paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d6bf7717-17a4-4dd6-8361-1ed75405df81 | quantifying-performance-of-bipedal-standing | 1711.07894 | null | http://arxiv.org/abs/1711.07894v1 | http://arxiv.org/pdf/1711.07894v1.pdf | Quantifying Performance of Bipedal Standing with Multi-channel EMG | Spinal cord stimulation has enabled humans with motor complete spinal cord
injury (SCI) to independently stand and recover some lost autonomic function.
Quantifying the quality of bipedal standing under spinal stimulation is
important for spinal rehabilitation therapies and for new strategies that seek
to combine spina... | ['Kun Ho Kim', 'Joel W. Burdick', 'Yanan Sui'] | 2017-11-21 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 5.37667096e-01 -1.80759765e-02 -6.51207089e-01 -1.18074015e-01
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-8.50646257e-01 1.16712546e+00 5.30557930e-01 2.70554740e-02
-1.45178825e-01 -1.17357403e-01 -7.76160598e-01 -4.34248835e-01
-2.10318819e-01 5.18217385e-01 1.59650091e-02 -4.51556861... | [6.879725456237793, 0.20546182990074158] |
9db5105d-2088-45a2-816a-72203b49a664 | a-deep-learning-framework-for-assessing | 1901.10435 | null | https://arxiv.org/abs/1901.10435v3 | https://arxiv.org/pdf/1901.10435v3.pdf | A Deep Learning Framework for Assessing Physical Rehabilitation Exercises | Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthc... | ['Y. Liao', 'M. Xian', 'A. Vakanski'] | 2019-01-29 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 1.18653089e-01 -1.04878172e-01 -3.34917843e-01 -7.84340054e-02
-7.98560560e-01 5.96878119e-02 1.93790123e-02 8.64094123e-03
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-4.96860296e-01 5.48854053e-01 -4.72055152e-02 -2.99168944... | [7.10115909576416, 0.2620053291320801] |
06ff65b5-37b2-4f26-91f9-a38939f07547 | hierarchical-and-fast-graph-similarity | 2005.07115 | null | https://arxiv.org/abs/2005.07115v7 | https://arxiv.org/pdf/2005.07115v7.pdf | CoSimGNN: Towards Large-scale Graph Similarity Computation | The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is important in many real-world applications. Computing exact GED values is typically an NP-hard problem and traditional algorithms usually achieve an unsatisfactory trade-off between accuracy and efficiency. Rece... | ['Yueyang Wang', 'Haoyan Xu', 'Ziheng Duan', 'Jie Feng', 'Runjian Chen'] | 2020-05-14 | null | null | null | null | ['3d-human-action-recognition', 'graph-similarity'] | ['computer-vision', 'graphs'] | [-2.73726159e-03 2.95751635e-02 -1.87349785e-02 -2.76419014e-01
-4.49992359e-01 -4.61091936e-01 3.96187633e-01 8.75919938e-01
-4.22163278e-01 4.41554368e-01 -1.98729709e-01 -2.08239838e-01
-3.76923352e-01 -1.51728761e+00 -6.44113839e-01 -4.68751848e-01
-6.03682041e-01 5.97982645e-01 5.65969169e-01 -3.74742806... | [7.123830318450928, 6.193305492401123] |
10723ebe-387b-43ae-a0b1-3c50b030a6cb | bronchusnet-region-and-structure-prior | 2205.06947 | null | https://arxiv.org/abs/2205.06947v2 | https://arxiv.org/pdf/2205.06947v2.pdf | BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification | CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians. The basis of airway analysis is bronchial tree reconstruction, which consists of bronchus segmentation and classification. However, there remains ... | ['Hong Shen', 'Guanbin Li', 'Haofeng Li', 'Yu Wang', 'huan zhang', 'Haifan Gong', 'Wenhao Huang'] | 2022-05-14 | null | null | null | null | ['unet-segmentation'] | ['computer-vision'] | [ 4.39202487e-01 3.13951880e-01 -4.25327390e-01 -2.93981284e-01
-9.62325752e-01 -3.25417310e-01 -2.76590395e-03 8.71030465e-02
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2.67415285e-01 8.58539164e-01 8.00730765e-01 1.58416435... | [15.048200607299805, -2.160701036453247] |
9f0d8a61-2910-48b4-b2d7-e0e756ff0ea8 | cascading-convolutional-color-constancy | 1912.11180 | null | https://arxiv.org/abs/1912.11180v1 | https://arxiv.org/pdf/1912.11180v1.pdf | Cascading Convolutional Color Constancy | Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to intrinsic appearance and label ambiguities caused by unknown illuminants, diverse reflection property of materials and extrinsic imaging fac... | ['Zhao-Xiang Zhang', 'Yanlin Qian', 'Ke Chen', 'Kui Jia', 'Huanglin Yu', 'Kaiqi Wang'] | 2019-12-24 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 4.95452493e-01 -9.16994572e-01 9.62763578e-02 -5.47750711e-01
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5.21215081e-01 -2.42002174e-01 5.48876636e-02 -7.79959857... | [10.516283988952637, -2.5699214935302734] |
67cc21a4-13ff-49a4-9701-bd8f40e7f99f | brain-effective-connectome-based-on-fmri-and | 2302.05451 | null | https://arxiv.org/abs/2302.05451v2 | https://arxiv.org/pdf/2302.05451v2.pdf | Brain Effective Connectome based on fMRI and DTI Data: Bayesian Causal Learning and Assessment | Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome (EC). Although current EC discovery methods have contributed to our understanding of brain organization, their performances are severely constrained by the short sample size and poor temporal resolution of fMRI data, and high dimens... | ['Babak Nadjar Araabi', 'Alireza Akhondi-Asl', 'Yamin Bagheri', 'Mahdi Dehshiri', 'Abdolmahdi Bagheri'] | 2023-02-10 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [-4.75028269e-02 -4.87891212e-02 -2.04694644e-01 -2.97335476e-01
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-9.18832064e-01 2.31441796e-01 2.16303959e-01 2.31464118... | [12.37670612335205, 3.4296979904174805] |
0a61c8cf-27fb-4d2e-a83c-0d7c8f7526e0 | gaitfi-robust-device-free-human | 2208.14326 | null | https://arxiv.org/abs/2208.14326v1 | https://arxiv.org/pdf/2208.14326v1.pdf | GaitFi: Robust Device-Free Human Identification via WiFi and Vision Multimodal Learning | As an important biomarker for human identification, human gait can be collected at a distance by passive sensors without subject cooperation, which plays an essential role in crime prevention, security detection and other human identification applications. At present, most research works are based on cameras and comput... | ['Lihua Xie', 'Chris Xiaoxuan Lu', 'Han Zou', 'Shenghai Yuan', 'Jianfei Yang', 'Lang Deng'] | 2022-08-30 | null | null | null | null | ['gait-recognition'] | ['computer-vision'] | [ 2.30255559e-01 -1.01788402e+00 -1.72611594e-01 -1.25573978e-01
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-2.90047169e-01 -1.93983749e-01 -1.90166887e-02 2.99128264... | [14.252493858337402, 1.460525631904602] |
707c2383-90ce-4279-86fa-52583b6cf785 | learning-to-factorize-and-relight-a-city | 2008.02796 | null | https://arxiv.org/abs/2008.02796v1 | https://arxiv.org/pdf/2008.02796v1.pdf | Learning to Factorize and Relight a City | We propose a learning-based framework for disentangling outdoor scenes into temporally-varying illumination and permanent scene factors. Inspired by the classic intrinsic image decomposition, our learning signal builds upon two insights: 1) combining the disentangled factors should reconstruct the original image, and 2... | ['Tinghui Zhou', 'Alexei A. Efros', 'Andrew Liu', 'Shiry Ginosar', 'Noah Snavely'] | 2020-08-06 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2473_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490528.pdf | eccv-2020-8 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 1.90157339e-01 -5.09342670e-01 -3.87327932e-02 -1.53541937e-01
-6.57857120e-01 -1.18676376e+00 5.44692099e-01 -6.14831030e-01
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-3.59037906e-01 -5.62074780e-01 -8.93171430e-01 -8.75843048e-01
-3.22438449e-01 -3.21426541e-01 -1.73307076e-01 -2.80745663... | [9.343363761901855, -3.052455186843872] |
9eddd23c-73ac-462e-b427-2b6345dde52e | rapid-randomized-restarts-for-multi-agent | 1706.02794 | null | http://arxiv.org/abs/1706.02794v1 | http://arxiv.org/pdf/1706.02794v1.pdf | Rapid Randomized Restarts for Multi-Agent Path Finding Solvers | Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in
artificial intelligence and robotics. It has many real-world applications for
which existing MAPF solvers use various heuristics. However, these solvers are
deterministic and perform poorly on "hard" instances typically characterized by
many agents i... | ['Glenn Wagner', 'Sven Koenig', 'Liron Cohen', 'T. K. Satish Kumar', 'Howie Choset'] | 2017-06-08 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-1.47146344e-01 4.84596163e-01 -2.17532635e-01 -1.79106556e-02
-5.33990800e-01 -9.61546779e-01 4.99596655e-01 2.84178823e-01
-3.59963328e-01 1.29145181e+00 -2.45791391e-01 -6.33078098e-01
-7.61229694e-01 -1.13642609e+00 -1.17113912e+00 -6.13883913e-01
-6.43487692e-01 1.28073168e+00 5.61812758e-01 -4.40401495... | [4.95452356338501, 1.9091202020645142] |
dcaa0656-9000-49cf-a6aa-0dcec2cc84c8 | guided-exploration-of-data-summaries | 2205.13956 | null | https://arxiv.org/abs/2205.13956v1 | https://arxiv.org/pdf/2205.13956v1.pdf | Guided Exploration of Data Summaries | Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that are collectively diverse to be representative. Unifor... | ['Aurélien Personnaz', 'Sihem Amer-Yahia', 'Brit Youngmann'] | 2022-05-27 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 3.83862466e-01 4.94644105e-01 -3.64264578e-01 -3.07341814e-01
-1.34791160e+00 -4.98590320e-01 4.74229604e-01 8.00956190e-01
-1.61492378e-01 1.01505291e+00 1.01354682e+00 -6.60842583e-02
-4.43838328e-01 -6.59444273e-01 -6.55789316e-01 -5.14289677e-01
-2.27941096e-01 9.57863271e-01 -1.20820299e-01 1.21684775... | [12.511327743530273, 9.441153526306152] |
6cc357ab-8676-40ad-862c-2806b26ba6b5 | molecule-design-by-latent-space-energy-based | 2306.14902 | null | https://arxiv.org/abs/2306.14902v1 | https://arxiv.org/pdf/2306.14902v1.pdf | Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting | Generation of molecules with desired chemical and biological properties such as high drug-likeness, high binding affinity to target proteins, is critical for drug discovery. In this paper, we propose a probabilistic generative model to capture the joint distribution of molecules and their properties. Our model assumes ... | ['Ying Nian Wu', 'Tian Han', 'Bo Pang', 'Deqian Kong'] | 2023-06-09 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 4.78726119e-01 -1.16037294e-01 -6.76935196e-01 -1.76251143e-01
-5.59767902e-01 -4.61552441e-01 7.76553750e-01 4.22132671e-01
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1.18617497e-01 7.21273661e-01 2.34181628e-01 5.77004068... | [4.975209712982178, 5.711024284362793] |
4cbe2213-95e1-4728-9a5d-f48ba2e183ee | correcting-the-misuse-a-method-for-the | null | null | https://aclanthology.org/2020.deelio-1.1 | https://aclanthology.org/2020.deelio-1.1.pdf | Correcting the Misuse: A Method for the Chinese Idiom Cloze Test | The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context. Chinese idiom is a type of Chinese idiomatic expression. The common misuse of Chinese idioms leads to error in corpus and causes error in th... | ['Hongbo Wang', 'Tan Yang', 'Hongsheng Zhao', 'Xinyu Wang'] | null | null | null | null | emnlp-deelio-2020-11 | ['cloze-test'] | ['natural-language-processing'] | [ 5.17745987e-02 -1.15867361e-01 -2.79847980e-01 -5.12387812e-01
-7.07119942e-01 -6.43875539e-01 1.41612262e-01 -1.96748048e-01
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6.87719762e-01 1.11059475e+00 1.19200289e-01 -5.22104084... | [10.885123252868652, 9.202057838439941] |
dfc1b04b-cc51-4c55-a4a8-bbabd7d2e887 | ynu-hpcc-at-semeval-2021-task-5-using-a | null | null | https://aclanthology.org/2021.semeval-1.112 | https://aclanthology.org/2021.semeval-1.112.pdf | YNU-HPCC at SemEval-2021 Task 5: Using a Transformer-based Model with Auxiliary Information for Toxic Span Detection | Toxic span detection requires the detection of spans that make a text toxic instead of simply classifying the text. In this paper, a transformer-based model with auxiliary information is proposed for SemEval-2021 Task 5. The proposed model was implemented based on the BERT-CRF architecture. It consists of three parts: ... | ['Xuejie Zhang', 'Jin Wang', 'Ruijun Chen'] | 2021-08-01 | null | null | null | semeval-2021 | ['toxic-spans-detection'] | ['natural-language-processing'] | [ 1.67992979e-01 8.58792961e-02 -9.64134783e-02 -4.36128765e-01
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2.54482418e-01 3.94412369e-01 5.17624676e-01 -6.33397549... | [8.985345840454102, 10.564370155334473] |
f630a55f-0168-4727-a84a-3b94bffdb4f1 | disentanglement-in-a-gan-for-unconditional | 2307.01673 | null | https://arxiv.org/abs/2307.01673v1 | https://arxiv.org/pdf/2307.01673v1.pdf | Disentanglement in a GAN for Unconditional Speech Synthesis | Can we develop a model that can synthesize realistic speech directly from a latent space, without explicit conditioning? Despite several efforts over the last decade, previous adversarial and diffusion-based approaches still struggle to achieve this, even on small-vocabulary datasets. To address this, we propose AudioS... | ['Herman Kamper', 'Matthew Baas'] | 2023-07-04 | null | null | null | null | ['voice-conversion', 'image-generation', 'disentanglement', 'voice-conversion', 'speech-enhancement', 'speaker-verification', 'speech-synthesis'] | ['audio', 'computer-vision', 'methodology', 'speech', 'speech', 'speech', 'speech'] | [ 5.19297838e-01 3.02797735e-01 -1.52737141e-01 -1.45124659e-01
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1.53246462e-01 4.04182106e-01 -2.50958890e-01 -3.29090476... | [15.111006736755371, 6.386163234710693] |
ede90b55-d507-4470-8454-83cabe9ce100 | dynamic-negative-example-construction-for | null | null | https://aclanthology.org/2022.ccl-1.83 | https://aclanthology.org/2022.ccl-1.83.pdf | Dynamic Negative Example Construction for Grammatical Error Correction using Contrastive Learning | “Grammatical error correction (GEC) aims at correcting texts with different types of grammatical errors into natural and correct forms. Due to the difference of error type distribution and error density, current grammatical error correction systems may over-correct writings and produce a low precision. To address this ... | ['Li Xia', 'Zhuang Junbin', 'He Junyi'] | null | null | null | null | ccl-2022-10 | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 1.43067613e-01 1.29975438e-01 -1.40076708e-02 -4.82817024e-01
-5.13464510e-01 -2.52077371e-01 2.48601980e-04 4.47742671e-01
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3.42443347e-01 2.59042412e-01 1.03479318e-01 -5.63781381... | [11.011369705200195, 10.76950740814209] |
930e0029-b863-4a71-9dfd-fc9365c5a01c | sphereflow-6-dof-scene-flow-from-rgb-d-pairs | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Hornacek_SphereFlow_6_DoF_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Hornacek_SphereFlow_6_DoF_2014_CVPR_paper.pdf | SphereFlow: 6 DoF Scene Flow from RGB-D Pairs | We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primar... | ['Andrew Fitzgibbon', 'Michael Hornacek', 'Carsten Rother'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['occlusion-handling'] | ['computer-vision'] | [ 1.95163682e-01 9.17532369e-02 1.78338900e-01 -2.63792556e-02
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-2.22584624e-02 4.22742456e-01 3.90074074e-01 -3.14077348... | [8.712102890014648, -2.11586332321167] |
ce6dc914-c5f0-4c30-8940-273a30ff91f2 | influence-of-mother-tongue-on-english-accent | null | null | https://aclanthology.org/W14-5109 | https://aclanthology.org/W14-5109.pdf | Influence of Mother Tongue on English Accent | null | ['R. Krishnan', 'G. Radha Krishna'] | 2014-12-01 | null | null | null | ws-2014-12 | ['text-independent-speaker-recognition'] | ['speech'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.230069160461426, 3.812074661254883] |
1f1d2658-d50b-4ee6-9708-ff75e3a9ea03 | stability-of-graph-neural-networks-to | 1910.09655 | null | https://arxiv.org/abs/1910.09655v1 | https://arxiv.org/pdf/1910.09655v1.pdf | Stability of Graph Neural Networks to Relative Perturbations | Graph neural networks (GNNs), consisting of a cascade of layers applying a graph convolution followed by a pointwise nonlinearity, have become a powerful architecture to process signals supported on graphs. Graph convolutions (and thus, GNNs), rely heavily on knowledge of the graph for operation. However, in many pract... | ['Fernando Gama', 'Alejandro Ribeiro', 'Joan Bruna'] | 2019-10-21 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 1.33851230e-01 2.62255669e-01 2.47501791e-01 -1.31908566e-01
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-5.41041017e-01 -1.10729225e-03 3.48109663e-01 -3.56286705... | [6.796566009521484, 6.091092109680176] |
372a0b0b-bfe3-47d0-9f0d-f3fb7348052f | relaxed-attention-for-transformer-models | 2209.09735 | null | https://arxiv.org/abs/2209.09735v1 | https://arxiv.org/pdf/2209.09735v1.pdf | Relaxed Attention for Transformer Models | The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and - for natural language processing tasks - lead to an implicitly learned internal language model in the autoregressive transformer decoder complicating the integration of external language models. In this pape... | ['Tim Fingscheidt', 'Zhengyang Li', 'Björn Möller', 'Timo Lohrenz'] | 2022-09-20 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 2.68849194e-01 6.21946216e-01 -2.44436398e-01 -1.86857477e-01
-1.36042440e+00 -4.30240393e-01 5.70633709e-01 -2.29460657e-01
-4.64428782e-01 5.92169225e-01 6.60067499e-01 -7.47910202e-01
5.38561881e-01 -3.18955123e-01 -1.11250114e+00 -4.46085006e-01
4.41074073e-01 6.49238765e-01 -5.47689535e-02 -4.80938375... | [11.06336498260498, 7.502165794372559] |
61eaae61-3685-4ec4-8745-dcfa0ce0b023 | stable-and-effective-one-step-method-for | null | null | https://ieeexplore.ieee.org/document/9413460 | https://ieeexplore.ieee.org/document/9413460 | Stable and Effective One-Step Method for Person Search | Person search, which requires both pedestrian detection and person re-identification, is a challenging computer vision task applied to real-world scenarios. The challenges faced by detection and re-identification, such as occlusion, poor illumination, confusing background, are still urgent for person search. In additio... | ['Abdulmotaleb El Saddik', 'Rokia Abdeen', 'Jie Yang', 'Xinyao Wang', 'Xuezhi Xiang', 'Ning Lv'] | 2021-05-13 | null | null | null | ieee-international-conference-on-acoustics-10 | ['person-search'] | ['computer-vision'] | [-1.92476884e-01 -7.90233552e-01 2.45894119e-01 -3.65804315e-01
-8.18203747e-01 -3.31239551e-01 3.93607974e-01 -1.31823719e-01
-1.16364717e+00 7.11478770e-01 -7.66156912e-02 9.39422399e-02
2.26214767e-01 -3.48689824e-01 -3.38439435e-01 -7.29197860e-01
3.85586500e-01 4.27514136e-01 4.16643769e-01 2.71935195... | [14.79964542388916, 0.8364032506942749] |
ba2bedfd-8832-4509-952b-797b6f98c364 | probabilistic-regular-tree-priors-for | 2306.08506 | null | https://arxiv.org/abs/2306.08506v1 | https://arxiv.org/pdf/2306.08506v1.pdf | Probabilistic Regular Tree Priors for Scientific Symbolic Reasoning | Symbolic Regression (SR) allows for the discovery of scientific equations from data. To limit the large search space of possible equations, prior knowledge has been expressed in terms of formal grammars that characterize subsets of arbitrary strings. However, there is a mismatch between context-free grammars required t... | ['Steffen Staab', 'Wolfgang Nowak', 'Amin Totounferoush', 'Tim Schneider'] | 2023-06-14 | null | null | null | null | ['symbolic-regression', 'bayesian-inference'] | ['knowledge-base', 'methodology'] | [ 5.67624152e-01 5.74105799e-01 -9.73622128e-02 -5.47003329e-01
-5.73183060e-01 -6.03020787e-01 3.60107332e-01 5.43118827e-02
1.48128167e-01 9.22338128e-01 -1.68158591e-01 -1.19753790e+00
-6.32271171e-01 -1.11018956e+00 -9.79947448e-01 -3.05542916e-01
-1.37709990e-01 8.49640489e-01 1.98451862e-01 -4.44314033... | [8.551045417785645, 6.544299602508545] |
ac945c50-0234-4f2b-b969-32cafdca5fcc | random-forest-classifier-for-eeg-based | 2106.04510 | null | https://arxiv.org/abs/2106.04510v1 | https://arxiv.org/pdf/2106.04510v1.pdf | Random Forest classifier for EEG-based seizure prediction | Epileptic seizure prediction has gained considerable interest in the computational Epilepsy research community. This paper presents a Machine Learning based method for epileptic seizure prediction which outperforms state-of-the art methods. We compute a probability for a given epoch, of being pre-ictal against interict... | ['Mario Chavez', 'Remy Ben Messaoud'] | 2021-06-02 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [ 1.84711218e-01 1.43301725e-01 2.70838410e-01 -3.99370581e-01
-6.81177974e-01 -2.38061666e-01 5.12619376e-01 4.30269450e-01
-3.20007116e-01 1.14721501e+00 -1.04483046e-01 -4.55709696e-01
-3.37376177e-01 -5.40659189e-01 -2.73120254e-01 -7.17868030e-01
-9.50006425e-01 2.54615486e-01 4.31266189e-01 3.44070911... | [13.23590087890625, 3.527452230453491] |
c7e5a8f1-8f65-4ac5-a4cf-199500e781d9 | selc-self-ensemble-label-correction-improves | 2205.01156 | null | https://arxiv.org/abs/2205.01156v1 | https://arxiv.org/pdf/2205.01156v1.pdf | SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels | Deep neural networks are prone to overfitting noisy labels, resulting in poor generalization performance. To overcome this problem, we present a simple and effective method self-ensemble label correction (SELC) to progressively correct noisy labels and refine the model. We look deeper into the memorization behavior in ... | ['Wenbo He', 'Yangdi Lu'] | 2022-05-02 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.70419681e-01 4.33660159e-03 1.69279814e-01 -7.41611600e-01
-7.90656626e-01 -4.77475882e-01 5.14549434e-01 4.43773896e-01
-6.69574142e-01 1.15602386e+00 -3.68269160e-02 -1.37410536e-01
5.14541306e-02 -6.27197921e-01 -6.56544089e-01 -7.79373884e-01
1.73864588e-01 3.10340077e-01 7.49664083e-02 -1.54792368... | [9.35177230834961, 3.8557381629943848] |
18e0f256-85a2-43c0-b36b-7ce657a00d78 | 2-speed-network-ensemble-for-efficient | 2203.08267 | null | https://arxiv.org/abs/2203.08267v1 | https://arxiv.org/pdf/2203.08267v1.pdf | 2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips | The ever-growing volume of satellite imagery data presents a challenge for industry and governments making data-driven decisions based on the timely analysis of very large data sets. Commonly used deep learning algorithms for automatic classification of satellite images are time and resource-intensive to train. The cos... | ['Sanjoy Paul', 'Nagesh Shukla', 'Biswajeet Pradhan', 'Subrata Chakraborty', 'Michael James Horry'] | 2022-03-15 | null | null | null | null | ['satellite-image-classification'] | ['computer-vision'] | [ 1.42668813e-01 -4.30211991e-01 6.77987039e-02 -5.12987852e-01
-5.24886250e-01 -5.19867837e-01 2.93719620e-01 -7.16560185e-02
-7.85770595e-01 7.85745323e-01 -3.45721483e-01 -4.36710119e-01
-1.19618796e-01 -1.07273257e+00 -7.43784666e-01 -9.29305792e-01
-3.92345577e-01 6.67374849e-01 2.41191462e-01 -3.35698575... | [9.59597110748291, -1.4468575716018677] |
705e4bf8-292f-493b-8d97-9d543ae2f0de | chatgpt-vs-google-a-comparative-study-of | 2307.01135 | null | https://arxiv.org/abs/2307.01135v1 | https://arxiv.org/pdf/2307.01135v1.pdf | ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience | The advent of ChatGPT, a large language model-powered chatbot, has prompted questions about its potential implications for traditional search engines. In this study, we investigate the differences in user behavior when employing search engines and chatbot tools for information-seeking tasks. We carry out a randomized o... | ['Hailiang Chen', 'Yue Feng', 'Ruiyun Xu'] | 2023-07-03 | null | null | null | null | ['chatbot', 'misinformation', 'management', 'chatbot'] | ['methodology', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-7.36673474e-01 2.15403676e-01 -4.94450003e-01 2.06355408e-01
-6.33448482e-01 -7.02029407e-01 5.79263866e-01 1.90757111e-01
-6.05219483e-01 3.13327700e-01 1.76175207e-01 -9.21163738e-01
-2.83898443e-01 -3.39603931e-01 2.93392956e-01 -9.22557060e-03
5.64370275e-01 2.17568949e-01 2.37692118e-01 -3.42521042... | [12.203276634216309, 7.763009548187256] |
2d05e106-53f6-49be-bceb-b078b9c89d3b | nextdoor-gpu-based-graph-sampling-for | 2009.06693 | null | https://arxiv.org/abs/2009.06693v4 | https://arxiv.org/pdf/2009.06693v4.pdf | Accelerating Graph Sampling for Graph Machine Learning using GPUs | Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches that are suitable for training a DNN. However, sampling time can be a significant fraction of traini... | ['Marco Serafini', 'Arjun Guha', 'Sandeep Polisetty', 'Abhinav Jangda'] | 2020-09-14 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [-1.80358559e-01 9.93807986e-02 -5.16400218e-01 -3.94242913e-01
-6.28949344e-01 -6.09129965e-01 4.55747426e-01 5.64099848e-01
-1.77612573e-01 4.22721505e-01 1.81219742e-01 -8.29988301e-01
2.66553015e-01 -1.65547621e+00 -5.51998913e-01 -5.27705491e-01
-3.97193879e-01 8.77969921e-01 1.49103865e-01 -6.36414140... | [6.988565444946289, 5.783038139343262] |
fe103ec3-8dd1-48bd-b81f-001b0aaeb2d2 | xkd-cross-modal-knowledge-distillation-with | 2211.13929 | null | https://arxiv.org/abs/2211.13929v4 | https://arxiv.org/pdf/2211.13929v4.pdf | XKD: Cross-modal Knowledge Distillation with Domain Alignment for Video Representation Learning | We present XKD, a novel self-supervised framework to learn meaningful representations from unlabelled video clips. XKD is trained with two pseudo tasks. First, masked data reconstruction is performed to learn individual representations from audio and visual streams. Next, self-supervised cross-modal knowledge distillat... | ['Ali Etemad', 'Pritam Sarkar'] | 2022-11-25 | null | null | null | null | ['action-classification', 'self-supervised-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 4.48122770e-01 5.60800254e-04 -2.17490584e-01 -2.64423132e-01
-1.19715548e+00 -5.49950957e-01 6.77843153e-01 2.71218698e-02
-6.26685619e-01 7.19529152e-01 2.39129901e-01 1.06381342e-01
-9.61470231e-02 -4.48014289e-01 -1.06520379e+00 -5.80476224e-01
-2.18448088e-01 2.70085961e-01 3.03762197e-01 -1.18729763... | [9.720733642578125, 1.048330545425415] |
6f2cf0d1-8976-4730-8fdb-917fc129e5c7 | self-organization-map-based-texture-feature | 1408.4143 | null | http://arxiv.org/abs/1408.4143v1 | http://arxiv.org/pdf/1408.4143v1.pdf | Self Organization Map based Texture Feature Extraction for Efficient Medical Image Categorization | Texture is one of the most important properties of visual surface that helps
in discriminating one object from another or an object from background. The
self-organizing map (SOM) is an excellent tool in exploratory phase of data
mining. It projects its input space on prototypes of a low-dimensional regular
grid that ca... | ['Mohammed M. Abdelsamea', 'Marghny H. Mohamed'] | 2014-07-14 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 2.61412740e-01 -9.50248837e-02 -2.60111243e-01 -5.11223733e-01
2.60224212e-02 -2.83918619e-01 4.86415923e-01 7.31121480e-01
-3.68271291e-01 3.40854019e-01 4.33915198e-01 -9.67966095e-02
-7.12706625e-01 -1.02551234e+00 2.09135208e-02 -9.08386707e-01
-3.23500007e-01 6.46759510e-01 5.44823825e-01 -6.45532608... | [14.988771438598633, -2.674238681793213] |
db378c1c-8d24-4ead-9e78-ca6f09b273cb | the-ebible-corpus-data-and-model-benchmarks | 2304.09919 | null | https://arxiv.org/abs/2304.09919v1 | https://arxiv.org/pdf/2304.09919v1.pdf | The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages | Efficiently and accurately translating a corpus into a low-resource language remains a challenge, regardless of the strategies employed, whether manual, automated, or a combination of the two. Many Christian organizations are dedicated to the task of translating the Holy Bible into languages that lack a modern translat... | ['Marcus Schwarting', 'Jonathan Robie', 'Joel Mathew', 'Michael Martin', 'Colin Leong', 'Taeho Jang', 'Ulf Hermjakob', 'Damien Daspit', 'David Baines', 'Vesa Akerman'] | 2023-04-19 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 1.65643513e-01 -1.14957444e-01 -4.56858307e-01 -4.67684925e-01
-1.39976788e+00 -9.49976802e-01 8.23072851e-01 -2.38429904e-01
-5.40738881e-01 1.33226502e+00 3.91562343e-01 -9.01539743e-01
3.78819972e-01 -7.45301008e-01 -6.61660314e-01 -4.53266621e-01
4.46825802e-01 1.41287053e+00 -3.91166210e-01 -7.68540621... | [11.559547424316406, 10.396551132202148] |
62582aab-da69-4272-883d-7e47273f602b | fisheyeex-polar-outpainting-for-extending-the | 2206.05844 | null | https://arxiv.org/abs/2206.05844v1 | https://arxiv.org/pdf/2206.05844v1.pdf | FisheyeEX: Polar Outpainting for Extending the FoV of Fisheye Lens | Fisheye lens gains increasing applications in computational photography and assisted driving because of its wide field of view (FoV). However, the fisheye image generally contains invalid black regions induced by its imaging model. In this paper, we present a FisheyeEX method that extends the FoV of the fisheye lens by... | ['Yao Zhao', 'Yunchao Wei', 'Chunyu Lin', 'Kang Liao'] | 2022-06-12 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 2.25938603e-01 5.53979464e-02 1.93116844e-01 -3.56114209e-01
-7.03790411e-02 -4.63794827e-01 4.50410903e-01 -7.00398147e-01
-2.18072310e-01 5.80150723e-01 1.03898667e-01 -6.91432878e-02
1.20516464e-01 -8.37646961e-01 -9.98941481e-01 -7.96356618e-01
6.52112305e-01 -3.16427827e-01 6.69392288e-01 -3.34455281... | [10.185526847839355, -2.5118086338043213] |
5e80f980-d627-4779-bed0-49d75b0f9d59 | neural-frailty-machine-beyond-proportional | 2303.10358 | null | https://arxiv.org/abs/2303.10358v1 | https://arxiv.org/pdf/2303.10358v1.pdf | Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions | We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis to capture unobserved heterogeneity among individuals, at the same time being able to leverage the strong appr... | ['Weiqiang Wang', 'Tianyi Zhang', 'Tengfei Liu', 'Ming Zheng', 'Wen Yu', 'Mingzhe Wu', 'Jiawei Qiao', 'Ruofan Wu'] | 2023-03-18 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-3.68446231e-01 4.38248143e-02 -5.36067426e-01 -5.67437887e-01
-7.88896918e-01 -3.65853421e-02 2.15442404e-01 3.69950049e-02
-2.76647747e-01 1.08393848e+00 3.27006638e-01 -5.37749588e-01
-3.61045599e-01 -7.23770201e-01 -7.37292171e-01 -8.30206931e-01
-7.32646942e-01 4.34003949e-01 -2.68039197e-01 2.90954430... | [7.7844367027282715, 5.591602325439453] |
facbf9f6-dfe7-4af0-864f-d52e7006920a | undeepvo-monocular-visual-odometry-through | 1709.06841 | null | http://arxiv.org/abs/1709.06841v2 | http://arxiv.org/pdf/1709.06841v2.pdf | UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning | We propose a novel monocular visual odometry (VO) system called UnDeepVO in
this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera
and the depth of its view by using deep neural networks. There are two salient
features of the proposed UnDeepVO: one is the unsupervised deep learning
scheme, and th... | ['Sen Wang', 'Dongbing Gu', 'Zhiqiang Long', 'Ruihao Li'] | 2017-09-20 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-4.77661550e-01 -1.39126286e-01 -1.69282913e-01 -2.56381005e-01
1.13926098e-01 -3.21016133e-01 6.13402009e-01 -8.12649071e-01
-4.62776691e-01 6.00225627e-01 4.97725010e-02 1.21946819e-01
1.53095424e-01 -5.53056955e-01 -9.36292827e-01 -5.28131127e-01
2.65315026e-01 6.44911885e-01 2.06916884e-01 -2.21929811... | [8.05415153503418, -2.1975905895233154] |
13455ebe-aa26-4987-a914-61de0e66779b | schema-guided-user-satisfaction-modeling-for | 2305.16798 | null | https://arxiv.org/abs/2305.16798v1 | https://arxiv.org/pdf/2305.16798v1.pdf | Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues | User Satisfaction Modeling (USM) is one of the popular choices for task-oriented dialogue systems evaluation, where user satisfaction typically depends on whether the user's task goals were fulfilled by the system. Task-oriented dialogue systems use task schema, which is a set of task attributes, to encode the user's t... | ['Gabriella Kazai', 'Emine Yilmaz', 'Nikolaos Aletras', 'Animesh Prasad', 'Yunlong Jiao', 'Yue Feng'] | 2023-05-26 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 0.1683778 0.26042747 -0.34057957 -0.9541179 -0.5202768 -0.24036759
0.51764125 0.20284894 -0.51210976 0.58183867 0.551256 -0.06851896
-0.1070813 -0.37660313 0.19238245 -0.18547675 0.41735533 1.1922768
-0.03222709 -0.82043874 0.3824455 -0.39319494 -1.4137475 0.6352698
1.0910239 1.1650611 0.48... | [12.75547981262207, 7.942605018615723] |
c5e53ccd-cc5e-4e27-8430-5fb54dc25de1 | single-stage-visual-query-localization-in | 2306.09324 | null | https://arxiv.org/abs/2306.09324v1 | https://arxiv.org/pdf/2306.09324v1.pdf | Single-Stage Visual Query Localization in Egocentric Videos | Visual Query Localization on long-form egocentric videos requires spatio-temporal search and localization of visually specified objects and is vital to build episodic memory systems. Prior work develops complex multi-stage pipelines that leverage well-established object detection and tracking methods to perform VQL. Ho... | ['Kristen Grauman', 'Santhosh Kumar Ramakrishnan', 'Hanwen Jiang'] | 2023-06-15 | null | null | null | null | ['temporal-localization'] | ['computer-vision'] | [-7.37362206e-01 -5.04747331e-01 -5.93213797e-01 -2.91926235e-01
-8.05505872e-01 -6.65861487e-01 5.28961599e-01 6.27771989e-02
-4.82271522e-01 3.67037207e-01 3.80495846e-01 1.16314486e-01
6.49950802e-02 -4.64306295e-01 -9.66588259e-01 -5.49966916e-02
-2.57161379e-01 3.87985140e-01 6.64356232e-01 3.25947315... | [8.632163047790527, 0.3533104956150055] |
531f6b98-7a5f-4ccc-81ff-ea27bc6a6622 | multilingual-ner-transfer-for-low-resource | 1902.00193 | null | https://arxiv.org/abs/1902.00193v4 | https://arxiv.org/pdf/1902.00193v4.pdf | Massively Multilingual Transfer for NER | In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a `massive' setting with many such models. This setting raises the problem of poor transfer, pa... | ['Afshin Rahimi', 'Yuan Li', 'Trevor Cohn'] | 2019-02-01 | massively-multilingual-transfer-for-ner | https://aclanthology.org/P19-1015 | https://aclanthology.org/P19-1015.pdf | acl-2019-7 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 1.61255583e-01 9.37316269e-02 -6.21060669e-01 -4.23786044e-01
-1.75670409e+00 -6.73791885e-01 9.48583782e-01 -1.52035639e-01
-7.52460361e-01 9.09414351e-01 4.03534234e-01 -3.77696693e-01
1.80406824e-01 -4.96845871e-01 -8.33292544e-01 -5.97455800e-01
-3.90961654e-02 8.78973424e-01 2.84820586e-01 -3.35091203... | [11.012696266174316, 9.791007041931152] |
398ff5ea-0b02-4792-b0ce-f852d11d62d1 | mutual-context-network-for-jointly-estimating | 1901.01874 | null | https://arxiv.org/abs/1901.01874v4 | https://arxiv.org/pdf/1901.01874v4.pdf | Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions | In this work, we address two coupled tasks of gaze prediction and action recognition in egocentric videos by exploring their mutual context. Our assumption is that in the procedure of performing a manipulation task, what a person is doing determines where the person is looking at, and the gaze point reveals gaze and no... | ['Yoichi Sato', 'Zhenqiang Li', 'Minjie Cai', 'Yifei Huang'] | 2019-01-07 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 3.66104126e-01 -1.93753168e-02 -6.02446377e-01 -6.29492760e-01
-1.33280205e-02 -9.90906134e-02 4.60582793e-01 -7.10393071e-01
-2.57699788e-01 4.56857860e-01 7.63705790e-01 2.92657167e-01
1.60569064e-02 -1.09872915e-01 -5.85163593e-01 -8.91886890e-01
-3.67570892e-02 -1.93303823e-01 -6.81061819e-02 1.78424805... | [13.991453170776367, 0.054675135761499405] |
8d38bc9b-2b6a-44fe-a31b-6f2ede3c2250 | saliency-based-fire-detection-using-texture | 1912.10059 | null | https://arxiv.org/abs/1912.10059v1 | https://arxiv.org/pdf/1912.10059v1.pdf | Saliency Based Fire Detection Using Texture and Color Features | Due to industry deployment and extension of urban areas, early warning systems have an essential role in giving emergency. Fire is an event that can rapidly spread and cause injury, death, and damage. Early detection of fire could significantly reduce these injuries. Video-based fire detection is a low cost and fast me... | ['Shadrokh Samavi', 'Nader Karimi', 'Maedeh Jamali'] | 2019-12-20 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 3.56918216e-01 -1.12151015e+00 1.27546368e-02 1.42601863e-01
-1.59615308e-01 -4.85327572e-01 5.95275998e-01 2.93090940e-01
-6.49523735e-01 7.56022334e-01 1.34628505e-01 -1.03465550e-01
-2.47109845e-01 -1.20502722e+00 -1.25493079e-01 -7.57189274e-01
-4.10924435e-01 -2.59976476e-01 1.08515513e+00 -1.01182558... | [9.168943405151367, -1.1983873844146729] |
d93c17a4-c054-41f7-a5bd-819c8d83b856 | knowledge-graph-and-text-jointly-embedding | null | null | https://aclanthology.org/D14-1167 | https://aclanthology.org/D14-1167.pdf | Knowledge Graph and Text Jointly Embedding | null | ['Jianwen Zhang', 'Jianlin Feng', 'Zhen Wang', 'Zheng Chen'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.483160018920898, 3.820765495300293] |
7f5b269a-4426-4fd8-a5ef-9e6051a07e0e | gridnet-with-automatic-shape-prior | 1705.08943 | null | http://arxiv.org/abs/1705.08943v2 | http://arxiv.org/pdf/1705.08943v2.pdf | GridNet with automatic shape prior registration for automatic MRI cardiac segmentation | In this paper, we propose a fully automatic MRI cardiac segmentation method
based on a novel deep convolutional neural network (CNN) designed for the 2017
ACDC MICCAI challenge. The novelty of our network comes with its embedded shape
prior and its loss function tailored to the cardiac anatomy. Our model includes
a car... | ['Pierre-Marc Jodoin', 'Olivier Humbert', 'Clement Zotti', 'Alain Lalande', 'Zhiming Luo'] | 2017-05-24 | null | null | null | null | ['image-cropping', 'cardiac-segmentation'] | ['computer-vision', 'medical'] | [ 8.69816393e-02 4.02640700e-01 1.47688419e-01 -4.19485062e-01
-5.11179090e-01 -3.59304547e-01 1.73149660e-01 4.00698811e-01
-6.73360348e-01 5.54088354e-01 -1.88190296e-01 -1.07247636e-01
1.62942603e-01 -6.22055411e-01 -5.95266759e-01 -7.64122069e-01
-4.34813708e-01 6.77123308e-01 2.67747551e-01 1.02884673... | [14.227895736694336, -2.392822265625] |
c935a7a5-28a4-43fa-a745-e2ca5ebe3e84 | seggroup-seg-level-supervision-for-3d | 2012.10217 | null | https://arxiv.org/abs/2012.10217v5 | https://arxiv.org/pdf/2012.10217v5.pdf | SegGroup: Seg-Level Supervision for 3D Instance and Semantic Segmentation | Most existing point cloud instance and semantic segmentation methods rely heavily on strong supervision signals, which require point-level labels for every point in the scene. However, such strong supervision suffers from large annotation costs, arousing the need to study efficient annotating. In this paper, we discove... | ['Jie zhou', 'Jiwen Lu', 'Yi Wei', 'Yueqi Duan', 'An Tao'] | 2020-12-18 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 1.09541900e-01 5.30078113e-01 -6.34694874e-01 -7.67654240e-01
-9.65775251e-01 -7.41390824e-01 2.54384279e-01 4.62505966e-01
-2.25868776e-01 2.24638730e-01 -3.76612544e-01 -3.21179390e-01
2.05403805e-01 -7.72967279e-01 -1.03143549e+00 -4.51588690e-01
3.99761891e-04 7.60470152e-01 9.13848042e-01 3.32519472... | [8.013051986694336, -3.180424213409424] |
5d6bc172-c5db-4605-81ac-6feb378d8b84 | 6d-rotation-representation-for-unconstrained | 2202.12555 | null | https://arxiv.org/abs/2202.12555v2 | https://arxiv.org/pdf/2202.12555v2.pdf | 6D Rotation Representation For Unconstrained Head Pose Estimation | In this paper, we present a method for unconstrained end-to-end head pose estimation. We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D rotation matrix representation for efficient and robust direct regression. This way... | ['Ayoub Al-Hamadi', 'Ahmed A. Abdelrahman', 'Thorsten Hempel'] | 2022-02-25 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.75930494e-01 3.98064703e-01 -3.75065565e-01 -6.93446577e-01
-1.13901365e+00 -3.32801610e-01 3.99296373e-01 -4.93662149e-01
-6.57024980e-01 6.70027375e-01 3.97975534e-01 -3.15216243e-01
1.71861410e-01 -1.85307398e-01 -8.35669577e-01 -6.29838824e-01
1.47021011e-01 6.53807104e-01 -1.24497347e-01 -2.77260035... | [13.656578063964844, 0.2563970983028412] |
33bdfb67-0630-4544-9b95-2e329bbc9262 | adapting-pre-trained-language-models-for | 2302.13812 | null | https://arxiv.org/abs/2302.13812v1 | https://arxiv.org/pdf/2302.13812v1.pdf | Adapting Pre-trained Language Models for Quantum Natural Language Processing | The emerging classical-quantum transfer learning paradigm has brought a decent performance to quantum computational models in many tasks, such as computer vision, by enabling a combination of quantum models and classical pre-trained neural networks. However, using quantum computing with pre-trained models has yet to be... | ['Qun Liu', 'Christina Lioma', 'Yudong Zhu', 'Benyou Wang', 'Qiuchi Li'] | 2023-02-24 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 4.56731111e-01 2.53981918e-01 1.83569882e-02 -4.25837457e-01
-1.03459442e+00 -3.77717078e-01 7.96220601e-01 2.08900124e-01
-5.32155037e-01 6.50086045e-01 -1.06055573e-01 -6.97140574e-01
2.38202497e-01 -1.17508352e+00 -7.62693405e-01 -4.34393287e-01
-9.88111421e-02 5.82270563e-01 2.29903776e-02 -6.49611175... | [5.570859909057617, 4.968654632568359] |
9e27a389-e6f3-4215-92db-42175cf6220e | one-shot-action-recognition-towards-novel | 2102.08997 | null | https://arxiv.org/abs/2102.08997v4 | https://arxiv.org/pdf/2102.08997v4.pdf | One-shot action recognition in challenging therapy scenarios | One-shot action recognition aims to recognize new action categories from a single reference example, typically referred to as the anchor example. This work presents a novel approach for one-shot action recognition in the wild that computes motion representations robust to variable kinematic conditions. One-shot action ... | ['Ana C. Murillo', 'Luis Montesano', 'Alexandre Bernardino', 'Jose Santos-Victor', 'Laura Santos', 'Alberto Sabater'] | 2021-02-17 | null | null | null | null | ['one-shot-3d-action-recognition'] | ['computer-vision'] | [ 7.83666730e-01 2.57436261e-02 -4.69202340e-01 -1.03885055e-01
-1.13033295e+00 -2.23108053e-01 8.21752846e-01 -2.48197109e-01
-3.28918159e-01 4.41211611e-01 7.40727067e-01 5.22327363e-01
-2.22400755e-01 -9.66245160e-02 -1.24767728e-01 -8.03590536e-01
-2.85024017e-01 5.38479388e-01 3.21365982e-01 -1.73189402... | [8.084908485412598, 0.6032102704048157] |
8cc7fc8d-430e-4bde-ba30-439ed6285510 | ontology-based-production-simulation-with | null | null | https://www.mdpi.com/2076-3417/12/3/1608 | https://www.mdpi.com/2076-3417/12/3/1608 | Ontology-Based Production Simulation with OntologySim | Imagine the possibility to save a simulation at any time, modify or analyze it, and restart again with exactly the same state. The conceptualization and its concrete manifestation in the implementation OntologySim is demonstrated in this paper. The presented approach of a fully ontology-based simulation can solve curre... | ['Gisela Lanza', 'Andreas Kuhnle', 'Lars Kiefer', 'Marvin Carl May'] | 2022-02-03 | null | null | null | mdpi-2022-2 | ['manufacturing-simulation'] | ['knowledge-base'] | [-2.27795482e-01 2.37134606e-01 3.20626318e-01 4.20109555e-02
5.26207507e-01 -8.34255934e-01 1.11117923e+00 4.60362256e-01
-1.53024659e-01 5.06706834e-01 -1.63938388e-01 -1.75037578e-01
-8.56627524e-01 -1.17288244e+00 -3.30760568e-01 -3.41145098e-01
-1.89523071e-01 9.45398748e-01 3.45548511e-01 -9.47174013... | [8.824394226074219, 6.94346809387207] |
7e905474-31d2-4a1a-b0bf-c9b5c705baa6 | neural-approaches-to-entity-centric | 2304.07625 | null | https://arxiv.org/abs/2304.07625v1 | https://arxiv.org/pdf/2304.07625v1.pdf | Neural Approaches to Entity-Centric Information Extraction | Artificial Intelligence (AI) has huge impact on our daily lives with applications such as voice assistants, facial recognition, chatbots, autonomously driving cars, etc. Natural Language Processing (NLP) is a cross-discipline of AI and Linguistics, dedicated to study the understanding of the text. This is a very challe... | ['Klim Zaporojets'] | 2023-04-15 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [-9.92126837e-02 4.00742531e-01 -2.28163943e-01 -3.59731942e-01
-4.81627546e-02 -8.56803179e-01 8.20573032e-01 5.77464104e-01
-5.86937964e-01 1.07726419e+00 4.02219802e-01 -1.27536893e-01
-2.60898679e-01 -8.96097481e-01 -3.89100790e-01 -3.89027655e-01
1.47676095e-01 9.49313045e-01 5.71791947e-01 -6.45391047... | [9.500386238098145, 9.101840019226074] |
a94fd564-4dbe-4637-b615-424556430ebf | generating-negative-samples-by-manipulating | null | null | https://aclanthology.org/2021.naacl-main.120 | https://aclanthology.org/2021.naacl-main.120.pdf | Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model | Evaluating the quality of responses generated by open-domain conversation systems is a challenging task. This is partly because there can be multiple appropriate responses to a given dialogue history. Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this var... | ['Jong Park', 'Wonsuk Yang', 'Eugene Jang', 'ChaeHun Park'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 5.34540772e-01 4.34086412e-01 1.14352621e-01 -8.61599743e-01
-1.05971849e+00 -7.02788949e-01 6.44296229e-01 2.80848920e-01
-4.94168043e-01 9.04204607e-01 5.28924286e-01 -2.59115219e-01
-1.90686863e-02 -6.75496459e-01 -8.73706676e-03 -3.87726635e-01
6.52267814e-01 7.03422248e-01 2.43608251e-01 -4.60319668... | [12.7877197265625, 8.052569389343262] |
ae5a68e7-4bf7-4def-82ee-e75537888cac | dad-data-free-adversarial-defense-at-test | 2204.01568 | null | https://arxiv.org/abs/2204.01568v2 | https://arxiv.org/pdf/2204.01568v2.pdf | DAD: Data-free Adversarial Defense at Test Time | Deep models are highly susceptible to adversarial attacks. Such attacks are carefully crafted imperceptible noises that can fool the network and can cause severe consequences when deployed. To encounter them, the model requires training data for adversarial training or explicit regularization-based techniques. However,... | ['Anirban Chakraborty', 'Ruchit Rawal', 'Gaurav Kumar Nayak'] | 2022-04-04 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 5.05072057e-01 1.13858588e-01 3.22275937e-01 -2.43858635e-01
-9.30621803e-01 -1.15734160e+00 4.67430890e-01 -3.86551931e-03
-5.91228545e-01 7.84382284e-01 -3.75382900e-01 -3.48458022e-01
1.28598630e-01 -8.69568050e-01 -1.02972257e+00 -8.73074055e-01
-1.29948765e-01 3.05921078e-01 2.28101283e-01 -1.62006915... | [5.600977420806885, 7.830865859985352] |
5ab8fcac-7db3-44be-9f03-7114610a28bb | situational-grounding-within-multimodal | 1902.01886 | null | http://arxiv.org/abs/1902.01886v1 | http://arxiv.org/pdf/1902.01886v1.pdf | Situational Grounding within Multimodal Simulations | In this paper, we argue that simulation platforms enable a novel type of
embodied spatial reasoning, one facilitated by a formal model of object and
event semantics that renders the continuous quantitative search space of an
open-world, real-time environment tractable. We provide examples for how a
semantically-informe... | ['Nikhil Krishnaswamy', 'James Pustejovsky'] | 2019-02-05 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-3.84893030e-01 1.88616648e-01 2.99338609e-01 -1.53379902e-01
-2.65042782e-01 -9.44228530e-01 8.67630899e-01 3.03266346e-01
-5.61224401e-01 6.55380130e-01 8.69832188e-02 -6.28645301e-01
-2.95501202e-01 -1.34866309e+00 -4.83674973e-01 -2.40829796e-01
-5.65041065e-01 6.24015629e-01 2.83564270e-01 -7.18827426... | [4.082743167877197, 1.187160611152649] |
b687fa9c-d68f-4ccb-8736-6d9857d19c39 | two-level-graph-neural-network | 2201.01190 | null | https://arxiv.org/abs/2201.01190v1 | https://arxiv.org/pdf/2201.01190v1.pdf | Two-level Graph Neural Network | Graph Neural Networks (GNNs) are recently proposed neural network structures for the processing of graph-structured data. Due to their employed neighbor aggregation strategy, existing GNNs focus on capturing node-level information and neglect high-level information. Existing GNNs therefore suffer from representational ... | ['Edwin R Hancock', 'Zhihong Zhang', 'Chengyu Sun', 'Xing Ai'] | 2022-01-03 | null | null | null | null | ['subgraph-counting'] | ['graphs'] | [ 3.89445841e-01 1.62870675e-01 -4.12979931e-01 -1.37820318e-01
1.87959090e-01 -6.51187375e-02 2.34708250e-01 3.19150776e-01
-4.16631758e-01 6.78926766e-01 -7.18396679e-02 -3.70614439e-01
-7.18379438e-01 -1.42802846e+00 -6.06763124e-01 -6.97710752e-01
-6.13225043e-01 1.35498032e-01 2.81986624e-01 -1.44765675... | [7.022201061248779, 6.214372634887695] |
6e48f2dd-5d42-4ed5-bab5-a48520e60d86 | improving-scene-text-recognition-for | 2304.08592 | null | https://arxiv.org/abs/2304.08592v1 | https://arxiv.org/pdf/2304.08592v1.pdf | Improving Scene Text Recognition for Character-Level Long-Tailed Distribution | Despite the recent remarkable improvements in scene text recognition (STR), the majority of the studies focused mainly on the English language, which only includes few number of characters. However, STR models show a large performance degradation on languages with a numerous number of characters (e.g., Chinese and Kore... | ['Jaegul Choo', 'Jungsoo Lee', 'Sunghyo Chung', 'Sunghyun Park'] | 2023-03-31 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 3.07387531e-01 -5.80201983e-01 -8.54559839e-02 -2.73940653e-01
-2.84206390e-01 -4.46853936e-01 5.91338575e-01 -1.59160066e-02
-5.58398366e-01 6.86127305e-01 9.93956178e-02 -3.99619877e-01
2.68306017e-01 -8.13597500e-01 -7.66723752e-01 -6.01349175e-01
4.40453559e-01 2.98165709e-01 2.84160733e-01 -7.90031925... | [11.895709037780762, 2.198019027709961] |
683b89b3-c133-4db8-89e6-57aa4b9fb682 | open-vocabulary-detr-with-conditional | 2203.11876 | null | https://arxiv.org/abs/2203.11876v2 | https://arxiv.org/pdf/2203.11876v2.pdf | Open-Vocabulary DETR with Conditional Matching | Open-vocabulary object detection, which is concerned with the problem of detecting novel objects guided by natural language, has gained increasing attention from the community. Ideally, we would like to extend an open-vocabulary detector such that it can produce bounding box predictions based on user inputs in form of ... | ['Chen Change Loy', 'Chen Huang', 'Kaiyang Zhou', 'Wei Li', 'Yuhang Zang'] | 2022-03-22 | null | null | null | null | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 3.75289649e-01 1.04024813e-01 5.67640513e-02 -3.80457252e-01
-1.02574134e+00 -6.23671710e-01 6.20035768e-01 1.53157249e-01
-5.13090789e-01 2.54103094e-01 -2.26406828e-01 -1.54493898e-01
3.38050544e-01 -5.93395293e-01 -1.01529908e+00 -3.37819815e-01
1.30877405e-01 5.84881604e-01 4.78122830e-01 -6.95767552... | [9.726065635681152, 1.536392331123352] |
845aa6a3-3589-4d26-b5fb-cb4666197d90 | harmonious-feature-learning-for-interactive | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.pdf | Harmonious Feature Learning for Interactive Hand-Object Pose Estimation | Joint hand and object pose estimation from a single image is extremely challenging as serious occlusion often occurs when the hand and object interact. Existing approaches typically first extract coarse hand and object features from a single backbone, then further enhance them with reference to each other via inter... | ['Shaoli Huang', 'Zengsheng Kuang', 'Huan Yao', 'Changxing Ding', 'Zhifeng Lin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['hand-pose-estimation', 'hand-object-pose'] | ['computer-vision', 'computer-vision'] | [-3.62552732e-01 -2.79299527e-01 -1.36842981e-01 -1.55269429e-01
-5.45011342e-01 -3.36586982e-01 4.43402231e-01 -3.91947627e-01
-4.18290436e-01 3.83435100e-01 2.74141759e-01 4.18697000e-01
4.09701206e-02 -5.44702590e-01 -7.54521906e-01 -9.19252455e-01
2.64046490e-01 3.32919151e-01 5.46137393e-01 -3.05810589... | [6.74315881729126, -0.7636508941650391] |
85b73227-6d5a-4648-88aa-6e44de1fe598 | improving-arabic-text-categorization-using | null | null | https://aclanthology.org/2020.wanlp-1.21 | https://aclanthology.org/2020.wanlp-1.21.pdf | Improving Arabic Text Categorization Using Transformer Training Diversification | Automatic categorization of short texts, such as news headlines and social media posts, has many applications ranging from content analysis to recommendation systems. In this paper, we use such text categorization i.e., labeling the social media posts to categories like ‘sports’, ‘politics’, ‘human-rights’ among others... | ['Bernard J. Jansen', 'Joni Salminen', 'Jung Soon-Gyo', 'Kareem Darwish', 'Ahmed Abdelali', 'Shammur Absar Chowdhury'] | null | null | null | null | coling-wanlp-2020-12 | ['text-categorization'] | ['natural-language-processing'] | [-2.85842627e-01 1.56506434e-01 -2.10251063e-01 -2.62396872e-01
-4.52566713e-01 -7.78283358e-01 1.06320202e+00 8.29699397e-01
-6.40994310e-01 3.87403399e-01 6.66461587e-01 -4.54425573e-01
6.15942553e-02 -9.71372008e-01 -2.46107250e-01 -4.82806176e-01
4.38346043e-02 4.98589665e-01 3.12071383e-01 -9.66511846... | [10.195062637329102, 10.060270309448242] |
9ffd554e-a545-4df6-a0ff-9c5c52ad27eb | using-multiple-instance-learning-for | 2203.13896 | null | https://arxiv.org/abs/2203.13896v2 | https://arxiv.org/pdf/2203.13896v2.pdf | Using Multiple Instance Learning for Explainable Solar Flare Prediction | In this work we leverage a weakly-labeled dataset of spectral data from NASAs IRIS satellite for the prediction of solar flares using the Multiple Instance Learning (MIL) paradigm. While standard supervised learning models expect a label for every instance, MIL relaxes this and only considers bags of instances to be la... | ['Martin Melchior', 'Cédric Huwyler'] | 2022-03-25 | null | null | null | null | ['solar-flare-prediction'] | ['time-series'] | [ 3.64405990e-01 -1.21163949e-01 -4.22619969e-01 -3.63597184e-01
-7.88843274e-01 -8.34493756e-01 6.70810103e-01 3.90524030e-01
2.40958452e-01 8.39792788e-01 -8.74907337e-03 -2.62242913e-01
-5.30488729e-01 -7.78871536e-01 -4.24130619e-01 -1.02480245e+00
-3.07615310e-01 5.47455847e-01 9.85976309e-02 -3.25032502... | [6.62171745300293, 2.78182053565979] |
bc8136bc-4ad0-4f4f-bfba-331decb644b1 | feature-calibration-network-for-occluded | 2212.05717 | null | https://arxiv.org/abs/2212.05717v1 | https://arxiv.org/pdf/2212.05717v1.pdf | Feature Calibration Network for Occluded Pedestrian Detection | Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-... | ['Qi Tian', 'Xiaopeng Zhang', 'Jianzhuang Liu', 'Baochang Zhang', 'Qixiang Ye', 'Tianliang Zhang'] | 2022-12-12 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-3.14154173e-03 -5.52049205e-02 2.12529644e-01 -4.66749877e-01
-1.20977415e-02 -2.12908089e-01 6.38185620e-01 1.37802258e-01
-6.56989694e-01 5.90766072e-01 5.57383746e-02 9.13329199e-02
3.89026821e-01 -1.04496062e+00 -6.08724415e-01 -9.88352001e-01
-1.09386735e-01 -3.32117602e-02 8.11232030e-01 -2.30752364... | [8.07052230834961, -0.5729885101318359] |
dc6917c2-93c7-4ed3-9f7b-4b7793ea4d6d | from-a-glance-to-gotcha-interactive-facial | 2007.15683 | null | https://arxiv.org/abs/2007.15683v1 | https://arxiv.org/pdf/2007.15683v1.pdf | From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback | Facial image retrieval plays a significant role in forensic investigations where an untrained witness tries to identify a suspect from a massive pool of images. However, due to the difficulties in describing human facial appearances verbally and directly, people naturally tend to depict by referring to well-known exist... | ['Alexander Hauptmann', 'Xinru Yang', 'Haozhi Qi', 'Mingyang Li'] | 2020-07-30 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [ 2.52859056e-01 4.19479236e-02 1.88795757e-02 -5.85001230e-01
-9.84680653e-01 -6.12631381e-01 6.21201456e-01 1.69839457e-01
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-1.42860770e-01 -2.84863055e-01 -2.73018420e-01 -4.93264228e-01
-9.04085487e-02 7.00806916e-01 3.10962051e-01 -4.26099561... | [12.863560676574707, 1.0189911127090454] |
fb1cf2a4-0421-4445-8e26-fc6d3942ee21 | where-did-you-learn-that-from-surprising | 2109.03975 | null | https://arxiv.org/abs/2109.03975v3 | https://arxiv.org/pdf/2109.03975v3.pdf | Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning | While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to membership inference attacks. In such attacking systems, the advers... | ['Doina Precup', 'Alexander Wong', 'Hossein Aboutalebi', 'Susan Amin', 'Maziar Gomrokchi'] | 2021-09-08 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [-1.08040139e-01 1.62877724e-01 1.34176850e-01 -1.18052468e-01
-5.64774036e-01 -9.19716537e-01 4.92094159e-01 2.11256102e-01
-5.44307947e-01 8.45410168e-01 -5.57573080e-01 -6.69921637e-01
-1.21135838e-01 -1.18669534e+00 -1.15007329e+00 -8.74038279e-01
-5.50480485e-01 2.41996907e-02 -1.51358426e-01 -1.10942543... | [5.669051647186279, 7.541394233703613] |
bd3d0735-02c9-4710-8c06-fa403e68cf37 | using-adaptive-experiments-to-rapidly-help | 2208.05092 | null | https://arxiv.org/abs/2208.05092v1 | https://arxiv.org/pdf/2208.05092v1.pdf | Using Adaptive Experiments to Rapidly Help Students | Adaptive experiments can increase the chance that current students obtain better outcomes from a field experiment of an instructional intervention. In such experiments, the probability of assigning students to conditions changes while more data is being collected, so students can be assigned to interventions that are l... | ['Joseph Jay Williams', 'Andrew Petersen', 'Anna Rafferty', 'Jacob Nogas', 'Hammad Shaikh', 'Qi Yin Zheng', 'Angela Zavaleta-Bernuy'] | 2022-08-10 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 4.04325187e-01 1.04591183e-01 -4.59530175e-01 -3.90284657e-01
-3.24983388e-01 -6.44664526e-01 4.91355121e-01 4.77740973e-01
-8.00576568e-01 9.37502444e-01 2.79612869e-01 -1.15621531e+00
-4.00361478e-01 -9.01019931e-01 -9.22688127e-01 -3.20269525e-01
2.87582487e-01 1.45409524e-01 4.51171696e-01 2.02870056... | [10.181479454040527, 7.174191951751709] |
9a393467-3af6-42b8-a541-3105784446ca | game-up-game-aware-mode-enumeration-and | 2305.17600 | null | https://arxiv.org/abs/2305.17600v1 | https://arxiv.org/pdf/2305.17600v1.pdf | GAME-UP: Game-Aware Mode Enumeration and Understanding for Trajectory Prediction | Interactions between road agents present a significant challenge in trajectory prediction, especially in cases involving multiple agents. Because existing diversity-aware predictors do not account for the interactive nature of multi-agent predictions, they may miss these important interaction outcomes. In this paper, w... | ['Guy Rosman', 'Naomi Leonard', 'Avinash Balachandran', 'John Leonard', 'Yen-Ling Kuo', 'Xin Huang', 'Xiongyi Cui', 'Jonathan DeCastro', 'Yanxia Zhang', 'Oswin So', 'Justin Lidard'] | 2023-05-28 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-1.15028721e-04 3.84091973e-01 -5.46482861e-01 -2.49303365e-03
-1.05231535e+00 -7.22437322e-01 6.57855809e-01 4.65176962e-02
-1.66800857e-01 1.11524105e+00 5.34250319e-01 -6.32008314e-01
-6.01039648e-01 -1.02646339e+00 -8.45121682e-01 -2.46502995e-01
-6.78470850e-01 9.55493689e-01 6.82514191e-01 -7.18388379... | [5.421816349029541, 1.1200640201568604] |
2d7ccdaa-8df2-4a3e-96b4-44139a34e1b4 | black-box-vs-gray-box-a-case-study-on | 2305.15189 | null | https://arxiv.org/abs/2305.15189v2 | https://arxiv.org/pdf/2305.15189v2.pdf | Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts | In this paper, we present a method for table tennis ball trajectory filtering and prediction. Our gray-box approach builds on a physical model. At the same time, we use data to learn parameters of the dynamics model, of an extended Kalman filter, and of a neural model that infers the ball's initial condition. We demons... | ['Joerg Stueckler', 'Michael Muehlebach', 'Dieter Buechler', 'Hao Ma', 'Philip Tobuschat', 'Jan Achterhold'] | 2023-05-24 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-1.12629183e-01 4.31646585e-01 -1.32281855e-01 1.83164194e-01
-1.18156746e-01 -4.30518508e-01 3.46820593e-01 -1.01375774e-01
-6.93944097e-01 7.52322137e-01 -6.93433434e-02 -1.78155452e-01
-2.64329374e-01 -7.03382075e-01 -1.21130204e+00 -3.67899358e-01
-1.32731766e-01 8.18403780e-01 6.34493232e-01 -6.30150855... | [4.741697311401367, 1.1084847450256348] |
b0708dc0-c5e1-4ba7-92c4-03592dd5a520 | adaptive-similarity-embedding-for | null | null | https://ieeexplore.ieee.org/document/8970566 | https://iitkgpmail.iitkgp.ac.in/service/home/~/?auth=co&loc=en_US&id=10916&part=2 | Adaptive Similarity Embedding for Unsupervised Multi-View Feature Selection | Multi-view learning has become a significant research topic in image processing, data mining, and machine learning due
to the proliferation of multi-view data. Considering the difficulty in obtaining labeled data in many real applications, we focus on the
multi-view unsupervised feature selection problem. Most existi... | ['Cheng Zeng', 'Shengzi Sun', 'Yuan Wan'] | 2020-01-27 | null | null | null | ieee-xplore-2020-1 | ['multi-view-learning'] | ['computer-vision'] | [ 2.26342306e-02 -6.28349960e-01 -2.87184119e-01 -2.51446128e-01
-2.82323956e-01 -2.55043834e-01 2.99299449e-01 -1.38637811e-01
-2.29747146e-01 2.94205219e-01 4.19803739e-01 3.87980044e-01
-4.78596091e-01 -7.15783179e-01 -1.86264757e-02 -9.89336550e-01
2.72979885e-01 1.27854884e-01 2.58401364e-01 -7.05845878... | [8.34209156036377, 4.575998783111572] |
e96acd4c-b4b0-4639-8feb-df370a43dfd8 | template-instance-loss-for-offline | 1910.05545 | null | https://arxiv.org/abs/1910.05545v1 | https://arxiv.org/pdf/1910.05545v1.pdf | Template-Instance Loss for Offline Handwritten Chinese Character Recognition | The long-standing challenges for offline handwritten Chinese character recognition (HCCR) are twofold: Chinese characters can be very diverse and complicated while similarly looking, and cursive handwriting (due to increased writing speed and infrequent pen lifting) makes strokes and even characters connected together ... | ['Chi-Keung Tang', 'Dan Meng', 'Cewu Lu', 'Yao Xiao'] | 2019-10-12 | null | null | null | null | ['offline-handwritten-chinese-character', 'offline-handwritten-chinese-character'] | ['computer-vision', 'natural-language-processing'] | [ 2.36557680e-03 -5.58443248e-01 -1.46912217e-01 -4.83146787e-01
-4.68847930e-01 -6.87462866e-01 4.01423335e-01 -5.37346601e-01
-4.11767840e-01 4.85850275e-01 -1.92547351e-01 -2.87787318e-01
7.38813654e-02 -2.83530086e-01 -5.83821952e-01 -7.10096180e-01
1.08328424e-01 4.68825251e-01 7.08895130e-03 -7.81087503... | [11.894505500793457, 2.4509899616241455] |
6bd69402-4b65-495f-aeb2-1439b8c3b84f | decoupling-representation-and-classifier-for | 1910.09217 | null | https://arxiv.org/abs/1910.09217v2 | https://arxiv.org/pdf/1910.09217v2.pdf | Decoupling Representation and Classifier for Long-Tailed Recognition | The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions usually involve class-balancing strategies, e.g., by loss re-weighting, data re-sampling, or transfer learning from head- to tail-classes, ... | ['Marcus Rohrbach', 'Yannis Kalantidis', 'Zhicheng Yan', 'Saining Xie', 'Jiashi Feng', 'Bingyi Kang', 'Albert Gordo'] | 2019-10-21 | null | https://openreview.net/forum?id=r1gRTCVFvB | https://openreview.net/pdf?id=r1gRTCVFvB | iclr-2020-1 | ['long-tail-learning-with-class-descriptors'] | ['methodology'] | [ 8.25352967e-02 -2.15397641e-01 -4.18502182e-01 -5.92891335e-01
-8.56786728e-01 -5.22439718e-01 5.84156632e-01 1.56909347e-01
-5.59258103e-01 6.58983707e-01 1.41957536e-01 -4.15575683e-01
-9.91406366e-02 -7.29746103e-01 -9.48081374e-01 -5.84044933e-01
2.99393982e-02 5.77808738e-01 1.46876678e-01 -2.24821046... | [9.509749412536621, 2.8889870643615723] |
ecd85b9d-6421-462f-84df-60ee4e70898e | synthesis-of-stabilizing-recurrent | 2204.00122 | null | https://arxiv.org/abs/2204.00122v3 | https://arxiv.org/pdf/2204.00122v3.pdf | Synthesis of Stabilizing Recurrent Equilibrium Network Controllers | We propose a parameterization of a nonlinear dynamic controller based on the recurrent equilibrium network, a generalization of the recurrent neural network. We derive constraints on the parameterization under which the controller guarantees exponential stability of a partially observed dynamical system with sector bou... | ['Peter Seiler', 'Murat Arcak', 'Fangda Gu', 'He Yin', 'Neelay Junnarkar'] | 2022-03-31 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 3.80807042e-01 9.18792069e-01 -5.53282559e-01 2.83449560e-01
-2.14390680e-01 -7.92097867e-01 3.28143895e-01 -7.55870700e-01
-1.22889504e-01 9.82633650e-01 -2.62738198e-01 -5.93895793e-01
-3.12513113e-01 -1.62814021e-01 -8.77848506e-01 -9.29229259e-01
-4.15671319e-02 1.05452813e-01 -3.76370609e-01 -2.93281168... | [4.904115676879883, 2.4131534099578857] |
31a4972d-0a0a-4359-8342-913ddbe3d23f | self-supervised-scene-flow-estimation-with-4d | 2203.01137 | null | https://arxiv.org/abs/2203.01137v4 | https://arxiv.org/pdf/2203.01137v4.pdf | Self-Supervised Scene Flow Estimation with 4-D Automotive Radar | Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy. While estimating the scene flow from LiDAR has progressed recently, it remains largely unknown how to estimate the scene flow from a 4-D radar - an increasingly popul... | ['Chris Xiaoxuan Lu', 'Jianning Deng', 'Yimin Deng', 'Zhijun Pan', 'Fangqiang Ding'] | 2022-03-02 | null | null | null | null | ['motion-segmentation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.49133587e-01 -4.69429463e-01 -3.66856754e-01 -4.61655915e-01
-4.86860722e-01 -6.75785065e-01 5.97901762e-01 -3.40508461e-01
-5.55239260e-01 7.44641304e-01 -4.20520812e-01 -5.59297800e-01
-1.35967717e-01 -8.45764160e-01 -3.42319936e-01 -6.10287368e-01
-2.17906803e-01 7.26398170e-01 4.31499541e-01 -9.18680131... | [8.231975555419922, -1.9903998374938965] |
9de2eee8-0471-43d2-8b84-0588b873669e | eliminating-background-bias-for-robust-person | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Tian_Eliminating_Background-Bias_for_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Tian_Eliminating_Background-Bias_for_CVPR_2018_paper.pdf | Eliminating Background-Bias for Robust Person Re-Identification | Person re-identification is an important topic in intelligent surveillance and computer vision. It aims to accurately measure visual similarities between person images for determining whether two images correspond to the same person. State-of-the-art methods mainly utilize deep learning based approaches for learning vi... | ['Junjie Yan', 'Xuesen Zhang', 'Shihua Li', 'Hongsheng Li', 'Xiaogang Wang', 'Shuai Yi', 'Maoqing Tian', 'Jianping Shi'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['human-parsing'] | ['computer-vision'] | [ 7.03354627e-02 -3.95807803e-01 6.03571460e-02 -5.82625985e-01
-7.80992433e-02 -3.89825344e-01 8.14317763e-01 -1.01650260e-01
-6.81185126e-01 5.30168116e-01 1.94114313e-01 9.56573263e-02
3.90272915e-01 -8.39621007e-01 -7.97655165e-01 -6.33608222e-01
5.85821643e-03 2.03034490e-01 2.49860674e-01 1.34083554... | [14.700393676757812, 0.9161237478256226] |
02f676c6-b87d-4a68-b875-04ec6d01f36a | texta3-activation-anomaly-analysis | 2003.01801 | null | https://arxiv.org/abs/2003.01801v3 | https://arxiv.org/pdf/2003.01801v3.pdf | $\text{A}^3$: Activation Anomaly Analysis | Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-en... | ['Konstantin Böttinger', 'Jan-Philipp Schulze', 'Philip Sperl'] | 2020-03-03 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 4.44091588e-01 1.10988826e-01 -2.46697769e-01 -6.22071743e-01
-4.94091600e-01 -4.66247022e-01 6.54508591e-01 5.13300896e-01
-2.79583544e-01 3.30717951e-01 -1.41574606e-01 -4.52583522e-01
-7.87885413e-02 -6.29223645e-01 -5.98490298e-01 -6.88276827e-01
-3.65971446e-01 5.33711672e-01 3.56997281e-01 3.49001996... | [7.604815483093262, 2.420433521270752] |
1d5dbab5-2c26-4f56-9c9e-cdded5b07662 | cattle-detection-occlusion-problem | 2212.11418 | null | https://arxiv.org/abs/2212.11418v1 | https://arxiv.org/pdf/2212.11418v1.pdf | Cattle Detection Occlusion Problem | The management of cattle over a huge area is still a challenging problem in the farming sector. With evolution in technology, Unmanned aerial vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative to manual animal censuses for livestock estimation since they are less risky and expensive.... | ['Vaishnavi Mendu', 'Bhavya Sehgal', 'Aparna Mendu'] | 2022-12-21 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 8.25536251e-02 -5.25788069e-02 1.32499397e-01 -1.57470316e-01
6.39686704e-01 -1.97743699e-01 -3.13140824e-02 -2.14126155e-01
-6.39995933e-01 8.07483196e-01 -6.09355271e-01 -1.25551194e-01
-9.78958905e-02 -8.62472534e-01 -6.60156608e-01 -4.82482642e-01
-4.19205785e-01 3.70215684e-01 4.18601662e-01 -3.86724710... | [8.568172454833984, -0.8881064057350159] |
2f977227-3d7a-4745-b608-78ba98ea6644 | multi-attention-network-for-compressed-video | 2207.12622 | null | https://arxiv.org/abs/2207.12622v1 | https://arxiv.org/pdf/2207.12622v1.pdf | Multi-Attention Network for Compressed Video Referring Object Segmentation | Referring video object segmentation aims to segment the object referred by a given language expression. Existing works typically require compressed video bitstream to be decoded to RGB frames before being segmented, which increases computation and storage requirements and ultimately slows the inference down. This may h... | ['Guorong Li', 'Qingming Huang', 'Laiyun Qing', 'Shuhui Wang', 'Zhenjun Han', 'Yuankai Qi', 'Dexiang Hong', 'Weidong Chen'] | 2022-07-26 | null | null | null | null | ['referring-expression-segmentation', 'referring-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.42527086e-01 -2.17046991e-01 -3.58757466e-01 -3.45094413e-01
-8.77797365e-01 -3.26969206e-01 2.66597539e-01 -3.33107203e-01
-5.84823012e-01 2.70350367e-01 2.90655252e-02 -1.27798170e-01
6.19190820e-02 -7.64861345e-01 -9.01521623e-01 -6.51507616e-01
4.37125772e-01 1.42380903e-02 4.60652292e-01 -5.94691224... | [9.406329154968262, 0.2397836297750473] |
85af4a5a-edf5-456b-8df0-c6e679379553 | online-abuse-detection-the-value-of | null | null | https://aclanthology.org/W19-1303 | https://aclanthology.org/W19-1303.pdf | Online abuse detection: the value of preprocessing and neural attention models | We propose an attention-based neural network approach to detect abusive speech in online social networks. Our approach enables more effective modeling of context and the semantic relationships between words. We also empirically evaluate the value of text pre-processing techniques in addressing the challenge of out-of-v... | ['Robin Cohen', 'Lukasz Golab', 'Dhruv Kumar'] | 2019-06-01 | null | null | null | ws-2019-6 | ['abuse-detection'] | ['natural-language-processing'] | [-4.80374806e-02 1.92737624e-01 -3.88599277e-01 -3.27483922e-01
-5.14699936e-01 -2.37207144e-01 5.66173017e-01 3.12193185e-01
-6.57414675e-01 4.18747276e-01 7.08905816e-01 -4.22794223e-01
-4.96305525e-02 -5.37733316e-01 -2.63745815e-01 1.00474814e-02
-2.66126007e-01 3.11562479e-01 2.33951688e-01 -7.88658559... | [8.801804542541504, 10.560930252075195] |
66ae6221-cd66-408e-8eca-25c0c89e0dda | pava-a-novel-path-based-valley-seeking | 2306.07503 | null | https://arxiv.org/abs/2306.07503v1 | https://arxiv.org/pdf/2306.07503v1.pdf | PaVa: a novel Path-based Valley-seeking clustering algorithm | Clustering methods are being applied to a wider range of scenarios involving more complex datasets, where the shapes of clusters tend to be arbitrary. In this paper, we propose a novel Path-based Valley-seeking clustering algorithm for arbitrarily shaped clusters. This work aims to seek the valleys among clusters and t... | ['Shuangzhe Liu', 'Tiefeng Ma', 'Conan Liu', 'Lin Ma'] | 2023-06-13 | null | null | null | null | ['clustering'] | ['methodology'] | [-2.98331175e-02 -4.00200844e-01 3.54847498e-02 -2.15936497e-01
-3.07910144e-01 -6.57878041e-01 3.87203246e-01 3.32103580e-01
-3.89658928e-01 2.80274481e-01 -2.15764835e-01 -1.24910243e-01
-6.49761617e-01 -9.49370384e-01 -1.91045895e-01 -1.02979398e+00
-2.17063591e-01 6.11401498e-01 5.46048224e-01 1.92890745... | [7.590150833129883, 4.59266996383667] |
a527b048-a1e5-4144-b9c9-355614667ba2 | semantic-graph-convolutional-networks-for-3d | 1904.03345 | null | https://arxiv.org/abs/1904.03345v3 | https://arxiv.org/pdf/1904.03345v3.pdf | Semantic Graph Convolutional Networks for 3D Human Pose Regression | In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks... | ['Long Zhao', 'Xi Peng', 'Dimitris N. Metaxas', 'Yu Tian', 'Mubbasir Kapadia'] | 2019-04-06 | semantic-graph-convolutional-networks-for-3d-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Semantic_Graph_Convolutional_Networks_for_3D_Human_Pose_Regression_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Semantic_Graph_Convolutional_Networks_for_3D_Human_Pose_Regression_CVPR_2019_paper.pdf | cvpr-2019-6 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 4.98650745e-02 5.81749678e-01 -2.73793727e-01 -4.63736713e-01
1.56879917e-01 -8.20981711e-02 3.11879128e-01 -1.62292585e-01
-3.15721840e-01 5.23156226e-01 9.14253592e-02 -2.19504349e-02
-1.70881912e-01 -9.70827103e-01 -9.68975425e-01 -2.41886690e-01
-3.53091508e-01 6.05852067e-01 6.54672608e-02 -5.32517731... | [7.062404155731201, -0.5803235173225403] |
50ba1e0b-61f5-4ba7-aa29-5e9007e3daf9 | direct-measurement-of-arbitrary-quantum | 2101.05556 | null | https://arxiv.org/abs/2101.05556v3 | https://arxiv.org/pdf/2101.05556v3.pdf | Direct measurement of density-matrix elements using a phase-shifting technique Tianfeng | A direct measurement protocol allows reconstructing specific elements of the density matrix of a quantum state without using quantum state tomography. However, the direct measurement protocols to date are primarily based on weak or strong measurements with an ancillary pointer, which interacts with the investigated sys... | ['XiaoQi Zhou', 'Changliang Ren', 'Tianfeng Feng'] | 2021-01-14 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.18339521e-01 7.77472463e-03 1.00315601e-01 -2.52682239e-01
-7.66372025e-01 -5.61372459e-01 2.91808516e-01 -7.15057254e-02
-7.21128225e-01 1.13337612e+00 -5.13047338e-01 -6.61844432e-01
-1.74726710e-01 -1.28842998e+00 -3.61986458e-01 -1.25475478e+00
8.26195627e-02 6.52179837e-01 -1.12973051e-02 -3.50408763... | [5.603762149810791, 4.888608455657959] |
ab944a3f-68b4-4c5f-a6cd-434a71311b18 | introducing-tales-of-tribute-ai-competition | 2305.08234 | null | https://arxiv.org/abs/2305.08234v1 | https://arxiv.org/pdf/2305.08234v1.pdf | Introducing Tales of Tribute AI Competition | This paper presents a new AI challenge, the Tales of Tribute AI Competition (TOTAIC), based on a two-player deck-building card game released with the High Isle chapter of The Elder Scrolls Online. Currently, there is no other AI competition covering Collectible Card Games (CCG) genre, and there has never been one that ... | ['Damian Kowalik', 'Dominik Budzki', 'Katarzyna Polak', 'Radosław Miernik', 'Jakub Kowalski'] | 2023-05-14 | null | null | null | null | ['card-games'] | ['playing-games'] | [ 5.41944355e-02 2.67682344e-01 8.32265913e-02 2.93880016e-01
-1.00137389e+00 -8.99261653e-01 5.26568592e-01 -2.65042305e-01
-7.78691947e-01 1.37261915e+00 -1.15463249e-01 -4.00753826e-01
-5.14250875e-01 -7.61856139e-01 -5.37863016e-01 -3.78306448e-01
-3.53110313e-01 1.16777980e+00 4.47355956e-01 -1.02328181... | [3.479564905166626, 1.5097851753234863] |
91e963e8-3e6a-4eda-83c9-bf9946f466ca | kgboost-a-classification-based-knowledge-base | 2112.09340 | null | https://arxiv.org/abs/2112.09340v1 | https://arxiv.org/pdf/2112.09340v1.pdf | KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling | Knowledge base completion is formulated as a binary classification problem in this work, where an XGBoost binary classifier is trained for each relation using relevant links in knowledge graphs (KGs). The new method, named KGBoost, adopts a modularized design and attempts to find hard negative samples so as to train a ... | ['C. -C. Jay Kuo', 'Bin Wang', 'Xiou Ge', 'Yun-Cheng Wang'] | 2021-12-17 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-1.02270424e-01 5.94440877e-01 -1.33332157e+00 -4.63414192e-01
-4.67815548e-01 -8.32806602e-02 3.30763519e-01 4.58176970e-01
-8.34488869e-02 1.11482620e+00 -1.43563911e-01 -4.12436217e-01
-6.91613853e-01 -1.17115843e+00 -8.57589364e-01 -3.76660436e-01
-3.63495618e-01 8.88478816e-01 9.23747849e-03 -1.83299825... | [8.813680648803711, 7.912006855010986] |
9598d837-f812-42ff-9e4b-1b8ab595d2fd | multi-perspective-scientific-document | null | null | https://aclanthology.org/2022.sdp-1.33 | https://aclanthology.org/2022.sdp-1.33.pdf | Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS) | It is well recognized that creating summaries of scientific texts can be difficult. For each given document, the majority of summarizing research believes there is only one best gold summary. Having just one gold summary limits our capacity to assess the effectiveness of summarizing algorithms because creating summarie... | ['Abbas Akkasi'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'extractive-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.17089784e-01 6.08391404e-01 -2.07428873e-01 -2.76474297e-01
-1.43022585e+00 -6.77457809e-01 8.44510674e-01 9.33447301e-01
-2.24951804e-01 9.40126836e-01 8.90120327e-01 -5.67654252e-01
-1.79049745e-01 -6.16771817e-01 -6.49848461e-01 -3.81341428e-01
3.56838524e-01 7.07918346e-01 -2.30458856e-01 -1.17529154... | [12.438920974731445, 9.610702514648438] |
09d21ee9-c3f2-4cf7-970f-9fe64e87bcbe | my-way-of-telling-a-story-persona-based | 1906.06401 | null | https://arxiv.org/abs/1906.06401v1 | https://arxiv.org/pdf/1906.06401v1.pdf | "My Way of Telling a Story": Persona based Grounded Story Generation | Visual storytelling is the task of generating stories based on a sequence of images. Inspired by the recent works in neural generation focusing on controlling the form of text, this paper explores the idea of generating these stories in different personas. However, one of the main challenges of performing this task is ... | ['Alan W. black', 'Khyathi Raghavi Chandu', 'Shrimai Prabhumoye', 'Ruslan Salakhutdinov'] | 2019-06-14 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.81071782e-01 5.23934066e-01 2.70480335e-01 -5.01848936e-01
-5.94605744e-01 -5.43815911e-01 1.23037577e+00 -2.71173716e-01
1.06303710e-02 7.34109044e-01 9.62773085e-01 8.21076706e-02
4.40427154e-01 -7.97850311e-01 -6.85039818e-01 -3.81282240e-01
5.54488897e-01 7.40644574e-01 6.68406412e-02 -3.86208177... | [11.165410041809082, 0.7519330978393555] |
2319434f-87d7-4dc3-95b2-3f00e472f1d6 | digital-twin-tracking-dataset-dttd-a-new-rgb | 2302.05991 | null | https://arxiv.org/abs/2302.05991v2 | https://arxiv.org/pdf/2302.05991v2.pdf | Digital Twin Tracking Dataset (DTTD): A New RGB+Depth 3D Dataset for Longer-Range Object Tracking Applications | Digital twin is a problem of augmenting real objects with their digital counterparts. It can underpin a wide range of applications in augmented reality (AR), autonomy, and UI/UX. A critical component in a good digital-twin system is real-time, accurate 3D object tracking. Most existing works solve 3D object tracking th... | ['Allen Y. Yang', 'Zekun Wang', 'Yichen Chen', 'Adam Chang', 'Chuanyu Pan', 'Seth Z. Zhao', 'Weiyu Feng'] | 2023-02-12 | null | null | null | null | ['3d-object-tracking', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 1.80759951e-01 -2.52880931e-01 -1.18214592e-01 -1.70871958e-01
-6.81679487e-01 -7.81098127e-01 3.01112562e-01 -3.31084728e-01
-7.43449479e-02 8.42167363e-02 -2.26343840e-01 -2.78680712e-01
7.47708697e-03 -6.54281974e-01 -9.93494928e-01 -4.46797580e-01
7.16484338e-02 7.30636120e-01 5.17675340e-01 -2.97050357... | [7.072671890258789, -2.175894021987915] |
1a1b8324-c4cb-4e20-bdbf-194bd7c3b9fc | adaptive-dnn-surgery-for-selfish-inference | 2306.12185 | null | https://arxiv.org/abs/2306.12185v1 | https://arxiv.org/pdf/2306.12185v1.pdf | Adaptive DNN Surgery for Selfish Inference Acceleration with On-demand Edge Resource | Deep Neural Networks (DNNs) have significantly improved the accuracy of intelligent applications on mobile devices. DNN surgery, which partitions DNN processing between mobile devices and multi-access edge computing (MEC) servers, can enable real-time inference despite the computational limitations of mobile devices. H... | ['Song Guo', 'Jianxin Liao', 'Haifeng Sun', 'Jingyu Wang', 'Qi Qi', 'Dezhi Chen', 'Xiang Yang'] | 2023-06-21 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-1.28884792e-01 -1.43215939e-01 -4.41143036e-01 7.57998005e-02
-4.28341888e-02 -6.05720758e-01 -9.70061049e-02 -4.47657973e-01
-5.97080529e-01 6.99487686e-01 -2.02659756e-01 -7.15562940e-01
-4.02655602e-01 -8.63995671e-01 -5.44019103e-01 -6.66129053e-01
1.52229935e-01 7.87835002e-01 3.86540204e-01 1.91001981... | [8.123985290527344, 2.8489725589752197] |
e4bec2ec-0ce4-4d3e-a438-f4ccd4b0db58 | compositional-exemplars-for-in-context | 2302.05698 | null | https://arxiv.org/abs/2302.05698v3 | https://arxiv.org/pdf/2302.05698v3.pdf | Compositional Exemplars for In-context Learning | Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task via a prompt consisting of input-output examples as the demonstration, without any parameter updates. The performance of ICL is highly dominated by the quality of the selected in-c... | ['Lingpeng Kong', 'Tao Yu', 'Jiangtao Feng', 'Zhiyong Wu', 'Jiacheng Ye'] | 2023-02-11 | null | null | null | null | ['point-processes', 'semantic-parsing', 'open-domain-question-answering'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 4.88283306e-01 1.49838567e-01 -1.98609605e-01 -5.28473020e-01
-1.23835742e+00 -9.34172392e-01 7.44411945e-01 2.20338166e-01
-4.20175493e-01 6.42841160e-01 2.11801142e-01 -5.69240749e-01
-7.38814622e-02 -6.00936651e-01 -1.06547856e+00 -3.17450017e-01
2.18013421e-01 5.70943296e-01 -1.98897451e-01 -2.62331545... | [10.797913551330566, 8.374439239501953] |
3dd9f375-ac8e-4ac0-b864-7eb7951c05b1 | airiva-a-deep-generative-model-of-adaptive | 2304.13737 | null | https://arxiv.org/abs/2304.13737v1 | https://arxiv.org/pdf/2304.13737v1.pdf | AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires | Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens. However, the extreme diversity of the adaptive immune repertoire presents challenges in reliably identifying disease-spe... | ['Edward Meeds', 'Julia Greissl', 'Javier Gonzalez', 'Javier Zazo', 'Rebecca Elyanow', 'Steven Woodhouse', 'Max Ilse', 'Sahra Ghalebikesabi', 'Paidamoyo Chapfuwa', 'Niranjani Prasad', 'Melanie F. Pradier'] | 2023-04-26 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 8.93487334e-01 -6.09073102e-01 -3.81831437e-01 -4.88533705e-01
-7.73082495e-01 -6.91409528e-01 5.02361238e-01 7.95440823e-02
-3.54734898e-01 9.62970078e-01 2.59630650e-01 -4.65977460e-01
-3.01497400e-01 -5.29209197e-01 -7.64253318e-01 -1.05758643e+00
-2.50623584e-01 1.46345472e+00 -4.23900813e-01 3.53545300... | [5.007588863372803, 5.467929363250732] |
baf4ad9a-f0c7-48b8-a31a-c1aadf6ca3dd | explicit-visual-prompting-for-low-level | 2303.10883 | null | https://arxiv.org/abs/2303.10883v2 | https://arxiv.org/pdf/2303.10883v2.pdf | Explicit Visual Prompting for Low-Level Structure Segmentations | We consider the generic problem of detecting low-level structures in images, which includes segmenting the manipulated parts, identifying out-of-focus pixels, separating shadow regions, and detecting concealed objects. Whereas each such topic has been typically addressed with a domain-specific solution, we show that a ... | ['Xiaodong Cun', 'Chi-Man Pun', 'Xi Shen', 'Weihuang Liu'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_Explicit_Visual_Prompting_for_Low-Level_Structure_Segmentations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_Explicit_Visual_Prompting_for_Low-Level_Structure_Segmentations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-manipulation-detection', 'defocus-blur-detection', 'visual-prompting', 'camouflaged-object-segmentation', 'foreground-segmentation', 'shadow-detection'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 3.58408034e-01 2.17143402e-01 -2.73879647e-01 -4.09143388e-01
-1.12621307e+00 -8.06420326e-01 4.30613041e-01 6.09384477e-02
-2.92796493e-01 1.77948728e-01 -5.38582392e-02 -3.18394184e-01
1.75756857e-01 -4.20756936e-01 -1.05805218e+00 -9.10595059e-01
1.30261898e-01 1.09301887e-01 7.64703691e-01 1.81681603... | [9.592723846435547, 0.3347044587135315] |
44f78ff4-8f80-47cb-806e-38f1049df899 | concurrent-subsidiary-supervision-for | 2207.13247 | null | https://arxiv.org/abs/2207.13247v1 | https://arxiv.org/pdf/2207.13247v1.pdf | Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation | The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains. Prior DA works show that pretext tasks could be used to mitigate this domain shift by learning domain invariant representations. However, in practice, we find that most existing pretext task... | ['R. Venkatesh Babu', 'Varun Jampani', 'Hiran Sarkar', 'Akshay Kulkarni', 'Suvaansh Bhambri', 'Jogendra Nath Kundu'] | 2022-07-27 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.24955374e-01 -8.55638459e-02 -4.23125625e-01 -5.63255668e-01
-9.23533380e-01 -7.82542288e-01 6.72351301e-01 -3.80071588e-02
-4.80311096e-01 1.02846980e+00 2.62194872e-01 -2.58674145e-01
-2.35399976e-01 -5.44230163e-01 -4.65215713e-01 -4.84734505e-01
3.98760825e-01 6.80907130e-01 2.49214858e-01 -5.57646036... | [10.3314790725708, 3.1171531677246094] |
55bbf5be-61e3-45fd-a9fb-bc7be628c385 | pulmonary-arteryvein-classification-in-ct | null | null | https://doi.org/10.1109/TMI.2018.2833385 | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214740/pdf/nihms982442.pdf | Pulmonary Artery–Vein Classification in CT Images Using Deep Learning | Recent studies show that pulmonary vascular diseases may specifically affect arteries or veins through different physiologic mechanisms. To detect changes in the two vascular trees, physicians manually analyze the chest computed tomography (CT) image of the patients in search of abnormalities. This process is time cons... | ['Raúl San José Estépar', 'Maria J. Ledesma-Carbayo', 'David Bermejo-Pelaez', 'Daniel Jimenez-Carretero', 'Pietro Nardelli', 'Farbod N. Rahaghi', 'George R. Washko'] | 2018-05-04 | null | null | null | ieee-transactions-on-medical-imaging-volume | ['pulmonary-arteryvein-classification', 'pulmorary-vessel-segmentation', '3d-medical-imaging-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 1.53992236e-01 -1.86338052e-02 -8.43564942e-02 -1.43661290e-01
-2.76684612e-01 -4.47016120e-01 2.63054013e-01 2.29388759e-01
-5.87446332e-01 7.20523119e-01 -1.12709492e-01 -9.37496483e-01
-1.96062148e-01 -1.13998151e+00 -5.37844449e-02 -7.08502948e-01
-3.08755010e-01 1.06060779e+00 5.75945020e-01 1.77638978... | [15.150286674499512, -2.1813182830810547] |
3f13ddca-0d77-4d92-9d90-326480464c98 | efficient-federated-learning-with-spike | 2205.14315 | null | https://arxiv.org/abs/2205.14315v1 | https://arxiv.org/pdf/2205.14315v1.pdf | Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition | With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a la... | ['Yi Wu', 'Shengli Xie', 'Dusit Niyato', 'Jiawen Kang', 'Bo Li', 'Zhe Zhang', 'Kan Xie'] | 2022-05-28 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 1.60693944e-01 -4.11511302e-01 -4.58138824e-01 -6.77842259e-01
-1.81875572e-01 -4.71366346e-01 3.07481587e-01 -4.36940730e-01
-5.62805116e-01 6.79247677e-01 -4.43914443e-01 -6.87497735e-01
-1.64461672e-01 -9.23630774e-01 -7.05873549e-01 -9.62418020e-01
2.98627079e-01 1.23051591e-01 3.52857083e-01 1.88017651... | [7.918078422546387, -0.6112276911735535] |
fc6163f2-69cc-4818-b371-7ad08fb01ffa | bopr-body-aware-part-regressor-for-human | 2303.11675 | null | https://arxiv.org/abs/2303.11675v2 | https://arxiv.org/pdf/2303.11675v2.pdf | BoPR: Body-aware Part Regressor for Human Shape and Pose Estimation | This paper presents a novel approach for estimating human body shape and pose from monocular images that effectively addresses the challenges of occlusions and depth ambiguity. Our proposed method BoPR, the Body-aware Part Regressor, first extracts features of both the body and part regions using an attention-guided me... | ['Ying Shan', 'Jifeng Ning', 'Shaoli Huang', 'Yongkang Cheng'] | 2023-03-21 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [-1.55166954e-01 1.59942359e-01 -5.75454116e-01 -3.46501797e-01
-4.10778791e-01 -2.51176506e-01 3.08510482e-01 -3.21576327e-01
-1.14766791e-01 5.42194545e-01 6.39351726e-01 4.13119346e-01
1.91358313e-01 -3.84437054e-01 -7.69767284e-01 -4.18026388e-01
5.13307303e-02 4.09794241e-01 2.45413288e-01 -1.12837784... | [7.057725429534912, -0.9316050410270691] |
55b3305c-7d7e-4377-a427-85f7a7a5ea5d | study-of-frequency-domain-exponential | 2201.05501 | null | https://arxiv.org/abs/2201.05501v1 | https://arxiv.org/pdf/2201.05501v1.pdf | Study of Frequency domain exponential functional link network filters | The exponential functional link network (EFLN) filter has attracted tremendous interest due to its enhanced nonlinear modeling capability. However, the computational complexity will dramatically increase with the dimension growth of the EFLN-based filter. To improve the computational efficiency, we propose a novel freq... | ['Y. Yu', 'R. C. de Lamareb', 'S. Tana', 'T. Yu'] | 2022-01-12 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.45572424e-01 -1.99044093e-01 6.42376244e-01 2.28719767e-02
-2.99613059e-01 -3.70631039e-01 -1.69056524e-02 -5.59523463e-01
-5.00677109e-01 8.28252614e-01 1.05152883e-01 -2.69129574e-01
-8.14076066e-01 -2.12920293e-01 -1.28831476e-01 -8.90154898e-01
-2.49903411e-01 -2.81134695e-01 1.07128985e-01 -2.41083264... | [15.099013328552246, 5.771141529083252] |
b23664f3-ed5a-4dfb-a7ad-b4fe8e533f8d | improved-ccg-parsing-with-semi-supervised | null | null | https://aclanthology.org/Q14-1026 | https://aclanthology.org/Q14-1026.pdf | Improved CCG Parsing with Semi-supervised Supertagging | Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings en... | ['Mike Lewis', 'Mark Steedman'] | 2014-01-01 | null | null | null | tacl-2014-1 | ['ccg-supertagging'] | ['natural-language-processing'] | [ 1.47300661e-01 7.75737524e-01 -2.86976218e-01 -6.55453205e-01
-9.44472253e-01 -7.79730737e-01 4.04003590e-01 7.67894745e-01
-8.36701155e-01 6.52919054e-01 4.58156973e-01 -6.59815192e-01
-1.46839404e-02 -7.77205169e-01 -4.67528343e-01 -4.79827285e-01
-1.37251884e-01 6.60668671e-01 5.14087915e-01 -9.99406800... | [10.318443298339844, 9.758899688720703] |
510c9b26-11fb-47c9-bdf7-72e9c985dc47 | functional-segmentation-through-dynamic-mode | 1905.10218 | null | https://arxiv.org/abs/1905.10218v1 | https://arxiv.org/pdf/1905.10218v1.pdf | Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images | Quantification of kidney function in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) requires careful segmentation of the renal region of interest (ROI). Traditionally, human experts are required to manually delineate the kidney ROI across multiple images in the dynamic sequence. This approach is costly,... | ['Kevin Wells', 'Miroslaw Bober', 'Isky Gorden', 'Santosh Tirunagari', 'Norman Poh', 'David Windridge'] | 2019-05-24 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 4.46518302e-01 1.46488860e-01 3.30732793e-01 -4.15331841e-01
-6.43106639e-01 -6.12371147e-01 3.56412649e-01 1.86209753e-01
-6.87686384e-01 6.39751256e-01 -2.55031642e-02 -3.26380581e-01
-4.21039730e-01 -5.63868821e-01 -2.86914170e-01 -8.89914274e-01
-4.60673004e-01 8.77850175e-01 3.76653731e-01 2.32803687... | [14.052169799804688, -2.4981682300567627] |
8ffae3cc-05d6-428a-99e7-0e0061d968fe | improving-lidar-3d-object-detection-via-range | 2306.05663 | null | https://arxiv.org/abs/2306.05663v1 | https://arxiv.org/pdf/2306.05663v1.pdf | Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization | In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets. Existing 3D object detectors tend to perform well on the point cloud regions closer to the LiDAR sensor as opposed to on regions that ar... | ['Bingbing Liu', 'Mrigank Rochan', 'Yannis Y. He', 'Alaap Grandhi', 'Eduardo R. Corral-Soto'] | 2023-06-09 | null | null | null | null | ['3d-object-detection'] | ['computer-vision'] | [-1.77612111e-01 -3.62710178e-01 -1.00327075e-01 -4.13100541e-01
-5.55399835e-01 -5.64918041e-01 3.92163604e-01 4.48320895e-01
-7.05434859e-01 5.98195083e-02 -2.66449481e-01 -4.29681391e-01
1.39318824e-01 -1.03173804e+00 -5.88888407e-01 -3.22442919e-01
-5.49882241e-02 1.11888480e+00 1.01707542e+00 9.48745310... | [7.754235744476318, -2.6837055683135986] |
8965cc50-5731-49b1-995a-d0c4044b6e91 | one-shot-segmentation-in-clutter | 1803.09597 | null | http://arxiv.org/abs/1803.09597v2 | http://arxiv.org/pdf/1803.09597v2.pdf | One-Shot Segmentation in Clutter | We tackle the problem of one-shot segmentation: finding and segmenting a
previously unseen object in a cluttered scene based on a single instruction
example. We propose a novel dataset, which we call $\textit{cluttered
Omniglot}$. Using a baseline architecture combining a Siamese embedding for
detection with a U-net fo... | ['Claudio Michaelis', 'Matthias Bethge', 'Alexander S. Ecker'] | 2018-03-26 | one-shot-segmentation-in-clutter-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2303 | http://proceedings.mlr.press/v80/michaelis18a/michaelis18a.pdf | icml-2018-7 | ['one-shot-segmentation', 'foreground-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.02766657e-01 1.37212604e-01 -5.17436489e-02 -4.26958799e-01
-8.49651694e-01 -7.62509346e-01 1.99211046e-01 1.51893213e-01
-7.14498937e-01 4.64252681e-01 -3.58112723e-01 -6.12861097e-01
-1.54821062e-02 -3.11910540e-01 -9.40471172e-01 -6.41013324e-01
-4.22352590e-02 7.09937811e-01 8.23443890e-01 2.83406768... | [9.283905982971191, 0.05174309015274048] |
b2d48b6c-d68b-4337-a107-a8092a6599d3 | geometric-pooling-maintaining-more-useful | 2306.12341 | null | https://arxiv.org/abs/2306.12341v1 | https://arxiv.org/pdf/2306.12341v1.pdf | Geometric Pooling: maintaining more useful information | Graph Pooling technology plays an important role in graph node classification tasks. Sorting pooling technologies maintain large-value units for pooling graphs of varying sizes. However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sortin... | ['Shuai Wang', 'Hongsen Zhao', 'Shuyue Zhou', 'Ruihua Zhang', 'Yanxia Bao', 'Kenan Lou', 'Yang shen', 'Jia Liu', 'Hao Xu'] | 2023-06-21 | null | null | null | null | ['node-classification'] | ['graphs'] | [ 1.29096344e-01 4.62756425e-01 -4.79385763e-01 9.38050523e-02
-1.05301410e-01 -4.08103138e-01 2.04238147e-01 3.83414656e-01
-1.25571772e-01 8.17246914e-01 2.21842706e-01 1.12556785e-01
-1.46123454e-01 -1.25666749e+00 -1.62310258e-01 -7.21233189e-01
-6.92726552e-01 -4.44226235e-01 6.18580222e-01 -2.53046840... | [7.064884662628174, 6.256589889526367] |
86a2997d-b32d-4c15-ac2b-d18fa48fd13b | deep-unsupervised-learning-using-spike-timing | 2307.04054 | null | https://arxiv.org/abs/2307.04054v1 | https://arxiv.org/pdf/2307.04054v1.pdf | Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity | Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work,... | ['Abhronil Sengupta', 'Sen Lu'] | 2023-07-08 | null | null | null | null | ['clustering'] | ['methodology'] | [ 3.46947938e-01 -3.04995269e-01 2.53653258e-01 -6.37124956e-01
-3.11095327e-01 -5.45333624e-01 3.70620638e-01 -1.69545636e-01
-7.99232483e-01 9.01414990e-01 -3.36049885e-01 -1.09226488e-01
-2.19033718e-01 -6.07461333e-01 -1.13272023e+00 -1.02548945e+00
-2.11130127e-01 2.95188367e-01 5.25205135e-01 2.07862020... | [8.223857879638672, 2.4957525730133057] |
3adf992c-ac25-46ea-87f9-b120d59355dc | cfilt-iit-bombay-lt-edi-eacl2021-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.29 | https://aclanthology.org/2021.ltedi-1.29.pdf | CFILT IIT Bombay@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion using Multilingual Representation fromTransformers | With the internet becoming part and parcel of our lives, engagement in social media has increased a lot. Identifying and eliminating offensive content from social media has become of utmost priority to prevent any kind of violence. However, detecting encouraging, supportive and positive content is equally important to ... | ['Pushpak Bhattacharyya', 'Prince Kumar', 'Pankaj Singh'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-1.80904225e-01 2.76696295e-01 -3.71710926e-01 -1.76404729e-01
-1.13810694e+00 -7.19985843e-01 9.52076435e-01 2.06672236e-01
-5.89457154e-01 1.01325953e+00 6.82572246e-01 -6.02371335e-01
-1.07644901e-01 -4.06761199e-01 -3.60205412e-01 -3.02578360e-01
9.78517979e-02 4.26634192e-01 9.22879055e-02 -4.63933945... | [8.988812446594238, 10.631540298461914] |
c3f59511-3314-4083-ae07-15fc3732be64 | interpreting-deep-urban-sound-classification | 2111.10235 | null | https://arxiv.org/abs/2111.10235v1 | https://arxiv.org/pdf/2111.10235v1.pdf | Interpreting deep urban sound classification using Layer-wise Relevance Propagation | After constructing a deep neural network for urban sound classification, this work focuses on the sensitive application of assisting drivers suffering from hearing loss. As such, clear etiology justifying and interpreting model predictions comprise a strong requirement. To this end, we used two different representation... | ['Stavros Ntalampiras', 'Marco Colussi'] | 2021-11-19 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 2.21102446e-01 1.95215896e-01 2.95905083e-01 -6.47947431e-01
-7.71956444e-01 3.38151725e-03 2.18325734e-01 2.09094822e-01
-1.32645771e-01 6.24183536e-01 4.35037553e-01 -4.53668594e-01
-3.91563714e-01 -9.02343094e-01 -5.82465529e-01 -5.48598468e-01
-4.55438383e-02 -6.05590865e-02 -4.53800596e-02 -3.72027338... | [15.184419631958008, 5.2258100509643555] |
8de1f18e-041d-4572-aab6-8c8b2ca2aaed | all-you-need-is-boundary-toward-arbitrary | 1911.09550 | null | https://arxiv.org/abs/1911.09550v1 | https://arxiv.org/pdf/1911.09550v1.pdf | All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting | Recently, end-to-end text spotting that aims to detect and recognize text from cluttered images simultaneously has received particularly growing interest in computer vision. Different from the existing approaches that formulate text detection as bounding box extraction or instance segmentation, we localize a set of poi... | ['Mengchao He', 'Yongchao Xu', 'HUI ZHANG', 'Mingkun Yang', 'Wenyu Liu', 'Hao Wang', 'Yongpan Wang', 'Xiang Bai', 'Pu Lu'] | 2019-11-21 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.91056651e-01 -5.11959732e-01 1.33896157e-01 -3.30873132e-01
-9.39494312e-01 -8.29190731e-01 7.94889748e-01 1.72468379e-01
-3.64956260e-01 9.22531039e-02 -1.27784297e-01 -2.47197941e-01
2.25316063e-01 -4.01244044e-01 -6.31172121e-01 -4.08468395e-01
6.96971595e-01 8.69164407e-01 3.03576261e-01 2.95932591... | [11.958138465881348, 2.271026849746704] |
abd8c231-7998-4fb5-9ed6-735751199e5b | the-impact-of-z_score-on-twitter-sentiment | null | null | https://aclanthology.org/S14-2113 | https://aclanthology.org/S14-2113.pdf | The Impact of Z\_score on Twitter Sentiment Analysis | null | ["Frederic B{\\'e}chet", 'Patrice Bellot', 'Hussam Hamdan'] | 2014-08-01 | null | null | null | semeval-2014-8 | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.295073986053467, 3.6454503536224365] |
58d928ee-8600-4e75-8b3c-558840614c31 | attention-compilation-and-solver-based | 2306.06755 | null | https://arxiv.org/abs/2306.06755v1 | https://arxiv.org/pdf/2306.06755v1.pdf | Attention, Compilation, and Solver-based Symbolic Analysis are All You Need | In this paper we present a Java-to-Python (J2P) and Python-to-Java (P2J) back-to-back code translation method, and associated tool called CoTran, based on large language models (LLMs). Our method leverages the attention mechanism of LLMs, compilation, and symbolic execution-based test generation for equivalence testing... | ['Vijay Ganesh', 'Aryan Mahajan', 'Gautham Kishore', 'Haoyang Ju', 'Piyush Jha', 'Prithwish Jana'] | 2023-06-11 | null | null | null | null | ['code-translation'] | ['computer-code'] | [ 4.03898247e-02 -2.84553766e-01 -3.48298937e-01 -3.20567012e-01
-1.38331223e+00 -7.68699646e-01 5.54452538e-01 6.47464022e-02
2.83722226e-02 6.52454853e-01 -3.26055437e-01 -9.47480381e-01
5.41681230e-01 -8.19106758e-01 -1.12557673e+00 6.45394996e-02
-8.78568273e-03 2.41396785e-01 1.27697960e-01 -5.00386804... | [7.728301525115967, 7.833614349365234] |
f3ef0b0e-e44d-494f-be88-1c3e3713dc3f | slide-constrain-parse-repeat-synchronous | 2305.17273 | null | https://arxiv.org/abs/2305.17273v1 | https://arxiv.org/pdf/2305.17273v1.pdf | Slide, Constrain, Parse, Repeat: Synchronous SlidingWindows for Document AMR Parsing | The sliding window approach provides an elegant way to handle contexts of sizes larger than the Transformer's input window, for tasks like language modeling. Here we extend this approach to the sequence-to-sequence task of document parsing. For this, we exploit recent progress in transition-based parsing to implement a... | ['Salim Roukos', 'Radu Florian', 'Ramon Fernandez Astudillo', 'Tahira Naseem', 'Sadhana Kumaravel'] | 2023-05-26 | null | null | null | null | ['amr-parsing'] | ['natural-language-processing'] | [ 6.61964476e-01 5.00114918e-01 -4.28533442e-02 -5.33742189e-01
-1.44772851e+00 -9.25374806e-01 2.63956696e-01 5.02274632e-01
-3.12421292e-01 1.86875984e-01 1.80184618e-01 -1.26092410e+00
3.62284333e-01 -7.80973196e-01 -7.10615754e-01 7.50122070e-02
-1.58037126e-01 3.34772110e-01 7.95693099e-01 -2.37458304... | [10.383515357971191, 9.59814167022705] |
178f5286-9a23-49c8-bd08-86a5add44753 | rafola-a-rationale-annotated-corpus-for | 2205.02684 | null | https://arxiv.org/abs/2205.02684v1 | https://arxiv.org/pdf/2205.02684v1.pdf | RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labour | Forced labour is the most common type of modern slavery, and it is increasingly gaining the attention of the research and social community. Recent studies suggest that artificial intelligence (AI) holds immense potential for augmenting anti-slavery action. However, AI tools need to be developed transparently in coopera... | ['Riza Batista-Navarro', 'Viktor Schlegel', 'Erick Mendez Guzman'] | 2022-05-05 | null | https://aclanthology.org/2022.lrec-1.386 | https://aclanthology.org/2022.lrec-1.386.pdf | lrec-2022-6 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 8.35962176e-01 6.04752004e-01 -5.59233010e-01 -5.01352012e-01
-5.60170949e-01 -6.36407852e-01 1.06381893e+00 4.24481153e-01
-5.90874732e-01 8.91932726e-01 8.06811929e-01 -6.72922790e-01
-5.77204347e-01 -4.75393206e-01 -2.04976186e-01 -4.76786435e-01
3.72206062e-01 1.01092505e+00 -2.50085026e-01 -4.79020447... | [9.471138000488281, 9.577008247375488] |
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