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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d3e8e689-f462-4002-8339-9a3c97f736d6 | deep-representation-learning-of-tissue | 2305.1559 | null | https://arxiv.org/abs/2305.15590v2 | https://arxiv.org/pdf/2305.15590v2.pdf | Deep Representation Learning of Tissue Metabolome and Computed Tomography Images Annotates Non-invasive Classification and Prognosis Prediction of NSCLC | The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Con... | ['Eric O Aboagye', 'Marco A Calzado', 'Joram M. Posma', 'Richard Lee', 'Ángel Salvatierra', 'Marina Álvarez-Benito', 'Paula Moreno', 'OCTAPUS-AI', 'Mitchell Chen', 'Sumeet Hindocha', 'Kristofer Linton-Reid', 'Marc Boubnovski Martell'] | 2023-05-24 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [ 4.26694453e-01 -9.96633098e-02 -4.99083310e-01 -1.38418168e-01
-1.12051582e+00 -5.21403372e-01 4.84232306e-01 5.87728381e-01
-5.04890501e-01 7.51503289e-01 3.35390866e-01 -5.98718524e-01
-1.67499110e-01 -6.08189106e-01 -1.98507592e-01 -1.30229461e+00
-3.52260023e-01 7.83712029e-01 -5.32761455e-01 3.67777765... | [15.012991905212402, -2.8357532024383545] |
4d02896b-dd00-479f-ad68-cf921b1bb8e1 | non-contact-heart-rate-measurement-from | 2304.14789 | null | https://arxiv.org/abs/2304.14789v1 | https://arxiv.org/pdf/2304.14789v1.pdf | Non-Contact Heart Rate Measurement from Deteriorated Videos | Remote photoplethysmography (rPPG) offers a state-of-the-art, non-contact methodology for estimating human pulse by analyzing facial videos. Despite its potential, rPPG methods can be susceptible to various artifacts, such as noise, occlusions, and other obstructions caused by sunglasses, masks, or even involuntary fac... | ['Miguel Bordallo López', 'Olli Silvén', 'Constantino Álvarez Casado', 'Le Nguyen', 'Nhi Nguyen'] | 2023-04-28 | null | null | null | null | ['heart-rate-estimation'] | ['medical'] | [ 5.43549538e-01 -1.38780490e-01 1.37435868e-01 -1.62919641e-01
-6.33609831e-01 -4.67909634e-01 2.66250193e-01 -3.21472436e-01
-1.90457895e-01 7.60414243e-01 3.51834476e-01 1.47151470e-03
1.29104930e-03 -4.89035338e-01 -4.12739038e-01 -8.04121017e-01
-5.74694201e-02 -2.77671903e-01 -1.64800659e-01 1.99341312... | [13.865635871887207, 2.6870622634887695] |
26f011f4-9673-4559-ad84-d2a0d4eed190 | damuel-a-large-multilingual-dataset-for | 2306.09288 | null | https://arxiv.org/abs/2306.09288v1 | https://arxiv.org/pdf/2306.09288v1.pdf | DaMuEL: A Large Multilingual Dataset for Entity Linking | We present DaMuEL, a large Multilingual Dataset for Entity Linking containing data in 53 languages. DaMuEL consists of two components: a knowledge base that contains language-agnostic information about entities, including their claims from Wikidata and named entity types (PER, ORG, LOC, EVENT, BRAND, WORK_OF_ART, MANUF... | ['Milan Straka', 'David Kubeša'] | 2023-06-15 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-7.97530770e-01 4.96549904e-01 -5.16492605e-01 1.82729468e-01
-8.98482621e-01 -1.03882682e+00 7.13277996e-01 8.04722667e-01
-6.09383881e-01 1.09058762e+00 6.46165788e-01 6.57204241e-02
-1.15528055e-01 -1.10589445e+00 -6.69930458e-01 -1.24969468e-01
3.08654845e-01 7.04376221e-01 4.69444513e-01 -3.21289599... | [9.497920989990234, 8.938844680786133] |
1d216d73-bb6a-470f-8afc-75372eb5aeab | improving-japanese-semantic-role-labeling | null | null | https://aclanthology.org/Y18-1058 | https://aclanthology.org/Y18-1058.pdf | Improving Japanese semantic-role-labeling performance with transfer learning as case for limited resources of tagged corpora on aggregated language | null | ['Hitoshi Ueda', 'Tatsuhiko Abo', 'Masaya Iizuka', 'Yoshihiko Inada', 'Masahiro Taguchi', 'Yasuhiro Ishihara', 'Koichi Takeuchi', 'Takuya Okamura'] | null | null | null | null | paclic-2018-12 | ['semantic-role-labeling'] | ['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... | [-7.129366397857666, 3.7449145317077637] |
eb45ce21-37ad-42b4-8228-04809a6bd4c9 | controllable-image-enhancement | 2206.08488 | null | https://arxiv.org/abs/2206.08488v1 | https://arxiv.org/pdf/2206.08488v1.pdf | Controllable Image Enhancement | Editing flat-looking images into stunning photographs requires skill and time. Automated image enhancement algorithms have attracted increased interest by generating high-quality images without user interaction. However, the quality assessment of a photograph is subjective. Even in tone and color adjustments, a single ... | ['Kyoung Mu Lee', 'Heewon Kim'] | 2022-06-16 | null | null | null | null | ['photo-retouching'] | ['computer-vision'] | [ 9.06635702e-01 -1.37853593e-01 2.75596350e-01 -5.32600701e-01
-7.66823292e-01 -4.91244406e-01 3.66188645e-01 -2.95740485e-01
-4.66951221e-01 4.54042166e-01 -1.78092763e-01 -1.83719277e-01
-1.17431656e-01 -6.53129160e-01 -7.25023150e-01 -6.90441012e-01
3.18693072e-01 -3.58723819e-01 1.35352015e-01 -6.43677786... | [11.006376266479492, -2.119178056716919] |
62e0cc2d-9749-4480-a6e1-f91afcf1eefb | self-supervised-contrastive-pre-training-for | 2206.08496 | null | https://arxiv.org/abs/2206.08496v3 | https://arxiv.org/pdf/2206.08496v3.pdf | Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency | Pre-training on time series poses a unique challenge due to the potential mismatch between pre-training and target domains, such as shifts in temporal dynamics, fast-evolving trends, and long-range and short-cyclic effects, which can lead to poor downstream performance. While domain adaptation methods can mitigate thes... | ['Marinka Zitnik', 'Theodoros Tsiligkaridis', 'Ziyuan Zhao', 'Xiang Zhang'] | 2022-06-17 | null | null | null | null | ['hand-gesture-recognition', 'hand-gesture-recognition-1', 'gesture-recognition', 'fault-detection'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [ 5.26833057e-01 -2.46190071e-01 -3.51359904e-01 -3.73823613e-01
-9.24508035e-01 -5.41251361e-01 3.11839253e-01 -2.32260585e-01
-2.81652421e-01 5.43694854e-01 4.55040157e-01 -1.93873838e-01
-4.75576848e-01 -3.90661091e-01 -6.82294905e-01 -6.63768947e-01
-6.22608721e-01 1.42524019e-01 9.72942710e-02 -1.67095661... | [7.492997169494629, 3.023341417312622] |
ad0ae3fb-b126-4208-a573-97aa28615655 | visual-instruction-tuning | 2304.08485 | null | https://arxiv.org/abs/2304.08485v1 | https://arxiv.org/pdf/2304.08485v1.pdf | Visual Instruction Tuning | Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored in the multimodal field. In this paper, we present the first attempt to use language-only GPT-4 to generate multimodal language-image instruc... | ['Yong Jae Lee', 'Qingyang Wu', 'Chunyuan Li', 'Haotian Liu'] | 2023-04-17 | null | null | null | null | ['science-question-answering', 'instruction-following'] | ['miscellaneous', 'natural-language-processing'] | [ 2.07523674e-01 2.56349206e-01 -2.47546166e-01 -3.59519422e-01
-1.38388753e+00 -5.24301708e-01 7.80106664e-01 -1.47152603e-01
-3.75164390e-01 3.37134331e-01 9.07199755e-02 -7.91092515e-01
5.66600561e-01 -3.31463426e-01 -1.35654652e+00 -4.45697844e-01
2.46583149e-01 1.03342736e+00 -3.52516435e-02 -3.63081068... | [10.961844444274902, 1.5863548517227173] |
7da4ccac-80f6-44ab-86d9-064f288bdc75 | learning-difference-equations-with-structured | 2307.01238 | null | https://arxiv.org/abs/2307.01238v1 | https://arxiv.org/pdf/2307.01238v1.pdf | Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction | People with diabetes must carefully monitor their blood glucose levels, especially after eating. Blood glucose regulation requires a proper combination of food intake and insulin boluses. Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Although traditional m... | ['J. Ignacio Hidalgo', 'Gabriel Kronberger', 'J. Manuel Velasco', 'David Joedicke', 'Daniel Parra'] | 2023-07-03 | null | null | null | null | ['clustering'] | ['methodology'] | [ 2.44072363e-01 4.75794315e-01 -4.20235962e-01 -5.82810760e-01
-2.39600003e-01 -2.17410251e-01 -1.01567216e-01 8.54912519e-01
-8.53947923e-02 8.99587035e-01 3.43832821e-01 -3.01683009e-01
-4.91646618e-01 -7.54469395e-01 -3.68303657e-01 -6.84294045e-01
-5.79418123e-01 8.08731556e-01 -4.89192277e-01 2.77527254... | [8.124436378479004, 5.622197151184082] |
0a243589-61d9-476f-8422-a200b3ec4b22 | interpretability-of-machine-learning-recent | 2305.00537 | null | https://arxiv.org/abs/2305.00537v1 | https://arxiv.org/pdf/2305.00537v1.pdf | Interpretability of Machine Learning: Recent Advances and Future Prospects | The proliferation of machine learning (ML) has drawn unprecedented interest in the study of various multimedia contents such as text, image, audio and video, among others. Consequently, understanding and learning ML-based representations have taken center stage in knowledge discovery in intelligent multimedia research ... | ['Ling Guan', 'Lei Gao'] | 2023-04-30 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 6.69729888e-01 6.24289624e-02 -5.43316483e-01 -5.33940017e-01
-3.74955624e-01 -1.63625732e-01 5.05536497e-01 3.61684471e-01
-1.37864307e-01 6.04885578e-01 7.33246207e-02 -3.56097132e-01
-3.12048882e-01 -6.10922277e-01 -3.52426022e-01 -6.87034369e-01
-1.25942603e-01 2.44834781e-01 -4.48132902e-01 7.86799192... | [10.402058601379395, 1.7087620496749878] |
93f7834e-3960-4926-ad06-069875c835db | italic-an-italian-intent-classification | 2306.08502 | null | https://arxiv.org/abs/2306.08502v1 | https://arxiv.org/pdf/2306.08502v1.pdf | ITALIC: An Italian Intent Classification Dataset | Recent large-scale Spoken Language Understanding datasets focus predominantly on English and do not account for language-specific phenomena such as particular phonemes or words in different lects. We introduce ITALIC, the first large-scale speech dataset designed for intent classification in Italian. The dataset compri... | ['Elena Baralis', 'Luca Cagliero', 'Eliana Pastor', 'Giuseppe Attanasio', 'Luca Colomba', 'Lorenzo Vaiani', 'Moreno La Quatra', 'Alkis Koudounas'] | 2023-06-14 | null | null | null | null | ['classification-1', 'spoken-language-understanding', 'intent-classification', 'spoken-language-understanding'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'speech'] | [-1.41469106e-01 1.23233356e-01 -3.11893106e-01 -5.53326249e-01
-1.18602610e+00 -8.09035838e-01 7.42258370e-01 1.17209516e-01
-5.96432090e-01 6.07890725e-01 8.57658863e-01 -3.57453495e-01
4.21369880e-01 -2.31018901e-01 -5.11215746e-01 -1.62498981e-01
1.11532308e-01 8.54060411e-01 1.30540863e-01 -2.08619967... | [14.135594367980957, 6.935575008392334] |
3c006b22-fc97-42e9-9504-283f749519a2 | speech-dereverberation-with-a-reverberation | 2210.11089 | null | https://arxiv.org/abs/2210.11089v6 | https://arxiv.org/pdf/2210.11089v6.pdf | Speech Dereverberation with a Reverberation Time Shortening Target | This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be s... | ['Xiaofei Li', 'Wenye Zhu', 'Rui Zhou'] | 2022-10-20 | null | null | null | null | ['speech-denoising', 'speech-dereverberation'] | ['speech', 'speech'] | [-1.35048598e-01 -2.07055047e-01 3.74876171e-01 -1.34209841e-01
-4.14405853e-01 -2.25234732e-01 1.43716618e-01 -1.39445022e-01
-3.15510809e-01 7.14471579e-01 3.61956447e-01 -5.05250514e-01
-1.56816602e-01 -5.99779427e-01 -4.07983720e-01 -9.46702659e-01
-2.79000819e-01 -4.06967491e-01 1.58055335e-01 -5.03336251... | [15.091310501098633, 5.902318477630615] |
8eb28b3a-6df1-4829-987d-c899f29a5481 | machine-learning-model-interpretability-for | 1610.09045 | null | http://arxiv.org/abs/1610.09045v1 | http://arxiv.org/pdf/1610.09045v1.pdf | Machine Learning Model Interpretability for Precision Medicine | Interpretability of machine learning models is critical for data-driven
precision medicine efforts. However, highly predictive models are generally
complex and are difficult to interpret. Here using Model-Agnostic Explanations
algorithm, we show that complex models such as random forest can be made
interpretable. Using... | [] | 2016-10-28 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 1.58799425e-01 6.54647946e-01 -3.32412750e-01 -6.22745335e-01
-2.01577663e-01 -3.55732650e-01 1.13200076e-01 4.94102895e-01
1.39865577e-01 1.24478698e+00 3.49652052e-01 -1.09296668e+00
-6.66808486e-01 -4.85735387e-01 -5.38781941e-01 -2.84351766e-01
-2.28892446e-01 1.05718613e+00 -3.44981283e-01 7.24965148... | [8.322891235351562, 5.8629913330078125] |
7ad1ed59-28a0-46f7-ba52-687471a41ea8 | bi-vlgm-bi-level-class-severity-aware-vision | 2305.12231 | null | https://arxiv.org/abs/2305.12231v1 | https://arxiv.org/pdf/2305.12231v1.pdf | Bi-VLGM : Bi-Level Class-Severity-Aware Vision-Language Graph Matching for Text Guided Medical Image Segmentation | Medical reports with substantial information can be naturally complementary to medical images for computer vision tasks, and the modality gap between vision and language can be solved by vision-language matching (VLM). However, current vision-language models distort the intra-model relation and mainly include class inf... | ['Yuan Yixuan', 'Liu Jie', 'Chen Wenting'] | 2023-05-20 | null | null | null | null | ['graph-matching'] | ['graphs'] | [ 4.10613477e-01 1.94381565e-01 -4.86482143e-01 -5.61369538e-01
-9.93725181e-01 -2.73595184e-01 4.47878927e-01 4.66571897e-01
-2.67957121e-01 1.15901686e-01 4.07633483e-01 -3.48869830e-01
-2.46179730e-01 -7.36969352e-01 -3.74025017e-01 -5.30318797e-01
2.80822337e-01 1.48399323e-01 5.01280785e-01 4.00615099... | [10.767301559448242, 1.4589191675186157] |
9c0a0fe6-f35b-4362-9895-4663133627e0 | wtr-a-test-collection-for-web-table-retrieval | 2105.02354 | null | https://arxiv.org/abs/2105.02354v1 | https://arxiv.org/pdf/2105.02354v1.pdf | WTR: A Test Collection for Web Table Retrieval | We describe the development, characteristics and availability of a test collection for the task of Web table retrieval, which uses a large-scale Web Table Corpora extracted from the Common Crawl. Since a Web table usually has rich context information such as the page title and surrounding paragraphs, we not only provid... | ['Brian D. Davison', 'Shuo Zhang', 'Zhiyu Chen'] | 2021-05-05 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 7.03463936e-03 -2.07034677e-01 -5.55843353e-01 -2.14279518e-01
-1.73379672e+00 -1.04249310e+00 7.49343991e-01 8.01453471e-01
-2.75336146e-01 7.69034564e-01 4.99001563e-01 -1.87107801e-01
-1.53182879e-01 -9.45233881e-01 -5.50367236e-01 -9.92710367e-02
-5.68464436e-02 7.77723849e-01 8.98276567e-01 -4.71772999... | [9.754591941833496, 7.930352210998535] |
8290ebf5-ada5-4ebc-8895-c8ed3e697603 | fooling-polarization-based-vision-using | 2303.1789 | null | https://arxiv.org/abs/2303.17890v1 | https://arxiv.org/pdf/2303.17890v1.pdf | Fooling Polarization-based Vision using Locally Controllable Polarizing Projection | Polarization is a fundamental property of light that encodes abundant information regarding surface shape, material, illumination and viewing geometry. The computer vision community has witnessed a blossom of polarization-based vision applications, such as reflection removal, shape-from-polarization, transparent object... | ['Yinqiang Zheng', 'Ko Nishino', 'Shohei Nobuhara', 'Zhihang Zhong', 'Zhuoxiao Li'] | 2023-03-31 | null | null | null | null | ['reflection-removal', 'color-constancy'] | ['computer-vision', 'computer-vision'] | [ 6.66549206e-01 3.23499382e-01 5.26474059e-01 2.20784381e-01
-2.66855001e-01 -1.09293973e+00 2.79885799e-01 -6.13680065e-01
-5.16199470e-01 4.61238861e-01 -2.46605054e-01 -7.50272930e-01
2.68258512e-01 -7.64275491e-01 -7.22613633e-01 -1.30713379e+00
5.87938488e-01 -1.91983148e-01 2.25976259e-01 -1.53888687... | [10.380586624145508, -2.677485227584839] |
16b11805-6f56-4b0c-8fbe-7eded3f7d154 | interpretability-of-multivariate-brain-maps | 1603.08704 | null | http://arxiv.org/abs/1603.08704v1 | http://arxiv.org/pdf/1603.08704v1.pdf | Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification | Brain decoding is a popular multivariate approach for hypothesis testing in
neuroimaging. It is well known that the brain maps derived from weights of
linear classifiers are hard to interpret because of high correlations between
predictors, low signal to noise ratios, and the high dimensionality of
neuroimaging data. T... | ['Seyed Mostafa Kia'] | 2016-03-29 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.62688482e-01 1.02163658e-01 2.73874968e-01 -7.41973758e-01
-4.20025975e-01 -3.13706994e-01 4.04208660e-01 3.29778016e-01
-5.15252948e-01 6.74134851e-01 1.42813427e-02 -3.93071294e-01
-6.63265944e-01 -2.83888757e-01 -5.98091781e-01 -7.80963957e-01
-2.46905819e-01 4.52156991e-01 -1.58291221e-01 2.95618594... | [12.687114715576172, 3.397921562194824] |
9316ab3c-1f65-40b3-94ae-77595b56c883 | n-reference-transfer-learning-for-saliency | 2007.05104 | null | https://arxiv.org/abs/2007.05104v1 | https://arxiv.org/pdf/2007.05104v1.pdf | $n$-Reference Transfer Learning for Saliency Prediction | Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to predict saliency maps on images in new domains that lack sufficient data for data-hungry models. To solve this problem, we propose a few-shot... | ['Mohan S. Kankanhalli', 'Yongkang Wong', 'Yan Luo', 'Qi Zhao'] | 2020-07-09 | null | null | null | null | ['saliency-prediction-1'] | ['computer-vision'] | [ 2.14773536e-01 3.67088476e-03 -5.18732190e-01 -5.04493415e-01
-8.33039343e-01 2.38905824e-03 3.17005217e-01 -1.69513505e-02
-2.41265818e-01 8.43019247e-01 1.70676962e-01 1.01133041e-01
9.94725376e-02 -6.15074456e-01 -6.67476952e-01 -5.25064468e-01
2.94850886e-01 2.05925271e-01 7.52606630e-01 -2.38286152... | [9.779558181762695, -0.31541258096694946] |
cdb6461e-1f86-484b-b12f-c6c9f997d0c7 | flownet3d-geometric-losses-for-deep-scene | 1912.01438 | null | https://arxiv.org/abs/1912.01438v3 | https://arxiv.org/pdf/1912.01438v3.pdf | FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation | We present FlowNet3D++, a deep scene flow estimation network. Inspired by classical methods, FlowNet3D++ incorporates geometric constraints in the form of point-to-plane distance and angular alignment between individual vectors in the flow field, into FlowNet3D. We demonstrate that the addition of these geometric loss ... | ['Henry Howard-Jenkins', 'Shuda Li', 'Zirui Wang', 'Min Chen', 'Victor Adrian Prisacariu'] | 2019-12-03 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-3.51546258e-01 -2.53259987e-01 2.30272561e-02 -2.40462348e-01
-2.35007107e-01 -6.53577745e-01 7.09521711e-01 8.81607085e-02
-3.17997932e-01 5.05631864e-01 6.71107531e-01 -3.05041730e-01
2.36433983e-01 -8.77134144e-01 -4.31882501e-01 -1.08176194e-01
-4.30051923e-01 2.55409449e-01 5.42592704e-01 -7.51551613... | [8.678671836853027, -1.970813512802124] |
75635496-15e8-48aa-8de7-584f9c2089b3 | from-a-bird-s-eye-view-to-see-joint-camera | 2212.09298 | null | https://arxiv.org/abs/2212.09298v1 | https://arxiv.org/pdf/2212.09298v1.pdf | From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration | We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration. This is a very challenging problem since its only input is several RGB images from different first-person views (FPVs) for a multi-person scene, without the BEV image and the calibrat... | ['Song Wang', 'Feifan Wang', 'Wei Feng', 'Ruize Han', 'Zekun Qian'] | 2022-12-19 | null | null | null | null | ['camera-calibration', 'camera-localization'] | ['computer-vision', 'computer-vision'] | [-1.23905540e-01 -2.89394706e-01 6.15104795e-01 -4.31657553e-01
-6.33486152e-01 -7.66300797e-01 3.01566780e-01 -4.18669283e-01
-4.13885862e-01 3.90254110e-01 9.55348462e-02 2.79141366e-01
3.81672412e-01 -4.60166425e-01 -8.26123893e-01 -7.45298386e-01
7.37610996e-01 3.91979635e-01 5.70495307e-01 -1.83316041... | [7.223688125610352, -1.009044885635376] |
252175bb-fbbb-4342-ae8b-a01e00b0cd15 | illiterate-dall-cdot-e-learns-to-compose | null | null | https://openreview.net/forum?id=h0OYV0We3oh | https://openreview.net/pdf?id=h0OYV0We3oh | Illiterate DALL$\cdot$E Learns to Compose | DALL$\cdot$E has shown an impressive ability of composition-based systematic generalization in image generation. This is possible because it utilizes the dataset of text-image pairs where the text provides the source of compositionality. Following this result, an important extending question is whether this composition... | ['Sungjin Ahn', 'Fei Deng', 'Gautam Singh'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['systematic-generalization'] | ['reasoning'] | [ 7.13097990e-01 6.45836234e-01 2.20900148e-01 -1.75255075e-01
-9.52389657e-01 -4.10482079e-01 8.33686709e-01 -3.79598856e-01
-2.82892048e-01 8.43183279e-01 4.33727205e-02 -2.33304635e-01
-2.86056716e-02 -9.98359382e-01 -1.09074128e+00 -8.89013529e-01
1.25511944e-01 6.63703084e-01 1.61708727e-01 -4.51235175... | [11.31167221069336, -0.18442033231258392] |
0b4657d2-a494-4386-ba3a-c9ef20923765 | cook-gen-robust-generative-modeling-of | 2306.01805 | null | https://arxiv.org/abs/2306.01805v1 | https://arxiv.org/pdf/2306.01805v1.pdf | Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes | As people become more aware of their food choices, food computation models have become increasingly popular in assisting people in maintaining healthy eating habits. For example, food recommendation systems analyze recipe instructions to assess nutritional contents and provide recipe recommendations. The recent and rem... | ['Amit Sheth', 'Vignesh Narayanan', 'Yuxin Zi', 'Renjith Prasad', 'Kanak Raj', 'Kaushik Roy', 'Revathy Venkataramanan'] | 2023-06-01 | null | null | null | null | ['food-recommendation'] | ['miscellaneous'] | [ 2.05775231e-01 -1.03056215e-01 -2.49258727e-01 -4.40607131e-01
-3.59797180e-01 -8.48942757e-01 5.20288169e-01 7.59591758e-01
-5.40564023e-02 3.06668669e-01 8.24491382e-01 -3.13662350e-01
1.23252824e-01 -1.23312271e+00 -7.28276610e-01 -6.09174728e-01
6.12514131e-02 5.56519628e-01 -4.41634774e-01 -6.41602099... | [11.521666526794434, 4.531466484069824] |
7dfa3827-5938-4dd3-89b2-5a6c0d700900 | fine-grained-temporal-relation-extraction | 1902.0139 | null | https://arxiv.org/abs/1902.01390v2 | https://arxiv.org/pdf/1902.01390v2.pdf | Fine-Grained Temporal Relation Extraction | We present a novel semantic framework for modeling temporal relations and event durations that maps pairs of events to real-valued scales. We use this framework to construct the largest temporal relations dataset to date, covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to t... | ['Benjamin Van Durme', 'Siddharth Vashishtha', 'Aaron Steven White'] | 2019-02-04 | fine-grained-temporal-relation-extraction-1 | https://aclanthology.org/P19-1280 | https://aclanthology.org/P19-1280.pdf | acl-2019-7 | ['temporal-relation-extraction'] | ['natural-language-processing'] | [-2.71402299e-01 4.34392333e-01 -7.27705121e-01 -9.12190259e-01
-6.95153952e-01 -8.52088690e-01 9.01400864e-01 4.54536289e-01
-4.66124535e-01 9.61421907e-01 7.38404751e-01 -2.45216355e-01
-2.47662157e-01 -9.13215578e-01 -4.64628011e-01 -3.78327863e-03
-8.01604927e-01 6.50939584e-01 5.55714428e-01 -4.43790317... | [9.124157905578613, 9.17470932006836] |
47bdcc8e-1c88-42e7-82ba-db97a2e059f8 | huruf-an-application-for-arabic-handwritten | 2212.0861 | null | https://arxiv.org/abs/2212.08610v2 | https://arxiv.org/pdf/2212.08610v2.pdf | Huruf: An Application for Arabic Handwritten Character Recognition Using Deep Learning | Handwriting Recognition has been a field of great interest in the Artificial Intelligence domain. Due to its broad use cases in real life, research has been conducted widely on it. Prominent work has been done in this field focusing mainly on Latin characters. However, the domain of Arabic handwritten character recogni... | ['Md. Hasanul Kabir', 'Tasnim Ahmed', 'Sabbir Ahmed', 'Chowdhury Mohammad Abdullah', 'Fairuz Shaiara', 'Minhaz Kamal'] | 2022-12-16 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 6.77236468e-02 -4.00537521e-01 2.91988969e-01 -6.21406972e-01
-2.44326312e-02 -5.98860681e-01 5.87885201e-01 8.31288770e-02
-7.77160406e-01 7.03132331e-01 -1.96064860e-01 -1.26089618e-01
-1.22382417e-01 -5.73374212e-01 -3.61634940e-01 -9.66965139e-01
1.01550318e-01 4.37718540e-01 3.49124312e-01 -2.22145066... | [11.831430435180664, 2.672022819519043] |
f6e90b6c-7aeb-47c5-bc82-13bf0e5634a6 | the-natural-language-pipeline-neural-text | null | null | https://aclanthology.org/2020.nl4xai-1.5 | https://aclanthology.org/2020.nl4xai-1.5.pdf | The Natural Language Pipeline, Neural Text Generation and Explainability | End-to-end encoder-decoder approaches to data-to-text generation are often black boxes whose predictions are difficult to explain. Breaking up the end-to-end model into sub-modules is a natural way to address this problem. The traditional pre-neural Natural Language Generation (NLG) pipeline provides a framework for br... | ['Claire Gardent', 'Albert Gatt', 'Juliette Faille'] | null | null | null | null | acl-nl4xai-inlg-2020-11 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.66871327e-01 1.32422781e+00 -2.54753828e-01 -7.69077241e-01
-9.36461627e-01 -3.64611953e-01 9.13369358e-01 -1.08885385e-01
1.88821405e-01 8.64302397e-01 8.21257532e-01 -5.66328466e-01
4.53586638e-01 -7.28133619e-01 -9.44504678e-01 1.04230933e-01
3.43400389e-01 7.52495587e-01 -4.56510484e-01 -4.41212654... | [11.676753997802734, 8.961003303527832] |
8bba6353-3e22-4133-b3f1-6171f340d23b | openshape-scaling-up-3d-shape-representation | 2305.10764 | null | https://arxiv.org/abs/2305.10764v2 | https://arxiv.org/pdf/2305.10764v2.pdf | OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding | We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To... | ['Hao Su', 'Fatih Porikli', 'Hong Cai', 'Shizhong Han', 'Xuanlin Li', 'Yinhao Zhu', 'Kaiming Kuang', 'Ruoxi Shi', 'Minghua Liu'] | 2023-05-18 | null | null | null | null | ['3d-shape-representation', '3d-classification', 'zero-shot-transfer-3d-point-cloud', 'open-set-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'miscellaneous'] | [-1.40727907e-01 -1.27145022e-01 -1.75479919e-01 -3.05610240e-01
-1.10456848e+00 -8.11639369e-01 8.40888917e-01 1.72460496e-01
-9.76676568e-02 6.40937537e-02 3.92214835e-01 1.04588248e-01
2.97216207e-01 -7.59858549e-01 -9.12149668e-01 -3.27570856e-01
3.19433600e-01 9.07845914e-01 7.23697692e-02 -2.31151223... | [8.152865409851074, -3.3014943599700928] |
ab255369-11cf-4ad8-b4f1-8594411691c7 | hardware-trojan-attacks-on-neural-networks | 1806.05768 | null | http://arxiv.org/abs/1806.05768v1 | http://arxiv.org/pdf/1806.05768v1.pdf | Hardware Trojan Attacks on Neural Networks | With the rising popularity of machine learning and the ever increasing demand
for computational power, there is a growing need for hardware optimized
implementations of neural networks and other machine learning models. As the
technology evolves, it is also plausible that machine learning or artificial
intelligence wil... | ['Yingjie Lao', 'Joseph Clements'] | 2018-06-14 | null | null | null | null | ['neural-network-security'] | ['miscellaneous'] | [ 6.06006086e-01 -1.46486992e-02 -1.17462270e-01 -2.43967012e-01
5.73846698e-03 -8.93841267e-01 2.13992372e-01 -1.55799389e-01
-7.28859484e-01 3.74769896e-01 -7.42908716e-01 -1.06213856e+00
5.13911359e-02 -8.60351682e-01 -1.04424477e+00 -7.20688879e-01
-2.13168278e-01 -1.61239192e-01 2.99054533e-01 -5.52157052... | [5.649139404296875, 7.620660781860352] |
efd3615d-73b8-49d5-a9ce-995d77b59cfa | a-preliminary-approach-for-learning | 2001.04432 | null | https://arxiv.org/abs/2001.04432v1 | https://arxiv.org/pdf/2001.04432v1.pdf | A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children | The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision support systems. We adapted a boosted Statistical Relational Learning (SRL) framework to learn probabilistic rules from clinical hospital reco... | ['Neel Shah', 'Lakshmi Raman', 'Abdelaziz Farhat', 'Michael A. Skinner', 'Sriraam Natarajan'] | 2020-01-13 | null | null | null | null | ['respiratory-failure'] | ['medical'] | [ 6.69181123e-02 6.96077347e-01 -6.04178846e-01 -9.77226615e-01
-5.55140138e-01 -3.09404612e-01 1.14065461e-01 1.22658312e+00
-2.25531533e-01 9.33594644e-01 3.75957280e-01 -8.17283273e-01
-7.67451048e-01 -8.92648220e-01 -4.43959385e-01 -3.99335951e-01
-2.27081731e-01 1.00682092e+00 8.39749128e-02 4.07967985... | [8.419625282287598, 8.589703559875488] |
21fee6df-c079-4003-aaf6-c43b0ebe8b1c | temporal-stability-in-predictive-process | 1712.04165 | null | http://arxiv.org/abs/1712.04165v3 | http://arxiv.org/pdf/1712.04165v3.pdf | Temporal Stability in Predictive Process Monitoring | Predictive process monitoring is concerned with the analysis of events
produced during the execution of a business process in order to predict as
early as possible the final outcome of an ongoing case. Traditionally,
predictive process monitoring methods are optimized with respect to accuracy.
However, in environments ... | ['Fabrizio Maria Maggi', 'Anna Leontjeva', 'Irene Teinemaa', 'Marlon Dumas'] | 2017-12-12 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 5.20492852e-01 -9.27241985e-03 -2.55004108e-01 -4.34739769e-01
-3.61060500e-01 -3.00075024e-01 8.60350847e-01 8.59502554e-01
-2.89523870e-01 5.86829185e-01 2.15463303e-02 -5.54414392e-01
-5.96721649e-01 -1.07271552e+00 -2.04753861e-01 -4.22609031e-01
-3.02076638e-01 4.41073269e-01 1.22239411e-01 2.66808599... | [8.531810760498047, 5.996852397918701] |
1b34a267-4023-4180-a23c-38bbbbc8ca47 | context-aware-proposal-network-for-temporal | 2206.09082 | null | https://arxiv.org/abs/2206.09082v1 | https://arxiv.org/pdf/2206.09082v1.pdf | Context-aware Proposal Network for Temporal Action Detection | This technical report presents our first place winning solution for temporal action detection task in CVPR-2022 AcitivityNet Challenge. The task aims to localize temporal boundaries of action instances with specific classes in long untrimmed videos. Recent mainstream attempts are based on dense boundary matchings and e... | ['Nong Sang', 'Yuanjie Shao', 'Changxin Gao', 'Shiwei Zhang', 'Huaxin Zhang', 'Xiang Wang'] | 2022-06-18 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 5.09142160e-01 4.08182181e-02 -5.16459525e-01 -3.16474773e-02
-1.07760096e+00 -3.22739065e-01 7.71476269e-01 -1.99407876e-01
-4.57382351e-01 6.37306392e-01 5.62393486e-01 2.21542135e-01
-8.29901844e-02 -3.98048997e-01 -3.91420364e-01 -5.49025595e-01
-4.62746680e-01 1.69462770e-01 1.01584673e+00 -4.33658808... | [8.310942649841309, 0.43152111768722534] |
50f77769-9f75-437d-919d-723b20cc9f0e | few-shot-learning-of-accurate-folding | 2208.09652 | null | https://arxiv.org/abs/2208.09652v1 | https://arxiv.org/pdf/2208.09652v1.pdf | Few-Shot Learning of Accurate Folding Landscape for Protein Structure Prediction | Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and therapeutical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern ... | ['Yi Qin Gao', 'YuAn Liu', 'Lijiang Yang', 'Boxin Xue', 'Yi Isaac Yang', 'Diqing Chen', 'Fan Yu', 'Ningxi Ni', 'Jialiang Yu', 'Zidong Wang', 'Min Wang', 'Haotian Chu', 'Mengyun Chen', 'Sirui Liu', 'Jun Zhang'] | 2022-08-20 | null | null | null | null | ['protein-design'] | ['medical'] | [ 4.32902247e-01 -3.80804054e-02 -2.55712494e-02 -2.96005160e-01
-8.44510913e-01 -6.17997766e-01 3.10324520e-01 -1.27264503e-02
-6.24326766e-02 1.41242445e+00 4.27769274e-02 -4.71573710e-01
9.28751454e-02 -6.63170934e-01 -1.00996125e+00 -1.26960611e+00
2.29398489e-01 8.50033581e-01 2.33365506e-01 -5.50499141... | [4.714763641357422, 5.582594871520996] |
cbf4da58-a708-4218-af88-d33a3af9e495 | tukey-inspired-video-object-segmentation | 1811.07958 | null | http://arxiv.org/abs/1811.07958v2 | http://arxiv.org/pdf/1811.07958v2.pdf | Tukey-Inspired Video Object Segmentation | We investigate the problem of strictly unsupervised video object
segmentation, i.e., the separation of a primary object from background in video
without a user-provided object mask or any training on an annotated dataset. We
find foreground objects in low-level vision data using a John Tukey-inspired
measure of "outlie... | ['Jason J. Corso', 'Brent A. Griffin'] | 2018-11-19 | null | null | null | null | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 5.60685575e-01 -1.30391598e-01 7.36777857e-02 -2.77577370e-01
-8.94828141e-01 -9.17752683e-01 5.01636505e-01 2.80027032e-01
-5.73774219e-01 3.19343388e-01 -3.41090947e-01 -1.24950241e-02
1.37089705e-02 -3.45926285e-01 -1.08746886e+00 -9.70065892e-01
-2.83125583e-02 6.22360706e-01 8.53526413e-01 4.47001129... | [9.0302734375, -0.3196549713611603] |
2ae7a165-a289-4021-915a-0a7a1e88ce9b | catching-image-retrieval-generalization | 2306.13357 | null | https://arxiv.org/abs/2306.13357v1 | https://arxiv.org/pdf/2306.13357v1.pdf | Catching Image Retrieval Generalization | The concepts of overfitting and generalization are vital for evaluating machine learning models. In this work, we show that the popular Recall@K metric depends on the number of classes in the dataset, which limits its ability to estimate generalization. To fix this issue, we propose a new metric, which measures retriev... | ['Ivan Karpukhin', 'Maksim Zhdanov'] | 2023-06-23 | null | null | null | null | ['metric-learning', 'metric-learning', 'retrieval'] | ['computer-vision', 'methodology', 'methodology'] | [-2.02670872e-01 -5.73318303e-01 -4.49303716e-01 -5.68980217e-01
-7.84347713e-01 -6.94140315e-01 5.19654512e-01 4.59584177e-01
-5.82860649e-01 5.11895776e-01 -3.89792398e-02 -5.46234176e-02
-4.73260075e-01 -7.76584685e-01 -3.21670741e-01 -5.14079750e-01
1.85310245e-01 -7.08878273e-03 1.80981494e-02 -1.39153033... | [9.428749084472656, 3.0729188919067383] |
450a414f-6748-4bc0-b60c-68313a0a1779 | blind-image-deblurring-with-local-maximum | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Blind_Image_Deblurring_With_Local_Maximum_Gradient_Prior_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Blind_Image_Deblurring_With_Local_Maximum_Gradient_Prior_CVPR_2019_paper.pdf | Blind Image Deblurring With Local Maximum Gradient Prior | Blind image deblurring aims to recover sharp image from a blurred one while the blur kernel is unknown. To solve this ill-posed problem, a great amount of image priors have been explored and employed in this area. In this paper, we present a blind deblurring method based on Local Maximum Gradient (LMG) prior. Our work ... | [' Guixu Zhang', ' Tingting Wang', ' Faming Fang', 'Liang Chen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.11840832e-01 -4.76423711e-01 1.94303662e-01 -1.45482898e-01
-1.94197774e-01 -3.63395065e-01 6.11809373e-01 -4.40785229e-01
-1.89332515e-01 8.61782730e-01 2.37890735e-01 5.79334646e-02
-3.08683753e-01 -4.82955933e-01 -4.29088742e-01 -8.82543623e-01
4.09726836e-02 -3.28593016e-01 9.50525627e-02 -1.15340687... | [11.595423698425293, -2.7497875690460205] |
22f575ea-9f08-4062-9691-f6323a14f66e | dense-regression-network-for-video-grounding | 2004.03545 | null | https://arxiv.org/abs/2004.03545v1 | https://arxiv.org/pdf/2004.03545v1.pdf | Dense Regression Network for Video Grounding | We address the problem of video grounding from natural language queries. The key challenge in this task is that one training video might only contain a few annotated starting/ending frames that can be used as positive examples for model training. Most conventional approaches directly train a binary classifier using suc... | ['Peihao Chen', 'Haoming Xu', 'Runhao Zeng', 'Mingkui Tan', 'Wenbing Huang', 'Chuang Gan'] | 2020-04-07 | dense-regression-network-for-video-grounding-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Dense_Regression_Network_for_Video_Grounding_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zeng_Dense_Regression_Network_for_Video_Grounding_CVPR_2020_paper.pdf | cvpr-2020-6 | ['video-grounding', 'natural-language-moment-retrieval'] | ['computer-vision', 'computer-vision'] | [ 5.61501123e-02 9.03912783e-02 -5.83269596e-01 -4.62412477e-01
-9.30936515e-01 -3.90520602e-01 4.21468019e-01 -7.71564171e-02
-4.41212147e-01 6.28717124e-01 5.95108047e-02 -3.87539677e-02
5.03111124e-01 -6.74895525e-01 -1.36758780e+00 -5.61507046e-01
6.23579882e-03 3.04796010e-01 5.72054446e-01 8.30008741... | [9.887646675109863, 0.6968645453453064] |
8cd88620-06cb-430e-8e45-e15cc2bbe6f7 | tilt-then-blur-or-blur-then-tilt-clarifying | 2207.06377 | null | https://arxiv.org/abs/2207.06377v3 | https://arxiv.org/pdf/2207.06377v3.pdf | Tilt-then-Blur or Blur-then-Tilt? Clarifying the Atmospheric Turbulence Model | Imaging at a long distance often requires advanced image restoration algorithms to compensate for the distortions caused by atmospheric turbulence. However, unlike many standard restoration problems such as deconvolution, the forward image formation model of the atmospheric turbulence does not have a simple expression.... | ['Stanley H. Chan'] | 2022-07-13 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [ 4.60508943e-01 -3.40519637e-01 6.63833082e-01 -2.75556147e-01
2.23451644e-01 -5.05943894e-01 6.02320969e-01 -3.56519759e-01
-2.60160565e-01 5.96020699e-01 3.32285821e-01 -6.76218033e-01
-4.27487969e-01 -3.44215363e-01 -3.63955468e-01 -1.12856102e+00
2.27604926e-01 7.23169744e-02 1.80065870e-01 -9.60506219... | [11.69257640838623, -2.746903896331787] |
6e661c1a-1579-4291-a5e6-a7abbd4e1cfd | when-regression-meets-manifold-learning-for | 1805.064 | null | http://arxiv.org/abs/1805.06400v1 | http://arxiv.org/pdf/1805.06400v1.pdf | When Regression Meets Manifold Learning for Object Recognition and Pose Estimation | In this work, we propose a method for object recognition and pose estimation
from depth images using convolutional neural networks. Previous methods
addressing this problem rely on manifold learning to learn low dimensional
viewpoint descriptors and employ them in a nearest neighbor search on an
estimated descriptor sp... | ['Sergey Zakharov', 'Mai Bui', 'Slobodan Ilic', 'Nassir Navab', 'Shadi Albarqouni'] | 2018-05-16 | null | null | null | null | ['pose-retrieval'] | ['computer-vision'] | [ 3.82787101e-02 4.85112071e-02 -2.51821756e-01 -4.27054733e-01
-1.39254045e+00 -5.61468422e-01 8.24893951e-01 2.08174974e-01
-6.31305218e-01 3.64360064e-01 6.28311038e-02 3.36390734e-01
-5.42982519e-01 -5.61439693e-01 -9.46409106e-01 -6.16032124e-01
5.86402901e-02 8.37056458e-01 -7.87203163e-02 1.32176206... | [7.855602264404297, -2.241067886352539] |
4c0c9c97-17f0-483a-a8cc-7ca3fa9a6c75 | bella-black-box-model-explanations-by-local | 2305.11311 | null | https://arxiv.org/abs/2305.11311v1 | https://arxiv.org/pdf/2305.11311v1.pdf | BELLA: Black box model Explanations by Local Linear Approximations | In recent years, understanding the decision-making process of black-box models has become not only a legal requirement but also an additional way to assess their performance. However, the state of the art post-hoc interpretation approaches rely on synthetic data generation. This introduces uncertainty and can hurt the ... | ['Fabian Suchanek', 'Albert Bifet', 'Nedeljko Radulovic'] | 2023-05-18 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-8.22514370e-02 7.36784995e-01 -5.36706269e-01 -6.61953986e-01
-7.14210272e-01 -6.55037165e-01 8.59292686e-01 3.15028727e-01
-5.19146360e-02 1.09763539e+00 7.48672336e-02 -8.14693868e-01
-2.77209610e-01 -6.84991181e-01 -8.04503202e-01 -4.70486253e-01
9.06798914e-02 5.47460973e-01 1.09771788e-01 -1.07180446... | [8.752216339111328, 5.706368923187256] |
f471ab67-543f-42d1-9170-a28c0a5d7a3b | topic-guided-coherence-modeling-for-sentence | null | null | https://aclanthology.org/D19-1232 | https://aclanthology.org/D19-1232.pdf | Topic-Guided Coherence Modeling for Sentence Ordering by Preserving Global and Local Information | We propose a novel topic-guided coherence modeling (TGCM) for sentence ordering. Our attention based pointer decoder directly utilize sentence vectors in a permutation-invariant manner, without being compressed into a single fixed-length vector as the paragraph representation. Thus, TGCM can improve global dependencies... | ['Kyong-Ho Lee', 'Cheolheon Shin', 'Byungkook Oh', 'Seungmin Seo', 'Eunju Jo'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['sentence-ordering'] | ['natural-language-processing'] | [ 1.70277208e-01 4.30267351e-03 -6.29361808e-01 -6.98175132e-01
-8.51632059e-01 -5.39571159e-02 2.96063572e-01 5.63914120e-01
-3.45354944e-01 7.15451181e-01 1.17350304e+00 -1.81009218e-01
-6.63599297e-02 -4.54821199e-01 -6.22545958e-01 -3.78847659e-01
-4.39252146e-02 2.39083216e-01 4.58034664e-01 -2.34601706... | [12.23622989654541, 9.33604907989502] |
6380a1ea-f571-4481-bcc4-d4f7a532d6d8 | shading-annotations-in-the-wild | 1705.01156 | null | http://arxiv.org/abs/1705.01156v1 | http://arxiv.org/pdf/1705.01156v1.pdf | Shading Annotations in the Wild | Understanding shading effects in images is critical for a variety of vision
and graphics problems, including intrinsic image decomposition, shadow removal,
image relighting, and inverse rendering. As is the case with other vision
tasks, machine learning is a promising approach to understanding shading - but
there is li... | ['Balazs Kovacs', 'Sean Bell', 'Kavita Bala', 'Noah Snavely'] | 2017-05-02 | shading-annotations-in-the-wild-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Kovacs_Shading_Annotations_in_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Kovacs_Shading_Annotations_in_CVPR_2017_paper.pdf | cvpr-2017-7 | ['shadow-removal', 'intrinsic-image-decomposition', 'image-relighting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.49115288e-01 5.88197000e-02 5.65200031e-01 -7.28616834e-01
-7.00129628e-01 -6.94307029e-01 3.54540050e-01 -1.13525592e-01
-3.58968787e-02 4.62804198e-01 5.16439319e-01 -4.28366005e-01
6.09076917e-01 -8.07552457e-01 -6.62543714e-01 -4.62952971e-01
3.54536682e-01 3.01278889e-01 3.25556457e-01 -3.74651134... | [9.725348472595215, -3.018434762954712] |
8c1de538-65ee-440e-b933-2aa7fda6679e | weakly-supervised-forced-alignment-of | 2306.00996 | null | https://arxiv.org/abs/2306.00996v1 | https://arxiv.org/pdf/2306.00996v1.pdf | Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling | The study of speech disorders can benefit greatly from time-aligned data. However, audio-text mismatches in disfluent speech cause rapid performance degradation for modern speech aligners, hindering the use of automatic approaches. In this work, we propose a simple and effective modification of alignment graph construc... | ['Vassilis Katsouros', 'Athanasios Katsamanis', 'Georgios Paraskevopoulos', 'Theodoros Kouzelis'] | 2023-05-30 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 3.32956225e-01 2.50534505e-01 9.80334803e-02 -4.40503985e-01
-1.41894150e+00 -7.01848269e-01 1.78005114e-01 2.73833066e-01
-4.03847694e-01 4.24985588e-01 5.62382758e-01 -4.37342286e-01
1.46116465e-01 1.15241837e-02 -3.49391282e-01 -4.56632644e-01
-8.04251134e-02 5.72249293e-01 8.76526311e-02 -5.37870117... | [14.433728218078613, 6.847911834716797] |
f7854d31-1c47-4832-889a-7e565c10ecb6 | autotransfer-automl-with-knowledge-transfer | 2303.07669 | null | https://arxiv.org/abs/2303.07669v1 | https://arxiv.org/pdf/2303.07669v1.pdf | AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks | AutoML has demonstrated remarkable success in finding an effective neural architecture for a given machine learning task defined by a specific dataset and an evaluation metric. However, most present AutoML techniques consider each task independently from scratch, which requires exploring many architectures, leading to ... | ['Jure Leskovec', 'Jiaju Liu', 'Jiaxuan You', 'Kaidi Cao'] | 2023-03-14 | null | null | null | null | ['automl'] | ['methodology'] | [ 9.31500420e-02 -2.68464983e-01 -4.09187116e-02 -1.38680443e-01
-6.28861785e-01 -7.75549769e-01 5.02295911e-01 -2.59477887e-02
-4.88788337e-01 2.45043203e-01 1.05353639e-01 -2.67929167e-01
-6.72692060e-01 -4.83188719e-01 -6.57023311e-01 -4.84892666e-01
1.73247695e-01 6.90576494e-01 1.62994564e-02 -1.80078715... | [8.7780179977417, 3.3779296875] |
cea4521c-1456-482c-9449-aa5275df9475 | decoupled-multi-task-learning-with-cyclical | 2203.14448 | null | https://arxiv.org/abs/2203.14448v1 | https://arxiv.org/pdf/2203.14448v1.pdf | Decoupled Multi-task Learning with Cyclical Self-Regulation for Face Parsing | This paper probes intrinsic factors behind typical failure cases (e.g. spatial inconsistency and boundary confusion) produced by the existing state-of-the-art method in face parsing. To tackle these problems, we propose a novel Decoupled Multi-task Learning with Cyclical Self-Regulation (DML-CSR) for face parsing. Spec... | ['Stefanos Zafeiriou', 'Ying Li', 'Zheng Zhu', 'Jiankang Deng', 'Qingping Zheng'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_Decoupled_Multi-Task_Learning_With_Cyclical_Self-Regulation_for_Face_Parsing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_Decoupled_Multi-Task_Learning_With_Cyclical_Self-Regulation_for_Face_Parsing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['face-parsing'] | ['computer-vision'] | [ 2.60408700e-01 3.11763167e-01 1.46450745e-02 -6.79246604e-01
-8.24617982e-01 -5.13399243e-01 2.56910712e-01 -2.81441718e-01
2.49778386e-02 2.87656724e-01 -1.45397484e-01 -1.86984271e-01
8.99450779e-02 -6.24094784e-01 -9.68125582e-01 -5.76155126e-01
1.39742494e-01 2.66801268e-01 1.39610648e-01 -3.67095545... | [13.435489654541016, 0.6584419012069702] |
95539aee-4b61-4470-a238-a784b46aa0b3 | don-t-take-it-literally-an-edit-invariant-1 | null | null | https://openreview.net/forum?id=kQWGURqrAW_ | https://openreview.net/pdf?id=kQWGURqrAW_ | Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation | Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a target sequence with a generated sequence. Such training objective is sub-optimal when the target sequence is not perfect, e.g., when the t... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.63180685e-01 -1.06831044e-01 -4.00110707e-03 -4.13256198e-01
-1.11002493e+00 -6.16670012e-01 6.66834474e-01 -5.86972199e-02
-5.69137156e-01 9.91656244e-01 1.48499176e-01 -4.39760059e-01
5.77042460e-01 -8.54581654e-01 -1.14064932e+00 -6.16720676e-01
1.48778677e-01 2.95936555e-01 -1.52696148e-01 -3.33763987... | [11.71264362335205, 9.623723983764648] |
c21a461d-cc18-499a-845d-1a621e7d3596 | an-enhanced-deep-convolutional-encoder | null | null | https://link.springer.com/chapter/10.1007/978-3-319-60663-7_18 | https://link.springer.com/chapter/10.1007/978-3-319-60663-7_18 | An Enhanced Deep Convolutional Encoder-Decoder Network for Road Segmentation on Aerial Imagery | Object classification from images is among the many practical examples where deep learning algorithms have successfully been applied. In this paper, we present an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Several aspects of the proposed method are enhance... | ['Teerapong Panboonyuen'] | 2017-06-20 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.46523994e-01 1.36653148e-02 -1.68277584e-02 -5.56759298e-01
-5.41586161e-01 -5.06838679e-01 5.51862895e-01 -3.54070544e-01
-6.76145852e-01 8.17162156e-01 -1.44271329e-01 -3.91627073e-01
-1.10453553e-01 -9.73615468e-01 -1.00784183e+00 -5.70796490e-01
-2.76296794e-01 4.80198003e-02 5.47935545e-01 -1.63997710... | [9.157733917236328, -1.0376887321472168] |
e8e51306-aff3-4e31-8784-dfb2b99f9f4f | information-plane-analysis-of-deep-neural-1 | null | null | https://openreview.net/forum?id=B1l0wp4tvr | https://openreview.net/pdf?id=B1l0wp4tvr | Information Plane Analysis of Deep Neural Networks via Matrix--Based Renyi's Entropy and Tensor Kernels | Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization ability. However, it is by no means obvious how to estimate mutual information (MI) between each hidden layer and the input/desired output, ... | ['Robert Jenssen', 'Jose Principe', 'Shujian Yu', 'Michael Kampffmeyer', 'Sigurd Løkse', 'Kristoffer Wickstrøm'] | 2019-09-25 | null | null | null | null | ['information-plane'] | ['methodology'] | [-1.20235030e-02 1.92163572e-01 2.11380683e-02 -7.70130306e-02
9.89772156e-02 -6.31550968e-01 5.07116318e-01 -3.70822139e-02
-5.59202611e-01 5.42601347e-01 -2.94818670e-01 -3.95616770e-01
-6.72982872e-01 -7.76364684e-01 -7.11786628e-01 -9.94312108e-01
-5.11472166e-01 1.77555770e-01 2.63531506e-01 -8.63950048... | [7.9372735023498535, 3.5939571857452393] |
09595f24-20fd-4b13-bc97-a104b80ddb18 | improving-accented-speech-recognition-with | 2303.07924 | null | https://arxiv.org/abs/2303.07924v1 | https://arxiv.org/pdf/2303.07924v1.pdf | Improving Accented Speech Recognition with Multi-Domain Training | Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets. However, they still lack generalization capability and are not robust to domain shifts like accent variations. In this work, we use speech audio representing four ... | ['Yannick Estève', 'Lucas Maison'] | 2023-03-14 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 8.46379697e-02 -1.27173185e-01 -1.36307508e-01 -6.36453629e-01
-1.13030720e+00 -8.39938402e-01 6.35099649e-01 8.08544531e-02
-6.80771470e-01 9.38747704e-01 3.57250035e-01 -3.95805299e-01
4.83823717e-02 -2.83975214e-01 -4.90841717e-01 -4.11528915e-01
1.02893971e-01 4.48051184e-01 2.26940081e-01 -6.12480104... | [14.365167617797852, 6.653918743133545] |
b37f08d5-d22c-4d29-8f56-f21c3c2e88c8 | mvco-dot-multi-view-contrastive-domain | 2304.07465 | null | https://arxiv.org/abs/2304.07465v1 | https://arxiv.org/pdf/2304.07465v1.pdf | MvCo-DoT:Multi-View Contrastive Domain Transfer Network for Medical Report Generation | In clinical scenarios, multiple medical images with different views are usually generated at the same time, and they have high semantic consistency. However, the existing medical report generation methods cannot exploit the rich multi-view mutual information of medical images. Therefore, in this work, we propose the fi... | ['Thomas Lukasiewicz', 'Junyang Chen', 'Wenting Xu', 'Zhenghua Xu', 'Xiangtao Wang', 'Ruizhi Wang'] | 2023-04-15 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [-1.32439816e-02 3.53651911e-01 -3.62387121e-01 -4.87834156e-01
-1.35249054e+00 -3.70814204e-01 5.26825368e-01 -1.11998789e-01
1.23779275e-01 8.56803000e-01 7.07835317e-01 -1.47746474e-01
-7.71932900e-02 -6.34257495e-01 -6.77954257e-01 -5.56789994e-01
3.35213721e-01 4.79837120e-01 -7.34062940e-02 2.52989143... | [15.032275199890137, -1.4103494882583618] |
7cb80578-1224-4360-a0c2-ea05a9ceddd2 | modelling-arbitrary-complex-dielectric | 2109.01928 | null | https://arxiv.org/abs/2109.01928v1 | https://arxiv.org/pdf/2109.01928v1.pdf | Modelling Arbitrary Complex Dielectric Properties -- an automated implementation for gprMax | There is a need to accurately simulate materials with complex electromagnetic properties when modelling Ground Penetrating Radar (GPR), as many objects encountered with GPR contain water, e.g. soils, curing concrete, and water-filled pipes. One of widely-used open-source software that simulates electromagnetic wave pro... | ['Antonios Giannopoulos', 'Craig Warren', 'Iraklis Giannakis', 'Sylwia Majchrowska'] | 2021-09-04 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-8.16528425e-02 -2.90747136e-01 1.00196254e+00 -6.87974393e-02
-4.11705494e-01 -1.97111726e-01 1.93220913e-01 -7.71566257e-02
-3.67539585e-01 8.61630261e-01 -1.20431393e-01 -7.82974839e-01
-3.01727027e-01 -1.30752480e+00 1.09108403e-01 -8.72570932e-01
-2.21285135e-01 7.03645766e-01 4.51717347e-01 -5.00568628... | [6.795579433441162, 1.6407396793365479] |
027d8f73-2015-45ab-b058-dec2ddadf45a | a-geometrically-aware-auto-encoder-for-multi | 2302.01616 | null | https://arxiv.org/abs/2302.01616v3 | https://arxiv.org/pdf/2302.01616v3.pdf | A geometrically aware auto-encoder for multi-texture synthesis | We propose an auto-encoder architecture for multi-texture synthesis. The approach relies on both a compact encoder accounting for second order neural statistics and a generator incorporating adaptive periodic content. Images are embedded in a compact and geometrically consistent latent space, where the texture represen... | ['Sidonie Lefebvre', 'Yann Gousseau', 'Pierrick Chatillon'] | 2023-02-03 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 1.69275299e-01 1.30170763e-01 -2.28887454e-01 -2.21146286e-01
-7.65800118e-01 -2.52675325e-01 9.93595839e-01 -4.41293776e-01
2.03265011e-01 7.42965579e-01 6.29564404e-01 3.31421345e-01
7.11542964e-02 -8.17402601e-01 -8.67612958e-01 -1.02330303e+00
-3.32108960e-02 4.17670876e-01 1.13626756e-01 -2.17743292... | [11.485966682434082, -0.504715085029602] |
f336c09b-ae2d-4146-99f1-eaee0ff13437 | dbpedia-abstracts-a-large-scale-open | null | null | https://aclanthology.org/L16-1532 | https://aclanthology.org/L16-1532.pdf | DBpedia Abstracts: A Large-Scale, Open, Multilingual NLP Training Corpus | The ever increasing importance of machine learning in Natural Language Processing is accompanied by an equally increasing need in large-scale training and evaluation corpora. Due to its size, its openness and relative quality, the Wikipedia has already been a source of such data, but on a limited scale. This paper intr... | ['Martin Br{\\"u}mmer', 'Sebastian Hellmann', 'Milan Dojchinovski'] | 2016-05-01 | dbpedia-abstracts-a-large-scale-open-1 | https://aclanthology.org/L16-1532 | https://aclanthology.org/L16-1532.pdf | lrec-2016-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [-5.81127644e-01 6.21569395e-01 -5.86331964e-01 -1.65739655e-01
-5.92833042e-01 -9.81024027e-01 8.95704389e-01 8.75102401e-01
-9.53318000e-01 1.33367777e+00 5.43167710e-01 -2.07800359e-01
-1.43483102e-01 -8.14870119e-01 -6.84521556e-01 1.40406698e-01
-4.59983796e-01 1.13040960e+00 5.39068341e-01 -5.46535492... | [9.475006103515625, 8.887776374816895] |
1056c8fc-44cd-46c3-9057-0b916f3c3db5 | reporting-existing-datasets-for-automatic | 2306.12292 | null | https://arxiv.org/abs/2306.12292v1 | https://arxiv.org/pdf/2306.12292v1.pdf | Reporting existing datasets for automatic epilepsy diagnosis and seizure detection | More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep... | ['Nidhi Goel', 'Sakshi Tiwari', 'Palak Handa'] | 2023-06-21 | null | null | null | null | ['seizure-detection'] | ['medical'] | [-3.17575008e-01 -2.50064820e-01 3.68268073e-01 -4.80275661e-01
-7.39375174e-01 -9.34751853e-02 1.93848744e-01 1.41821176e-01
-3.66269112e-01 1.23974609e+00 3.64113480e-01 1.38434440e-01
-3.47856969e-01 -4.74897653e-01 -2.19816461e-01 -6.99730217e-01
-8.83559942e-01 5.35383165e-01 -4.32056576e-01 4.28941138... | [13.219476699829102, 3.506120443344116] |
242b44d2-1011-4775-9140-ecabfa14ceec | from-argument-search-to-argumentative | null | null | https://aclanthology.org/2021.sigdial-1.39 | https://aclanthology.org/2021.sigdial-1.39.pdf | From Argument Search to Argumentative Dialogue: A Topic-independent Approach to Argument Acquisition for Dialogue Systems | Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations. Since the manual acquisition of these argument structures is highly time-consuming, the corresponding systems are infle... | ['Stefan Ultes', 'Wolfgang Minker', 'Johannes Daxenberger', 'Isabel Feustel', 'Carolin Schindler', 'Niklas Rach'] | null | null | null | null | sigdial-acl-2021-7 | ['relation-classification'] | ['natural-language-processing'] | [ 1.95442036e-01 1.00207746e+00 -1.22101650e-01 -2.31953621e-01
-7.56505311e-01 -9.12352681e-01 1.25230658e+00 1.00233698e+00
-5.81466258e-01 1.15442109e+00 3.84459406e-01 -8.25371861e-01
-4.36918646e-01 -9.79578733e-01 -4.89214137e-02 -2.20104277e-01
2.33625561e-01 9.80674386e-01 5.89323580e-01 -9.19196963... | [9.692276954650879, 9.544038772583008] |
abcf98cf-1d35-4660-9f4a-6d11d8597836 | learning-disentangled-representations-in-1 | 2307.03077 | null | https://arxiv.org/abs/2307.03077v1 | https://arxiv.org/pdf/2307.03077v1.pdf | Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions | Signed graphs are complex systems that represent trust relationships or preferences in various domains. Learning node representations in such graphs is crucial for many mining tasks. Although real-world signed relationships can be influenced by multiple latent factors, most existing methods often oversimplify the model... | ['Jinhong Jung', 'Geonwoo Ko'] | 2023-07-06 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 1.44777343e-01 3.71875942e-01 -6.16954565e-01 -7.34826803e-01
1.45967588e-01 -7.80228853e-01 8.13028991e-01 -9.70488414e-02
4.16069254e-02 4.99402523e-01 6.10032320e-01 -4.18510318e-01
-3.07969242e-01 -8.56737375e-01 -6.91300392e-01 -5.06549478e-01
-5.15272796e-01 4.80677158e-01 -5.92156872e-02 -3.15482438... | [7.25329065322876, 6.226616859436035] |
2b767930-6ef4-4aba-a43a-6f4d41833b9d | self-learning-locally-optimal-hypertuning | 2210.10783 | null | https://arxiv.org/abs/2210.10783v1 | https://arxiv.org/pdf/2210.10783v1.pdf | Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials | Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation ... | ['Francisco Javier Montans', 'Ricardo Callado', 'Miguel Angel Sanz', 'Luis Saucedo-Mora', 'Miguel Diaz-Lago', 'Ismael Ben-Yelun'] | 2022-10-19 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 2.62323231e-01 9.50100049e-02 1.41750559e-01 -1.51254401e-01
-7.37065196e-01 -2.48418283e-02 2.95591056e-01 6.15749300e-01
-4.76866364e-01 8.69150639e-01 -4.03361320e-01 -2.27900311e-01
-8.09865832e-01 -8.92053425e-01 -4.60243881e-01 -1.05044031e+00
-4.73339856e-01 7.89408326e-01 5.87610960e-01 -4.48231071... | [6.678933143615723, 2.6414856910705566] |
80ea58ca-ce7d-4332-adaa-97992f86e53b | stand-alone-inter-frame-attention-in-video-1 | 2206.06931 | null | https://arxiv.org/abs/2206.06931v1 | https://arxiv.org/pdf/2206.06931v1.pdf | Stand-Alone Inter-Frame Attention in Video Models | Motion, as the uniqueness of a video, has been critical to the development of video understanding models. Modern deep learning models leverage motion by either executing spatio-temporal 3D convolutions, factorizing 3D convolutions into spatial and temporal convolutions separately, or computing self-attention along temp... | ['Tao Mei', 'Jiebo Luo', 'Ting Yao', 'Yingwei Pan', 'Zhaofan Qiu', 'Fuchen Long'] | 2022-06-14 | stand-alone-inter-frame-attention-in-video | http://openaccess.thecvf.com//content/CVPR2022/html/Long_Stand-Alone_Inter-Frame_Attention_in_Video_Models_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Long_Stand-Alone_Inter-Frame_Attention_in_Video_Models_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-classification'] | ['computer-vision'] | [-0.26087043 -0.29416034 -0.07403517 -0.30848968 -0.40505713 -0.56211925
0.4872163 -0.46275002 -0.4020021 0.4341501 0.29445583 -0.0210753
0.07518449 -0.72683495 -1.0305592 -0.7891805 -0.02675223 -0.17317224
0.61379445 -0.06677594 0.00643632 0.41282228 -1.1873306 0.42888266
0.6583695 1.3065356 0.3... | [8.998528480529785, 0.23565573990345] |
7f0a3139-e1b2-4e8d-8caa-d381616c56e1 | complementary-network-with-adaptive-receptive | 2001.03893 | null | https://arxiv.org/abs/2001.03893v1 | https://arxiv.org/pdf/2001.03893v1.pdf | Complementary Network with Adaptive Receptive Fields for Melanoma Segmentation | Automatic melanoma segmentation in dermoscopic images is essential in computer-aided diagnosis of skin cancer. Existing methods may suffer from the hole and shrink problems with limited segmentation performance. To tackle these issues, we propose a novel complementary network with adaptive receptive filed learning. Ins... | ['Zhen Chen', 'Yixuan Yuan', 'Xiaoqing Guo'] | 2020-01-12 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.65469408e-01 2.27971762e-01 -2.28496134e-01 -2.13998452e-01
-4.24937963e-01 -2.85305887e-01 2.55897284e-01 2.59163906e-03
-7.79179335e-01 5.94018459e-01 -6.91175312e-02 -2.92628825e-01
-1.52580654e-02 -8.25632155e-01 -2.78087705e-01 -1.07957625e+00
4.59359437e-01 -1.46862999e-01 6.08261347e-01 7.30093494... | [15.607069969177246, -2.9114456176757812] |
7ca3aed5-6d38-48d3-8c27-3b47217a45d2 | analysis-of-sparse-subspace-clustering | 2204.00723 | null | https://arxiv.org/abs/2204.00723v1 | https://arxiv.org/pdf/2204.00723v1.pdf | Analysis of Sparse Subspace Clustering: Experiments and Random Projection | Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner. As a technique that is used in many domains, such as face clustering, plant categorization, image segmentation, document classification, clustering is considered one of the mos... | ['Enrico Au-Yeung', 'Mehmet F. Demirel'] | 2022-04-01 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 1.83637947e-01 -3.56107146e-01 -1.38056234e-01 -3.84318650e-01
-1.71972886e-01 -7.98214138e-01 3.78905624e-01 2.16666698e-01
-6.64187968e-02 9.55519602e-02 1.72093660e-01 -1.44235522e-01
-3.58784556e-01 -5.98809004e-01 -1.97300762e-01 -1.02407205e+00
-1.08501658e-01 6.16763294e-01 2.11568922e-01 2.22859859... | [7.693382740020752, 4.49768590927124] |
b7502cb4-ec0c-4a5c-9ad3-9885fe7d8cee | a-cross-study-analysis-of-drug-response | 2104.08961 | null | https://arxiv.org/abs/2104.08961v2 | https://arxiv.org/pdf/2104.08961v2.pdf | A cross-study analysis of drug response prediction in cancer cell lines | To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study to assess model accuracy. While an essential first step, cross validat... | ['Rick Stevens', 'Yitan Zhu', 'George Zaki', 'Hyunseung Yoo', 'Justin M. Wozniak', 'Eric Stahlberg', 'Maulik Shukla', 'Alexander Partin', 'Sergei Maslov', 'Pinyi Lu', 'Stewart He', 'Jason Gans', 'Ya Ju Fan', 'Yvonne Evrard', 'Veronika Dubinkina', 'Xiaotian Duan', 'James Doroshow', 'Judith Cohn', 'Austin Clyde', 'Cristi... | 2021-04-18 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 2.59036005e-01 -5.23219109e-01 -5.80438256e-01 -1.73388720e-01
-1.16128194e+00 -6.62714481e-01 5.11910677e-01 7.39084482e-01
-5.92492342e-01 1.13139081e+00 1.40113205e-01 -8.94127548e-01
-2.94799179e-01 -4.42699373e-01 -6.22367263e-01 -7.06849217e-01
-8.32520425e-04 4.46670294e-01 2.36957315e-02 7.60231763... | [15.181254386901855, -2.9655954837799072] |
d15b2701-c4c5-4b87-86b1-2b540bab94f7 | machine-learning-for-classification-of-1 | 2209.02249 | null | https://arxiv.org/abs/2209.02249v1 | https://arxiv.org/pdf/2209.02249v1.pdf | Machine Learning For Classification Of Antithetical Emotional States | Emotion Classification through EEG signals has achieved many advancements. However, the problems like lack of data and learning the important features and patterns have always been areas with scope for improvement both computationally and in prediction accuracy. This works analyses the baseline machine learning classif... | ['Yusuf Uzzaman Khan', 'Rajat Maheshwari', 'Jeevanshi Sharma'] | 2022-09-06 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [-1.38709515e-01 -2.38651466e-02 5.73981255e-02 -7.24147022e-01
-6.31943107e-01 -2.57897042e-02 3.28283608e-01 2.77408034e-01
-6.08083308e-01 9.52426076e-01 -1.05656952e-01 1.94251373e-01
-3.79634947e-01 -4.61131036e-01 -3.92694503e-01 -7.06059098e-01
-6.42261028e-01 7.51084164e-02 -2.49252930e-01 -2.70891994... | [13.183027267456055, 3.490743637084961] |
18b6e27e-c929-4af5-aade-dab5e115adcc | online-decision-transformer | 2202.05607 | null | https://arxiv.org/abs/2202.05607v2 | https://arxiv.org/pdf/2202.05607v2.pdf | Online Decision Transformer | Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches similar to large-scale language modeling. However, any practical instantiation of RL also involves an online component, where policies pretra... | ['Aditya Grover', 'Amy Zhang', 'Qinqing Zheng'] | 2022-02-11 | null | null | null | null | ['d4rl'] | ['robots'] | [ 4.59040664e-02 3.40970516e-01 -7.19738841e-01 -2.10810974e-01
-9.97182906e-01 -7.52496123e-01 8.85575414e-01 2.50501223e-02
-7.17318833e-01 1.06192982e+00 2.81644434e-01 -6.99354827e-01
6.35056570e-02 -4.35582608e-01 -9.70135570e-01 -3.54548842e-01
-1.77994668e-01 7.62964487e-01 5.70699126e-02 -2.77361721... | [4.122016906738281, 1.8962018489837646] |
5b164e94-7892-494b-9811-a3118ffbd879 | dada-dialect-adaptation-via-dynamic | 2305.13406 | null | https://arxiv.org/abs/2305.13406v1 | https://arxiv.org/pdf/2305.13406v1.pdf | DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules | Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identificatio... | ['Diyi Yang', 'William Held', 'Yanchen Liu'] | 2023-05-22 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [-1.08129509e-01 -2.77240962e-01 -3.77792031e-01 -7.00036824e-01
-1.07254076e+00 -1.22327435e+00 6.06934965e-01 3.79504226e-02
-3.47791314e-01 4.83237743e-01 3.39380831e-01 -7.78206050e-01
-6.09332435e-02 -6.50966406e-01 -6.04304969e-01 -1.19372986e-01
2.81889826e-01 6.75732434e-01 1.31058425e-01 -8.48021150... | [11.093363761901855, 10.031608581542969] |
ab29a8cd-cac1-4978-b9ac-75baaa02b863 | autonomous-crater-detection-on-asteroids | 2204.00477 | null | https://arxiv.org/abs/2204.00477v1 | https://arxiv.org/pdf/2204.00477v1.pdf | Autonomous crater detection on asteroids using a fully-convolutional neural network | This paper shows the application of autonomous Crater Detection using the U-Net, a Fully-Convolutional Neural Network, on Ceres. The U-Net is trained on optical images of the Moon Global Morphology Mosaic based on data collected by the LRO and manual crater catalogues. The Moon-trained network will be tested on Dawn op... | ['Fabio Curti', 'Dario Spiller', 'Francesco Latorre'] | 2022-04-01 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.26726389e-01 1.64605938e-02 2.64981866e-01 -3.46362710e-01
-7.18260586e-01 -5.50248682e-01 7.71016419e-01 -4.51364011e-01
-7.58643985e-01 3.94862205e-01 -3.86639774e-01 -2.26024240e-01
1.53038368e-01 -9.94547069e-01 -7.12336779e-01 -8.74974787e-01
-2.62273282e-01 8.93696964e-01 2.23551735e-01 -3.90457004... | [8.616206169128418, -1.5213983058929443] |
a76d9632-ceee-4a5a-9584-b2ef7b22d59e | masked-vision-language-transformers-for-scene | 2211.04785 | null | https://arxiv.org/abs/2211.04785v1 | https://arxiv.org/pdf/2211.04785v1.pdf | Masked Vision-Language Transformers for Scene Text Recognition | Scene text recognition (STR) enables computers to recognize and read the text in various real-world scenes. Recent STR models benefit from taking linguistic information in addition to visual cues into consideration. We propose a novel Masked Vision-Language Transformers (MVLT) to capture both the explicit and the impli... | ['Jian Zhang', 'Weigang Qi', 'Shengming Zhang', 'Ying Peng', 'Jie Wu'] | 2022-11-09 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.97141516e-01 -4.59636748e-01 -6.42244592e-02 -4.11310852e-01
-5.53856730e-01 -2.31142014e-01 7.43772328e-01 -6.03261851e-02
-4.75291669e-01 1.45247459e-01 5.18854082e-01 -3.97099733e-01
5.29382825e-01 -5.89155078e-01 -7.23702371e-01 -5.07589579e-01
8.30256522e-01 2.02865079e-01 6.39393747e-01 4.35241871... | [11.827999114990234, 2.1226558685302734] |
ac17a083-e965-4daf-af8c-cba25d3a4eda | multimodal-learning-for-non-small-cell-lung | 2211.0328 | null | https://arxiv.org/abs/2211.03280v1 | https://arxiv.org/pdf/2211.03280v1.pdf | Multimodal Learning for Non-small Cell Lung Cancer Prognosis | This paper focuses on the task of survival time analysis for lung cancer. Although much progress has been made in this problem in recent years, the performance of existing methods is still far from satisfactory. Traditional and some deep learning-based survival time analyses for lung cancer are mostly based on textual ... | ['Steven Weidong Su', 'Sai Ho Ling', 'Fan Yang', 'Xiaoshui Huang', 'Yaxiong Wang', 'Yujiao Wu'] | 2022-11-07 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.18014418e-01 -2.59895593e-01 -7.82051325e-01 -3.89747739e-01
-1.14755857e+00 -3.49395812e-01 4.44799125e-01 3.57127279e-01
-5.87639630e-01 8.81780088e-01 4.18770611e-01 -9.35072601e-01
-9.25075635e-02 -8.11932504e-01 1.38866939e-02 -9.88355041e-01
-8.34028646e-02 7.84901738e-01 2.75232404e-01 1.49357975... | [15.28002643585205, -2.4676408767700195] |
76503bfa-1bc8-405f-98c3-188411214d50 | a-deep-relevance-matching-model-for-ad-hoc | 1711.08611 | null | http://arxiv.org/abs/1711.08611v1 | http://arxiv.org/pdf/1711.08611v1.pdf | A Deep Relevance Matching Model for Ad-hoc Retrieval | In recent years, deep neural networks have led to exciting breakthroughs in
speech recognition, computer vision, and natural language processing (NLP)
tasks. However, there have been few positive results of deep models on ad-hoc
retrieval tasks. This is partially due to the fact that many important
characteristics of t... | ['W. Bruce Croft', 'Yixing Fan', 'Jiafeng Guo', 'Qingyao Ai'] | 2017-11-23 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 2.21606672e-01 -3.45055014e-01 -2.39945009e-01 -4.16263312e-01
-1.32547045e+00 -3.69544655e-01 9.37884450e-01 4.60516483e-01
-5.19366622e-01 1.15442641e-01 4.03258175e-01 -8.74181166e-02
-5.80771923e-01 -8.55288208e-01 -4.79895890e-01 -2.41656378e-01
3.36843967e-01 9.15495157e-01 2.17377052e-01 -6.71694100... | [11.410110473632812, 7.735055446624756] |
aebd3a2a-1507-4698-be9b-25804db680a2 | clip2tv-an-empirical-study-on-transformer | 2111.0561 | null | https://arxiv.org/abs/2111.05610v2 | https://arxiv.org/pdf/2111.05610v2.pdf | CLIP2TV: Align, Match and Distill for Video-Text Retrieval | Modern video-text retrieval frameworks basically consist of three parts: video encoder, text encoder and the similarity head. With the success on both visual and textual representation learning, transformer based encoders and fusion methods have also been adopted in the field of video-text retrieval. In this report, we... | ['Lili Zhao', 'Dedan Chang', 'Sheng Chen', 'Weiqi Sun', 'Jingyu Liu', 'Zijian Gao'] | 2021-11-10 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 2.92991728e-01 -8.01440358e-01 -3.13462615e-01 -6.31396621e-02
-1.42905390e+00 -4.32723582e-01 9.89613175e-01 1.54547185e-01
-4.35643494e-01 3.77627134e-01 4.90900815e-01 1.47684380e-01
-4.07197297e-01 -2.46770546e-01 -4.45281416e-01 -5.67629039e-01
6.03904016e-02 4.49999511e-01 3.01595032e-01 -2.60924101... | [10.449665069580078, 0.8480726480484009] |
13229c80-631b-4e97-9a4a-70384f7b679a | learning-uncertainty-with-artificial-neural | 2105.05559 | null | https://arxiv.org/abs/2105.05559v1 | https://arxiv.org/pdf/2105.05559v1.pdf | Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes | Artificial neural networks will always make a prediction, even when completely uncertain and regardless of the consequences. This obliviousness of uncertainty is a major obstacle towards their adoption in practice. Techniques exist, however, to estimate the two major types of uncertainty: model uncertainty and observat... | ['Jochen De Weerdt', 'Hans Weytjens'] | 2021-05-12 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 8.77951756e-02 4.87299800e-01 -1.34626508e-01 -7.68230021e-01
-2.78721571e-01 -1.46590695e-01 6.75103366e-01 3.18231761e-01
-3.23905975e-01 1.13641441e+00 -2.19365433e-01 -4.17590350e-01
-6.18294597e-01 -1.09898162e+00 -6.66538179e-01 -6.62438095e-01
-2.66943395e-01 8.11938107e-01 2.37502694e-01 2.96811432... | [7.400041103363037, 3.8447766304016113] |
5d616c95-1802-4990-a8d5-b224f32e28d0 | frob-few-shot-robust-model-for-classification | null | null | https://openreview.net/forum?id=mZsZy481_F | https://openreview.net/pdf?id=mZsZy481_F | FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection | Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims mainly due to rarity and the limited samples in the few-shot setting, and because of adversarial attacks. Accomplishing these aims is important for critical systems in safety, security, and defence. In paral... | ['Sotirios A. Tsaftaris', 'Mehrdad Yaghoobi', 'Nikolaos Dionelis'] | 2021-09-29 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 7.65542760e-02 3.74837592e-02 -8.48003477e-02 -3.35930586e-02
-9.99899268e-01 -3.87898684e-01 6.37550294e-01 2.82610089e-01
-6.03494793e-02 4.34807986e-01 -2.78104872e-01 6.85163736e-02
-1.23855129e-01 -8.39844227e-01 -8.88781190e-01 -7.41104901e-01
-2.01672554e-01 2.40085527e-01 6.05278790e-01 -1.85084686... | [7.822861194610596, 2.427135705947876] |
01acbf8d-405e-467b-a550-5a723a2bc1a0 | coco-a-coupled-contrastive-framework-for | 2306.04979 | null | https://arxiv.org/abs/2306.04979v2 | https://arxiv.org/pdf/2306.04979v2.pdf | CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification | Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, ho... | ['Xiao Luo', 'Xian-Sheng Hua', 'Chong Chen', 'Zeyu Ma', 'Long Lan', 'Mengzhu Wang', 'Li Shen', 'Nan Yin'] | 2023-06-08 | null | null | null | null | ['graph-classification', 'graph-representation-learning'] | ['graphs', 'methodology'] | [ 1.73448861e-01 2.57199287e-01 -3.36496234e-01 -3.86085808e-01
-2.02914655e-01 -7.81594455e-01 4.51858729e-01 1.61568522e-01
1.02172956e-01 4.83011186e-01 2.33916163e-01 -2.08020538e-01
-2.20216468e-01 -9.65946853e-01 -5.87392211e-01 -5.13934433e-01
8.16354677e-02 7.38115251e-01 2.25095078e-01 -4.66892272... | [7.292663097381592, 6.243254661560059] |
932e38b6-fbc8-4ac0-ae56-fbda8f5a79db | mmcr4nlp-multilingual-multiway-corpora | 1710.01025 | null | http://arxiv.org/abs/1710.01025v3 | http://arxiv.org/pdf/1710.01025v3.pdf | MMCR4NLP: Multilingual Multiway Corpora Repository for Natural Language Processing | Multilinguality is gradually becoming ubiquitous in the sense that more and
more researchers have successfully shown that using additional languages help
improve the results in many Natural Language Processing tasks. Multilingual
Multiway Corpora (MMC) contain the same sentence in multiple languages. Such
corpora have ... | ['Sadao Kurohashi', 'Raj Dabre'] | 2017-10-03 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-1.69916913e-01 -4.21023965e-01 -5.76121390e-01 -4.11512077e-01
-1.43816626e+00 -1.14680481e+00 5.57861567e-01 5.66314101e-01
-8.02968264e-01 1.28808129e+00 3.51598084e-01 -9.89246309e-01
2.38231644e-01 -3.58526260e-01 -6.71346307e-01 -2.64630079e-01
2.83424109e-01 7.79755294e-01 1.74462467e-01 -4.43188608... | [10.62663745880127, 10.113409996032715] |
ccc42f46-a67c-4a68-be5f-557d660d2d9a | improving-partition-block-based-acoustic-echo | 2008.03944 | null | https://arxiv.org/abs/2008.03944v1 | https://arxiv.org/pdf/2008.03944v1.pdf | improving partition-block-based acoustic echo canceler in under-modeling scenarios | Recently, a partitioned-block-based frequency-domain Kalman filter (PFKF) has been proposed for acoustic echo cancellation. Compared with the normal frequency-domain Kalman filter, the PFKF utilizes the partitioned-block structure, resulting in both fast convergence and low time-latency. We present an analysis of the s... | ['Jing Lu', 'Wenzhi Fan'] | 2020-08-10 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [-1.29772887e-01 -3.66947711e-01 3.87602955e-01 -1.41668737e-01
-7.02189803e-01 -2.94423789e-01 1.75805658e-01 -4.60266650e-01
-2.50765234e-01 7.14241147e-01 2.67330140e-01 -5.77458203e-01
-4.45952684e-01 -1.64900944e-01 -3.48960817e-01 -1.04058194e+00
-3.63182634e-01 -4.84903097e-01 2.33675644e-01 2.42813662... | [15.123194694519043, 5.745194435119629] |
98b43b5c-dae9-44e1-b3e5-9173dd83f28a | inferring-disease-correlation-from-healthcare | 1510.03051 | null | http://arxiv.org/abs/1510.03051v1 | http://arxiv.org/pdf/1510.03051v1.pdf | Inferring disease correlation from healthcare data | Electronic Health Records maintained in health care settings are a potential
source of substantial clinical knowledge. The massive volume of data,
unstructured nature of records and obligatory requirement of domain
acquaintance together pose a challenge in knowledge extraction from it. The aim
of this study is to overc... | ['Anand Ashish', 'Priyadarshini Gargi'] | 2015-10-11 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.13075894e-01 4.19639081e-01 -4.63561416e-01 -3.41193497e-01
-3.54745120e-01 -3.36431265e-01 1.75840124e-01 1.12930501e+00
-5.45502082e-02 1.22814286e+00 6.29679084e-01 -3.14749777e-01
-1.26025403e+00 -8.95917892e-01 -1.41193494e-01 -4.95781392e-01
-5.34895539e-01 6.33458674e-01 -3.76682073e-01 -1.81680262... | [8.439491271972656, 8.604217529296875] |
445eb151-4e7b-4fb6-a6d3-9413ef7c3518 | task-adaptive-spatial-temporal-video-sampler | 2207.09759 | null | https://arxiv.org/abs/2207.09759v3 | https://arxiv.org/pdf/2207.09759v3.pdf | Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition | A primary challenge faced in few-shot action recognition is inadequate video data for training. To address this issue, current methods in this field mainly focus on devising algorithms at the feature level while little attention is paid to processing input video data. Moreover, existing frame sampling strategies may om... | ['Weiyao Lin', 'John See', 'Weixian Lv', 'Huabin Liu'] | 2022-07-20 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 4.48940426e-01 -4.19816464e-01 -4.74264294e-01 -1.43805668e-01
-6.59822643e-01 -3.21457535e-02 3.10195386e-01 -2.80787706e-01
-4.39467311e-01 4.20731664e-01 3.35645586e-01 4.18982133e-02
-2.03328785e-02 -4.80051905e-01 -5.77346027e-01 -8.15560520e-01
7.90368319e-02 -1.85035631e-01 6.64090097e-01 1.26469005... | [8.794281959533691, 0.3370566964149475] |
5e154e7b-6fa6-49cb-8fa6-908224e3f105 | equivariance-with-learned-canonicalization | 2211.06489 | null | https://arxiv.org/abs/2211.06489v3 | https://arxiv.org/pdf/2211.06489v3.pdf | Equivariance with Learned Canonicalization Functions | Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations. In this paper, we propose an alternative that avoids this architectural constraint by learning to produce canonical representations of the data. These canonicalization functions ... | ['Siamak Ravanbakhsh', 'Yoshua Bengio', 'Yan Zhang', 'Arnab Kumar Mondal', 'Sékou-Oumar Kaba'] | 2022-11-11 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [ 1.11415461e-01 3.78461868e-01 -3.61632377e-01 -5.48065424e-01
-3.40160340e-01 -9.66963112e-01 6.96169853e-01 -4.21289176e-01
-3.56437296e-01 4.38214213e-01 1.58374518e-01 -4.22685236e-01
-2.72243649e-01 -5.62389493e-01 -1.23781157e+00 -6.38393939e-01
-8.83249417e-02 7.69436955e-01 -2.41827834e-02 -2.94499129... | [8.84866714477539, 2.4843215942382812] |
c9b6f450-d517-43bc-b71d-679766c25318 | ingb-informed-nonlinear-granular-ball | 2307.01224 | null | https://arxiv.org/abs/2307.01224v1 | https://arxiv.org/pdf/2307.01224v1.pdf | INGB: Informed Nonlinear Granular Ball Oversampling Framework for Noisy Imbalanced Classification | In classification problems, the datasets are usually imbalanced, noisy or complex. Most sampling algorithms only make some improvements to the linear sampling mechanism of the synthetic minority oversampling technique (SMOTE). Nevertheless, linear oversampling has several unavoidable drawbacks. Linear oversampling is s... | ['GuoYing Wang', 'Yabin Shao', 'Qun Liu', 'Hao Zhou', 'Min Li'] | 2023-07-03 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 9.84145552e-02 -1.72474101e-01 -7.63888538e-01 -4.54047531e-01
-6.72723055e-01 3.16536650e-02 2.17289820e-01 -2.02940544e-03
-3.31060812e-02 1.22893918e+00 1.34028688e-01 -3.96582820e-02
-1.98599190e-01 -1.14385617e+00 -5.91682851e-01 -8.69617641e-01
2.56045312e-01 6.57459199e-01 1.73530713e-01 -8.79550725... | [8.819238662719727, 4.086338520050049] |
a2599866-35a3-40ed-9bbb-51fd5af27d95 | on-hyperbolic-embeddings-in-2d-object | 2203.08049 | null | https://arxiv.org/abs/2203.08049v3 | https://arxiv.org/pdf/2203.08049v3.pdf | On Hyperbolic Embeddings in 2D Object Detection | Object detection, for the most part, has been formulated in the euclidean space, where euclidean or spherical geodesic distances measure the similarity of an image region to an object class prototype. In this work, we study whether a hyperbolic geometry better matches the underlying structure of the object classificati... | ['Abhinav Valada', 'Alexander Braun', 'Christopher Lang'] | 2022-03-15 | null | null | null | null | ['zero-shot-object-detection'] | ['computer-vision'] | [-9.83506218e-02 -1.93170741e-01 -2.44090259e-01 -4.89401907e-01
-5.04732072e-01 -8.40763032e-01 7.48408258e-01 6.62713587e-01
-5.98606408e-01 -1.50168970e-01 -1.51063930e-02 -2.27679074e-01
-4.34433311e-01 -8.17905366e-01 -2.42433384e-01 -4.35723215e-01
-6.51299506e-02 2.63989449e-01 8.98116410e-01 7.30796084... | [9.484905242919922, 1.5046013593673706] |
f3f96753-59d6-4c76-bae4-1e6c220999c0 | rethinking-the-trigger-injecting-position-in | 2304.02277 | null | https://arxiv.org/abs/2304.02277v2 | https://arxiv.org/pdf/2304.02277v2.pdf | Rethinking the Trigger-injecting Position in Graph Backdoor Attack | Backdoor attacks have been demonstrated as a security threat for machine learning models. Traditional backdoor attacks intend to inject backdoor functionality into the model such that the backdoored model will perform abnormally on inputs with predefined backdoor triggers and still retain state-of-the-art performance o... | ['Stjepan Picek', 'Gorka Abad', 'Jing Xu'] | 2023-04-05 | null | null | null | null | ['backdoor-attack'] | ['adversarial'] | [ 1.37932360e-01 6.08197808e-01 -4.56716806e-01 1.25747621e-01
5.23447841e-02 -1.11442459e+00 6.05468929e-01 1.68551907e-01
8.93178731e-02 3.59149009e-01 -2.77251959e-01 -1.16488814e+00
-1.17527701e-01 -1.21874523e+00 -1.14475799e+00 -5.51297128e-01
-2.76317149e-01 -1.16652898e-01 6.33275330e-01 -4.16906595... | [5.778233528137207, 7.636438369750977] |
8fa7db5e-1b65-458e-bcfb-f40452176451 | obtaining-referential-word-meanings-from | null | null | https://aclanthology.org/P17-1023 | https://aclanthology.org/P17-1023.pdf | Obtaining referential word meanings from visual and distributional information: Experiments on object naming | We investigate object naming, which is an important sub-task of referring expression generation on real-world images. As opposed to mutually exclusive labels used in object recognition, object names are more flexible, subject to communicative preferences and semantically related to each other. Therefore, we investigate... | ['Sina Zarrie{\\ss}', 'David Schlangen'] | 2017-07-01 | null | null | null | acl-2017-7 | ['referring-expression-generation'] | ['computer-vision'] | [ 1.41923875e-01 -1.01579968e-02 -4.07852262e-01 -7.22281098e-01
-3.88656974e-01 -6.40089214e-01 1.03329265e+00 5.81341758e-02
-6.96446776e-01 5.27995169e-01 8.46578002e-01 -9.23889950e-02
-9.00230184e-02 -8.52651119e-01 -4.18686002e-01 -5.31525791e-01
1.70263320e-01 4.16296124e-01 -1.75092518e-01 -9.18549001... | [10.51675033569336, 2.1669788360595703] |
a9651cde-9528-4e0d-a0a2-0f473673d082 | contrastive-regularized-u-net-for-video | null | null | https://ieeexplore.ieee.org/abstract/document/10098744/ | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10098744 | Contrastive-Regularized U-Net for Video Anomaly Detection | Video anomaly detection aims to identify anomalous segments in a video. It is typically trained with weakly supervised video-level labels. This paper focuses on two crucial factors affecting the performance of video anomaly detection models. First, we explore how to capture the local and global temporal dependencies mo... | ['Joon Huang Chuah', 'Maylor Karhang Leung', 'Hui-Fuang Ng', 'Hung-Khoon Tan', 'Yu Tong Cheng', 'Kian Yu Gan'] | 2023-04-11 | null | null | null | ieee-access-2023-4 | ['video-anomaly-detection', 'anomaly-detection-in-surveillance-videos', 'anomaly-detection', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'computer-vision', 'methodology', 'methodology'] | [ 8.97685811e-02 -2.08453819e-01 -4.10917848e-01 -5.44020712e-01
-4.53111202e-01 -2.43726298e-01 4.63297427e-01 2.26169854e-01
-4.14395213e-01 2.68198878e-01 2.84502059e-01 -1.04584016e-01
1.82580069e-01 -4.68375176e-01 -9.40497220e-01 -6.61587298e-01
-6.06574118e-01 -2.94110198e-02 4.93909150e-01 9.80853289... | [7.853390216827393, 1.5923407077789307] |
03337c27-4fe9-4556-99c2-7e0bef24c5c4 | glean-generative-latent-bank-for-image-super | 2207.14812 | null | https://arxiv.org/abs/2207.14812v1 | https://arxiv.org/pdf/2207.14812v1.pdf | GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond | We show that pre-trained Generative Adversarial Networks (GANs) such as StyleGAN and BigGAN can be used as a latent bank to improve the performance of image super-resolution. While most existing perceptual-oriented approaches attempt to generate realistic outputs through learning with adversarial loss, our method, Gene... | ['Chen Change Loy', 'Jinwei Gu', 'Xintao Wang', 'Xiangyu Xu', 'Kelvin C. K. Chan'] | 2022-07-29 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.26362038e-01 2.42834315e-01 1.42422944e-01 -2.36563757e-01
-9.72563565e-01 -6.01816595e-01 6.65096581e-01 -9.46748614e-01
-8.84462241e-03 6.02958620e-01 2.66482353e-01 -2.56869256e-01
4.91417497e-01 -9.00933266e-01 -1.11817312e+00 -5.98936439e-01
3.77273023e-01 2.02776089e-01 -1.60783395e-01 -3.22489679... | [11.552242279052734, -0.6509395837783813] |
ec12364b-f03f-4e9a-a76c-683f3f08a310 | a-simple-local-minimal-intensity-prior-and-an | 1906.06642 | null | http://arxiv.org/abs/1906.06642v5 | http://arxiv.org/pdf/1906.06642v5.pdf | A Simple Local Minimal Intensity Prior and An Improved Algorithm for Blind Image Deblurring | Blind image deblurring is a long standing challenging problem in image
processing and low-level vision. Recently, sophisticated priors such as dark
channel prior, extreme channel prior, and local maximum gradient prior, have
shown promising effectiveness. However, these methods are computationally
expensive. Meanwhile,... | [] | 2020-10-29 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 1.95051506e-01 -4.90656585e-01 -5.10097742e-02 1.34449348e-01
-4.90212739e-01 -2.54590183e-01 3.92436326e-01 -6.67043984e-01
-2.08563313e-01 8.08654606e-01 3.90624136e-01 -5.47553487e-02
-1.03377670e-01 -3.82368147e-01 -4.62827951e-01 -1.05009735e+00
2.51351625e-01 -3.62928122e-01 1.60073042e-01 1.28850937... | [11.49273681640625, -2.679994583129883] |
83bff61a-977f-469c-a521-4d3c047239da | prosocialdialog-a-prosocial-backbone-for | 2205.12688 | null | https://arxiv.org/abs/2205.12688v2 | https://arxiv.org/pdf/2205.12688v2.pdf | ProsocialDialog: A Prosocial Backbone for Conversational Agents | Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale multi-turn dialogue dataset to teach conversational agents to respond to problematic content follow... | ['Maarten Sap', 'Yejin Choi', 'Gunhee Kim', 'Daniel Khashabi', 'Ximing Lu', 'Liwei Jiang', 'Youngjae Yu', 'Hyunwoo Kim'] | 2022-05-25 | null | null | null | null | ['dialogue-safety-prediction', 'rules-of-thumb-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.53471376e-02 9.81118262e-01 6.79255798e-02 -5.31233490e-01
-4.69569117e-01 -8.18877161e-01 9.92098808e-01 -1.62578002e-01
-1.27900466e-01 1.13669443e+00 1.05581510e+00 -2.54618049e-01
1.46555752e-01 -5.20122051e-01 7.14823082e-02 -2.86809236e-01
3.86672944e-01 8.86129498e-01 -3.09428990e-01 -8.44178677... | [12.834390640258789, 8.0730562210083] |
0f502e66-80c0-414e-8e15-52b88b9f0cfd | crowdsourcing-ground-truth-for-medical | 1701.02185 | null | http://arxiv.org/abs/1701.02185v2 | http://arxiv.org/pdf/1701.02185v2.pdf | Crowdsourcing Ground Truth for Medical Relation Extraction | Cognitive computing systems require human labeled data for evaluation, and
often for training. The standard practice used in gathering this data minimizes
disagreement between annotators, and we have found this results in data that
fails to account for the ambiguity inherent in language. We have proposed the
CrowdTruth... | ['Lora Aroyo', 'Anca Dumitrache', 'Chris Welty'] | 2017-01-09 | null | null | null | null | ['medical-relation-extraction'] | ['medical'] | [ 2.25764643e-02 8.91888916e-01 2.49850467e-01 -8.81277561e-01
-1.11328804e+00 -6.92999542e-01 4.66178477e-01 7.97760248e-01
-9.14123654e-01 1.14257479e+00 4.53411132e-01 -2.54483193e-01
-9.78836939e-02 -4.93692905e-01 -5.09616375e-01 -2.86440134e-01
2.68400967e-01 1.16451621e+00 2.59461850e-01 -3.19694161... | [9.686211585998535, 4.804385662078857] |
8845d842-f80f-4ebb-b9a9-831eaa44c5f5 | stem-unsupervised-structural-embedding-for | 2112.00712 | null | https://arxiv.org/abs/2112.00712v2 | https://arxiv.org/pdf/2112.00712v2.pdf | STEM: Unsupervised STructural EMbedding for Stance Detection | Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-parti... | ['Oren Tsur', 'Dan Vilenchik', 'Vladyslav Kozhukhov', 'Ron Korenblum Pick'] | 2021-12-01 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 4.28983122e-02 6.42833054e-01 -6.95006847e-01 -4.72286612e-01
-3.66852313e-01 -7.73616791e-01 1.11642838e+00 7.22245395e-01
-1.10195920e-01 5.24065673e-01 1.14049542e+00 -4.62198704e-01
1.74202070e-01 -8.68146956e-01 -4.63274181e-01 -4.02754009e-01
1.34672225e-01 4.26533312e-01 1.49848446e-01 -4.77724195... | [8.832176208496094, 10.060871124267578] |
66d15c00-2db4-4bfc-aa38-4141cb174887 | temporal-segment-networks-towards-good | 1608.00859 | null | http://arxiv.org/abs/1608.00859v1 | http://arxiv.org/pdf/1608.00859v1.pdf | Temporal Segment Networks: Towards Good Practices for Deep Action Recognition | Deep convolutional networks have achieved great success for visual
recognition in still images. However, for action recognition in videos, the
advantage over traditional methods is not so evident. This paper aims to
discover the principles to design effective ConvNet architectures for action
recognition in videos and l... | ['Luc van Gool', 'Xiaoou Tang', 'Yu Qiao', 'Limin Wang', 'Zhe Wang', 'Yuanjun Xiong', 'Dahua Lin'] | 2016-08-02 | null | null | null | null | ['multimodal-activity-recognition'] | ['computer-vision'] | [ 2.88714677e-01 -2.12602526e-01 -6.80733562e-01 -2.96539843e-01
-3.36280257e-01 3.55681293e-02 4.68454510e-01 -8.56291711e-01
-4.39177126e-01 6.93004012e-01 2.68285125e-01 -3.83752026e-02
-2.76918322e-01 -3.99817526e-01 -8.77221823e-01 -8.37092578e-01
-4.77499872e-01 1.23477001e-02 4.69437867e-01 -7.19966516... | [8.46163272857666, 0.6142162680625916] |
0f485ea0-484d-4f4b-89ed-49146d3f55f6 | a-strategy-oriented-bayesian-soft-actor | 2303.04193 | null | https://arxiv.org/abs/2303.04193v1 | https://arxiv.org/pdf/2303.04193v1.pdf | A Strategy-Oriented Bayesian Soft Actor-Critic Model | Adopting reasonable strategies is challenging but crucial for an intelligent agent with limited resources working in hazardous, unstructured, and dynamic environments to improve the system's utility, decrease the overall cost, and increase mission success probability. This paper proposes a novel hierarchical strategy d... | ['Ramviyas Parasuraman', 'Qin Yang'] | 2023-03-07 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-4.65547532e-01 5.57841539e-01 -3.58190387e-01 -1.29306326e-02
-3.50872785e-01 -1.10323861e-01 6.34371459e-01 -5.67690544e-02
-7.73642063e-01 1.29400563e+00 2.34607071e-01 -2.55495340e-01
-7.01336443e-01 -5.19123197e-01 -4.92213577e-01 -9.66321766e-01
-1.62982285e-01 8.37340772e-01 8.90168011e-01 -5.70649087... | [4.170470237731934, 2.077409267425537] |
d729acfe-9131-47e5-9d4d-10d9e7dceac8 | cascaded-interactional-targeting-network-for | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhou_Cascaded_Interactional_Targeting_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhou_Cascaded_Interactional_Targeting_CVPR_2016_paper.pdf | Cascaded Interactional Targeting Network for Egocentric Video Analysis | Knowing how hands move and what object is being manipulated are two key sub-tasks for analyzing first-person (egocentric) action. However, lack of fully annotated hand data as well as imprecise foreground segmentation make either sub-task challenging. This work aims to explicitly address these two issues via introducin... | ['Yang Zhou', 'Richang Hong', 'Bingbing Ni', 'Qi Tian', 'Xiaokang Yang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['foreground-segmentation', 'hand-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.14280713e-01 9.26983356e-02 -4.00980771e-01 -3.05806965e-01
-5.72278261e-01 -4.63854074e-01 5.71898639e-01 -7.28407443e-01
-4.48352516e-01 6.96356475e-01 4.41419333e-01 7.49353915e-02
1.64751619e-01 -5.79167843e-01 -8.49777818e-01 -8.91329050e-01
2.65024245e-01 4.35422301e-01 5.74927092e-01 1.12999314... | [7.70041561126709, 0.1854279637336731] |
a0ee90fa-4e3e-4754-94f3-094a7f46c194 | the-foes-of-neural-networks-data-efficiency | null | null | https://openreview.net/forum?id=X6_vet6HWX | https://openreview.net/pdf?id=X6_vet6HWX | The Foes of Neural Network’s Data Efficiency Among Unnecessary Input Dimensions | Input dimensions are unnecessary for a given task when the target function can be expressed without such dimensions. Object's background in image recognition or redundant sentences in text classification are examples of unnecessary dimensions that are often present in datasets. Deep neural networks achieve remarkable g... | ['Xavier Boix', 'Tomotake Sasaki', 'Sanjana Srivastava', "Vanessa D'Amario"] | 2021-01-01 | null | null | null | null | ['foveation'] | ['computer-vision'] | [ 6.26219213e-01 7.91244060e-02 1.11937739e-01 -4.78599161e-01
-4.85967584e-02 -4.69693184e-01 3.10079962e-01 1.79997504e-01
-7.73351371e-01 3.53532016e-01 2.30137438e-01 -6.77476525e-01
-5.67457974e-01 -7.36604810e-01 -7.67286599e-01 -5.75782597e-01
3.52031112e-01 -1.67197242e-01 -1.54084802e-01 -3.89384687... | [8.646174430847168, 3.6194229125976562] |
c0a30995-ba73-43e6-b26d-983e148d1a4e | task-planning-in-robotics-an-empirical | 1804.08229 | null | http://arxiv.org/abs/1804.08229v3 | http://arxiv.org/pdf/1804.08229v3.pdf | Task Planning in Robotics: an Empirical Comparison of PDDL-based and ASP-based Systems | Robots need task planning algorithms to sequence actions toward accomplishing
goals that are impossible through individual actions. Off-the-shelf task
planners can be used by intelligent robotics practitioners to solve a variety
of planning problems. However, many different planners exist, each with
different strengths... | ['Piyush Khandelwal', 'Yuqian Jiang', 'Shiqi Zhang', 'Peter Stone'] | 2018-04-23 | null | null | null | null | ['robot-task-planning'] | ['robots'] | [ 2.76734203e-01 6.45879626e-01 -3.48685950e-01 -3.31459165e-01
-4.15413558e-01 -6.15593910e-01 5.17017245e-01 8.15534294e-02
-2.19443634e-01 8.24475765e-01 4.90974724e-01 -4.47837144e-01
-6.56957567e-01 -8.33685160e-01 -3.44762176e-01 -2.17602521e-01
-1.18762650e-01 1.10642278e+00 8.24711502e-01 -6.16558790... | [4.4195942878723145, 0.9395314455032349] |
145c6d1f-c3d9-40fb-809e-625cd17862d5 | generating-reasonable-and-diversified-story | null | null | https://aclanthology.org/C18-1088 | https://aclanthology.org/C18-1088.pdf | Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training | Story generation is a challenging problem in artificial intelligence (AI) and has received a lot of interests in the natural language processing (NLP) community. Most previous work tried to solve this problem using Sequence to Sequence (Seq2Seq) model trained with Maximum Likelihood Estimation (MLE). However, the pure ... | ['Xiao Ding', 'Zhongyang Li', 'Ting Liu'] | 2018-08-01 | generating-reasonable-and-diversified-story-1 | https://aclanthology.org/C18-1088 | https://aclanthology.org/C18-1088.pdf | coling-2018-8 | ['cloze-test'] | ['natural-language-processing'] | [ 6.12696707e-01 4.48033571e-01 3.07456050e-02 -2.90781915e-01
-1.17460263e+00 -7.43090749e-01 9.91606593e-01 -2.86431491e-01
-2.53208548e-01 1.18412995e+00 8.15558612e-01 1.27313748e-01
5.19620359e-01 -8.85883510e-01 -8.36175621e-01 -4.31789279e-01
2.09918767e-01 7.54016519e-01 -2.69520562e-02 -5.67837417... | [11.731973648071289, 8.908620834350586] |
40b19155-c33c-49cd-9064-dcb261cd70ba | a-reinforcement-learning-approach-to-improve | 2106.00654 | null | https://arxiv.org/abs/2106.00654v1 | https://arxiv.org/pdf/2106.00654v1.pdf | A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT | Recent research has shown the potential of using available mobile fog devices (such as smartphones, drones, domestic and industrial robots) as relays to minimize communication outages between sensors and destination devices, where localized Internet-of-Things services (e.g., manufacturing process control, health and se... | ['Ivana Dusparic', 'Maxime Gueriau', 'Babatunji Omoniwa'] | 2021-06-01 | null | null | null | null | ['industrial-robots'] | ['robots'] | [-1.72460765e-01 5.21317482e-01 -3.06057215e-01 2.14930788e-01
-2.62380056e-02 -6.03241384e-01 2.66293705e-01 1.39325157e-01
-1.45264462e-01 1.26675093e+00 -4.23311353e-01 -1.66197211e-01
-2.36704245e-01 -1.18207836e+00 -3.16416055e-01 -1.15039635e+00
-3.42816889e-01 1.30741432e-01 3.22042406e-01 -1.80482522... | [5.858743667602539, 1.71694815158844] |
0e5bc697-e2f3-4a38-a8b6-43a7922a65ba | activeglae-a-benchmark-for-deep-active | 2306.10087 | null | https://arxiv.org/abs/2306.10087v1 | https://arxiv.org/pdf/2306.10087v1.pdf | ActiveGLAE: A Benchmark for Deep Active Learning with Transformers | Deep active learning (DAL) seeks to reduce annotation costs by enabling the model to actively query instance annotations from which it expects to learn the most. Despite extensive research, there is currently no standardized evaluation protocol for transformer-based language models in the field of DAL. Diverse experime... | ['Bernhard Sick', 'Bernd Bischl', 'Moritz Wirth', 'Denis Huseljic', 'Matthias Aßenmacher', 'Lukas Rauch'] | 2023-06-16 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 1.56098604e-01 1.02433242e-01 -7.98148274e-01 -5.10861933e-01
-1.35388017e+00 -6.88578367e-01 6.77438736e-01 2.26581365e-01
-8.74346614e-01 5.80864191e-01 3.25097114e-01 -3.12016189e-01
-1.83974117e-01 -3.75955999e-01 -4.24226671e-01 -8.37147385e-02
7.79759511e-02 6.71690941e-01 2.59525567e-01 -6.99207112... | [9.661767959594727, 4.461142539978027] |
3fd20a78-5ce2-4448-8acf-b3af8daac052 | feature-based-decipherment-for-machine | null | null | https://aclanthology.org/J18-3006 | https://aclanthology.org/J18-3006.pdf | Feature-Based Decipherment for Machine Translation | Orthographic similarities across languages provide a strong signal for unsupervised probabilistic transduction (decipherment) for closely related language pairs. The existing decipherment models, however, are not well suited for exploiting these orthographic similarities. We propose a log-linear model with latent varia... | ['Parker Riley', 'Iftekhar Naim', 'Daniel Gildea'] | 2018-09-01 | null | null | null | cl-2018-9 | ['decipherment'] | ['natural-language-processing'] | [-8.23771115e-03 -5.05713046e-01 -2.77326196e-01 -4.01531756e-01
-7.88359284e-01 -7.44153261e-01 9.38404083e-01 1.27137780e-01
-5.12126446e-01 7.93928266e-01 4.94346261e-01 -3.25460494e-01
-2.81414613e-02 -8.69180918e-01 -8.26095879e-01 -5.31560838e-01
1.74766287e-01 7.48552144e-01 -1.44943550e-01 6.03557862... | [11.166964530944824, 9.58345890045166] |
11ff9bb3-5044-45f0-b7d6-d8f8b37b4031 | sketch3t-test-time-training-for-zero-shot | 2203.14691 | null | https://arxiv.org/abs/2203.14691v1 | https://arxiv.org/pdf/2203.14691v1.pdf | Sketch3T: Test-Time Training for Zero-Shot SBIR | Zero-shot sketch-based image retrieval typically asks for a trained model to be applied as is to unseen categories. In this paper, we question to argue that this setup by definition is not compatible with the inherent abstract and subjective nature of sketches, i.e., the model might transfer well to new categories, but... | ['Yi-Zhe Song', 'Tao Xiang', 'Pinaki Nath Chowdhury', 'Vaishnav Potlapalli', 'Ayan Kumar Bhunia', 'Aneeshan Sain'] | 2022-03-28 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sain_Sketch3T_Test-Time_Training_for_Zero-Shot_SBIR_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sain_Sketch3T_Test-Time_Training_for_Zero-Shot_SBIR_CVPR_2022_paper.pdf | cvpr-2022-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 2.27042139e-01 -2.16628417e-01 -2.07544878e-01 -5.70405543e-01
-8.35037887e-01 -9.07395482e-01 8.51963103e-01 -3.28144729e-01
-2.25212216e-01 3.59468490e-01 -7.94038624e-02 2.62011252e-02
-1.82872042e-01 -8.15955997e-01 -7.41900086e-01 -4.29744661e-01
2.77083218e-01 8.45237792e-01 3.05078745e-01 -2.76928782... | [11.598742485046387, 0.6689432263374329] |
2c05782f-9791-4a6d-a5a7-4edf964a4be2 | improving-visual-relationship-detection-using | 1809.00204 | null | http://arxiv.org/abs/1809.00204v1 | http://arxiv.org/pdf/1809.00204v1.pdf | Improving Visual Relationship Detection using Semantic Modeling of Scene Descriptions | Structured scene descriptions of images are useful for the automatic
processing and querying of large image databases. We show how the combination
of a semantic and a visual statistical model can improve on the task of mapping
images to their associated scene description. In this paper we consider scene
descriptions wh... | ['Stephan Baier', 'Yunpu Ma', 'Volker Tresp'] | 2018-09-01 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.35034651e-01 2.78731193e-02 -3.18838447e-01 -4.96029139e-01
-2.80717701e-01 -3.04216534e-01 9.86410975e-01 6.48365438e-01
-9.54256654e-02 4.59053904e-01 -1.21597829e-03 -1.40301570e-01
-1.63444638e-01 -8.94751906e-01 -1.01300323e+00 -2.76883632e-01
-9.78674591e-02 7.26949513e-01 8.83463025e-01 -1.25702977... | [10.267148971557617, 1.6766589879989624] |
67092234-1f89-4039-b7b2-ba5d84196fb2 | matroids-hitting-sets-and-unsupervised | 1705.08992 | null | http://arxiv.org/abs/1705.08992v2 | http://arxiv.org/pdf/1705.08992v2.pdf | Matroids Hitting Sets and Unsupervised Dependency Grammar Induction | This paper formulates a novel problem on graphs: find the minimal subset of
edges in a fully connected graph, such that the resulting graph contains all
spanning trees for a set of specifed sub-graphs. This formulation is motivated
by an un-supervised grammar induction problem from computational linguistics.
We present... | ['Vahab Mirrokni', 'Leonid Peshkin', 'Virginia Savova', 'Nicholas Harvey', 'David Karger'] | 2017-05-24 | null | null | null | null | ['dependency-grammar-induction'] | ['natural-language-processing'] | [ 6.17351174e-01 8.97196651e-01 -5.23899913e-01 -3.97998005e-01
-2.47320473e-01 -7.11442471e-01 1.93613559e-01 3.50369304e-01
1.28637671e-01 7.40229487e-01 -2.48111412e-01 -6.92530751e-01
-5.62047362e-01 -1.15058970e+00 -5.98154187e-01 -4.12455380e-01
-7.01882958e-01 8.63673508e-01 2.75515258e-01 -4.48476933... | [7.066451549530029, 5.3870930671691895] |
fe49393e-479d-4341-8c7b-dd2d8c4fe62b | tree-structured-parzen-estimator | 2304.11127 | null | https://arxiv.org/abs/2304.11127v3 | https://arxiv.org/pdf/2304.11127v3.pdf | Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance | Recent advances in many domains require more and more complicated experiment design. Such complicated experiments often have many parameters, which necessitate parameter tuning. Tree-structured Parzen estimator (TPE), a Bayesian optimization method, is widely used in recent parameter tuning frameworks. Despite its popu... | ['Shuhei Watanabe'] | 2023-04-21 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [-3.30536783e-01 -4.86388355e-01 -5.43719411e-01 -3.48104417e-01
-9.23297882e-01 -7.35177219e-01 4.53962952e-01 -2.00447038e-01
-4.50813591e-01 8.36154103e-01 2.24350333e-01 -3.87671083e-01
-3.07719171e-01 -4.30097193e-01 -5.66340566e-01 -8.09557915e-01
1.54123962e-01 3.96899015e-01 1.75428301e-01 1.00206152... | [8.102594375610352, 3.9026646614074707] |
16c0f66a-25db-4ebd-ae82-08c6782ddb75 | moccasin-efficient-tensor-rematerialization | 2304.14463 | null | https://arxiv.org/abs/2304.14463v2 | https://arxiv.org/pdf/2304.14463v2.pdf | Moccasin: Efficient Tensor Rematerialization for Neural Networks | The deployment and training of neural networks on edge computing devices pose many challenges. The low memory nature of edge devices is often one of the biggest limiting factors encountered in the deployment of large neural network models. Tensor rematerialization or recompute is a way to address high memory requiremen... | ['Bistra Dilkina', 'Christopher Lott', 'Harris Teague', 'Haoming Li', 'Burak Bartan'] | 2023-04-27 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-2.68578846e-02 1.13554932e-01 -5.05593009e-02 -2.33132094e-01
1.18717819e-01 -4.46735829e-01 -3.77865578e-03 9.50567424e-02
-6.60993636e-01 7.74078012e-01 -5.54796219e-01 -5.93171895e-01
-4.17339057e-01 -9.40463901e-01 -9.44659531e-01 -5.59047997e-01
-3.95736188e-01 5.43158948e-01 4.28570099e-02 -3.01908404... | [8.41599178314209, 3.2758729457855225] |
ad0be8c6-7a64-45ae-8f0e-2f3061dd82d8 | predicting-rare-events-by-shrinking-towards | 2305.187 | null | https://arxiv.org/abs/2305.18700v1 | https://arxiv.org/pdf/2305.18700v1.pdf | Predicting Rare Events by Shrinking Towards Proportional Odds | Training classifiers is difficult with severe class imbalance, but many rare events are the culmination of a sequence with much more common intermediate outcomes. For example, in online marketing a user first sees an ad, then may click on it, and finally may make a purchase; estimating the probability of purchases is d... | ['Jacob Bien', 'Gregory Faletto'] | 2023-05-30 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 4.30592775e-01 2.59448826e-01 -7.59820700e-01 -6.95637822e-01
-6.16074264e-01 -4.42306101e-01 3.33049238e-01 6.22891128e-01
-4.68945444e-01 8.16442788e-01 1.98390633e-01 -4.84028876e-01
-3.42034817e-01 -6.80474699e-01 -7.99316585e-01 -5.41351676e-01
-3.33661020e-01 5.77784598e-01 -1.05358675e-01 1.79831013... | [8.42539119720459, 4.713194370269775] |
3ab87a7a-53ff-4f1a-8ed8-0600dcf36729 | emotion-recognition-in-conversation-using | 2207.07238 | null | https://arxiv.org/abs/2207.07238v1 | https://arxiv.org/pdf/2207.07238v1.pdf | Emotion Recognition in Conversation using Probabilistic Soft Logic | Creating agents that can both appropriately respond to conversations and understand complex human linguistic tendencies and social cues has been a long standing challenge in the NLP community. A recent pillar of research revolves around emotion recognition in conversation (ERC); a sub-field of emotion recognition that ... | ['Lise Getoor', 'William Wang', 'Charles Dickens', 'Connor Pryor', 'Alon Albalak', 'Pegah Jandaghi', 'Eriq Augustine'] | 2022-07-14 | null | null | null | null | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.08437903e-01 6.71258628e-01 1.20954767e-01 -8.17111075e-01
-2.26790339e-01 -5.68184495e-01 1.14724946e+00 5.12798786e-01
-3.55332762e-01 5.49244702e-01 9.50604975e-01 -1.94315195e-01
1.48312366e-02 -8.37434053e-01 -4.04208601e-01 -2.35384881e-01
-1.78924665e-01 6.76444411e-01 -2.39575446e-01 -7.14547813... | [12.980374336242676, 6.275015830993652] |
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