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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3c49634d-e24d-4421-910e-965cf8aa7ded | self-supervised-monocular-scene-flow | 2004.04143 | null | https://arxiv.org/abs/2004.04143v2 | https://arxiv.org/pdf/2004.04143v2.pdf | Self-Supervised Monocular Scene Flow Estimation | Scene flow estimation has been receiving increasing attention for 3D environment perception. Monocular scene flow estimation -- obtaining 3D structure and 3D motion from two temporally consecutive images -- is a highly ill-posed problem, and practical solutions are lacking to date. We propose a novel monocular scene fl... | ['Junhwa Hur', 'Stefan Roth'] | 2020-04-08 | self-supervised-monocular-scene-flow-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Hur_Self-Supervised_Monocular_Scene_Flow_Estimation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Hur_Self-Supervised_Monocular_Scene_Flow_Estimation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['scene-flow-estimation'] | ['computer-vision'] | [-1.31659895e-01 -5.26811957e-01 -4.55733448e-01 -3.65185648e-01
-5.02189696e-01 -5.79967141e-01 4.65236366e-01 -6.69772923e-01
-3.69098872e-01 7.69696057e-01 4.34495836e-01 -1.70786589e-01
2.53133357e-01 -4.72605526e-01 -6.60471618e-01 -4.25601363e-01
-4.36049774e-02 1.75453559e-01 1.49252802e-01 4.38788146... | [8.645561218261719, -1.9650688171386719] |
1b12023d-78c5-46f0-b69e-d80c0d2436b2 | steerable-wavelet-scattering-for-3d-atomic | 1812.02320 | null | http://arxiv.org/abs/1812.02320v2 | http://arxiv.org/pdf/1812.02320v2.pdf | Steerable Wavelet Scattering for 3D Atomic Systems with Application to Li-Si Energy Prediction | A general machine learning architecture is introduced that uses wavelet
scattering coefficients of an inputted three dimensional signal as features.
Solid harmonic wavelet scattering transforms of three dimensional signals were
previously introduced in a machine learning framework for the regression of
properties of sm... | ['Xavier Brumwell', 'Paul Sinz', 'Kwang Jin Kim', 'Yue Qi', 'Matthew Hirn'] | 2018-11-21 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 4.36467648e-01 -2.57604085e-02 -1.74329296e-01 -5.44222653e-01
-8.80398333e-01 8.59831853e-05 8.57219934e-01 1.44158214e-01
-4.87269700e-01 7.86748528e-01 1.40793100e-01 1.29098788e-01
-1.30001396e-01 -1.06470239e+00 -5.46448648e-01 -1.30471921e+00
-2.68336892e-01 8.68650377e-01 1.36110127e-01 -2.80234247... | [5.339661121368408, 5.266302108764648] |
ea42c26c-6a31-4f3b-be6d-5045e462aa5e | n-singer-a-non-autoregressive-korean-singing | 2106.15205 | null | https://arxiv.org/abs/2106.15205v2 | https://arxiv.org/pdf/2106.15205v2.pdf | N-Singer: A Non-Autoregressive Korean Singing Voice Synthesis System for Pronunciation Enhancement | Recently, end-to-end Korean singing voice systems have been designed to generate realistic singing voices. However, these systems still suffer from a lack of robustness in terms of pronunciation accuracy. In this paper, we propose N-Singer, a non-autoregressive Korean singing voice system, to synthesize accurate and pr... | ['Hoon-Young Cho', 'Young-Ik Kim', 'Min-Ji Lee', 'Hanbin Bae', 'Tae-Woo Kim', 'Gyeong-Hoon Lee'] | 2021-06-29 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-3.71811867e-01 -3.90044481e-01 2.52444923e-01 1.57210782e-01
-1.08214700e+00 -7.24443674e-01 3.61509137e-02 -6.67984903e-01
-3.07287369e-02 5.00243008e-01 4.22915250e-01 -1.00007527e-01
5.48189998e-01 -5.59087336e-01 -5.05343080e-01 -7.98010945e-01
7.21550658e-02 -3.85114141e-02 -9.51597989e-02 -3.42311382... | [15.506766319274902, 6.179930210113525] |
ff41ac60-7dad-438d-a8ba-d22aefda7d9b | analyzing-koopman-approaches-to-physics | 2010.00399 | null | https://arxiv.org/abs/2010.00399v2 | https://arxiv.org/pdf/2010.00399v2.pdf | Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting | Accurately predicting sea-surface temperature weeks to months into the future is an important step toward long term weather forecasting. Standard atmosphere-ocean coupled numerical models provide accurate sea-surface forecasts on the scale of a few days to a few weeks, but many important weather systems require greater... | ['Andrew August', 'Wenwei Xu', 'Julian Rice'] | 2020-09-15 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-4.48523045e-01 -4.27890062e-01 4.07944292e-01 -4.35815632e-01
-4.47656900e-01 -6.34142160e-01 8.90329838e-01 -1.84574515e-01
-4.03085858e-01 9.47968841e-01 8.39558691e-02 -6.75851941e-01
6.31318539e-02 -9.23211813e-01 -3.34694177e-01 -9.70995605e-01
-5.85682809e-01 1.85147211e-01 1.62688084e-02 -7.98433006... | [6.47027063369751, 3.0392255783081055] |
9a91b01b-e31d-48b5-8a79-7778cea97149 | are-we-making-real-progress-in-simulated | 1912.06321 | null | https://arxiv.org/abs/1912.06321v2 | https://arxiv.org/pdf/1912.06321v2.pdf | Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance? | Does progress in simulation translate to progress on robots? If one method outperforms another in simulation, how likely is that trend to hold in reality on a robot? We examine this question for embodied PointGoal navigation, developing engineering tools and a research paradigm for evaluating a simulator by its sim2rea... | ['Abhishek Kadian', 'Sonia Chernova', 'Erik Wijmans', 'Stefan Lee', 'Manolis Savva', 'Dhruv Batra', 'Alexander Clegg', 'Joanne Truong', 'Aaron Gokaslan'] | 2019-12-13 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-3.23500425e-01 6.32249191e-02 2.81561464e-01 -6.02391772e-02
-4.60522115e-01 -8.18855584e-01 6.47471666e-01 -1.01168185e-01
-7.15562940e-01 8.67278516e-01 -1.48085818e-01 -8.36090267e-01
-1.80172876e-01 -7.64632404e-01 -9.33532178e-01 -3.04182708e-01
-8.79722297e-01 6.29800618e-01 5.99642396e-01 -8.00394356... | [4.639543056488037, 0.9841850399971008] |
3f61135c-b9e1-49e7-a49c-c196d8962439 | risk-aware-reward-shaping-of-reinforcement | 2306.03220 | null | https://arxiv.org/abs/2306.03220v1 | https://arxiv.org/pdf/2306.03220v1.pdf | Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving | Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment. Nevertheless, the reward function for an RL agent, which is significant to its performance, is challenging to be de... | ['Zhiyong Sun', 'Zhiqiang Ma', 'Sofie Haesaert', 'Zengjie Zhang', 'Lin-Chi Wu'] | 2023-06-05 | null | null | null | null | ['openai-gym', 'motion-planning'] | ['playing-games', 'robots'] | [-2.59270757e-01 3.75457078e-01 -6.27009988e-01 -2.38284484e-01
-5.75760186e-01 -4.10124421e-01 5.13928890e-01 -3.00881058e-01
-6.21286154e-01 9.25734758e-01 1.29845649e-01 -6.08932793e-01
-3.35555404e-01 -6.59034491e-01 -7.02202201e-01 -6.96789145e-01
-5.04998088e-01 1.38793096e-01 1.74135268e-01 -6.17538631... | [4.916833400726318, 1.622194528579712] |
66c689f1-b1cd-4ec9-b869-ec86207a83a8 | mesonet-a-compact-facial-video-forgery | 1809.00888 | null | http://arxiv.org/abs/1809.00888v1 | http://arxiv.org/pdf/1809.00888v1.pdf | MesoNet: a Compact Facial Video Forgery Detection Network | This paper presents a method to automatically and efficiently detect face
tampering in videos, and particularly focuses on two recent techniques used to
generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional
image forensics techniques are usually not well suited to videos due to the
compression tha... | ['Junichi Yamagishi', 'Isao Echizen', 'Vincent Nozick', 'Darius Afchar'] | 2018-09-04 | null | null | null | null | ['image-forensics', 'fake-image-detection'] | ['computer-vision', 'computer-vision'] | [ 9.51888040e-02 -1.21725872e-01 2.11349964e-01 1.97924048e-01
-2.72254854e-01 -3.42132896e-01 6.90472066e-01 -4.79976535e-01
-3.13179702e-01 7.37498701e-01 -1.64005339e-01 -1.25684813e-01
8.47085863e-02 -8.07039917e-01 -8.51300120e-01 -9.43427265e-01
-5.66989362e-01 -1.91330370e-02 2.65887856e-01 -1.05587514... | [12.573691368103027, 1.1084764003753662] |
47e2110a-2c22-4e5f-a8cf-b23e06fb2af1 | semi-supervised-learning-for-neural-keyphrase | 1808.06773 | null | https://arxiv.org/abs/1808.06773v2 | https://arxiv.org/pdf/1808.06773v2.pdf | Semi-Supervised Learning for Neural Keyphrase Generation | We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large amounts of labeled data, which is only applicable to resource-rich dom... | ['Hai Ye', 'Lu Wang'] | 2018-08-21 | semi-supervised-learning-for-neural-keyphrase-1 | https://aclanthology.org/D18-1447 | https://aclanthology.org/D18-1447.pdf | emnlp-2018-10 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 4.10129637e-01 -9.10418630e-02 -6.40399575e-01 -9.65741202e-02
-1.54462171e+00 -9.27488387e-01 9.10693467e-01 3.54298800e-01
-4.91821975e-01 1.22729850e+00 5.43028712e-01 -2.07585812e-01
2.54927099e-01 -5.87800980e-01 -9.30549979e-01 -4.72783834e-01
1.92860812e-01 4.79452074e-01 2.09414661e-02 -1.32895306... | [12.31902027130127, 8.874542236328125] |
b8f69584-e91d-4f13-aec5-78512543b914 | unsupervised-feature-learning-of-human | 1812.02592 | null | http://arxiv.org/abs/1812.02592v1 | http://arxiv.org/pdf/1812.02592v1.pdf | Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold | An unsupervised human action modeling framework can provide useful
pose-sequence representation, which can be utilized in a variety of pose
analysis applications. In this work we propose a novel temporal pose-sequence
modeling framework, which can embed the dynamics of 3D human-skeleton joints to
a continuous latent sp... | ['R. Venkatesh Babu', 'Maharshi Gor', 'Jogendra Nath Kundu', 'Phani Krishna Uppala'] | 2018-12-06 | null | null | null | null | ['fine-grained-action-recognition'] | ['computer-vision'] | [ 0.24826929 0.19790398 -0.34004217 -0.2559172 -0.82741874 -0.23672159
0.8975464 -0.6069137 -0.26939297 0.543449 0.8146414 0.33966014
-0.19418672 -0.4509422 -0.9044548 -0.6988937 0.00723821 0.60052
-0.06601509 -0.21758309 -0.09703316 0.5547472 -1.3666073 0.2818844
0.44215962 0.60061234 -0.0245... | [7.330533504486084, -0.3137703537940979] |
d0a79a4d-0e9a-4437-b3ff-10318e50b71c | icd-coding-from-clinical-text-using-multi | 1912.00862 | null | https://arxiv.org/abs/1912.00862v1 | https://arxiv.org/pdf/1912.00862v1.pdf | ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network | Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. Ho... | ['Hong Yu', 'Fei Li'] | 2019-11-25 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [-1.36445239e-01 -1.64307907e-01 -2.38943666e-01 -5.65947652e-01
-5.46364605e-01 -3.66321295e-01 5.63377738e-02 3.32842588e-01
-2.51044780e-01 2.81038314e-01 4.40896034e-01 -6.44761622e-01
-8.37329328e-02 -8.87531221e-01 -5.76330900e-01 -4.70061693e-03
2.19027661e-02 3.04173321e-01 6.58749640e-02 -1.20671406... | [8.000649452209473, 6.825688362121582] |
485b044a-3360-42d0-b3c4-25fd033cbead | edassistant-supporting-exploratory-data | 2112.07858 | null | https://arxiv.org/abs/2112.07858v1 | https://arxiv.org/pdf/2112.07858v1.pdf | EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In-Situ Code Search and Recommendation | Using computational notebooks (e.g., Jupyter Notebook), data scientists rationalize their exploratory data analysis (EDA) based on their prior experience and external knowledge such as online examples. For novices or data scientists who lack specific knowledge about the dataset or problem to investigate, effectively ob... | ['Jian Zhao', 'Chengnian Sun', 'Justin Leung', 'Yizhi Zhang', 'Xingjun Li'] | 2021-12-15 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-9.40636098e-01 -1.25946984e-01 -2.75314480e-01 -2.78449237e-01
-3.92609358e-01 -9.50157940e-01 4.23624665e-01 7.15053439e-01
-2.28610784e-01 -1.19731344e-01 2.69999295e-01 -9.27374363e-01
-4.79477912e-01 -5.45990646e-01 -3.64191890e-01 1.32798985e-01
1.22754768e-01 2.19409838e-01 -2.59793788e-01 7.91540295... | [8.466347694396973, 7.260227203369141] |
c4f5e49e-0469-4078-b574-076a484e0886 | p-2-sdf-for-neural-indoor-scene | 2303.00236 | null | https://arxiv.org/abs/2303.00236v1 | https://arxiv.org/pdf/2303.00236v1.pdf | P$^2$SDF for Neural Indoor Scene Reconstruction | Given only a set of images, neural implicit surface representation has shown its capability in 3D surface reconstruction. However, as the nature of per-scene optimization is based on the volumetric rendering of color, previous neural implicit surface reconstruction methods usually fail in low-textured regions, includin... | ['Shenghua Gao', 'Lina Cao', 'Zhengyu Zhang', 'Zhengxin Li', 'Ruoyu Wang', 'Jinpeng Yu', 'Jing Li'] | 2023-03-01 | null | null | null | null | ['indoor-scene-reconstruction'] | ['computer-vision'] | [ 4.56059247e-01 2.21081510e-01 8.33233446e-02 -3.83616120e-01
-6.70874596e-01 -1.84732541e-01 2.46364474e-01 -5.46640716e-02
-1.19106956e-01 6.59488380e-01 -2.93170270e-02 4.59104888e-02
-1.46103248e-01 -1.12472463e+00 -1.12331176e+00 -8.51973414e-01
3.16430703e-02 5.11519849e-01 2.13386044e-01 -1.76662073... | [8.875767707824707, -3.0162830352783203] |
2838d20a-7b29-40e5-84a1-4db77ed5a879 | voice-privacy-with-smart-digital-assistants | 2104.11038 | null | https://arxiv.org/abs/2104.11038v1 | https://arxiv.org/pdf/2104.11038v1.pdf | Voice Privacy with Smart Digital Assistants in Educational Settings | The emergence of voice-assistant devices ushers in delightful user experiences not just on the smart home front, but also in diverse educational environments from classrooms to personalized-learning/tutoring. However, the use of voice as an interaction modality also could result in exposure of user's identity, and hind... | ['Ravi Kokku', 'Aditya Vempaty', 'Mohammad Niknazar'] | 2021-03-24 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 3.62535149e-01 5.39882779e-01 2.34476477e-01 -2.82136083e-01
-6.18223071e-01 -1.08498645e+00 4.48871553e-01 9.35820788e-02
-2.68589735e-01 5.54775536e-01 3.81008059e-01 -5.90083838e-01
-1.96963027e-02 -4.36177403e-01 -3.37195128e-01 -5.03900170e-01
5.84339976e-01 9.30445418e-02 1.47566885e-01 6.81515709... | [14.007795333862305, 5.840723514556885] |
feffbfa9-983d-4308-a820-a6a703abc7ef | point-supervised-single-cell-segmentation-via | 2304.10671 | null | https://arxiv.org/abs/2304.10671v2 | https://arxiv.org/pdf/2304.10671v2.pdf | Point-supervised Single-cell Segmentation via Collaborative Knowledge Sharing | Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the annotation burden. This paper focuses on a weakly-supervised training setting for sin... | ['Ji Yu'] | 2023-04-20 | null | null | null | null | ['cell-segmentation', 'self-learning'] | ['medical', 'natural-language-processing'] | [ 1.15435258e-01 1.64213985e-01 -9.22740698e-02 -3.65191221e-01
-1.07553959e+00 -7.67318070e-01 3.53691936e-01 4.23049659e-01
-8.29161823e-01 1.00405717e+00 -2.63253689e-01 3.55250351e-02
-5.90115339e-02 -6.23462796e-01 -7.72777855e-01 -1.24088573e+00
3.27028602e-01 7.16071606e-01 3.70112598e-01 1.57603368... | [14.549227714538574, -3.0220470428466797] |
bc051876-493e-47a9-8340-224ca5c5b3e9 | falsification-before-extrapolation-in-causal | 2209.13708 | null | https://arxiv.org/abs/2209.13708v3 | https://arxiv.org/pdf/2209.13708v3.pdf | Falsification before Extrapolation in Causal Effect Estimation | Randomized Controlled Trials (RCTs) represent a gold standard when developing policy guidelines. However, RCTs are often narrow, and lack data on broader populations of interest. Causal effects in these populations are often estimated using observational datasets, which may suffer from unobserved confounding and select... | ['David Sontag', 'Ming-Chieh Shih', 'Michael Oberst', 'Zeshan Hussain'] | 2022-09-27 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [ 4.48489398e-01 1.48730725e-01 -1.28747046e+00 -3.66099894e-01
-9.62345660e-01 -5.40494919e-01 4.87573296e-01 6.32210314e-01
-4.78393495e-01 1.20558953e+00 5.05190909e-01 -8.82838666e-01
-4.57436293e-01 -7.09675491e-01 -1.01458430e+00 -5.59627235e-01
-5.28815687e-01 3.32837462e-01 -2.64663041e-01 6.51222706... | [7.9599385261535645, 5.274258136749268] |
35fe9e2e-2421-4f54-a5c0-f71cb20feb70 | learning-augmented-algorithms-for-online | 2112.05353 | null | https://arxiv.org/abs/2112.05353v2 | https://arxiv.org/pdf/2112.05353v2.pdf | Learning-Augmented Algorithms for Online Steiner Tree | This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the onli... | ['Benjamin Moseley', 'Chenyang Xu'] | 2021-12-10 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 1.16113424e-01 6.32321954e-01 -8.85805905e-01 -2.16173992e-01
-7.38025665e-01 -1.11557078e+00 -3.36028516e-01 3.84735465e-01
-4.01676707e-02 7.48490810e-01 -3.27576280e-01 -9.21038032e-01
-6.51490510e-01 -7.10008323e-01 -1.06417847e+00 -5.52414477e-01
-5.59992671e-01 1.23319948e+00 4.57331419e-01 -5.08661792... | [4.700952529907227, 3.4894657135009766] |
efb05fb6-c01c-4f1f-b4ff-5cf5d3cd8116 | off-the-shelf-unsupervised-nmt | 1811.02278 | null | http://arxiv.org/abs/1811.02278v1 | http://arxiv.org/pdf/1811.02278v1.pdf | Off-the-Shelf Unsupervised NMT | We frame unsupervised machine translation (MT) in the context of multi-task
learning (MTL), combining insights from both directions. We leverage
off-the-shelf neural MT architectures to train unsupervised MT models with no
parallel data and show that such models can achieve reasonably good
performance, competitive with... | ['John Glover', 'Sebastian Ruder', 'Chris Hokamp'] | 2018-11-06 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 2.86626965e-01 4.04700667e-01 -6.12586021e-01 -3.52985412e-01
-1.73416460e+00 -7.11133659e-01 9.47205007e-01 -3.82293552e-01
-6.28045201e-01 9.34501946e-01 4.72196728e-01 -7.48944402e-01
5.28693855e-01 -2.30313361e-01 -1.02792919e+00 -1.68167442e-01
4.76759255e-01 1.09726739e+00 -1.19366303e-01 -4.25323278... | [11.59795093536377, 10.380215644836426] |
7108b22d-05c0-464c-9f2b-20ed09e48421 | 3d-hand-pose-tracking-and-estimation-using | 1610.07214 | null | http://arxiv.org/abs/1610.07214v1 | http://arxiv.org/pdf/1610.07214v1.pdf | 3D Hand Pose Tracking and Estimation Using Stereo Matching | 3D hand pose tracking/estimation will be very important in the next
generation of human-computer interaction. Most of the currently available
algorithms rely on low-cost active depth sensors. However, these sensors can be
easily interfered by other active sources and require relatively high power
consumption. As a resu... | ['Xiaobin Xu', 'Jianbo Jiao', 'Mingliang Chen', 'Qingxiong Yang', 'Liangqiong Qu', 'Jiawei Zhang'] | 2016-10-23 | null | null | null | null | ['hand-segmentation'] | ['computer-vision'] | [ 4.43865247e-02 -5.91495395e-01 -1.39466956e-01 4.43590954e-02
-4.37183470e-01 -6.30700648e-01 2.36414984e-01 -1.26784503e-01
-6.71588480e-01 4.90725964e-01 -2.99987167e-01 2.70460993e-01
-7.62744918e-02 -5.00906944e-01 -4.15848404e-01 -8.36652815e-01
2.56416947e-01 1.02669859e+00 7.90310085e-01 -1.09182680... | [6.559937477111816, -0.6529669761657715] |
282a7aa9-8ff5-4c0c-80dc-2041f6ac730d | a-multi-level-deep-ensemble-model-for-skin | 1807.08488 | null | http://arxiv.org/abs/1807.08488v1 | http://arxiv.org/pdf/1807.08488v1.pdf | A Multi-Level Deep Ensemble Model for Skin Lesion Classification in Dermoscopy Images | A multi-level deep ensemble (MLDE) model that can be trained in an 'end to
end' manner is proposed for skin lesion classification in dermoscopy images. In
this model, four pre-trained ResNet-50 networks are used to characterize the
multiscale information of skin lesions and are combined by using an adaptive
weighting s... | ['Yutong Xie', 'Jianpeng Zhang', 'Yong Xia'] | 2018-07-23 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.36630988e-01 1.40108481e-01 -1.71804488e-01 -2.29904950e-01
-7.53082633e-01 -2.31251463e-01 4.79421049e-01 4.44976926e-01
-5.65941632e-01 3.54986012e-01 1.42123222e-01 -1.99980870e-01
-1.62379220e-01 -5.90364397e-01 -3.19755375e-01 -6.59274518e-01
-6.15713634e-02 -1.44544721e-01 2.90153533e-01 1.84463318... | [15.718025207519531, -2.9992690086364746] |
5e97b2d3-59a7-4752-9f52-56949e8734c2 | few-shot-object-counting-and-detection | 2207.10988 | null | https://arxiv.org/abs/2207.10988v2 | https://arxiv.org/pdf/2207.10988v2.pdf | Few-shot Object Counting and Detection | We tackle a new task of few-shot object counting and detection. Given a few exemplar bounding boxes of a target object class, we seek to count and detect all objects of the target class. This task shares the same supervision as the few-shot object counting but additionally outputs the object bounding boxes along with t... | ['Minh Hoai', 'Khoi Nguyen', 'Chau Pham', 'Thanh Nguyen'] | 2022-07-22 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 2.12793618e-01 -3.27239782e-01 -1.51564023e-02 -3.35707724e-01
-9.92019653e-01 -3.25235754e-01 7.32248604e-01 1.40870467e-01
-7.68491447e-01 6.06631041e-01 -2.94559598e-01 3.63856316e-01
5.30716419e-01 -8.05801690e-01 -8.47696960e-01 -5.43561161e-01
2.27549434e-01 6.38341725e-01 8.21647942e-01 2.64973223... | [9.005167961120605, 0.5657517313957214] |
5dfd99a0-bcdb-4299-b38c-0dff05814cd4 | real-time-detection-tracking-and | 1803.06077 | null | http://arxiv.org/abs/1803.06077v2 | http://arxiv.org/pdf/1803.06077v2.pdf | Real-time Detection, Tracking, and Classification of Moving and Stationary Objects using Multiple Fisheye Images | The ability to detect pedestrians and other moving objects is crucial for an
autonomous vehicle. This must be done in real-time with minimum system
overhead. This paper discusses the implementation of a surround view system to
identify moving as well as static objects that are close to the ego vehicle.
The algorithm wo... | ['Ragunathan', 'Iljoo Baek', 'Geng Yan', 'Rajkumar', 'Albert Davies'] | 2018-03-16 | null | null | null | null | ['moving-object-detection'] | ['computer-vision'] | [-3.52193415e-01 -1.02804996e-01 2.22738177e-01 -3.78295690e-01
-2.75656223e-01 -6.23718739e-01 4.02790815e-01 -2.92814493e-01
-4.91369635e-01 5.98580778e-01 -1.09881498e-01 -4.24134105e-01
3.42896819e-01 -6.35834157e-01 -7.04563260e-01 -6.45253837e-01
-4.29910608e-02 1.58568144e-01 1.02666271e+00 7.57321641... | [8.015373229980469, -1.2895967960357666] |
ef8a60ad-7d08-4883-a5b7-ddbc664fd532 | dataci-a-platform-for-data-centric-ai-on | 2306.15538 | null | https://arxiv.org/abs/2306.15538v2 | https://arxiv.org/pdf/2306.15538v2.pdf | DataCI: A Platform for Data-Centric AI on Streaming Data | We introduce DataCI, a comprehensive open-source platform designed specifically for data-centric AI in dynamic streaming data settings. DataCI provides 1) an infrastructure with rich APIs for seamless streaming dataset management, data-centric pipeline development and evaluation on streaming scenarios, 2) an carefully ... | ['Yuanming Li', 'Yizheng Huang', 'Huaizheng Zhang'] | 2023-06-27 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.93939470e-02 -3.99437189e-01 -7.17085674e-02 -4.77294683e-01
-4.60444570e-01 -7.16857791e-01 4.14822936e-01 5.97850621e-01
-2.85834968e-01 -7.17200860e-02 5.47251523e-01 -3.63889933e-02
-1.61638647e-01 -6.59135401e-01 -4.10593003e-01 -5.24633288e-01
-8.57670605e-01 6.71582401e-01 7.01268911e-01 -4.80523109... | [8.495954513549805, -0.28072142601013184] |
843fd2ae-8f33-46ad-8561-73a5988b18ab | speech-driven-video-editing-via-an-audio | 2301.04474 | null | https://arxiv.org/abs/2301.04474v3 | https://arxiv.org/pdf/2301.04474v3.pdf | Speech Driven Video Editing via an Audio-Conditioned Diffusion Model | Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditory speech recording, the lip and jaw motions are re-synchronized withou... | ['Hugh Jordan', 'Maciej Zięba', 'Michał Stypułkowski', 'Peter Corcoran', 'Rachel McDonnell', 'Shubhajit Basak', 'Dan Bigioi'] | 2023-01-10 | null | null | null | null | ['face-model'] | ['computer-vision'] | [ 3.2647008e-01 3.5292909e-01 2.4786524e-01 -2.8974035e-01
-1.0061537e+00 -4.9002612e-01 9.5776671e-01 -5.7430899e-01
-1.2749852e-01 3.4380171e-01 5.9641683e-01 1.6875149e-01
2.0470966e-01 -1.5321331e-01 -6.7446202e-01 -5.4697239e-01
-7.2175890e-02 1.0539145e-01 -5.6440972e-02 -1.6992371e-01
4.5665773e-03... | [13.229667663574219, -0.4376731812953949] |
2f2b0383-d0a3-4d00-929c-88a5b008123e | a-fast-and-lightweight-network-for-low-light | 2304.02978 | null | https://arxiv.org/abs/2304.02978v1 | https://arxiv.org/pdf/2304.02978v1.pdf | A Fast and Lightweight Network for Low-Light Image Enhancement | Low-light images often suffer from severe noise, low brightness, low contrast, and color deviation. While several low-light image enhancement methods have been proposed, there remains a lack of efficient methods that can simultaneously solve all of these problems. In this paper, we introduce FLW-Net, a Fast and LightWe... | ['Chunhui Wang', 'Guohui YANG', 'Yanwu Xu', 'Yue Wang', 'Yong Li', 'Rao Fu', 'Junde Wu', 'Xiaoguang Di', 'Yu Zhang'] | 2023-04-06 | null | null | null | null | ['image-enhancement', 'low-light-image-enhancement'] | ['computer-vision', 'computer-vision'] | [ 3.42415124e-01 -7.14026570e-01 6.45305812e-02 -4.11522478e-01
-5.79712093e-01 -8.43570977e-02 1.26071349e-01 -2.26089701e-01
-6.13977730e-01 6.73310459e-01 -4.56913114e-02 -1.09057061e-01
-4.04749438e-02 -8.61109436e-01 -4.36267167e-01 -8.75352442e-01
4.67607439e-01 -7.36951470e-01 3.94804180e-01 -2.02781558... | [10.790146827697754, -2.472576856613159] |
217f057f-87ea-4233-b6c6-24ce778cf298 | lps-lt-edi-acl2022-an-ensemble-approach-about | null | null | https://aclanthology.org/2022.ltedi-1.24 | https://aclanthology.org/2022.ltedi-1.24.pdf | LPS@LT-EDI-ACL2022:An Ensemble Approach about Hope Speech Detection | The task shared by sponsor about Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI-ACL-2022.The goal of this task is to identify whether a given comment contains hope speech or not,and hope is considered significant for the well-being, recuperation and restoration of human life.Our work aims to cha... | ['Yue Zhu'] | null | null | null | null | ltedi-acl-2022-5 | ['hope-speech-detection'] | ['natural-language-processing'] | [-5.00387728e-01 3.80916297e-01 -6.24540687e-01 -3.92817706e-01
-8.35227132e-01 6.87869266e-02 7.47154832e-01 5.78817725e-01
-3.96203816e-01 9.73898590e-01 1.51563215e+00 2.55033281e-02
-1.06496751e-01 -4.21073109e-01 1.39569700e-01 -1.77724868e-01
6.35409653e-02 3.97317529e-01 -4.53803539e-01 -6.05522215... | [9.010466575622559, 10.712047576904297] |
be2cfd1f-3c6e-439c-9a7b-5345f1738be2 | improving-candidate-retrieval-with-entity-1 | 2202.13404 | null | https://arxiv.org/abs/2202.13404v3 | https://arxiv.org/pdf/2202.13404v3.pdf | Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking | Entity linking (EL) is the task of linking entity mentions in a document to referent entities in a knowledge base (KB). Many previous studies focus on Wikipedia-derived KBs. There is little work on EL over Wikidata, even though it is the most extensive crowdsourced KB. The scale of Wikidata can open up many new real-wo... | ['ChengXiang Zhai', 'Heng Ji', 'Tuan Manh Lai'] | 2022-02-27 | null | https://aclanthology.org/2022.findings-acl.292 | https://aclanthology.org/2022.findings-acl.292.pdf | findings-acl-2022-5 | ['pgtask'] | ['natural-language-processing'] | [-4.06601727e-01 1.09464526e-01 -6.52277589e-01 4.68301401e-02
-1.24783409e+00 -7.87027657e-01 5.32450378e-01 5.69287419e-01
-9.95615244e-01 1.12307537e+00 5.66800654e-01 1.51643381e-01
-7.92397335e-02 -9.64648485e-01 -8.07862699e-01 -2.44833827e-01
1.82716995e-01 1.06323552e+00 7.92878568e-01 -4.30601269... | [9.481307029724121, 8.80034351348877] |
11ad6358-dc14-48b8-b034-92a1e0dc8a66 | machine-unlearning-learning-polluting-and | 2111.14609 | null | https://arxiv.org/abs/2111.14609v2 | https://arxiv.org/pdf/2111.14609v2.pdf | An Investigation on Learning, Polluting, and Unlearning the Spam Emails for Lifelong Learning | Machine unlearning for security is studied in this context. Several spam email detection methods exist, each of which employs a different algorithm to detect undesired spam emails. But these models are vulnerable to attacks. Many attackers exploit the model by polluting the data, which are trained to the model in vario... | ['Ripon Patgiri', 'Nithish Bhupathi', 'Kyathi Puppaala', 'Nishchal Parne'] | 2021-11-26 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.12772211e-01 6.66772574e-03 9.80232731e-02 -2.99828518e-02
-2.99622342e-02 -6.64330304e-01 6.97453737e-01 -1.00244798e-01
-6.42391145e-01 8.53956282e-01 -1.46890491e-01 -9.36513007e-01
-1.04218505e-01 -9.70593691e-01 -4.63047534e-01 -5.75281560e-01
2.94460386e-01 4.51957345e-01 6.35762155e-01 -2.84849197... | [7.731541633605957, 9.923995018005371] |
82c36afb-af81-45fa-bb49-de2e826c825c | zero-shot-aspect-based-scientific-document | null | null | https://openreview.net/forum?id=iEqUccu6yII | https://openreview.net/pdf?id=iEqUccu6yII | Zero-Shot Aspect-Based Scientific Document Summarization using Self-Supervised Pre-training | We study the zero-shot setting for the aspect-based scientific document summarization task. Summarizing scientific documents with respect to an aspect can remarkably improve document assistance systems and readers experience. However, existing large-scale datasets contain a limited variety of aspects, causing summariza... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 3.30571294e-01 3.92703980e-01 -7.41393387e-01 -3.19998354e-01
-1.42563808e+00 -5.21625698e-01 7.05950558e-01 6.46303177e-01
-3.59181404e-01 9.38890159e-01 9.34659064e-01 -5.05655818e-02
-6.99127242e-02 -4.50155675e-01 -7.20773757e-01 -4.16725844e-01
3.50678444e-01 7.93334842e-01 3.46259147e-01 -1.43469915... | [12.39635944366455, 9.467986106872559] |
980e71fd-64cd-498a-819c-e1d8b700e60a | semantic-segmentation-assisted-instance | 2208.04766 | null | https://arxiv.org/abs/2208.04766v1 | https://arxiv.org/pdf/2208.04766v1.pdf | Semantic Segmentation-Assisted Instance Feature Fusion for Multi-Level 3D Part Instance Segmentation | Recognizing 3D part instances from a 3D point cloud is crucial for 3D structure and scene understanding. Several learning-based approaches use semantic segmentation and instance center prediction as training tasks and fail to further exploit the inherent relationship between shape semantics and part instances. In this ... | ['Yang Liu', 'Xin Tong', 'ChunYu Sun'] | 2022-08-09 | null | null | null | null | ['3d-instance-segmentation-1', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.33135968e-01 1.96280852e-01 -3.08177799e-01 -6.15459800e-01
-7.86264300e-01 -6.43771172e-01 4.65528846e-01 3.03333431e-01
1.45402461e-01 6.50989562e-02 2.04708111e-02 3.88591439e-02
-9.58350599e-02 -8.11611712e-01 -9.17552173e-01 -2.27654696e-01
8.53669569e-02 7.51040637e-01 8.85206580e-01 -1.31123990... | [8.010801315307617, -3.1636037826538086] |
f95c72a2-ccc5-4c95-8b66-ae99b1a94943 | towards-streaming-image-understanding | 2005.10420 | null | https://arxiv.org/abs/2005.10420v2 | https://arxiv.org/pdf/2005.10420v2.pdf | Towards Streaming Perception | Embodied perception refers to the ability of an autonomous agent to perceive its environment so that it can (re)act. The responsiveness of the agent is largely governed by latency of its processing pipeline. While past work has studied the algorithmic trade-off between latency and accuracy, there has not been a clear m... | ['Yu-Xiong Wang', 'Mengtian Li', 'Deva Ramanan'] | 2020-05-21 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/3553_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123470460.pdf | eccv-2020-8 | ['real-time-multi-object-tracking'] | ['computer-vision'] | [ 6.22098327e-01 -2.37259772e-02 1.92746725e-02 -3.62893850e-01
-5.50829649e-01 -8.57049465e-01 8.06252301e-01 5.55925250e-01
-7.32802033e-01 1.73781261e-01 1.29450038e-01 -1.31930903e-01
-1.18990280e-01 -7.30533063e-01 -7.15090096e-01 -7.26229608e-01
-4.49902475e-01 2.06015423e-01 7.96319664e-01 -2.61142492... | [8.421772956848145, -0.6509853005409241] |
268d0b47-7340-492c-a155-c3b75090157a | medical-concept-normalization-in-user-1 | null | null | https://aclanthology.org/2020.louhi-1.3 | https://aclanthology.org/2020.louhi-1.3.pdf | Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings | Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a clinical knowledge base. It is much beyond simple string matching and requires a deep semantic understanding of concept mentions. Recent research approach concept normaliza... | ['Sivanesan Sangeetha', 'Katikapalli Subramanyam Kalyan'] | null | null | null | null | emnlp-louhi-2020-11 | ['medical-concept-normalization', 'clinical-knowledge'] | ['medical', 'miscellaneous'] | [ 5.76249182e-01 3.17867994e-01 -4.17965263e-01 -3.99551094e-01
-6.62808478e-01 -2.79199809e-01 4.01186287e-01 1.38200307e+00
-9.45485830e-01 5.09086609e-01 6.42981112e-01 -1.67830870e-01
-3.33355248e-01 -1.05162978e+00 -1.47116423e-01 -7.18127549e-01
1.27011999e-01 6.17544532e-01 1.09521382e-01 -4.00430590... | [8.523159980773926, 8.551177978515625] |
51c11f4f-f68a-4ae4-b2a8-4233bf754d10 | a-comprehensive-review-on-the-nilm-algorithms | 2102.12578 | null | https://arxiv.org/abs/2102.12578v2 | https://arxiv.org/pdf/2102.12578v2.pdf | A Comprehensive Review on the NILM Algorithms for Energy Disaggregation | The housing structures have changed with urbanization and the growth due to the construction of high-rise buildings all around the world requires end-use appliance energy conservation and management in real-time. This shift also came along with smart-meters which enabled the estimation of appliance-specific power consu... | ['Abbas Kouzani', 'Mohiuddin Ahmed', 'M. A. Parvez Mahmud', 'Adnan Anwar', 'Akriti Verma'] | 2021-02-20 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-3.24716806e-01 -1.93274707e-01 1.95808172e-01 -5.27839661e-01
-4.91345674e-01 -2.87324250e-01 5.69589674e-01 -1.31146550e-01
9.00492668e-02 9.14169729e-01 4.42090243e-01 5.06547466e-02
-6.90948665e-02 -1.24938476e+00 -1.42197043e-01 -1.21419084e+00
2.65436750e-02 4.86371636e-01 -5.84161758e-01 -6.34013787... | [16.048173904418945, 7.572981357574463] |
9e0f869b-0f54-424b-adad-37515d922623 | sfharmony-source-free-domain-adaptation-for | 2303.15965 | null | https://arxiv.org/abs/2303.15965v1 | https://arxiv.org/pdf/2303.15965v1.pdf | SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis | To represent the biological variability of clinical neuroimaging populations, it is vital to be able to combine data across scanners and studies. However, different MRI scanners produce images with different characteristics, resulting in a domain shift known as the `harmonisation problem'. Additionally, neuroimaging da... | ['Ana IL Namburete', 'Mark Jenkinson', 'Nicola K Dinsdale'] | 2023-03-28 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 3.33551228e-01 1.95195328e-03 4.68196580e-04 -7.40443587e-01
-9.27430570e-01 -5.95027089e-01 3.99149179e-01 2.08660156e-01
-7.13993311e-01 6.63296461e-01 1.42556459e-01 -8.19281489e-02
-3.24998140e-01 -2.40210235e-01 -5.24825752e-01 -8.07530701e-01
-2.13736609e-01 5.97908735e-01 3.29704881e-01 3.08464378... | [14.42003345489502, -1.9508531093597412] |
c3eef19f-b880-4251-b4eb-145727eca87c | bertmap-a-bert-based-ontology-alignment | 2112.02682 | null | https://arxiv.org/abs/2112.02682v4 | https://arxiv.org/pdf/2112.02682v4.pdf | BERTMap: A BERT-based Ontology Alignment System | Ontology alignment (a.k.a ontology matching (OM)) plays a critical role in knowledge integration. Owing to the success of machine learning in many domains, it has been applied in OM. However, the existing methods, which often adopt ad-hoc feature engineering or non-contextual word embeddings, have not yet outperformed ... | ['Ian Horrocks', 'Denvar Antonyrajah', 'Jiaoyan Chen', 'Yuan He'] | 2021-12-05 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 4.17972535e-01 6.57933533e-01 -3.93953979e-01 -3.61490846e-01
-2.85415202e-01 -1.06265821e-01 6.45778894e-01 8.95140886e-01
-3.93158793e-01 5.02963841e-01 4.51487154e-01 -2.24930167e-01
-8.32678795e-01 -8.71234536e-01 -2.65227288e-01 -1.87622622e-01
-8.06687996e-02 1.02762544e+00 2.98760623e-01 -5.00997841... | [9.098376274108887, 8.091619491577148] |
6f8279bf-d214-4af5-9042-81d671d978cd | constructing-a-knowledge-graph-from-textual | 2305.00382 | null | https://arxiv.org/abs/2305.00382v2 | https://arxiv.org/pdf/2305.00382v2.pdf | Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database | Knowledge graphs have shown promise for several cybersecurity tasks, such as vulnerability assessment and threat analysis. In this work, we present a new method for constructing a vulnerability knowledge graph from information in the National Vulnerability Database (NVD). Our approach combines named entity recognition ... | ['Leon Moonen', 'Pierre Lison', 'Anders Mølmen Høst'] | 2023-04-30 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'named-entity-recognition-ner'] | ['graphs', 'methodology', 'natural-language-processing'] | [-2.67368227e-01 3.66711348e-01 -3.84679675e-01 1.07979029e-01
-1.10355057e-01 -9.88462269e-01 4.24279660e-01 8.54491591e-01
-3.53209287e-01 4.99533355e-01 3.63481849e-01 -1.09248400e+00
-4.64062750e-01 -1.54266584e+00 -3.96531940e-01 1.25747561e-01
-5.00006557e-01 5.20499870e-02 5.79290092e-01 -4.18052465... | [6.569391250610352, 7.471474647521973] |
86f38fdb-f1d3-4efe-8084-4e98f686c3df | ffa-ir-towards-an-explainable-and-reliable | null | null | https://openreview.net/pdf?id=FgYTwJbjbf | https://openreview.net/pdf?id=FgYTwJbjbf | FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark | The automatic generation of long and coherent medical reports given medical images (e.g. Chest X-ray and Fundus Fluorescein Angiography (FFA)) has great potential to support clinical practice. Researchers have explored advanced methods from computer vision and natural language processing to incorporate medical domain k... | ['Xiaojun Chang', 'Xiaodan Liang', 'Karin Verspoor', 'Flora D Salim', 'Yizhi Liu', 'Mengke Li', 'Caineng Pan', 'Zhong Liu', 'Xin Chen', 'Cong Wang', 'Xiaoyun Zhao', 'Yuetian Weng', 'Rui Liu', 'Wenjia Cai', 'Mingjie Li'] | 2021-08-19 | null | null | null | thirty-fifth-conference-on-neural-information | ['medical-report-generation'] | ['medical'] | [ 1.77813709e-01 5.18293262e-01 -4.28649008e-01 -4.68881756e-01
-1.25568151e+00 -3.73257399e-01 4.95670706e-01 2.55763412e-01
-5.13951480e-02 1.07043719e+00 6.98920965e-01 -4.66922015e-01
-3.47825401e-02 -5.12681663e-01 -3.19711834e-01 -2.44250327e-01
5.79121150e-02 3.36505592e-01 -3.60052407e-01 3.67403567... | [15.04604721069336, -1.3830312490463257] |
3ab2db93-0ebb-4444-9daf-0f815cfe3918 | deep-compressed-pneumonia-detection-for-low | 1911.02007 | null | https://arxiv.org/abs/1911.02007v1 | https://arxiv.org/pdf/1911.02007v1.pdf | Deep Compressed Pneumonia Detection for Low-Power Embedded Devices | Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accuracy and performance, etc. On the contrast, the large-scale network brings high requirements of both memory storage and computation resource,... | ['Caiwen Ding', 'Sheng Lin', 'Hongjia Li', 'Ning Liu', 'Yanzhi Wang'] | 2019-11-04 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 2.20266446e-01 -1.65052429e-01 -5.09732246e-01 -2.59144604e-01
-1.72668099e-01 1.58617675e-01 -1.90103948e-01 1.08867466e-01
-7.38744080e-01 6.73939645e-01 6.35583475e-02 -2.61040956e-01
-3.54764551e-01 -1.07446802e+00 -1.67928427e-01 -7.73483694e-01
-1.49431545e-02 2.02652082e-01 4.51585144e-01 5.03693759... | [8.538002967834473, 3.0167288780212402] |
85a43ebc-c96e-42fc-a724-06d3b37787a1 | covert-communication-based-on-the-poisoning | 2306.01342 | null | https://arxiv.org/abs/2306.01342v1 | https://arxiv.org/pdf/2306.01342v1.pdf | Covert Communication Based on the Poisoning Attack in Federated Learning | Covert communication has become an important area of research in computer security. It involves hiding specific information on a carrier for message transmission and is often used to transmit private data, military secrets, and even malware. In deep learning, many methods have been developed for hiding information in m... | ['Rong Wang', 'Junchuan Liang'] | 2023-06-02 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 4.86988753e-01 6.42297268e-02 -2.98961788e-01 -1.64737985e-01
-6.10037684e-01 -7.46092200e-01 6.28127217e-01 6.34133965e-02
-5.14841974e-02 7.88641214e-01 -8.75528753e-02 -6.38043046e-01
1.42404824e-01 -1.01867771e+00 -8.89569998e-01 -9.40910637e-01
-4.73858029e-01 1.06333502e-01 7.53208026e-02 -2.84844786... | [5.723423004150391, 7.652279376983643] |
34a23611-eebc-4086-b145-f5302e4d8e52 | sketch-tokens-a-learned-mid-level | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Lim_Sketch_Tokens_A_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Lim_Sketch_Tokens_A_2013_CVPR_paper.pdf | Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection | We propose a novel approach to both learning and detecting local contour-based representations for mid-level features. Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images. Patches of human generated contours are clustered to form sketch tok... | ['Piotr Dollar', 'C. L. Zitnick', 'Joseph J. Lim'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['contour-detection'] | ['computer-vision'] | [ 2.92481512e-01 -2.78236091e-01 -2.10891113e-01 -3.57537150e-01
-1.15254426e+00 -7.83974409e-01 8.89048696e-01 4.86397177e-01
-5.10792613e-01 3.08282077e-01 -1.60830226e-02 -4.79393378e-02
5.35957456e-01 -9.27512050e-01 -6.26606941e-01 -3.91683012e-01
-4.76659209e-01 1.54943019e-01 9.25190091e-01 -4.72176485... | [9.375835418701172, 0.2615113854408264] |
dd682367-4d01-47ec-8fa3-386e87ea3468 | selecting-optimal-context-sentences-for-event | null | null | https://www.aaai.org/AAAI22Papers/AAAI-3912.ManH.pdf | https://www.aaai.org/AAAI22Papers/AAAI-3912.ManH.pdf | Selecting Optimal Context Sentences for Event-Event Relation Extraction | Understanding events entails recognizing the structural and temporal orders between event mentions to build event structures/ graphs for input documents. To achieve this goal, our work addresses the problems of subevent relation extraction
(SRE) and temporal event relation extraction (TRE) that aim to predict subevent... | ['and Thien Huu Nguyen', 'Linh Van Ngo', 'Nghia Ngo Trung', 'Hieu Man Duc Trong'] | 2022-04-02 | null | null | null | aaai-2022-4 | ['temporal-relation-extraction', 'temporal-relation-classification', 'relation-classification', 'event-relation-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.10235727e-01 2.64940262e-01 -2.90383250e-01 -5.13251901e-01
-1.08140862e+00 -4.03697848e-01 8.42726767e-01 9.87590671e-01
-5.00042796e-01 9.08573866e-01 6.20819509e-01 -3.94744724e-01
-4.62332487e-01 -1.06451738e+00 -5.97690225e-01 -2.76104152e-01
-5.44280648e-01 4.31049764e-01 4.21560258e-01 -1.86565682... | [9.075793266296387, 9.16012954711914] |
00e46241-8931-4229-afb2-e61c53cbd0f6 | investigating-the-relation-between-chaos-and | 2008.12756 | null | https://arxiv.org/abs/2008.12756v2 | https://arxiv.org/pdf/2008.12756v2.pdf | Investigating the relation between chaos and the three body problem | We review the properties of fractals, the Mandelbrot set and how deterministic chaos ties to the picture. A detailed study on three body systems, one of the major applications of chaos theory was undertaken. Systems belonging to different families produced till date were studied and their properties were analysed. We t... | ['Vishak Vikranth', 'T. S. Sachin Venkatesh'] | 2020-08-28 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-3.14679921e-01 -1.09967068e-01 5.03230155e-01 1.17218927e-01
2.12636605e-01 -6.91969514e-01 9.23649371e-01 7.27434829e-02
-3.03981960e-01 9.57628608e-01 2.17253808e-02 -3.73865098e-01
-6.42185569e-01 -5.92053235e-01 1.52326033e-01 -1.14676034e+00
-6.48338139e-01 6.78581715e-01 6.11902535e-01 -8.39455903... | [5.728281021118164, 4.144344329833984] |
f81bb261-5736-4aa2-875d-784109c8a5f2 | few-shot-learning-with-noisy-labels | 2204.05494 | null | https://arxiv.org/abs/2204.05494v2 | https://arxiv.org/pdf/2204.05494v2.pdf | Few-shot Learning with Noisy Labels | Few-shot learning (FSL) methods typically assume clean support sets with accurately labeled samples when training on novel classes. This assumption can often be unrealistic: support sets, no matter how small, can still include mislabeled samples. Robustness to label noise is therefore essential for FSL methods to be pr... | ['Tal Hassner', 'Vladan Petrovic', 'Samrudhdhi B. Rangrej', 'Kevin J Liang'] | 2022-04-12 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Liang_Few-Shot_Learning_With_Noisy_Labels_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Liang_Few-Shot_Learning_With_Noisy_Labels_CVPR_2022_paper.pdf | cvpr-2022-1 | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 3.15521896e-01 1.34532571e-01 -1.87517807e-01 -6.36329234e-01
-1.25933921e+00 -4.26105648e-01 7.25930989e-01 1.33798663e-02
-2.99018055e-01 7.31205106e-01 1.94260329e-01 1.16432279e-01
2.91575231e-02 -5.56325138e-01 -7.67157733e-01 -5.56084633e-01
4.26848419e-02 5.33465028e-01 6.32649124e-01 -1.65978417... | [9.854036331176758, 2.828157901763916] |
2616a886-2ff6-40f0-8fc6-a99dfbbcee85 | learning-to-execute-programs-with-instruction | 2010.12621 | null | https://arxiv.org/abs/2010.12621v1 | https://arxiv.org/pdf/2010.12621v1.pdf | Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks | Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from leveraging program structure like control flow graphs, but they are not well-suited to tasks like program execution that require far more seq... | ['Daniel Tarlow', 'Hugo Larochelle', 'Charles Sutton', 'David Bieber'] | 2020-10-23 | null | http://proceedings.neurips.cc/paper/2020/hash/62326dc7c4f7b849d6f013ba46489d6c-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/62326dc7c4f7b849d6f013ba46489d6c-Paper.pdf | neurips-2020-12 | ['program-repair', 'learning-to-execute', 'systematic-generalization', 'program-repair'] | ['computer-code', 'computer-code', 'reasoning', 'reasoning'] | [ 2.94089943e-01 4.01330292e-01 -5.48470736e-01 -1.62503928e-01
-3.96393687e-01 -3.23310465e-01 2.74676025e-01 4.14846018e-02
-1.73720028e-02 1.12856776e-01 3.09633344e-01 -1.20563900e+00
-1.12599675e-02 -1.00076640e+00 -1.10596836e+00 -2.16559008e-01
-3.33104849e-01 2.25406408e-01 2.17992276e-01 -3.78261179... | [7.721888542175293, 7.64915657043457] |
44f28cab-9feb-4e39-a4bc-8e316fc4e025 | end-to-end-face-parsing-via-interlinked | 2002.04831 | null | https://arxiv.org/abs/2002.04831v2 | https://arxiv.org/pdf/2002.04831v2.pdf | End-to-End Face Parsing via Interlinked Convolutional Neural Networks | Face parsing is an important computer vision task that requires accurate pixel segmentation of facial parts (such as eyes, nose, mouth, etc.), providing a basis for further face analysis, modification, and other applications. Interlinked Convolutional Neural Networks (iCNN) was proved to be an effective two-stage model... | ['Xiaolin Hu', 'Valentin Yiu', 'Liang Tang', 'Zi Yin'] | 2020-02-12 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 2.35285401e-01 4.08991247e-01 -7.21870810e-02 -6.50764942e-01
-4.66278523e-01 -4.29317206e-01 3.03678989e-01 -5.25473535e-01
-2.98199832e-01 3.73883963e-01 -2.02799991e-01 -3.46710473e-01
4.35986847e-01 -7.06560254e-01 -9.27144706e-01 -5.32745481e-01
3.55398297e-01 2.61916041e-01 3.50764066e-01 9.85904317... | [13.440844535827637, 0.6436769366264343] |
bc337f42-7340-4787-829f-df91ab509928 | prob-probabilistic-objectness-for-open-world | 2212.01424 | null | https://arxiv.org/abs/2212.01424v1 | https://arxiv.org/pdf/2212.01424v1.pdf | PROB: Probabilistic Objectness for Open World Object Detection | Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - whi... | ['Serena Yeung', 'Kuan-Chieh Wang', 'Orr Zohar'] | 2022-12-02 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-world-object-detection'] | ['computer-vision'] | [ 1.52927384e-01 2.86863089e-01 -1.72551617e-01 -1.94392487e-01
-8.89534771e-01 -5.68466961e-01 5.43308854e-01 1.66152954e-01
-3.70753527e-01 5.71916938e-01 -2.57721484e-01 -8.63574073e-02
1.38290629e-01 -8.25085700e-01 -7.53996491e-01 -6.37439668e-01
-1.56916101e-02 7.40625262e-01 1.00323284e+00 1.80689827... | [9.34732437133789, 1.46369469165802] |
9aa0be6b-7f56-4273-94cd-489aa60a5c56 | mimo-detection-under-hardware-impairments | 2306.05146 | null | https://arxiv.org/abs/2306.05146v1 | https://arxiv.org/pdf/2306.05146v1.pdf | MIMO Detection under Hardware Impairments: Learning with Noisy Labels | This paper considers a data detection problem in multiple-input multiple-output (MIMO) communication systems with hardware impairments. To address challenges posed by nonlinear and unknown distortion in received signals, two learning-based detection methods, referred to as model-driven and data-driven, are presented. T... | ['H. Vincent Poor', 'Yo-Seb Jeon', 'Seunghyeon Jeon', 'Jinman Kwon'] | 2023-06-08 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 4.17489320e-01 -1.35473981e-01 1.75689366e-02 -2.85102099e-01
-1.17151737e+00 2.93826293e-02 1.33349672e-01 -7.08989128e-02
-4.60800231e-01 9.08452928e-01 3.33720818e-02 -5.21102309e-01
-8.64624381e-02 -4.71260190e-01 -4.90420192e-01 -8.67323697e-01
-2.38334417e-01 -3.05311680e-01 3.55791152e-02 8.95337909... | [6.431744575500488, 1.4596154689788818] |
106f0375-58a4-4cda-aa15-4684b3e2d89d | a-systematic-and-analytical-review-of-the | 2003.04452 | null | http://arxiv.org/abs/2003.04452v2 | http://arxiv.org/pdf/2003.04452v2.pdf | A Systematic and Analytical Review of the Socioeconomic and Environmental Impact of the Deployed High-Speed Rail (HSR) Systems on the World | The installation of high-speed rail in the world during the last two decades
resulted in significant socioeconomic and environmental changes. The U.S. has
the longest rail network in the world, but the focus is on carrying a wide
variety of loads including coal, farm crops, industrial products, commercial
goods, and mi... | [] | 2020-03-18 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-5.40723264e-01 -2.11312696e-01 -5.96308708e-01 1.87631845e-01
-4.77833748e-01 -5.72270811e-01 4.89104271e-01 4.23437357e-02
-6.88337564e-01 9.72401917e-01 1.92211345e-01 -1.02393830e+00
-2.46761695e-01 -1.36202633e+00 -2.75226027e-01 -4.78923798e-01
2.03621924e-01 5.05695976e-02 8.15423802e-02 -9.93055820... | [6.051041126251221, 2.060622215270996] |
ca1621f4-a99f-4327-a1cc-32a507571af2 | regular-decision-processes-for-grid-worlds | 2111.03647 | null | https://arxiv.org/abs/2111.03647v2 | https://arxiv.org/pdf/2111.03647v2.pdf | Regular Decision Processes for Grid Worlds | Markov decision processes are typically used for sequential decision making under uncertainty. For many aspects however, ranging from constrained or safe specifications to various kinds of temporal (non-Markovian) dependencies in task and reward structures, extensions are needed. To that end, in recent years interest h... | ['Martijn van Otterlo', 'Nicky Lenaers'] | 2021-11-05 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 9.70112830e-02 2.51668304e-01 -3.17472667e-01 -3.66158158e-01
-4.02608335e-01 -7.42275357e-01 7.24669039e-01 2.44541407e-01
-4.61907238e-01 1.11922431e+00 -1.81051508e-01 -5.93594611e-01
-7.24131763e-01 -6.76234841e-01 -2.91441739e-01 -5.41008174e-01
-6.74705148e-01 7.70390570e-01 6.42389178e-01 -9.43803340... | [4.415437698364258, 2.1676557064056396] |
7a54a386-c975-474d-8802-76f572204310 | simple-prediction-of-immiscible-metal | 2101.12343 | null | https://arxiv.org/abs/2101.12343v1 | https://arxiv.org/pdf/2101.12343v1.pdf | Simple prediction of immiscible metal alloying based on metastability analysis | It has been known that even though two elemental metals, $X$ and $Y$, are immiscible, they can form alloys on surfaces of other metal $Z$. In order to understand such surface alloying of immiscible metals, we study the energetic stability of binary alloys, $XZ$ and $YZ$, in several structures with various coordination ... | ['Jun Onoe', 'Junji Yuhara', 'Shota Ono'] | 2021-01-29 | null | null | null | null | ['formation-energy'] | ['miscellaneous'] | [ 1.88766588e-02 4.72055048e-01 -2.41933659e-01 3.71596813e-02
-1.82331368e-01 1.44839302e-01 3.69408697e-01 -2.82009870e-01
-7.04070702e-02 1.06111538e+00 -4.66510862e-01 -1.50460243e-01
1.35758370e-01 -1.39172363e+00 -6.39909565e-01 -1.13218653e+00
1.23802729e-01 7.32114136e-01 6.33928120e-01 -7.91382611... | [5.377104759216309, 4.909825325012207] |
8176a1cb-5dc9-4b3b-8fff-54f47635747e | multiframe-scene-flow-with-piecewise-rigid | 1710.02124 | null | http://arxiv.org/abs/1710.02124v1 | http://arxiv.org/pdf/1710.02124v1.pdf | Multiframe Scene Flow with Piecewise Rigid Motion | We introduce a novel multiframe scene flow approach that jointly optimizes
the consistency of the patch appearances and their local rigid motions from
RGB-D image sequences. In contrast to the competing methods, we take advantage
of an oversegmentation of the reference frame and robust optimization
techniques. We formu... | ['Jan Kautz', 'Matthias Nießner', 'Kihwan Kim', 'Didier Stricker', 'Robert Maier', 'Vladislav Golyanik'] | 2017-10-05 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.21466108e-01 -2.88297057e-01 -5.45881949e-02 -1.57718405e-01
-6.14025831e-01 -7.45963216e-01 5.37346184e-01 -3.52765173e-01
-2.42925480e-01 5.37472367e-01 7.51294494e-02 -2.49027275e-02
4.91862595e-02 -4.68326420e-01 -6.85037494e-01 -5.99063814e-01
2.51954913e-01 4.04800236e-01 4.62819129e-01 -2.58038461... | [8.615194320678711, -2.018214225769043] |
9b2f0652-ee32-4a2f-805c-f374ca03e62e | long-range-arena-a-benchmark-for-efficient-1 | 2011.04006 | null | https://arxiv.org/abs/2011.04006v1 | https://arxiv.org/pdf/2011.04006v1.pdf | Long Range Arena: A Benchmark for Efficient Transformers | Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer m... | ['Donald Metzler', 'Sebastian Ruder', 'Liu Yang', 'Jinfeng Rao', 'Philip Pham', 'Dara Bahri', 'Yikang Shen', 'Samira Abnar', 'Mostafa Dehghani', 'Yi Tay'] | 2020-11-08 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 4.09386754e-01 -3.94225866e-01 -2.79252883e-03 -3.35400403e-01
-1.00613427e+00 -9.05421793e-01 8.87388110e-01 -7.10066855e-02
-4.25242752e-01 6.01182818e-01 2.24573076e-01 -5.42413235e-01
-2.87678838e-01 -6.58873022e-01 -8.21243525e-01 -4.41623807e-01
1.47181582e-02 7.00675488e-01 -8.82934257e-02 -4.00724173... | [10.453051567077637, 2.092665672302246] |
ef9932b7-689f-46a3-bbe2-c208a93361ca | deepfake-video-forensics-based-on-transfer | 2004.14178 | null | https://arxiv.org/abs/2004.14178v1 | https://arxiv.org/pdf/2004.14178v1.pdf | Deepfake Video Forensics based on Transfer Learning | Deeplearning has been used to solve complex problems in various domains. As it advances, it also creates applications which become a major threat to our privacy, security and even to our Democracy. Such an application which is being developed recently is the "Deepfake". Deepfake models can create fake images and videos... | ['Tejeswinee K', 'Ragul M', 'Rahul U', 'Raja Vignesh K'] | 2020-04-29 | null | null | null | null | ['video-forensics'] | ['computer-vision'] | [-2.51081049e-01 2.84794141e-02 -5.78145124e-02 -2.77967602e-01
-9.40878019e-02 -4.57411200e-01 6.11341894e-01 -5.04587710e-01
-3.95478427e-01 5.15067160e-01 -2.08250329e-01 -3.36683005e-01
2.49199480e-01 -6.68128431e-01 -8.45385134e-01 -6.98269486e-01
-3.51548158e-02 3.92094590e-02 2.57964015e-01 -6.45811483... | [12.57412052154541, 1.1897196769714355] |
bea65b2d-0771-45e2-9117-d9acc9cec76c | efficient-subsampling-for-generating-high | 2103.11166 | null | https://arxiv.org/abs/2103.11166v5 | https://arxiv.org/pdf/2103.11166v5.pdf | Efficient Subsampling of Realistic Images From GANs Conditional on a Class or a Continuous Variable | Recently, subsampling or refining images generated from unconditional GANs has been actively studied to improve the overall image quality. Unfortunately, these methods are often observed less effective or inefficient in handling conditional GANs (cGANs) -- conditioning on a class (aka class-conditional GANs) or a conti... | ['William J. Welch', 'Z. Jane Wang', 'Yongwei Wang', 'Xin Ding'] | 2021-03-20 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 6.43304527e-01 2.77893960e-01 -2.77261704e-01 -2.05619082e-01
-1.27714109e+00 -4.27962005e-01 8.26396108e-01 -4.25634354e-01
-1.56944409e-01 1.11939573e+00 -1.91869643e-02 -1.08352408e-01
2.40146443e-01 -1.07870150e+00 -8.41253281e-01 -1.06410182e+00
4.44550276e-01 2.13816568e-01 -1.45032793e-01 2.14139774... | [11.590516090393066, -0.2890143096446991] |
cb61a04d-4c65-46f7-bcf1-00ce8533740e | progressive-sub-graph-clustering-algorithm | 2305.12703 | null | https://arxiv.org/abs/2305.12703v1 | https://arxiv.org/pdf/2305.12703v1.pdf | Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification | Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker verification tasks. In this paper, we propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaus... | ['Pengyuan Zhang', 'Wenchao Wang', 'Zhenduo Zhao', 'Jingze Lu', 'Zhuo Li'] | 2023-05-22 | null | null | null | null | ['graph-clustering', 'pseudo-label', 'speaker-verification'] | ['graphs', 'miscellaneous', 'speech'] | [ 2.67672986e-02 1.27168521e-01 -4.16902423e-01 -8.33475828e-01
-1.01450646e+00 -6.69398904e-01 2.80592442e-01 -1.85397286e-02
6.70334324e-02 6.17714047e-01 2.08216935e-01 -3.55096281e-01
-3.03754449e-01 -2.84608364e-01 -8.85125026e-02 -9.04834986e-01
2.41744563e-01 5.51724613e-01 3.11754704e-01 2.29881480... | [14.859437942504883, 1.196321725845337] |
980c70d4-dd2a-46ae-9d32-cb3ef1f402b8 | which-k-space-sampling-schemes-is-good-for | 2103.08516 | null | https://arxiv.org/abs/2103.08516v1 | https://arxiv.org/pdf/2103.08516v1.pdf | Which K-Space Sampling Schemes is good for Motion Artifact Detection in Magnetic Resonance Imaging? | Motion artifacts are a common occurrence in the Magnetic Resonance Imaging (MRI) exam. Motion during acquisition has a profound impact on workflow efficiency, often requiring a repeat of sequences. Furthermore, motion artifacts may escape notice by technologists, only to be revealed at the time of reading by the radiol... | ['Khan A. Wahid', 'Ekta Walia', 'Mohammad Reza Mohebbian'] | 2021-03-15 | null | null | null | null | ['motion-detection'] | ['computer-vision'] | [ 1.85225740e-01 -4.05837297e-01 -8.89418274e-02 -1.73882693e-02
-4.90947485e-01 -5.20209193e-01 2.43976966e-01 -1.91741109e-01
-6.60088778e-01 4.18141603e-01 1.68989390e-01 -5.27002692e-01
-4.47483599e-01 -2.23819777e-01 -3.76459330e-01 -9.52744603e-01
-3.01749229e-01 9.81350988e-02 5.25216818e-01 2.22256333... | [13.740094184875488, -2.440864086151123] |
5628ef86-7514-4a32-bbc8-df523ab21b2c | deep-point-cloud-reconstruction-1 | 2111.11704 | null | https://arxiv.org/abs/2111.11704v2 | https://arxiv.org/pdf/2111.11704v2.pdf | Deep Point Cloud Reconstruction | Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. In this paper, we advocate that jointly solving these tasks leads to significant improvement for point cloud rec... | ['In So Kweon', 'Jaesik Park', 'Francois Rameau', 'Byeongin Joung', 'Jaesung Choe'] | 2021-11-23 | deep-point-cloud-reconstruction | https://openreview.net/forum?id=mKDtUtxIGJ | https://openreview.net/pdf?id=mKDtUtxIGJ | iclr-2022-4 | ['point-cloud-completion', 'point-cloud-reconstruction', 'point-set-upsampling'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.76952381e-02 1.09617643e-01 3.30990583e-01 -3.46570730e-01
-5.98243237e-01 -1.21555507e-01 5.78713417e-01 4.67775352e-02
-2.40653098e-01 4.63158965e-01 7.96832591e-02 -1.80714950e-02
-1.61524445e-01 -1.01580667e+00 -9.13454115e-01 -4.71029848e-01
3.20556723e-02 8.58889341e-01 3.02817523e-01 -2.89252609... | [8.331769943237305, -3.544970989227295] |
c06ef930-aaf4-49aa-9beb-9d98686be94f | interpretable-machine-learning-for-detection | 2210.11235 | null | https://arxiv.org/abs/2210.11235v3 | https://arxiv.org/pdf/2210.11235v3.pdf | Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis | Ransomware has appeared as one of the major global threats in recent days. The alarming increasing rate of ransomware attacks and new ransomware variants intrigue the researchers to constantly examine the distinguishing traits of ransomware and refine their detection strategies. Application Programming Interface (API) ... | ['Kaushik Roy', 'Madhuri Siddula', 'Rawshan Ara Mowri'] | 2022-10-16 | null | null | null | null | ['interpretable-machine-learning'] | ['methodology'] | [-6.72249421e-02 -6.40550733e-01 -5.44828951e-01 -1.75544575e-01
-8.77186134e-02 -1.04675782e+00 5.15563011e-01 2.91908951e-03
-1.76987812e-01 2.75153011e-01 3.46289352e-02 -1.02213931e+00
-3.92932147e-01 -6.21528566e-01 -6.28687665e-02 -4.31412756e-01
-2.07536831e-03 2.97113568e-01 3.51471275e-01 -1.69860363... | [14.399721145629883, 9.671430587768555] |
45307256-b613-42c2-ad28-ee116c8ee57f | sex-detection-in-the-early-stage-of | 2305.02325 | null | https://arxiv.org/abs/2305.02325v1 | https://arxiv.org/pdf/2305.02325v1.pdf | Sex Detection in the Early Stage of Fertilized Chicken Eggs via Image Recognition | Culling newly hatched male chicks in industrial hatcheries poses a serious ethical problem. Both laying and broiler breeders need males, but it is a problem because they are produced more than needed. Being able to determine the sex of chicks in the egg at the beginning or early stage of incubation can eliminate ethica... | ['Efendi Nasibov', 'Ufuk Asil'] | 2023-05-03 | null | null | null | null | ['morphological-analysis'] | ['natural-language-processing'] | [-1.49205521e-01 2.07078122e-02 2.09544569e-01 -5.46970181e-02
8.24517727e-01 -8.06780696e-01 -1.56015888e-01 4.96542424e-01
-6.85238123e-01 5.79283774e-01 -5.14077187e-01 -3.15195441e-01
2.20825542e-02 -7.74102449e-01 -4.65047359e-01 -7.13022232e-01
3.35631073e-02 -4.30675521e-02 4.33467597e-01 4.27068211... | [14.443156242370605, -2.940850019454956] |
1ba8a4e1-7bc3-46b1-9442-59c560210c62 | adaptive-graph-convolutional-network-with | 2003.06167 | null | https://arxiv.org/abs/2003.06167v1 | https://arxiv.org/pdf/2003.06167v1.pdf | Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection | Co-saliency detection aims to discover the common and salient foregrounds from a group of relevant images. For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC). Three major contributions have been made, and are experimentally shown to have substantial practical ... | ['Jin Chen', 'Shiwen Shen', 'Tengpeng Li', 'Kaihua Zhang', 'Bo Liu', 'Qingshan Liu'] | 2020-03-13 | adaptive-graph-convolutional-network-with-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Zhang_Adaptive_Graph_Convolutional_Network_With_Attention_Graph_Clustering_for_Co-Saliency_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_Adaptive_Graph_Convolutional_Network_With_Attention_Graph_Clustering_for_Co-Saliency_CVPR_2020_paper.pdf | cvpr-2020-6 | ['co-saliency-detection'] | ['computer-vision'] | [ 3.94783884e-01 1.75733238e-01 -1.41571268e-01 -1.69306815e-01
-7.73106694e-01 -1.96114972e-01 5.01483679e-01 5.23686856e-02
6.76383525e-02 1.74877211e-01 2.63610959e-01 -1.16013721e-01
1.52042389e-01 -3.60953003e-01 -9.05367315e-01 -5.29366314e-01
-4.70417738e-02 1.28109872e-01 6.87318861e-01 1.32284775... | [9.722726821899414, -0.2636004090309143] |
81ac36f2-9a6f-429e-b2b4-ae8efeeeaf4c | inr-v-a-continuous-representation-space-for | 2210.16579 | null | https://arxiv.org/abs/2210.16579v2 | https://arxiv.org/pdf/2210.16579v2.pdf | INR-V: A Continuous Representation Space for Video-based Generative Tasks | Generating videos is a complex task that is accomplished by generating a set of temporally coherent images frame-by-frame. This limits the expressivity of videos to only image-based operations on the individual video frames needing network designs to obtain temporally coherent trajectories in the underlying image space... | ['C. V. Jawahar', 'Vinay P Namboodiri', 'Aditya Agarwal', 'Bipasha Sen'] | 2022-10-29 | null | null | null | null | ['video-generation', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 4.67242509e-01 2.81437278e-01 -1.93092510e-01 -2.35312730e-01
-7.36288548e-01 -4.53660131e-01 6.36162758e-01 -9.51647997e-01
1.48937525e-02 1.00849140e+00 4.40928370e-01 8.31058398e-02
1.58189908e-01 -6.13180578e-01 -1.55775464e+00 -8.10591638e-01
-8.49703550e-02 1.53508157e-01 -3.06863517e-01 -1.32348984... | [10.839298248291016, -0.6124202013015747] |
2cee712a-a18e-4c71-ba34-dbbf3caa4877 | artificial-life-sustainable-self-replicating | 2105.13971 | null | https://arxiv.org/abs/2105.13971v2 | https://arxiv.org/pdf/2105.13971v2.pdf | Artificial life: sustainable self-replicating systems | Nature has found one method of organizing living matter, but maybe other options exist -- not yet discovered -- on how to create life. To study the life "as it could be" is the objective of an interdisciplinary field called Artificial Life (commonly abbreviated as ALife). The word "artificial" refers to the fact that h... | ['Jitka Cejkova', 'Carlos Gershenson'] | 2021-05-27 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 7.83925131e-02 2.56007165e-01 1.77358299e-01 2.43144289e-01
7.53682256e-01 -8.01762223e-01 8.76054645e-01 -2.64723450e-01
-1.58587113e-01 1.08231640e+00 1.51956543e-01 -4.13731545e-01
2.48619169e-01 -1.06661475e+00 -4.70138401e-01 -1.16397488e+00
1.63162380e-01 3.18058759e-01 4.48050201e-02 -6.83106601... | [5.593796253204346, 4.181454658508301] |
e7b1b019-34a2-4ddb-ae54-d25ba2d93068 | robust-one-shot-segmentation-of-brain-tissues | 2211.14521 | null | https://arxiv.org/abs/2211.14521v3 | https://arxiv.org/pdf/2211.14521v3.pdf | Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation | One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for ... | ['Qiang Li', 'Zhiwei Wang', 'Ran Duan', 'Sheng Wang', 'Xiaoyu Zeng', 'Jinxin Lv'] | 2022-11-26 | null | null | null | null | ['one-shot-segmentation'] | ['computer-vision'] | [ 3.09902877e-01 2.68019050e-01 -6.25376264e-03 -4.14334893e-01
-8.99869025e-01 -5.56685269e-01 3.86658996e-01 -2.94595003e-01
-4.32909757e-01 5.21389127e-01 -2.91420016e-02 1.78900547e-02
5.03225103e-02 -4.93670046e-01 -7.09318995e-01 -1.14096534e+00
2.78900564e-01 4.22035038e-01 5.11038363e-01 4.75317519... | [14.392355918884277, -2.2677383422851562] |
3ecf118f-f0c4-4a4d-82fd-01383ab5106c | multi-resolution-beta-divergence-nmf-for | 2007.03893 | null | https://arxiv.org/abs/2007.03893v3 | https://arxiv.org/pdf/2007.03893v3.pdf | Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing | Many datasets are obtained as a resolution trade-off between two adversarial dimensions; for example between the frequency and the temporal resolutions for the spectrogram of an audio signal, and between the number of wavelengths and the spatial resolution for a hyper/multi-spectral image. To perform blind source separ... | ['Cédric Févotte', 'Nicolas Gillis', 'Valentin Leplat'] | 2020-07-08 | null | null | null | null | ['hyperspectral-unmixing'] | ['computer-vision'] | [ 5.39893031e-01 -5.44230819e-01 3.18932772e-01 2.14087531e-01
-1.01409280e+00 -6.07736111e-01 2.96927124e-01 -1.92296356e-01
-4.62704003e-01 6.87463164e-01 -1.15950666e-01 -1.32079750e-01
-7.45440602e-01 -6.51556849e-01 -3.82756561e-01 -1.02292049e+00
-1.47879168e-01 -1.99710056e-02 -2.02619061e-01 -3.26706946... | [10.060131072998047, -2.0127081871032715] |
16ea58b4-abfc-4e00-bbf6-45e739e82a5c | multilingual-complex-word-identification | null | null | https://aclanthology.org/R19-2013 | https://aclanthology.org/R19-2013.pdf | Multilingual Complex Word Identification: Convolutional Neural Networks with Morphological and Linguistic Features | The paper is about our experiments with Complex Word Identification system using deep learning approach with word embeddings and engineered features. | ['Kim Cheng SHEANG'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['complex-word-identification'] | ['natural-language-processing'] | [-4.13829416e-01 -1.68103769e-01 -3.16771448e-01 -2.45273232e-01
1.48351684e-01 -5.30521452e-01 6.20462656e-01 1.21866755e-01
-1.03456223e+00 3.03277314e-01 4.79363531e-01 -8.42634201e-01
1.13157310e-01 -7.84303248e-01 3.32275748e-01 1.18945040e-01
-3.47530812e-01 6.42205536e-01 -1.35679737e-01 -7.81832397... | [10.456299781799316, 10.156827926635742] |
6d158000-9792-4006-8cde-bd02cd1f4605 | realheponet-a-robust-single-stage-convnet-for | 2011.01890 | null | https://arxiv.org/abs/2011.01890v1 | https://arxiv.org/pdf/2011.01890v1.pdf | RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild | Human head pose estimation in images has applications in many fields such as human-computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Netwo... | ['Manuel J. Marín-Jiménez', 'Rafael Muñoz-Salinas', 'Francisco J. Madrid-Cuevas', 'Rafael Berral-Soler'] | 2020-11-03 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-2.05005065e-01 2.13111833e-01 2.79507548e-01 -4.45546925e-01
-4.89255667e-01 -1.96656391e-01 3.31220478e-01 -1.29289478e-01
-7.83582509e-01 6.48191154e-01 -1.02838442e-01 -3.06834072e-01
1.50166795e-01 -6.71354711e-01 -7.33144343e-01 -6.54351890e-01
-2.06773430e-01 3.82366538e-01 2.87772864e-01 -1.69218391... | [13.668924331665039, 0.2891647517681122] |
357d7d01-782b-42d9-87f9-6431206470a0 | spatially-regularized-parametric-map | 1911.03786 | null | https://arxiv.org/abs/1911.03786v2 | https://arxiv.org/pdf/1911.03786v2.pdf | Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting | Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency, adoption of MRF into the clinics is hindered by its dictionary matching-based reconstruction, which is computationally demanding and lacks scala... | ['Benjamin Marty', 'Pierre G. Carlier', 'Olivier Scheidegger', 'Fabian Balsiger', 'Mauricio Reyes', 'Alain Jungo'] | 2019-11-09 | null | null | null | null | ['magnetic-resonance-fingerprinting'] | ['medical'] | [ 3.19669634e-01 5.12701645e-02 -1.32001206e-01 -4.31815654e-01
-9.15870249e-01 -2.58620441e-01 1.19416453e-01 1.45449057e-01
-6.44596815e-01 8.51007342e-01 7.88432285e-02 -7.94890150e-02
-6.37880147e-01 -5.25406480e-01 -7.10020542e-01 -8.23717058e-01
-7.55499959e-01 7.82066584e-01 2.26890460e-01 2.50046421... | [13.515033721923828, -2.37788724899292] |
4b94dded-b1af-4cb6-8519-353c7489fa90 | advancing-referring-expression-segmentation | 2305.12452 | null | https://arxiv.org/abs/2305.12452v1 | https://arxiv.org/pdf/2305.12452v1.pdf | Advancing Referring Expression Segmentation Beyond Single Image | Referring Expression Segmentation (RES) is a widely explored multi-modal task, which endeavors to segment the pre-existing object within a single image with a given linguistic expression. However, in broader real-world scenarios, it is not always possible to determine if the described object exists in a specific image.... | ['Rui Zhao', 'Feng Zhu', 'Xie Chi', 'Zhao Zhang', 'Yixuan Wu'] | 2023-05-21 | null | null | null | null | ['co-saliency-detection', 'referring-expression', 'referring-expression-segmentation', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 0.26821464 -0.0370755 -0.16865744 -0.48081124 -0.86522514 -0.48586568
0.56338686 0.15766022 -0.4188552 0.28413746 0.14787894 0.09176537
0.36138752 -0.43165997 -0.72085875 -0.4931409 0.38306946 0.33414233
0.33845982 -0.29932952 0.10673436 0.32803497 -1.5586662 0.4072142
0.64067274 1.066105 0.6... | [10.1730375289917, 1.21395742893219] |
e56a505c-4658-4038-96b3-874c83708a77 | acgnet-action-complement-graph-network-for | 2112.10977 | null | https://arxiv.org/abs/2112.10977v1 | https://arxiv.org/pdf/2112.10977v1.pdf | ACGNet: Action Complement Graph Network for Weakly-supervised Temporal Action Localization | Weakly-supervised temporal action localization (WTAL) in untrimmed videos has emerged as a practical but challenging task since only video-level labels are available. Existing approaches typically leverage off-the-shelf segment-level features, which suffer from spatial incompleteness and temporal incoherence, thus limi... | ['Di Huang', 'Jie Qin', 'Zichen Yang'] | 2021-12-21 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization'] | ['computer-vision', 'computer-vision'] | [ 1.80044889e-01 -2.74380147e-01 -6.23524249e-01 -1.39782175e-01
-5.32182097e-01 -3.42236698e-01 5.77068388e-01 -5.64964488e-03
-4.18775350e-01 5.04940808e-01 4.28596944e-01 3.21792811e-02
-5.37748188e-02 -2.48417631e-01 -5.92430830e-01 -8.68901670e-01
-3.01354468e-01 -4.08708125e-01 6.65248334e-01 -2.64020283... | [8.48635482788086, 0.6669294834136963] |
f288b0fc-5bb5-4870-92a1-219372d88f24 | neural-enquirer-learning-to-query-tables-in | null | null | https://aclanthology.org/W16-0105 | https://aclanthology.org/W16-0105.pdf | Neural Enquirer: Learning to Query Tables in Natural Language | null | ['Zhengdong Lu', 'Pengcheng Yin', 'Hang Li', 'Kao Ben'] | 2016-06-01 | null | null | null | ws-2016-6 | ['learning-to-execute'] | ['computer-code'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.420971870422363, 3.9701995849609375] |
5992616c-6fe6-4314-a48d-6ee059056a6f | learning-multi-dimensional-edge-feature-based | 2205.01782 | null | https://arxiv.org/abs/2205.01782v2 | https://arxiv.org/pdf/2205.01782v2.pdf | Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognition | The activations of Facial Action Units (AUs) mutually influence one another. While the relationship between a pair of AUs can be complex and unique, existing approaches fail to specifically and explicitly represent such cues for each pair of AUs in each facial display. This paper proposes an AU relationship modelling a... | ['Hatice Gunes', 'Linlin Shen', 'Weicheng Xie', 'Siyang Song', 'Cheng Luo'] | 2022-05-02 | null | null | null | null | ['facial-action-unit-detection'] | ['computer-vision'] | [ 1.55943632e-01 1.78630292e-01 -3.03841513e-02 -5.84225178e-01
-1.87200040e-01 -1.87813073e-01 5.50172508e-01 -2.22567990e-01
1.33121148e-01 6.07153736e-02 3.57794911e-02 3.64512026e-01
-1.77150462e-02 -1.01397872e+00 -7.27018833e-01 -7.75652468e-01
-3.46158832e-01 1.88279822e-01 2.88987253e-02 -4.57094610... | [13.658355712890625, 1.5728684663772583] |
78626ec7-93a0-4baf-ae54-c53f1dc470ed | triple-consistency-loss-for-pairing | 1811.03492 | null | http://arxiv.org/abs/1811.03492v1 | http://arxiv.org/pdf/1811.03492v1.pdf | Triple consistency loss for pairing distributions in GAN-based face synthesis | Generative Adversarial Networks have shown impressive results for the task of
object translation, including face-to-face translation. A key component behind
the success of recent approaches is the self-consistency loss, which encourages
a network to recover the original input image when the output generated for a
desir... | ['Enrique Sanchez', 'Michel Valstar'] | 2018-11-08 | null | null | null | null | ['face-to-face-translation'] | ['computer-vision'] | [ 5.05133510e-01 6.11385167e-01 1.05456956e-01 -3.43805611e-01
-8.87545228e-01 -8.28666270e-01 7.21777797e-01 -5.93702018e-01
-1.56207800e-01 8.09573352e-01 -3.26649994e-02 -8.23644176e-03
1.17143698e-01 -7.93349624e-01 -9.97527421e-01 -9.09949481e-01
2.57946610e-01 6.13739312e-01 -4.15980034e-02 -3.05511624... | [11.79932975769043, -0.39382246136665344] |
be484c7b-7f2e-4c88-95c7-3dcd6f58f691 | few-shot-model-adaptation-for-customized | 2104.09457 | null | https://arxiv.org/abs/2104.09457v1 | https://arxiv.org/pdf/2104.09457v1.pdf | Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal | Despite excellent progress has been made, the performance of deep learning based algorithms still heavily rely on specific datasets, which are difficult to extend due to labor-intensive labeling. Moreover, because of the advancement of new applications, initial definition of data annotations might not always meet the r... | ['Yu-Wing Tai', 'Weinong Wang', 'Bingkun Liu', 'Zhen Wei'] | 2021-04-19 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 2.14782685e-01 3.07084993e-02 -1.58446670e-01 -6.26280725e-01
-7.42573917e-01 -1.99880913e-01 4.42801505e-01 -3.80270720e-01
-3.02431673e-01 3.28530133e-01 -2.66449153e-01 1.70871958e-01
9.14312303e-02 -4.78274018e-01 -4.43928927e-01 -6.46855116e-01
3.13838124e-01 5.27545631e-01 2.63626456e-01 -2.91960210... | [13.479117393493652, 1.330198049545288] |
ba947728-56c3-4d8f-ac43-20b39bbe5ccf | comospeech-one-step-speech-and-singing-voice | 2305.06908 | null | https://arxiv.org/abs/2305.06908v1 | https://arxiv.org/pdf/2305.06908v1.pdf | CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model | Denoising diffusion probabilistic models (DDPMs) have shown promising performance for speech synthesis. However, a large number of iterative steps are required to achieve high sample quality, which restricts the inference speed. Maintaining sample quality while increasing sampling speed has become a challenging task. I... | ['Yike Guo', 'Qifeng Liu', 'Jie Chen', 'Xu Tan', 'Wei Xue', 'Zhen Ye'] | 2023-05-11 | null | null | null | null | ['speech-synthesis', 'singing-voice-synthesis'] | ['speech', 'speech'] | [-2.82647014e-01 -3.81793343e-02 -8.38847086e-02 -1.19860418e-01
-1.32148516e+00 -3.36480409e-01 7.33673453e-01 -3.03015321e-01
-2.17047185e-01 4.38035458e-01 3.74398351e-01 -4.26403493e-01
2.31022164e-01 -6.42908275e-01 -5.32119513e-01 -1.01978195e+00
4.65186536e-01 4.10663486e-01 3.85984480e-01 1.70206770... | [15.088391304016113, 6.222421646118164] |
236847f1-8f78-4e3f-ba5b-fdeb40f28da5 | 2110-06794 | 2110.06794 | null | https://arxiv.org/abs/2110.06794v2 | https://arxiv.org/pdf/2110.06794v2.pdf | The Layout Generation Algorithm of Graphic Design Based on Transformer-CVAE | Graphic design is ubiquitous in people's daily lives. For graphic design, the most time-consuming task is laying out various components in the interface. Repetitive manual layout design will waste a lot of time for professional graphic designers. Existing templates are usually rudimentary and not suitable for most desi... | ['Xiaodong Xie', 'Dangqing Huang', 'Mengxi Guo'] | 2021-10-08 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-4.06033009e-01 7.64278928e-03 3.16707343e-01 -1.60005778e-01
1.45210713e-01 -6.64573133e-01 4.87556279e-01 -3.97955686e-01
1.57314599e-01 3.76956880e-01 4.01005715e-01 -4.42216009e-01
-2.69577950e-01 -9.06551063e-01 -3.08687210e-01 -3.00445735e-01
5.09170115e-01 5.42038023e-01 -4.44313139e-01 -6.66299090... | [11.312917709350586, -0.15699486434459686] |
39b93ace-34b1-4b56-9bd0-1b8a51c760c7 | exploring-the-grounding-issues-in-image | 2305.14616 | null | https://arxiv.org/abs/2305.14616v1 | https://arxiv.org/pdf/2305.14616v1.pdf | Exploring the Grounding Issues in Image Caption | This paper explores the grounding issue concerning multimodal semantic representation from a computational cognitive-linguistic view. Five perceptual properties of groundedness are annotated and analyzed: Affordance, Perceptual salience, Object number, Gaze cueing, and Ecological Niche Association (ENA). We annotated s... | ['Shu-Kai Hsieh', 'Yu-Hsiang Tseng', 'Po-Ya Angela Wang', 'Hsin-Yu Chou', 'Pin-Er Chen'] | 2023-05-24 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 4.86483395e-01 2.62145638e-01 -1.98901474e-01 -6.00055218e-01
-1.71449661e-01 -8.48820686e-01 8.11758697e-01 9.62927461e-01
-4.28195477e-01 2.22968161e-01 1.18054509e+00 -3.13026339e-01
-1.41680524e-01 -4.47525620e-01 -4.92109597e-01 -3.57352555e-01
-5.49311042e-02 -2.29635268e-01 -3.02392781e-01 -5.24359345... | [10.775086402893066, 1.6524487733840942] |
3423fd5c-1df9-42eb-ae13-12b67518a536 | car-pose-in-context-accurate-pose-estimation | 1912.04363 | null | https://arxiv.org/abs/1912.04363v1 | https://arxiv.org/pdf/1912.04363v1.pdf | Car Pose in Context: Accurate Pose Estimation with Ground Plane Constraints | Scene context is a powerful constraint on the geometry of objects within the scene in cases, such as surveillance, where the camera geometry is unknown and image quality may be poor. In this paper, we describe a method for estimating the pose of cars in a scene jointly with the ground plane that supports them. We formu... | ['Gregory D. Hager', 'Weichao Qiu', 'Michael Peven', 'Alan L. Yuille', 'Pengfei Li'] | 2019-12-09 | null | null | null | null | ['car-pose-estimation'] | ['computer-vision'] | [ 2.31087375e-02 -1.51879236e-01 3.83632518e-02 -3.00001085e-01
-9.72811222e-01 -9.69996572e-01 6.69902384e-01 -6.48937970e-02
-3.65330279e-01 2.04097301e-01 -3.10622919e-02 -2.96041191e-01
2.52435148e-01 -4.55752671e-01 -1.04303265e+00 -6.62057996e-01
1.53329164e-01 5.79018652e-01 6.99237525e-01 1.04514509... | [7.751688003540039, -2.416954755783081] |
d30aa701-17d3-4455-94ec-24dc69ff1ce6 | tunaoil-a-tuning-algorithm-strategy-for | 2208.02606 | null | https://arxiv.org/abs/2208.02606v1 | https://arxiv.org/pdf/2208.02606v1.pdf | TunaOil: A Tuning Algorithm Strategy for Reservoir Simulation Workloads | Reservoir simulations for petroleum fields and seismic imaging are known as the most demanding workloads for high-performance computing (HPC) in the oil and gas (O&G) industry. The optimization of the simulator numerical parameters plays a vital role as it could save considerable computational efforts. State-of-the-art... | ['Josep Lluís Berral', 'José Roberto Pereira Rodrigues', 'David Buchaca Prats', 'Felipe Albuquerque Portella'] | 2022-08-04 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [-3.72578710e-01 -5.54598093e-01 4.57712650e-01 2.03966144e-02
-6.62036479e-01 -6.89587533e-01 5.53495407e-01 5.49460351e-01
-4.26904798e-01 5.66002250e-01 5.60108759e-02 -5.37161112e-01
-1.99819952e-01 -9.67922151e-01 -6.15539193e-01 -9.23911452e-01
-5.38916767e-01 6.63028836e-01 4.02044594e-01 -1.91455901... | [6.370100498199463, 3.353407382965088] |
8d4259d4-720a-4539-921d-c2102a0ff2f1 | noveld-a-simple-yet-effective-exploration | null | null | http://proceedings.neurips.cc/paper/2021/hash/d428d070622e0f4363fceae11f4a3576-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d428d070622e0f4363fceae11f4a3576-Paper.pdf | NovelD: A Simple yet Effective Exploration Criterion | Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. Previous exploration methods (e.g., RND) have achieved strong results in multiple hard tasks. However, if there are multiple novel areas to explore, these methods often focus quickly on one without sufficiently trying oth... | ['Yuandong Tian', 'Joseph E. Gonzalez', 'Kurt Keutzer', 'Yi Wu', 'Xiaolong Wang', 'Huazhe Xu', 'Tianjun Zhang'] | 2021-12-01 | null | https://openreview.net/forum?id=CYUzpnOkFJp | https://openreview.net/pdf?id=CYUzpnOkFJp | neurips-2021-12 | ['montezumas-revenge', 'nethack'] | ['playing-games', 'playing-games'] | [-1.07310705e-01 2.05216885e-01 -8.72456729e-02 2.31226385e-01
-7.96996117e-01 -8.03876758e-01 3.54307443e-01 6.93474114e-02
-8.07731748e-01 1.30580699e+00 -4.01993617e-02 -6.67414546e-01
-5.75577140e-01 -8.71301651e-01 -8.25610161e-01 -9.28969443e-01
-4.74438220e-01 8.34634244e-01 1.72182664e-01 -6.08653486... | [3.8701369762420654, 1.7086776494979858] |
cf106dc6-f3a5-4fff-bed2-3ee861856d2f | all-weather-deep-outdoor-lighting-estimation-1 | 1906.04909 | null | https://arxiv.org/abs/1906.04909v1 | https://arxiv.org/pdf/1906.04909v1.pdf | All-Weather Deep Outdoor Lighting Estimation | We present a neural network that predicts HDR outdoor illumination from a single LDR image. At the heart of our work is a method to accurately learn HDR lighting from LDR panoramas under any weather condition. We achieve this by training another CNN (on a combination of synthetic and real images) to take as input an LD... | ['Jean-François Lalonde', 'Jonathan Eisenmann', 'Yannick Hold-Geoffroy', 'Sunil Hadap', 'Jinsong Zhang', 'Kalyan Sunkavalli'] | 2019-06-12 | all-weather-deep-outdoor-lighting-estimation | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_All-Weather_Deep_Outdoor_Lighting_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_All-Weather_Deep_Outdoor_Lighting_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['lighting-estimation'] | ['computer-vision'] | [ 4.80257899e-01 -1.98028609e-01 1.72404960e-01 -5.89164019e-01
-5.45837402e-01 -7.60860741e-01 6.78971589e-01 -8.16974103e-01
-6.56899363e-02 6.45081162e-01 1.64553121e-01 -3.33342969e-01
6.08773470e-01 -8.39668095e-01 -1.12412202e+00 -7.30230868e-01
3.73693645e-01 3.34708720e-01 -1.22857057e-01 -1.84429094... | [9.827959060668945, -2.939762830734253] |
3897ddc8-cc6d-4ab9-bcb6-fbd5449cc268 | sentence-ambiguity-grammaticality-and | 2210.06928 | null | https://arxiv.org/abs/2210.06928v2 | https://arxiv.org/pdf/2210.06928v2.pdf | Sentence Ambiguity, Grammaticality and Complexity Probes | It is unclear whether, how and where large pre-trained language models capture subtle linguistic traits like ambiguity, grammaticality and sentence complexity. We present results of automatic classification of these traits and compare their viability and patterns across representation types. We demonstrate that templat... | ['Ondřej Bojar', 'Vilém Zouhar', 'Sunit Bhattacharya'] | 2022-10-13 | null | null | null | null | ['sentence-ambiguity'] | ['miscellaneous'] | [ 2.03075260e-01 1.90512672e-01 1.76232204e-01 -6.17443621e-01
-5.39669573e-01 -7.60708153e-01 7.37076938e-01 3.52598071e-01
-4.82658803e-01 2.81200111e-01 6.76812768e-01 -3.67464274e-01
-1.60772637e-01 -5.87973475e-01 -4.55603331e-01 -4.26751345e-01
-2.37303838e-01 4.07901108e-01 -5.98071739e-02 -3.55046302... | [10.811280250549316, 9.420488357543945] |
da5a8380-b88b-467e-8a05-7642eed2ac48 | simple-yet-effective-bridge-reasoning-for | 1909.07597 | null | https://arxiv.org/abs/1909.07597v2 | https://arxiv.org/pdf/1909.07597v2.pdf | Simple yet Effective Bridge Reasoning for Open-Domain Multi-Hop Question Answering | A key challenge of multi-hop question answering (QA) in the open-domain setting is to accurately retrieve the supporting passages from a large corpus. Existing work on open-domain QA typically relies on off-the-shelf information retrieval (IR) techniques to retrieve \textbf{answer passages}, i.e., the passages containi... | ['William Yang Wang', 'Hong Wang', 'Wenhan Xiong', 'Shiyu Chang', 'Xiaoxiao Guo', 'Murray Campbell', 'Mo Yu'] | 2019-09-17 | simple-yet-effective-bridge-reasoning-for-1 | https://aclanthology.org/D19-5806 | https://aclanthology.org/D19-5806.pdf | ws-2019-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.74370553e-02 5.05013287e-01 8.12670663e-02 -3.20974179e-02
-2.20365810e+00 -1.03071010e+00 3.05631727e-01 5.73912442e-01
-4.91674423e-01 7.20074117e-01 6.05344594e-01 -5.13272107e-01
-2.75552511e-01 -9.37871516e-01 -1.06641626e+00 -3.19602668e-01
4.60480332e-01 9.64978755e-01 6.99030519e-01 -8.11536372... | [11.318596839904785, 7.96478271484375] |
f1eea86b-4528-40d1-8d66-676e16a026c0 | supervised-contrastive-learning-approach-for | 2207.03153 | null | https://arxiv.org/abs/2207.03153v1 | https://arxiv.org/pdf/2207.03153v1.pdf | Supervised Contrastive Learning Approach for Contextual Ranking | Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine tuning. This paper proposes a simple yet effective method to improve ran... | ['Avishek Anand', 'Koustav Rudra', 'Jurek Leonhardt', 'Abhijit Anand'] | 2022-07-07 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 2.97291428e-01 6.25715032e-02 -2.88879275e-01 -7.25946248e-01
-1.81013870e+00 -5.24424970e-01 1.19560266e+00 5.30231655e-01
-7.37669706e-01 9.57570314e-01 5.96356988e-01 -7.56174996e-02
-6.92823589e-01 -5.38255036e-01 -8.66371870e-01 -6.62104726e-01
-3.85071844e-01 1.04350579e+00 1.02159023e-01 -4.61685210... | [11.411486625671387, 7.571452617645264] |
b4733a01-3398-419a-89df-d477caf51bfe | vocal-tract-length-perturbation-for-text | 2011.12536 | null | https://arxiv.org/abs/2011.12536v2 | https://arxiv.org/pdf/2011.12536v2.pdf | Vocal Tract Length Perturbation for Text-Dependent Speaker Verification with Autoregressive Prediction Coding | In this letter, we propose a vocal tract length (VTL) perturbation method for text-dependent speaker verification (TD-SV), in which a set of TD-SV systems are trained, one for each VTL factor, and score-level fusion is applied to make a final decision. Next, we explore the bottleneck (BN) feature extracted by training ... | ['Zheng-Hua Tan', 'Achintya kr. Sarkar'] | 2020-11-25 | null | null | null | null | ['text-dependent-speaker-verification'] | ['speech'] | [ 2.87199497e-01 -2.61070222e-01 2.05915779e-01 -5.17866254e-01
-1.42880440e+00 -4.54123050e-01 4.54819471e-01 -2.01499864e-01
-3.19908708e-01 8.68946612e-02 5.65306425e-01 -4.73178655e-01
2.96719730e-01 8.54784921e-02 -2.97731191e-01 -1.01590765e+00
9.57969725e-02 1.42168537e-01 -1.92342371e-01 -9.04597640... | [14.378482818603516, 6.087107181549072] |
d3fb5c5c-3ba0-4f93-a2fb-13c79a70e4f6 | how-can-objects-help-action-recognition-1 | 2306.11726 | null | https://arxiv.org/abs/2306.11726v1 | https://arxiv.org/pdf/2306.11726v1.pdf | How can objects help action recognition? | Current state-of-the-art video models process a video clip as a long sequence of spatio-temporal tokens. However, they do not explicitly model objects, their interactions across the video, and instead process all the tokens in the video. In this paper, we investigate how we can use knowledge of objects to design better... | ['Cordelia Schmid', 'Chen Sun', 'Anurag Arnab', 'Xingyi Zhou'] | 2023-06-20 | how-can-objects-help-action-recognition | http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_How_Can_Objects_Help_Action_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_How_Can_Objects_Help_Action_Recognition_CVPR_2023_paper.pdf | cvpr-2023-1 | ['action-recognition-in-videos'] | ['computer-vision'] | [ 1.00581832e-01 -1.84438646e-01 -2.96746671e-01 -1.01055823e-01
-6.46832645e-01 -6.29997492e-01 6.77959442e-01 1.89744428e-01
-8.01566839e-01 3.73279065e-01 2.70020992e-01 1.26098007e-01
2.69048065e-01 -6.62150681e-01 -1.06243372e+00 -3.67337078e-01
9.50722620e-02 1.39850184e-01 7.60441124e-01 2.57511020... | [9.136466979980469, 0.427143931388855] |
4867e9aa-698c-488b-a6a6-1c9c0cb5c555 | semeval-2016-task-3-community-question-1 | 1912.01972 | null | https://arxiv.org/abs/1912.01972v1 | https://arxiv.org/pdf/1912.01972v1.pdf | SemEval-2016 Task 3: Community Question Answering | This paper describes the SemEval--2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question--Comment Similarity (subtask A), Question--Question Similarity (B), and Question--External Comment Similarity (C). For Arabic, we had another subtask: Reran... | ['Walid Magdy', 'Lluís Màrquez', 'Abed Alhakim Freihat', 'James Glass', 'Preslav Nakov', 'Hamdy Mubarak', 'Bilal Randeree', 'Alessandro Moschitti'] | 2019-12-03 | semeval-2016-task-3-community-question | https://aclanthology.org/S16-1083 | https://aclanthology.org/S16-1083.pdf | semeval-2016-6 | ['question-similarity'] | ['natural-language-processing'] | [-1.07253335e-01 -7.96975195e-02 5.39743960e-01 -3.31094325e-01
-1.50950658e+00 -9.12066996e-01 8.86271894e-01 6.44457996e-01
-7.09023118e-01 7.76302993e-01 5.13490379e-01 -4.01109993e-01
2.08193958e-02 -1.89521506e-01 -3.58147949e-01 -2.61117250e-01
9.41252783e-02 5.77359915e-01 6.47573888e-01 -7.05065548... | [11.369216918945312, 8.019886016845703] |
6b0e0544-8c5e-4ff8-b9ff-930af9335d1d | semantic-aware-scene-recognition | 1909.02410 | null | https://arxiv.org/abs/1909.02410v3 | https://arxiv.org/pdf/1909.02410v3.pdf | Semantic-Aware Scene Recognition | Scene recognition is currently one of the top-challenging research fields in computer vision. This may be due to the ambiguity between classes: images of several scene classes may share similar objects, which causes confusion among them. The problem is aggravated when images of a particular scene class are notably diff... | ['Álvaro García-Martín', 'Jesús Bescós', 'Marcos Escudero-Viñolo', 'Alejandro López-Cifuentes'] | 2019-09-05 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 7.78828979e-01 -3.46679479e-01 1.35708362e-01 -6.62275553e-01
-5.03181159e-01 -5.56290567e-01 6.41506076e-01 3.88597697e-01
-6.25442922e-01 3.80119652e-01 5.76506630e-02 5.12214974e-02
-1.45488814e-01 -8.02931249e-01 -7.77108669e-01 -1.06187272e+00
5.31824887e-01 1.06360376e-01 3.39678675e-01 -1.95133090... | [9.492964744567871, -0.45026350021362305] |
ee921906-1562-4cfd-b249-699ff12ebd60 | bayesian-inference-on-brain-computer | 2304.07401 | null | https://arxiv.org/abs/2304.07401v1 | https://arxiv.org/pdf/2304.07401v1.pdf | Bayesian inference on Brain-Computer Interface using the GLASS Model | The brain-computer interface (BCI) enables individuals with severe physical impairments to communicate with the world. BCIs offer computational neuroscience opportunities and challenges in converting real-time brain activities to computer commands and are typically framed as a classification problem. This article focus... | ['Jian Kang', 'Jane E. Huggins', 'Bangyao Zhao'] | 2023-04-14 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 2.21360013e-01 -3.37895662e-01 1.60653725e-01 -7.29543790e-02
-9.58270669e-01 -1.56595990e-01 4.45453048e-01 -4.22160715e-01
-3.21466684e-01 9.97469425e-01 5.94479501e-01 -3.51244599e-01
-6.26057267e-01 -2.05982268e-01 -5.92715740e-01 -9.65194464e-01
-4.53290254e-01 -1.28829882e-01 -2.65497386e-01 3.75466138... | [13.096738815307617, 3.3481109142303467] |
4affc42d-c30e-4d40-aa72-7c945e917e34 | dsvo-direct-stereo-visual-odometry | 1810.03963 | null | https://arxiv.org/abs/1810.03963v2 | https://arxiv.org/pdf/1810.03963v2.pdf | DSVO: Direct Stereo Visual Odometry | This paper proposes a novel approach to stereo visual odometry without stereo matching. It is particularly robust in scenes of repetitive high-frequency textures. Referred to as DSVO (Direct Stereo Visual Odometry), it operates directly on pixel intensities, without any explicit feature matching, and is thus efficient ... | ['Junaed Sattar', 'Jiawei Mo'] | 2018-09-19 | null | null | null | null | ['stereo-matching', 'monocular-visual-odometry'] | ['computer-vision', 'robots'] | [-2.44516926e-03 -1.61905289e-01 -5.27481325e-02 -3.39035764e-02
-5.91243319e-02 -3.87296289e-01 8.57675731e-01 -2.95358688e-01
-4.83384818e-01 5.74689686e-01 7.24058002e-02 1.37189473e-03
2.72517860e-01 -5.30493796e-01 -6.35598958e-01 -6.22500837e-01
3.39092195e-01 1.11095953e+00 7.80243278e-01 -3.39915842... | [7.809682369232178, -2.219496011734009] |
47a9ae1b-de4d-4eb7-9b9b-08e60045ec6d | a-lightweight-hybrid-cnn-lstm-model-for-ecg | 2209.00988 | null | https://arxiv.org/abs/2209.00988v1 | https://arxiv.org/pdf/2209.00988v1.pdf | A lightweight hybrid CNN-LSTM model for ECG-based arrhythmia detection | Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to heart str... | ['Fahimeh Nasimi', 'Nima Alamatsaz', 'Hamidreza Payan', 'Mohammadreza Yazdchi', 'Leyla s Tabatabaei', 'Negin Alamatsaz'] | 2022-08-29 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 4.15744781e-01 -3.97810072e-01 1.64757431e-01 -1.40070423e-01
-6.61004543e-01 -4.22948778e-01 1.09878760e-02 2.39724621e-01
-4.09248561e-01 8.78845096e-01 -3.96244735e-01 -4.81322318e-01
-3.78791034e-01 -4.92814183e-01 -1.56638607e-01 -7.52148449e-01
-4.65784043e-01 3.49494606e-01 -4.36506450e-01 1.74887791... | [14.282060623168945, 3.2794275283813477] |
76a71b2d-e719-49ec-94b5-3af1917a6c86 | convolutional-neural-networks-deceived-by | 1811.10565 | null | http://arxiv.org/abs/1811.10565v1 | http://arxiv.org/pdf/1811.10565v1.pdf | Convolutional Neural Networks Deceived by Visual Illusions | Visual illusions teach us that what we see is not always what it is
represented in the physical world. Its special nature make them a fascinating
tool to test and validate any new vision model proposed. In general, current
vision models are based on the concatenation of linear convolutions and
non-linear operations. In... | ['Marcelo Bertalmío', 'Javier Vazquez-Corral', 'Adrián Martín', 'Alexander Gomez-Villa'] | 2018-11-26 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [ 1.05796747e-01 -6.42896220e-02 5.15471339e-01 -2.60480583e-01
5.90117753e-01 -6.94367945e-01 9.26968873e-01 -6.54669926e-02
-3.12022984e-01 4.10356581e-01 1.72435477e-01 -2.72831529e-01
2.09056854e-01 -6.35763168e-01 -8.42174411e-01 -7.83705771e-01
2.27480158e-01 -2.13639036e-01 3.13728780e-01 -6.90685689... | [10.079373359680176, 2.314777374267578] |
9dfaa168-449f-4695-83c5-fc4d14cf093d | sentence-level-recurrent-topic-model-letting | 1604.02038 | null | http://arxiv.org/abs/1604.02038v2 | http://arxiv.org/pdf/1604.02038v2.pdf | Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves | We propose Sentence Level Recurrent Topic Model (SLRTM), a new topic model
that assumes the generation of each word within a sentence to depend on both
the topic of the sentence and the whole history of its preceding words in the
sentence. Different from conventional topic models that largely ignore the
sequential orde... | ['Tie-Yan Liu', 'Fei Tian', 'Bin Gao', 'Di He'] | 2016-04-07 | null | null | null | null | ['short-text-conversation'] | ['natural-language-processing'] | [ 1.08432092e-01 4.87438977e-01 -3.49277169e-01 -4.48555410e-01
-6.94994748e-01 -2.07632169e-01 1.15720546e+00 3.97569835e-02
1.55513495e-01 9.59740818e-01 1.02631843e+00 -3.15246165e-01
2.97288924e-01 -9.27893102e-01 -5.12479901e-01 -6.63992763e-01
7.38437707e-03 4.59430218e-01 3.53539139e-01 -5.34286737... | [10.40390396118164, 7.000944137573242] |
b7bec9fa-ca54-4d0b-8c48-d19e6bc9c566 | conservative-offline-distributional | 2107.06106 | null | https://arxiv.org/abs/2107.06106v2 | https://arxiv.org/pdf/2107.06106v2.pdf | Conservative Offline Distributional Reinforcement Learning | Many reinforcement learning (RL) problems in practice are offline, learning purely from observational data. A key challenge is how to ensure the learned policy is safe, which requires quantifying the risk associated with different actions. In the online setting, distributional RL algorithms do so by learning the distri... | ['Osbert Bastani', 'Dinesh Jayaraman', 'Yecheng Jason Ma'] | 2021-07-12 | null | http://proceedings.neurips.cc/paper/2021/hash/a05d886123a54de3ca4b0985b718fb9b-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/a05d886123a54de3ca4b0985b718fb9b-Paper.pdf | neurips-2021-12 | ['distributional-reinforcement-learning', 'd4rl'] | ['methodology', 'robots'] | [-3.99847507e-01 7.26447284e-01 -3.40111524e-01 -7.46070594e-02
-1.22615612e+00 -5.89389741e-01 4.52846438e-01 4.89463657e-01
-9.63317335e-01 9.57094908e-01 1.31832466e-01 -5.00034153e-01
-4.34456617e-01 -7.31077731e-01 -1.08985329e+00 -9.34620380e-01
-7.53013849e-01 7.12637722e-01 -1.36816546e-01 -1.51774511... | [4.243551731109619, 2.471790075302124] |
f44e3f7a-575c-489c-90e5-f43a580a9934 | dynint-dynamic-interaction-modeling-for-large | 2301.08139 | null | https://arxiv.org/abs/2301.08139v1 | https://arxiv.org/pdf/2301.08139v1.pdf | DynInt: Dynamic Interaction Modeling for Large-scale Click-Through Rate Prediction | Learning feature interactions is the key to success for the large-scale CTR prediction in Ads ranking and recommender systems. In industry, deep neural network-based models are widely adopted for modeling such problems. Researchers proposed various neural network architectures for searching and modeling the feature int... | ['Liubo Li', 'YaChen Yan'] | 2023-01-03 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-3.26960415e-01 -4.67281252e-01 -4.72605705e-01 -7.02660620e-01
-3.12737703e-01 -7.12471962e-01 4.93995398e-01 -3.19041789e-01
-2.30529726e-01 3.20186704e-01 9.59631056e-02 -3.70989054e-01
-5.04684210e-01 -8.23588073e-01 -7.16465950e-01 -4.88608241e-01
-3.01446974e-01 7.17910051e-01 4.33853328e-01 -5.05252004... | [10.136177062988281, 5.4773359298706055] |
121ed9c9-3da5-460c-bd8b-77370638cc26 | x-cal-explicit-calibration-for-survival-1 | 2101.05346 | null | https://arxiv.org/abs/2101.05346v1 | https://arxiv.org/pdf/2101.05346v1.pdf | X-CAL: Explicit Calibration for Survival Analysis | Survival analysis models the distribution of time until an event of interest, such as discharge from the hospital or admission to the ICU. When a model's predicted number of events within any time interval is similar to the observed number, it is called well-calibrated. A survival model's calibration can be measured us... | ['Rajesh Ranganath', 'Adler J. Perotte', 'Aahlad Puli', 'Xintian Han', 'Mark Goldstein'] | 2021-01-13 | x-cal-explicit-calibration-for-survival | http://proceedings.neurips.cc/paper/2020/hash/d4a93297083a23cc099f7bd6a8621131-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/d4a93297083a23cc099f7bd6a8621131-Paper.pdf | neurips-2020-12 | ['length-of-stay-prediction'] | ['medical'] | [-3.62573341e-02 1.10107400e-01 -3.68893147e-01 -9.31675732e-01
-1.20437789e+00 -4.17185903e-01 3.46512169e-01 6.04022682e-01
-6.16352439e-01 9.91204083e-01 4.50418770e-01 -5.81409931e-01
-2.58899450e-01 -8.04704070e-01 -6.14415407e-01 -7.33728707e-01
-4.83232200e-01 7.70950496e-01 -1.92984611e-01 1.66067481... | [7.8232502937316895, 5.630885601043701] |
cb330354-dcf6-47b8-98db-fcf22d340d35 | evolutionary-multitasking-for-semantic-web | 1902.06370 | null | http://arxiv.org/abs/1902.06370v1 | http://arxiv.org/pdf/1902.06370v1.pdf | Evolutionary Multitasking for Semantic Web Service Composition | Web services are basic functions of a software system to support the concept
of service-oriented architecture. They are often composed together to provide
added values, known as web service composition. Researchers often employ
Evolutionary Computation techniques to efficiently construct composite services
with near-op... | ['Hui Ma', 'Sven Hartmann', 'Gang Chen', 'Chen Wang'] | 2019-02-18 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 3.59965175e-01 -3.26180607e-01 2.90348470e-01 -4.44671243e-01
-6.56008124e-01 -5.09216726e-01 2.47928873e-01 -2.69184351e-01
-2.16873601e-01 4.57341611e-01 -1.72835160e-02 8.69318098e-02
-5.47856569e-01 -7.35061288e-01 -4.67189103e-01 -7.17042327e-01
1.72470361e-01 6.79080248e-01 4.62563187e-01 -4.75777060... | [8.594792366027832, 6.931784152984619] |
31e2cea6-c566-4d46-887e-f739900234fb | are-pretrained-transformers-robust-in-intent | 2106.04564 | null | https://arxiv.org/abs/2106.04564v3 | https://arxiv.org/pdf/2106.04564v3.pdf | Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection | Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few-shot intent recognition tasks and then illustrate the vulnerability of pre-trained Transformer-based models against samples that are in-dom... | ['Caiming Xiong', 'Ye Liu', 'Zhiwei Liu', 'JianGuo Zhang', 'Philip S. Yu', 'Yao Wan', 'Kazuma Hashimoto'] | 2021-06-08 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 2.01476678e-01 -1.75640061e-01 -3.98348987e-01 -4.15684640e-01
-1.10034752e+00 -3.84515077e-01 6.80245578e-01 1.42057657e-01
-2.48307079e-01 2.89111406e-01 4.42447931e-01 -2.66127378e-01
6.57767281e-02 -6.18272483e-01 -1.92467332e-01 -1.51823774e-01
2.33146213e-02 3.48846525e-01 5.23938298e-01 -3.99193197... | [11.98349666595459, 7.592231750488281] |
cf6e5a2f-bab4-421c-b6ed-23469bd86f12 | leveraging-factored-action-spaces-for | 2305.01738 | null | https://arxiv.org/abs/2305.01738v1 | https://arxiv.org/pdf/2305.01738v1.pdf | Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare | Many reinforcement learning (RL) applications have combinatorial action spaces, where each action is a composition of sub-actions. A standard RL approach ignores this inherent factorization structure, resulting in a potential failure to make meaningful inferences about rarely observed sub-action combinations; this is p... | ['Jenna Wiens', 'Finale Doshi-Velez', 'Michael W. Sjoding', 'Maggie Makar', 'Shengpu Tang'] | 2023-05-02 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 6.85035661e-02 2.99822509e-01 -6.79920495e-01 -5.22736683e-02
-8.75340760e-01 -6.30309999e-01 3.61106306e-01 2.17119768e-01
-6.59708977e-01 1.29687083e+00 2.38986582e-01 -7.57033408e-01
-4.76569891e-01 -8.02625895e-01 -8.63790810e-01 -6.55912399e-01
-5.24688125e-01 4.61403191e-01 -5.41642345e-02 -2.54980605... | [4.148134708404541, 2.561164617538452] |
e11811e7-3cd6-48c1-a351-cc2445c23cc3 | improving-the-domain-adaptation-of-retrieval | 2210.02627 | null | https://arxiv.org/abs/2210.02627v1 | https://arxiv.org/pdf/2210.02627v1.pdf | Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering | Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint... | ['Suranga Nanayakkara', 'Rajib Rana', 'Tharindu Kaluarachchi', 'Elliott Wen', 'Rivindu Weerasekera', 'Shamane Siriwardhana'] | 2022-10-06 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-8.86869952e-02 6.10680163e-01 8.00805092e-02 -3.93255830e-01
-1.43755054e+00 -9.09821570e-01 4.39438403e-01 1.86027229e-01
-5.61142802e-01 1.05950379e+00 4.67811286e-01 -1.87248051e-01
-1.49913535e-01 -7.39194453e-01 -9.02924180e-01 -6.70031756e-02
2.73397207e-01 1.26126194e+00 5.70153534e-01 -9.05251563... | [11.303743362426758, 7.987534999847412] |
cca61e03-02b2-46b2-af56-a1a19cf07359 | the-ubuntu-dialogue-corpus-a-large-dataset-1 | 1506.08909 | null | http://arxiv.org/abs/1506.08909v3 | http://arxiv.org/pdf/1506.08909v3.pdf | The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems | This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost
1 million multi-turn dialogues, with a total of over 7 million utterances and
100 million words. This provides a unique resource for research into building
dialogue managers based on neural language models that can make use of large
amounts o... | ['Ryan Lowe', 'Iulian Serban', 'Nissan Pow', 'Joelle Pineau'] | 2015-06-30 | the-ubuntu-dialogue-corpus-a-large-dataset | https://aclanthology.org/W15-4640 | https://aclanthology.org/W15-4640.pdf | ws-2015-9 | ['conversational-response-selection'] | ['natural-language-processing'] | [-3.39760870e-01 6.40305161e-01 -2.68608451e-01 -8.35556746e-01
-8.37513387e-01 -7.26247013e-01 9.80239034e-01 8.89010057e-02
-5.53210557e-01 1.32395637e+00 7.56911516e-01 -5.46274781e-01
4.85572815e-01 -5.24860859e-01 6.56719282e-02 -1.08948581e-01
-2.06609562e-01 1.15824294e+00 1.79947227e-01 -9.80796754... | [12.75489330291748, 7.967925548553467] |
82568499-547c-4803-b941-3bef93e77bc1 | enhancing-low-resource-ner-using-assisting | 2306.06477 | null | https://arxiv.org/abs/2306.06477v1 | https://arxiv.org/pdf/2306.06477v1.pdf | Enhancing Low Resource NER Using Assisting Language And Transfer Learning | Named Entity Recognition (NER) is a fundamental task in NLP that is used to locate the key information in text and is primarily applied in conversational and search systems. In commercial applications, NER or comparable slot-filling methods have been widely deployed for popular languages. NER is used in applications su... | ['Dipali Kadam', 'Raviraj Joshi', 'Parth Patil', 'Onkar Litake', 'Aparna Ranade', 'Maithili Sabane'] | 2023-06-10 | null | null | null | null | ['named-entity-recognition-ner', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [-5.98108053e-01 -2.03633547e-01 -3.13640833e-01 -2.81081468e-01
-1.07027388e+00 -9.85199213e-01 7.75088966e-01 4.01798755e-01
-1.03520322e+00 1.26993573e+00 4.55809593e-01 -4.25462902e-01
5.56770116e-02 -5.50965905e-01 -2.59388030e-01 -2.37713024e-01
1.83143869e-01 9.18666184e-01 2.50408679e-01 -5.25473773... | [9.877628326416016, 9.75691032409668] |
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