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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ccd7c7b3-bcb4-4d4a-ab85-4164f819e396 | arrhythmia-classification-using-cgan | 2202.00569 | null | https://arxiv.org/abs/2202.00569v4 | https://arxiv.org/pdf/2202.00569v4.pdf | Arrhythmia Classification using CGAN-augmented ECG Signals | ECG databases are usually highly imbalanced due to the abundance of Normal ECG and scarcity of abnormal cases. As such, deep learning classifiers trained on imbalanced datasets usually perform poorly, especially on minor classes. One solution is to generate realistic synthetic ECG signals using Generative Adversarial N... | ['John J. Prevost', 'Fatemeh Afghah', 'Edmond Adib'] | 2022-01-26 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 5.82353830e-01 2.73082405e-01 2.15592161e-01 -8.33806321e-02
-1.14943409e+00 -5.79942107e-01 3.27740967e-01 -7.71575943e-02
-1.50427386e-01 1.16550779e+00 1.31179824e-01 -1.98720500e-01
-8.17873776e-02 -8.12690020e-01 -3.57427895e-01 -9.76655185e-01
-1.85504347e-01 6.51166916e-01 -6.34133667e-02 -6.25727251... | [14.278203010559082, 3.129563093185425] |
cbb8707c-39b4-4a61-bda6-caf98f96f894 | nature-language-reasoning-a-survey | 2303.14725 | null | https://arxiv.org/abs/2303.14725v2 | https://arxiv.org/pdf/2303.14725v2.pdf | Natural Language Reasoning, A Survey | This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for natural language reasoning in NLP, based on both philosophy and NLP scenarios, discuss what types of tasks requ... | ['Benyou Wang', 'Prayag Tiwari', 'Hongbo Zhang', 'Fei Yu'] | 2023-03-26 | null | null | null | null | ['multi-hop-question-answering', 'philosophy', 'mathematical-reasoning', 'logical-reasoning'] | ['knowledge-base', 'miscellaneous', 'natural-language-processing', 'reasoning'] | [ 2.05752671e-01 1.09973335e+00 -3.98677528e-01 -5.90374887e-01
-9.02146846e-02 -9.42222297e-01 7.58863628e-01 5.42381525e-01
-4.91200507e-01 9.95268106e-01 5.53930700e-01 -8.72233272e-01
-7.03220248e-01 -1.12038398e+00 -3.89342844e-01 -2.03282475e-01
1.85445994e-01 6.98429763e-01 1.77659437e-01 -6.41484857... | [9.180093765258789, 7.12153434753418] |
f1cb92e4-ec87-49c6-9faf-bf1eac56b115 | utility-oriented-underwater-image-quality | 2205.03574 | null | https://arxiv.org/abs/2205.03574v1 | https://arxiv.org/pdf/2205.03574v1.pdf | Utility-Oriented Underwater Image Quality Assessment Based on Transfer Learning | The widespread image applications have greatly promoted the vision-based tasks, in which the Image Quality Assessment (IQA) technique has become an increasingly significant issue. For user enjoyment in multimedia systems, the IQA exploits image fidelity and aesthetics to characterize user experience; while for other ta... | ['Patrick Le Callet', 'Ke Gu', 'Tiesong Zhao', 'Honggang Liao', 'Rongfu Lin', 'Weiling Chen'] | 2022-05-07 | null | null | null | null | ['fish-detection'] | ['computer-vision'] | [ 1.20024905e-01 -2.30033368e-01 4.04252827e-01 -4.82484519e-01
-5.79557121e-01 -1.37736484e-01 2.95012444e-01 1.24693848e-01
-4.96136874e-01 2.59193987e-01 2.07833722e-01 3.08228843e-02
-3.66629392e-01 -1.06929207e+00 -6.20164633e-01 -7.45866060e-01
-1.73058376e-01 -3.33022743e-01 1.31899148e-01 -2.45520741... | [10.702264785766602, -3.5378506183624268] |
d0e87ae7-c19f-4e3e-ae8d-defc87147839 | learning-lightness-from-human-judgement-on | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Narihira_Learning_Lightness_From_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Narihira_Learning_Lightness_From_2015_CVPR_paper.pdf | Learning Lightness From Human Judgement on Relative Reflectance | We develop a new approach to inferring lightness, the perceived reflectance of surfaces, from a single image. Classic methods view this problem from the perspective of intrinsic image decomposition, where an image is separated into reflectance and shading components. Rather than reason about reflectance and shading t... | ['Takuya Narihira', 'Stella X. Yu', 'Michael Maire'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 1.01311815e+00 8.75925869e-02 3.49117905e-01 -8.66308749e-01
-6.52106941e-01 -3.52612823e-01 4.94080305e-01 -3.58714253e-01
1.08094625e-02 2.95953929e-01 2.11380899e-01 -2.29293182e-02
1.88662112e-01 -1.09645998e+00 -8.60421002e-01 -8.44708145e-01
6.61036551e-01 1.57283887e-01 5.84973358e-02 -3.13585848... | [9.861172676086426, -2.9681179523468018] |
6a592448-4e97-4310-b332-6de73646d882 | eegminer-discovering-interpretable-features | 2110.10009 | null | https://arxiv.org/abs/2110.10009v2 | https://arxiv.org/pdf/2110.10009v2.pdf | EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters | Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions. However, the relevant features are not readily apparent and accessible. To mine informative latent representations from multichannel recordings of ongoing EEG acti... | ['Stefanos Zafeiriou', 'Yannis Panagakis', 'Nikolaos Laskaris', 'Dimitrios A. Adamos', 'Stylianos Bakas', 'Siegfried Ludwig'] | 2021-10-19 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 4.35327679e-01 9.85038802e-02 1.10966310e-01 -5.40536284e-01
-6.04051530e-01 -7.35230565e-01 7.12990761e-01 1.58603072e-01
-5.41436136e-01 5.40002048e-01 6.26360774e-01 3.18941295e-01
-7.68380165e-01 -2.59571642e-01 -4.01173294e-01 -5.89479566e-01
-5.94297826e-01 1.58038258e-03 -3.49599302e-01 1.83292821... | [12.886730194091797, 3.4430789947509766] |
428d797f-d045-4f45-9d53-80a859dfbf88 | learning-jpeg-compression-artifacts-for-image | 2108.12947 | null | https://arxiv.org/abs/2108.12947v2 | https://arxiv.org/pdf/2108.12947v2.pdf | Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization | Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editi... | ['Changick Kim', 'Heung-Kyu Lee', 'In-Jae Yu', 'Seung-Hun Nam', 'Myung-Joon Kwon'] | 2021-08-30 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 6.04035914e-01 -6.54309392e-01 -2.12680712e-01 -1.33651614e-01
-5.66408098e-01 -5.07124722e-01 3.64326805e-01 -1.01779982e-01
-4.67197925e-01 1.46770775e-01 3.30523471e-03 -5.07873356e-01
3.86158288e-01 -6.97877526e-01 -9.80398178e-01 -6.26242638e-01
-2.96010554e-01 -4.33627933e-01 9.26026553e-02 8.32003132... | [12.303071022033691, 0.9517441391944885] |
1aff20ea-6cf6-481f-ad26-d6ea756028a4 | non-decreasing-quantile-function-network-with-1 | 2105.06696 | null | https://arxiv.org/abs/2105.06696v1 | https://arxiv.org/pdf/2105.06696v1.pdf | Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning | Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address. One is how to ensure the validity of the learned quantile function, the other is how to efficiently utilize the distribution information. This paper attem... | ['Liwen Zhang', 'Qi Kuang', 'Zhoufan Zhu', 'Fan Zhou'] | 2021-05-14 | non-decreasing-quantile-function-network-with | https://openreview.net/forum?id=f_GA2IU9-K- | https://openreview.net/pdf?id=f_GA2IU9-K- | null | ['distributional-reinforcement-learning'] | ['methodology'] | [-4.16535228e-01 3.92912365e-02 -3.67440015e-01 -3.25551331e-01
-1.01162219e+00 -5.55849731e-01 1.23168588e-01 1.61772761e-02
-5.14632940e-01 1.21963036e+00 -1.30429240e-02 -4.85704213e-01
-5.62372029e-01 -9.53033626e-01 -6.01943552e-01 -8.03546846e-01
-4.13605094e-01 4.96481329e-01 2.33933538e-01 -4.31611478... | [4.045472621917725, 2.571512460708618] |
8f99f9cf-3f75-486f-8cfe-a086a14bb378 | profilesr-gan-a-gan-based-super-resolution | 2107.09523 | null | https://arxiv.org/abs/2107.09523v2 | https://arxiv.org/pdf/2107.09523v2.pdf | ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles | It is a common practice for utilities to down-sample smart meter measurements from high resolution (e.g. 1-min or 1-sec) to low resolution (e.g. 15-, 30- or 60-min) to lower the data transmission and storage cost. However, down-sampling can remove high-frequency components from time-series load profiles, making them un... | ['Ning Lu', 'Yiyan Li', 'Lidong Song'] | 2021-07-18 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.72906682e-01 -3.07935953e-01 5.15834056e-03 -2.84185886e-01
-1.17459357e+00 -4.56722498e-01 2.66148597e-01 -8.80383700e-02
2.47360885e-01 1.00569367e+00 2.63337761e-01 -1.66208699e-01
-2.03434899e-01 -1.21212709e+00 -3.02496344e-01 -8.75187337e-01
-2.70007700e-01 6.07476942e-03 -2.07403481e-01 -2.25304976... | [15.222539901733398, 6.038623332977295] |
016922ae-5035-420f-bae7-6c44487f4a07 | piano-skills-assessment | 2101.04884 | null | https://arxiv.org/abs/2101.04884v2 | https://arxiv.org/pdf/2101.04884v2.pdf | Piano Skills Assessment | Can a computer determine a piano player's skill level? Is it preferable to base this assessment on visual analysis of the player's performance or should we trust our ears over our eyes? Since current CNNs have difficulty processing long video videos, how can shorter clips be sampled to best reflect the players skill le... | ['Brendan Morris', 'Jaiden Reddy', 'Paritosh Parmar'] | 2021-01-13 | null | null | null | null | ['action-quality-assessment', 'skills-evaluation', 'skills-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-5.16523719e-02 -1.53410971e-01 -1.28170013e-01 -1.36781812e-01
-1.06901073e+00 -1.03751242e+00 -3.43631580e-02 -1.13489203e-01
-5.85085094e-01 1.06363259e-01 5.41832745e-01 3.51117589e-02
-2.24265441e-01 -4.79622871e-01 -2.02211410e-01 -3.22781622e-01
1.18517026e-01 3.13064128e-01 4.70071375e-01 -3.84109408... | [7.73577356338501, 0.26029351353645325] |
755d74c4-8202-40cb-b71c-98ef5b30ee59 | joint-3d-localization-and-classification-of | 1906.04749 | null | https://arxiv.org/abs/1906.04749v1 | https://arxiv.org/pdf/1906.04749v1.pdf | Joint 3D Localization and Classification of Space Debris using a Multispectral Rotating Point Spread Function | We consider the problem of joint three-dimensional (3D) localization and material classification of unresolved space debris using a multispectral rotating point spread function (RPSF). The use of RPSF allows one to estimate the 3D locations of point sources from their rotated images acquired by a single 2D sensor array... | ['Sudhakar Prasad', 'Grey Ballard', 'Chao Wang', 'Robert Plemmons'] | 2019-06-11 | null | null | null | null | ['material-classification'] | ['computer-vision'] | [ 1.98584393e-01 -6.68636680e-01 1.40647709e-01 3.41974854e-01
-1.08619499e+00 -8.78246307e-01 4.95215297e-01 -5.27823605e-02
-4.18337643e-01 7.17920661e-01 -1.32966086e-01 -7.08716959e-02
-6.22126579e-01 -6.83054686e-01 -5.40000498e-01 -1.00939631e+00
3.98941711e-03 4.72967714e-01 3.33322883e-01 2.37685874... | [10.071433067321777, -2.079148530960083] |
3f5e691d-b0b0-4948-ac85-22efc527b7b7 | semi-supervised-3d-face-reconstruction-with | null | null | https://openreview.net/forum?id=H1lK5kBKvr | https://openreview.net/pdf?id=H1lK5kBKvr | Semi-supervised 3D Face Reconstruction with Nonlinear Disentangled Representations | Recovering 3D geometry shape, albedo and lighting from a single image has wide applications in many areas, which is also a typical ill-posed problem. In order to eliminate the ambiguity, face prior knowledge like linear 3D morphable models (3DMM) learned from limited scan data are often adopted to the reconstruction pr... | ['Xiaokang Yang', 'Guangtao Zhai', 'Chao Ma', 'Yudong Guo', 'Juyong Zhang', 'Zhongpai Gao'] | 2019-09-25 | null | null | null | null | ['3d-face-reconstruction', 'facial-editing', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.08606008e-02 -2.27733105e-02 -1.49001330e-01 -6.48539186e-01
-3.10391456e-01 -3.68372947e-01 3.86654764e-01 -8.35349143e-01
-5.93626276e-02 5.21172822e-01 -6.83724461e-03 3.57444495e-01
2.25641280e-01 -6.44883990e-01 -8.47588241e-01 -8.79491270e-01
4.05385822e-01 3.37922722e-01 -4.87980992e-01 -2.31070980... | [12.911275863647461, -0.03857969492673874] |
adfe4088-191d-4163-9257-51d3ce1636dc | diffuse-map-guiding-unsupervised-generative | 2205.11951 | null | https://arxiv.org/abs/2205.11951v2 | https://arxiv.org/pdf/2205.11951v2.pdf | Diffuse Map Guiding Unsupervised Generative Adversarial Network for SVBRDF Estimation | Reconstructing materials in the real world has always been a difficult problem in computer graphics. Accurately reconstructing the material in the real world is critical in the field of realistic rendering. Traditionally, materials in computer graphics are mapped by an artist, then mapped onto a geometric model by coor... | ['Hongnan Chen', 'Zhiyao Luo'] | 2022-05-24 | null | null | null | null | ['svbrdf-estimation'] | ['computer-vision'] | [ 5.78115940e-01 -2.99426932e-02 4.81465042e-01 -7.88305625e-02
-4.81150448e-01 -4.66345519e-01 5.39829433e-01 -5.91964483e-01
2.26395026e-01 8.09851706e-01 -1.34954631e-01 -2.79126853e-01
1.35653645e-01 -1.43265045e+00 -9.21310127e-01 -8.21327984e-01
4.62283820e-01 3.03787500e-01 3.43994379e-01 -2.37450778... | [9.500858306884766, -3.17021107673645] |
ad7ed517-629d-45e6-b239-cec40e7efea9 | evolutionary-framework-for-two-stage | 1903.01885 | null | http://arxiv.org/abs/1903.01885v1 | http://arxiv.org/pdf/1903.01885v1.pdf | Evolutionary framework for two-stage stochastic resource allocation problems | Resource allocation problems are a family of problems in which resources must
be selected to satisfy given demands. This paper focuses on the two-stage
stochastic generalization of resource allocation problems where future demands
are expressed in a finite number of possible scenarios. The goal is to select
cost effect... | ['Fábio L. Usberti', 'Evandro C. Bracht', 'Mário C. San Felice', 'Pedro H. D. B. Hokama'] | 2018-11-29 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 6.79621816e-01 -1.12111308e-01 -5.52117467e-01 -2.01118171e-01
-3.21092784e-01 -4.06805217e-01 9.82011296e-03 -2.16775224e-01
-2.46493578e-01 1.19672227e+00 -1.44021779e-01 -3.04416746e-01
-9.62026536e-01 -9.43457723e-01 7.59128332e-02 -7.51278222e-01
-1.07068844e-01 1.11482990e+00 3.06311280e-01 -3.75255108... | [5.398467540740967, 3.2222445011138916] |
dda04f99-ad93-4b60-b10b-3a340ce9f3a8 | unleashing-the-power-of-user-reviews | 2306.15541 | null | https://arxiv.org/abs/2306.15541v1 | https://arxiv.org/pdf/2306.15541v1.pdf | Unleashing the Power of User Reviews: Exploring Airline Choices at Catania Airport, Italy | This study aims to investigate the possible relationship between the mechanisms of social influence and the choice of airline, through the use of new tools, with the aim of understanding whether they can contribute to a better understanding of the factors influencing the decisions of consumers in the aviation sector. W... | ['Antonio Picone', 'Vincenzo Miracula'] | 2023-06-27 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-4.48999465e-01 -8.39499198e-03 -3.08399856e-01 -7.06013963e-02
-2.86253184e-01 -7.30186641e-01 5.75674534e-01 7.66330004e-01
-5.37525773e-01 2.32502267e-01 4.43645507e-01 -6.03742838e-01
-2.54968762e-01 -9.26349759e-01 -3.54467630e-01 -3.88244092e-01
-2.31995359e-02 -1.83970705e-01 -1.56266406e-01 -7.48195529... | [10.686920166015625, 6.8262410163879395] |
2d2a7aa3-7d7a-4517-8c83-b8f3e07d6bdc | bayesian-persuasion-in-sequential-trials | 2110.09594 | null | https://arxiv.org/abs/2110.09594v3 | https://arxiv.org/pdf/2110.09594v3.pdf | Bayesian Persuasion in Sequential Trials | We consider a Bayesian persuasion or information design problem where the sender tries to persuade the receiver to take a particular action via a sequence of signals. This we model by considering multi-phase trials with different experiments conducted based on the outcomes of prior experiments. In contrast to most of t... | ['Grant Schoenebeck', 'Vijay G. Subramanian', 'Shih-Tang Su'] | 2021-10-18 | null | null | null | null | ['persuasion-strategies'] | ['computer-vision'] | [ 7.54972458e-01 5.60713887e-01 -4.84785795e-01 -3.06759536e-01
-8.31122518e-01 -7.08229721e-01 5.78012109e-01 3.62400264e-01
-9.38134909e-01 8.93926442e-01 1.19599812e-01 -9.51202691e-01
-7.14060605e-01 -5.94838917e-01 -7.01633036e-01 -8.43855500e-01
2.81033576e-01 8.95196736e-01 -1.83405817e-01 3.08682323... | [7.912792682647705, 5.2510905265808105] |
57d4f134-265c-46a0-ba15-f2822a5743e3 | scale-invariant-adversarial-attack-for | 2201.12527 | null | https://arxiv.org/abs/2201.12527v1 | https://arxiv.org/pdf/2201.12527v1.pdf | Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses | Efficient and effective attacks are crucial for reliable evaluation of defenses, and also for developing robust models. Projected Gradient Descent (PGD) attack has been demonstrated to be one of the most successful adversarial attacks. However, the effect of the standard PGD attack can be easily weakened by rescaling t... | ['Daoqiang Zhang', 'Zhongnian Li', 'Tao Zhang', 'Mengting Xu'] | 2022-01-29 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 1.83148175e-01 -3.88579905e-01 3.71335521e-02 -2.89435893e-01
-5.27046680e-01 -1.20689917e+00 7.94098318e-01 -3.79202098e-01
-4.95101035e-01 4.64483410e-01 1.87479798e-02 -5.69095910e-01
-1.19408913e-01 -8.12042832e-01 -5.89659870e-01 -9.01543260e-01
-1.89281836e-01 -3.00499558e-01 4.36422229e-01 -6.50782108... | [5.558252334594727, 7.914463520050049] |
b60f468d-ddd9-4e53-b384-bb0f51cab2f1 | the-challenges-of-htr-model-training-feedback | 2212.11146 | null | https://arxiv.org/abs/2212.11146v3 | https://arxiv.org/pdf/2212.11146v3.pdf | The Challenges of HTR Model Training: Feedback from the Project Donner le gout de l'archive a l'ere numerique | The arrival of handwriting recognition technologies offers new possibilities for research in heritage studies. However, it is now necessary to reflect on the experiences and the practices developed by research teams. Our use of the Transkribus platform since 2018 has led us to search for the most significant ways to im... | ['Deslandres Dominique', 'Gohier Maxime', 'Verret Farah', 'Couture Beatrice'] | 2022-12-13 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 8.81064832e-02 -6.04729168e-02 4.69099171e-02 -2.97032654e-01
-7.79006481e-01 -8.55535567e-01 6.24288261e-01 -1.60721093e-01
-5.69280267e-01 3.70705068e-01 5.64587831e-01 -5.86327493e-01
-3.32736969e-03 -6.00650072e-01 -4.87791061e-01 -2.93287188e-01
5.59535921e-01 5.99282146e-01 -9.96867567e-02 -3.99430990... | [11.838233947753906, 2.5776703357696533] |
6ba718b7-df97-4427-b461-bb371b436660 | from-unsupervised-to-few-shot-graph-anomaly | 2202.05525 | null | https://arxiv.org/abs/2202.05525v1 | https://arxiv.org/pdf/2202.05525v1.pdf | From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach | Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce. Existing efforts in graph anomaly detection typically only consider the information in a single scale (view), thus inevitably limiting their capability in capturing anomalous pattern... | ['Yi-Ping Phoebe Chen', 'Shirui Pan', 'Khoa T. Phan', 'Lianhua Chi', 'Yixin Liu', 'Ming Jin', 'Yu Zheng'] | 2022-02-11 | null | null | null | null | ['graph-anomaly-detection'] | ['graphs'] | [ 1.14858367e-01 -5.43899238e-02 -2.94846799e-02 -3.93089920e-01
-2.39020914e-01 -3.03240627e-01 3.86457741e-01 7.41466939e-01
9.69241709e-02 1.91370174e-01 -5.76447807e-02 -1.85707286e-01
-2.56625891e-01 -1.10723984e+00 -5.09382367e-01 -5.54554462e-01
-4.32264626e-01 3.02537590e-01 3.87038767e-01 -2.76990950... | [6.618313312530518, 5.759117603302002] |
2c79fd3b-87ec-4f55-9912-fd543194e776 | image-provenance-analysis-at-scale | 1801.06510 | null | http://arxiv.org/abs/1801.06510v2 | http://arxiv.org/pdf/1801.06510v2.pdf | Image Provenance Analysis at Scale | Prior art has shown it is possible to estimate, through image processing and
computer vision techniques, the types and parameters of transformations that
have been applied to the content of individual images to obtain new images.
Given a large corpus of images and a query image, an interesting further step
is to retrie... | ['Michael Parowski', 'Walter J. Scheirer', 'Kevin W. Bowyer', 'Joel Brogan', 'Daniel Moreira', 'Anderson Rocha', 'Allan Pinto', 'Patrick J. Flynn', 'Aparna Bharati'] | 2018-01-19 | null | null | null | null | ['authorship-verification'] | ['natural-language-processing'] | [ 6.52554214e-01 -5.36229461e-02 1.43359080e-01 -2.08300039e-01
-5.42555928e-01 -8.95952225e-01 9.50085342e-01 6.91469550e-01
-6.00423634e-01 3.33388776e-01 4.49254662e-02 -1.03342846e-01
1.40786514e-01 -7.93492377e-01 -1.00263453e+00 -5.34362853e-01
-1.59022987e-01 2.97011018e-01 5.88268936e-01 -1.01905540... | [12.366605758666992, 1.0184673070907593] |
adb7b837-d019-4872-b003-df6920a9de57 | humor-detection-in-english-hindi-code-mixed | 1806.05513 | null | http://arxiv.org/abs/1806.05513v1 | http://arxiv.org/pdf/1806.05513v1.pdf | Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System | The tremendous amount of user generated data through social networking sites
led to the gaining popularity of automatic text classification in the field of
computational linguistics over the past decade. Within this domain, one problem
that has drawn the attention of many researchers is automatic humor detection
in tex... | ['Manish Shrivastava', 'Ankush Khandelwal', 'Syed S. Akhtar', 'Sahil Swami'] | 2018-06-14 | humor-detection-in-english-hindi-code-mixed-1 | https://aclanthology.org/L18-1193 | https://aclanthology.org/L18-1193.pdf | lrec-2018-5 | ['humor-detection'] | ['natural-language-processing'] | [-6.21427000e-01 -1.53074130e-01 1.20087698e-01 -7.01092631e-02
-2.10853472e-01 -5.34594655e-01 5.31304479e-01 5.09077191e-01
-1.74269423e-01 5.82100213e-01 6.42759204e-01 -3.21024060e-01
4.73946571e-01 -7.05574453e-01 4.51833084e-02 -2.87115097e-01
1.16659477e-01 -8.61721337e-02 2.06380367e-01 -7.20899343... | [8.955350875854492, 10.8219633102417] |
a53cb31b-a7ab-4bee-90cb-28cdd182604b | ndjir-neural-direct-and-joint-inverse | 2302.00675 | null | https://arxiv.org/abs/2302.00675v1 | https://arxiv.org/pdf/2302.00675v1.pdf | NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Object | The goal of inverse rendering is to decompose geometry, lights, and materials given pose multi-view images. To achieve this goal, we propose neural direct and joint inverse rendering, NDJIR. Different from prior works which relies on some approximations of the rendering equation, NDJIR directly addresses the integrals ... | ['Takuya Narihira', 'Kazuki Yoshiyama'] | 2023-02-02 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 4.10735458e-01 -6.64689243e-02 7.60807991e-01 -3.51138711e-01
-5.26219785e-01 -4.74482119e-01 7.94573665e-01 -5.22958815e-01
2.51078457e-01 5.93421519e-01 1.22839585e-02 -1.41752616e-01
-3.14651608e-01 -1.27598965e+00 -5.82016528e-01 -4.56716985e-01
3.27703327e-01 9.46598291e-01 3.36161584e-01 -2.06947416... | [9.663744926452637, -3.1272757053375244] |
60bd774c-df37-4680-a860-cb25b4d343ba | adaptive-multi-teacher-knowledge-distillation | 2306.06634 | null | https://arxiv.org/abs/2306.06634v1 | https://arxiv.org/pdf/2306.06634v1.pdf | Adaptive Multi-Teacher Knowledge Distillation with Meta-Learning | Multi-Teacher knowledge distillation provides students with additional supervision from multiple pre-trained teachers with diverse information sources. Most existing methods explore different weighting strategies to obtain a powerful ensemble teacher, while ignoring the student with poor learning ability may not benefi... | ['Can Wang', 'Defang Chen', 'Hailin Zhang'] | 2023-06-11 | null | null | null | null | ['meta-learning'] | ['methodology'] | [-1.59604460e-01 1.30485326e-01 -5.25774002e-01 -4.86405462e-01
-4.50689852e-01 -4.96193081e-01 2.54956782e-01 7.30289072e-02
-3.80525351e-01 9.19801056e-01 1.29492015e-01 -2.26508245e-01
-4.62257475e-01 -9.12717342e-01 -4.43649113e-01 -9.26714420e-01
7.08899617e-01 2.11514354e-01 2.32294336e-01 -2.89033204... | [9.516715049743652, 3.379037618637085] |
5717e949-f007-489d-bc5c-a85de20a6040 | towards-complex-artificial-life | 1805.06366 | null | http://arxiv.org/abs/1805.06366v1 | http://arxiv.org/pdf/1805.06366v1.pdf | Towards Complex Artificial Life | An object-oriented combinator chemistry was used to construct an artificial
organism with a system architecture possessing characteristics necessary for
organisms to evolve into more complex forms. This architecture supports
modularity by providing a mechanism for the construction of executable modules
called $methods$... | ['Lance R. Williams'] | 2018-05-16 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.63805291e-01 1.14573650e-01 3.79852355e-01 2.75638044e-01
5.72842896e-01 -6.45123839e-01 6.70759737e-01 9.81724113e-02
-2.24926963e-01 5.69865644e-01 -4.43098575e-01 -3.14573824e-01
-3.39339375e-01 -1.19349253e+00 -3.62582356e-01 -8.64686728e-01
-7.31978655e-01 3.21643353e-01 5.90012014e-01 -2.90846407... | [5.614199638366699, 4.185136795043945] |
a0a6ac45-f3f6-4956-8de2-d48f28fdc897 | basn-learning-steganography-with-binary | 1907.04362 | null | https://arxiv.org/abs/1907.04362v1 | https://arxiv.org/pdf/1907.04362v1.pdf | BASN -- Learning Steganography with Binary Attention Mechanism | Secret information sharing through image carrier has aroused much research attention in recent years with images' growing domination on the Internet and mobile applications. However, with the booming trend of convolutional neural networks, image steganography is facing a more significant challenge from neural-network-a... | ['Yang Yang'] | 2019-07-09 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 8.32077205e-01 2.48137355e-01 -1.66563615e-01 1.22823484e-01
2.49443009e-01 -7.91766271e-02 2.86998063e-01 -6.73239291e-01
-3.30044955e-01 3.12227398e-01 -8.95808712e-02 -4.87919182e-01
1.37514725e-01 -8.72116923e-01 -4.61129606e-01 -8.87607515e-01
-1.91205531e-01 -4.77020502e-01 3.82751733e-01 -4.20975238... | [4.294663429260254, 8.062056541442871] |
4924c6b0-6689-49d5-894f-2ee729ee742b | named-entity-recognition-only-from-word | 1909.00164 | null | https://arxiv.org/abs/1909.00164v2 | https://arxiv.org/pdf/1909.00164v2.pdf | Named Entity Recognition Only from Word Embeddings | Deep neural network models have helped named entity (NE) recognition achieve amazing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Efforts have been made to replace human annotations with external knowledge (e.g., NE dictionary, part-of-spee... | ['Junlang Zhan', 'Ying Luo', 'Hai Zhao'] | 2019-08-31 | null | https://aclanthology.org/2020.emnlp-main.723 | https://aclanthology.org/2020.emnlp-main.723.pdf | emnlp-2020-11 | ['type-prediction'] | ['computer-code'] | [ 2.44760718e-02 -1.31500736e-02 -2.75180489e-01 -6.87499106e-01
-7.48749614e-01 -5.65184295e-01 3.66949618e-01 7.64279254e-03
-9.38728809e-01 8.10179532e-01 4.19132471e-01 -1.98536173e-01
2.32625976e-01 -9.09695268e-01 -4.20655638e-01 -3.70919019e-01
2.19335333e-01 5.37701428e-01 8.33334997e-02 -8.35350007... | [9.670619010925293, 9.427018165588379] |
3a524748-47af-4af5-9b39-a6058ad94236 | synthetic-ct-generation-from-mri-using-3d | 2305.19467 | null | https://arxiv.org/abs/2305.19467v1 | https://arxiv.org/pdf/2305.19467v1.pdf | Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion Model | Magnetic resonance imaging (MRI)-based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error-prone image registration, ultimately reducing patient radiation dose and setup uncertainty. We propose an MRI-to-CT transformer-based denoising d... | ['Xiaofeng Yang', 'Hui Mao', 'David S. Yu', 'Pretesh Patel', 'Justin Roper', 'Junbo Peng', 'Chih-Wei Chang', 'Yuheng Li', 'Richard L. J. Qiu', 'Tonghe Wang', 'Jacob Wynne', 'Elham Abouei', 'Shaoyan Pan'] | 2023-05-31 | null | null | null | null | ['image-registration', 'ms-ssim', 'anatomy'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 4.44251597e-01 1.36550069e-01 3.48496139e-01 -3.15524071e-01
-1.13160014e+00 -3.54437590e-01 6.63223386e-01 1.00853242e-01
-6.55314267e-01 6.25757992e-01 4.14447874e-01 -3.06757241e-01
-2.69468457e-01 -9.37987685e-01 -3.51040035e-01 -9.71065938e-01
-2.62534767e-01 8.00394654e-01 5.55684745e-01 2.10090727... | [13.626974105834961, -2.499552011489868] |
9842ae22-17c6-4d78-a7fc-9ed031e344d2 | boxcars-improving-fine-grained-recognition-of | 1703.00686 | null | http://arxiv.org/abs/1703.00686v3 | http://arxiv.org/pdf/1703.00686v3.pdf | BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillance | In this paper, we focus on fine-grained recognition of vehicles mainly in
traffic surveillance applications. We propose an approach that is orthogonal to
recent advancements in fine-grained recognition (automatic part discovery and
bilinear pooling). In addition, in contrast to other methods focused on
fine-grained rec... | ['Jakub Špaňhel', 'Adam Herout', 'Jakub Sochor'] | 2017-03-02 | null | null | null | null | ['vehicle-pose-estimation'] | ['computer-vision'] | [ 1.36260465e-01 -1.57147467e-01 9.00097415e-02 -4.60140586e-01
-5.78782439e-01 -6.62793279e-01 8.57612729e-01 -2.81996876e-01
-5.04021108e-01 5.03206909e-01 -1.94923267e-01 -2.54177719e-01
4.03328799e-02 -9.77553248e-01 -1.27070415e+00 -8.40379059e-01
2.16581136e-01 4.90825772e-01 6.01068974e-01 7.16464594... | [8.221046447753906, -0.8083351254463196] |
6309bdc2-698d-4232-814a-9ca48dd2d23d | improving-the-modality-representation-with | 2210.15824 | null | https://arxiv.org/abs/2210.15824v3 | https://arxiv.org/pdf/2210.15824v3.pdf | Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis | Modality representation learning is an important problem for multimodal sentiment analysis (MSA), since the highly distinguishable representations can contribute to improving the analysis effect. Previous works of MSA have usually focused on multimodal fusion strategies, and the deep study of modal representation learn... | ['Limin Sun', 'Hongsong Zhu', 'Yimo Ren', 'Jie Liu', 'Hong Li', 'Xin Zheng', 'Peipei Liu'] | 2022-10-28 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 3.92260045e-01 -1.67463616e-01 -9.08003822e-02 -3.26010555e-01
-9.21117783e-01 -2.81351417e-01 7.79835880e-01 1.72394022e-01
-1.60049453e-01 4.51797068e-01 5.39734662e-01 9.33775082e-02
-1.06557868e-01 -6.11910105e-01 -5.51878989e-01 -1.18393004e+00
4.14194882e-01 1.53910384e-01 -8.24407861e-02 -6.31220877... | [13.077596664428711, 5.007893085479736] |
af6fb4fd-d802-4813-b4f6-25eec9582ba8 | benchmarking-the-impact-of-noise-on-deep | 2303.13915 | null | https://arxiv.org/abs/2303.13915v1 | https://arxiv.org/pdf/2303.13915v1.pdf | Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG | Electrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise efficiently, but its influence on the accuracy of these methods is sti... | ['Nicolai Spicher', 'Dagmar Krefting', 'Henning Dathe', 'Ennio Idrobo-Avila', 'Philip Gemke', 'Theresa Bender'] | 2023-03-24 | null | null | null | null | ['atrial-fibrillation-detection'] | ['medical'] | [ 1.23014741e-01 -2.59214312e-01 3.20276380e-01 -5.71968675e-01
-1.11719787e+00 -7.03737080e-01 2.03969866e-01 4.96541977e-01
-7.49907017e-01 7.01615393e-01 4.19663489e-02 -3.95374358e-01
-3.64068866e-01 -5.90856493e-01 -4.30906534e-01 -8.80646765e-01
-3.09574515e-01 3.82808775e-01 -3.75875831e-01 1.29393294... | [14.318840980529785, 3.2930819988250732] |
06826626-8872-4ee9-b6f6-a049b77056b8 | musiac-an-extensible-generative-framework-for | 2202.05528 | null | https://arxiv.org/abs/2202.05528v1 | https://arxiv.org/pdf/2202.05528v1.pdf | MusIAC: An extensible generative framework for Music Infilling Applications with multi-level Control | We present a novel music generation framework for music infilling, with a user friendly interface. Infilling refers to the task of generating musical sections given the surrounding multi-track music. The proposed transformer-based framework is extensible for new control tokens as the added music control tokens such as ... | ['Dorien Herremans', 'Thor Magnusson', 'Chris Kiefer', 'Ivor Simpson', 'Rui Guo'] | 2022-02-11 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.88596058e-02 -7.68597648e-02 5.57615645e-02 2.93530762e-01
-6.15801096e-01 -9.11123037e-01 5.49743652e-01 7.20865801e-02
6.16830774e-02 5.76151729e-01 4.64154810e-01 8.94456804e-02
-4.61101145e-01 -8.29527557e-01 -4.16008711e-01 -4.27082062e-01
-9.04323906e-02 1.46480650e-01 2.95836091e-01 -5.46416640... | [15.99519157409668, 5.433119297027588] |
c163f1ef-b5e9-4fb7-8a7d-d7191ee914bf | self-supervised-real-time-video-stabilization | 2111.05980 | null | https://arxiv.org/abs/2111.05980v1 | https://arxiv.org/pdf/2111.05980v1.pdf | Self-Supervised Real-time Video Stabilization | Videos are a popular media form, where online video streaming has recently gathered much popularity. In this work, we propose a novel method of real-time video stabilization - transforming a shaky video to a stabilized video as if it were stabilized via gimbals in real-time. Our framework is trainable in a self-supervi... | ['In So Kweon', 'Jaesik Park', 'Jinsoo Choi'] | 2021-11-10 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 2.93658286e-01 -1.29028201e-01 -1.13707945e-01 -5.52664734e-02
-6.47831023e-01 -7.88340092e-01 2.77533740e-01 2.24407226e-01
-4.92374927e-01 5.55819929e-01 4.32823263e-02 -1.79541856e-01
2.99222887e-01 -3.97888243e-01 -1.17775011e+00 -7.66700327e-01
7.71353394e-02 -2.21423075e-01 6.84072495e-01 2.11885702... | [10.62294864654541, -1.3856219053268433] |
1f636fa6-18c1-4b84-9ec5-b96cfd4a20ac | hoiclip-efficient-knowledge-transfer-for-hoi | 2303.15786 | null | https://arxiv.org/abs/2303.15786v2 | https://arxiv.org/pdf/2303.15786v2.pdf | HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models | Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions. Recently, Contrastive Language-Image Pre-training (CLIP) has shown great potential in providing interaction prior for HOI detectors via knowledge distillation. However, such approaches often rely on large-scal... | ['Xuming He', 'Yongfei Liu', 'Longtian Qiu', 'Shan Ning'] | 2023-03-28 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ning_HOICLIP_Efficient_Knowledge_Transfer_for_HOI_Detection_With_Vision-Language_Models_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ning_HOICLIP_Efficient_Knowledge_Transfer_for_HOI_Detection_With_Vision-Language_Models_CVPR_2023_paper.pdf | cvpr-2023-1 | ['human-object-interaction-detection'] | ['computer-vision'] | [ 2.67327160e-01 1.75209213e-02 -3.62428516e-01 -2.21050501e-01
-9.80693221e-01 -3.87159646e-01 5.72681010e-01 -1.17813930e-01
-2.63186961e-01 3.67847770e-01 4.52498525e-01 1.87138841e-01
2.07046241e-01 -4.47872818e-01 -1.11468947e+00 -4.52425808e-01
9.82118547e-02 2.48912305e-01 3.08460534e-01 -2.40186155... | [9.628889083862305, 1.4010441303253174] |
20f2c611-28f3-462a-a352-4fa55d339a52 | efficient-video-segmentation-models-with-per | 2202.12427 | null | https://arxiv.org/abs/2202.12427v1 | https://arxiv.org/pdf/2202.12427v1.pdf | Efficient Video Segmentation Models with Per-frame Inference | Most existing real-time deep models trained with each frame independently may produce inconsistent results across the temporal axis when tested on a video sequence. A few methods take the correlations in the video sequence into account,e.g., by propagating the results to the neighboring frames using optical flow or ext... | ['Jingdong Wang', 'Changqian Yu', 'Chunhua Shen', 'Yifan Liu'] | 2022-02-24 | null | null | null | null | ['image-matting', 'video-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [-3.15047875e-02 -3.11115414e-01 -4.73150611e-01 -5.02162755e-01
-6.75502956e-01 -5.19910812e-01 3.26664388e-01 -3.71291906e-01
-5.24017274e-01 7.52577722e-01 -1.73674718e-01 -1.18155047e-01
8.68497938e-02 -6.29932404e-01 -1.01684153e+00 -6.27221286e-01
-1.19216785e-01 7.22923055e-02 6.77843750e-01 1.74111351... | [9.177655220031738, -0.10053399205207825] |
d709ca85-8a7e-4db6-8c5e-c705e62d11ee | stock-movement-prediction-from-tweets-and | null | null | https://aclanthology.org/P18-1183 | https://aclanthology.org/P18-1183.pdf | Stock Movement Prediction from Tweets and Historical Prices | Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or... | ['Yumo Xu', 'Shay B. Cohen'] | 2018-07-01 | null | null | null | acl-2018-7 | ['stock-trend-prediction', 'stock-market-prediction'] | ['time-series', 'time-series'] | [-4.09530073e-01 -2.92220414e-01 -3.53632450e-01 -2.18646646e-01
-9.18234169e-01 -5.65650403e-01 1.07425117e+00 -5.80676675e-01
-9.41641331e-02 7.77521551e-01 4.57376182e-01 -1.07348591e-01
-1.78118601e-01 -8.70782733e-01 -6.94978476e-01 -7.00541735e-01
-3.46320182e-01 9.84467506e-01 1.92802250e-01 -1.86028957... | [6.8355793952941895, 3.4699034690856934] |
f4fdb41b-f5af-4655-aacc-243d38a8ea0e | chart-rcnn-efficient-line-chart-data | 2211.14362 | null | https://arxiv.org/abs/2211.14362v1 | https://arxiv.org/pdf/2211.14362v1.pdf | Chart-RCNN: Efficient Line Chart Data Extraction from Camera Images | Line Chart Data Extraction is a natural extension of Optical Character Recognition where the objective is to recover the underlying numerical information a chart image represents. Some recent works such as ChartOCR approach this problem using multi-stage networks combining OCR models with object detection frameworks. H... | ['Haoshuai Zhou', 'Linkai Li', 'Congxi Lu', 'Shufan Li'] | 2022-11-25 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 6.18046522e-01 -1.95154354e-01 -7.10165501e-02 -5.32850146e-01
-6.82091117e-01 -1.00230801e+00 5.72989225e-01 7.07362145e-02
-1.81947559e-01 4.86277282e-01 -5.76876216e-02 -2.85648137e-01
3.19411218e-01 -7.71609962e-01 -1.02866876e+00 -1.17356412e-01
4.83066499e-01 2.89439917e-01 2.93718010e-01 -1.11110054... | [11.621419906616211, 2.255239725112915] |
e7df1829-68cf-4e5b-a384-502c8f12643c | discohead-audio-and-video-driven-talking-head | 2303.07697 | null | https://arxiv.org/abs/2303.07697v1 | https://arxiv.org/pdf/2303.07697v1.pdf | DisCoHead: Audio-and-Video-Driven Talking Head Generation by Disentangled Control of Head Pose and Facial Expressions | For realistic talking head generation, creating natural head motion while maintaining accurate lip synchronization is essential. To fulfill this challenging task, we propose DisCoHead, a novel method to disentangle and control head pose and facial expressions without supervision. DisCoHead uses a single geometric trans... | ['Gyeongsu Chae', 'Sungwoo Park', 'SeungHyun Lee', 'Sunwon Hong', 'Geumbyeol Hwang'] | 2023-03-14 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [-2.73815274e-01 3.93427461e-01 -8.43001753e-02 -2.57993698e-01
-7.87135839e-01 -4.14088845e-01 5.35542786e-01 -9.19107735e-01
-2.51445740e-01 4.74598438e-01 5.25220811e-01 1.90123767e-01
6.01856172e-01 -1.09617554e-01 -8.12332511e-01 -9.37223315e-01
2.44025171e-01 1.67680338e-01 1.79263707e-02 -3.46070863... | [13.19989013671875, -0.4321385622024536] |
c110a81e-1661-4369-ad80-a813b0b70a0c | a-unified-survey-on-anomaly-novelty-open-set | 2110.14051 | null | https://arxiv.org/abs/2110.14051v5 | https://arxiv.org/pdf/2110.14051v5.pdf | A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges | Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label significantly compromises the reliability of a model. The problem has gained significant attention due to i... | ['Mohammad Sabokrou', 'Mohammad Hossein Rohban', 'Yixuan Li', 'Dan Hendrycks', 'Hossein Mirzaei', 'Mohammadreza Salehi'] | 2021-10-26 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 1.01689599e-01 -7.45884180e-02 -3.99073184e-01 -4.33519810e-01
-5.79093814e-01 -8.02904069e-01 4.55093682e-01 4.88245219e-01
8.97605810e-03 6.15212977e-01 -4.62823451e-01 -4.88807142e-01
-4.51735735e-01 -6.49442613e-01 -4.17064041e-01 -6.90395892e-01
-2.55133808e-01 6.40818477e-01 1.29274428e-01 1.45015180... | [7.718326091766357, 2.583534002304077] |
1e4bfd28-af0d-4ad6-9c68-c1e744c4cb02 | rstgen-imbuing-fine-grained-interpretable | 2205.12590 | null | https://arxiv.org/abs/2205.12590v1 | https://arxiv.org/pdf/2205.12590v1.pdf | RSTGen: Imbuing Fine-Grained Interpretable Control into Long-FormText Generators | In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models. To this end, we propose RSTGen, a framework that utilises Rhetorical Structure Theory (RST), a classical language theory, to control the discourse structure, semantics and topics of generated text. F... | ['Yulan He', 'Ritabrata Dutta', 'Rilwan A. Adewoyin'] | 2022-05-25 | null | https://aclanthology.org/2022.naacl-main.133 | https://aclanthology.org/2022.naacl-main.133.pdf | naacl-2022-7 | ['story-generation'] | ['natural-language-processing'] | [ 4.61014628e-01 1.28591311e+00 -1.59483507e-01 9.48563814e-02
-7.97735929e-01 -7.32018769e-01 1.59733796e+00 3.93027574e-01
2.10931078e-02 1.14783549e+00 1.32417929e+00 -4.49860722e-01
-7.64827384e-03 -8.17096710e-01 -3.88284773e-01 2.22558845e-02
8.35950300e-02 8.68231654e-01 2.07187623e-01 -8.88585031... | [11.548778533935547, 9.062111854553223] |
307a851a-c5e9-411f-ba11-e961679b4d30 | layout-based-causal-inference-for-object | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Layout-Based_Causal_Inference_for_Object_Navigation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Layout-Based_Causal_Inference_for_Object_Navigation_CVPR_2023_paper.pdf | Layout-Based Causal Inference for Object Navigation | Previous works for ObjectNav task attempt to learn the association (e.g. relation graph) between the visual inputs and the goal during training. Such association contains the prior knowledge of navigating in training environments, which is denoted as the experience. The experience performs a positive effect on help... | ['Shuqiang Jiang', 'Xinyao Yu', 'Yubing Bai', 'Weijie Li', 'Xinhang Song', 'Sixian Zhang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [-1.28994184e-02 2.38233015e-01 1.79122925e-01 -4.10371065e-01
1.74793124e-01 -3.41603577e-01 5.14223754e-01 1.49522960e-01
-6.74043953e-01 6.96599483e-01 2.28409141e-01 -1.44363225e-01
-4.40362543e-01 -8.56169403e-01 -1.23889887e+00 -9.08519685e-01
-1.41625211e-01 1.32452995e-01 4.61206555e-01 -1.55385330... | [4.497059345245361, 0.5689058899879456] |
70f0bd37-cbd9-4b75-9e70-695dc6b38f88 | sar-image-despeckling-based-on-nonlocal | 1611.07559 | null | http://arxiv.org/abs/1611.07559v1 | http://arxiv.org/pdf/1611.07559v1.pdf | Sar image despeckling based on nonlocal similarity sparse decomposition | This letter presents a method of synthetic aperture radar (SAR) image
despeckling aimed to preserve the detail information while suppressing speckle
noise. This method combines the nonlocal self-similarity partition and a
proposed modified sparse decomposition. The nonlocal partition method groups a
series of structure... | ['Cheng-Wei Sang', 'Quisong Xia', 'Hong Sun'] | 2016-11-22 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 5.47652125e-01 -6.93964124e-01 2.04190388e-01 -2.39900693e-01
-8.27762008e-01 -2.86234111e-01 3.88741612e-01 -2.94380546e-01
-9.41965953e-02 4.31962490e-01 6.82842553e-01 2.76711702e-01
-5.09957135e-01 -7.62275815e-01 -2.08268300e-01 -1.13712931e+00
7.82159567e-02 2.14976341e-01 -3.88314319e-03 -1.78301066... | [10.442651748657227, -2.004106044769287] |
48040ebf-da3b-411f-b6b2-c5c940512dde | galois-boosting-deep-reinforcement-learning | 2205.13728 | null | https://arxiv.org/abs/2205.13728v1 | https://arxiv.org/pdf/2205.13728v1.pdf | GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis | Despite achieving superior performance in human-level control problems, unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence (e.g., logic deduction and reuse), thus it behaves ineffectively than humans regarding learning and generalization in complex problems. Previous works attempt to directl... | ['Yang Liu', 'Jianye Hao', 'Yi Li', 'Yan Zheng', 'Hao Zhang', 'Tianpei Yang', 'Zhiming Li', 'Yushi Cao'] | 2022-05-27 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [-7.14714304e-02 2.99537599e-01 -5.60268879e-01 -2.88875937e-01
-3.31660032e-01 -7.14806795e-01 5.40850639e-01 3.57301198e-02
1.76209718e-01 7.08486378e-01 2.60366332e-02 -9.15585637e-01
-2.74319470e-01 -1.22525918e+00 -1.10826683e+00 -1.10692978e-01
6.78578541e-02 1.68456078e-01 3.34974885e-01 -4.45187598... | [9.142034530639648, 7.198415756225586] |
6997cc53-f544-42e1-be6c-976032574202 | pv2tea-patching-visual-modality-to-textual | 2306.01016 | null | https://arxiv.org/abs/2306.01016v1 | https://arxiv.org/pdf/2306.01016v1.pdf | PV2TEA: Patching Visual Modality to Textual-Established Information Extraction | Information extraction, e.g., attribute value extraction, has been extensively studied and formulated based only on text. However, many attributes can benefit from image-based extraction, like color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation diffi... | ['Xian Li', 'Carl Yang', 'Jingbo Shang', 'Chenwei Zhang', 'Nasser Zalmout', 'Rongmei Lin', 'Hejie Cui'] | 2023-06-01 | null | null | null | null | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 6.47711992e-01 4.97001968e-02 -3.79063308e-01 -6.46998107e-01
-1.32146323e+00 -7.28032887e-01 5.99733889e-01 2.07297832e-01
-4.17250752e-01 6.57599509e-01 1.69308439e-01 1.96166113e-02
1.86246842e-01 -3.79423469e-01 -7.77927816e-01 -8.83281291e-01
2.81847626e-01 3.66986901e-01 -3.82237136e-02 7.39805549... | [10.77523422241211, 1.3511593341827393] |
80b4b623-4f73-41c7-9ed0-2a813cba315f | synopses-of-movie-narratives-a-video-language-1 | 2203.05711 | null | https://arxiv.org/abs/2203.05711v4 | https://arxiv.org/pdf/2203.05711v4.pdf | Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding | Despite recent advances of AI, story understanding remains an open and under-investigated problem. We collect, preprocess, and publicly release a video-language story dataset, Synopses of Movie Narratives (SyMoN), containing 5,193 video summaries of popular movies and TV series with a total length of 869 hours. SyMoN c... | ['Yangfeng Ji', 'Boyang Li', 'Qin Chao', 'Yidan Sun'] | 2022-03-11 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 3.61415476e-01 -2.84411430e-01 -6.25104368e-01 -2.51361549e-01
-1.04674006e+00 -9.31906044e-01 1.07229698e+00 3.02013248e-01
-9.70924273e-02 6.05060995e-01 1.19459832e+00 3.28618407e-01
-1.08722508e-01 -3.64591271e-01 -7.07125604e-01 -1.46287799e-01
-1.25204548e-01 3.31510216e-01 1.78438902e-01 -3.12320381... | [10.502279281616211, 0.8036318421363831] |
1ed08b4b-f559-4b45-8a79-3dbdc8a013f3 | 2305-14704 | 2305.14704 | null | https://arxiv.org/abs/2305.14704v2 | https://arxiv.org/pdf/2305.14704v2.pdf | An Evaluation on Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation | To speed up online testing, adaptive traffic experimentation through multi-armed bandit algorithms is rising as an essential complementary alternative to the fixed horizon A/B testing. Based on recent research on best arm identification and statistical inference with adaptively collected data, this paper derives and ev... | ['Ted Yuan', 'Zezhong Zhang'] | 2023-05-24 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.95068028e-02 -2.11981654e-01 -7.90600598e-01 -3.45608145e-01
-8.45927298e-01 -4.78174001e-01 3.40191871e-01 -4.31913555e-01
-3.64374965e-01 1.39676809e+00 -2.70864725e-01 -1.00812936e+00
-9.81504440e-01 -5.62463760e-01 -7.90776432e-01 -8.68102014e-01
-3.03878009e-01 9.23328757e-01 5.41399777e-01 1.55392522... | [4.530822277069092, 3.2678725719451904] |
917c071d-8dce-439b-a574-1a8f3c07ff1e | drotrack-high-speed-drone-based-object | 2005.00828 | null | https://arxiv.org/abs/2005.00828v1 | https://arxiv.org/pdf/2005.00828v1.pdf | DroTrack: High-speed Drone-based Object Tracking Under Uncertainty | We present DroTrack, a high-speed visual single-object tracking framework for drone-captured video sequences. Most of the existing object tracking methods are designed to tackle well-known challenges, such as occlusion and cluttered backgrounds. The complex motion of drones, i.e., multiple degrees of freedom in three-d... | ['Flora Salim', 'Ali Hamdi', 'Du Yong Kim'] | 2020-05-02 | null | null | null | null | ['drone-based-object-tracking'] | ['computer-vision'] | [-4.08146948e-01 -6.33727193e-01 -1.06029108e-01 -1.67921945e-01
-4.94324803e-01 -8.86547625e-01 3.32933992e-01 -2.48869091e-01
-6.85964942e-01 4.82122183e-01 -3.94466072e-01 1.42831802e-01
1.26984492e-01 -5.13317227e-01 -8.97778749e-01 -7.31639206e-01
-2.91033477e-01 3.30034673e-01 7.79919744e-01 8.80400613... | [6.470572471618652, -2.1865131855010986] |
b27a655d-6836-4c28-a71a-8022d3fdb48a | sentiment-analysis-for-emotional-speech | null | null | https://aclanthology.org/2020.coling-main.440 | https://aclanthology.org/2020.coling-main.440.pdf | Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System | As smart speakers and conversational robots become ubiquitous, the demand for expressive speech synthesis has increased. In this paper, to control the emotional parameters of the speech synthesis according to certain dialogue contents, we construct a news dataset with emotion labels ({``}positive,{''} {``}negative,{''}... | ['Tetsunori Kobayashi', 'Yoichi Matsuyama', 'Ryota Ando', 'Hiroaki Takatsu'] | 2020-12-01 | null | null | null | coling-2020-8 | ['emotional-speech-synthesis', 'expressive-speech-synthesis'] | ['speech', 'speech'] | [-1.65324569e-01 7.55803406e-01 -1.65880978e-01 -7.22152710e-01
-5.05836189e-01 -4.24407661e-01 5.60972154e-01 -2.04562426e-01
-3.31006348e-01 1.02406764e+00 4.25556332e-01 -8.25323686e-02
4.98199373e-01 -4.50298667e-01 -3.70864719e-01 -6.15858257e-01
1.88464269e-01 5.53976119e-01 -1.08821794e-01 -3.40795457... | [13.01169490814209, 6.1897478103637695] |
3daaa7b3-0688-447b-8b65-a3ece4fd511c | leveraging-relational-information-for-1 | 2205.10056 | null | https://arxiv.org/abs/2205.10056v1 | https://arxiv.org/pdf/2205.10056v1.pdf | Leveraging Relational Information for Learning Weakly Disentangled Representations | Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of variation of the data in single isolated dimensions of the neural representation. We argue that such a de... | ['Davide Bacciu', 'Andrea Valenti'] | 2022-05-20 | leveraging-relational-information-for | https://openreview.net/forum?id=TNmJgFmz2k | https://openreview.net/pdf?id=TNmJgFmz2k | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 3.60340774e-01 5.41365564e-01 -3.33316028e-01 -2.12223679e-01
-6.48603082e-01 -9.29797947e-01 9.96390343e-01 -1.87519968e-01
-4.14799675e-02 6.80127740e-01 7.35755622e-01 -1.95278749e-01
-7.00483203e-01 -6.66515231e-01 -7.23466814e-01 -8.43734086e-01
6.66996017e-02 4.56508785e-01 -2.20000699e-01 -2.04284802... | [9.25931453704834, 4.870980739593506] |
b16381c9-ce5c-46ce-ba81-1bb93f855b04 | text-to-speech-synthesis-based-on-latent | 2212.08329 | null | https://arxiv.org/abs/2212.08329v1 | https://arxiv.org/pdf/2212.08329v1.pdf | Text-to-speech synthesis based on latent variable conversion using diffusion probabilistic model and variational autoencoder | Text-to-speech synthesis (TTS) is a task to convert texts into speech. Two of the factors that have been driving TTS are the advancements of probabilistic models and latent representation learning. We propose a TTS method based on latent variable conversion using a diffusion probabilistic model and the variational auto... | ['Tomoki Toda', 'Yusuke Yasuda'] | 2022-12-16 | null | null | null | null | ['text-to-speech-synthesis'] | ['speech'] | [ 4.38556522e-02 2.75842190e-01 -3.61097276e-01 -4.20427382e-01
-9.25899029e-01 -6.66421771e-01 9.77339208e-01 -6.74640715e-01
9.56203565e-02 5.01303732e-01 8.32387209e-01 -2.75720328e-01
2.13221446e-01 -7.79411793e-01 -5.94691813e-01 -7.77432978e-01
5.98404408e-01 8.37038517e-01 3.82214375e-02 -1.10705167... | [15.00573444366455, 6.563294410705566] |
ffc70f5a-ee0f-4032-ac31-627d92b854fe | direct-robot-configuration-space-construction | 2303.05653 | null | https://arxiv.org/abs/2303.05653v1 | https://arxiv.org/pdf/2303.05653v1.pdf | Direct Robot Configuration Space Construction using Convolutional Encoder-Decoders | Intelligent robots must be able to perform safe and efficient motion planning in their environments. Central to modern motion planning is the configuration space. Configuration spaces define the set of configurations of a robot that result in collisions with obstacles in the workspace, C-clsn, and the set of configurat... | ['Hod Lipson', 'Riya Gupta', 'Carl Gross', 'Christopher Benka'] | 2023-03-10 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.90994248e-01 1.99802220e-01 4.55438606e-02 -4.38529514e-02
-5.75299561e-01 -7.03133941e-01 4.79370683e-01 -7.32441545e-02
-6.33248746e-01 5.12035131e-01 1.41614974e-01 -5.83348930e-01
-2.76059300e-01 -6.10461056e-01 -9.08292174e-01 -2.96214491e-01
-4.27929997e-01 8.42431247e-01 2.42768109e-01 -5.62162817... | [4.726498603820801, 0.9186777472496033] |
516cd210-6a41-460a-9326-3677934227d4 | bayesian-analysis-of-dynamic-linear-topic | 1511.03947 | null | http://arxiv.org/abs/1511.03947v1 | http://arxiv.org/pdf/1511.03947v1.pdf | Bayesian Analysis of Dynamic Linear Topic Models | In dynamic topic modeling, the proportional contribution of a topic to a
document depends on the temporal dynamics of that topic's overall prevalence in
the corpus. We extend the Dynamic Topic Model of Blei and Lafferty (2006) by
explicitly modeling document level topic proportions with covariates and
dynamic structure... | ['Brian Howard', 'Surya T. Tokdar', 'David L. Banks', 'Chris Glynn'] | 2015-11-12 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [ 6.49982691e-02 6.18920103e-02 -5.91910124e-01 -2.67620683e-01
-1.06513965e+00 -5.62026501e-01 8.66194725e-01 5.26183426e-01
-3.43005359e-01 9.78476465e-01 3.70912045e-01 -6.89225852e-01
-2.89543778e-01 -7.73365021e-01 -7.77763128e-01 -5.36184371e-01
-3.73438776e-01 9.90749955e-01 2.41474852e-01 5.06655991... | [10.269942283630371, 6.903753280639648] |
e6a82332-517b-422c-95f4-bebc18cc0e2c | deep-clustering-with-a-constraint-for | 2303.03036 | null | https://arxiv.org/abs/2303.03036v1 | https://arxiv.org/pdf/2303.03036v1.pdf | Deep Clustering with a Constraint for Topological Invariance based on Symmetric InfoNCE | We consider the scenario of deep clustering, in which the available prior knowledge is limited. In this scenario, few existing state-of-the-art deep clustering methods can perform well for both non-complex topology and complex topology datasets. To address the problem, we propose a constraint utilizing symmetric InfoNC... | ['Takafumi Kanamori', 'Yusaku Hino', 'Kaito Goto', 'Hiroki Waida', 'Yuichiro Wada', 'Yuhui Zhang'] | 2023-03-06 | null | null | null | null | ['deep-clustering', 'deep-clustering'] | ['miscellaneous', 'natural-language-processing'] | [-7.72479951e-01 -5.16275585e-01 2.04082385e-01 -2.67196625e-01
-1.04565300e-01 -4.88390267e-01 2.27751344e-01 -1.98903337e-01
-3.21215838e-01 5.33143520e-01 -9.29328054e-03 -1.68699339e-01
-4.47872311e-01 -7.73866236e-01 -5.29105902e-01 -1.06796229e+00
-2.81704813e-01 8.95872474e-01 2.52648592e-01 4.59075458... | [9.076977729797363, 3.3687026500701904] |
33d26b30-7848-4bbc-877d-fcf2366a54e7 | viewnet-a-novel-projection-based-backbone | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_ViewNet_A_Novel_Projection-Based_Backbone_With_View_Pooling_for_Few-Shot_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_ViewNet_A_Novel_Projection-Based_Backbone_With_View_Pooling_for_Few-Shot_CVPR_2023_paper.pdf | ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud Classification | Although different approaches have been proposed for 3D point cloud-related tasks, few-shot learning (FSL) of 3D point clouds still remains under-explored. In FSL, unlike traditional supervised learning, the classes of training and test data do not overlap, and a model needs to recognize unseen classes from only a ... | ['Senem Velipasalar', 'Minmin Yang', 'Jiajing Chen'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['few-shot-point-cloud-classification', 'point-cloud-classification'] | ['computer-vision', 'computer-vision'] | [-1.41509771e-01 -1.40165329e-01 -1.76866397e-01 -4.30983484e-01
-5.45645058e-01 -5.39210081e-01 6.98980093e-01 -5.92663251e-02
-3.03677190e-03 1.05066232e-01 -9.66536626e-02 -4.52687517e-02
5.58199994e-02 -1.09846008e+00 -9.08173978e-01 -5.80699921e-01
5.18415980e-02 3.68120581e-01 6.87462389e-01 -1.63404882... | [8.07878589630127, -3.3366682529449463] |
e4f0d702-2004-4622-a76d-8d452536d02f | computing-education-in-the-era-of-generative | 2306.02608 | null | https://arxiv.org/abs/2306.02608v1 | https://arxiv.org/pdf/2306.02608v1.pdf | Computing Education in the Era of Generative AI | The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning. Very recent advances in artificial intelligence have resulted in code generation models that can produce source code from natural... | ['Sami Sarsa', 'Eddie Antonio Santos', 'Brent N. Reeves', 'Andrew Luxton-Reilly', 'Juho Leinonen', 'Arto Hellas', 'James Finnie-Ansley', 'Brett A. Becker', 'James Prather', 'Paul Denny'] | 2023-06-05 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 4.44944166e-02 3.61607790e-01 1.94672607e-02 -3.73362184e-01
-4.87452328e-01 -8.10075462e-01 3.02412063e-01 8.06499183e-01
-2.62928724e-01 3.27468634e-01 1.78015143e-01 -9.33339179e-01
-3.18543106e-01 -8.71221483e-01 -7.05985308e-01 -1.75230056e-01
5.70985153e-02 8.76594707e-02 2.19296739e-01 -3.54355991... | [9.831528663635254, 7.3534393310546875] |
e26d04b2-693f-4ea0-8eac-39c4bf3c3001 | hp-gan-probabilistic-3d-human-motion | 1711.09561 | null | http://arxiv.org/abs/1711.09561v1 | http://arxiv.org/pdf/1711.09561v1.pdf | HP-GAN: Probabilistic 3D human motion prediction via GAN | Predicting and understanding human motion dynamics has many applications,
such as motion synthesis, augmented reality, security, and autonomous vehicles.
Due to the recent success of generative adversarial networks (GAN), there has
been much interest in probabilistic estimation and synthetic data generation
using deep ... | ['Zicheng Liu', 'John Kender', 'Emad Barsoum'] | 2017-11-27 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [ 3.76055658e-01 4.33298379e-01 3.64586376e-02 -1.30166799e-01
-9.05182779e-01 -3.46548319e-01 7.92051852e-01 -8.91605854e-01
-2.41899729e-01 1.10613871e+00 4.99381542e-01 2.72633974e-02
4.51642305e-01 -8.59603047e-01 -1.11781561e+00 -7.46025503e-01
6.94933254e-03 5.93290091e-01 4.36788797e-01 -1.67839006... | [7.299492835998535, -0.10407783091068268] |
1c201a88-23b4-49a7-a545-99808cfe0b97 | vvc-extension-scheme-for-object-detection | 2305.18782 | null | https://arxiv.org/abs/2305.18782v1 | https://arxiv.org/pdf/2305.18782v1.pdf | VVC Extension Scheme for Object Detection Using Contrast Reduction | In recent years, video analysis using Artificial Intelligence (AI) has been widely used, due to the remarkable development of image recognition technology using deep learning. In 2019, the Moving Picture Experts Group (MPEG) has started standardization of Video Coding for Machines (VCM) as a video coding technology for... | ['Hiroshi Watanabe', 'Kein Yamada', 'Taiju Watanabe', 'Takahiro Shindo'] | 2023-05-30 | null | null | null | null | ['video-compression'] | ['computer-vision'] | [ 5.22523701e-01 -3.41147900e-01 -1.35391429e-01 1.33619770e-01
3.96336615e-02 -2.34849602e-02 2.26972550e-01 -1.80919051e-01
-5.51371813e-01 4.23582464e-01 -1.98460668e-02 -1.06752686e-01
3.87575179e-01 -8.72394979e-01 -4.82218742e-01 -7.06114650e-01
1.14803314e-01 -4.24760818e-01 5.48826456e-01 2.72596121... | [11.175915718078613, -1.5825532674789429] |
8cfbac87-70b1-45b4-aafd-56b2264e8fc3 | powerplanningdl-reliability-aware-framework | 2005.01386 | null | https://arxiv.org/abs/2005.01386v2 | https://arxiv.org/pdf/2005.01386v2.pdf | PowerPlanningDL: Reliability-Aware Framework for On-Chip Power Grid Design using Deep Learning | With the increase in the complexity of chip designs, VLSI physical design has become a time-consuming task, which is an iterative design process. Power planning is that part of the floorplanning in VLSI physical design where power grid networks are designed in order to provide adequate power to all the underlying funct... | ['Sukanta Dey', 'Sukumar Nandi', 'Gaurav Trivedi'] | 2020-05-04 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [-9.16796401e-02 -1.04212416e-02 -3.44022423e-01 -1.44536451e-01
-4.53529507e-01 -3.40372562e-01 3.02909344e-01 2.47569263e-01
1.47387415e-01 9.36793685e-01 -1.01078607e-01 -4.79498237e-01
-4.53460544e-01 -9.26720262e-01 -3.17689985e-01 -7.59642005e-01
-1.36620045e-01 5.21883309e-01 -9.44636390e-03 -1.91381592... | [5.933703899383545, 3.346686363220215] |
6a4d6284-30d6-4d99-b9d8-3d873afa87df | learning-affinity-via-spatial-propagation-1 | null | null | https://arxiv.org/pdf/1710.01020.pdf | https://arxiv.org/pdf/1710.01020.pdf | Learning Affinity via Spatial Propagation Network | In this paper, we propose spatial propagation networks for learning the affinity matrix for vision tasks. We show that by constructing a row/column linear propagation
model, the spatially varying transformation matrix exactly constitutes an affinity
matrix that models dense, global pairwise relationships of an image.... | ['Jan Kautz', 'Ming-Hsuan Yang', 'Guangyu Zhong', 'Jinwei Gu', 'Shalini De Mello', 'Sifei Liu'] | 2017-10-03 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 2.27516755e-01 1.41534552e-01 -3.59814465e-02 -6.23511672e-01
-5.14614284e-01 -5.47494650e-01 3.27408999e-01 -2.11136397e-02
-5.11394382e-01 5.24868332e-02 -1.39568165e-01 -1.29080787e-01
-2.79190361e-01 -9.18365359e-01 -1.09682965e+00 -8.49065900e-01
1.78656161e-01 6.82326555e-01 3.66713196e-01 -2.64037937... | [9.655721664428711, 0.5238730311393738] |
f57ad705-c41c-4f7b-9828-953590a3dc85 | multi-stage-distillation-framework-for-cross-2 | 2209.05869 | null | https://arxiv.org/abs/2209.05869v1 | https://arxiv.org/pdf/2209.05869v1.pdf | Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching | Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks. However, the student model needs to be large in this operation. Otherwise, its performance will drop sharply, thus making it impractical to ... | ['Xuefeng Yang', 'Qi Ju', 'Zhe Zhao', 'Yuejian Fang', 'Weijie Liu', 'Kunbo Ding'] | 2022-09-13 | multi-stage-distillation-framework-for-cross-1 | https://aclanthology.org/2022.findings-naacl.167 | https://aclanthology.org/2022.findings-naacl.167.pdf | findings-naacl-2022-7 | ['xlm-r'] | ['natural-language-processing'] | [-3.67969126e-02 -2.32942745e-01 -5.55676639e-01 -4.37422574e-01
-1.06314170e+00 -3.87741268e-01 3.47296864e-01 2.21674219e-01
-6.50042057e-01 4.57289428e-01 -1.96144562e-02 -7.52005100e-01
1.03225209e-01 -5.46472490e-01 -7.14726985e-01 -3.22817773e-01
3.51462156e-01 3.58079046e-01 2.96039701e-01 -7.24694505... | [13.631701469421387, 7.037595272064209] |
df844211-4dbf-4ceb-836d-98de495c2b00 | game-theoretic-algorithms-for-conditional | 2208.09551 | null | https://arxiv.org/abs/2208.09551v1 | https://arxiv.org/pdf/2208.09551v1.pdf | Game-Theoretic Algorithms for Conditional Moment Matching | A variety of problems in econometrics and machine learning, including instrumental variable regression and Bellman residual minimization, can be formulated as satisfying a set of conditional moment restrictions (CMR). We derive a general, game-theoretic strategy for satisfying CMR that scales to nonlinear problems, is ... | ['Zhiwei Steven Wu', 'J. Andrew Bagnell', 'Sanjiban Choudhury', 'Gokul Swamy'] | 2022-08-19 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-4.47512530e-02 2.99114048e-01 -5.19118845e-01 -2.67742306e-01
-1.27955842e+00 -7.70463407e-01 5.89451969e-01 -2.52488226e-01
-3.49397212e-01 1.01249886e+00 -1.35316700e-01 -8.92406166e-01
-9.03560162e-01 -2.87569433e-01 -4.40413445e-01 -7.06188381e-01
-2.87040442e-01 5.97047567e-01 -3.55742246e-01 -4.68432941... | [6.525765895843506, 4.097433090209961] |
36b4bc3b-bfab-4b78-a659-6e9fc740ab02 | differentially-private-distributed-data | 1910.12832 | null | https://arxiv.org/abs/1910.12832v2 | https://arxiv.org/pdf/1910.12832v2.pdf | Differentially Private Distributed Data Summarization under Covariate Shift | We envision AI marketplaces to be platforms where consumers, with very less data for a target task, can obtain a relevant model by accessing many private data sources with vast number of data samples. One of the key challenges is to construct a training dataset that matches a target task without compromising on privacy... | ['Venkata Sitaramagiridharganesh Ganapavarapu', 'Roman Vaculin', 'Karthikeyan Shanmugam', 'Kanthi Sarpatwar', 'Ashish Jagmohan'] | 2019-10-28 | differentially-private-distributed-data-1 | http://papers.nips.cc/paper/9589-differentially-private-distributed-data-summarization-under-covariate-shift | http://papers.nips.cc/paper/9589-differentially-private-distributed-data-summarization-under-covariate-shift.pdf | neurips-2019-12 | ['data-summarization'] | ['miscellaneous'] | [ 2.48018280e-01 4.49264646e-02 -3.05887163e-01 -4.92395818e-01
-1.46386516e+00 -1.17706263e+00 1.54851168e-01 4.25726950e-01
-4.53043312e-01 8.39914560e-01 -1.04187123e-01 -1.35544553e-01
-3.78545046e-01 -9.09028590e-01 -9.50733066e-01 -1.14281940e+00
-3.05921197e-01 4.66774344e-01 -7.11854696e-02 -1.24001674... | [5.872924327850342, 6.698272705078125] |
d4fcb9db-b9e4-4a6a-9f1c-cd510368e80f | actor-director-critic-a-novel-deep | 2301.03887 | null | https://arxiv.org/abs/2301.03887v1 | https://arxiv.org/pdf/2301.03887v1.pdf | Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework | In this paper, we propose actor-director-critic, a new framework for deep reinforcement learning. Compared with the actor-critic framework, the director role is added, and action classification and action evaluation are applied simultaneously to improve the decision-making performance of the agent. Firstly, the actions... | ['Yuanlin Zhang', 'Yonghong Song', 'Zongwei Liu'] | 2023-01-10 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [-2.30556130e-01 -1.02153920e-01 -2.19987676e-01 -2.40936037e-02
-2.24808753e-01 -4.85805385e-02 3.32203478e-01 2.49857139e-02
-9.28997457e-01 8.72157693e-01 -5.43109290e-02 9.11689177e-02
-1.30533114e-01 -8.98378968e-01 -4.66588378e-01 -9.98233497e-01
9.46771502e-02 3.50845397e-01 4.92189676e-01 -7.38527626... | [4.036343574523926, 2.065160036087036] |
31555700-8920-4f63-9937-186e1f75211b | flightbert-a-non-autoregressive-multi-horizon | 2305.01658 | null | https://arxiv.org/abs/2305.01658v1 | https://arxiv.org/pdf/2305.01658v1.pdf | FlightBERT++: A Non-autoregressive Multi-Horizon Flight Trajectory Prediction Framework | Flight Trajectory Prediction (FTP) is an essential task in Air Traffic Control (ATC), which can assist air traffic controllers to manage airspace more safely and efficiently. Existing approaches generally perform multi-horizon FTP tasks in an autoregressive manner, which is prone to suffer from error accumulation and l... | ['Yi Lin', 'Jianwei Zhang', 'Zheng Zhang', 'Dongyue Guo'] | 2023-05-02 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [ 4.62187111e-01 -2.56379485e-01 -3.42727631e-01 -2.44433627e-01
-4.74181622e-01 -3.55012864e-01 4.09060925e-01 -9.82753113e-02
-2.02145785e-01 6.12036109e-01 2.31066197e-01 -4.77873355e-01
-3.50632221e-01 -8.89626324e-01 -5.55458546e-01 -6.19007111e-01
-2.25938261e-01 -2.79128477e-02 2.23432377e-01 -2.24868819... | [6.935773849487305, 2.7537682056427] |
20549a74-dd86-471c-ac9b-a38ff5c187c0 | policy-learning-for-active-target-tracking | 2212.01498 | null | https://arxiv.org/abs/2212.01498v2 | https://arxiv.org/pdf/2212.01498v2.pdf | Policy Learning for Active Target Tracking over Continuous SE(3) Trajectories | This paper proposes a novel model-based policy gradient algorithm for tracking dynamic targets using a mobile robot, equipped with an onboard sensor with limited field of view. The task is to obtain a continuous control policy for the mobile robot to collect sensor measurements that reduce uncertainty in the target sta... | ['Nikolay Atanasov', 'Arash Asgharivaskasi', 'Shumon Koga', 'Pengzhi Yang'] | 2022-12-03 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-2.37404592e-02 4.87139374e-01 -6.33888006e-01 -2.33788729e-01
-5.48060834e-01 -3.36814702e-01 3.26755941e-01 -2.94704467e-01
-9.79909778e-01 7.56266892e-01 -9.99903604e-02 -3.41504246e-01
-3.39637816e-01 -4.53603059e-01 -9.73106325e-01 -7.81476617e-01
-9.75607932e-02 4.45355505e-01 -1.28406703e-01 -5.23744933... | [4.658564567565918, 2.18918776512146] |
caeb04ec-5f1a-460a-a8be-189f139843df | questions-for-flat-minima-optimization-of | 2202.00661 | null | https://arxiv.org/abs/2202.00661v5 | https://arxiv.org/pdf/2202.00661v5.pdf | When Do Flat Minima Optimizers Work? | Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers. Two methods have received significant attention due to their scalability: 1. Stochastic Weight Avera... | ['Matt J. Kusner', 'Ricardo Silva', 'Linqing Liu', 'Jean Kaddour'] | 2022-02-01 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 2.54378021e-02 -1.08951055e-01 -3.86685878e-01 -6.95288897e-01
-8.54160786e-01 -3.92455488e-01 3.32047135e-01 3.04634362e-01
-6.64186478e-01 6.21902108e-01 4.27192330e-01 -3.28686446e-01
-3.75813484e-01 -6.28288507e-01 -7.06865311e-01 -5.83874702e-01
-1.41390994e-01 3.01134944e-01 5.63114956e-02 -1.11132771... | [8.25529956817627, 3.523998498916626] |
07e67162-9d77-4280-aec8-b63c774f3e97 | efficientad-accurate-visual-anomaly-detection | 2303.14535 | null | https://arxiv.org/abs/2303.14535v1 | https://arxiv.org/pdf/2303.14535v1.pdf | EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies | Detecting anomalies in images is an important task, especially in real-time computer vision applications. In this work, we focus on computational efficiency and propose a lightweight feature extractor that processes an image in less than a millisecond on a modern GPU. We then use a student-teacher approach to detect an... | ['Rebecca König', 'Lars Heckler', 'Kilian Batzner'] | 2023-03-25 | null | null | null | null | ['semi-supervised-anomaly-detection'] | ['computer-vision'] | [ 2.13263869e-01 -4.00319338e-01 5.06830513e-01 -2.94768244e-01
-3.51682127e-01 -3.69789153e-01 4.74149704e-01 6.75174057e-01
-4.55187351e-01 1.04110986e-01 -8.34078610e-01 -5.03641665e-01
1.20083578e-01 -9.41326082e-01 -8.02513063e-01 -8.83469641e-01
-1.79040447e-01 3.00736099e-01 6.29790962e-01 1.43814400... | [7.663540363311768, 2.210428237915039] |
e247d3ff-e010-4575-8c9b-23364df64463 | understanding-and-mitigating-multi-sided | 2111.05564 | null | https://arxiv.org/abs/2111.05564v1 | https://arxiv.org/pdf/2111.05564v1.pdf | Understanding and Mitigating Multi-Sided Exposure Bias in Recommender Systems | Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to optimize utilities not just for the end user, but also for other actors such as item sellers or produ... | ['Masoud Mansoury'] | 2021-11-10 | null | null | null | null | ['exposure-fairness'] | ['adversarial'] | [-1.99102640e-01 -3.63270119e-02 -3.06230485e-01 -4.51426804e-01
-9.00419354e-02 -6.98373079e-01 3.24479610e-01 3.03949416e-01
-3.11850518e-01 5.19668102e-01 4.58650082e-01 -4.69984740e-01
-6.69411480e-01 -9.88434196e-01 -2.89352477e-01 -5.33957243e-01
1.66832462e-01 1.76554680e-01 -8.96959379e-02 -5.46108425... | [9.695348739624023, 5.644174575805664] |
5e8f746f-4d8e-47b2-8373-be3fd9625045 | eeny-meeny-miny-moe-how-to-choose-data-for | 2210.14465 | null | https://arxiv.org/abs/2210.14465v1 | https://arxiv.org/pdf/2210.14465v1.pdf | Eeny, meeny, miny, moe. How to choose data for morphological inflection | Data scarcity is a widespread problem in numerous natural language processing (NLP) tasks for low-resource languages. Within morphology, the labour-intensive work of tagging/glossing data is a serious bottleneck for both NLP and language documentation. Active learning (AL) aims to reduce the cost of data annotation by ... | ['Mans Hulden', 'Saliha Muradoglu'] | 2022-10-26 | null | null | null | null | ['morphological-inflection'] | ['natural-language-processing'] | [ 2.77299523e-01 2.73735195e-01 -2.34907046e-01 -3.62442017e-01
-1.23752713e+00 -8.94263923e-01 4.53322887e-01 6.66930795e-01
-8.54193747e-01 7.58501291e-01 4.22415942e-01 -7.37495780e-01
-1.56191081e-01 -5.18320382e-01 -5.25505126e-01 -6.38754189e-01
2.23863780e-01 8.07857096e-01 1.11274784e-02 -5.26802801... | [10.758749961853027, 9.466996192932129] |
b012529a-52b4-499a-971f-1a117cef7e99 | robust-counterfactual-inferences-using | 1808.07569 | null | http://arxiv.org/abs/1808.07569v1 | http://arxiv.org/pdf/1808.07569v1.pdf | Robust Counterfactual Inferences using Feature Learning and their Applications | In a wide variety of applications, including personalization, we want to
measure the difference in outcome due to an intervention and thus have to deal
with counterfactual inference. The feedback from a customer in any of these
situations is only 'bandit feedback' - that is, a partial feedback based on
whether we chose... | ['Abhimanyu Mitra', 'Sushant Kumar', 'Kannan Achan'] | 2018-08-22 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.62089151e-01 2.09004238e-01 -7.76381016e-01 -3.76206249e-01
-5.90437829e-01 -4.89441901e-01 5.57173133e-01 5.85798025e-01
-7.78246522e-01 9.18771565e-01 5.67247093e-01 -7.52532244e-01
-4.39769298e-01 -8.93754363e-01 -9.30403829e-01 -8.57185841e-01
-2.49109487e-03 5.80751777e-01 -1.78370833e-01 -4.77806143... | [8.367703437805176, 5.411685466766357] |
ac4c7daf-4d6f-423a-b499-7383cd3555e4 | gatortron-a-large-clinical-language-model-to | 2203.03540 | null | https://arxiv.org/abs/2203.03540v3 | https://arxiv.org/pdf/2203.03540v3.pdf | GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records | There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinica... | ['Tanja Magoc', 'Ying Zhang', 'Mona G Flores', 'Cheryl Martin', 'Colin Compas', 'Christopher Parisien', 'Kaleb E Smith', 'Hoo Chang Shin', 'Nima PourNejatian', 'Aokun Chen', 'Yonghui Wu', 'Jiang Bian', 'Elizabeth A Shenkman', 'William R Hogan', 'Duane A Mitchell', 'Gloria Lipori', 'Christopher A Harle', 'Xi Yang'] | 2022-02-02 | null | null | null | null | ['medical-relation-extraction', 'clinical-concept-extraction'] | ['medical', 'medical'] | [ 8.63025114e-02 3.38367134e-01 -1.92757100e-01 -3.77910882e-01
-9.69210327e-01 -3.91119212e-01 1.02175698e-01 7.26878643e-01
-4.97756809e-01 6.46594167e-01 5.91991365e-01 -8.51814032e-01
-1.44322127e-01 -7.06717372e-01 -3.04904878e-01 -2.23903820e-01
-1.77480057e-01 1.06330609e+00 -4.40679044e-01 -3.09761427... | [8.59883975982666, 8.548994064331055] |
28fa7c83-e8eb-453e-af28-f30b502828f8 | adversarial-synthesis-learning-enables | 1712.07695 | null | http://arxiv.org/abs/1712.07695v1 | http://arxiv.org/pdf/1712.07695v1.pdf | Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth | A lack of generalizability is one key limitation of deep learning based
segmentation. Typically, one manually labels new training images when
segmenting organs in different imaging modalities or segmenting abnormal organs
from distinct disease cohorts. The manual efforts can be alleviated if one is
able to reuse manual... | ['Bennett A. Landman', 'Richard G. Abramson', 'Albert Assad', 'Shunxing Bao', 'Zhoubing Xu', 'Yuankai Huo'] | 2017-12-20 | null | null | null | null | ['splenomegaly-segmentation-on-multi-modal-mri'] | ['medical'] | [ 3.98474574e-01 4.95575517e-01 1.41119942e-01 -3.96200716e-01
-9.53363597e-01 -8.74881268e-01 3.95226032e-01 -2.13598326e-01
-4.44166541e-01 8.36987257e-01 -1.36460379e-01 -3.75589520e-01
3.66284639e-01 -7.15127110e-01 -7.04293370e-01 -7.75329113e-01
2.08341330e-01 7.61530876e-01 2.49802470e-01 1.86493456... | [14.285225868225098, -2.2306711673736572] |
a7fcb22f-eeac-4729-afa0-2f09f4be0273 | guir-mup-2022-towards-generating-topic-aware | null | null | https://aclanthology.org/2022.sdp-1.34 | https://aclanthology.org/2022.sdp-1.34.pdf | GUIR @ MuP 2022: Towards Generating Topic-aware Multi-perspective Summaries for Scientific Documents | This paper presents our approach for the MuP 2022 shared task —-Multi-Perspective Scientific Document Summarization, where the objective is to enable summarization models to explore methods for generating multi-perspective summaries for scientific papers. We explore two orthogonal ways to cope with this task. The first... | ['Nazli Goharian', 'Sajad Sotudeh'] | null | null | null | null | sdp-coling-2022-10 | ['scientific-article-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.86002026e-02 5.38892448e-01 -1.46553874e-01 -1.39883935e-01
-1.68196285e+00 -7.11305916e-01 8.99196327e-01 4.80504990e-01
-2.59879112e-01 1.14794385e+00 1.10411608e+00 -2.13011354e-01
7.03996941e-02 -3.69512171e-01 -8.08185399e-01 -3.95329356e-01
1.15119226e-01 5.86859524e-01 1.98947433e-02 -1.52978212... | [12.556046485900879, 9.614302635192871] |
79747bee-b5e5-4cec-8d98-76598f0dbffb | specific-investments-under-negotiated | 2303.14515 | null | https://arxiv.org/abs/2303.14515v1 | https://arxiv.org/pdf/2303.14515v1.pdf | Specific investments under negotiated transfer pricing: effects of different surplus sharing parameters on managerial performance: An agent-based simulation with fuzzy Q-learning agents | This paper focuses on a decentralized profit-center firm that uses negotiated transfer pricing as an instrument to coordinate the production process. Moreover, the firm's headquarters gives its divisions full authority over operating decisions and it is assumed that each division can additionally make an upfront invest... | ['Christian Mitsch'] | 2023-03-25 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.58814859e-01 6.47650659e-01 -4.73300129e-01 1.58472165e-01
-1.36850134e-01 -6.17589414e-01 9.50234011e-02 2.72200089e-02
-5.73132575e-01 1.05737317e+00 -1.41943544e-01 -3.22554350e-01
-6.57284617e-01 -9.54688013e-01 -1.42283395e-01 -8.63223255e-01
2.77389407e-01 7.63669372e-01 -2.82497913e-01 -3.00460875... | [4.260954856872559, 2.9026646614074707] |
30ae32c7-7bc9-4f5a-81c2-b38f53afa5d7 | amr-to-text-generation-with-graph-transformer | null | null | https://aclanthology.org/2020.tacl-1.2 | https://aclanthology.org/2020.tacl-1.2.pdf | AMR-To-Text Generation with Graph Transformer | Abstract meaning representation (AMR)-to-text generation is the challenging task of generating natural language texts from AMR graphs, where nodes represent concepts and edges denote relations. The current state-of-the-art methods use graph-to-sequence models; however, they still cannot significantly outperform the pre... | ['Xiaojun Wan', 'Tianming Wang', 'Hanqi Jin'] | 2020-01-01 | null | null | null | tacl-2020-1 | ['graph-to-sequence'] | ['natural-language-processing'] | [ 6.94048345e-01 6.45857811e-01 -1.10181637e-01 -2.28670269e-01
-7.30250299e-01 -4.07041818e-01 1.00577652e+00 3.28408092e-01
-4.41347361e-02 8.92364919e-01 7.21103251e-01 -4.76421952e-01
2.78713644e-01 -1.07443428e+00 -7.75609553e-01 -2.96506733e-01
1.20698340e-01 6.77467048e-01 3.71950641e-02 -6.26514375... | [10.356513977050781, 8.365818977355957] |
026acdd7-1580-4960-a05d-72e0c25f0bec | comprehensive-evaluation-of-no-reference-1 | 2011.07950 | null | https://arxiv.org/abs/2011.07950v1 | https://arxiv.org/pdf/2011.07950v1.pdf | Comprehensive evaluation of no-reference image quality assessment algorithms on authentic distortions | Objective image quality assessment deals with the prediction of digital images' perceptual quality. No-reference image quality assessment predicts the quality of a given input image without any knowledge or information about its pristine (distortion free) counterpart. Machine learning algorithms are heavily used in no-... | ['Domonkos Varga'] | 2020-10-26 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 2.29968965e-01 -3.59933168e-01 -8.83348286e-02 -3.71059775e-01
-1.17200077e+00 -3.97817343e-01 3.71176124e-01 3.56915176e-01
-3.55709881e-01 6.80777133e-01 -3.10197920e-02 -1.04208015e-01
-8.13607201e-02 -5.83495796e-01 -5.13006330e-01 -9.06826496e-01
-1.47691593e-01 -2.41674930e-01 2.16944814e-01 -1.11694232... | [11.773789405822754, -1.904897689819336] |
ca8ae175-279d-4de5-96ac-79bcc06d4716 | lscp-locally-selective-combination-in | 1812.01528 | null | http://arxiv.org/abs/1812.01528v2 | http://arxiv.org/pdf/1812.01528v2.pdf | LSCP: Locally Selective Combination in Parallel Outlier Ensembles | In unsupervised outlier ensembles, the absence of ground truth makes the
combination of base outlier detectors a challenging task. Specifically,
existing parallel outlier ensembles lack a reliable way of selecting competent
base detectors, affecting accuracy and stability, during model combination. In
this paper, we pr... | ['Zheng Li', 'Maciej K. Hryniewicki', 'Zain Nasrullah', 'Yue Zhao'] | 2018-12-04 | null | null | null | null | ['outlier-ensembles'] | ['methodology'] | [-3.13261032e-01 -7.51821220e-01 1.17451914e-01 -6.42284676e-02
-1.18229377e+00 -3.56854916e-01 5.95455647e-01 4.85569119e-01
-4.03409451e-01 5.46117544e-01 3.60719077e-02 8.88446420e-02
-2.23473072e-01 -4.69433755e-01 -5.68100989e-01 -8.45146835e-01
-1.92703068e-01 5.45975566e-01 4.02571350e-01 1.83386635... | [7.586382865905762, 2.691741466522217] |
744229a7-f64f-41c7-a797-30cce148808d | neural-program-repair-systems-challenges-and | 2202.10868 | null | https://arxiv.org/abs/2202.10868v2 | https://arxiv.org/pdf/2202.10868v2.pdf | Neural Program Repair: Systems, Challenges and Solutions | Automated Program Repair (APR) aims to automatically fix bugs in the source code. Recently, as advances in Deep Learning (DL) field, there is a rise of Neural Program Repair (NPR) studies, which formulate APR as a translation task from buggy code to correct code and adopt neural networks based on encoder-decoder archit... | ['Bin Luo', 'Jidong Ge', 'Chuanyi Li', 'Wenkang Zhong'] | 2022-02-22 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [-0.05321573 0.191861 -0.5353088 -0.35074255 -0.7286398 -0.33887127
-0.09774721 0.15763718 0.15543945 0.53853405 0.07489485 -0.61450356
0.19576462 -0.7736008 -1.1116349 -0.13514072 -0.00898371 -0.37688515
0.11187957 -0.27539384 0.48292577 -0.05001323 -1.4239157 0.39636245
0.9998652 0.6039018 0.... | [7.597423076629639, 7.752069473266602] |
5069a673-e4c1-4484-81af-1029dde7378f | open-set-recognition-with-gradient-based | 2206.08229 | null | https://arxiv.org/abs/2206.08229v1 | https://arxiv.org/pdf/2206.08229v1.pdf | Open-Set Recognition with Gradient-Based Representations | Neural networks for image classification tasks assume that any given image during inference belongs to one of the training classes. This closed-set assumption is challenged in real-world applications where models may encounter inputs of unknown classes. Open-set recognition aims to solve this problem by rejecting unkno... | ['Ghassan AlRegib', 'Jinsol Lee'] | 2022-06-16 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 6.83935702e-01 2.42557779e-01 -4.81039345e-01 -6.79565847e-01
-6.53683186e-01 -6.93617642e-01 4.07603234e-01 2.15483367e-01
-4.90967542e-01 7.01049805e-01 -5.96046686e-01 -3.26785803e-01
-1.52533501e-01 -8.66410613e-01 -1.12437427e+00 -5.65782309e-01
4.22157384e-02 8.98230076e-01 1.47180527e-01 2.54637897... | [9.544711112976074, 3.0263195037841797] |
606ddf0c-ba63-4770-9857-bee53c8beb35 | crystal-transformer-self-learning-neural | 2204.11953 | null | https://arxiv.org/abs/2204.11953v1 | https://arxiv.org/pdf/2204.11953v1.pdf | Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials | Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for p... | ['Jianjun Hu', 'Fanglin Chen', 'Edirisuriya M. D. Siriwardane', 'Stanislav Stefanov', 'Yuqi Song', 'Qinyang Li', 'Lai Wei'] | 2022-04-25 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.15445411e-01 2.33826712e-01 -6.89911544e-02 -2.36502439e-01
-6.15465641e-01 -4.70497131e-01 5.83073199e-01 1.11522801e-01
1.15686506e-01 1.32673275e+00 1.59164533e-01 -6.14455342e-01
1.94051534e-01 -1.23529112e+00 -1.04387343e+00 -1.11725104e+00
3.63116831e-01 9.80538070e-01 -8.51172507e-02 -2.72588581... | [5.127840042114258, 5.408814907073975] |
8541c411-88fc-4fc0-87be-453c4bda3496 | clear-the-fog-combat-value-assessment-in | 1811.12627 | null | http://arxiv.org/abs/1811.12627v2 | http://arxiv.org/pdf/1811.12627v2.pdf | Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders | StarCraft, one of the most popular real-time strategy games, is a compelling
environment for artificial intelligence research for both micro-level unit
control and macro-level strategic decision making. In this study, we address an
eminent problem concerning macro-level decision making, known as the
'fog-of-war', which... | ['Changhyeon Bae', 'Hyungu Kahng', 'Young Joon Park', 'Yoon Sang Cho', 'Junseung Lee', 'Iljoo Yoon', 'Hyunjin Choi', 'Hyunjae Lee', 'Gonie Ahn', 'Yonghyun Jeong', 'Seoung Bum Kim', 'Hyungrok Do', 'Uk Jo', 'Hankyu Lee', 'Daehun Jun'] | 2018-11-30 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [ 3.22207771e-02 6.68475851e-02 -3.14809307e-02 -1.37316748e-01
-3.11200827e-01 -4.57349956e-01 5.30661345e-01 -4.17945050e-02
-8.88483405e-01 8.86781931e-01 8.68018866e-02 -4.05971408e-01
2.42566857e-02 -1.01888704e+00 -4.89674777e-01 -5.79787791e-01
-1.63198307e-01 3.38282347e-01 4.30889398e-01 -8.60633194... | [3.5622565746307373, 1.555107831954956] |
9bb74dce-917f-4257-8629-87028db0f45d | user-satisfaction-modeling-with-domain | null | null | https://aclanthology.org/2022.sigdial-1.59 | https://aclanthology.org/2022.sigdial-1.59.pdf | User Satisfaction Modeling with Domain Adaptation in Task-oriented Dialogue Systems | User Satisfaction Estimation (USE) is crucial in helping measure the quality of a task-oriented dialogue system. However, the complex nature of implicit responses poses challenges in detecting user satisfaction, and most datasets are limited in size or not available to the public due to user privacy policies. Unlike ta... | ['Georg Groh', 'Bernhard Pflugfelder', 'Mingyang Ma', 'Yan Pan'] | null | null | null | null | sigdial-acl-2022-9 | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 2.56180108e-01 2.54357487e-01 -2.55232513e-01 -1.00490093e+00
-9.71724868e-01 -4.90180433e-01 5.24610579e-01 7.53184855e-02
-5.20057797e-01 9.75404203e-01 6.06322944e-01 1.62674591e-01
2.92365462e-01 -4.41993028e-01 2.27986854e-02 -3.19557875e-01
3.78352642e-01 7.45337427e-01 7.00854063e-02 -8.19876790... | [12.784561157226562, 7.934021472930908] |
81f60e72-24bf-4e75-8b76-de5bb1794d11 | modeling-and-recognition-of-smart-grid-faults | 1407.7008 | null | http://arxiv.org/abs/1407.7008v2 | http://arxiv.org/pdf/1407.7008v2.pdf | Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification | Detecting faults in electrical power grids is of paramount importance, either
from the electricity operator and consumer viewpoints. Modern electric power
grids (smart grids) are equipped with smart sensors that allow to gather
real-time information regarding the physical status of all the component
elements belonging ... | ['Enrico De Santis', 'Alireza Sadeghian', 'Antonello Rizzi', 'Lorenzo Livi'] | 2014-07-25 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 1.45465480e-02 -3.38967413e-01 2.71808922e-01 -1.48775578e-01
-3.28712195e-01 -5.66706777e-01 4.67728645e-01 6.67927861e-01
-4.01173756e-02 8.89311790e-01 -5.13175726e-01 -3.39435071e-01
-6.67253733e-01 -9.78768885e-01 -1.01028932e-02 -1.09202087e+00
-1.97079271e-01 8.10030341e-01 -5.60286529e-02 -2.22368360... | [6.465163707733154, 2.4062306880950928] |
c2d81f18-db3c-4b10-bf7f-7f29a0f364bb | monocular-real-time-full-body-capture-with | 2012.06087 | null | https://arxiv.org/abs/2012.06087v2 | https://arxiv.org/pdf/2012.06087v2.pdf | Monocular Real-time Full Body Capture with Inter-part Correlations | We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational efficiency. Unlike pr... | ['Feng Xu', 'Christian Theobalt', 'Ayush Tewari', 'Ikhsanul Habibie', 'Marc Habermann', 'Yuxiao Zhou'] | 2020-12-11 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Monocular_Real-Time_Full_Body_Capture_With_Inter-Part_Correlations_CVPR_2021_paper.pdf | cvpr-2021-1 | ['face-model'] | ['computer-vision'] | [-7.79646933e-02 -1.40222525e-02 7.19006360e-02 -4.23767358e-01
-4.47510719e-01 -5.88790894e-01 4.07139778e-01 -8.98428977e-01
-6.77003190e-02 5.22758722e-01 7.89606050e-02 2.66429067e-01
4.01079863e-01 -4.89370435e-01 -6.59319162e-01 -7.07407415e-01
2.61827037e-02 7.74863303e-01 -2.90819436e-01 -6.30686283... | [13.105039596557617, -0.06154513359069824] |
d7370e65-6cae-462e-893a-53207ccef749 | celebv-hq-a-large-scale-video-facial | 2207.12393 | null | https://arxiv.org/abs/2207.12393v1 | https://arxiv.org/pdf/2207.12393v1.pdf | CelebV-HQ: A Large-Scale Video Facial Attributes Dataset | Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields. However, the academic community still lacks a video dataset with diverse facial attribute annotations, which is crucial for the research on face-r... | ['Chen Change Loy', 'Ziwei Liu', 'Li Zhang', 'Siwei Tang', 'Liming Jiang', 'Wentao Zhu', 'Wayne Wu', 'Hao Zhu'] | 2022-07-25 | null | null | null | null | ['video-generation', 'unconditional-video-generation'] | ['computer-vision', 'computer-vision'] | [-1.97889790e-01 -4.37164724e-01 -1.50202483e-01 -5.93456924e-01
-5.13628006e-01 -1.84843823e-01 4.00305122e-01 -4.56357002e-01
-4.02293392e-02 7.05101252e-01 3.98026884e-01 4.40405756e-01
1.75975990e-02 -4.84173566e-01 -5.19554377e-01 -8.99953008e-01
-9.15562883e-02 -1.25036687e-01 -3.00706685e-01 -1.81345433... | [12.935416221618652, 0.17907829582691193] |
d561b245-7242-4300-bfed-ac9a9e19f025 | global-norm-aware-pooling-for-pose-robust | 1808.00435 | null | http://arxiv.org/abs/1808.00435v1 | http://arxiv.org/pdf/1808.00435v1.pdf | Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate | In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block,
which reweights local features in a convolutional neural network (CNN)
adaptively according to their L2 norms and outputs a global feature vector with
a global average pooling layer. Our GNAP block is designed to give dynamic
weights to local fea... | ['Zhen Han', 'Xiang Gao', 'Jia Guo', 'Yang Liu', 'Sheng Chen'] | 2018-08-01 | null | null | null | null | ['robust-face-recognition'] | ['computer-vision'] | [ 4.16923203e-02 -2.51266688e-01 -1.38943508e-01 -5.74438870e-01
-5.13089538e-01 -3.32203507e-01 4.09248054e-01 -7.18904138e-01
-4.09428149e-01 2.96081662e-01 1.55212536e-01 1.36565328e-01
3.76811773e-02 -7.63426900e-01 -8.08939815e-01 -9.73080039e-01
-1.99523523e-01 -4.18542027e-01 7.05515966e-02 9.74779353... | [13.25134563446045, 0.6959513425827026] |
81f960ba-a365-45ad-b6c5-3f75d3d80ba8 | generative-modeling-for-small-data-object | 1910.07169 | null | https://arxiv.org/abs/1910.07169v1 | https://arxiv.org/pdf/1910.07169v1.pdf | Generative Modeling for Small-Data Object Detection | This paper explores object detection in the small data regime, where only a limited number of annotated bounding boxes are available due to data rarity and annotation expense. This is a common challenge today with machine learning being applied to many new tasks where obtaining training data is more challenging, e.g. i... | ['Li-Jia Li', 'Jia Deng', 'Tomas Pfister', 'Michael Muelly', 'Lanlan Liu'] | 2019-10-16 | generative-modeling-for-small-data-object-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_Generative_Modeling_for_Small-Data_Object_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_Generative_Modeling_for_Small-Data_Object_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['small-data'] | ['computer-vision'] | [ 4.38489646e-01 3.53559226e-01 1.47564799e-01 -2.33205333e-01
-9.53389406e-01 -4.23439026e-01 4.56875831e-01 4.48852241e-01
-8.33291829e-01 6.80110574e-01 -1.18730284e-01 -2.75419623e-01
2.35197857e-01 -6.81333780e-01 -7.62201607e-01 -9.01295841e-01
1.29024103e-01 8.59597862e-01 5.37349701e-01 5.24266176... | [15.010220527648926, -2.423823356628418] |
52964014-da4f-4834-a19b-1d211f943472 | visual-relationship-detection-with-language | 1608.00187 | null | http://arxiv.org/abs/1608.00187v1 | http://arxiv.org/pdf/1608.00187v1.pdf | Visual Relationship Detection with Language Priors | Visual relationships capture a wide variety of interactions between pairs of
objects in images (e.g. "man riding bicycle" and "man pushing bicycle").
Consequently, the set of possible relationships is extremely large and it is
difficult to obtain sufficient training examples for all possible
relationships. Because of t... | ['Li Fei-Fei', 'Michael Bernstein', 'Cewu Lu', 'Ranjay Krishna'] | 2016-07-31 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [-7.13536702e-03 -1.27408892e-01 -4.56321865e-01 -6.81303859e-01
-2.57250309e-01 -6.86032712e-01 9.10989404e-01 3.13487560e-01
-1.82523802e-01 4.12393421e-01 2.09093675e-01 -3.64008874e-01
-1.09171867e-01 -7.75397241e-01 -9.90331829e-01 -2.22081915e-01
-2.89964527e-01 5.77285945e-01 5.02316833e-01 -2.43860990... | [10.2926664352417, 1.592879056930542] |
6104635e-8262-445d-9d06-7c2f4ff9b438 | algorithmic-trading-in-a-microstructural | 1705.01446 | null | https://arxiv.org/abs/1705.01446v3 | https://arxiv.org/pdf/1705.01446v3.pdf | Algorithmic trading in a microstructural limit order book model | We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox point processes with intensities that only depend on the state of ... | ['Huyên Pham', 'Côme Huré', 'Frédéric Abergel'] | 2017-05-03 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-4.49363589e-01 -2.81315178e-01 -2.41716087e-01 2.38834117e-02
-4.06683236e-01 -8.35421324e-01 5.98949790e-01 1.44392192e-01
-4.72972393e-01 7.58774281e-01 -5.99460416e-02 -3.27951640e-01
-5.72940886e-01 -9.16322887e-01 -6.55476511e-01 -6.94780946e-01
-3.44720602e-01 1.31251812e+00 7.78825358e-02 -3.12503567... | [4.849298477172852, 3.956382989883423] |
f8e7d834-1ed3-47cf-bb81-25a649b0c857 | chemical-detection-and-indexing-in-pubmed | null | null | https://biocreative.bioinformatics.udel.edu/media/store/files/2021/TRACK2_pos_03_BC7_submission_136.pdf | https://biocreative.bioinformatics.udel.edu/media/store/files/2021/TRACK2_pos_03_BC7_submission_136.pdf | Chemical detection and indexing in PubMed full text articles using deep learning and rule-based methods | Identifying chemicals in biomedical scientific literature is a crucial task for drug development research. The BioCreative NLM-Chem challenge promoted the development of automatic systems that can identify chemicals in full-text articles and decide which chemical concepts are relevant to be indexed. This work describes... | ['Sérgio Matos', 'João Rafael Almeida', 'João Figueira Silva', 'Rui Antunes', 'Tiago Almeida'] | 2021-11-08 | null | null | null | biocreative-vii-challenge-evaluation-workshop | ['chemical-indexing'] | ['natural-language-processing'] | [ 2.87756294e-01 2.03003377e-01 -1.90168217e-01 -6.22632615e-02
-9.40467358e-01 -8.60179722e-01 1.00218081e+00 1.12622988e+00
-7.78217673e-01 1.07700217e+00 2.86921620e-01 -3.51853251e-01
-4.21455503e-01 -7.60455668e-01 -7.88142145e-01 -8.88639867e-01
2.25402117e-01 7.58791983e-01 -1.09161705e-01 1.06928855... | [8.509193420410156, 8.733352661132812] |
098072cd-02df-4afe-97c5-b6adb7751526 | identifying-trades-using-technical-analysis | 2304.09936 | null | https://arxiv.org/abs/2304.09936v1 | https://arxiv.org/pdf/2304.09936v1.pdf | Identifying Trades Using Technical Analysis and ML/DL Models | The importance of predicting stock market prices cannot be overstated. It is a pivotal task for investors and financial institutions as it enables them to make informed investment decisions, manage risks, and ensure the stability of the financial system. Accurate stock market predictions can help investors maximize the... | ['Prof. Pramila M. Chawan', 'Nirmit Deliwala', 'Meet Parekh', 'Mann Doshi', 'Aayush Shah'] | 2023-04-12 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-7.60105491e-01 -5.27899027e-01 -6.00241601e-01 -5.11510558e-02
-1.53273612e-01 -5.85039556e-01 2.97234356e-01 3.17455590e-01
-3.55242133e-01 5.62963605e-01 2.64960587e-01 -7.37041891e-01
1.25068560e-01 -1.07105744e+00 -4.51239124e-02 -4.87188339e-01
-4.04226296e-02 1.90858662e-01 7.16790408e-02 -2.34697998... | [4.481984615325928, 4.196460247039795] |
b3bccf0c-17d7-4403-b8ff-207005039fc3 | multi-crossre-a-multi-lingual-multi-domain | 2305.10985 | null | https://arxiv.org/abs/2305.10985v1 | https://arxiv.org/pdf/2305.10985v1.pdf | Multi-CrossRE A Multi-Lingual Multi-Domain Dataset for Relation Extraction | Most research in Relation Extraction (RE) involves the English language, mainly due to the lack of multi-lingual resources. We propose Multi-CrossRE, the broadest multi-lingual dataset for RE, including 26 languages in addition to English, and covering six text domains. Multi-CrossRE is a machine translated version of ... | ['Barbara Plank', 'Rob van der Goot', 'Sampo Pyysalo', 'Filip Ginter', 'Elisa Bassignana'] | 2023-05-18 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [-2.99167901e-01 2.60549992e-01 -7.06481338e-01 -1.35745555e-01
-1.30130816e+00 -8.90844166e-01 6.72710180e-01 -7.50142187e-02
-5.76904595e-01 1.45654416e+00 5.50284505e-01 -4.20709431e-01
1.53192624e-01 -6.80549562e-01 -7.01208055e-01 6.46210238e-02
1.81168765e-01 7.62767494e-01 7.79716447e-02 -5.54593623... | [10.550929069519043, 9.441484451293945] |
ac93416c-847f-4f19-b9de-46ab14a35145 | multi-level-contrast-network-for-wearables | 2208.07547 | null | https://arxiv.org/abs/2208.07547v1 | https://arxiv.org/pdf/2208.07547v1.pdf | Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition | Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications. In recent years, the deep learning-based HAR models have achieved impressive recognition performance. However, most HAR algorithms are susceptible to the multi-class windows problem th... | ['Robert C. Qiu', 'Wenxian Yu', 'Ling Pei', 'Lei Chu', 'Songpengcheng Xia'] | 2022-08-16 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 3.15922976e-01 -4.73241746e-01 -3.26599479e-01 -2.35644639e-01
-7.73304880e-01 -9.16230772e-03 2.38583490e-01 1.13940522e-01
-3.69803369e-01 6.64830387e-01 4.96368855e-01 3.16303849e-01
-1.09935813e-01 -5.21020651e-01 -3.65919918e-01 -9.63494003e-01
-1.31651342e-01 -4.37697709e-01 2.36074135e-01 3.59119445... | [7.736607074737549, 0.850323498249054] |
bffd2a61-0d88-4a06-9f35-2aea90f47671 | text-to-audio-grounding-based-novel-metric | 2210.06354 | null | https://arxiv.org/abs/2210.06354v1 | https://arxiv.org/pdf/2210.06354v1.pdf | Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity | Automatic Audio Captioning (AAC) refers to the task of translating an audio sample into a natural language (NL) text that describes the audio events, source of the events and their relationships. Unlike NL text generation tasks, which rely on metrics like BLEU, ROUGE, METEOR based on lexical semantics for evaluation, t... | ['Sunil Kumar Kopparapu', 'Rupayan Chakraborty', 'Swapnil Bhosale'] | 2022-10-03 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 6.58478916e-01 2.41576493e-01 1.32845163e-01 -1.84139639e-01
-1.23745441e+00 -6.26273453e-01 9.29897666e-01 5.99997461e-01
-1.65183157e-01 9.79864836e-01 1.13460243e+00 4.42126133e-02
1.13879731e-02 -3.99660796e-01 -5.54017067e-01 -1.75507545e-01
3.89184840e-02 2.53354818e-01 1.68621495e-01 -1.78873558... | [15.336762428283691, 4.831185817718506] |
7d463332-cb81-4d1b-b8f9-36d42d3d9993 | factual-a-benchmark-for-faithful-and | 2305.17497 | null | https://arxiv.org/abs/2305.17497v2 | https://arxiv.org/pdf/2305.17497v2.pdf | FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing | Textual scene graph parsing has become increasingly important in various vision-language applications, including image caption evaluation and image retrieval. However, existing scene graph parsers that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail... | ['Terry Yue Zhuo', 'Quan Hung Tran', 'Donghong Ji', 'Fei Li', 'Gholamreza Haffari', 'Lizhen Qu', 'Yuyang Chai', 'Zhuang Li'] | 2023-05-27 | null | null | null | null | ['image-captioning', 'graph-similarity'] | ['computer-vision', 'graphs'] | [ 5.37250876e-01 2.10404575e-01 -1.22292139e-01 -5.64519763e-01
-1.03709209e+00 -7.20468700e-01 5.60749829e-01 3.30741554e-01
6.12187723e-04 4.17471170e-01 2.73014635e-01 -1.00054115e-01
3.62690955e-01 -7.51752496e-01 -1.12065530e+00 -4.40358996e-01
5.56471825e-01 3.97866338e-01 4.61322874e-01 -6.62939772... | [10.499361991882324, 1.5132032632827759] |
dd7401de-56ae-4eae-b4bf-1aeefb548903 | deep-rgb-d-saliency-detection-with-depth | 2103.11832 | null | https://arxiv.org/abs/2103.11832v1 | https://arxiv.org/pdf/2103.11832v1.pdf | Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion | RGB-D salient object detection (SOD) is usually formulated as a problem of classification or regression over two modalities, i.e., RGB and depth. Hence, effective RGBD feature modeling and multi-modal feature fusion both play a vital role in RGB-D SOD. In this paper, we propose a depth-sensitive RGB feature modeling sc... | ['Xi Li', 'Songyuan Li', 'Huanyu Wang', 'Wenhu Zhang', 'Peng Sun'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Sun_Deep_RGB-D_Saliency_Detection_With_Depth-Sensitive_Attention_and_Automatic_Multi-Modal_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Sun_Deep_RGB-D_Saliency_Detection_With_Depth-Sensitive_Attention_and_Automatic_Multi-Modal_CVPR_2021_paper.pdf | cvpr-2021-1 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.53497830e-01 3.71346064e-02 8.88197124e-02 -1.38836980e-01
-1.04332149e+00 -1.12687133e-01 2.97842711e-01 1.80030428e-02
-3.17463964e-01 3.27842563e-01 3.24393898e-01 6.57324269e-02
-2.30338916e-01 -7.59119034e-01 -4.95945364e-01 -9.95889187e-01
5.31977892e-01 -2.79532629e-03 6.03887856e-01 -4.60848302... | [9.69139575958252, -0.8251104950904846] |
437d4d72-0c9c-4948-b27a-0c951c03fe0a | improving-dialogue-act-classification-for | 1806.00522 | null | http://arxiv.org/abs/1806.00522v1 | http://arxiv.org/pdf/1806.00522v1.pdf | Improving Dialogue Act Classification for Spontaneous Arabic Speech and Instant Messages at Utterance Level | The ability to model and automatically detect dialogue act is an important
step toward understanding spontaneous speech and Instant Messages. However, it
has been difficult to infer a dialogue act from a surface utterance because it
highly depends on the context of the utterance and speaker linguistic
knowledge; especi... | ['AbdelRahim Elmadany', 'Sherif Abdou', 'Mervat Gheith'] | 2018-05-30 | improving-dialogue-act-classification-for-2 | https://aclanthology.org/L18-1020 | https://aclanthology.org/L18-1020.pdf | lrec-2018-5 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 5.61866723e-03 6.56082511e-01 1.73814744e-01 -7.19714761e-01
-6.45638943e-01 -6.89126432e-01 9.79371250e-01 3.42974931e-01
-1.72634438e-01 1.00347841e+00 6.22039914e-01 -2.96590924e-01
-1.90567616e-02 -5.28072774e-01 2.57889122e-01 -4.77988422e-01
4.86362390e-02 8.19706976e-01 1.67586073e-01 -6.71723068... | [12.808451652526855, 7.897951126098633] |
776fddf9-a53b-4d35-9e76-573d3a869739 | multi-level-context-ultra-aggregation-for | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Nie_Multi-Level_Context_Ultra-Aggregation_for_Stereo_Matching_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Nie_Multi-Level_Context_Ultra-Aggregation_for_Stereo_Matching_CVPR_2019_paper.pdf | Multi-Level Context Ultra-Aggregation for Stereo Matching | Exploiting multi-level context information to cost volume can improve the performance of learning-based stereo matching methods. In recent years, 3-D Convolution Neural Networks (3-D CNNs) show the advantages in regularizing cost volume but are limited by unary features learning in matching cost computation. However, e... | [' Yongtian Wang', ' Yue Liu', ' Deng-Ping Fan', ' Zhengfa Liang', ' Yun Liu', ' Ming-Ming Cheng', 'Guang-Yu Nie'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [ 1.08089916e-01 -6.71106100e-01 -2.17219695e-01 -6.07946157e-01
-3.76005977e-01 -5.37758023e-02 5.82104325e-01 -2.54532024e-02
-5.94753861e-01 5.08784413e-01 3.85832250e-01 6.78685531e-02
-4.19948483e-03 -1.24612820e+00 -7.67639697e-01 -5.42322040e-01
4.44655456e-02 8.09648037e-02 5.19379020e-01 -3.06124657... | [8.89608383178711, -2.2205333709716797] |
ac471f66-38f9-4f54-bb57-eaf26a77c8dc | spherical-convolutional-neural-network-for-3d | 1805.07872 | null | http://arxiv.org/abs/1805.07872v2 | http://arxiv.org/pdf/1805.07872v2.pdf | Spherical Convolutional Neural Network for 3D Point Clouds | We propose a neural network for 3D point cloud processing that exploits
`spherical' convolution kernels and octree partitioning of space. The proposed
metric-based spherical kernels systematically quantize point neighborhoods to
identify local geometric structures in data, while maintaining the properties
of translatio... | ['Ajmal Mian', 'Huan Lei', 'Naveed Akhtar'] | 2018-05-21 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-3.21035951e-01 -1.67488337e-01 2.74889544e-02 -3.63353819e-01
-6.06434569e-02 -6.86670601e-01 5.67740738e-01 3.04324865e-01
-3.39855701e-01 7.07942108e-03 -1.38701499e-01 -3.68159175e-01
-4.42351371e-01 -1.16812909e+00 -9.04931724e-01 -4.17809427e-01
-7.94892490e-01 5.90338349e-01 4.59685862e-01 -5.58738895... | [7.944940090179443, -3.6841373443603516] |
6edabdae-ed99-4332-864c-d2951f05477d | hybrid-classical-quantum-deep-learning-models | 2108.01125 | null | https://arxiv.org/abs/2108.01125v1 | https://arxiv.org/pdf/2108.01125v1.pdf | Hybrid Classical-Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack | Image classification must work for autonomous vehicles (AV) operating on public roads, and actions performed based on image misclassification can have serious consequences. Traffic sign images can be misclassified by an adversarial attack on machine learning models used by AVs for traffic sign recognition. To make clas... | ['Mashrur Chowdhury', 'Dimitra Michalaka', 'Judith Mwakalonge', 'Gurcan Comert', 'Frank Ngeni', 'Zadid Khan', 'Fahim Ahmed', 'Sakib Mahmud Khan', 'Reek Majumder'] | 2021-08-02 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 3.52526754e-01 2.97322929e-01 1.17381059e-01 -2.02756017e-01
-7.71158516e-01 -6.02405787e-01 6.55407429e-01 -6.33756340e-01
-6.10842645e-01 4.57010239e-01 -6.03636801e-01 -1.05228019e+00
3.45110357e-01 -1.24035740e+00 -9.70522523e-01 -9.07754898e-01
1.80528332e-02 2.49412477e-01 7.50425577e-01 -6.96230412... | [5.628922462463379, 5.051033020019531] |
65a96c84-e0f4-4c28-9c5a-3e7b3651c970 | a-comprehensive-empirical-analysis-on-cross | 2106.12797 | null | https://arxiv.org/abs/2106.12797v1 | https://arxiv.org/pdf/2106.12797v1.pdf | A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language | We analyze the process of creating word embedding feature representations designed for a learning task when annotated data is scarce, for example, in depressive language detection from Tweets. We start with a rich word embedding pre-trained from a large general dataset, which is then augmented with embeddings learned f... | ['Osmar Zaiane', 'Randy Goebel', 'Nawshad Farruque'] | 2021-06-24 | null | null | null | null | ['data-ablation'] | ['computer-vision'] | [ 3.73815969e-02 4.75711197e-01 -2.25454494e-01 -5.74930429e-01
-7.92598128e-01 -1.71756238e-01 7.36230314e-01 7.94455886e-01
-8.35182667e-01 5.80122650e-01 8.01586986e-01 7.55296424e-02
1.12122901e-01 -1.03632176e+00 -3.56024176e-01 -4.76782739e-01
-8.02930072e-02 7.20157623e-01 -8.42376798e-02 -8.67239714... | [10.458312034606934, 8.711012840270996] |
cfa7723f-59bc-42f4-8f68-670adc72868d | scops-self-supervised-co-part-segmentation | 1905.01298 | null | https://arxiv.org/abs/1905.01298v1 | https://arxiv.org/pdf/1905.01298v1.pdf | SCOPS: Self-Supervised Co-Part Segmentation | Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations. Existing works on part segmentation is dominated by supervised approaches that rely on large amounts of manual annotations and can not generalize to unseen object categories. We propose... | ['Ming-Hsuan Yang', 'Varun Jampani', 'Wei-Chih Hung', 'Sifei Liu', 'Jan Kautz', 'Pavlo Molchanov'] | 2019-05-03 | scops-self-supervised-co-part-segmentation-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Hung_SCOPS_Self-Supervised_Co-Part_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Hung_SCOPS_Self-Supervised_Co-Part_Segmentation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [-7.27536099e-04 2.83239502e-02 -3.22389275e-01 -6.76652372e-01
-5.70609689e-01 -8.08356941e-01 2.76466578e-01 6.26502186e-02
1.08934671e-01 3.79810363e-01 -1.03916824e-01 4.66592640e-01
5.51495627e-02 -5.54337621e-01 -1.07455635e+00 -1.86135098e-01
3.20484750e-02 8.48036647e-01 9.49805439e-01 3.04459292... | [9.304349899291992, 0.5434898138046265] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.