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0ae8ad1e-79d8-42d7-a2d3-1fd0899cf530 | temporal-knowledge-graph-reasoning-with-low | 2204.04783 | null | https://arxiv.org/abs/2204.04783v1 | https://arxiv.org/pdf/2204.04783v1.pdf | Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations | Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accurate but missing information. In this context, tensor decomposition has successfully modeled interactions between entities and relations. Th... | ['Günter Neumann', 'Saadullah Amin', 'Ioannis Dikeoulias'] | 2022-04-10 | null | https://aclanthology.org/2022.repl4nlp-1.12 | https://aclanthology.org/2022.repl4nlp-1.12.pdf | repl4nlp-acl-2022-5 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-3.25503498e-01 -2.69572347e-01 -6.69834554e-01 -3.09481502e-01
-2.77346849e-01 -6.80679321e-01 6.78012371e-01 4.66567636e-01
-3.86074752e-01 1.48840994e-01 4.06769753e-01 -3.25138360e-01
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-5.07432103e-01 6.87157512e-01 4.65922892e-01 -3.14686686... | [8.552275657653809, 7.89233922958374] |
e30166e3-1e50-4877-b64d-44c28008d5f0 | dermatologist-like-explainable-ai-enhances | 2303.12806 | null | https://arxiv.org/abs/2303.12806v1 | https://arxiv.org/pdf/2303.12806v1.pdf | Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma | Although artificial intelligence (AI) systems have been shown to improve the accuracy of initial melanoma diagnosis, the lack of transparency in how these systems identify melanoma poses severe obstacles to user acceptance. Explainable artificial intelligence (XAI) methods can help to increase transparency, but most XA... | ['Titus J. Brinker', 'Eva Krieghoff-Henning', 'Stefan Fröhling', 'Kamran Ghoreschi', 'Jochen S. Utikal', 'Bastian Schilling', 'Matthias Goebeler', 'Konstantin Drexler', 'Sebastian Haferkamp', 'Michael Erdmann', 'Markus V. Heppt', 'Frank Friedrich Gellrich', 'Wiebke Sondermann', 'Sören Korsing', 'Gabriela Poch', 'Mar Ll... | 2023-03-17 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 2.62591928e-01 1.12567246e+00 -4.39959794e-01 -6.54226780e-01
-4.87847954e-01 -5.56184292e-01 3.51926476e-01 5.18860102e-01
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5.22285938e-01 6.51966631e-01 -2.68519998e-01 2.72413224... | [8.653736114501953, 5.8241868019104] |
02d7bc30-25f9-43d7-b354-e91ec6815a94 | dude-deep-unsigned-distance-embeddings-for-hi | 2011.0257 | null | https://arxiv.org/abs/2011.02570v2 | https://arxiv.org/pdf/2011.02570v2.pdf | DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces | High fidelity representation of shapes with arbitrary topology is an important problem for a variety of vision and graphics applications. Owing to their limited resolution, classical discrete shape representations using point clouds, voxels and meshes produce low quality results when used in these applications. Several... | ['Maneesh Singh', 'Laszlo Jeni', 'Aurobrata Ghosh', 'Sarthak Sharma', 'Rahul Venkatesh'] | 2020-11-04 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [ 1.16355792e-01 -4.56623593e-03 1.86547026e-01 -1.50972798e-01
-5.24674058e-01 -5.95172346e-01 8.14950049e-01 2.49575928e-01
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-2.54136082e-02 -1.41163838e+00 -8.04715633e-01 -6.02068245e-01
1.04172416e-01 6.42081618e-01 8.17628726e-02 -3.55781257... | [8.616474151611328, -3.6587820053100586] |
3e6ec533-5035-4dd6-9c45-6508656d7bdf | learning-free-iris-segmentation-revisited-a | 1901.01575 | null | http://arxiv.org/abs/1901.01575v1 | http://arxiv.org/pdf/1901.01575v1.pdf | Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples | Subject matching performance in iris biometrics is contingent upon fast,
high-quality iris segmentation. In many cases, iris biometrics acquisition
equipment takes a number of images in sequence and combines the segmentation
and matching results for each image to strengthen the result. To date,
segmentation has occurre... | ['Walter Scheirer', 'Camila Carballo', 'Jeffery Kinnison', 'Mateusz Trokielewicz', 'Adam Czajka'] | 2019-01-06 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 4.86120075e-01 -2.47248858e-01 -2.69449428e-02 -3.99172068e-01
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9.33254287e-02 5.76112211e-01 -7.32530933e-03 2.31695011... | [3.7429420948028564, -3.6317481994628906] |
03c54bc3-b6a9-4c47-8848-c957390e2982 | efficient-detection-of-botnet-traffic-by | 2107.02896 | null | https://arxiv.org/abs/2107.02896v1 | https://arxiv.org/pdf/2107.02896v1.pdf | Efficient Detection of Botnet Traffic by features selection and Decision Trees | Botnets are one of the online threats with the biggest presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze large amounts of network traffic data. In this work, we focus on increasing the performance on botnet traffi... | ['Enrique Alegre', 'Eduardo Fidalgo', 'Víctor González-Castro', 'Javier Velasco-Mata'] | 2021-06-30 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-1.04197234e-01 -6.29730582e-01 -1.15030371e-01 2.22932864e-02
-4.84011583e-02 -4.63875681e-01 6.59660876e-01 1.76768824e-01
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-3.41278404e-01 6.58781052e-01 8.84911239e-01 -4.88271266... | [5.2309794425964355, 7.239346981048584] |
dbd9b945-b543-4f4c-bb2f-3b6c2945f2bd | unconstrained-monocular-3d-human-pose | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Yu_Unconstrained_Monocular_3D_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Yu_Unconstrained_Monocular_3D_2013_CVPR_paper.pdf | Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-Modality Regression Forest | This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in uncon... | ['Roberto Cipolla', 'Tae-Kyun Kim', 'Tsz-Ho Yu'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 3.27324748e-01 1.03464007e-01 -1.41464069e-01 -1.17426496e-02
-5.18075585e-01 -4.60754395e-01 5.55917323e-01 -4.06974673e-01
-5.84490955e-01 5.48040152e-01 2.86251843e-01 5.43530464e-01
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-1.99139401e-01 5.75103104e-01 4.63901550e-01 -2.98763454... | [7.098392963409424, -0.9059575200080872] |
2d081236-9c51-4b4d-9065-e42fc6529315 | the-dku-dukeece-diarization-system-for-the | 2210.01677 | null | https://arxiv.org/abs/2210.01677v1 | https://arxiv.org/pdf/2210.01677v1.pdf | The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022 | This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detection model, a clustering-based diarization model, and a target-speaker voice activity detection-based overlap detection model. Overall, the sub... | ['Ming Li', 'Kangyue Wang', 'Yucong Zhang', 'Ming Cheng', 'Xiaoyi Qin', 'Weiqing Wang'] | 2022-10-04 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-8.26353282e-02 2.48894513e-01 -1.19340330e-01 -4.97754902e-01
-1.32412410e+00 -4.83479172e-01 6.20099425e-01 -1.97208717e-01
-4.78725016e-01 3.01411539e-01 7.26377666e-01 -2.35867679e-01
3.17028522e-01 2.75783271e-01 -3.13892394e-01 -5.99323869e-01
-8.51628035e-02 3.16199630e-01 8.99305567e-02 -1.39153242... | [14.470669746398926, 5.986634731292725] |
be19a46d-b8b9-4e7a-a3e8-220b146417b4 | improving-rnn-transducer-with-target-speaker | 2011.13393 | null | https://arxiv.org/abs/2011.13393v2 | https://arxiv.org/pdf/2011.13393v2.pdf | Improving RNN Transducer With Target Speaker Extraction and Neural Uncertainty Estimation | Target-speaker speech recognition aims to recognize target-speaker speech from noisy environments with background noise and interfering speakers. This work presents a joint framework that combines time-domain target-speaker speech extraction and Recurrent Neural Network Transducer (RNN-T). To stabilize the joint-traini... | ['Dong Yu', 'Meng Yu', 'Shinji Watanabe', 'Chao Weng', 'Chunlei Zhang', 'Jiatong Shi'] | 2020-11-26 | null | null | null | null | ['speech-extraction'] | ['speech'] | [ 4.60826367e-01 2.28921577e-01 2.44984940e-01 -5.03330410e-01
-1.48038280e+00 -2.65848607e-01 1.93429410e-01 -5.53169429e-01
-2.41199777e-01 2.56971657e-01 4.28071558e-01 -4.45925057e-01
2.19936639e-01 5.80766499e-02 -4.29144889e-01 -8.52134466e-01
1.17240779e-01 -3.99329737e-02 -4.63209860e-03 -8.69232640... | [14.588767051696777, 6.1720428466796875] |
33f1e755-f285-46d5-b1a3-42cfd0c4fbb8 | rethinking-ai-explainability-and-plausibility | 2303.17707 | null | https://arxiv.org/abs/2303.17707v2 | https://arxiv.org/pdf/2303.17707v2.pdf | The XAI Alignment Problem: Rethinking How Should We Evaluate Human-Centered AI Explainability Techniques | Setting proper evaluation objectives for explainable artificial intelligence (XAI) is vital for making XAI algorithms follow human communication norms, support human reasoning processes, and fulfill human needs for AI explanations. In this position paper, we examine the most pervasive human-grounded concept in XAI eval... | ['Ghassan Hamarneh', 'Xiaoxiao Li', 'Weina Jin'] | 2023-03-30 | null | null | null | null | ['object-localization'] | ['computer-vision'] | [ 3.51301670e-01 1.12565100e+00 -2.93444782e-01 -6.61985219e-01
-8.13436508e-02 -4.99095052e-01 9.78276134e-01 6.76394925e-02
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-5.66770434e-01 -6.33350492e-01 -6.37010634e-01 -4.87779155e-02
4.77362901e-01 7.77828753e-01 -6.32242322e-01 1.70526773... | [8.969355583190918, 5.95249605178833] |
75134729-78f0-4b5b-a9a5-3fee56cbbd1a | a-systematic-framework-to-discover-pattern | 1711.06955 | null | http://arxiv.org/abs/1711.06955v1 | http://arxiv.org/pdf/1711.06955v1.pdf | A systematic framework to discover pattern for web spam classification | Web spam is a big problem for search engine users in World Wide Web. They use
deceptive techniques to achieve high rankings. Although many researchers have
presented the different approach for classification and web spam detection
still it is an open issue in computer science. Analyzing and evaluating these
websites ca... | ['Jiang Xiaohui', 'Yuan Chi', 'Wang Yongli', 'Jelodar Hamed'] | 2017-11-19 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [-3.66457850e-01 -5.13481677e-01 -1.99543178e-01 -1.99579671e-02
-7.11354196e-01 -1.06372404e+00 7.82269835e-01 1.77303076e-01
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-2.94602931e-01 -1.17470098e+00 -1.94827065e-01 -3.88626516e-01
-5.08902445e-02 4.57831085e-01 1.17632520e+00 -4.25577253... | [7.822587013244629, 10.010924339294434] |
1a3c89ae-dff7-4076-a6a3-0ca44bab7f54 | distance-sensitive-offline-reinforcement | 2205.11027 | null | https://arxiv.org/abs/2205.11027v3 | https://arxiv.org/pdf/2205.11027v3.pdf | When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning | In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas. Unfortunately, existing offline RL methods are often over-conservative, inevitably hurting generalization performance outside data distribution. In our study,... | ['Ya-Qin Zhang', 'Jingjing Liu', 'Xiangyu Zhu', 'Haoran Xu', 'Xianyuan Zhan', 'Jianxiong Li'] | 2022-05-23 | null | null | null | null | ['d4rl'] | ['robots'] | [-4.22966301e-01 3.50608766e-01 -5.50858140e-01 -1.05600610e-01
-8.00352335e-01 -8.44005883e-01 2.24551216e-01 1.82817429e-01
-6.64799392e-01 1.04551721e+00 -4.18503731e-02 -4.16729838e-01
-2.59820282e-01 -7.38708913e-01 -1.05204821e+00 -7.83141434e-01
-2.48950928e-01 5.61393142e-01 9.88774747e-02 -4.07685399... | [4.125679016113281, 2.301802396774292] |
839a9a97-36c6-4912-afc4-69b720fd0978 | optimizing-slam-evaluation-footprint-through | 2209.06316 | null | https://arxiv.org/abs/2209.06316v2 | https://arxiv.org/pdf/2209.06316v2.pdf | Optimizing SLAM Evaluation Footprint Through Dynamic Range Coverage Analysis of Datasets | Simultaneous Localization and Mapping (SLAM) is considered an ever-evolving problem due to its usage in many applications. Evaluation of SLAM is done typically using publicly available datasets which are increasing in number and the level of difficulty. Each dataset provides a certain level of dynamic range coverage th... | ['Hong Zhang', 'Islam Ali'] | 2022-09-13 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 3.87585193e-01 -4.43837970e-01 4.10298593e-02 -6.37705982e-01
-6.87561393e-01 -7.03356028e-01 4.22081411e-01 6.87316597e-01
-5.84405601e-01 8.61505210e-01 -7.77557939e-02 -2.28158571e-02
-7.92193711e-01 -9.15598452e-01 -6.10145152e-01 -4.68752831e-01
-4.03168082e-01 7.08229184e-01 3.98358256e-01 -3.15180302... | [7.316073894500732, -2.045354127883911] |
ae4a3f3f-db7b-4d64-bc70-d61666413fd2 | 190910390 | 1909.1039 | null | https://arxiv.org/abs/1909.10390v1 | https://arxiv.org/pdf/1909.10390v1.pdf | GNTeam at 2018 n2c2: Feature-augmented BiLSTM-CRF for drug-related entity recognition in hospital discharge summaries | Monitoring the administration of drugs and adverse drug reactions are key parts of pharmacovigilance. In this paper, we explore the extraction of drug mentions and drug-related information (reason for taking a drug, route, frequency, dosage, strength, form, duration, and adverse events) from hospital discharge summarie... | ['Haifa Alrdahi', 'Goran Nenadic', 'Maksim Belousov', 'Ghada Alfattni', 'Nikola Milosevic'] | 2019-09-23 | null | null | null | null | ['entity-extraction'] | ['natural-language-processing'] | [ 8.10637251e-02 1.96266055e-01 -5.00027180e-01 -4.13086325e-01
-9.52057838e-01 -4.46457356e-01 3.15133214e-01 8.61954629e-01
-7.79925823e-01 7.33240545e-01 6.74510300e-01 -5.94607890e-01
-6.23806566e-03 -7.28397787e-01 -5.43651521e-01 -3.92500728e-01
-3.20183814e-01 5.97861052e-01 -5.69650054e-01 4.54246700... | [8.414437294006348, 8.706585884094238] |
b3232ac6-6ef0-4d78-8d84-b48fb77c5e0d | graph-similarity-using-pagerank-and | 2002.05158 | null | https://arxiv.org/abs/2002.05158v2 | https://arxiv.org/pdf/2002.05158v2.pdf | Fast and Scalable Complex Network Descriptor Using PageRank and Persistent Homology | The PageRank of a graph is a scalar function defined on the node set of the graph which encodes nodes centrality information of the graph. In this article, we use the PageRank function along with persistent homology to obtain a scalable graph descriptor and utilize it to compare the similarities between graphs. For a g... | ['Mustafa Hajij', 'Elizabeth Munch', 'Paul Rosen'] | 2020-02-12 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-3.36868197e-01 4.06624705e-01 -1.71250463e-01 2.78459787e-02
-2.54822314e-01 -7.87499905e-01 1.97670057e-01 8.23996365e-01
-2.50335395e-01 2.03840807e-01 5.02938963e-02 -2.02214420e-01
-2.78514653e-01 -1.42757738e+00 -6.56005144e-01 -1.73286319e-01
-1.02876174e+00 4.03885126e-01 5.39542854e-01 -2.97313124... | [7.086126804351807, 5.672863483428955] |
02af9966-0e0a-4c93-8d1c-25b50104c920 | no-reference-image-quality-assessment-with-1 | null | null | https://www.mdpi.com/2076-3417/12/1/101 | https://www.mdpi.com/2076-3417/12/1/101 | No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion | No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyd... | ['Domonkos Varga'] | 2021-12-23 | null | null | null | applied-sciences-2021-12 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.68223590e-01 -6.39524698e-01 1.35499820e-01 -4.39756393e-01
-9.18568373e-01 -2.65821606e-01 4.38984513e-01 -4.87488881e-02
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6.43606950e-03 -1.03395417e-01 1.61993176e-01 -4.12640721... | [11.809365272521973, -1.8432087898254395] |
fa8888a2-a33e-4685-ae42-2100f4715951 | mask-reference-image-quality-assessment | 2302.1377 | null | https://arxiv.org/abs/2302.13770v2 | https://arxiv.org/pdf/2302.13770v2.pdf | Mask Reference Image Quality Assessment | Understanding semantic information is an essential step in knowing what is being learned in both full-reference (FR) and no-reference (NR) image quality assessment (IQA) methods. However, especially for many severely distorted images, even if there is an undistorted image as a reference (FR-IQA), it is difficult to per... | ['Anlong Ming', 'Limin Liu', 'Shuai He', 'Pengxiang Xiao'] | 2023-02-27 | null | null | null | null | ['image-quality-assessment'] | ['computer-vision'] | [ 5.48692524e-01 -1.18114553e-01 3.10389232e-02 -3.43648374e-01
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3.02530348e-01 -1.15860589e-01 3.20329517e-01 -3.92636567... | [11.743398666381836, -1.8976538181304932] |
0a038e35-efc7-4399-89aa-7661f18e152f | balanced-mixture-of-supernets-for-learning | 2306.11982 | null | https://arxiv.org/abs/2306.11982v1 | https://arxiv.org/pdf/2306.11982v1.pdf | Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture | Downsampling layers, including pooling and strided convolutions, are crucial components of the convolutional neural network architecture that determine both the granularity/scale of image feature analysis as well as the receptive field size of a given layer. To fully understand this problem, we analyse the performance ... | ['Marco Pedersoli', 'Matthew Toews', 'Mehraveh Javan'] | 2023-06-21 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-1.14772163e-01 -2.96176314e-01 1.43299818e-01 -2.61195719e-01
1.63954988e-01 -5.77057123e-01 3.37423116e-01 -3.53592681e-04
-8.56957376e-01 4.97634441e-01 6.45440221e-02 -1.79979518e-01
-3.47305655e-01 -8.50070715e-01 -9.03236926e-01 -8.42472613e-01
2.49222964e-02 2.23532710e-02 7.91149735e-01 -2.61596859... | [8.729609489440918, 2.8569869995117188] |
4146a025-c8e8-4b1c-aeb2-d50ccd154557 | object-detection-with-deep-reinforcement | 2208.04511 | null | https://arxiv.org/abs/2208.04511v1 | https://arxiv.org/pdf/2208.04511v1.pdf | Object Detection with Deep Reinforcement Learning | Object localization has been a crucial task in computer vision field. Methods of localizing objects in an image have been proposed based on the features of the attended pixels. Recently researchers have proposed methods to formulate object localization as a dynamic decision process, which can be solved by a reinforceme... | ['Ruofeng Li', 'Manoosh Samiei'] | 2022-08-09 | null | null | null | null | ['active-object-localization'] | ['computer-vision'] | [-6.09303731e-03 -5.38332909e-02 -1.17437966e-01 -4.92521673e-01
-6.54543579e-01 -5.00245929e-01 7.24748135e-01 1.53164968e-01
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-2.71892399e-01 -6.09542847e-01 -5.12721956e-01 -9.69674349e-01
-5.65417707e-02 3.59119058e-01 6.53163612e-01 2.17119902... | [9.329421043395996, 0.6355604529380798] |
d68afc92-a84c-4f8f-8e79-cf931adcfb8b | pre-training-contextualized-world-models-with | 2305.18499 | null | https://arxiv.org/abs/2305.18499v1 | https://arxiv.org/pdf/2305.18499v1.pdf | Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning | Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains. Recent work has investigated such unsupervised pre-training methods for model-based reinforcement learning (MBRL) but is limited to domain-specific or simulated data. In this paper, we stud... | ['Mingsheng Long', 'Chaoyi Deng', 'Haoyu Ma', 'Jialong Wu'] | 2023-05-29 | null | null | null | null | ['unsupervised-pre-training', 'model-based-reinforcement-learning'] | ['methodology', 'reasoning'] | [ 1.10025570e-01 -2.15377986e-01 -4.23839271e-01 -2.00054199e-01
-2.43228734e-01 -2.91030467e-01 5.49150348e-01 -3.56723905e-01
-3.87852162e-01 7.10614681e-01 4.05573472e-02 8.37168843e-02
-7.37909600e-02 -6.26028717e-01 -1.07626879e+00 -8.61326933e-01
-1.68904319e-01 2.33347118e-02 3.61376494e-01 -4.63544667... | [7.468553066253662, -0.04944642633199692] |
b61fed4b-3bbc-4eee-b6e8-0ded94388731 | semi-supervised-counterfactual-explanations-1 | 2303.12634 | null | https://arxiv.org/abs/2303.12634v1 | https://arxiv.org/pdf/2303.12634v1.pdf | Semi-supervised counterfactual explanations | Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different output or a target output. A valid counterfactual explanation should have likely feature values. Here, we address the challenge of generating cou... | ['Satyam Dwivedi', 'Sumanta Mukherjee', 'Shravan Kumar Sajja'] | 2023-03-22 | semi-supervised-counterfactual-explanations | https://openreview.net/forum?id=o6ndFLB1DST | https://openreview.net/pdf?id=o6ndFLB1DST | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 5.31472802e-01 8.94002140e-01 -5.47478497e-01 -5.24153411e-01
-5.35307407e-01 -4.44148332e-01 1.05752158e+00 -5.62564805e-02
-4.24527638e-02 1.36626434e+00 6.71602428e-01 -6.76318169e-01
-3.21225405e-01 -7.98078954e-01 -1.09199476e+00 -5.25814354e-01
7.07257092e-02 5.79170167e-01 -3.75375718e-01 3.21921766... | [8.652080535888672, 5.625154495239258] |
98f93a91-9a3a-48db-89ee-80123b6d936b | multi-hypothesis-3d-human-pose-estimation | 2210.11179 | null | https://arxiv.org/abs/2210.11179v1 | https://arxiv.org/pdf/2210.11179v1.pdf | Multi-hypothesis 3D human pose estimation metrics favor miscalibrated distributions | Due to depth ambiguities and occlusions, lifting 2D poses to 3D is a highly ill-posed problem. Well-calibrated distributions of possible poses can make these ambiguities explicit and preserve the resulting uncertainty for downstream tasks. This study shows that previous attempts, which account for these ambiguities via... | ['Fabian H. Sinz', 'Mohammad Bashiri', 'R. James Cotton', 'Paweł A. Pierzchlewicz'] | 2022-10-20 | null | null | null | null | ['multi-hypotheses-3d-human-pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-5.53936735e-02 3.18714380e-01 -2.78421789e-01 -3.12832177e-01
-1.12779403e+00 -1.41238764e-01 4.63884860e-01 -2.76922524e-01
-2.68761545e-01 1.06925225e+00 4.75080580e-01 5.61654717e-02
-1.01758003e-01 -6.27962887e-01 -9.44033325e-01 -4.93478894e-01
-7.47737139e-02 9.97187078e-01 4.35531288e-01 1.72535017... | [7.115455150604248, -1.0833992958068848] |
dfae93c0-4a22-4b2d-8c3d-07642fa5d461 | nlp-cuet-lt-edi-eacl2021-multilingual-code | 2103.00464 | null | https://arxiv.org/abs/2103.00464v1 | https://arxiv.org/pdf/2103.00464v1.pdf | NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner | In recent years, several systems have been developed to regulate the spread of negativity and eliminate aggressive, offensive or abusive contents from the online platforms. Nevertheless, a limited number of researches carried out to identify positive, encouraging and supportive contents. In this work, our goal is to id... | ['Mohammed Moshiul Hoque', 'Omar Sharif', 'Eftekhar Hossain'] | 2021-02-28 | null | https://aclanthology.org/2021.ltedi-1.25 | https://aclanthology.org/2021.ltedi-1.25.pdf | eacl-ltedi-2021-4 | ['multilingual-text-classification', 'hope-speech-detection'] | ['miscellaneous', 'natural-language-processing'] | [-3.85861546e-01 9.17819068e-02 -2.89014071e-01 -2.02967495e-01
-3.19212228e-01 -3.44612658e-01 9.26179886e-01 3.56650054e-01
-4.58522141e-01 1.01315892e+00 5.18709064e-01 -5.19456446e-01
-3.38149279e-01 -7.02917516e-01 -1.75018087e-01 -3.04836661e-01
-2.04161808e-01 -2.96535529e-02 -2.73550600e-01 -9.05355275... | [8.926568031311035, 10.669601440429688] |
d27b3655-9403-477a-b274-b7f38e621feb | time-matters-multi-scale-temporalization-of | 1801.05853 | null | http://arxiv.org/abs/1801.05853v1 | http://arxiv.org/pdf/1801.05853v1.pdf | Time Matters: Multi-scale Temporalization of Social Media Popularity | The evolution of social media popularity exhibits rich temporality, i.e.,
popularities change over time at various levels of temporal granularity. This
is influenced by temporal variations of public attentions or user activities.
For example, popularity patterns of street snap on Flickr are observed to
depict distincti... | ['Wen-Huang Cheng', 'Yongdong Zhang', 'Bo Wu', 'Tao Mei'] | 2017-12-12 | null | null | null | null | ['social-media-popularity-prediction', 'social-media-popularity-prediction'] | ['miscellaneous', 'time-series'] | [-3.55938524e-01 -9.43804801e-01 -6.14532292e-01 -1.10099159e-01
-3.17941785e-01 -4.47898597e-01 4.24639106e-01 2.82510400e-01
-9.59972143e-02 4.06903803e-01 6.13182127e-01 9.32141766e-02
-2.31503308e-01 -6.84554458e-01 -6.68538392e-01 -5.90392768e-01
-2.73839623e-01 1.74504146e-01 2.73349077e-01 -1.60064548... | [9.635824203491211, 5.467423439025879] |
7d3b58f6-3b95-4c97-abe9-b7a8108c73e2 | xalign-cross-lingual-fact-to-text-alignment | 2202.00291 | null | https://arxiv.org/abs/2202.00291v2 | https://arxiv.org/pdf/2202.00291v2.pdf | XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages | Multiple critical scenarios (like Wikipedia text generation given English Infoboxes) need automated generation of descriptive text in low resource (LR) languages from English fact triples. Previous work has focused on English fact-to-text (F2T) generation. To the best of our knowledge, there has been no previous attemp... | ['Vasudeva Varma', 'Manish Gupta', 'Anubhav Sharma', 'Bhavyajeet Singh', 'Shivprasad Sagare', 'Tushar Abhishek'] | 2022-02-01 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 9.61815342e-02 7.10246623e-01 -3.32331359e-01 -5.00388205e-01
-1.53546453e+00 -8.10084224e-01 1.06905568e+00 -2.07314994e-02
-2.62201309e-01 1.58125544e+00 8.31692576e-01 -5.65699637e-01
2.51333505e-01 -1.01939499e+00 -1.02932966e+00 1.02302119e-01
3.38830173e-01 1.01404238e+00 -1.66447699e-01 -5.81294715... | [11.478921890258789, 9.461713790893555] |
803f1815-49b7-4f09-8dc6-b7c2bc4634a8 | lsta-long-short-term-attention-for-egocentric | 1811.10698 | null | http://arxiv.org/abs/1811.10698v3 | http://arxiv.org/pdf/1811.10698v3.pdf | LSTA: Long Short-Term Attention for Egocentric Action Recognition | Egocentric activity recognition is one of the most challenging tasks in video
analysis. It requires a fine-grained discrimination of small objects and their
manipulation. While some methods base on strong supervision and attention
mechanisms, they are either annotation consuming or do not take spatio-temporal
patterns ... | ['Swathikiran Sudhakaran', 'Oswald Lanz', 'Sergio Escalera'] | 2018-11-26 | lsta-long-short-term-attention-for-egocentric-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Sudhakaran_LSTA_Long_Short-Term_Attention_for_Egocentric_Action_Recognition_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Sudhakaran_LSTA_Long_Short-Term_Attention_for_Egocentric_Action_Recognition_CVPR_2019_paper.pdf | cvpr-2019-6 | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 1.20619088e-02 -6.13310337e-01 -4.43523049e-01 -2.50366986e-01
-4.57154721e-01 -4.45398539e-01 6.73875511e-01 -9.45518091e-02
-6.09941304e-01 3.15109611e-01 5.78077614e-01 2.83847481e-01
-1.33336663e-01 -4.12134320e-01 -7.41694808e-01 -6.33084774e-01
-4.77465242e-01 1.17680550e-01 4.26831096e-01 5.60660027... | [8.415735244750977, 0.6257859468460083] |
b4304a3e-ce17-4dc6-b74f-3589f1c3bc50 | clinical-language-understanding-evaluation | 2209.14377 | null | https://arxiv.org/abs/2209.14377v1 | https://arxiv.org/pdf/2209.14377v1.pdf | Clinical Language Understanding Evaluation (CLUE) | Clinical language processing has received a lot of attention in recent years, resulting in new models or methods for disease phenotyping, mortality prediction, and other tasks. Unfortunately, many of these approaches are tested under different experimental settings (e.g., data sources, training and testing splits, metr... | ['Dina Demner-Fushman', 'Travis R. Goodwin'] | 2022-09-28 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [ 1.98064655e-01 -1.29089087e-01 -3.59104127e-01 -5.68251848e-01
-8.39763880e-01 -4.26937222e-01 4.61169451e-01 9.81681764e-01
-4.58302408e-01 5.91343701e-01 4.89118248e-01 -6.41633213e-01
-2.22701222e-01 -5.24610102e-01 1.64918199e-01 -2.90177435e-01
-3.63049835e-01 8.14602554e-01 -9.43699386e-03 1.80559546... | [8.456910133361816, 8.393970489501953] |
631d0ce6-d577-4a0c-91c6-ddd7634560c6 | perada-parameter-efficient-and-generalizable | 2302.06637 | null | https://arxiv.org/abs/2302.06637v1 | https://arxiv.org/pdf/2302.06637v1.pdf | PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees | Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high communication and computation costs or (2) overfit to local data, which can be limited in scope, and are vulnerable to evolved test samples... | ['Anima Anandkumar', 'Bo Li', 'Chaowei Xiao', 'Daguang Xu', 'Wenda Chu', 'De-An Huang', 'Chulin Xie'] | 2023-02-13 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.53448009e-01 -3.10149759e-01 -4.52113926e-01 -5.00727654e-01
-1.21894324e+00 -6.58836365e-01 -3.10175139e-02 -9.31089297e-02
-3.55714411e-01 8.17432582e-01 -1.87655911e-02 -2.64232635e-01
-4.96594220e-01 -7.44715750e-01 -9.91244555e-01 -8.96633923e-01
-3.17851335e-01 7.37861514e-01 1.29758298e-01 1.81029648... | [5.848239421844482, 6.272885322570801] |
0af30134-17e5-4c64-8ac1-527aaad2bf35 | comparing-computational-architectures-for | 2210.04107 | null | https://arxiv.org/abs/2210.04107v1 | https://arxiv.org/pdf/2210.04107v1.pdf | Comparing Computational Architectures for Automated Journalism | The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representat... | ['Fabio G. Cozman', 'Marcos M. José', 'João Gabriel M. Campos', 'Yan V. Sym'] | 2022-10-08 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 8.20744131e-03 9.10920799e-01 3.44978720e-01 -4.01884466e-01
-8.01660836e-01 -5.26964843e-01 1.14373314e+00 1.92536220e-01
-3.68520230e-01 1.06099296e+00 7.66816139e-01 -3.67855161e-01
1.84490144e-01 -9.80280757e-01 -4.70806867e-01 -2.53183931e-01
4.47905928e-01 1.06462359e+00 -2.82987475e-01 -4.43753898... | [11.594347953796387, 9.059464454650879] |
e4e875e1-c058-45e7-8910-f002a738651f | do-we-train-on-test-data-the-impact-of-near | 2304.04653 | null | https://arxiv.org/abs/2304.04653v1 | https://arxiv.org/pdf/2304.04653v1.pdf | Do We Train on Test Data? The Impact of Near-Duplicates on License Plate Recognition | This work draws attention to the large fraction of near-duplicates in the training and test sets of datasets widely adopted in License Plate Recognition (LPR) research. These duplicates refer to images that, although different, show the same license plate. Our experiments, conducted on the two most popular datasets in ... | ['David Menotti', 'Rodrigo Minetto', 'Alceu S. Britto Jr.', 'Valter Estevam', 'Rayson Laroca'] | 2023-04-10 | null | null | null | null | ['license-plate-recognition'] | ['computer-vision'] | [-1.31712094e-01 -4.77904797e-01 -8.97659361e-03 -4.72120792e-01
-1.07963550e+00 -9.01203930e-01 8.89413476e-01 -6.32364690e-01
-4.17039752e-01 7.00106740e-01 -5.38097806e-02 -2.99308300e-01
5.07822409e-02 -5.38345993e-01 -1.05185652e+00 -5.03887475e-01
2.85678267e-01 5.54751515e-01 2.56561995e-01 2.15727016... | [9.845483779907227, -4.907479763031006] |
c4a8d58a-cfbc-4408-a055-4d4e6c41d7f0 | online-training-through-time-for-spiking | 2210.04195 | null | https://arxiv.org/abs/2210.04195v2 | https://arxiv.org/pdf/2210.04195v2.pdf | Online Training Through Time for Spiking Neural Networks | Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with surrogate gradients (SG) is popularly used to achieve high performance i... | ['Zhouchen Lin', 'Di He', 'Zongpeng Zhang', 'Qingyan Meng', 'Mingqing Xiao'] | 2022-10-09 | null | null | null | null | ['event-data-classification', 'gesture-recognition'] | ['computer-vision', 'computer-vision'] | [ 5.93943819e-02 -4.65616673e-01 2.66509652e-01 -2.29593948e-01
-6.87970370e-02 -2.24614158e-01 1.88182741e-01 -1.80720061e-01
-7.70430923e-01 9.92686093e-01 -3.29348534e-01 -2.55389154e-01
-1.22026257e-01 -9.02082145e-01 -1.25472331e+00 -9.05635715e-01
-4.96846586e-02 -5.15830927e-02 5.49444079e-01 -3.54582965... | [8.2272310256958, 2.4872119426727295] |
5eb47235-de91-45e1-a7c3-185875c1fd5a | uncertainty-aware-blind-image-quality | 2005.13983 | null | https://arxiv.org/abs/2005.13983v6 | https://arxiv.org/pdf/2005.13983v6.pdf | Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild | Performance of blind image quality assessment (BIQA) models has been significantly boosted by end-to-end optimization of feature engineering and quality regression. Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthet... | ['Kede Ma', 'Xiaokang Yang', 'Weixia Zhang', 'Guangtao Zhai'] | 2020-05-28 | null | null | null | null | ['blind-image-quality-assessment'] | ['computer-vision'] | [ 3.00955139e-02 -2.39726439e-01 4.69918758e-01 -5.89399874e-01
-1.39724720e+00 -6.82283342e-01 6.16615713e-01 -2.02089086e-01
-4.58222806e-01 5.67169368e-01 3.40038925e-01 -8.74800608e-02
-2.34247565e-01 -5.00510037e-01 -9.10695910e-01 -6.18889213e-01
-3.82565111e-02 2.80541092e-01 -3.40917140e-01 6.71620443... | [11.888123512268066, -1.801232099533081] |
7a8f5e96-eff3-469d-be64-bec8347809b5 | language-id-prediction-from-speech-using-self | 2104.11985 | null | https://arxiv.org/abs/2104.11985v1 | https://arxiv.org/pdf/2104.11985v1.pdf | Language ID Prediction from Speech Using Self-Attentive Pooling and 1D-Convolutions | This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. For many low-resource and endangered languages, only single-speaker recordings may be... | ['Nikolay Mikhaylovskiy', 'Roman Bedyakin'] | 2021-04-24 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [-6.16265126e-02 -7.30723068e-02 -2.00155482e-01 -6.15178764e-01
-1.04229653e+00 -7.51910567e-01 6.33392632e-01 -1.31449535e-01
-8.52969468e-01 6.62335038e-01 4.55734402e-01 -6.60721123e-01
4.60502297e-01 2.28721015e-02 -3.67392719e-01 -2.29593039e-01
-7.81411454e-02 7.55301118e-01 -2.61223316e-03 -3.69259745... | [14.143684387207031, 6.653419494628906] |
3f2b1c08-87cb-4166-aad5-4139b1eacc1c | what-leads-to-generalization-of-object | 2008.057 | null | https://arxiv.org/abs/2008.05700v1 | https://arxiv.org/pdf/2008.05700v1.pdf | What leads to generalization of object proposals? | Object proposal generation is often the first step in many detection models. It is lucrative to train a good proposal model, that generalizes to unseen classes. This could help scaling detection models to larger number of classes with fewer annotations. Motivated by this, we study how a detection model trained on a sma... | ['Rui Wang', 'Dhruv Mahajan', 'Vignesh Ramanathan'] | 2020-08-13 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 1.07080355e-01 4.92789894e-01 -6.96612075e-02 -4.74946052e-01
-5.30050218e-01 -6.41495407e-01 6.79632723e-01 1.34963512e-01
-6.18531704e-01 5.06595910e-01 -2.36321673e-01 -1.22924127e-01
2.74406940e-01 -8.57225716e-01 -7.35195935e-01 -5.02975523e-01
4.44310494e-02 5.81335127e-01 9.39334095e-01 -9.32508707... | [9.360478401184082, 1.597768783569336] |
1af98938-77b1-42f3-8543-ae80580e424f | graph-contrastive-learning-with-multi | 2307.04322 | null | https://arxiv.org/abs/2307.04322v1 | https://arxiv.org/pdf/2307.04322v1.pdf | Graph Contrastive Learning with Multi-Objective for Personalized Product Retrieval in Taobao Search | In e-commerce search, personalized retrieval is a crucial technique for improving user shopping experience. Recent works in this domain have achieved significant improvements by the representation learning paradigm, e.g., embedding-based retrieval (EBR) and collaborative filtering (CF). EBR methods do not sufficiently ... | ['Xiaoyi Zeng', 'Qingwen Liu', 'Yun Zhong', 'Sen Li', 'Chao Zhang', 'Longbin Li'] | 2023-07-10 | null | null | null | null | ['contrastive-learning', 'graph-learning', 'contrastive-learning', 'representation-learning', 'retrieval', 'collaborative-filtering'] | ['computer-vision', 'graphs', 'methodology', 'methodology', 'methodology', 'miscellaneous'] | [-1.36367440e-01 -4.82336462e-01 -6.30878150e-01 -3.65137428e-01
-7.49457777e-01 -4.93869156e-01 2.14212343e-01 4.72968519e-01
-4.56169516e-01 2.07217395e-01 2.67469317e-01 -2.33352631e-01
-9.60658967e-01 -1.06666398e+00 -5.48154354e-01 -4.49559689e-01
-3.38966399e-01 4.18560207e-01 2.64042497e-01 -6.27399564... | [10.182642936706543, 5.6233229637146] |
2ae4aa78-b3d4-4955-88e1-85d874d9af4c | multi-head-attention-mechanism-learning-for | 2307.04075 | null | https://arxiv.org/abs/2307.04075v1 | https://arxiv.org/pdf/2307.04075v1.pdf | Multi-Head Attention Mechanism Learning for Cancer New Subtypes and Treatment Based on Cancer Multi-Omics Data | Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omics data and clinical features among subtypes of different cancers. Therefore, the identification and discovery of cancer subtypes are crucial for the diagnosis, treatment, and prognosis of cancer. In this... | ['Shaoliang Peng', 'Liwen Xu', 'Pengfei Rong', 'Zhichao Feng', 'Lian Wang', 'Yutao Dou', 'Dazhen Liu', 'Liangrui Pan'] | 2023-07-09 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [-3.71493638e-01 -2.75511622e-01 -4.07103807e-01 -1.13500886e-01
-7.77310073e-01 -2.35799029e-01 3.63208681e-01 6.28483951e-01
-1.90365002e-01 4.92768019e-01 2.99914092e-01 -2.88573265e-01
-5.84804952e-01 -9.41724181e-01 -3.45237970e-01 -1.31559718e+00
2.08351970e-01 4.89258677e-01 -4.60203528e-01 1.16050206... | [6.013308525085449, 5.692407131195068] |
390e8e26-d559-4c3b-856f-12542921daf8 | guiding-users-to-where-to-give-color-hints | 2210.1427 | null | https://arxiv.org/abs/2210.14270v1 | https://arxiv.org/pdf/2210.14270v1.pdf | Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization | Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime. However, another approach, which actively informs the user of the most effective regions to... | ['Jaegul Choo', 'Daesik Kim', 'Mohammad Azam Khan', 'Haneol Lee', 'Yeojeong Park', 'Juntae Kim', 'Soyoung Yang', 'Junsoo Lee', 'Youngin Cho'] | 2022-10-25 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 3.87665220e-02 -2.60933399e-01 -4.61972617e-02 -3.07690740e-01
-3.22696894e-01 -6.92615271e-01 5.83895385e-01 2.33606860e-01
-3.65968704e-01 3.40636969e-01 9.17565301e-02 -4.65098143e-01
1.66011810e-01 -7.51879036e-01 -3.29937249e-01 -4.36330795e-01
2.85438418e-01 1.47858456e-01 3.32759619e-01 -1.72916681... | [11.405937194824219, -1.0073308944702148] |
93405ef5-2ed6-4874-9d11-359d934523c0 | detection-of-gravitational-waves-using | 1910.08245 | null | https://arxiv.org/abs/1910.08245v1 | https://arxiv.org/pdf/1910.08245v1.pdf | Detection of gravitational waves using topological data analysis and convolutional neural network: An improved approach | The gravitational wave detection problem is challenging because the noise is typically overwhelming. Convolutional neural networks (CNNs) have been successfully applied, but require a large training set and the accuracy suffers significantly in the case of low SNR. We propose an improved method that employs a feature e... | ['Jae-Hun Jung', 'Christopher Bresten'] | 2019-10-18 | null | null | null | null | ['gravitational-wave-detection'] | ['miscellaneous'] | [ 4.04398292e-01 -2.96440274e-01 4.64829892e-01 5.32035753e-02
-7.96505988e-01 -6.01231158e-01 6.24637306e-01 -1.43068284e-01
-6.82075202e-01 6.91512167e-01 -1.82073534e-01 -1.23190694e-01
-2.12372899e-01 -1.05273843e+00 -4.88357663e-01 -1.12025297e+00
-3.95683229e-01 3.11315298e-01 8.69344056e-01 -5.88550091... | [7.567573070526123, 3.0552990436553955] |
afab361d-930a-4faf-ab6e-06529918ad31 | generative-sequential-recommendation-with | 2306.11114 | null | https://arxiv.org/abs/2306.11114v1 | https://arxiv.org/pdf/2306.11114v1.pdf | Generative Sequential Recommendation with GPTRec | Sequential recommendation is an important recommendation task that aims to predict the next item in a sequence. Recently, adaptations of language models, particularly Transformer-based models such as SASRec and BERT4Rec, have achieved state-of-the-art results in sequential recommendation. In these models, item ids repl... | ['Craig Macdonald', 'Aleksandr V. Petrov'] | 2023-06-19 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-1.16231576e-01 -2.75021911e-01 -4.25425470e-01 -1.84715867e-01
-5.79836309e-01 -7.15767801e-01 7.37164915e-01 -3.75179350e-02
-4.22668993e-01 4.35525179e-01 8.37949395e-01 -4.60792273e-01
-5.38022399e-01 -9.32871759e-01 -6.36484623e-01 -5.13390362e-01
-1.92720041e-01 7.15280354e-01 1.01476490e-01 -6.23187184... | [10.127577781677246, 5.713368892669678] |
3f4f8294-0e22-4c4c-86d2-e3deb2b49bb2 | when-a-computer-cracks-a-joke-automated | 2109.08702 | null | https://arxiv.org/abs/2109.08702v1 | https://arxiv.org/pdf/2109.08702v1.pdf | When a Computer Cracks a Joke: Automated Generation of Humorous Headlines | Automated news generation has become a major interest for new agencies in the past. Oftentimes headlines for such automatically generated news articles are unimaginative as they have been generated with ready-made templates. We present a computationally creative approach for headline generation that can generate humoro... | ['Mika Hämäläinen', 'Khalid Alnajjar'] | 2021-09-17 | null | null | null | null | ['headline-generation', 'news-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-1.05182163e-01 9.87590253e-01 7.33767301e-02 -2.58171201e-01
-8.46958995e-01 -5.85934341e-01 1.05630529e+00 1.00749843e-01
-3.56262863e-01 1.28412688e+00 1.21558642e+00 -8.87081251e-02
3.10478359e-01 -5.83285570e-01 -3.03773552e-01 -1.18834004e-01
4.90829915e-01 7.70304739e-01 -2.32230090e-02 -8.16087127... | [12.237801551818848, 9.379389762878418] |
6ab24947-d9f9-4833-a183-64f958102314 | deep-curiosity-search-intra-life-exploration | 1806.00553 | null | http://arxiv.org/abs/1806.00553v3 | http://arxiv.org/pdf/1806.00553v3.pdf | Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problems | Traditional exploration methods in RL require agents to perform random
actions to find rewards. But these approaches struggle on sparse-reward domains
like Montezuma's Revenge where the probability that any random action sequence
leads to reward is extremely low. Recent algorithms have performed well on such
tasks by e... | ['Jeff Clune', 'Christopher Stanton'] | 2018-06-01 | null | https://openreview.net/forum?id=BkeDEoCctQ | https://openreview.net/pdf?id=BkeDEoCctQ | null | ['montezumas-revenge'] | ['playing-games'] | [-2.35860065e-01 1.26376480e-01 -2.75640100e-01 5.46870120e-02
-6.74430728e-01 -8.30809057e-01 8.42611194e-01 -9.17335972e-02
-1.09506965e+00 1.42637205e+00 1.83529213e-01 -4.57393348e-01
-2.17651129e-01 -8.40301514e-01 -8.63304436e-01 -6.87724292e-01
-7.61113703e-01 8.55677128e-01 1.06553838e-01 -5.71216047... | [3.8818957805633545, 1.638156533241272] |
b2d2413f-214b-406f-8a0b-25bdea309ab2 | revisiting-data-augmentation-in-model | 2305.13232 | null | https://arxiv.org/abs/2305.13232v1 | https://arxiv.org/pdf/2305.13232v1.pdf | Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study | The excellent performance of deep neural networks is usually accompanied by a large number of parameters and computations, which have limited their usage on the resource-limited edge devices. To address this issue, abundant methods such as pruning, quantization and knowledge distillation have been proposed to compress ... | ['Kaisheng Ma', 'Linfeng Zhang', 'Muzhou Yu'] | 2023-05-22 | null | null | null | null | ['model-compression'] | ['methodology'] | [ 2.88579553e-01 1.26530960e-01 -4.84937340e-01 -4.07531470e-01
2.58440226e-02 6.14963211e-02 3.25887948e-01 1.77411377e-01
-6.12118602e-01 6.73637092e-01 7.19938427e-02 -4.08451915e-01
-2.49703247e-02 -9.42101002e-01 -7.25930393e-01 -7.91490495e-01
2.23019019e-01 2.46958867e-01 2.42144749e-01 -1.94449052... | [8.544032096862793, 3.117164134979248] |
e2ad35e4-6219-41ad-ab9d-21370aa7c226 | analyzing-the-impact-of-climate-change-on | 2302.01887 | null | https://arxiv.org/abs/2302.01887v2 | https://arxiv.org/pdf/2302.01887v2.pdf | Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach | Natural language processing (NLP) is a promising approach for analyzing large volumes of climate-change and infrastructure-related scientific literature. However, best-in-practice NLP techniques require large collections of relevant documents (corpus). Furthermore, NLP techniques using machine learning and deep learnin... | ['Prasanna Balaprakash', 'Leslie-Anne Levy', 'John K Hutchison', 'Duane R. Verner', 'Joshua David Bergerson', 'Tanwi Mallick'] | 2023-02-03 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 1.85668021e-01 4.44450676e-02 -3.09406906e-01 -3.88563901e-01
-1.03272021e+00 -1.03258717e+00 8.31681430e-01 9.97571826e-01
-3.38172495e-01 6.22713506e-01 4.46761936e-01 -1.01821125e+00
-1.24823473e-01 -1.12368369e+00 -7.57445395e-01 -5.56398809e-01
-3.72314937e-02 7.13233232e-01 -8.86863917e-02 1.46873727... | [10.328680038452148, 7.23297643661499] |
b1e16304-dd0a-4076-b744-510200129955 | fredom-fairness-domain-adaptation-approach-to | 2304.02135 | null | https://arxiv.org/abs/2304.02135v1 | https://arxiv.org/pdf/2304.02135v1.pdf | FREDOM: Fairness Domain Adaptation Approach to Semantic Scene Understanding | Although Domain Adaptation in Semantic Scene Segmentation has shown impressive improvement in recent years, the fairness concerns in the domain adaptation have yet to be well defined and addressed. In addition, fairness is one of the most critical aspects when deploying the segmentation models into human-related real-w... | ['Khoa Luu', 'Jackson Cothren', 'Bhiksha Raj', 'Ngan Le', 'Thanh-Dat Truong'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Truong_FREDOM_Fairness_Domain_Adaptation_Approach_to_Semantic_Scene_Understanding_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Truong_FREDOM_Fairness_Domain_Adaptation_Approach_to_Semantic_Scene_Understanding_CVPR_2023_paper.pdf | cvpr-2023-1 | ['scene-segmentation'] | ['computer-vision'] | [ 1.19137824e-01 3.87343645e-01 -9.09288228e-02 -8.47293496e-01
-3.53680074e-01 1.31226435e-01 4.48521703e-01 -4.19568531e-02
-6.76276565e-01 8.13233614e-01 -1.21295363e-01 -1.13068543e-01
-1.58579439e-01 -6.85262620e-01 -4.73478615e-01 -7.37133861e-01
4.51540470e-01 3.46460223e-01 3.43631834e-01 -3.21329862... | [9.599556922912598, 1.466704249382019] |
a337aaf6-f8a6-4806-98db-959bdf8edcf5 | resper-computationally-modelling-resisting | 2101.10545 | null | https://arxiv.org/abs/2101.10545v1 | https://arxiv.org/pdf/2101.10545v1.pdf | RESPER: Computationally Modelling Resisting Strategies in Persuasive Conversations | Modelling persuasion strategies as predictors of task outcome has several real-world applications and has received considerable attention from the computational linguistics community. However, previous research has failed to account for the resisting strategies employed by an individual to foil such persuasion attempts... | ['Carolyn Penstein Rosé', 'Haogang Bao', 'Xinru Yan', 'Meredith Riggs', 'Surya Shekhar Chakraborty', 'Rishabh Joshi', 'Sayan Sinha', 'Ritam Dutt'] | 2021-01-26 | null | https://aclanthology.org/2021.eacl-main.7 | https://aclanthology.org/2021.eacl-main.7.pdf | eacl-2021-2 | ['persuasion-strategies'] | ['computer-vision'] | [ 7.59190857e-01 7.24470198e-01 -1.93884388e-01 -6.98917270e-01
-3.96772772e-01 -5.53930223e-01 1.28864312e+00 2.85881966e-01
-5.97575903e-01 7.49271393e-01 1.07536352e+00 -8.73803675e-01
-4.58179533e-01 -3.96032363e-01 -7.14156181e-02 -3.23927045e-01
2.90666103e-01 3.91328663e-01 -3.53994101e-01 -4.57735986... | [12.723514556884766, 7.960143089294434] |
7faaa1c6-0f0b-4094-ab51-024dfd09e755 | multi-view-active-fine-grained-recognition | 2206.01153 | null | https://arxiv.org/abs/2206.01153v1 | https://arxiv.org/pdf/2206.01153v1.pdf | Multi-View Active Fine-Grained Recognition | As fine-grained visual classification (FGVC) being developed for decades, great works related have exposed a key direction -- finding discriminative local regions and revealing subtle differences. However, unlike identifying visual contents within static images, for recognizing objects in the real physical world, discr... | ['Zhanyu Ma', 'Yongbin Li', 'Ting-En Lin', 'Dongliang Chang', 'Heqing Wang', 'Wenqing Yu', 'Ruoyi Du'] | 2022-06-02 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.56481262e-02 -3.41547787e-01 -3.28142077e-01 -3.90598506e-01
-4.14475381e-01 -7.21443653e-01 6.43291771e-01 -1.87687963e-01
3.39982286e-03 5.36497891e-01 2.33129129e-01 -8.34567547e-02
-7.61341676e-02 -7.18185723e-01 -6.78950131e-01 -1.00971985e+00
8.31783488e-02 2.07954571e-01 4.02327687e-01 -1.71522930... | [9.690919876098633, 1.940043568611145] |
71b9582d-fd7b-4198-a075-06191caf9e8f | counterfactual-explanation-and-causal | 2009.08856 | null | https://arxiv.org/abs/2009.08856v2 | https://arxiv.org/pdf/2009.08856v2.pdf | Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control | We propose an architecture for training generative models of counterfactual conditionals of the form, 'can we modify event A to cause B instead of C?', motivated by applications in robot control. Using an 'adversarial training' paradigm, an image-based deep neural network model is trained to produce small and realistic... | ['Simón C. Smith', 'Subramanian Ramamoorthy'] | 2020-09-18 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 7.58288443e-01 9.20193732e-01 7.98093528e-02 -1.70311421e-01
-2.55862087e-01 -6.62061930e-01 1.22139633e+00 -2.40978688e-01
-5.75664222e-01 9.60331440e-01 6.12618960e-02 -6.10397637e-01
-4.29852635e-01 -8.95849526e-01 -1.30685592e+00 -7.97684610e-01
-4.59085070e-02 3.84897768e-01 -1.48318827e-01 -3.89608055... | [4.502810478210449, 1.8757786750793457] |
ff6e57aa-5df2-4998-8554-2f07e1c41438 | rethinking-class-relations-absolute-relative | 2001.03919 | null | https://arxiv.org/abs/2001.03919v4 | https://arxiv.org/pdf/2001.03919v4.pdf | Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning | The majority of existing few-shot learning methods describe image relations with binary labels. However, such binary relations are insufficient to teach the network complicated real-world relations, due to the lack of decision smoothness. Furthermore, current few-shot learning models capture only the similarity via rel... | ['Songlei Jian', 'Piotr Koniusz', 'Hongdong Li', 'Hongguang Zhang', 'Philip H. S. Torr'] | 2020-01-12 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Rethinking_Class_Relations_Absolute-Relative_Supervised_and_Unsupervised_Few-Shot_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification'] | ['computer-vision', 'computer-vision'] | [ 5.61325550e-01 3.12566608e-01 -4.57807690e-01 -6.54723406e-01
2.09794529e-02 -3.13401222e-01 7.19135463e-01 6.94436252e-01
-2.43655160e-01 6.27530515e-01 -1.73383877e-01 2.32580483e-01
-4.21538264e-01 -1.09462476e+00 -7.59669781e-01 -4.43190902e-01
5.15531786e-02 6.38421535e-01 5.14055669e-01 -1.71896845... | [10.168471336364746, 2.5303750038146973] |
3ae90784-eba3-4e18-b6ce-03bb8c9e3947 | how-old-is-gpt-the-humbel-framework-for | 2305.14195 | null | https://arxiv.org/abs/2305.14195v2 | https://arxiv.org/pdf/2305.14195v2.pdf | How Old is GPT?: The HumBEL Framework for Evaluating Language Models using Human Demographic Data | While large pre-trained language models (LMs) find greater use across NLP, existing evaluation protocols do not consider how LM language use aligns with particular human demographic groups, which can be an important consideration in conversational AI applications. To remedy this gap, we consider how LM language skills ... | ['Malihe Alikhani', 'Jennifer C. Gates', 'Anthony Sicilia'] | 2023-05-23 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.61453649e-01 6.89945579e-01 -2.13428900e-01 -1.91119641e-01
-8.91304910e-01 -3.77566040e-01 4.69550133e-01 2.71284282e-01
-7.40896821e-01 4.50550795e-01 7.89738774e-01 -6.69919252e-01
-3.12465996e-01 -3.02674115e-01 -9.12359655e-02 -1.73116148e-01
1.02056101e-01 8.71100247e-01 -9.43025500e-02 -2.18855411... | [10.810999870300293, 9.657529830932617] |
a5e31e92-90cc-4294-b1ec-3150dc1ea842 | audio-cover-song-identification-using | 1712.00166 | null | https://arxiv.org/abs/1712.00166v2 | https://arxiv.org/pdf/1712.00166v2.pdf | Audio Cover Song Identification using Convolutional Neural Network | In this paper, we propose a new approach to cover song identification using a CNN (convolutional neural network). Most previous studies extract the feature vectors that characterize the cover song relation from a pair of songs and used it to compute the (dis)similarity between the two songs. Based on the observation th... | ['Sang Keun Choe', 'Sungkyun Chang', 'Kyogu Lee', 'Juheon Lee'] | 2017-12-01 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 5.60422003e-01 -5.28347909e-01 -3.08014117e-02 -2.63343066e-01
-6.16706133e-01 -8.46554160e-01 3.21913093e-01 1.43256122e-02
-1.53965473e-01 4.00228560e-01 9.52548981e-02 2.70775408e-01
-4.66841340e-01 -1.25534809e+00 -7.99433768e-01 -5.52965701e-01
-3.99260014e-01 4.26419765e-01 -1.14020087e-01 -1.03251934... | [15.777467727661133, 5.180514812469482] |
546b30d0-935e-4d11-8066-fd0d59c1e2e0 | bwbaugh-hierarchical-sentiment-analysis-with | null | null | https://aclanthology.org/S13-2090 | https://aclanthology.org/S13-2090.pdf | bwbaugh : Hierarchical sentiment analysis with partial self-training | null | ['Wesley Baugh'] | 2013-06-01 | null | null | null | semeval-2013-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3230366706848145, 3.8184146881103516] |
9084bb86-b7a8-4d5b-b176-aaae36fd843d | homography-from-two-orientation-and-scale | 1906.11927 | null | https://arxiv.org/abs/1906.11927v1 | https://arxiv.org/pdf/1906.11927v1.pdf | Homography from two orientation- and scale-covariant features | This paper proposes a geometric interpretation of the angles and scales which the orientation- and scale-covariant feature detectors, e.g. SIFT, provide. Two new general constraints are derived on the scales and rotations which can be used in any geometric model estimation tasks. Using these formulas, two new constrain... | ['Zuzana Kukelova', 'Daniel Barath'] | 2019-06-27 | homography-from-two-orientation-and-scale-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Barath_Homography_From_Two_Orientation-_and_Scale-Covariant_Features_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Barath_Homography_From_Two_Orientation-_and_Scale-Covariant_Features_ICCV_2019_paper.pdf | iccv-2019-10 | ['homography-estimation'] | ['computer-vision'] | [-5.66508994e-02 -2.06110895e-01 -1.94342464e-01 -2.21558452e-01
-2.13307127e-01 -8.41412783e-01 6.34109497e-01 9.37300995e-02
-4.87450153e-01 5.65782011e-01 -3.33765835e-01 1.29775077e-01
-1.92272291e-01 -7.36153483e-01 -4.67667490e-01 -6.23458326e-01
3.99908982e-02 4.25591767e-01 2.63892829e-01 -2.20799953... | [7.912501811981201, -2.31968092918396] |
698b1093-bc4e-4e86-8f5e-3d83e6707e47 | sutav-a-turkish-audio-visual-database | null | null | https://aclanthology.org/L12-1262 | https://aclanthology.org/L12-1262.pdf | SUTAV: A Turkish Audio-Visual Database | This paper contains information about the ''''''``Sabanci University Turkish Audio-Visual (SUTAV)'''''''' database. The main aim of collecting SUTAV database was to obtain a large audio-visual collection of spoken words, numbers and sentences in Turkish language. The database was collected between 2006 and 2010 during ... | ['Ibrahim Saygin Topkaya', 'Hakan Erdogan'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['person-identification', 'audio-visual-speech-recognition'] | ['computer-vision', 'speech'] | [-1.78655609e-01 -4.01310384e-01 2.14197308e-01 -3.66511256e-01
-1.04973328e+00 -3.32359016e-01 4.66170818e-01 2.19878197e-01
-5.30792117e-01 6.10472381e-01 2.88416594e-01 -1.69316277e-01
1.95207670e-01 -4.48922068e-01 -5.15636504e-01 -8.76369178e-01
9.17932391e-02 1.98241100e-01 4.78434283e-03 -1.69821069... | [14.318987846374512, 5.1222076416015625] |
0029b3f2-514e-4e6b-8ae9-6770144cf9e2 | epic-ensemble-of-partial-point-clouds-for | 2303.11419 | null | https://arxiv.org/abs/2303.11419v2 | https://arxiv.org/pdf/2303.11419v2.pdf | EPiC: Ensemble of Partial Point Clouds for Robust Classification | Robust point cloud classification is crucial for real-world applications, as consumer-type 3D sensors often yield partial and noisy data, degraded by various artifacts. In this work we propose a general ensemble framework, based on partial point cloud sampling. Each ensemble member is exposed to only partial input data... | ['Guy Gilboa', 'Meir Yossef Levi'] | 2023-03-20 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-3.04395556e-02 -3.86550635e-01 -1.94902578e-03 -3.38203758e-02
-1.06247294e+00 -7.79907167e-01 5.89714348e-01 3.82494256e-02
-8.10699984e-02 8.03449035e-01 -2.01471031e-01 -7.79886618e-02
-5.27469106e-02 -8.37957025e-01 -1.07836676e+00 -1.10157037e+00
-2.43529961e-01 2.94214755e-01 3.07356060e-01 -1.69888631... | [7.686522483825684, -2.8788421154022217] |
f36c6d10-6e7f-446f-8aa9-cf31dd7cd6cd | gres-generalized-referring-expression-1 | 2306.00968 | null | https://arxiv.org/abs/2306.00968v1 | https://arxiv.org/pdf/2306.00968v1.pdf | GRES: Generalized Referring Expression Segmentation | Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object described by a given language expression. Existing classic RES datasets and methods commonly support single-target expressions only, i.e., one expression refers to one target object. Multi-target and no-target expressions are no... | ['Xudong Jiang', 'Henghui Ding', 'Chang Liu'] | 2023-06-01 | gres-generalized-referring-expression | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_GRES_Generalized_Referring_Expression_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_GRES_Generalized_Referring_Expression_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['referring-expression', 'generalized-referring-expression-segmentation', 'referring-expression-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.29843056e-01 -2.74327397e-02 -3.14791918e-01 -5.53907692e-01
-9.29176867e-01 -5.49763501e-01 4.39491093e-01 -1.75512701e-01
-3.05887520e-01 5.11346102e-01 -7.66022727e-02 -2.02653304e-01
2.31347248e-01 -6.11072421e-01 -6.55998707e-01 -5.05141675e-01
4.77723658e-01 3.38930100e-01 5.69008887e-01 -4.02050823... | [10.240772247314453, 1.170952320098877] |
1466f9c9-cc5b-431c-8604-be94ad4afee2 | action-unit-detection-with-region-adaptation | 1704.03067 | null | http://arxiv.org/abs/1704.03067v1 | http://arxiv.org/pdf/1704.03067v1.pdf | Action Unit Detection with Region Adaptation, Multi-labeling Learning and Optimal Temporal Fusing | Action Unit (AU) detection becomes essential for facial analysis. Many
proposed approaches face challenging problems in dealing with the alignments of
different face regions, in the effective fusion of temporal information, and in
training a model for multiple AU labels. To better address these problems, we
propose a d... | ['Zhigang Zhu', 'Wei Li', 'Farnaz Abitahi'] | 2017-04-10 | action-unit-detection-with-region-adaptation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Action_Unit_Detection_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Action_Unit_Detection_CVPR_2017_paper.pdf | cvpr-2017-7 | ['action-unit-detection'] | ['computer-vision'] | [ 2.86765486e-01 -1.98926240e-01 -2.44443938e-01 -1.46439925e-01
-7.67867148e-01 3.91860912e-03 2.46600911e-01 -2.62717128e-01
-1.50133207e-01 2.53200531e-01 -7.66078159e-02 3.85572255e-01
2.24957854e-01 -7.67979741e-01 -6.03967071e-01 -9.50794160e-01
-3.07836324e-01 -1.58742871e-02 3.79008204e-01 1.04238456... | [13.591064453125, 1.6044058799743652] |
715abdc4-a379-40cf-9c3d-d3cd66c95d23 | autoshape-an-autoencoder-shapelet-approach | 2208.04313 | null | https://arxiv.org/abs/2208.04313v2 | https://arxiv.org/pdf/2208.04313v2.pdf | AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering | Time series shapelets are discriminative subsequences that have been recently found effective for time series clustering (TSC). The shapelets are convenient for interpreting the clusters. Thus, the main challenge for TSC is to discover high-quality variable-length shapelets to discriminate different clusters. In this p... | ['Grace Lai-Hung Wong', 'Daphne Ngar-yin Mah', 'Sourav S Bhowmick', 'Jianliang Xu', 'Byron Choi', 'Guozhong Li'] | 2022-08-06 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-5.68078995e-01 -6.70002222e-01 9.55896974e-02 -1.65290296e-01
-6.56207860e-01 -7.39181399e-01 3.78776997e-01 1.28628597e-01
-2.45578066e-01 2.22119287e-01 9.67186540e-02 -1.08527921e-01
-6.63696408e-01 -5.46312392e-01 -4.52488601e-01 -1.16420949e+00
-6.27322793e-01 5.03106058e-01 -3.53042722e-01 9.73335002... | [7.323685646057129, 3.2488014698028564] |
11750aca-58c2-4bb2-8278-b36181292b22 | learning-robust-video-synchronization-without | 1610.05985 | null | http://arxiv.org/abs/1610.05985v3 | http://arxiv.org/pdf/1610.05985v3.pdf | Learning Robust Video Synchronization without Annotations | Aligning video sequences is a fundamental yet still unsolved component for a
broad range of applications in computer graphics and vision. Most classical
image processing methods cannot be directly applied to related video problems
due to the high amount of underlying data and their limit to small changes in
appearance.... | ['Ido Freeman', 'Patrick Wieschollek', 'Hendrik P. A. Lensch'] | 2016-10-19 | null | null | null | null | ['video-synchronization', 'video-alignment'] | ['computer-vision', 'computer-vision'] | [ 4.95596558e-01 -4.86973882e-01 -2.41396993e-01 -3.13377321e-01
-3.88598591e-01 -8.01767886e-01 6.45753801e-01 -1.52831096e-02
-5.01859128e-01 5.14617682e-01 -8.26347023e-02 -9.14606005e-02
-1.51044717e-02 -3.00997376e-01 -6.70666873e-01 -6.32744908e-01
-3.04783404e-01 3.69447708e-01 5.38734674e-01 -2.49606580... | [8.61434555053711, -0.5172134041786194] |
6031aa14-859d-47b3-aea5-7057a6f110d3 | tadil-task-agnostic-domain-incremental | 2306.11955 | null | https://arxiv.org/abs/2306.11955v1 | https://arxiv.org/pdf/2306.11955v1.pdf | TADIL: Task-Agnostic Domain-Incremental Learning through Task-ID Inference using Transformer Nearest-Centroid Embeddings | Machine Learning (ML) models struggle with data that changes over time or across domains due to factors such as noise, occlusion, illumination, or frequency, unlike humans who can learn from such non independent and identically distributed data. Consequently, a Continual Learning (CL) approach is indispensable, particu... | ['David Ellison', 'Ajay Dholakia', 'Jordi Guitart', 'Peini Liu', 'Gusseppe Bravo-Rocca'] | 2023-06-21 | null | null | null | null | ['incremental-learning'] | ['methodology'] | [ 2.22574651e-01 -3.24342281e-01 -1.68932959e-01 -5.05706191e-01
-8.64680886e-01 -7.20071256e-01 6.86189175e-01 4.82699037e-01
-6.97745979e-01 6.53549910e-01 -1.58974975e-01 -1.72480464e-01
-2.18986139e-01 -4.29375112e-01 -8.22983027e-01 -5.20982206e-01
-6.26278445e-02 8.50951493e-01 5.87036967e-01 1.38992086... | [9.877602577209473, 2.773505210876465] |
85871c5f-f59a-401f-ba06-dcfa565cdeff | sea-sentence-encoder-assembly-for-video | 2011.12091 | null | https://arxiv.org/abs/2011.12091v1 | https://arxiv.org/pdf/2011.12091v1.pdf | SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries | Retrieving unlabeled videos by textual queries, known as Ad-hoc Video Search (AVS), is a core theme in multimedia data management and retrieval. The success of AVS counts on cross-modal representation learning that encodes both query sentences and videos into common spaces for semantic similarity computation. Inspired ... | ['Gang Yang', 'Jiaqi Ji', 'Chaoxi Xu', 'Fangming Zhou', 'Xirong Li'] | 2020-11-24 | null | null | null | null | ['ad-hoc-video-search'] | ['computer-vision'] | [ 2.78673768e-01 -6.11010969e-01 -2.92769551e-01 -3.02501649e-01
-1.15949214e+00 -4.09241229e-01 5.86172581e-01 1.91545472e-01
-4.66889322e-01 5.84680319e-01 5.61307192e-01 9.19419341e-03
-7.01969340e-02 -3.74920964e-01 -9.47060287e-01 -4.56386417e-01
7.80640021e-02 4.93745133e-02 3.95803988e-01 -2.19459057... | [10.331851959228516, 0.9297490119934082] |
ac093da3-fa09-4771-8ac0-28bf575ce71a | synergetic-reconstruction-from-2d-pose-and-3d | 2001.05613 | null | https://arxiv.org/abs/2001.05613v2 | https://arxiv.org/pdf/2001.05613v2.pdf | Synergetic Reconstruction from 2D Pose and 3D Motion for Wide-Space Multi-Person Video Motion Capture in the Wild | Although many studies have investigated markerless motion capture, the technology has not been applied to real sports or concerts. In this paper, we propose a markerless motion capture method with spatiotemporal accuracy and smoothness from multiple cameras in wide-space and multi-person environments. The proposed meth... | ['Yoshihiko Nakamura', 'Takuya Ohashi', 'Yosuke Ikegami'] | 2020-01-16 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [-9.65972468e-02 -4.25564468e-01 -2.82824665e-01 1.09726235e-01
-6.93037987e-01 -3.29265356e-01 2.64841497e-01 -5.00354528e-01
-5.26887059e-01 5.90924084e-01 2.60295331e-01 4.24287021e-01
1.98773459e-01 -4.16670233e-01 -6.39757812e-01 -4.33106035e-01
-3.04178391e-02 3.48093331e-01 9.43125963e-01 3.95873524... | [7.229074478149414, -0.8978018760681152] |
4b3bd3a3-e6cc-4e7e-a285-46a879a7656f | multi-granularity-generator-for-temporal | 1811.11524 | null | http://arxiv.org/abs/1811.11524v2 | http://arxiv.org/pdf/1811.11524v2.pdf | Multi-granularity Generator for Temporal Action Proposal | Temporal action proposal generation is an important task, aiming to localize
the video segments containing human actions in an untrimmed video. In this
paper, we propose a multi-granularity generator (MGG) to perform the temporal
action proposal from different granularity perspectives, relying on the video
visual featu... | ['Shih-Fu Chang', 'Yuan Liu', 'Yifeng Zhang', 'Lin Ma', 'Wei Liu'] | 2018-11-28 | multi-granularity-generator-for-temporal-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Multi-Granularity_Generator_for_Temporal_Action_Proposal_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Multi-Granularity_Generator_for_Temporal_Action_Proposal_CVPR_2019_paper.pdf | cvpr-2019-6 | ['temporal-action-proposal-generation'] | ['computer-vision'] | [ 4.61370170e-01 -1.30127892e-01 -3.32628578e-01 -8.41024145e-03
-7.60177016e-01 -1.40832037e-01 7.67972946e-01 -6.53485879e-02
-4.64806795e-01 5.08775592e-01 5.93211710e-01 2.34413236e-01
1.57773450e-01 -6.52045667e-01 -6.10165060e-01 -8.91755819e-01
-3.31543274e-02 -1.33695379e-02 7.72694886e-01 2.27056053... | [8.537554740905762, 0.48761439323425293] |
c17420d2-3a94-420e-9e47-9d2495679f0d | universal-domain-adaptation-from-foundation | 2305.11092 | null | https://arxiv.org/abs/2305.11092v1 | https://arxiv.org/pdf/2305.11092v1.pdf | Universal Domain Adaptation from Foundation Models | Foundation models (e.g., CLIP or DINOv2) have shown their impressive learning and transferring capabilities on a wide range of visual tasks, by training on a large corpus of data and adapting to specific downstream tasks. It is, however, interesting that foundation models have not been fully explored for universal doma... | ['Kui Jia', 'Bin Deng'] | 2023-05-18 | null | null | null | null | ['universal-domain-adaptation'] | ['computer-vision'] | [ 9.81564298e-02 -2.81290203e-01 -4.61726457e-01 -4.10580575e-01
-9.28808570e-01 -6.73639953e-01 7.98195779e-01 -3.07070732e-01
-4.26400542e-01 8.41182351e-01 1.03315756e-01 -1.56964928e-01
2.74153024e-01 -2.89091945e-01 -6.45030379e-01 -7.20581234e-01
7.02369064e-02 5.97553015e-01 6.01939559e-01 -1.04694225... | [10.122467041015625, 2.6717028617858887] |
99e96940-5679-4875-be3f-48110ef91cdc | fedet-a-communication-efficient-federated | 2306.15347 | null | https://arxiv.org/abs/2306.15347v1 | https://arxiv.org/pdf/2306.15347v1.pdf | FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer | Federated Learning (FL) has been widely concerned for it enables decentralized learning while ensuring data privacy. However, most existing methods unrealistically assume that the classes encountered by local clients are fixed over time. After learning new classes, this assumption will make the model's catastrophic for... | ['Jing Xiao', 'Jianzong Wang', 'Xiaoyang Qu', 'Chenghao Liu'] | 2023-06-27 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [-1.91247120e-01 -7.01774359e-02 -1.76970869e-01 -3.50769371e-01
-7.42295742e-01 -4.48281884e-01 2.44946927e-01 -2.82489937e-02
-4.17549312e-01 1.13607299e+00 -2.84514967e-02 -6.03577234e-02
-4.10812311e-02 -8.26552749e-01 -9.40396011e-01 -9.90915835e-01
6.61181435e-02 3.85660619e-01 3.02755386e-01 2.71894813... | [5.8534932136535645, 6.328486442565918] |
b8a24e15-2acb-447d-85ca-871698ae10fc | multipar-supervised-irregular-tensor | 2208.00993 | null | https://arxiv.org/abs/2208.00993v2 | https://arxiv.org/pdf/2208.00993v2.pdf | MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning | Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender systems and Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the... | ['Sivasubramanium Bhavani', 'Xiaoqian Jiang', 'Joyce C Ho', 'Li Xiong', 'Jian Lou', 'Yifei Ren'] | 2022-08-01 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-3.82924139e-01 -4.56270278e-01 -2.35175744e-01 -2.84298211e-01
-5.08124411e-01 -4.09740150e-01 -1.08232811e-01 3.32007319e-01
1.91489860e-01 3.85890096e-01 8.21514547e-01 -4.54271972e-01
-8.25176060e-01 -4.62963551e-01 -1.95481023e-03 -7.08735466e-01
-6.26426995e-01 4.64214474e-01 -2.05895498e-01 -1.89640790... | [6.465212821960449, 5.963247776031494] |
43a6fab4-d34f-497c-86ab-7a4796296646 | grammatical-error-detection-and-correction | null | null | https://aclanthology.org/W14-1710 | https://aclanthology.org/W14-1710.pdf | Grammatical Error Detection and Correction using a Single Maximum Entropy Model | null | ['Zhongye Jia', 'Peilu Wang', 'Hai Zhao'] | 2014-06-01 | null | null | null | ws-2014-6 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.423455715179443, 3.640964984893799] |
dcbc8545-083f-4623-a4dc-0a728f3b2406 | saliency-guided-local-full-reference-image | null | null | https://www.mdpi.com/2624-6120/3/3/28 | https://www.mdpi.com/2624-6120/3/3/28 | Saliency-Guided Local Full-Reference Image Quality Assessment | Research and development of image quality assessment (IQA) algorithms have been in the focus of the computer vision and image processing community for decades. The intent of IQA methods is to estimate the perceptual quality of digital images correlating as high as possible with human judgements. Full-reference image qu... | ['Domonkos Varga'] | 2022-06-29 | null | null | null | signals-2022-6 | ['image-quality-estimation'] | ['computer-vision'] | [ 2.55908400e-01 -3.20640117e-01 -3.06673581e-03 -1.61067173e-01
-5.27124166e-01 -1.88218970e-02 3.55647177e-01 5.11842251e-01
-3.53971452e-01 4.64366674e-01 8.29669684e-02 -2.31724270e-02
-1.67497024e-01 -7.20018804e-01 -1.88932821e-01 -6.58804059e-01
1.50654437e-02 -4.36732918e-01 5.84478021e-01 -1.16943397... | [11.72187614440918, -1.9510142803192139] |
a7c85c94-44a1-4110-baa8-a708852366e1 | semantic-visual-guided-transformer-for-few | 2303.15494 | null | https://arxiv.org/abs/2303.15494v1 | https://arxiv.org/pdf/2303.15494v1.pdf | Semantic-visual Guided Transformer for Few-shot Class-incremental Learning | Few-shot class-incremental learning (FSCIL) has recently attracted extensive attention in various areas. Existing FSCIL methods highly depend on the robustness of the feature backbone pre-trained on base classes. In recent years, different Transformer variants have obtained significant processes in the feature represen... | ['Qinmu Peng', 'Chengxiang Lei', 'Jingyi Zhang', 'Sichao Fu', 'Wenhao Qiu'] | 2023-03-27 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 3.64576161e-01 -1.11533493e-01 -2.63573796e-01 -4.47909236e-01
-6.02009535e-01 -4.52182651e-01 7.24505305e-01 -6.29255697e-02
-9.17034149e-02 4.23624605e-01 1.12715378e-01 8.70569202e-04
-1.64799407e-01 -8.58225405e-01 -6.63280666e-01 -8.50722551e-01
2.20369741e-01 3.04322124e-01 5.91955602e-01 -2.03193143... | [9.89794921875, 2.5240213871002197] |
afded3b7-1c67-498a-8c04-0c2c2ccb6975 | cped-a-large-scale-chinese-personalized-and-1 | 2205.14727 | null | https://arxiv.org/abs/2205.14727v1 | https://arxiv.org/pdf/2205.14727v1.pdf | CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI | Human language expression is based on the subjective construal of the situation instead of the objective truth conditions, which means that speakers' personalities and emotions after cognitive processing have an important influence on conversation. However, most existing datasets for conversational AI ignore human pers... | ['Xiangmin Xu', 'Qianfeng Tie', 'Wenjing Han', 'Minlie Huang', 'Jianxin Pang', 'Xiaofen Xing', 'Weiquan Fan', 'YiRong Chen'] | 2022-05-29 | null | null | null | null | ['personality-trait-recognition', 'personality-recognition-in-conversation', 'emotion-recognition-in-conversation', 'dialog-act-classification', 'emotional-dialogue-acts', 'personalized-and-emotional-conversation', 'open-domain-dialog', 'conversational-response-generation'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-2.88165003e-01 3.62323731e-01 8.22779164e-02 -7.54911840e-01
-2.95490623e-01 -3.93183529e-01 7.31990397e-01 -4.12258267e-01
-1.31684497e-01 8.31945717e-01 8.34617972e-01 4.48797613e-01
2.37007767e-01 -5.55919647e-01 1.20891230e-02 -6.40514314e-01
1.19234391e-01 6.20629013e-01 -4.94746268e-01 -6.77080274... | [13.025507926940918, 6.30573034286499] |
89514220-9923-4470-ae23-425574a5e9ff | reviewing-evolution-of-learning-functions-and | 2305.14397 | null | https://arxiv.org/abs/2305.14397v1 | https://arxiv.org/pdf/2305.14397v1.pdf | Reviewing Evolution of Learning Functions and Semantic Information Measures for Understanding Deep Learning | A new trend in deep learning, represented by Mutual Information Neural Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is emerging. In this trend, similarity functions and Estimated Mutual Information (EMI) are used as learning and objective functions. Coincidentally, EMI is essentially the same ... | ['Chenguang Lu'] | 2023-05-23 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [-1.06970116e-01 2.60038167e-01 -2.04938099e-01 -3.32067102e-01
-2.52737254e-01 -2.89844066e-01 7.01862812e-01 -1.12867951e-01
-8.22240233e-01 8.55751455e-01 1.08514763e-01 9.30156372e-03
-7.02685237e-01 -9.90871906e-01 -3.41376513e-01 -7.73008943e-01
-1.73085183e-01 2.96646178e-01 -9.30420831e-02 8.39836746... | [7.9361467361450195, 3.588212251663208] |
357dc3d4-5563-47d8-b01a-0ab3f0157c46 | testing-of-detection-tools-for-ai-generated | 2306.15666 | null | https://arxiv.org/abs/2306.15666v2 | https://arxiv.org/pdf/2306.15666v2.pdf | Testing of Detection Tools for AI-Generated Text | Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionali... | ['Lorna Waddington', 'Petr Šigut', 'Olumide Popoola', 'Jean Guerrero-Dib', 'Tomáš Foltýnek', 'Sonja Bjelobaba', 'Alla Anohina-Naumeca', 'Debora Weber-Wulff'] | 2023-06-21 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [ 3.07933360e-01 5.03211617e-01 -1.71158478e-01 2.28047565e-01
-9.43058431e-01 -1.02799320e+00 1.11290169e+00 2.76348025e-01
-1.70683071e-01 5.69571614e-01 3.65157098e-01 -8.53290319e-01
1.03115007e-01 -5.88761628e-01 -3.08270454e-01 -3.45792264e-01
6.15688026e-01 6.38852298e-01 -2.62955993e-01 -5.22511899... | [8.5275239944458, 10.01546859741211] |
77deb21c-3359-41e7-97e2-fb5fb99c41c0 | unsupervised-visible-infrared-person-re | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Unsupervised_Visible-Infrared_Person_Re-Identification_via_Progressive_Graph_Matching_and_Alternate_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Unsupervised_Visible-Infrared_Person_Re-Identification_via_Progressive_Graph_Matching_and_Alternate_CVPR_2023_paper.pdf | Unsupervised Visible-Infrared Person Re-Identification via Progressive Graph Matching and Alternate Learning | Unsupervised visible-infrared person re-identification is a challenging task due to the large modality gap and the unavailability of cross-modality correspondences. Cross-modality correspondences are very crucial to bridge the modality gap. Some existing works try to mine cross-modality correspondences, but they fo... | ['Mang Ye', 'Zesen Wu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['person-re-identification', 'graph-matching'] | ['computer-vision', 'graphs'] | [ 1.99105665e-01 -2.35427186e-01 -2.04532132e-01 -1.86730638e-01
-7.13386297e-01 -2.94571042e-01 4.79062051e-01 1.54273286e-01
-1.51429832e-01 3.36392730e-01 3.69245827e-01 2.21931145e-01
-5.11406302e-01 -8.43808413e-01 -3.56599212e-01 -6.44632459e-01
5.00047028e-01 1.69644475e-01 6.99893087e-02 -2.16257408... | [14.72988224029541, 0.9946154952049255] |
f7e766d1-762a-421a-92b9-aee94bd4db00 | decomposed-knowledge-distillation-for-class | 2210.05941 | null | https://arxiv.org/abs/2210.05941v1 | https://arxiv.org/pdf/2210.05941v1.pdf | Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation | Class-incremental semantic segmentation (CISS) labels each pixel of an image with a corresponding object/stuff class continually. To this end, it is crucial to learn novel classes incrementally without forgetting previously learned knowledge. Current CISS methods typically use a knowledge distillation (KD) technique fo... | ['Bumsub Ham', 'Junghyup Lee', 'SangHoon Lee', 'Youngmin Oh', 'Donghyeon Baek'] | 2022-10-12 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 5.92276931e-01 2.43533224e-01 -2.52017498e-01 -4.48223531e-01
-2.71011710e-01 -8.27932596e-01 3.48136276e-01 3.09368551e-01
-4.86129314e-01 9.02819276e-01 -4.74256426e-01 -2.47093230e-01
6.99013355e-04 -9.89735961e-01 -9.54710066e-01 -9.84012425e-01
2.76807934e-01 2.59279698e-01 7.60360837e-01 1.69486806... | [9.40876293182373, 2.2631661891937256] |
a0dc900a-0ecf-443b-bdce-1305c3b921cd | automatic-objects-removal-for-scene | 1501.0597 | null | http://arxiv.org/abs/1501.05970v1 | http://arxiv.org/pdf/1501.05970v1.pdf | Automatic Objects Removal for Scene Completion | With the explosive growth of web-based cameras and mobile devices, billions
of photographs are uploaded to the internet. We can trivially collect a huge
number of photo streams for various goals, such as 3D scene reconstruction and
other big data applications. However, this is not an easy task due to the fact
the retri... | ['Kun Hua', 'Jianjun Yang', 'Yin Wang', 'Ju Shen', 'Wei Wang', 'Honggang Wang'] | 2015-01-23 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 3.98887187e-01 -3.04046482e-01 3.40722680e-01 -2.54356295e-01
-4.97716844e-01 -5.15555918e-01 4.52989101e-01 -9.51891094e-02
-1.11606017e-01 5.48730314e-01 1.40062049e-01 -1.67285446e-02
4.30997349e-02 -7.95280397e-01 -6.81073785e-01 -6.06802106e-01
3.88312697e-01 5.09474039e-01 5.29010773e-01 9.05234143... | [9.042449951171875, -2.531083345413208] |
d2344266-aac7-4de6-9c1c-d89c76194e35 | revisit-the-fundamental-theorem-of-linear | 2108.04432 | null | https://arxiv.org/abs/2108.04432v1 | https://arxiv.org/pdf/2108.04432v1.pdf | Revisit the Fundamental Theorem of Linear Algebra | This survey is meant to provide an introduction to the fundamental theorem of linear algebra and the theories behind them. Our goal is to give a rigorous introduction to the readers with prior exposure to linear algebra. Specifically, we provide some details and proofs of some results from (Strang, 1993). We then descr... | ['Jun Lu'] | 2021-08-10 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 1.09373681e-01 1.22926710e-02 -2.63779789e-01 -4.01361063e-02
-1.59388304e-01 -7.48914599e-01 3.69118422e-01 -3.22472364e-01
8.62584561e-02 5.19715846e-01 7.30862692e-02 -7.16075480e-01
-8.57508957e-01 -5.45665383e-01 -5.10172665e-01 -9.05739248e-01
-7.51244545e-01 -1.30158424e-01 -5.29428244e-01 -3.71661752... | [7.4805707931518555, 4.224154949188232] |
722de6bb-a3e9-489e-b7d4-93cb1068e1cc | nuig-at-tiad-combining-unsupervised-nlp-and | null | null | https://aclanthology.org/2020.globalex-1.15 | https://aclanthology.org/2020.globalex-1.15.pdf | NUIG at TIAD: Combining Unsupervised NLP and Graph Metrics for Translation Inference | In this paper, we present the NUIG system at the TIAD shard task. This system includes graph-based metrics calculated using novel algorithms, with an unsupervised document embedding tool called ONETA and an unsupervised multi-way neural machine translation method. The results are an improvement over our previous system... | ['John Philip McCrae', 'Mihael Arcan'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['document-embedding'] | ['methodology'] | [ 1.31731898e-01 9.56446007e-02 -2.87793010e-01 -2.99949646e-01
-6.19496226e-01 -3.96011025e-01 1.17832196e+00 2.14160994e-01
-7.46228516e-01 8.34763348e-01 4.85875815e-01 -4.09435123e-01
-3.03533614e-01 -8.21356118e-01 -8.81294087e-02 -4.85530347e-01
-3.12049925e-01 1.01576734e+00 5.57277322e-01 -6.56127512... | [10.600967407226562, 8.748727798461914] |
854e0a1a-3648-41a5-87df-5fdf3a6ac029 | interpretable-machine-learning-based-on | 2305.1567 | null | https://arxiv.org/abs/2305.15670v1 | https://arxiv.org/pdf/2305.15670v1.pdf | Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons | In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations. Recently, with the recognition that interpretabil... | ['Jie Chen', 'Aijun Zhang', 'Agus Sudjianto', 'Vijayan N. Nair', 'Linwei Hu'] | 2023-05-25 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-6.25796616e-03 1.34432644e-01 -2.71782398e-01 -6.26636803e-01
-3.48270595e-01 -3.87363315e-01 4.96887326e-01 3.04348916e-01
1.86347261e-01 8.67527723e-01 -2.77949665e-02 -7.84327984e-01
-6.69916153e-01 -4.86350656e-01 -8.99106801e-01 -4.49964225e-01
-3.06032717e-01 4.26385045e-01 -2.06286535e-01 -5.21470867... | [8.434959411621094, 5.338274955749512] |
98c74a2f-b3ba-48d5-9c66-53fb95ca7a7b | knowledge-cross-distillation-for-membership | 2111.01363 | null | https://arxiv.org/abs/2111.01363v3 | https://arxiv.org/pdf/2111.01363v3.pdf | Knowledge Cross-Distillation for Membership Privacy | A membership inference attack (MIA) poses privacy risks for the training data of a machine learning model. With an MIA, an attacker guesses if the target data are a member of the training dataset. The state-of-the-art defense against MIAs, distillation for membership privacy (DMP), requires not only private data for pr... | ['Hikaru Tsuchida', 'Isamu Teranishi', 'Junki Mori', 'Kunihiro Ito', 'Batnyam Enkhtaivan', 'Rishav Chourasia'] | 2021-11-02 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 1.23256646e-01 3.43685985e-01 -1.84938997e-01 -4.67932224e-01
-1.09318304e+00 -1.10827875e+00 4.10911769e-01 1.34056151e-01
-3.58173311e-01 1.13932550e+00 -4.05006409e-01 -7.54668653e-01
7.52467737e-02 -1.48105705e+00 -1.01988733e+00 -9.25274134e-01
6.11748286e-02 5.12537718e-01 9.07491427e-04 -8.80246311... | [5.9221014976501465, 7.092617511749268] |
cd20a9b5-e94c-4a8f-b4b4-5b84dab123ae | back-attention-knowledge-transfer-for-low | 1906.01183 | null | https://arxiv.org/abs/1906.01183v4 | https://arxiv.org/pdf/1906.01183v4.pdf | Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition | In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the considerable amount of labeled resources. However, most low-resource languages do not have such an abu... | ['Chunyan Miao', 'Huanhuan Chen', 'Huixiong Yi', 'Shengfei Lyu', 'Yong liu', 'Linghao Sun'] | 2019-06-04 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [-2.81733930e-01 -1.93071529e-01 -3.45414996e-01 -3.72715592e-01
-8.32718432e-01 -5.10278285e-01 3.63142699e-01 -2.00735524e-01
-9.97118413e-01 8.25753927e-01 6.43156528e-01 -1.03561960e-01
3.24328214e-01 -9.60397243e-01 -6.75588012e-01 -1.18909262e-01
5.64883828e-01 5.24275243e-01 -3.06006670e-01 -3.12019914... | [9.909741401672363, 9.686399459838867] |
3962fb86-8e9d-428d-bd2f-2274c7f690ff | thompson-sampling-for-combinatorial-pure-1 | 2206.0915 | null | https://arxiv.org/abs/2206.09150v1 | https://arxiv.org/pdf/2206.09150v1.pdf | Thompson Sampling for (Combinatorial) Pure Exploration | Existing methods of combinatorial pure exploration mainly focus on the UCB approach. To make the algorithm efficient, they usually use the sum of upper confidence bounds within arm set $S$ to represent the upper confidence bound of $S$, which can be much larger than the tight upper confidence bound of $S$ and leads to ... | ['Jun Zhu', 'Siwei Wang'] | 2022-06-18 | thompson-sampling-for-combinatorial-pure | https://openreview.net/forum?id=7N-6ZLyFUXz | https://openreview.net/pdf?id=7N-6ZLyFUXz | null | ['thompson-sampling'] | ['methodology'] | [-1.25140920e-01 4.08828259e-01 -8.57264996e-01 -7.18555599e-02
-1.59998763e+00 -9.49747562e-01 5.58950100e-03 1.23092411e-02
-3.68094295e-01 1.20822656e+00 -2.26009905e-01 -1.00487888e+00
-6.93916857e-01 -1.01142764e+00 -1.02833033e+00 -8.00299346e-01
-2.54430294e-01 1.00956535e+00 1.84820026e-01 9.93846431... | [4.543247699737549, 3.3196988105773926] |
9f17bd19-a88b-49d9-ae31-b89f1e56f013 | artificial-pupil-dilation-for-data-1 | 2212.12733 | null | https://arxiv.org/abs/2212.12733v1 | https://arxiv.org/pdf/2212.12733v1.pdf | Artificial Pupil Dilation for Data Augmentation in Iris Semantic Segmentation | Biometrics is the science of identifying an individual based on their intrinsic anatomical or behavioural characteristics, such as fingerprints, face, iris, gait, and voice. Iris recognition is one of the most successful methods because it exploits the rich texture of the human iris, which is unique even for twins and ... | ['Andres Valenzuela', 'David A. Benalcazar', 'Daniel P. Benalcazar'] | 2022-12-24 | artificial-pupil-dilation-for-data | https://ieeexplore.ieee.org/document/9935749 | https://github.com/dpbenalcazar/ArtificialDilation/blob/main/benalcazar2022dilation.pdf | 2022-ieee-sixth-ecuador-technical-chapters | ['iris-recognition', 'pupil-dilation', 'iris-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 3.23806763e-01 1.95722971e-02 -5.21526039e-01 -4.72583741e-01
-8.92693922e-02 -4.70523387e-01 2.50022173e-01 3.97598632e-02
-2.45477483e-01 2.40139440e-01 2.33745333e-02 -2.45676219e-01
-1.86425466e-02 -5.20020545e-01 -2.97995448e-01 -6.02251351e-01
2.03649297e-01 4.27728087e-01 -1.89108163e-01 2.60422707... | [3.7452142238616943, -3.6305134296417236] |
55fc8946-49e5-4ad9-a7a4-c2857d808dda | small-coupling-expansion-for-multiple | 2210.03463 | null | https://arxiv.org/abs/2210.03463v2 | https://arxiv.org/pdf/2210.03463v2.pdf | Small Coupling Expansion for Multiple Sequence Alignment | The alignment of biological sequences such as DNA, RNA, and proteins, is one of the basic tools that allow to detect evolutionary patterns, as well as functional/structural characterizations between homologous sequences in different organisms. Typically, state-of-the-art bioinformatics tools are based on profile models... | ['Andrea Pagnani', 'Louise Budzynski'] | 2022-10-07 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 5.07569671e-01 -3.77766877e-01 1.36184484e-01 -3.15593541e-01
-1.73682913e-01 -4.71946687e-01 4.32455271e-01 6.59805417e-01
-5.34478068e-01 9.58052397e-01 1.02719657e-01 -4.98738199e-01
-2.11717427e-01 -6.02671623e-01 -6.41010344e-01 -1.14237571e+00
-1.23051912e-01 5.32844424e-01 5.83632708e-01 -4.83178467... | [4.839676856994629, 5.192108631134033] |
4f3b62f0-2796-420d-991a-99bdc29cf291 | sparseness-meets-deepness-3d-human-pose | 1511.09439 | null | http://arxiv.org/abs/1511.09439v2 | http://arxiv.org/pdf/1511.09439v2.pdf | Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video | This paper addresses the challenge of 3D full-body human pose estimation from
a monocular image sequence. Here, two cases are considered: (i) the image
locations of the human joints are provided and (ii) the image locations of
joints are unknown. In the former case, a novel approach is introduced that
integrates a spar... | ['Xiaowei Zhou', 'Spyridon Leonardos', 'Kosta Derpanis', 'Kostas Daniilidis', 'Menglong Zhu'] | 2015-11-30 | sparseness-meets-deepness-3d-human-pose-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhou_Sparseness_Meets_Deepness_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhou_Sparseness_Meets_Deepness_CVPR_2016_paper.pdf | cvpr-2016-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-3.11712548e-02 3.61128300e-01 -1.45368144e-01 -3.08440149e-01
-7.69348443e-01 -2.03208223e-01 6.18681431e-01 -4.70887303e-01
-6.14411891e-01 6.87417328e-01 3.21692914e-01 3.24957103e-01
1.25704437e-01 -8.15957859e-02 -1.02516270e+00 -6.82894766e-01
-4.87347357e-02 7.86929190e-01 1.17027201e-01 2.06783898... | [7.016590118408203, -0.9877634048461914] |
5f1a0621-0430-476d-bc23-26a667b67bdf | optimal-gradient-sliding-and-its-application | 2205.15136 | null | https://arxiv.org/abs/2205.15136v1 | https://arxiv.org/pdf/2205.15136v1.pdf | Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity | We study structured convex optimization problems, with additive objective $r:=p + q$, where $r$ is ($\mu$-strongly) convex, $q$ is $L_q$-smooth and convex, and $p$ is $L_p$-smooth, possibly nonconvex. For such a class of problems, we proposed an inexact accelerated gradient sliding method that can skip the gradient com... | ['Gesualdo Scutari', 'Alexander Gasnikov', 'Ekaterina Borodich', 'Aleksandr Beznosikov', 'Dmitry Kovalev'] | 2022-05-30 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.28809023e-01 2.06478015e-01 5.77987023e-02 -1.40348420e-01
-1.21896267e+00 -4.97759968e-01 -2.00310707e-01 4.93536025e-01
-9.06795323e-01 9.74067926e-01 -4.46407795e-01 -3.06741804e-01
-8.27284694e-01 -1.03601682e+00 -9.95973587e-01 -1.03131902e+00
-9.22464788e-01 6.91095412e-01 -2.40311008e-02 -3.61331612... | [6.363692283630371, 4.606513023376465] |
a051109b-6bb3-43cd-bc6d-d69fe59be29a | text-classification-and-clustering-with | 2107.14597 | null | https://arxiv.org/abs/2107.14597v1 | https://arxiv.org/pdf/2107.14597v1.pdf | Text Classification and Clustering with Annealing Soft Nearest Neighbor Loss | We define disentanglement as how far class-different data points from each other are, relative to the distances among class-similar data points. When maximizing disentanglement during representation learning, we obtain a transformed feature representation where the class memberships of the data points are preserved. If... | ['Abien Fred Agarap'] | 2021-07-23 | null | null | null | null | ['text-clustering'] | ['natural-language-processing'] | [-1.68782398e-02 8.56308639e-02 -4.69486862e-01 -5.69501042e-01
-8.58377457e-01 -8.49588573e-01 8.60987902e-01 5.59846878e-01
-3.31062853e-01 5.59797704e-01 5.48543096e-01 -2.49762863e-01
-3.20183754e-01 -7.64944077e-01 -3.11755121e-01 -7.59122670e-01
4.50550057e-02 1.03163135e+00 -4.20188576e-01 -4.63049337... | [10.294556617736816, 6.711891174316406] |
4f906550-d214-45a7-8dc2-f188d0ce938c | data-and-physics-driven-learning-models-for | 2204.01706 | null | https://arxiv.org/abs/2204.01706v1 | https://arxiv.org/pdf/2204.01706v1.pdf | Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers | Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning. However, deep learning models are not the sovereign remedy for medical image analys... | ['Guang Yang', 'Yonina C. Eldar', 'Daniel Rueckert', 'Pietro Lio', 'Zidong Wang', 'Yang Li', 'Zhifan Gao', 'Yinzhe Wu', 'Huanjun Wu', 'Yang Nan', 'Yingying Fang', 'Jiahao Huang'] | 2022-04-01 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 4.06612933e-01 2.23192304e-01 -1.33867353e-01 -5.98220885e-01
-1.07835698e+00 -3.30026895e-01 3.80670667e-01 1.29970402e-01
-3.60759825e-01 5.15886009e-01 4.70671117e-01 -5.58048546e-01
-6.12920642e-01 -4.53526646e-01 -4.40116554e-01 -1.05294335e+00
-5.34625888e-01 8.15072715e-01 8.98710266e-02 -4.18464616... | [14.18740463256836, -2.4251739978790283] |
62501cb4-c73c-4fa0-b00d-d87c45c9ea9c | sequence-level-knowledge-distillation-for-1 | 2305.13899 | null | https://arxiv.org/abs/2305.13899v1 | https://arxiv.org/pdf/2305.13899v1.pdf | Sequence-Level Knowledge Distillation for Class-Incremental End-to-End Spoken Language Understanding | The ability to learn new concepts sequentially is a major weakness for modern neural networks, which hinders their use in non-stationary environments. Their propensity to fit the current data distribution to the detriment of the past acquired knowledge leads to the catastrophic forgetting issue. In this work we tackle ... | ['Alessio Brutti', 'Daniele Falavigna', 'Muqiao Yang', 'Umberto Cappellazzo'] | 2023-05-23 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 2.44512394e-01 2.61436909e-01 1.11045815e-01 -2.50963479e-01
-7.28532493e-01 -4.11856532e-01 4.69959348e-01 1.95259765e-01
-7.92299986e-01 9.37554121e-01 3.43385905e-01 -4.05275881e-01
-1.92303911e-01 -6.31884754e-01 -7.98683226e-01 -6.16010010e-01
1.32056430e-01 5.15470326e-01 5.76458931e-01 -2.61194080... | [9.967093467712402, 3.6413612365722656] |
87e6c796-4f8c-497d-8a9c-80c093b8bace | semantic-annotation-aggregation-with | null | null | https://aclanthology.org/C16-1168 | https://aclanthology.org/C16-1168.pdf | Semantic Annotation Aggregation with Conditional Crowdsourcing Models and Word Embeddings | In modern text annotation projects, crowdsourced annotations are often aggregated using item response models or by majority vote. Recently, item response models enhanced with generative data models have been shown to yield substantial benefits over those with conditional or no data models. However, suitable generative ... | ['Paul Felt', 'Kevin Seppi', 'Eric Ringger'] | 2016-12-01 | semantic-annotation-aggregation-with-1 | https://aclanthology.org/C16-1168 | https://aclanthology.org/C16-1168.pdf | coling-2016-12 | ['text-annotation'] | ['natural-language-processing'] | [ 7.73535594e-02 4.39911097e-01 -2.27965221e-01 -8.84628057e-01
-1.11092699e+00 -5.29872715e-01 9.05294776e-01 2.80011475e-01
-5.15044212e-01 9.39776301e-01 7.27992475e-01 9.91776064e-02
8.52706805e-02 -6.30865872e-01 -5.48309207e-01 -3.23346227e-01
4.88509506e-01 9.61312175e-01 2.13461056e-01 -3.35940003... | [10.797410011291504, 7.973968505859375] |
6a426b82-8522-4697-a200-36a22587175a | on-the-design-of-deep-priors-for-unsupervised | 2104.07161 | null | https://arxiv.org/abs/2104.07161v1 | https://arxiv.org/pdf/2104.07161v1.pdf | On the Design of Deep Priors for Unsupervised Audio Restoration | Unsupervised deep learning methods for solving audio restoration problems extensively rely on carefully tailored neural architectures that carry strong inductive biases for defining priors in the time or spectral domain. In this context, lot of recent success has been achieved with sophisticated convolutional network c... | ['Andreas Spanias', 'Jayaraman J. Thiagarajan', 'Vivek Sivaraman Narayanaswamy'] | 2021-04-14 | null | null | null | null | ['audio-denoising'] | ['audio'] | [ 5.38725078e-01 -2.08459675e-01 2.44476929e-01 -3.45091611e-01
-1.00059879e+00 -4.37148124e-01 3.51687640e-01 -1.30058378e-01
-2.14767337e-01 5.68945050e-01 4.53282267e-01 -1.10366359e-01
-4.37525004e-01 -5.16214073e-01 -6.76856577e-01 -7.09932268e-01
-1.06320441e-01 -1.33724824e-01 -1.41074523e-01 -2.48654574... | [15.359269142150879, 5.541706085205078] |
504a450c-e55f-4988-9de3-c5158eb7525d | canonicalizing-open-knowledge-bases | null | null | https://suchanek.name/work/publications/cikm2014.pdf | https://suchanek.name/work/publications/cikm2014.pdf | Canonicalizing Open Knowledge Bases | Open information extraction approaches have led to the creation of large knowledge bases from the Web. The problem with such methods is that their entities and relations are not canonicalized, leading to redundant and ambiguous facts. For example, they may store hBarack Obama, was born in, Honolului and hObama, place o... | ['Geremy Heitz', 'Luis Galárraga'] | 2014-11-03 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [-2.47664034e-01 5.40605009e-01 -4.65916395e-01 -6.22217469e-02
-4.56642509e-01 -9.02013183e-01 4.19138759e-01 5.11208296e-01
-2.85346836e-01 1.45214474e+00 3.46587330e-01 -2.22002998e-01
-6.17251694e-01 -1.09129584e+00 -5.44049561e-01 -2.44313732e-01
-2.19438761e-01 9.90537107e-01 6.03121743e-02 -3.95773947... | [9.321702003479004, 8.427739143371582] |
153203ee-416e-45a2-9427-3ef433b6da60 | policy-representation-via-diffusion | 2305.13122 | null | https://arxiv.org/abs/2305.13122v1 | https://arxiv.org/pdf/2305.13122v1.pdf | Policy Representation via Diffusion Probability Model for Reinforcement Learning | Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of exploration. The diffusion probability model is powerful to learn complicated multimodal distributions, which has shown promising and potential ... | ['Zhouchen Lin', 'Binbin Zhou', 'Shiting Wen', 'Cong Fang', 'Yiming Yang', 'Yucun Zhong', 'Fenghao Lei', 'Zhixiong Huang', 'Long Yang'] | 2023-05-22 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-4.54414040e-01 -6.04367666e-02 -8.46378803e-01 3.87840420e-01
-5.79511762e-01 -6.56515837e-01 6.33313835e-01 -7.03039020e-02
-4.81332332e-01 1.10163355e+00 1.71861112e-01 -7.50637233e-01
-3.75344634e-01 -6.53745174e-01 -6.99204862e-01 -1.08263624e+00
-4.29679304e-01 6.02901280e-01 -2.32828259e-01 -3.84012818... | [4.072254657745361, 2.273719549179077] |
6b28d2df-8839-45ba-99c2-1f13f5599879 | fpcd-an-open-aerial-vhr-dataset-for-farm-pond | 2302.14554 | null | https://arxiv.org/abs/2302.14554v1 | https://arxiv.org/pdf/2302.14554v1.pdf | FPCD: An Open Aerial VHR Dataset for Farm Pond Change Detection | Change detection for aerial imagery involves locating and identifying changes associated with the areas of interest between co-registered bi-temporal or multi-temporal images of a geographical location. Farm ponds are man-made structures belonging to the category of minor irrigation structures used to collect surface r... | ['G. Sivakumar', 'Om Damani', 'Rajiv Kumar', 'Chintan Tundia'] | 2023-02-28 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 2.25193977e-01 -2.28043124e-01 1.10710062e-01 -3.89058709e-01
-1.82820722e-01 -8.61214578e-01 6.72101080e-01 5.45810103e-01
-3.26241851e-01 5.83115101e-01 -1.28552422e-01 -5.05184889e-01
-2.72342831e-01 -1.25669444e+00 -7.33673871e-01 -7.46193945e-01
-6.21804118e-01 3.14611048e-01 4.32960242e-01 -2.89695591... | [9.378275871276855, -1.5291517972946167] |
d95e3c4e-8ba2-408d-97cf-0b5c98963986 | joint-estimation-of-room-geometry-and-modes | 1802.05879 | null | http://arxiv.org/abs/1802.05879v1 | http://arxiv.org/pdf/1802.05879v1.pdf | Joint Estimation of Room Geometry and Modes with Compressed Sensing | Acoustical behavior of a room for a given position of microphone and sound
source is usually described using the room impulse response. If we rely on the
standard uniform sampling, the estimation of room impulse response for
arbitrary positions in the room requires a large number of measurements. In
order to lower the ... | ['Hervé Lissek', 'Helena Peić Tukuljac', 'Pierre Vandergheynst', 'Thach Pham Vu'] | 2018-02-16 | null | null | null | null | ['room-impulse-response'] | ['audio'] | [ 3.25693786e-01 -6.29582703e-02 1.15840912e+00 -2.29082957e-01
-6.62335634e-01 -3.11826110e-01 1.81788921e-01 1.61501542e-01
-3.22220057e-01 5.20440519e-01 2.25115180e-01 -2.21068144e-01
-4.75240439e-01 -1.06530988e+00 -1.82605758e-01 -8.65616918e-01
2.57163253e-02 1.79201618e-01 1.49842417e-02 -1.41365185... | [15.143007278442383, 5.651333332061768] |
c370120e-9a95-46dc-b7df-ee6a8f25a058 | aggression-identification-in-social-media-a | null | null | https://aclanthology.org/2020.trac-1.5 | https://aclanthology.org/2020.trac-1.5.pdf | Aggression Identification in Social Media: a Transfer Learning Based Approach | The way people communicate have changed in many ways with the outbreak of social media. One of the aspects of social media is the ability for their information producers to hide, fully or partially, their identity during a discussion; leading to cyber-aggression and interpersonal aggression. Automatically monitoring us... | ['Josiane Mothe', 'Faneva risoa', 'Rami'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['aggression-identification'] | ['natural-language-processing'] | [-2.59529948e-01 2.64243454e-01 -2.39605531e-02 -6.02083094e-02
-3.06929439e-01 -4.13658351e-01 9.40598607e-01 3.01405132e-01
-5.92454314e-01 6.56419158e-01 7.07556665e-01 2.96295416e-02
-1.50336832e-01 -5.17497003e-01 -1.90910384e-01 -9.91667658e-02
-2.38603234e-01 3.97384644e-01 1.56724989e-01 -4.75180745... | [8.715832710266113, 10.508666038513184] |
45a7d06a-63e5-4d31-a680-8da44c7c2915 | usb-universal-scale-object-detection | 2103.14027 | null | https://arxiv.org/abs/2103.14027v3 | https://arxiv.org/pdf/2103.14027v3.pdf | USB: Universal-Scale Object Detection Benchmark | Benchmarks, such as COCO, play a crucial role in object detection. However, existing benchmarks are insufficient in scale variation, and their protocols are inadequate for fair comparison. In this paper, we introduce the Universal-Scale object detection Benchmark (USB). USB has variations in object scales and image dom... | ['Yosuke Shinya'] | 2021-03-25 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.75876066e-01 -5.29748559e-01 -3.29777002e-01 -3.94345552e-01
-6.76873386e-01 -5.36550343e-01 2.93676227e-01 -2.77123898e-01
-6.58060551e-01 5.31647742e-01 -2.38394916e-01 -1.12442032e-01
5.12198545e-02 -7.17277467e-01 -7.74890244e-01 -3.79618615e-01
-2.00549990e-01 1.85748830e-01 1.07867420e+00 -2.69403279... | [8.92793083190918, 0.02569383569061756] |
fca0c229-f233-4d99-83f8-6261652c9e50 | model-based-deep-learning-1 | 2306.04469 | null | https://arxiv.org/abs/2306.04469v1 | https://arxiv.org/pdf/2306.04469v1.pdf | Model-Based Deep Learning | Signal processing traditionally relies on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor ... | ['Yonina C. Eldar', 'Nir Shlezinger'] | 2023-06-05 | null | null | null | null | ['super-resolution', 'specificity'] | ['computer-vision', 'natural-language-processing'] | [ 2.59858370e-01 -4.21265751e-01 -1.29991323e-01 -2.50634134e-01
-5.35730720e-01 -3.94143283e-01 4.18825448e-01 -4.24733534e-02
-1.65133953e-01 5.95363915e-01 -2.25988343e-01 -1.31043240e-01
-5.64702153e-01 -7.73096859e-01 -6.67994380e-01 -8.60336125e-01
-5.39949477e-01 1.57371491e-01 -5.02625406e-02 -4.67422307... | [8.197980880737305, 2.272075891494751] |
7e46056a-1919-42fd-b0af-0eac4b7e243f | how-local-is-the-local-diversity-reinforcing | 1807.04219 | null | http://arxiv.org/abs/1807.04219v4 | http://arxiv.org/pdf/1807.04219v4.pdf | How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization | The large volume of video content and high viewing frequency demand automatic
video summarization algorithms, of which a key property is the capability of
modeling diversity. If videos are lengthy like hours-long egocentric videos, it
is necessary to track the temporal structures of the videos and enforce local
diversi... | ['Liqiang Wang', 'Tianbao Yang', 'Boqing Gong', 'Yandong Li'] | 2018-07-11 | how-local-is-the-local-diversity-reinforcing-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yandong_Li_How_Local_is_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yandong_Li_How_Local_is_ECCV_2018_paper.pdf | eccv-2018-9 | ['supervised-video-summarization'] | ['computer-vision'] | [ 1.11729644e-01 -5.04571907e-02 -3.00221890e-01 -1.63798407e-01
-6.16200447e-01 -3.68460417e-01 4.57466781e-01 1.87040679e-02
-3.24095845e-01 8.27754080e-01 4.19743150e-01 1.64256915e-01
-3.40681583e-01 -5.90653718e-01 -8.72768819e-01 -9.03127968e-01
-2.45621219e-01 8.66464004e-02 3.27945560e-01 1.90898567... | [10.426552772521973, 0.4073573350906372] |
8c5e9c54-398e-4a13-a573-1ac23f0cdb44 | polytuplet-loss-a-reverse-approach-to | 2304.01046 | null | https://arxiv.org/abs/2304.01046v2 | https://arxiv.org/pdf/2304.01046v2.pdf | Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Loss | The current trend in developing machine learning models for reading comprehension and logical reasoning tasks is focused on improving the models' abilities to understand and utilize logical rules. This work focuses on providing a novel loss function and accompanying model architecture that has more interpretable compon... | ['Ivan Rodriguez', 'Jeffrey Lu'] | 2023-04-03 | null | null | null | null | ['reading-comprehension', 'logical-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 4.52276796e-01 4.85020995e-01 -1.09651484e-01 -7.32553244e-01
-7.23579288e-01 -4.36635047e-01 5.66441298e-01 4.36013401e-01
-4.36272383e-01 6.72625840e-01 2.22612813e-01 -9.86079991e-01
-3.94305050e-01 -8.92535448e-01 -8.16640258e-01 -7.25058466e-02
2.60131776e-01 6.52214170e-01 1.54023796e-01 -1.87782317... | [10.007835388183594, 7.618768215179443] |
4ba502aa-e9b6-4e8e-ab03-7db26f91f978 | data-efficient-and-interpretable-tabular | 2203.02034 | null | https://arxiv.org/abs/2203.02034v2 | https://arxiv.org/pdf/2203.02034v2.pdf | Data-Efficient and Interpretable Tabular Anomaly Detection | Anomaly detection (AD) plays an important role in numerous applications. We focus on two understudied aspects of AD that are critical for integration into real-world applications. First, most AD methods cannot incorporate labeled data that are often available in practice in small quantities and can be crucial to achiev... | ['Tomas Pfister', 'Madeleine Udell', 'Sercan Arik', 'Jinsung Yoon', 'Chun-Hao Chang'] | 2022-03-03 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 3.26661259e-01 8.58505666e-02 -1.69433072e-01 -6.41548634e-01
-9.42048669e-01 -7.07210004e-01 4.92431343e-01 6.40991628e-01
-5.56197390e-02 6.65681005e-01 -1.27122030e-02 -3.51293087e-01
-2.38780707e-01 -8.10347736e-01 -6.99075162e-01 -5.26302159e-01
-2.38575265e-02 2.50734687e-01 6.09525032e-02 -3.68407033... | [7.628170490264893, 2.542316198348999] |
3933ffa6-e6df-45ea-9d99-105646c77f3e | interaction-level-membership-inference-attack | 2301.10964 | null | https://arxiv.org/abs/2301.10964v2 | https://arxiv.org/pdf/2301.10964v2.pdf | Interaction-level Membership Inference Attack Against Federated Recommender Systems | The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In FedRecs, users' attribute information and behavior data (i.e., user-item interaction data) are kept locally on their personal devices, therefor... | ['Hongzhi Yin', 'Tieke He', 'Lizhen Cui', 'Quoc Viet Hung Nguyen', 'Chaoqun Yang', 'Wei Yuan'] | 2023-01-26 | null | null | null | null | ['inference-attack', 'membership-inference-attack'] | ['adversarial', 'computer-vision'] | [-2.32196376e-01 -4.47057724e-01 -8.29351917e-02 -5.30486763e-01
-2.73055792e-01 -1.14354765e+00 2.23663136e-01 -2.21087292e-01
-1.81491703e-01 5.34530818e-01 -4.58757207e-03 -7.50165403e-01
-2.44572088e-01 -9.89034712e-01 -5.70827067e-01 -7.38048196e-01
-8.29611495e-02 -3.87405396e-01 -4.67981771e-03 -9.04042646... | [5.8957929611206055, 6.778964519500732] |
6faefc78-c74a-4ffa-94ee-e484769ecf64 | distributional-constrained-reinforcement | 2302.01727 | null | https://arxiv.org/abs/2302.01727v1 | https://arxiv.org/pdf/2302.01727v1.pdf | Distributional constrained reinforcement learning for supply chain optimization | This work studies reinforcement learning (RL) in the context of multi-period supply chains subject to constraints, e.g., on production and inventory. We introduce Distributional Constrained Policy Optimization (DCPO), a novel approach for reliable constraint satisfaction in RL. Our approach is based on Constrained Poli... | ['Calvin Tsay', 'Antonio del Rio Chanona', 'Jaime Sabal Bermúdez'] | 2023-02-03 | null | null | null | null | ['policy-gradient-methods', 'distributional-reinforcement-learning'] | ['methodology', 'methodology'] | [ 2.20860783e-02 1.51245654e-01 -5.02303839e-01 -1.72137134e-02
-8.01939070e-01 -6.20746195e-01 2.50301570e-01 2.91080058e-01
-5.47194719e-01 1.20163918e+00 6.23793062e-03 -5.26384532e-01
-4.85198021e-01 -7.27366030e-01 -9.29706156e-01 -8.73492718e-01
-2.24019364e-01 5.66106915e-01 -1.60253733e-01 -1.17779247... | [4.296387195587158, 2.4677553176879883] |
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