate such drawings. To deal with large shape changes, w... | ['Ming-Hsuan Yang', 'Yijun Li', 'Yu-Ting Chang', 'Yi-Hsuan Tsai', 'Wei-Chih Hung', 'Deng Cai', 'Wenqing Chu'] | 2020-08-12 | null | null | null | null | ['caricature'] | ['computer-vision'] | [ 6.05785370e-01 4.20528263e-01 4.95523036e-01 -5.31494737e-01
-1.62838936e-01 -5.30425310e-01 6.59363031e-01 -4.14828032e-01
1.11836590e-01 5.62278986e-01 8.00197721e-02 4.38778624e-02
3.22350860e-01 -8.96169603e-01 -8.81164551e-01 -1.73306197e-01
5.97544432e-01 2.99985886e-01 -1.12272188e-01 -2.88710356... | [12.005443572998047, -0.44594117999076843] |
178edf45-0325-40c1-860f-403fdac4ba58 | d2net-a-denoising-and-dereverberation-network | null | null | https://ieeexplore.ieee.org/abstract/document/9979863 | http://www.apsipa.org/proceedings/2022/APSIPA%202022/ThPM1-2/1570833515.pdf | D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer | The simultaneous denoising and dereverberation for single-channel mixture speech under the complicated acoustic environment is considered to be a challengeable task. In this paper, we propose a denoising and dereverberation network named as D²Net in which a two-branch encoder (TBE) is designed to extract and selectivel... | ['and Ying Hu', 'Yadong Chen', 'Wenbing Wei', 'Liusong Wang'] | 2022-11-21 | null | null | null | apsipa-asc-2022-11 | ['speech-enhancement'] | ['speech'] | [-2.57415771e-01 -3.82893354e-01 4.87650424e-01 -3.66944999e-01
-1.23424852e+00 -4.28866446e-01 1.65155485e-01 -6.85455084e-01
-3.70094240e-01 3.49187523e-01 5.67417085e-01 -4.72218633e-01
1.06223419e-01 -2.19890103e-01 -4.34915513e-01 -1.02798712e+00
3.73961449e-01 -2.10801288e-01 -3.12577486e-02 -2.84138858... | [14.900410652160645, 5.96978235244751] |
3e9c78e2-8022-4c76-b044-1870c4b2c5f3 | data-efficient-french-language-modeling-with | 2306.01497 | null | https://arxiv.org/abs/2306.01497v1 | https://arxiv.org/pdf/2306.01497v1.pdf | Data-Efficient French Language Modeling with CamemBERTa | Recent advances in NLP have significantly improved the performance of language models on a variety of tasks. While these advances are largely driven by the availability of large amounts of data and computational power, they also benefit from the development of better training methods and architectures. In this paper, w... | ['Djamé Seddah', 'Benoît Sagot', 'Wissam Antoun'] | 2023-06-02 | null | null | null | null | ['dependency-parsing', 'part-of-speech-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-4.62489665e-01 -1.42566571e-02 -2.13309318e-01 -6.38989031e-01
-1.21144605e+00 -1.04248452e+00 6.47474051e-01 2.43944541e-01
-5.70206285e-01 8.69531572e-01 3.70141268e-01 -7.71383405e-01
1.99480817e-01 -5.36829293e-01 -8.11413407e-01 -1.35872766e-01
2.31995389e-01 7.01549709e-01 2.74018198e-01 -3.20199937... | [10.597936630249023, 9.878244400024414] |
ff049a98-3edb-445c-aec8-7ae35e14caf8 | exploration-of-unranked-items-in-safe-online | 2305.01202 | null | https://arxiv.org/abs/2305.01202v1 | https://arxiv.org/pdf/2305.01202v1.pdf | Exploration of Unranked Items in Safe Online Learning to Re-Rank | Bandit algorithms for online learning to rank (OLTR) problems often aim to maximize long-term revenue by utilizing user feedback. From a practical point of view, however, such algorithms have a high risk of hurting user experience due to their aggressive exploration. Thus, there has been a rising demand for safe explor... | ['Togashi Riku', 'Kenshi Abe', 'Kaito Ariu', 'Hiroaki Shiino'] | 2023-05-02 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.73529521e-01 1.49252862e-01 -8.56276691e-01 -2.85032272e-01
-1.19155800e+00 -7.05788612e-01 -4.23031934e-02 3.88862222e-01
-5.58354616e-01 1.22641158e+00 2.81243861e-01 -4.78424788e-01
-6.25869930e-01 -5.29843211e-01 -9.66907740e-01 -6.85208082e-01
-3.98331165e-01 4.60890859e-01 -2.28016928e-01 8.85231644... | [4.604611396789551, 3.3223555088043213] |
16985df8-995e-41f0-82b7-e15da1372e06 | incorporating-structured-sentences-with-time | 2304.04717 | null | https://arxiv.org/abs/2304.04717v1 | https://arxiv.org/pdf/2304.04717v1.pdf | Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction | Temporal relation prediction in incomplete temporal knowledge graphs (TKGs) is a popular temporal knowledge graph completion (TKGC) problem in both transductive and inductive settings. Traditional embedding-based TKGC models (TKGE) rely on structured connections and can only handle a fixed set of entities, i.e., the tr... | ['Yong Dou', 'Zhen Huang', 'Fenglong Su', 'Chengjin Xu', 'Zhongwu Chen'] | 2023-04-10 | null | null | null | null | ['knowledge-graph-completion', 'temporal-knowledge-graph-completion'] | ['knowledge-base', 'knowledge-base'] | [ 9.38421264e-02 3.98377389e-01 -8.25788856e-01 -3.20915222e-01
-4.11609381e-01 -6.71624124e-01 8.27731729e-01 9.68882665e-02
-2.78216690e-01 8.29490900e-01 4.25922751e-01 -3.64354998e-01
-3.97842735e-01 -1.15403664e+00 -9.19047117e-01 -6.55467510e-01
-5.09603083e-01 7.16287792e-01 4.80795026e-01 -4.71262783... | [8.626049041748047, 8.061673164367676] |
763ec004-67ca-4b92-9ace-20feeb175575 | towards-accurate-translation-via-semantically | 2306.12089 | null | https://arxiv.org/abs/2306.12089v1 | https://arxiv.org/pdf/2306.12089v1.pdf | Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints | Lexically-constrained NMT (LNMT) aims to incorporate user-provided terminology into translations. Despite its practical advantages, existing work has not evaluated LNMT models under challenging real-world conditions. In this paper, we focus on two important but under-studied issues that lie in the current evaluation pr... | ['Jaegul Choo', 'Cheonbok Park', 'Hyoung-Gyu Lee', 'Dayeon Ki', 'Koanho Lee', 'Yujin Baek'] | 2023-06-21 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 2.25704923e-01 1.03284582e-01 -4.14304882e-01 -3.38037372e-01
-7.71495342e-01 -8.12532008e-01 8.19138467e-01 -1.83937147e-01
-5.91978312e-01 7.48759747e-01 2.64581561e-01 -6.35876000e-01
3.84148248e-02 -3.71528953e-01 -7.64579654e-01 -2.67416239e-01
3.17243785e-01 6.91440284e-01 1.01470537e-01 -4.48749840... | [11.281661033630371, 9.958633422851562] |
94a4ae65-5002-47c2-8b2b-b79b3eb48132 | auto-encoding-progressive-generative | 1903.03477 | null | http://arxiv.org/abs/1903.03477v1 | http://arxiv.org/pdf/1903.03477v1.pdf | Auto-Encoding Progressive Generative Adversarial Networks For 3D Multi Object Scenes | 3D multi object generative models allow us to synthesize a large range of
novel 3D multi object scenes and also identify objects, shapes, layouts and
their positions. But multi object scenes are difficult to create because of the
dataset being multimodal in nature. The conventional 3D generative adversarial
models are ... | ['Pratik Kanani', 'Manan Oza', 'Vedant Singh', 'Himanshu Vaghela'] | 2019-03-08 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 2.31116757e-01 1.75155714e-01 8.64109874e-01 -2.75133252e-01
-7.29258955e-01 -6.45047665e-01 9.49397445e-01 -2.58915633e-01
1.15724958e-01 1.04027164e+00 7.99347758e-02 3.37370038e-01
-2.23139897e-01 -1.21009362e+00 -9.81219530e-01 -5.96958399e-01
2.02443153e-01 1.10098541e+00 1.24190114e-01 -2.29729474... | [11.664518356323242, -0.36715275049209595] |
1b95587b-b83f-44c4-a386-dc29a104f58f | reinforcement-learning-with-demonstrations | 2212.01509 | null | https://arxiv.org/abs/2212.01509v2 | https://arxiv.org/pdf/2212.01509v2.pdf | Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward | Reinforcement learning often suffer from the sparse reward issue in real-world robotics problems. Learning from demonstration (LfD) is an effective way to eliminate this problem, which leverages collected expert data to aid online learning. Prior works often assume that the learning agent and the expert aim to accompli... | ['Jianyu Chen', 'Chengming Shi', 'Zheng Wu', 'Jingyue Gao', 'Yanjiang Guo'] | 2022-12-03 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 5.47184199e-02 9.07786638e-02 -1.99614525e-01 -2.54727036e-01
-6.55332446e-01 -5.47190666e-01 5.21365225e-01 -2.29315124e-02
-7.84668028e-01 1.20245159e+00 1.07454829e-01 -4.70230021e-02
-1.66493967e-01 -2.78370023e-01 -9.74002957e-01 -6.11763895e-01
-3.44331115e-01 6.09190643e-01 3.19735974e-01 -2.09157541... | [4.29988431930542, 1.3532452583312988] |
7a10f4bb-5dc4-4643-8f3e-febb18681f34 | incremental-generalized-category-discovery | 2304.14310 | null | https://arxiv.org/abs/2304.14310v1 | https://arxiv.org/pdf/2304.14310v1.pdf | Incremental Generalized Category Discovery | We explore the problem of Incremental Generalized Category Discovery (IGCD). This is a challenging category incremental learning setting where the goal is to develop models that can correctly categorize images from previously seen categories, in addition to discovering novel ones. Learning is performed over a series of... | ['Oisin Mac Aodha', 'Bingchen Zhao'] | 2023-04-27 | null | null | null | null | ['fine-grained-visual-categorization', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 5.12187064e-01 -9.15466398e-02 -2.04789504e-01 -3.99387956e-01
-4.70532328e-01 -6.45714760e-01 6.30833745e-01 4.05582011e-01
-4.01103675e-01 6.32105768e-01 -2.01179951e-01 -2.14625418e-01
-1.72818631e-01 -5.44456422e-01 -7.63471901e-01 -6.48337066e-01
-2.86004126e-01 5.98647296e-01 4.09085959e-01 4.80337948... | [9.869730949401855, 3.217721462249756] |
78f0683c-e302-4d6b-b3d6-5f9e5d0947f1 | decoding-finger-movements-from-ecog-signals | null | null | https://www.frontiersin.org/articles/10.3389/fnins.2012.00029/full | https://www.frontiersin.org/articles/10.3389/fnins.2012.00029/full | Decoding finger movements from ECoG signals using switching linear models | One of the most interesting challenges in ECoG-based Brain-Machine Interface is movement prediction. Being able to perform such a prediction paves the way to high-degree precision command for a machine such as a robotic arm or robotic hands. As a witness of the BCI community increasing interest toward such a problem, t... | ['Alain Rakotomamonjy', 'Rémi Flamary'] | 2012-03-06 | null | null | null | front-neurosci-sec-neuroprosthetics-2012-3 | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.22381352e-01 1.79837167e-01 -1.44709066e-01 -5.53975776e-02
-3.30703229e-01 -2.97488272e-01 6.05067730e-01 -5.17215490e-01
-5.00908017e-01 8.06079090e-01 1.37250215e-01 -9.22278613e-02
-4.56760079e-01 -3.80854249e-01 -7.22413123e-01 -7.16660798e-01
-3.16077352e-01 5.41015625e-01 3.46904576e-01 -4.04153883... | [12.960836410522461, 3.392345428466797] |
d6bcc3b8-0f8b-49b9-977d-b4bff95fb33e | distributional-model-equivalence-for-risk | 2307.01708 | null | https://arxiv.org/abs/2307.01708v1 | https://arxiv.org/pdf/2307.01708v1.pdf | Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning | We consider the problem of learning models for risk-sensitive reinforcement learning. We theoretically demonstrate that proper value equivalence, a method of learning models which can be used to plan optimally in the risk-neutral setting, is not sufficient to plan optimally in the risk-sensitive setting. We leverage di... | ['Amir-Massoud Farahmand', 'Murat A. Erdogdu', 'Tyler Kastner'] | 2023-07-04 | null | null | null | null | ['distributional-reinforcement-learning'] | ['methodology'] | [ 1.04291372e-01 4.78381962e-01 -8.17832828e-01 -3.73948157e-01
-1.48777986e+00 -7.34605432e-01 3.20620716e-01 2.98077255e-01
-7.08338439e-01 8.82237732e-01 1.66416824e-01 -5.95859289e-01
-6.42956138e-01 -1.09533918e+00 -5.37604809e-01 -6.18592739e-01
-4.62964863e-01 7.84365714e-01 1.26116171e-01 -2.96014637... | [4.226290702819824, 2.5526862144470215] |
243e5f10-5d1f-4f1f-b777-d6bff3c5cf75 | design-of-novel-algorithm-and-architecture | 1409.4043 | null | http://arxiv.org/abs/1409.4043v1 | http://arxiv.org/pdf/1409.4043v1.pdf | Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications | This paper presents the development of a new algorithm for Gaussian based
color image enhancement system. The algorithm has been designed into
architecture suitable for FPGA/ASIC implementation. The color image enhancement
is achieved by first convolving an original image with a Gaussian kernel since
Gaussian distribut... | ['M. Ravishankar', 'M. C. Hanumantharaju', 'D. R. Rameshbabu'] | 2014-09-14 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.20034826e-01 -2.91910529e-01 2.16462851e-01 -2.93489277e-01
2.66434941e-02 -4.94651288e-01 2.66314119e-01 1.84273332e-01
-6.96781754e-01 5.57428658e-01 -3.14353883e-01 -6.22414529e-01
5.11014834e-02 -7.85508454e-01 -2.73039103e-01 -4.85397846e-01
-4.45894077e-02 -3.30603868e-01 4.90940243e-01 -2.68283649... | [9.625669479370117, -2.0247974395751953] |
5cda59aa-6681-4a90-a768-88818f977bb8 | a-convolutional-transformer-network-for-crack | 2302.11728 | null | https://arxiv.org/abs/2302.11728v1 | https://arxiv.org/pdf/2302.11728v1.pdf | A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness | Cracks play a crucial role in assessing the safety and durability of manufactured buildings. However, the long and sharp topological features and complex background of cracks make the task of crack segmentation extremely challenging. In this paper, we propose a novel convolutional-transformer network based on encoder-d... | ['Hong Zhang', 'Jinqiang Cui', 'Bingxi Liu', 'Huaqi Tao'] | 2023-02-23 | null | null | null | null | ['crack-segmentation'] | ['computer-vision'] | [-1.05844788e-01 -1.34009838e-01 -9.57882032e-02 -3.51313204e-01
-7.41627395e-01 -1.16567984e-01 2.86202990e-02 1.14320405e-02
1.10134427e-02 2.35594064e-01 2.14135215e-01 -1.10474296e-01
1.65749207e-01 -1.13488102e+00 -8.09869051e-01 -8.23550165e-01
1.54806674e-01 1.03659458e-01 7.28167832e-01 -2.07921386... | [7.5450568199157715, 1.4479886293411255] |
ede7dd3e-f852-4fcf-a7db-bc9b9296cf01 | few-shot-bioacoustic-event-detection-at-the-1 | 2306.09223 | null | https://arxiv.org/abs/2306.09223v1 | https://arxiv.org/pdf/2306.09223v1.pdf | Few-shot bioacoustic event detection at the DCASE 2023 challenge | Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of interest. This task ran as part of the DCASE challenge for the third time this year with an evaluation set expanded to include new animal species... | ['Dan Stowell', 'Vincent Lostanlen', 'Hanna Pamuła', 'Lisa Gill', 'Ariana Strandburg-Peshkin', 'Joe Morford', 'Ivan Kiskin', 'Frants Jensen', 'Michael Emmerson', 'Emily Grout', 'Helen Whitehead', 'Ester Vidaña-Vila', 'Shubhr Singh', 'Burooj Ghani', 'Ines Nolasco'] | 2023-06-15 | null | null | null | null | ['sound-event-detection'] | ['audio'] | [ 1.71545178e-01 -2.12091744e-01 5.64209819e-01 -1.63254872e-01
-1.26445699e+00 -6.83143318e-01 4.89401430e-01 6.91482425e-02
-7.05068707e-01 6.03271246e-01 4.63191837e-01 1.98418677e-01
-1.17003851e-01 -1.94334373e-01 -3.22446525e-01 -4.98422146e-01
-5.87979555e-01 6.65708333e-02 8.24062824e-01 -4.16792601... | [15.144271850585938, 5.107876777648926] |
a6be53be-ef21-4d03-a042-a5da724db575 | dkma-uld-domain-knowledge-augmented-multi-1 | 2203.06886 | null | https://arxiv.org/abs/2203.06886v1 | https://arxiv.org/pdf/2203.06886v1.pdf | DKMA-ULD: Domain Knowledge augmented Multi-head Attention based Robust Universal Lesion Detection | Incorporating data-specific domain knowledge in deep networks explicitly can provide important cues beneficial for lesion detection and can mitigate the need for diverse heterogeneous datasets for learning robust detectors. In this paper, we exploit the domain information present in computed tomography (CT) scans and p... | ['Lovekesh Vig', 'Monika Sharma', 'Meghal Dani', 'Manu Sheoran'] | 2022-03-14 | dkma-uld-domain-knowledge-augmented-multi | https://www.bmvc2021-virtualconference.com/conference/papers/paper_1249.html | https://www.bmvc2021-virtualconference.com/assets/papers/1249.pdf | british-machine-vision-conference-2021-11 | ['medical-object-detection'] | ['computer-vision'] | [ 2.11734906e-01 2.54652530e-01 -3.42717826e-01 -1.83583423e-01
-1.27781928e+00 -3.29810828e-01 3.98296535e-01 3.11498165e-01
-7.22005486e-01 4.30795372e-01 2.22335726e-01 -7.26944506e-02
1.32309169e-01 -7.03796506e-01 -7.74249971e-01 -8.56021166e-01
-1.36950031e-01 5.05678356e-01 8.46162021e-01 1.30967414... | [15.098742485046387, -2.2968008518218994] |
560e1ce1-5584-4f62-9eb8-574aeb4336c0 | reconstructing-vechicles-from-a-single-image | 1609.09468 | null | http://arxiv.org/abs/1609.09468v1 | http://arxiv.org/pdf/1609.09468v1.pdf | Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding | We present an approach for reconstructing vehicles from a single (RGB) image,
in the context of autonomous driving. Though the problem appears to be
ill-posed, we demonstrate that prior knowledge about how 3D shapes of vehicles
project to an image can be used to reason about the reverse process, i.e., how
shapes (back-... | ['Falak Chhaya', 'G. V. Sai Krishna', 'K. Madhava Krishna', 'J. Krishna Murthy'] | 2016-09-29 | null | null | null | null | ['road-scene-understanding'] | ['computer-vision'] | [ 1.42336592e-01 2.15457320e-01 5.63359028e-03 -6.06224537e-01
-9.01210546e-01 -9.90557432e-01 8.11947405e-01 -2.87576109e-01
-3.79472971e-01 1.70426160e-01 -4.76410501e-02 -3.00118357e-01
1.68341130e-01 -5.71366608e-01 -1.56101155e+00 -4.42306101e-01
3.62409592e-01 7.58101583e-01 3.70801091e-01 -1.33168310... | [7.836769104003906, -2.562614917755127] |
8eab834b-7840-400b-af32-d465ee387a37 | a-combined-cnn-and-lstm-model-for-arabic | 1807.02911 | null | http://arxiv.org/abs/1807.02911v3 | http://arxiv.org/pdf/1807.02911v3.pdf | A Combined CNN and LSTM Model for Arabic Sentiment Analysis | Deep neural networks have shown good data modelling capabilities when dealing
with challenging and large datasets from a wide range of application areas.
Convolutional Neural Networks (CNNs) offer advantages in selecting good
features and Long Short-Term Memory (LSTM) networks have proven good abilities
of learning seq... | ['Matthew England', 'Vasile Palade', 'Abdulaziz M. Alayba', 'Rahat Iqbal'] | 2018-07-09 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-1.02645829e-02 -5.19845188e-01 -9.82825831e-02 -4.67460722e-01
-3.20980340e-01 -4.47726309e-01 6.30486250e-01 3.48656207e-01
-7.84784555e-01 5.54639339e-01 1.16233900e-01 -3.22050482e-01
3.52531821e-02 -7.93550789e-01 -2.38639593e-01 -6.61287546e-01
-5.91678396e-02 2.12432027e-01 -2.51859039e-01 -8.02438915... | [11.15522289276123, 7.060948371887207] |
d7ee4113-c9f0-4a60-9588-70ff623ca589 | on-metrics-to-assess-the-transferability-of | 1912.06200 | null | https://arxiv.org/abs/1912.06200v1 | https://arxiv.org/pdf/1912.06200v1.pdf | On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring | To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same hous... | ['Wilfried Elmenreich', 'Stephen Makonin', 'Christoph Klemenjak', 'Anthony Faustine'] | 2019-12-12 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 2.20177829e-01 -1.21519841e-01 4.26769964e-02 -4.87902403e-01
-8.05035710e-01 -6.61948144e-01 8.02871883e-01 4.11629051e-01
-2.25161895e-01 8.54277372e-01 4.12530266e-02 -1.52432650e-01
-3.60482842e-01 -1.00466239e+00 -3.00417066e-01 -7.82511830e-01
-3.52381170e-01 5.96140325e-01 4.74366471e-02 6.41419459... | [6.0477752685546875, 2.6108806133270264] |
00c92588-b4d1-44b9-95db-8c20fd7e4904 | segmenting-transparent-object-in-the-wild | 2101.08461 | null | https://arxiv.org/abs/2101.08461v3 | https://arxiv.org/pdf/2101.08461v3.pdf | Segmenting Transparent Object in the Wild with Transformer | This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset. Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained cat... | ['Ping Luo', 'Ding Liang', 'Hang Xu', 'Peize Sun', 'Wenhai Wang', 'Wenjia Wang', 'Enze Xie'] | 2021-01-21 | null | null | null | null | ['transparent-objects'] | ['computer-vision'] | [ 1.77955609e-02 3.49639416e-01 -6.13647513e-03 -4.48564351e-01
-5.57499051e-01 -6.07975721e-01 2.62983322e-01 -2.88947344e-01
-1.72440216e-01 3.40845972e-01 7.94371143e-02 -1.40902251e-01
3.14200133e-01 -1.02508402e+00 -7.17177570e-01 -8.03960264e-01
3.18597645e-01 4.24923658e-01 8.50854814e-01 -1.67030931... | [9.542881965637207, 0.1917513608932495] |
e7705b23-4278-4263-8680-fed0563780cb | unsupervised-translation-of-programming | 2006.03511 | null | https://arxiv.org/abs/2006.03511v3 | https://arxiv.org/pdf/2006.03511v3.pdf | Unsupervised Translation of Programming Languages | A transcompiler, also known as source-to-source translator, is a system that converts source code from a high-level programming language (such as C++ or Python) to another. Transcompilers are primarily used for interoperability, and to port codebases written in an obsolete or deprecated language (e.g. COBOL, Python 2) ... | ['Marie-Anne Lachaux', 'Lowik Chanussot', 'Guillaume Lample', 'Baptiste Roziere'] | 2020-06-05 | null | http://proceedings.neurips.cc/paper/2020/hash/ed23fbf18c2cd35f8c7f8de44f85c08d-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/ed23fbf18c2cd35f8c7f8de44f85c08d-Paper.pdf | neurips-2020-12 | ['code-translation', 'unsupervised-machine-translation'] | ['computer-code', 'natural-language-processing'] | [ 1.88628267e-02 -2.73268044e-01 -4.17380959e-01 -4.01641369e-01
-9.47969198e-01 -1.05196714e+00 3.32790315e-01 -2.76116114e-02
-1.78899795e-01 5.54028392e-01 2.87549458e-02 -9.94205534e-01
4.51556295e-01 -8.20962727e-01 -1.22856832e+00 4.90272120e-02
3.20877194e-01 2.74600953e-01 2.05690376e-02 -6.11252010... | [7.695737838745117, 7.87480354309082] |
0bfae0d2-7fe5-4b88-b5a7-25ce8fefecbb | salient-region-segmentation | 1803.05759 | null | http://arxiv.org/abs/1803.05759v1 | http://arxiv.org/pdf/1803.05759v1.pdf | Salient Region Segmentation | Saliency prediction is a well studied problem in computer vision. Early
saliency models were based on low-level hand-crafted feature derived from
insights gained in neuroscience and psychophysics. In the wake of deep learning
breakthrough, a new cohort of models were proposed based on neural network
architectures, allo... | ['Sen He', 'Nicolas Pugeault'] | 2018-03-15 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 4.29732174e-01 4.03965175e-01 -2.68682122e-01 -2.71499425e-01
-5.03835022e-01 -2.29264200e-01 6.47369504e-01 3.97094011e-01
-4.09814954e-01 7.20497429e-01 1.89116120e-01 -4.42900509e-02
-1.55043751e-01 -2.06691101e-01 -9.68625426e-01 -7.27485478e-01
-4.84287553e-02 2.64479697e-01 6.53951943e-01 -3.63865674... | [10.046239852905273, 1.5379610061645508] |
5bb59449-98cc-4ca3-930c-1234674121e2 | a-data-variation-robust-learning-model-based | 2302.04438 | null | https://arxiv.org/abs/2302.04438v2 | https://arxiv.org/pdf/2302.04438v2.pdf | An information-theoretic learning model based on importance sampling | A crucial assumption underlying the most current theory of machine learning is that the training distribution is identical to the test distribution. However, this assumption may not hold in some real-world applications. In this paper, we develop a learning model based on principles of information theory by minimizing t... | ['Mengyao Li', 'Fei Gao', 'Lizhen Ji', 'Jiangshe Zhang'] | 2023-02-09 | null | null | null | null | ['face-verification'] | ['computer-vision'] | [ 5.20921290e-01 4.59163934e-01 3.60051319e-02 -6.40290678e-01
-7.82362282e-01 -2.32684791e-01 1.68141276e-01 2.35401645e-01
-5.93407214e-01 8.68494749e-01 -5.17136931e-01 -3.00526202e-01
-5.98199308e-01 -6.83999002e-01 -8.43877792e-01 -9.93987858e-01
1.05836116e-01 1.12343691e-01 -2.44309798e-01 3.18135560... | [7.033913612365723, 4.014965534210205] |
ee1d0c06-7838-4fa2-866c-753073ba1a28 | seec-semantic-vector-federation-across-edge | 2008.13298 | null | https://arxiv.org/abs/2008.13298v1 | https://arxiv.org/pdf/2008.13298v1.pdf | SEEC: Semantic Vector Federation across Edge Computing Environments | Semantic vector embedding techniques have proven useful in learning semantic representations of data across multiple domains. A key application enabled by such techniques is the ability to measure semantic similarity between given data samples and find data most similar to a given sample. State-of-the-art embedding app... | ['Graham Bent', 'Shalisha Witherspoon', 'Nirmit Desai', 'Dean Steuer'] | 2020-08-30 | null | null | null | null | ['learning-semantic-representations'] | ['methodology'] | [ 2.15272754e-01 9.16478038e-02 -7.83027947e-01 -2.38068432e-01
-7.41357505e-01 -5.75480580e-01 7.90843427e-01 7.97753632e-01
-2.82968372e-01 4.30531502e-01 3.45509559e-01 -3.32817912e-01
-4.74585325e-01 -1.00697184e+00 -6.12804651e-01 -4.11632240e-01
-2.29965672e-01 6.22854352e-01 1.45056486e-01 -3.41245919... | [8.694659233093262, 7.8178181648254395] |
3fdf5c22-8fb7-4b02-98a7-ed8b5f6f03f9 | semantic-preserving-linguistic-steganography | 2203.03795 | null | https://arxiv.org/abs/2203.03795v1 | https://arxiv.org/pdf/2203.03795v1.pdf | Semantic-Preserving Linguistic Steganography by Pivot Translation and Semantic-Aware Bins Coding | Linguistic steganography (LS) aims to embed secret information into a highly encoded text for covert communication. It can be roughly divided to two main categories, i.e., modification based LS (MLS) and generation based LS (GLS). Unlike MLS that hides secret data by slightly modifying a given text without impairing th... | ['Xinpeng Zhang', 'Guorui Feng', 'Biao Yi', 'Hanzhou Wu', 'Tianyu Yang'] | 2022-03-08 | null | null | null | null | ['steganalysis'] | ['computer-vision'] | [ 9.70661700e-01 5.85097313e-01 3.42738554e-02 -1.26121908e-01
-2.18291968e-01 -6.35794282e-01 6.55202031e-01 1.14211375e-02
-3.28574836e-01 6.68379247e-01 2.05191121e-01 -4.16526705e-01
5.61435640e-01 -1.18133867e+00 -7.59973407e-01 -8.12271774e-01
1.69807732e-01 -2.91536182e-01 4.42765236e-01 -4.78548408... | [4.328485488891602, 8.050503730773926] |
d091326f-38ec-450b-8c8d-93e13222df42 | robustsleepnet-transfer-learning-for | 2101.02452 | null | https://arxiv.org/abs/2101.02452v2 | https://arxiv.org/pdf/2101.02452v2.pdf | RobustSleepNet: Transfer learning for automated sleep staging at scale | Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on the visual inspection of 30-second epochs of polysomnography signals. Numerous automatic approaches h... | ['Valentin Thorey', 'Antoine Guillot'] | 2021-01-07 | null | null | null | null | ['sleep-staging', 'automatic-sleep-stage-classification'] | ['medical', 'medical'] | [ 1.71566736e-02 8.83232355e-02 -8.46587494e-02 -5.32342911e-01
-5.40503860e-01 -4.76251841e-01 4.64056991e-02 3.12725157e-01
-7.85877347e-01 7.59464920e-01 -1.03369161e-01 -3.33017588e-01
-2.50297245e-02 -3.54455501e-01 -1.36022881e-01 -6.78662658e-01
5.10366037e-02 8.68731797e-01 4.19088125e-01 -1.49146497... | [13.500575065612793, 3.5295426845550537] |
875fadbc-6bf0-4662-8e34-155eb1a3b718 | gazenerf-3d-aware-gaze-redirection-with | 2212.04823 | null | https://arxiv.org/abs/2212.04823v2 | https://arxiv.org/pdf/2212.04823v2.pdf | GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields | We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the face region and eyeballs are separate 3D structures that move in a coordinated yet independent fashi... | ['Otmar Hilliges', 'Xucong Zhang', 'Hyung Jin Chang', 'Shalini De Mello', 'Gengyan Li', 'Xi Wang', 'Xiangwei Shi', 'Alessandro Ruzzi'] | 2022-12-08 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ruzzi_GazeNeRF_3D-Aware_Gaze_Redirection_With_Neural_Radiance_Fields_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ruzzi_GazeNeRF_3D-Aware_Gaze_Redirection_With_Neural_Radiance_Fields_CVPR_2023_paper.pdf | cvpr-2023-1 | ['gaze-redirection'] | ['computer-vision'] | [ 1.96235567e-01 1.30152375e-01 -7.09516704e-02 -6.78325236e-01
-3.08903515e-01 -7.54720926e-01 9.83421504e-01 -5.12314677e-01
-2.36741841e-01 2.11015150e-01 5.78281939e-01 -3.10967594e-01
2.11151615e-01 -2.61688590e-01 -6.25571847e-01 -6.30358338e-01
1.69668645e-01 1.42460719e-01 -2.68302828e-01 -2.33969495... | [14.11436653137207, 0.045289695262908936] |
25057f50-335d-4981-a07e-bce80081fa3a | high-for-low-and-low-for-high-efficient | 1504.06201 | null | http://arxiv.org/abs/1504.06201v3 | http://arxiv.org/pdf/1504.06201v3.pdf | High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and its Applications to High-Level Vision | Most of the current boundary detection systems rely exclusively on low-level
features, such as color and texture. However, perception studies suggest that
humans employ object-level reasoning when judging if a particular pixel is a
boundary. Inspired by this observation, in this work we show how to predict
boundaries b... | ['Gedas Bertasius', 'Lorenzo Torresani', 'Jianbo Shi'] | 2015-04-23 | high-for-low-and-low-for-high-efficient-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Bertasius_High-for-Low_and_Low-for-High_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Bertasius_High-for-Low_and_Low-for-High_ICCV_2015_paper.pdf | iccv-2015-12 | ['object-proposal-generation'] | ['computer-vision'] | [ 6.02245331e-01 2.80936778e-01 -1.84040859e-01 -4.65378284e-01
-7.56906271e-01 -5.25271893e-01 6.13629282e-01 3.32906991e-01
-4.75100249e-01 2.23809555e-01 -3.79199147e-01 -2.81759590e-01
2.59923846e-01 -9.86963034e-01 -9.57388699e-01 -3.31540883e-01
1.02894373e-01 7.85319865e-01 9.56070364e-01 -1.51095092... | [9.497322082519531, 0.28920382261276245] |
65ff6ac3-ca0f-4490-bdf6-281876fce61a | target-aware-generative-augmentations-for | 2305.13284 | null | https://arxiv.org/abs/2305.13284v1 | https://arxiv.org/pdf/2305.13284v1.pdf | Target-Aware Generative Augmentations for Single-Shot Adaptation | In this paper, we address the problem of adapting models from a source domain to a target domain, a task that has become increasingly important due to the brittle generalization of deep neural networks. While several test-time adaptation techniques have emerged, they typically rely on synthetic toolbox data augmentatio... | ['Jayaraman J. Thiagarajan', 'Pavan Turaga', 'Rakshith Subramanyam', 'Kowshik Thopalli'] | 2023-05-22 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 4.45565850e-01 -1.62991494e-01 -9.98119563e-02 -5.34209609e-01
-1.03711808e+00 -5.92671335e-01 6.79325461e-01 -3.82165223e-01
-2.24937648e-01 7.85552502e-01 -4.52164635e-02 -2.16922805e-01
1.94311738e-02 -5.49426377e-01 -8.20748270e-01 -6.44587934e-01
3.35914254e-01 7.26711512e-01 1.01822935e-01 -1.85680941... | [10.127312660217285, 2.8119099140167236] |
d393766d-15fe-44cd-b33e-4eb9bd722cfb | feature-aggregated-queries-for-transformer | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cui_Feature_Aggregated_Queries_for_Transformer-Based_Video_Object_Detectors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cui_Feature_Aggregated_Queries_for_Transformer-Based_Video_Object_Detectors_CVPR_2023_paper.pdf | Feature Aggregated Queries for Transformer-Based Video Object Detectors | Video object detection needs to solve feature degradation situations that rarely happen in the image domain. One solution is to use the temporal information and fuse the features from the neighboring frames. With Transformer-based object detectors getting a better performance on the image domain tasks, recent works... | ['Yiming Cui'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['video-object-detection'] | ['computer-vision'] | [ 3.10378876e-02 -4.16378796e-01 -8.69765691e-03 -3.11564654e-01
-6.39213324e-01 -3.32011640e-01 4.86082613e-01 5.13212308e-02
-5.48518479e-01 2.80277610e-01 -1.72133282e-01 1.00951619e-01
9.86601710e-02 -7.75638878e-01 -6.87728405e-01 -6.16087019e-01
1.48063404e-02 1.45061165e-01 1.24584067e+00 -2.71481514... | [8.733757019042969, -0.17405490577220917] |
575cec9a-bc92-421c-8f0c-c7e39bb14897 | graph-neural-controlled-differential | 2112.03558 | null | https://arxiv.org/abs/2112.03558v1 | https://arxiv.org/pdf/2112.03558v1.pdf | Graph Neural Controlled Differential Equations for Traffic Forecasting | Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been propose... | ['Noseong Park', 'Jeehyun Hwang', 'Hwangyong Choi', 'Jeongwhan Choi'] | 2021-12-07 | null | null | null | null | ['spatio-temporal-forecasting'] | ['time-series'] | [-9.53059494e-02 -5.90920448e-01 -4.66373004e-02 -1.11693991e-02
-1.45810768e-01 -8.25053453e-02 7.57948697e-01 -6.56855851e-02
-3.28132361e-01 4.65528011e-01 2.43169606e-01 -6.19997501e-01
-9.89525300e-03 -7.86341906e-01 -4.85385329e-01 -7.36567616e-01
-8.50986466e-02 1.51219564e-02 9.24399436e-01 -3.93603921... | [6.5148468017578125, 2.075610876083374] |
370a03b6-e478-4b11-99e0-cf48d38db001 | an-investigation-of-evaluation-metrics-for | 2305.17364 | null | https://arxiv.org/abs/2305.17364v1 | https://arxiv.org/pdf/2305.17364v1.pdf | An Investigation of Evaluation Metrics for Automated Medical Note Generation | Recent studies on automatic note generation have shown that doctors can save significant amounts of time when using automatic clinical note generation (Knoll et al., 2022). Summarization models have been used for this task to generate clinical notes as summaries of doctor-patient conversations (Krishna et al., 2021; Ca... | ['Thomas Lin', 'George Michalopoulos', 'Wen-wai Yim', 'Asma Ben Abacha'] | 2023-05-27 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding', 'text-summarization'] | ['graphs', 'graphs', 'natural-language-processing'] | [ 2.63841838e-01 5.90096056e-01 -1.47250658e-02 -1.87419668e-01
-1.03403401e+00 -5.13874114e-01 6.98323011e-01 9.58361924e-01
-2.51663119e-01 1.00526190e+00 1.10254633e+00 -1.40085921e-01
-4.81058478e-01 -4.93091315e-01 3.06198716e-01 -4.29183215e-01
-1.97668392e-02 6.04091346e-01 -5.44324331e-02 -1.68153435... | [12.263518333435059, 9.455862998962402] |
1ab2d274-1ede-4e81-b63c-26253fceb963 | adversarial-self-attention-for-language | 2206.12608 | null | https://arxiv.org/abs/2206.12608v3 | https://arxiv.org/pdf/2206.12608v3.pdf | Adversarial Self-Attention for Language Understanding | Deep neural models (e.g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness. This paper advances the self-attention mechanism to its robust variant for Transformer-based pre-trained language models (e.g. BERT). We p... | ['Min Zhang', 'Fei Huang', 'Pengjun Xie', 'Hai Zhao', 'Ruixue Ding', 'Hongqiu Wu'] | 2022-06-25 | null | null | null | null | ['paraphrase-identification', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.31951430e-01 2.93627053e-01 -2.52341688e-01 -5.91297328e-01
-6.69815302e-01 -9.75861251e-01 9.45007920e-01 -5.31889349e-02
-5.40486217e-01 4.65400517e-01 2.29617804e-01 -5.69808543e-01
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1.18120566e-01 3.05538595e-01 3.25661778e-01 -5.37144721... | [10.541921615600586, 8.078655242919922] |
50770d07-99fc-4263-b537-b30b9fa64503 | less-is-more-learning-highlight-detection | 1903.00859 | null | http://arxiv.org/abs/1903.00859v1 | http://arxiv.org/pdf/1903.00859v1.pdf | Less is More: Learning Highlight Detection from Video Duration | Highlight detection has the potential to significantly ease video browsing,
but existing methods often suffer from expensive supervision requirements,
where human viewers must manually identify highlights in training videos. We
propose a scalable unsupervised solution that exploits video duration as an
implicit supervi... | ['Kristen Grauman', 'Deepti Ghadiyaram', 'Yannis Kalantidis', 'Bo Xiong'] | 2019-03-03 | less-is-more-learning-highlight-detection-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Xiong_Less_Is_More_Learning_Highlight_Detection_From_Video_Duration_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xiong_Less_Is_More_Learning_Highlight_Detection_From_Video_Duration_CVPR_2019_paper.pdf | cvpr-2019-6 | ['highlight-detection'] | ['computer-vision'] | [ 4.14771229e-01 -1.24301910e-01 -7.33789802e-01 -2.78714001e-01
-7.94022739e-01 -7.49515831e-01 2.72882253e-01 3.08912098e-01
-3.89452934e-01 2.98260748e-01 4.99488801e-01 -3.44301313e-02
2.81238407e-01 -4.13523257e-01 -7.93638706e-01 -5.29280305e-01
-5.45097649e-01 -3.49241436e-01 5.20115793e-01 2.64205247... | [10.152966499328613, 0.4836825728416443] |
c79c225d-7044-431a-a8ae-f415054ce021 | human-image-generation-a-comprehensive-survey | 2212.08896 | null | https://arxiv.org/abs/2212.08896v1 | https://arxiv.org/pdf/2212.08896v1.pdf | Human Image Generation: A Comprehensive Survey | Image and video synthesis has become a blooming topic in computer vision and machine learning communities along with the developments of deep generative models, due to its great academic and application value. Many researchers have been devoted to synthesizing high-fidelity human images as one of the most commonly seen... | ['Tieniu Tan', 'Liang Wang', 'Zhang Zhang', 'Zhen Jia'] | 2022-12-17 | null | null | null | null | ['person-recognition', 'virtual-try-on'] | ['computer-vision', 'computer-vision'] | [ 4.51729745e-01 3.12665962e-02 -9.39325765e-02 -3.27021599e-01
-3.13511491e-01 -1.60839781e-01 8.90512347e-01 -5.29260576e-01
-2.60902315e-01 7.18061328e-01 1.64858684e-01 2.24418879e-01
1.40294269e-01 -8.73789907e-01 -5.06067395e-01 -8.74665916e-01
4.46234822e-01 3.75539452e-01 -2.75694042e-01 -3.05404961... | [12.015395164489746, -0.8162513971328735] |
963408d3-00db-484a-8066-b43e791a7424 | fewrel-20-towards-more-challenging-few-shot | 1910.07124 | null | https://arxiv.org/abs/1910.07124v1 | https://arxiv.org/pdf/1910.07124v1.pdf | FewRel 2.0: Towards More Challenging Few-Shot Relation Classification | We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of instances? (2) Can they detect none-of-the-above (NOTA) relations? To construct FewRel 2.0, we build upon the FewRel dataset (Han et al., 2018) ... | ['Jie zhou', 'Tianyu Gao', 'Peng Li', 'Maosong Sun', 'Hao Zhu', 'Zhiyuan Liu', 'Xu Han'] | 2019-10-16 | fewrel-20-towards-more-challenging-few-shot-1 | https://aclanthology.org/D19-1649 | https://aclanthology.org/D19-1649.pdf | ijcnlp-2019-11 | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 1.24669977e-01 2.46984974e-01 -6.67307317e-01 -3.15233260e-01
-6.74704671e-01 -4.75719154e-01 8.28246474e-01 2.27088600e-01
-1.35780409e-01 8.92646790e-01 6.33234531e-02 -2.63556600e-01
-1.43488988e-01 -8.02145302e-01 -3.93777132e-01 -2.32308462e-01
3.08536515e-02 9.01680708e-01 7.27432966e-01 -6.28359795... | [9.38143253326416, 8.709239959716797] |
4311eabb-5355-4818-b6ae-ec5820977294 | mdvit-multi-domain-vision-transformer-for | 2307.02100 | null | https://arxiv.org/abs/2307.02100v1 | https://arxiv.org/pdf/2307.02100v1.pdf | MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets | Despite its clinical utility, medical image segmentation (MIS) remains a daunting task due to images' inherent complexity and variability. Vision transformers (ViTs) have recently emerged as a promising solution to improve MIS; however, they require larger training datasets than convolutional neural networks. To overco... | ['Rafeef Garbi', 'Ghassan Harmarneh', 'Nourhan Bayasi', 'Siyi Du'] | 2023-07-05 | null | null | null | null | ['skin-lesion-segmentation', 'medical-image-segmentation', 'lesion-segmentation', 'representation-learning', 'transfer-learning'] | ['medical', 'medical', 'medical', 'methodology', 'miscellaneous'] | [ 4.43329841e-01 1.05871983e-01 -6.08659327e-01 -2.85733551e-01
-9.92113233e-01 -6.53381109e-01 2.04109475e-01 7.12567195e-02
-6.18505001e-01 8.53627264e-01 -7.74144055e-03 -2.48402908e-01
-6.25227019e-02 -6.78600013e-01 -7.13478565e-01 -7.91739106e-01
4.18748140e-01 5.18224239e-01 5.81068575e-01 -1.22842252... | [14.674539566040039, -2.075057029724121] |
9385498c-254a-492e-b12b-1c571523a276 | convolutional-neural-network-with | 2202.06673 | null | https://arxiv.org/abs/2202.06673v1 | https://arxiv.org/pdf/2202.06673v1.pdf | Convolutional Neural Network with Convolutional Block Attention Module for Finger Vein Recognition | Convolutional neural networks have become a popular research in the field of finger vein recognition because of their powerful image feature representation. However, most researchers focus on improving the performance of the network by increasing the CNN depth and width, which often requires high computational effort. ... | ['Mingwen Wang', 'Zhongxia Zhang'] | 2022-02-14 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 2.65163988e-01 -5.12676001e-01 7.53031299e-02 -4.15156841e-01
-5.37183173e-02 -1.45820603e-01 1.20837882e-01 -2.12162748e-01
-5.79729974e-01 4.05174583e-01 2.08462462e-01 6.51240945e-02
9.79016274e-02 -8.89638782e-01 -4.93775278e-01 -7.94654906e-01
4.89681512e-01 1.08917337e-02 3.48759979e-01 7.99195766... | [13.141435623168945, 0.9668226838111877] |
cb1dee5c-d42a-471d-bf65-b4a547d53aa4 | deep-spectral-methods-a-surprisingly-strong | 2205.07839 | null | https://arxiv.org/abs/2205.07839v1 | https://arxiv.org/pdf/2205.07839v1.pdf | Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization | Unsupervised localization and segmentation are long-standing computer vision challenges that involve decomposing an image into semantically-meaningful segments without any labeled data. These tasks are particularly interesting in an unsupervised setting due to the difficulty and cost of obtaining dense image annotation... | ['Andrea Vedaldi', 'Iro Laina', 'Christian Rupprecht', 'Luke Melas-Kyriazi'] | 2022-05-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Melas-Kyriazi_Deep_Spectral_Methods_A_Surprisingly_Strong_Baseline_for_Unsupervised_Semantic_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Melas-Kyriazi_Deep_Spectral_Methods_A_Surprisingly_Strong_Baseline_for_Unsupervised_Semantic_CVPR_2022_paper.pdf | cvpr-2022-1 | ['unsupervised-semantic-segmentation', 'graph-partitioning'] | ['computer-vision', 'graphs'] | [ 6.21579349e-01 9.54813436e-02 -2.24653572e-01 -3.26858401e-01
-6.79184794e-01 -8.81927311e-01 2.48127177e-01 1.96596414e-01
-4.36566681e-01 2.58399457e-01 -1.37602612e-01 -1.12580629e-02
-3.38468980e-03 -4.62717712e-01 -8.24722171e-01 -7.16067135e-01
2.02439949e-01 5.14852345e-01 4.27434057e-01 1.89622328... | [9.53364086151123, 0.8052259087562561] |
5b4efda3-9752-48f8-bfa5-4be3bbbfb8a9 | exploiting-spectral-augmentation-for-code | 2010.07130 | null | https://arxiv.org/abs/2010.07130v1 | https://arxiv.org/pdf/2010.07130v1.pdf | Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification | Spoken language Identification (LID) systems are needed to identify the language(s) present in a given audio sample, and typically could be the first step in many speech processing related tasks such as automatic speech recognition (ASR). Automatic identification of the languages present in a speech signal is not only ... | ['Hemant Misra', 'Sundeep Teki', 'Pradeep Rangan'] | 2020-10-14 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-1.92877531e-01 -3.31169426e-01 1.58356637e-01 -1.63645476e-01
-1.28548861e+00 -6.58501029e-01 6.94657028e-01 -9.21343714e-02
-5.64094961e-01 6.95383787e-01 5.96671820e-01 -6.20926738e-01
3.68551016e-01 -2.29839593e-01 -2.01057151e-01 -6.34076059e-01
2.40315631e-01 6.44686341e-01 -6.18740823e-03 -3.89124334... | [14.178839683532715, 6.546756744384766] |
e562e1f5-169c-402f-810a-5a060646515e | automated-surface-texture-analysis-via | 2204.05968 | null | https://arxiv.org/abs/2204.05968v1 | https://arxiv.org/pdf/2204.05968v1.pdf | Automated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform | Surface roughness and texture are critical to the functional performance of engineering components. The ability to analyze roughness and texture effectively and efficiently is much needed to ensure surface quality in many surface generation processes, such as machining, surface mechanical treatment, etc. Discrete Wavel... | ['Yang Guo', 'Firas A. Khasawneh', 'Jisheng Chen', 'Melih C. Yesilli'] | 2022-04-12 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 9.65264261e-01 -2.28533953e-01 2.74860978e-01 -1.29592076e-01
-8.45916927e-01 -2.15111062e-01 3.28414619e-01 5.09476840e-01
-3.32870066e-01 3.41256201e-01 -3.14847410e-01 -2.15987965e-01
-3.58251691e-01 -1.11190724e+00 -2.02490687e-01 -8.74787986e-01
1.35748684e-02 2.17087492e-01 4.83401418e-01 -2.83035040... | [13.001444816589355, -2.811244249343872] |
c624e3ea-8337-42ea-9ea8-0330aedfe624 | unsupervised-vision-and-vision-motion | 2210.00413 | null | https://arxiv.org/abs/2210.00413v2 | https://arxiv.org/pdf/2210.00413v2.pdf | Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment | PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point. Recent studies solved this PointGoal navigation task with near-perfect success rate in photo-realistically simulated environments, under the assumptions with noiseless actuation and most importantly, p... | ['Chuan Lin', 'YongJie Li', 'Fuya Luo', 'Xianshi Zhang', 'Yijun Cao'] | 2022-10-02 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-1.17726713e-01 1.23718888e-01 6.99901208e-02 -3.25598359e-01
-3.10793191e-01 -3.96480858e-01 5.35153091e-01 -6.30418807e-02
-8.37129712e-01 1.10450566e+00 -2.94674814e-01 2.61610240e-01
-8.41657724e-03 -8.45160484e-01 -8.98409724e-01 -8.54984760e-01
-1.12726584e-01 5.34382582e-01 5.54414749e-01 -5.49089372... | [4.765561580657959, 0.58937007188797] |
30f8b007-4742-470f-94f5-a6fd5835ac71 | an-automatic-pipeline-for-atlas-based-fetal | 2205.07575 | null | https://arxiv.org/abs/2205.07575v1 | https://arxiv.org/pdf/2205.07575v1.pdf | An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis | The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. In this... | ['Miguel A', 'González Ballester', 'Gemma', 'Piella', 'Fàtima', 'Crispi', 'Eduard', 'Gratacós', 'Elisenda', 'Eixarch', 'Nadine', 'Hahner', 'Valentin', 'Comte', 'Laura', 'Segales', 'Francesca', 'Crovetto', 'Oualid', 'Benkarim', 'Ayako', 'Nakaki', 'Andrea', 'Urru'] | 2022-05-16 | null | null | null | null | ['brain-segmentation'] | ['medical'] | [-1.31958231e-01 4.11909491e-01 4.54190522e-01 -5.49399674e-01
-1.89995915e-01 -5.29982567e-01 4.61276472e-01 7.43338406e-01
-5.67243159e-01 3.88336331e-01 1.16138272e-01 -8.48646313e-02
-1.48995742e-01 -6.91041768e-01 -4.14668381e-01 -2.56867617e-01
-3.50930303e-01 1.07029641e+00 6.17587209e-01 4.43132520... | [14.126568794250488, -2.431640386581421] |
5c3d9b6a-f961-47d8-9325-da2c7dde9a68 | automated-essay-scoring-system-for-nonnative | null | null | https://aclanthology.org/2020.lrec-1.157 | https://aclanthology.org/2020.lrec-1.157.pdf | Automated Essay Scoring System for Nonnative Japanese Learners | In this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including content, organization, and language scores. In particular, we developed AES systems using two different approaches: a featur... | ['Satoru Katsumata', 'Hiroki Shimanaka', 'Mio Arai', 'Mamoru Komachi', 'Reo Hirao'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-2.29520142e-01 -1.69265922e-02 8.81731957e-02 -5.15094101e-01
-8.68599057e-01 -6.46704197e-01 3.32907945e-01 4.09185410e-01
-8.09872270e-01 7.34209299e-01 3.06604028e-01 -4.43928063e-01
-1.54348120e-01 -6.80272996e-01 -3.71439815e-01 -3.46189797e-01
4.56682831e-01 1.16377376e-01 1.24751404e-01 -3.48731965... | [11.278922080993652, 9.389843940734863] |
afd28a48-c95e-484e-bcaf-75eb79f453a8 | cluster-labeling-by-word-embeddings-and | null | null | https://aclanthology.org/U18-1008 | https://aclanthology.org/U18-1008.pdf | Cluster Labeling by Word Embeddings and WordNet's Hypernymy | Cluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection. Once assigned, the labels can play an important role in applications such as navigation, search and document classification. However, finding appropriately descriptive labels is still a chall... | ['Massimo Piccardi', 'Hanieh Poostchi'] | 2018-12-01 | cluster-labeling-by-word-embeddings-and-1 | https://aclanthology.org/U18-1008 | https://aclanthology.org/U18-1008.pdf | alta-2018-12 | ['learning-word-embeddings'] | ['methodology'] | [-2.88616538e-01 -5.96894510e-02 -5.48084021e-01 -5.47000051e-01
-3.47821236e-01 -8.67119014e-01 8.37311089e-01 9.51853573e-01
-8.24653566e-01 5.11519194e-01 4.47038710e-01 -6.78352714e-02
-5.47102988e-01 -6.39135480e-01 2.23124668e-01 -5.31761706e-01
2.71845274e-02 9.87197995e-01 2.65082359e-01 -3.99971716... | [10.178664207458496, 8.620189666748047] |
2916c0d5-3b21-42a6-90b3-baa8910dfd44 | lsoie-a-large-scale-dataset-for-supervised | 2101.11177 | null | https://arxiv.org/abs/2101.11177v1 | https://arxiv.org/pdf/2101.11177v1.pdf | LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction | Open Information Extraction (OIE) systems seek to compress the factual propositions of a sentence into a series of n-ary tuples. These tuples are useful for downstream tasks in natural language processing like knowledge base creation, textual entailment, and natural language understanding. However, current OIE datasets... | ['Stefan Larson', 'Jacob Solawetz'] | 2021-01-27 | null | https://aclanthology.org/2021.eacl-main.222 | https://aclanthology.org/2021.eacl-main.222.pdf | eacl-2021-2 | ['open-information-extraction'] | ['natural-language-processing'] | [ 3.22782919e-02 6.30201101e-01 -6.92412555e-01 -5.76861262e-01
-1.16411221e+00 -7.12408006e-01 5.41547656e-01 5.68771780e-01
-4.06725347e-01 1.16491210e+00 7.98705935e-01 -6.39580429e-01
-3.10347583e-02 -1.07850146e+00 -9.70705867e-01 4.69057947e-01
-7.95675814e-02 8.12030196e-01 2.07595125e-01 -5.44248641... | [9.738579750061035, 8.54740047454834] |
8aa12a2c-2a86-4a58-bf95-9413980c0d23 | no-fear-of-classifier-biases-neural-collapse | 2303.10058 | null | https://arxiv.org/abs/2303.10058v1 | https://arxiv.org/pdf/2303.10058v1.pdf | No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier | Data heterogeneity is an inherent challenge that hinders the performance of federated learning (FL). Recent studies have identified the biased classifiers of local models as the key bottleneck. Previous attempts have used classifier calibration after FL training, but this approach falls short in improving the poor feat... | ['Chao Wu', 'Tao Lin', 'Rui He', 'Xinyi Shang', 'Zexi Li'] | 2023-03-17 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [-2.49103278e-01 -1.41237959e-01 -4.39395338e-01 -8.16412091e-01
-6.05705619e-01 -5.37078559e-01 2.86985606e-01 -3.46002936e-01
-3.03317755e-01 7.08920598e-01 1.09566003e-01 -2.48733163e-01
-2.67822272e-03 -5.69135666e-01 -8.87598455e-01 -7.79524565e-01
-2.89611295e-02 2.68527210e-01 1.00858606e-01 -2.12100506... | [5.938273906707764, 6.280642986297607] |
89e7c62b-e0d6-4c0c-9910-fef8194d1cb1 | image-translation-for-medical-image | 2010.02745 | null | https://arxiv.org/abs/2010.02745v2 | https://arxiv.org/pdf/2010.02745v2.pdf | Image Translation for Medical Image Generation -- Ischemic Stroke Lesions | Deep learning based disease detection and segmentation algorithms promise to improve many clinical processes. However, such algorithms require vast amounts of annotated training data, which are typically not available in the medical context due to data privacy, legal obstructions, and non-uniform data acquisition proto... | ['Christian Federau', 'Jonathan Zopes', 'Moritz Platscher'] | 2020-10-05 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 6.03351116e-01 5.55310547e-01 9.98186693e-02 -3.73034060e-01
-1.15163207e+00 -4.94131774e-01 3.94086748e-01 1.68512329e-01
-7.03179359e-01 9.79977429e-01 -3.90762985e-02 -4.45333213e-01
-1.50157157e-02 -7.70815551e-01 -8.65031064e-01 -5.96735358e-01
-1.15710497e-01 7.93203533e-01 7.41032362e-02 2.23714441... | [14.262007713317871, -2.0787737369537354] |
37e46461-5140-4c7f-9d2d-f1af0165a373 | harflow3d-a-latency-oriented-3d-cnn | 2303.17218 | null | https://arxiv.org/abs/2303.17218v6 | https://arxiv.org/pdf/2303.17218v6.pdf | HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices | For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networks have proven to be highly effective, achieving state-of-the-art results. This study introduces a novel streaming architecture based toolflow for mapping such models onto FPGAs considering the model's inherent characteristics and the features of t... | ['Dimitrios Tzovaras', 'Christos-Savvas Bouganis', 'Alexander Montgomerie-Corcoran', 'Petros Toupas'] | 2023-03-30 | null | null | null | null | ['action-recognition-in-videos'] | ['computer-vision'] | [ 1.75259963e-01 -1.90438870e-02 -1.47102535e-01 -4.11030799e-01
5.72128184e-02 -3.04491192e-01 2.64091074e-01 -1.24515602e-02
-4.73563194e-01 1.81979686e-01 -3.20426635e-02 -6.55482829e-01
-6.49302080e-02 -7.26575315e-01 -6.78584933e-01 3.28601338e-02
-4.57293481e-01 2.47192308e-01 4.31930959e-01 -1.27358973... | [8.308221817016602, 2.739593029022217] |
d5219285-9e77-48f9-9510-978198b618c9 | multimodal-emotion-recognition-among-couples | 2212.13917 | null | https://arxiv.org/abs/2212.13917v1 | https://arxiv.org/pdf/2212.13917v1.pdf | Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches | Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide an insight into their emotional well-being in chronic disease management. The emotions of partner... | ['George Boateng'] | 2022-12-21 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [-1.32793322e-01 2.68895179e-01 -5.94974995e-01 -6.86843693e-01
-1.41715825e-01 -2.95007795e-01 -2.53459722e-01 5.04479647e-01
1.61041003e-02 8.53604138e-01 8.28900710e-02 3.38062167e-01
1.75216019e-01 -7.04300940e-01 5.58244050e-01 -4.70562398e-01
-2.70856827e-01 2.15081125e-01 -1.05765331e+00 -3.39884847... | [13.514711380004883, 2.7606632709503174] |
0164d459-cb59-4dae-9977-5980745c3d92 | towards-zero-shot-code-switched-speech | 2211.01458 | null | https://arxiv.org/abs/2211.01458v2 | https://arxiv.org/pdf/2211.01458v2.pdf | Towards Zero-Shot Code-Switched Speech Recognition | In this work, we seek to build effective code-switched (CS) automatic speech recognition systems (ASR) under the zero-shot setting where no transcribed CS speech data is available for training. Previously proposed frameworks which conditionally factorize the bilingual task into its constituent monolingual parts are a p... | ['Shinji Watanabe', 'Preethi Jyothi', 'Ondrej Klejch', 'Matthew Wiesner', 'Brian Yan'] | 2022-11-02 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [ 2.55269378e-01 4.27480005e-02 -1.19844615e-01 -4.04920757e-01
-1.34370279e+00 -8.89296710e-01 5.93028307e-01 -2.81041861e-01
-7.82346666e-01 3.11977088e-01 -2.69645780e-01 -1.02306736e+00
5.61881840e-01 -2.70869642e-01 -7.47665644e-01 -5.56251943e-01
4.17255372e-01 6.93265557e-01 -5.70645630e-02 -4.10683662... | [14.439079284667969, 7.063859939575195] |
ad8f3d9d-07f2-482d-8cd7-ad258947209d | automatic-skin-lesion-segmentation-on | 1808.06759 | null | http://arxiv.org/abs/1808.06759v1 | http://arxiv.org/pdf/1808.06759v1.pdf | Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging | We present a superpixel-based strategy for segmenting skin lesion on
dermoscopic images. The segmentation is carried out by over-segmenting the
original image using the SLIC algorithm, and then merge the resulting
superpixels into two regions: healthy skin and lesion. The mean RGB color of
each superpixel was used as m... | ['Jonathan Avendaño', 'Diego Patiño', 'John Willian Branch'] | 2018-08-21 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.51551950e-01 1.97171614e-01 -6.01078011e-02 3.78377661e-02
-3.27971160e-01 -6.04580998e-01 1.95584953e-01 3.83912742e-01
-5.50560057e-01 5.41223526e-01 -4.61928725e-01 -1.59747422e-01
2.33622938e-01 -5.49922645e-01 -3.89894038e-01 -8.35441887e-01
1.18856877e-01 3.79936695e-02 8.89703512e-01 2.99716741... | [15.607157707214355, -3.0402674674987793] |
e56f252f-002e-40cd-9d47-423a18e09f92 | stock-price-prediction-using-machine-learning | 2009.10819 | null | https://arxiv.org/abs/2009.10819v1 | https://arxiv.org/pdf/2009.10819v1.pdf | Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models | Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to m... | ['Jaydip Sen', 'Sidra Mehtab', 'Abhishek Dutta'] | 2020-09-20 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-6.30892754e-01 -2.87770003e-01 -4.07904088e-01 -2.97250330e-01
-1.43047035e-01 -4.93134022e-01 7.97265410e-01 -1.22735091e-01
-4.74268436e-01 1.03360724e+00 1.62506342e-01 -9.28374112e-01
-2.43316278e-01 -1.30285072e+00 -6.39062464e-01 -3.98171276e-01
-4.80805427e-01 3.28945696e-01 -1.16462752e-01 -6.58854723... | [4.515870094299316, 4.189015865325928] |
28bed18e-b443-4dfc-8f86-7c34da5735cf | feature-augmented-machine-reading | 2211.09438 | null | https://arxiv.org/abs/2211.09438v1 | https://arxiv.org/pdf/2211.09438v1.pdf | Feature-augmented Machine Reading Comprehension with Auxiliary Tasks | While most successful approaches for machine reading comprehension rely on single training objective, it is assumed that the encoder layer can learn great representation through the loss function we define in the predict layer, which is cross entropy in most of time, in the case that we first use neural networks to enc... | ['Yifeng Xie'] | 2022-11-17 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 6.70011878e-01 8.02282572e-01 -1.57329395e-01 -4.81441468e-01
-7.20694363e-01 -3.05210531e-01 3.71468484e-01 5.58360100e-01
-4.47766036e-01 7.33899474e-01 4.55995649e-01 -4.53568578e-01
-1.11327164e-01 -1.18873596e+00 -1.04241419e+00 -2.84271270e-01
3.06587189e-01 2.91946858e-01 6.60027936e-02 -2.83170342... | [11.24478530883789, 8.139999389648438] |
98021d72-7e7a-41b1-b218-c702789f3d15 | strubert-structure-aware-bert-for-table | 2203.14278 | null | https://arxiv.org/abs/2203.14278v1 | https://arxiv.org/pdf/2203.14278v1.pdf | StruBERT: Structure-aware BERT for Table Search and Matching | A large amount of information is stored in data tables. Users can search for data tables using a keyword-based query. A table is composed primarily of data values that are organized in rows and columns providing implicit structural information. A table is usually accompanied by secondary information such as the caption... | ['Jeff Heflin', 'Brian D. Davison', 'Shuo Zhang', 'Zhiyu Chen', 'Mohamed Trabelsi'] | 2022-03-27 | null | null | null | null | ['table-retrieval', 'table-search'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.82013342e-02 -1.34405911e-01 -6.53642118e-01 -5.02518058e-01
-1.50785041e+00 -8.83063734e-01 4.67882365e-01 1.24628913e+00
-2.07980916e-01 5.45532465e-01 6.67283595e-01 -9.80332196e-02
-2.68999636e-01 -9.66804445e-01 -9.07694578e-01 -4.82601970e-02
2.27633074e-01 8.45453560e-01 2.56666124e-01 -5.28248906... | [9.714163780212402, 7.8971099853515625] |
e0ee4231-9dc1-4de5-ac74-7bb7c348c9b6 | rcp-recurrent-closest-point-for-point-cloud | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gu_RCP_Recurrent_Closest_Point_for_Point_Cloud_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gu_RCP_Recurrent_Closest_Point_for_Point_Cloud_CVPR_2022_paper.pdf | RCP: Recurrent Closest Point for Point Cloud | 3D motion estimation including scene flow and point cloud registration has drawn increasing interest. Inspired by 2D flow estimation, recent methods employ deep neural networks to construct the cost volume for estimating accurate 3D flow. However, these methods are limited by the fact that it is difficult to define... | ['Ping Tan', 'Siyu Zhu', 'Zuozhuo Dai', 'Weihao Yuan', 'Chengzhou Tang', 'Xiaodong Gu'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['point-cloud-registration', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [-4.41176325e-01 -7.33192563e-01 -1.74739920e-02 -1.53791577e-01
-2.41436064e-01 -4.38918322e-01 3.85791093e-01 -1.22994736e-01
-5.15054703e-01 3.69154990e-01 -2.96941716e-02 -2.08414674e-01
-8.15085880e-03 -8.43840718e-01 -5.72137833e-01 -5.48477054e-01
-2.41346434e-01 3.83748263e-01 6.40858471e-01 -2.17172220... | [8.5460786819458, -2.0629398822784424] |
387b5218-f90c-46f5-b843-8908e7810423 | unsupervised-boundary-aware-language-model | 2210.15231 | null | https://arxiv.org/abs/2210.15231v1 | https://arxiv.org/pdf/2210.15231v1.pdf | Unsupervised Boundary-Aware Language Model Pretraining for Chinese Sequence Labeling | Boundary information is critical for various Chinese language processing tasks, such as word segmentation, part-of-speech tagging, and named entity recognition. Previous studies usually resorted to the use of a high-quality external lexicon, where lexicon items can offer explicit boundary information. However, to ensur... | ['Min Zhang', 'Meishan Zhang', 'Pengjun Xie', 'Yanzhao Zhang', 'Dingkun Long', 'Peijie Jiang'] | 2022-10-27 | null | null | null | null | ['chinese-named-entity-recognition', 'chinese-word-segmentation'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.46972007e-02 -1.29566684e-01 -3.35571140e-01 -5.11418104e-01
-8.50220680e-01 -7.38050520e-01 1.96022287e-01 5.29069565e-02
-6.43513262e-01 6.54888570e-01 2.60478377e-01 -5.93403399e-01
5.36562145e-01 -7.56804883e-01 -4.29723233e-01 -3.89939576e-01
3.57712865e-01 3.39316875e-01 3.46189708e-01 -1.37005001... | [9.997269630432129, 10.106274604797363] |
0129612c-b7f7-4214-a6e2-d92164d84e0f | learning-to-fuse-2d-and-3d-image-cues-for | 1611.05708 | null | http://arxiv.org/abs/1611.05708v3 | http://arxiv.org/pdf/1611.05708v3.pdf | Learning to Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation | Most recent approaches to monocular 3D human pose estimation rely on Deep
Learning. They typically involve regressing from an image to either 3D joint
coordinates directly or 2D joint locations from which 3D coordinates are
inferred. Both approaches have their strengths and weaknesses and we therefore
propose a novel a... | ['Pablo Márquez-Neila', 'Pascal Fua', 'Mathieu Salzmann', 'Bugra Tekin'] | 2016-11-17 | learning-to-fuse-2d-and-3d-image-cues-for-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Tekin_Learning_to_Fuse_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Tekin_Learning_to_Fuse_ICCV_2017_paper.pdf | iccv-2017-10 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.89088538e-01 1.04585923e-02 -3.20352428e-02 -4.68147576e-01
-8.05823326e-01 -4.43005323e-01 7.38053143e-01 -3.57546397e-02
-7.31114209e-01 6.30651474e-01 3.49252999e-01 4.20995727e-02
1.28505707e-01 -3.84414613e-01 -8.96997213e-01 -3.28362018e-01
-2.90573929e-02 8.36494684e-01 2.67505050e-02 -3.62316519... | [6.933110237121582, -0.9832189679145813] |
a1db8fa4-22f7-4bad-af7c-0cad349b2e6e | fsar-federated-skeleton-based-action | 2306.11046 | null | https://arxiv.org/abs/2306.11046v1 | https://arxiv.org/pdf/2306.11046v1.pdf | FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation | Existing skeleton-based action recognition methods typically follow a centralized learning paradigm, which can pose privacy concerns when exposing human-related videos. Federated Learning (FL) has attracted much attention due to its outstanding advantages in privacy-preserving. However, directly applying FL approaches ... | ['Chenyang Si', 'Min Zhang', 'Tianyu Guo', 'Shitong Sun', 'Hong Liu', 'Jingwen Guo'] | 2023-06-19 | null | null | null | null | ['skeleton-based-action-recognition', 'action-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 1.80280849e-01 -1.25801221e-01 -5.25647879e-01 -3.73387814e-01
-7.62032688e-01 -6.77368879e-01 3.68129790e-01 -2.13417962e-01
-2.51871467e-01 6.19525909e-01 3.82414430e-01 -4.93284985e-02
-2.97876388e-01 -5.65979838e-01 -8.77147615e-01 -1.02728236e+00
-2.65655935e-01 -1.21627683e-02 3.75139862e-01 1.37698859... | [5.837756156921387, 6.675413131713867] |
7aaac243-2c72-4f49-83b5-9261e7b9fdee | oriented-reppoints-for-aerial-object | 2105.11111 | null | https://arxiv.org/abs/2105.11111v4 | https://arxiv.org/pdf/2105.11111v4.pdf | Oriented RepPoints for Aerial Object Detection | In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings. Unlike the mainstreamed approaches regressing the bounding box orientations, this paper proposes an effective adaptive points learning approach to aerial object detection by taking... | ['Jianke Zhu', 'Kaixuan Hu', 'Yijie Chen', 'Wentong Li'] | 2021-05-24 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Oriented_RepPoints_for_Aerial_Object_Detection_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Oriented_RepPoints_for_Aerial_Object_Detection_CVPR_2022_paper.pdf | cvpr-2022-1 | ['object-detection-in-aerial-images'] | ['computer-vision'] | [-1.65984184e-01 -2.53162146e-01 -8.92630592e-02 -2.87928820e-01
-6.23796225e-01 -7.21141517e-01 4.05155808e-01 7.61991250e-04
-1.91965938e-01 3.88298512e-01 -3.36137414e-01 8.29300806e-02
-6.76359236e-01 -8.17309618e-01 -6.96336746e-01 -8.60640943e-01
-3.43678385e-01 2.73212314e-01 2.36146718e-01 -2.01536164... | [8.692573547363281, -0.77676922082901] |
27bf116c-1c9e-49b4-b44e-070291194fa1 | unsupervised-mutual-transformer-learning-for | 2305.02032 | null | https://arxiv.org/abs/2305.02032v1 | https://arxiv.org/pdf/2305.02032v1.pdf | Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification | Classification of gigapixel Whole Slide Images (WSIs) is an important prediction task in the emerging area of computational pathology. There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs. Mos... | ['Nasir Rajpoot', 'Naoufel Werghi', 'Talha Qaiser', 'Arif Mahmood', 'Sajid Javed'] | 2023-05-03 | null | null | null | null | ['whole-slide-images', 'multiple-instance-learning', 'pseudo-label'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 7.52018213e-01 2.60600924e-01 -5.37214160e-01 -5.26997328e-01
-1.33215773e+00 -3.97197932e-01 2.78376907e-01 5.04993081e-01
-4.25008088e-01 6.96483672e-01 -1.57724991e-01 -2.80381113e-01
-1.71868116e-01 -6.92066491e-01 -7.06455171e-01 -1.40731132e+00
4.09062564e-01 5.65353811e-01 1.33279011e-01 2.24351674... | [15.067697525024414, -2.7858808040618896] |
d65fc99d-fb5b-495c-8a93-c1f89c1a09a5 | semantic-code-search-for-smart-contracts | 2111.14139 | null | https://arxiv.org/abs/2111.14139v1 | https://arxiv.org/pdf/2111.14139v1.pdf | Semantic Code Search for Smart Contracts | Semantic code search technology allows searching for existing code snippets through natural language, which can greatly improve programming efficiency. Smart contracts, programs that run on the blockchain, have a code reuse rate of more than 90%, which means developers have a great demand for semantic code search tools... | ['Longxiang Gao', 'Jiangshan Yu', 'Yong Xiang', 'Chaochen Shi'] | 2021-11-28 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-5.26923597e-01 -3.07546526e-01 -7.52864242e-01 -8.25082138e-02
-8.00219238e-01 -7.88991094e-01 5.27068257e-01 -1.19339131e-01
-1.04176611e-01 1.21212468e-01 3.83265227e-01 -8.05685818e-01
1.59444213e-01 -6.30628467e-01 -7.44311631e-01 -3.71671438e-01
1.28641620e-01 2.06665322e-01 3.05144280e-01 -2.89435863... | [7.487397193908691, 8.067889213562012] |
ba9debeb-41c1-437b-8f25-55c08898d6f0 | metacorrection-domain-aware-meta-loss | 2103.05254 | null | https://arxiv.org/abs/2103.05254v1 | https://arxiv.org/pdf/2103.05254v1.pdf | MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain. Existing self-training based UDA approaches assign pseudo labels for target data and treat them as ground truth labels to fully leverage unlabeled target data for model adaptation. However,... | ['Yixuan Yuan', 'Baopu Li', 'Chen Yang', 'Xiaoqing Guo'] | 2021-03-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Guo_MetaCorrection_Domain-Aware_Meta_Loss_Correction_for_Unsupervised_Domain_Adaptation_in_CVPR_2021_paper.pdf | cvpr-2021-1 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 4.08677995e-01 2.21457586e-01 -3.78957421e-01 -6.49713337e-01
-9.24749672e-01 -4.33389217e-01 3.26420814e-01 -1.32814854e-01
-2.74977982e-01 5.86578608e-01 4.24060319e-03 3.74828838e-02
-5.05903810e-02 -8.61056685e-01 -8.02461147e-01 -8.62391174e-01
6.41229689e-01 6.02542520e-01 1.25240535e-01 6.82719722... | [9.766409873962402, 1.5468709468841553] |
41512081-d66c-4431-8681-7c79a5d5d0ad | adaptive-and-implicit-regularization-for | 2208.05640 | null | https://arxiv.org/abs/2208.05640v1 | https://arxiv.org/pdf/2208.05640v1.pdf | Adaptive and Implicit Regularization for Matrix Completion | The explicit low-rank regularization, e.g., nuclear norm regularization, has been widely used in imaging sciences. However, it has been found that implicit regularization outperforms explicit ones in various image processing tasks. Another issue is that the fixed explicit regularization limits the applicability to broa... | ['Bao Wang', 'Hongxia Wang', 'Tao Sun', 'Zhemin Li'] | 2022-08-11 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 1.77574277e-01 -9.61226225e-02 -1.99160099e-01 -3.35040867e-01
-5.68051696e-01 -3.06224227e-01 3.37036997e-01 -2.03624085e-01
-5.13717473e-01 4.85897899e-01 3.47810984e-01 3.38104963e-02
-2.65315354e-01 -2.26299360e-01 -5.65532982e-01 -1.04150081e+00
1.46712407e-01 -1.68229931e-03 1.78851753e-01 3.25244702... | [7.625667572021484, 4.2894697189331055] |
52f20bec-ce7c-4500-a759-c460d63292ba | dilbert-customized-pre-training-for-domain | 2109.00571 | null | https://arxiv.org/abs/2109.00571v1 | https://arxiv.org/pdf/2109.00571v1.pdf | DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction | The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pre-training procedure can naturally be sub-optimal in some cases. Particularly, fine-tuning a pre-trained language model on a source domain a... | ['Roi Reichart', 'Yftah Ziser', 'Entony Lekhtman'] | 2021-09-01 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.07575321e-01 -1.89114660e-02 -3.39041740e-01 -6.31058991e-01
-1.03608477e+00 -1.18930507e+00 9.22245681e-01 4.84617025e-01
-3.57212245e-01 7.12343574e-01 3.09645593e-01 -2.57834524e-01
1.60721183e-01 -6.78365707e-01 -4.08596277e-01 -6.44948125e-01
4.44157094e-01 9.00952220e-01 2.34610230e-01 -4.58793789... | [10.76356029510498, 7.9528303146362305] |
298b82be-b189-4cd8-84fa-def2c7786563 | remote-sensing-change-detection-with | 2304.06710 | null | https://arxiv.org/abs/2304.06710v1 | https://arxiv.org/pdf/2304.06710v1.pdf | Remote Sensing Change Detection With Transformers Trained from Scratch | Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-training on another CD dataset and then fine-tuning on the target benchmark. This current strategy is driven by the fact that transformers typica... | ['Fahad Shahbaz Khan', 'Salman Khan', 'Rao Muhammad Anwer', 'Sanath Narayan', 'Hisham Cholakkal', 'Mustansar Fiaz', 'Mubashir Noman'] | 2023-04-13 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.37369251e-01 -2.30527505e-01 2.35464554e-02 -4.55561429e-01
-1.03020692e+00 -2.03127712e-01 7.99238205e-01 1.44972011e-01
-6.22070670e-01 3.83581191e-01 3.55608106e-01 1.35584921e-01
1.20678239e-01 -6.01531267e-01 -8.97953033e-01 -7.96670914e-01
1.36987373e-01 1.49809137e-01 3.74445498e-01 -3.62572342... | [9.83020305633545, 0.1941520869731903] |
34883ebc-eb7b-48a5-8848-aef20b68a0d5 | taylorimnet-for-fast-3d-shape-reconstruction | 2201.06845 | null | https://arxiv.org/abs/2201.06845v1 | https://arxiv.org/pdf/2201.06845v1.pdf | TaylorImNet for Fast 3D Shape Reconstruction Based on Implicit Surface Function | Benefiting from the contiguous representation ability, deep implicit functions can extract the iso-surface of a shape at arbitrary resolution. However, utilizing the neural network with a large number of parameters as the implicit function prevents the generation speed of high-resolution topology because it needs to fo... | ['Shenghua Gao', 'Jiale Xu', 'Yuting Xiao'] | 2022-01-18 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-2.87438631e-01 1.41856775e-01 -5.69206290e-02 -1.98197886e-01
-9.07616973e-01 -5.45839846e-01 5.56878209e-01 -1.95954204e-01
-1.74280956e-01 6.21840358e-01 1.33266956e-01 -1.58520639e-01
5.83591089e-02 -1.27791643e+00 -1.17311120e+00 -2.85034120e-01
2.97976118e-02 8.09687078e-01 5.66439509e-01 -2.46290103... | [8.730974197387695, -3.6092123985290527] |
03c7f183-142f-414d-984d-0ff38cc7d870 | dnn-based-mask-estimation-for-distributed | 2011.01714 | null | https://arxiv.org/abs/2011.01714v1 | https://arxiv.org/pdf/2011.01714v1.pdf | DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays | Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In... | ['Slim Essid', 'Irina Illina', 'Romain Serizel', 'Nicolas Furnon'] | 2020-11-03 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 4.03649420e-01 1.21874258e-01 6.39544070e-01 -1.84525520e-01
-8.32393527e-01 -3.39538038e-01 2.84595907e-01 -1.40676558e-01
-6.82157755e-01 5.45201421e-01 5.76625824e-01 -1.76959783e-01
-5.93766630e-01 -4.98026133e-01 -5.12452006e-01 -1.13574719e+00
-3.96612853e-01 -6.70162439e-02 -5.09356894e-02 3.80189046... | [14.996808052062988, 5.900113582611084] |
01e217b9-c919-4d85-a2b8-0f00dd05ac41 | a-term-based-methodology-for-query | 1601.04615 | null | http://arxiv.org/abs/1601.04615v2 | http://arxiv.org/pdf/1601.04615v2.pdf | A Term-Based Methodology for Query Reformulation Understanding | Key to any research involving session search is the understanding of how a
user's queries evolve throughout the session. When a user creates a query
reformulation, he or she is consciously retaining terms from their original
query, removing others and adding new terms. By measuring the similarity
between queries we can... | ['Wang Jun', 'Yang Hui', 'Sloan Marc'] | 2016-01-19 | null | null | null | null | ['session-search'] | ['natural-language-processing'] | [ 4.26341116e-01 -1.07099950e-01 -4.60051447e-01 -5.50459564e-01
-8.44038069e-01 -1.08879340e+00 8.27452004e-01 6.94033027e-01
-7.31094897e-01 2.15664148e-01 4.03682560e-01 -8.45690906e-01
-3.76208067e-01 -4.87248033e-01 -4.19994414e-01 1.64432928e-01
-7.38422945e-02 4.99437094e-01 4.73294646e-01 -4.56343919... | [12.011751174926758, 7.709576606750488] |
a749a870-cbba-45c1-9dd1-5e999c828064 | reinforced-co-training | 1804.06035 | null | http://arxiv.org/abs/1804.06035v1 | http://arxiv.org/pdf/1804.06035v1.pdf | Reinforced Co-Training | Co-training is a popular semi-supervised learning framework to utilize a
large amount of unlabeled data in addition to a small labeled set. Co-training
methods exploit predicted labels on the unlabeled data and select samples based
on prediction confidence to augment the training. However, the selection of
samples in e... | ['Lei LI', 'William Yang Wang', 'Jiawei Wu'] | 2018-04-17 | reinforced-co-training-1 | https://aclanthology.org/N18-1113 | https://aclanthology.org/N18-1113.pdf | naacl-2018-6 | ['clickbait-detection'] | ['natural-language-processing'] | [ 2.67130286e-01 1.98400207e-03 -9.72475886e-01 -6.68706298e-01
-8.14365089e-01 -4.23797816e-01 3.35441738e-01 2.40915492e-01
-4.84836608e-01 9.94882584e-01 -1.08800188e-01 -4.30305600e-01
1.09090053e-01 -7.87755430e-01 -4.79300022e-01 -5.79288721e-01
3.85032415e-01 5.81619620e-01 2.70471841e-01 3.50260854... | [9.483733177185059, 3.9742233753204346] |
8f196365-9a9a-44bd-b235-7488f9a80dcd | egolocate-real-time-motion-capture | 2305.01599 | null | https://arxiv.org/abs/2305.01599v1 | https://arxiv.org/pdf/2305.01599v1.pdf | EgoLocate: Real-time Motion Capture, Localization, and Mapping with Sparse Body-mounted Sensors | Human and environment sensing are two important topics in Computer Vision and Graphics. Human motion is often captured by inertial sensors, while the environment is mostly reconstructed using cameras. We integrate the two techniques together in EgoLocate, a system that simultaneously performs human motion capture (moca... | ['Feng Xu', 'Christian Theobalt', 'Shaohua Pan', 'Vladislav Golyanik', 'Marc Habermann', 'Yuxiao Zhou', 'Xinyu Yi'] | 2023-05-02 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-1.97741494e-01 -4.93154585e-01 -2.27362394e-01 4.84650396e-02
-5.23963511e-01 -5.78485608e-01 5.51676810e-01 -3.84226650e-01
-4.86974716e-01 5.33279836e-01 2.42343187e-01 2.50453442e-01
4.66746062e-01 -4.13822442e-01 -7.12086618e-01 -5.41853487e-01
3.23273450e-01 1.93025902e-01 1.34639710e-01 9.87481400... | [7.55694055557251, -2.081800699234009] |
8215d4af-f709-439f-8af5-6cbbd6ecde56 | road-detection-via-a-dual-task-network-based | 2208.08116 | null | https://arxiv.org/abs/2208.08116v1 | https://arxiv.org/pdf/2208.08116v1.pdf | Road detection via a dual-task network based on cross-layer graph fusion modules | Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer conne... | ['Xueyun Chen', 'Hongkun Liu', 'Wurui Shi', 'Zican Hu'] | 2022-08-17 | null | null | null | null | ['edge-detection'] | ['computer-vision'] | [ 8.01672265e-02 -2.64076054e-01 -4.70348522e-02 -2.96715975e-01
-1.25157356e-01 1.14046670e-01 8.37655723e-01 -1.17660061e-01
-3.17846566e-01 5.15368879e-01 1.99411381e-02 -3.04035813e-01
-3.65045786e-01 -1.45408297e+00 -4.09966141e-01 -8.69549453e-01
-7.83023983e-02 -2.53033668e-01 8.20981383e-01 -5.64367473... | [9.270235061645508, -0.862943172454834] |
3ea754d8-fec4-44be-af7e-e2619f2ea731 | multi-channel-and-multi-microphone-acoustic | 2103.02552 | null | https://arxiv.org/abs/2103.02552v1 | https://arxiv.org/pdf/2103.02552v1.pdf | Multi-Channel and Multi-Microphone Acoustic Echo Cancellation Using A Deep Learning Based Approach | Building on the deep learning based acoustic echo cancellation (AEC) in the single-loudspeaker (single-channel) and single-microphone setup, this paper investigates multi-channel AEC (MCAEC) and multi-microphone AEC (MMAEC). We train a deep neural network (DNN) to predict the near-end speech from microphone signals wit... | ['DeLiang Wang', 'Hao Zhang'] | 2021-03-03 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 6.57550320e-02 -4.12920266e-01 9.48502541e-01 -1.43537238e-01
-1.23420084e+00 -4.81476486e-01 3.22572052e-01 -5.12134492e-01
-5.44430733e-01 1.77661657e-01 7.35833764e-01 -5.67365587e-01
-1.42036565e-02 -2.13088214e-01 -7.72473872e-01 -8.24030161e-01
-1.26401454e-01 -3.38540852e-01 -5.71824573e-02 -4.29560281... | [14.966389656066895, 6.008331775665283] |
67b808ed-68f2-455a-8d7a-13c3880501ed | puzzle-solving-without-search-or-human | 2109.02797 | null | https://arxiv.org/abs/2109.02797v1 | https://arxiv.org/pdf/2109.02797v1.pdf | Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach | The application of Generative Pre-trained Transformer (GPT-2) to learn text-archived game notation provides a model environment for exploring sparse reward gameplay. The transformer architecture proves amenable to training on solved text archives describing mazes, Rubik's Cube, and Sudoku solvers. The method benefits f... | ['Ryerson Burdick', 'David Noever'] | 2021-09-07 | null | null | null | null | ['rubik-s-cube'] | ['graphs'] | [-1.25720531e-01 4.75296021e-01 2.00260013e-01 2.14112118e-01
-1.01430011e+00 -9.58316505e-01 5.67675054e-01 -2.85833031e-01
-2.09569857e-01 1.10112703e+00 4.57006782e-01 -9.51524198e-01
-7.02876806e-01 -9.37279046e-01 -2.99167693e-01 -3.99480462e-01
-4.21171218e-01 8.77774000e-01 1.32965883e-02 -9.28119600... | [3.7462754249572754, 1.4301806688308716] |
f7b1709b-7af9-464c-af71-ea096586962f | provably-efficient-generalized-lagrangian | 2306.00212 | null | https://arxiv.org/abs/2306.00212v1 | https://arxiv.org/pdf/2306.00212v1.pdf | Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning | We examine online safe multi-agent reinforcement learning using constrained Markov games in which agents compete by maximizing their expected total rewards under a constraint on expected total utilities. Our focus is confined to an episodic two-player zero-sum constrained Markov game with independent transition functio... | ['Mihailo R. Jovanović', 'Zhaoran Wang', 'Zhuoran Yang', 'Xiaohan Wei', 'Dongsheng Ding'] | 2023-05-31 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-1.15275718e-01 3.78566027e-01 -3.82523417e-01 1.08228676e-01
-1.16655695e+00 -6.50439858e-01 2.77733225e-02 1.97008207e-01
-1.25418890e+00 1.30343020e+00 -2.57454157e-01 -2.97075182e-01
-4.93642509e-01 -8.10688555e-01 -7.64011919e-01 -9.19932008e-01
-6.36508226e-01 6.11393809e-01 -9.87973660e-02 -2.97068506... | [4.362606525421143, 2.8576531410217285] |
8016cb61-c2b5-40b5-a754-913522e60d9b | bidirectional-hierarchical-attention-networks | null | null | https://aclanthology.org/2021.findings-emnlp.51 | https://aclanthology.org/2021.findings-emnlp.51.pdf | Bidirectional Hierarchical Attention Networks based on Document-level Context for Emotion Cause Extraction | Emotion cause extraction (ECE) aims to extract the causes behind the certain emotion in text. Some works related to the ECE task have been published and attracted lots of attention in recent years. However, these methods neglect two major issues: 1) pay few attentions to the effect of document-level context information... | ['Yi Zhao', 'Guangming Lu', 'Guimin Hu'] | null | null | null | null | findings-emnlp-2021-11 | ['emotion-cause-extraction'] | ['natural-language-processing'] | [ 6.34949356e-02 -1.70481443e-01 3.27835754e-02 -3.94705713e-01
-2.94495702e-01 -3.53653282e-01 3.04607272e-01 2.85905868e-01
-4.58036482e-01 3.80563170e-01 6.44629776e-01 -7.39836246e-02
-2.33485922e-01 -8.79537523e-01 -2.20187098e-01 -4.12606925e-01
6.63521588e-02 -2.22855434e-01 1.90347627e-01 -2.98978180... | [12.625174522399902, 6.212848663330078] |
dc5878ba-3e0c-457e-a19f-8bc33e12e801 | configurable-spatial-temporal-hierarchical | 2305.07328 | null | https://arxiv.org/abs/2305.07328v1 | https://arxiv.org/pdf/2305.07328v1.pdf | Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection | Video anomaly detection (VAD) is a vital task with great practical applications in industrial surveillance, security system, and traffic control. Unlike previous unsupervised VAD methods that adopt a fixed structure to learn normality without considering different detection demands, we design a spatial-temporal hierarc... | ['Jing Liu', 'Zhaoyang Xia', 'Jing Teng', 'Chengxin Pang', 'Tian Wang', 'Yang Liu', 'Xinhua Zeng', 'Kai Cheng'] | 2023-05-12 | null | null | null | null | ['video-anomaly-detection', 'human-detection'] | ['computer-vision', 'computer-vision'] | [-9.42832828e-02 -4.96980786e-01 1.92651585e-01 -1.42263040e-01
-6.66410252e-02 -4.12804514e-01 1.25293821e-01 -2.38381371e-01
1.42033324e-01 -2.01351449e-01 -1.39843538e-01 -4.06816602e-01
-9.80125368e-02 -8.22052419e-01 -5.53208351e-01 -7.15311408e-01
-4.11736339e-01 -6.09515123e-02 8.46824944e-01 -2.43493557... | [7.897280216217041, 1.5824859142303467] |
1c224742-0665-4578-a202-7ccfb6f00af1 | sigmoidally-preconditioned-off-policy | 2205.10047 | null | https://arxiv.org/abs/2205.10047v6 | https://arxiv.org/pdf/2205.10047v6.pdf | The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy Measure | The popular Proximal Policy Optimization (PPO) algorithm approximates the solution in a clipped policy space. Does there exist better policies outside of this space? By using a novel surrogate objective that employs the sigmoid function (which provides an interesting way of exploration), we found that the answer is ``Y... | ['Yi Chang', 'Bei Jiang', 'Randy Goebel', 'Zhiwei Yang', 'Zhixiao Sun', 'Haiyin Piao', 'Hengshuai Yao', 'Hechang Chen', 'Dongcui Diao', 'Xing Chen'] | 2022-05-20 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.07018220e-01 1.20553285e-01 -9.08711553e-01 1.32148787e-01
-5.81600189e-01 -8.37551713e-01 5.87218523e-01 -5.57290465e-02
-7.30417550e-01 1.32328963e+00 2.84570694e-01 -7.31283247e-01
-2.06681132e-01 -3.65286618e-01 -8.88350010e-01 -7.29107141e-01
-1.81956485e-01 3.66960824e-01 2.49277622e-01 -4.22743946... | [4.108235836029053, 2.2584352493286133] |
f0c5eb27-91ef-46ea-ac6a-432b21489b1d | acoustic-echo-cancellation-by-combining | 2005.09237 | null | https://arxiv.org/abs/2005.09237v1 | https://arxiv.org/pdf/2005.09237v1.pdf | Acoustic Echo Cancellation by Combining Adaptive Digital Filter and Recurrent Neural Network | Acoustic Echo Cancellation (AEC) plays a key role in voice interaction. Due to the explicit mathematical principle and intelligent nature to accommodate conditions, adaptive filters with different types of implementations are always used for AEC, giving considerable performance. However, there would be some kinds of re... | ['Pei Zhao', 'Tengrong Su', 'Hua Huang', 'Lu Ma'] | 2020-05-19 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.59256216e-02 -2.63810039e-01 7.00940132e-01 8.41451064e-02
-2.32901484e-01 -2.48532131e-01 4.48564067e-02 -1.87611341e-01
-3.41051400e-01 4.89436537e-01 5.46954453e-01 -1.09721914e-01
-6.97852746e-02 -4.70891207e-01 -2.00757906e-01 -9.16749597e-01
-2.02412549e-02 -5.35837293e-01 4.98432338e-01 -4.10158277... | [15.10736083984375, 5.949859619140625] |
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