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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d29bef02-6a24-47f2-9a5a-738941d7a19b | modelling-sars-cov-2-coevolution-with-genetic | 2102.12365 | null | https://arxiv.org/abs/2102.12365v1 | https://arxiv.org/pdf/2102.12365v1.pdf | Modelling SARS-CoV-2 coevolution with genetic algorithms | At the end of 2020, policy responses to the SARS-CoV-2 outbreak have been shaken by the emergence of virus variants, impacting public health and policy measures worldwide. The emergence of these strains suspected to be more contagious, more severe, or even resistant to antibodies and vaccines, seem to have taken by sur... | ['Aymeric Vie'] | 2021-02-24 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 3.08959305e-01 -1.20147690e-01 3.15463156e-01 2.88834274e-01
3.57673138e-01 -6.19994819e-01 5.81602156e-01 1.81686759e-01
-4.99268711e-01 1.15176427e+00 -1.27383932e-01 -4.79455709e-01
-3.15975517e-01 -7.61709213e-01 -4.09361482e-01 -1.06524932e+00
-5.45058012e-01 8.14931750e-01 -2.92788208e-01 -7.97604740... | [5.72324800491333, 4.224107265472412] |
dca0f5ea-a015-4700-9905-d04003ec0592 | label-aware-double-transfer-learning-for | 1804.09021 | null | http://arxiv.org/abs/1804.09021v2 | http://arxiv.org/pdf/1804.09021v2.pdf | Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition | We study the problem of named entity recognition (NER) from electronic
medical records, which is one of the most fundamental and critical problems for
medical text mining. Medical records which are written by clinicians from
different specialties usually contain quite different terminologies and writing
styles. The dif... | ['Li-Heng Chen', 'Wei-Nan Zhang', 'Shaodian Zhang', 'Yong Yu', 'Jian Shen', 'Zhenghui Wang', 'Yimei Gao', 'Yanru Qu', 'Ken Chen', 'Gen Gu'] | 2018-04-24 | label-aware-double-transfer-learning-for-1 | https://aclanthology.org/N18-1001 | https://aclanthology.org/N18-1001.pdf | naacl-2018-6 | ['medical-named-entity-recognition'] | ['natural-language-processing'] | [-1.27814664e-02 1.07913181e-01 -2.85339564e-01 -4.58374739e-01
-1.07675505e+00 -4.21721548e-01 1.68282479e-01 3.08452785e-01
-8.26093793e-01 7.47650981e-01 3.07200193e-01 -2.94678658e-01
-1.56549111e-01 -5.07375658e-01 -3.54688764e-01 -6.02650821e-01
3.71333569e-01 5.25194585e-01 1.08219266e-01 -1.75506219... | [8.620674133300781, 8.884127616882324] |
21a82ebb-c5ee-41ae-873f-9508cd73c642 | open-source-frame-semantic-parsing | 2303.12788 | null | https://arxiv.org/abs/2303.12788v1 | https://arxiv.org/pdf/2303.12788v1.pdf | Open-source Frame Semantic Parsing | While the state-of-the-art for frame semantic parsing has progressed dramatically in recent years, it is still difficult for end-users to apply state-of-the-art models in practice. To address this, we present Frame Semantic Transformer, an open-source Python library which achieves near state-of-the-art performance on F... | ['David Chanin'] | 2023-03-22 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 6.97574439e-03 7.04928815e-01 -3.18703353e-01 -6.63549364e-01
-9.52887356e-01 -7.10121691e-01 6.42383814e-01 8.88675973e-02
-4.66514796e-01 7.65208542e-01 6.52643383e-01 -6.45109475e-01
5.51844358e-01 -7.89864898e-01 -8.42559934e-01 2.29087770e-01
2.97277391e-01 4.90081400e-01 6.89025819e-01 -4.98120487... | [10.33467960357666, 9.34820556640625] |
c1a37924-0c14-4845-804a-a90f263f4beb | icdar-2019-competition-on-large-scale-street | 1909.07741 | null | https://arxiv.org/abs/1909.07741v1 | https://arxiv.org/pdf/1909.07741v1.pdf | ICDAR 2019 Competition on Large-scale Street View Text with Partial Labeling -- RRC-LSVT | Robust text reading from street view images provides valuable information for various applications. Performance improvement of existing methods in such a challenging scenario heavily relies on the amount of fully annotated training data, which is costly and in-efficient to obtain. To scale up the amount of training dat... | ['Dimosthenis Karatzas', 'Errui Ding', 'Chun Chet Ng', 'Chee-Kheng Chng', 'Junyu Han', 'Yuliang Liu', 'Lianwen Jin', 'Jingtuo Liu', 'Chee Seng Chan', 'Zihan Ni', 'Yipeng Sun', 'Canjie Luo'] | 2019-09-17 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 5.00984788e-01 -2.98731863e-01 -1.90763772e-02 -3.92096370e-01
-1.10777199e+00 -6.76427186e-01 6.82124317e-01 1.17599577e-01
-4.97204900e-01 3.34949553e-01 1.71693444e-01 -9.29612815e-02
6.21223569e-01 -4.89581287e-01 -5.92371941e-01 -5.89932978e-01
6.34392679e-01 7.22680986e-01 3.15226734e-01 5.81753291... | [11.954310417175293, 2.293041467666626] |
b8f36839-7be2-408a-905d-b61d66463c1e | periodic-load-rejection-for-floating-offshore | 2104.0425 | null | https://arxiv.org/abs/2104.04250v1 | https://arxiv.org/pdf/2104.04250v1.pdf | Periodic Load Rejection for Floating Offshore Wind Turbines via Constrained Subspace Predictive Repetitive Control | Individual Pitch Control (IPC) is an effective control strategy to mitigate the blade loads on large-scale wind turbines. Since IPC usually requires high pitch actuation, the safety constraints of the pitch actuator should be taken into account when designing the controller. This paper introduces a constrained Subspace... | ['Jan-Willem van Wingerden', 'Riccardo M. G. Ferrari', 'Yichao Liu'] | 2021-04-09 | null | null | null | null | ['pitch-control'] | ['audio'] | [ 2.09638998e-02 3.59270066e-01 1.53679550e-01 6.08462930e-01
5.32575130e-01 -9.73829448e-01 9.57653448e-02 -3.30964215e-02
1.83528483e-01 6.96978867e-01 2.28248164e-01 -1.74240083e-01
-9.15837586e-01 -5.59597611e-01 -1.48321241e-01 -9.69535112e-01
-9.41459090e-02 -1.58137619e-01 1.03865430e-01 -4.55295175... | [5.402334213256836, 2.484386444091797] |
f978fa04-22a5-43b9-ac7b-734dd93b422a | complex-word-identification-convolutional | null | null | https://aclanthology.org/W18-0538 | https://aclanthology.org/W18-0538.pdf | Complex Word Identification: Convolutional Neural Network vs. Feature Engineering | We describe the systems of NLP-CIC team that participated in the Complex Word Identification (CWI) 2018 shared task. The shared task aimed to benchmark approaches for identifying complex words in English and other languages from the perspective of non-native speakers. Our goal is to compare two approaches: feature engi... | ['er', 'ro', "Daniel Alej P{\\'e}rez Alvarez", 'Segun Taofeek Aroyehun', 'Jason Angel', 'Alex Gelbukh'] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-3.37899476e-01 1.50196835e-01 1.25967085e-01 -1.71676591e-01
-1.12078679e+00 -8.88721883e-01 7.31271088e-01 9.13769454e-02
-1.09405029e+00 5.31818509e-01 2.98237741e-01 -4.56156760e-01
1.01198711e-01 -3.80172104e-01 -4.13190007e-01 -1.94682002e-01
6.05545491e-02 7.20852375e-01 -1.16578132e-01 -4.46237236... | [10.450173377990723, 10.439269065856934] |
14d97d37-c74b-49eb-885c-625e890d9aec | highly-accurate-quantum-chemical-property | 2303.16982 | null | https://arxiv.org/abs/2303.16982v2 | https://arxiv.org/pdf/2303.16982v2.pdf | Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+ | Recent developments in deep learning have made remarkable progress in speeding up the prediction of quantum chemical (QC) properties by removing the need for expensive electronic structure calculations like density functional theory. However, previous methods learned from 1D SMILES sequences or 2D molecular graphs fail... | ['Guolin Ke', 'Linfeng Zhang', 'Di He', 'Zhifeng Gao', 'Shuqi Lu'] | 2023-03-16 | null | null | null | null | ['graph-regression'] | ['graphs'] | [ 2.46682063e-01 -3.07248086e-01 -4.82555807e-01 -4.19190735e-01
-9.42940295e-01 -6.85022056e-01 2.90393412e-01 4.47778553e-01
-1.02413937e-01 1.19387937e+00 2.74978988e-02 -7.09627867e-01
1.85892373e-01 -9.42118645e-01 -1.12996268e+00 -9.53684032e-01
-9.62359011e-02 5.93199492e-01 -4.18051817e-02 -1.00635238... | [5.092316150665283, 5.712825775146484] |
0357a41b-cb3c-4288-9bf5-60d38537680d | learning-emotional-representations-from | 2306.05709 | null | https://arxiv.org/abs/2306.05709v1 | https://arxiv.org/pdf/2306.05709v1.pdf | Learning Emotional Representations from Imbalanced Speech Data for Speech Emotion Recognition and Emotional Text-to-Speech | Effective speech emotional representations play a key role in Speech Emotion Recognition (SER) and Emotional Text-To-Speech (TTS) tasks. However, emotional speech samples are more difficult and expensive to acquire compared with Neutral style speech, which causes one issue that most related works unfortunately neglect:... | ['Damian Borth', 'Jón Guðnason', 'Shijun Wang'] | 2023-06-09 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [ 7.13586956e-02 3.87350649e-01 -7.46558979e-02 -5.53257763e-01
-8.71049404e-01 -1.91527262e-01 3.08048069e-01 -3.06274205e-01
-1.91168785e-01 5.93764067e-01 4.90084946e-01 -1.00609407e-01
6.30851150e-01 -3.31811249e-01 -5.12712717e-01 -4.85474676e-01
1.64931834e-01 8.98392051e-02 -4.54146087e-01 -5.12690246... | [13.658821105957031, 5.848977565765381] |
a92ce856-fa96-4e6b-87a0-e44a40882977 | transfer-learning-on-electromyography-emg | 2210.06295 | null | https://arxiv.org/abs/2210.06295v2 | https://arxiv.org/pdf/2210.06295v2.pdf | Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond | Machine learning on electromyography (EMG) has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the assumption that the training and future data must be of the same data distribution. However, this assumption may not hold in many real-world applications. Model calibration... | ['Mohamad Sawan', 'Jie Yang', 'Di wu'] | 2022-10-03 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 5.07307887e-01 4.42745164e-02 -7.14100718e-01 -1.40344635e-01
-9.77857888e-01 -3.39358866e-01 1.15425726e-04 -5.17169833e-01
-4.93053436e-01 1.16444910e+00 -1.24139503e-01 -4.32334095e-02
-2.60734260e-01 -3.98090571e-01 -1.01748872e+00 -8.46481740e-01
-2.64666736e-01 3.04656953e-01 5.34514780e-04 -1.43663406... | [6.966719150543213, 0.2406071275472641] |
75f8eae1-b4c0-49e1-9d94-f04d99eec266 | generalized-separable-nonnegative-matrix | 1905.12995 | null | https://arxiv.org/abs/1905.12995v2 | https://arxiv.org/pdf/1905.12995v2.pdf | Generalized Separable Nonnegative Matrix Factorization | Nonnegative matrix factorization (NMF) is a linear dimensionality technique for nonnegative data with applications such as image analysis, text mining, audio source separation and hyperspectral unmixing. Given a data matrix $M$ and a factorization rank $r$, NMF looks for a nonnegative matrix $W$ with $r$ columns and a ... | ['Nicolas Gillis', 'Junjun Pan'] | 2019-05-30 | null | null | null | null | ['audio-source-separation', 'hyperspectral-unmixing'] | ['audio', 'computer-vision'] | [ 2.92640358e-01 -1.85564712e-01 -1.97409526e-01 -7.06852227e-02
-6.17657244e-01 -5.59413433e-01 -1.08260915e-01 -1.48439288e-01
-3.45224112e-01 5.66928506e-01 7.66085535e-02 -4.86812145e-01
-7.34836638e-01 -7.20590651e-01 -4.34165806e-01 -8.95995975e-01
-4.65088964e-01 2.93375760e-01 -6.78436995e-01 -2.21510485... | [7.31822395324707, 4.4997944831848145] |
a8721f59-fb50-4b9c-9faf-d16ece33eec1 | an-affective-robot-companion-for-assisting | 1807.09825 | null | http://arxiv.org/abs/1807.09825v1 | http://arxiv.org/pdf/1807.09825v1.pdf | An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario | Being able to recognize emotions in human users is considered a highly
desirable trait in Human-Robot Interaction (HRI) scenarios. However, most
contemporary approaches rarely attempt to apply recognized emotional features
in an active manner to modulate robot decision-making and dialogue for the
benefit of the user. I... | ['Barros Pablo', 'Sutherland Alexander', 'Churamani Nikhil'] | 2018-07-12 | null | null | null | null | ['dialogue-management', '2048'] | ['natural-language-processing', 'playing-games'] | [ 8.83621052e-02 8.85228992e-01 9.93454680e-02 -5.32370031e-01
9.64330956e-02 -1.95474163e-01 5.07230163e-01 1.01636752e-01
-5.33509552e-01 1.01062429e+00 1.11056790e-01 -4.53580543e-02
5.64098619e-02 -6.54304147e-01 -8.69166385e-03 -3.42968524e-01
-3.96590568e-02 5.92696607e-01 -2.44875088e-01 -9.61554348... | [13.143417358398438, 7.684025287628174] |
6bf465e8-4571-468d-868f-001990635c5b | comparing-offline-and-online-testing-of-deep | 1912.00805 | null | https://arxiv.org/abs/1912.00805v1 | https://arxiv.org/pdf/1912.00805v1.pdf | Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study | There is a growing body of research on developing testing techniques for Deep Neural Networks (DNN). We distinguish two general modes of testing for DNNs: Offline testing where DNNs are tested as individual units based on test datasets obtained independently from the DNNs under test, and online testing where DNNs are e... | ['Donghwan Shin', 'Fitash Ul Haq', 'Lionel Briand', 'Shiva Nejati'] | 2019-11-28 | null | null | null | null | ['dnn-testing'] | ['adversarial'] | [-9.43019614e-02 2.06168175e-01 1.12831198e-01 -5.78045309e-01
6.89062104e-02 -7.63588190e-01 2.73801178e-01 -2.49190733e-01
-5.05296826e-01 7.65408218e-01 -8.79685342e-01 -9.15869594e-01
-1.89288825e-01 -9.29508746e-01 -1.10671782e+00 -3.18863451e-01
-1.47886366e-01 5.23389459e-01 7.41220057e-01 -2.63442844... | [6.47033166885376, 7.623467922210693] |
ea802bb2-bd09-493e-8bcd-6e6de59f6f58 | an-adversarially-learned-turing-test-for | 2104.08231 | null | https://arxiv.org/abs/2104.08231v1 | https://arxiv.org/pdf/2104.08231v1.pdf | An Adversarially-Learned Turing Test for Dialog Generation Models | The design of better automated dialogue evaluation metrics offers the potential of accelerate evaluation research on conversational AI. However, existing trainable dialogue evaluation models are generally restricted to classifiers trained in a purely supervised manner, which suffer a significant risk from adversarial a... | ['Bill Dolan', 'Michel Galley', 'Yizhe Zhang', 'Xiang Gao'] | 2021-04-16 | null | null | null | null | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 3.90374064e-01 6.14760458e-01 1.47332534e-01 -3.17143738e-01
-1.12322009e+00 -1.31427944e+00 1.00569117e+00 -2.57632911e-01
-3.31346720e-01 9.51446533e-01 3.33742052e-01 -5.91373026e-01
3.37476820e-01 -8.19666564e-01 -3.68027717e-01 -3.92891586e-01
-1.60745140e-02 8.31216037e-01 -4.10544090e-02 -9.19597268... | [12.649599075317383, 8.23375415802002] |
2c30cab9-d740-4be4-a63c-7ade8c067209 | sceneednet-a-deep-learning-approach-for-scene | 1807.03464 | null | http://arxiv.org/abs/1807.03464v1 | http://arxiv.org/pdf/1807.03464v1.pdf | SceneEDNet: A Deep Learning Approach for Scene Flow Estimation | Estimating scene flow in RGB-D videos is attracting much interest of the
computer vision researchers, due to its potential applications in robotics. The
state-of-the-art techniques for scene flow estimation, typically rely on the
knowledge of scene structure of the frame and the correspondence between
frames. However, ... | ['Snehasis Mukherjee', 'Ravi Kumar Thakur'] | 2018-07-10 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [ 1.32567465e-01 -4.19639736e-01 1.30198345e-01 -3.06164384e-01
-1.57059059e-01 -3.13348442e-01 6.49021387e-01 -9.42577701e-03
-7.72464812e-01 5.54207325e-01 1.16347499e-01 -5.91727234e-02
1.11287616e-01 -7.33308673e-01 -6.85526907e-01 -5.05396008e-01
-1.23774208e-01 8.00785199e-02 4.87698883e-01 -3.28785181... | [8.676101684570312, -2.0002858638763428] |
7b8a2f13-6ed9-4aca-bf26-5b544587001d | real-time-video-super-resolution-by-joint | 2105.02794 | null | https://arxiv.org/abs/2105.02794v1 | https://arxiv.org/pdf/2105.02794v1.pdf | Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation | The state of the art in video super-resolution (SR) are techniques based on deep learning, but they perform poorly on real-world videos (see Figure 1). The reason is that training image-pairs are commonly created by downscaling a high-resolution image to produce a low-resolution counterpart. Deep models are therefore t... | ['Noam Levy', 'Shahar S. Yuval', 'Alex Itskovich', 'Noam Elron'] | 2021-05-06 | null | null | null | null | ['video-denoising', 'video-enhancement', 'tone-mapping'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.99115217e-01 -1.97556406e-01 -5.07484414e-02 -6.24222197e-02
-5.98295927e-01 -8.42043459e-02 3.07900846e-01 -3.64789158e-01
-5.14687598e-01 7.08610117e-01 -8.56736600e-02 -1.76271200e-02
1.64126620e-01 -9.38807607e-01 -9.24936175e-01 -9.51490283e-01
1.83277112e-02 -1.49990588e-01 6.45456016e-01 -4.96133983... | [11.139166831970215, -1.9827536344528198] |
5a91e889-4368-4e86-8ba8-1859c5c06e81 | 1m-parameters-are-enough-a-lightweight-cnn | 2306.16103 | null | https://arxiv.org/abs/2306.16103v2 | https://arxiv.org/pdf/2306.16103v2.pdf | 1M parameters are enough? A lightweight CNN-based model for medical image segmentation | Convolutional neural networks (CNNs) and Transformer-based models are being widely applied in medical image segmentation thanks to their ability to extract high-level features and capture important aspects of the image. However, there is often a trade-off between the need for high accuracy and the desire for low comput... | ['Van-Truong Pham', 'Thi-Thao Tran', 'Thanh-Thu Nguyen', 'Binh-Duong Dinh'] | 2023-06-28 | null | null | null | null | ['medical-image-segmentation'] | ['medical'] | [ 1.35787606e-01 7.36676455e-02 -1.75159216e-01 -3.57566118e-01
-5.73805809e-01 -2.14130625e-01 9.20909569e-02 1.43025592e-02
-6.21695280e-01 4.15740639e-01 -6.87742233e-02 -7.21377075e-01
8.53595957e-02 -1.04036891e+00 -5.71494222e-01 -5.08688390e-01
6.52287006e-02 -4.76767384e-02 6.32691085e-01 -5.53851537... | [14.53321647644043, -2.6128926277160645] |
5597a497-0319-4b48-8c29-e28739ecd227 | a-causal-lens-for-peeking-into-black-box | 2008.00357 | null | https://arxiv.org/abs/2008.00357v1 | https://arxiv.org/pdf/2008.00357v1.pdf | A Causal Lens for Peeking into Black Box Predictive Models: Predictive Model Interpretation via Causal Attribution | With the increasing adoption of predictive models trained using machine learning across a wide range of high-stakes applications, e.g., health care, security, criminal justice, finance, and education, there is a growing need for effective techniques for explaining such models and their predictions. We aim to address th... | ['Aria Khademi', 'Vasant Honavar'] | 2020-08-01 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [ 5.94517767e-01 6.82309389e-01 -6.97544217e-01 -5.26020825e-01
-2.79042393e-01 -4.12322640e-01 6.63438261e-01 2.35084578e-01
-3.10948074e-01 9.21352148e-01 4.33377743e-01 -8.47688317e-01
-6.30778193e-01 -8.91897619e-01 -1.22953248e+00 -4.64404464e-01
-1.15893632e-02 6.90230668e-01 -3.12812299e-01 3.00625175... | [8.397570610046387, 5.528372287750244] |
974d4556-c005-42a9-9115-5b499d847c6d | speaker-change-aware-crf-for-dialogue-act | 2004.02913 | null | https://arxiv.org/abs/2004.02913v3 | https://arxiv.org/pdf/2004.02913v3.pdf | Speaker-change Aware CRF for Dialogue Act Classification | Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer. CRF models the conditional probability of the target DA label sequence given the input utterance sequence. However, the task in... | ['Jean-Pierre Lorré', 'Antoine Jean-Pierre Tixier', 'Michalis Vazirgiannis', 'Guokan Shang'] | 2020-04-06 | null | https://aclanthology.org/2020.coling-main.40 | https://aclanthology.org/2020.coling-main.40.pdf | coling-2020-8 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 2.62424082e-01 4.80690092e-01 -1.77615613e-01 -1.03842449e+00
-3.61957282e-01 -6.24517977e-01 9.61072803e-01 -1.74306780e-01
-4.65087891e-01 8.38165998e-01 5.73818564e-01 -3.71021330e-01
6.80154085e-01 -3.37293774e-01 -1.59691185e-01 -5.58418453e-01
1.40364897e-02 6.07951283e-01 2.49661848e-01 -3.40413243... | [12.789055824279785, 7.692222595214844] |
f058295b-7fa8-422c-b835-8fa3b0938f1a | xmi-icu-explainable-machine-learning-model | 2305.06109 | null | https://arxiv.org/abs/2305.06109v1 | https://arxiv.org/pdf/2305.06109v1.pdf | XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients | Heart attack remain one of the greatest contributors to mortality in the United States and globally. Patients admitted to the intensive care unit (ICU) with diagnosed heart attack (myocardial infarction or MI) are at higher risk of death. In this study, we use two retrospective cohorts extracted from the eICU and MIMIC... | ['Tingting Zhu', 'Peter Watkinson', 'Munib Mesinovic'] | 2023-05-10 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [ 1.18302651e-01 -2.64934838e-01 6.21301346e-02 -3.06227267e-01
-7.93523848e-01 -4.37366843e-01 -1.76940411e-01 6.40402198e-01
-2.77962983e-01 7.60374248e-01 2.73457617e-01 -7.93121040e-01
-8.41574550e-01 -3.37391615e-01 -7.58876093e-03 -5.63103855e-01
-8.45495582e-01 7.85829484e-01 -4.15503591e-01 3.91094834... | [8.003293991088867, 6.129432201385498] |
f695b620-92cb-4b07-b5e1-689ecde8ab6d | distill-knowledge-from-nrsfm-for-weakly | 1908.06377 | null | https://arxiv.org/abs/1908.06377v1 | https://arxiv.org/pdf/1908.06377v1.pdf | Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning | We propose to learn a 3D pose estimator by distilling knowledge from Non-Rigid Structure from Motion (NRSfM). Our method uses solely 2D landmark annotations. No 3D data, multi-view/temporal footage, or object specific prior is required. This alleviates the data bottleneck, which is one of the major concern for supervis... | ['Chen Kong', 'Chaoyang Wang', 'Simon Lucey'] | 2019-08-18 | distill-knowledge-from-nrsfm-for-weakly-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Distill_Knowledge_From_NRSfM_for_Weakly_Supervised_3D_Pose_Learning_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Distill_Knowledge_From_NRSfM_for_Weakly_Supervised_3D_Pose_Learning_ICCV_2019_paper.pdf | iccv-2019-10 | ['weakly-supervised-3d-human-pose-estimation'] | ['computer-vision'] | [-1.14039212e-01 4.72996324e-01 -6.20916843e-01 -5.90848327e-01
-1.04448426e+00 -6.57268703e-01 2.72167414e-01 -1.71130687e-01
-5.02884209e-01 6.59887195e-01 2.45565116e-01 -3.95861864e-02
5.09159081e-03 -5.23497999e-01 -1.08081222e+00 -6.38050735e-01
3.62785310e-01 7.74925351e-01 3.24912220e-01 1.24024965... | [7.0955281257629395, -1.1691763401031494] |
207e1f6f-46e6-48a9-a474-1508b9fa3a5f | statistical-properties-of-color-matching | 2007.02197 | null | https://arxiv.org/abs/2007.02197v2 | https://arxiv.org/pdf/2007.02197v2.pdf | Statistical properties of color matching functions | In trichromats, color vision entails the projection of an infinite-dimensional space (the one containing all possible electromagnetic power spectra) onto the 3-dimensional space that modulates the activity of the three types of cones. This drastic reduction in dimensionality gives rise to metamerism, that is, the perce... | ['Inés Samengo', 'María da Fonseca'] | 2020-07-04 | null | null | null | null | ['metamerism'] | ['computer-vision'] | [ 4.50029224e-01 -6.05629206e-01 3.58621597e-01 -6.64937720e-02
2.35345624e-02 -8.02175105e-01 4.86562580e-01 -1.89904779e-01
-7.78554022e-01 5.29771984e-01 -6.24160748e-03 -3.10052037e-01
-1.30449310e-01 -6.35611951e-01 -4.35223281e-01 -1.00305057e+00
3.26789916e-01 3.96711342e-02 2.99363047e-01 1.29983081... | [10.094829559326172, 2.0486621856689453] |
9f5dc517-b56b-4b11-92e2-4b7872c543a7 | human-pose-transfer-by-adaptive-hierarchical | 2012.0694 | null | https://arxiv.org/abs/2012.06940v1 | https://arxiv.org/pdf/2012.06940v1.pdf | Human Pose Transfer by Adaptive Hierarchical Deformation | Human pose transfer, as a misaligned image generation task, is very challenging. Existing methods cannot effectively utilize the input information, which often fail to preserve the style and shape of hair and clothes. In this paper, we propose an adaptive human pose transfer network with two hierarchical deformation le... | ['Kun Li', 'Xingzi Liu', 'Jinsong Zhang'] | 2020-12-13 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 3.40837568e-01 1.11175008e-01 2.02159926e-01 -4.61697936e-01
-2.95065373e-01 -4.13130105e-01 1.62308306e-01 -5.46773374e-01
-3.05292666e-01 6.81177318e-01 1.53321594e-01 3.26090485e-01
3.58212054e-01 -1.03818607e+00 -9.27044868e-01 -6.95327759e-01
5.66853225e-01 5.10852575e-01 4.61625606e-01 -3.39555889... | [11.973681449890137, -0.8481130003929138] |
fc95d966-c016-4eb3-b866-8c6a39b9f330 | geometric-constraints-in-probabilistic | 2307.04493 | null | https://arxiv.org/abs/2307.04493v1 | https://arxiv.org/pdf/2307.04493v1.pdf | Geometric Constraints in Probabilistic Manifolds: A Bridge from Molecular Dynamics to Structured Diffusion Processes | Understanding the macroscopic characteristics of biological complexes demands precision and specificity in statistical ensemble modeling. One of the primary challenges in this domain lies in sampling from particular subsets of the state-space, driven either by existing structural knowledge or specific areas of interest... | ['Markus Lill', 'Justin Diamond'] | 2023-07-10 | null | null | null | null | ['denoising', 'specificity'] | ['computer-vision', 'natural-language-processing'] | [ 5.26853740e-01 -9.98770073e-02 1.66776255e-01 -8.34973082e-02
-4.31468904e-01 -6.81146204e-01 7.10036337e-01 2.77330011e-01
-4.14777726e-01 1.15412140e+00 1.20809287e-01 -1.45445690e-01
-8.75505388e-01 -8.51089537e-01 -6.58389986e-01 -1.47132134e+00
-9.09521505e-02 5.96695483e-01 -1.53059945e-01 -2.21868649... | [5.315295696258545, 5.085062503814697] |
b9d7124b-ad5e-4086-8a87-d572b226434e | linearly-scalable-learning-of-smooth-low | 2306.10287 | null | https://arxiv.org/abs/2306.10287v1 | https://arxiv.org/pdf/2306.10287v1.pdf | Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction | In many data science applications, the objective is to extract appropriately-ordered smooth low-dimensional data patterns from high-dimensional data sets. This is challenging since common sorting algorithms are primarily aiming at finding monotonic orderings in low-dimensional data, whereas typical dimension reduction ... | ['Lukas Pospisil', 'Illia Horenko'] | 2023-06-17 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 2.77639866e-01 -8.11529234e-02 -1.31219149e-01 -3.21808726e-01
-6.03385389e-01 -4.22560394e-01 1.15806304e-01 3.29767793e-01
-4.51296240e-01 5.08514881e-01 -8.68053585e-02 -3.39278251e-01
-1.07925558e+00 -7.25598752e-01 -4.36911017e-01 -9.64279294e-01
-6.94340885e-01 6.42479956e-01 -2.73915589e-01 1.88146666... | [7.584362506866455, 4.297757625579834] |
04bc18cf-10c3-4f7b-aa19-389b4be5e772 | image-reconstruction-with-predictive-filter | 1811.11482 | null | http://arxiv.org/abs/1811.11482v1 | http://arxiv.org/pdf/1811.11482v1.pdf | Image Reconstruction with Predictive Filter Flow | We propose a simple, interpretable framework for solving a wide range of
image reconstruction problems such as denoising and deconvolution. Given a
corrupted input image, the model synthesizes a spatially varying linear filter
which, when applied to the input image, reconstructs the desired output. The
model parameters... | ['Shu Kong', 'Charless Fowlkes'] | 2018-11-28 | null | null | null | null | ['lossy-compression-artifact-reduction'] | ['computer-vision'] | [ 7.44181693e-01 2.62553804e-02 1.05384260e-01 -2.20258534e-01
-6.92169785e-01 -6.46454930e-01 4.34997380e-01 -7.48525321e-01
-1.22112922e-01 8.18229914e-01 6.80283964e-01 -2.01014563e-01
-6.59091398e-02 -2.02885777e-01 -8.17562938e-01 -6.67801142e-01
1.30030438e-01 -7.86637142e-02 -5.54364882e-02 1.57020420... | [11.582866668701172, -2.3903050422668457] |
84b64dae-814a-4020-9f1e-4f23b8e3dd09 | anglicized-words-and-misspelled-cognates-in | null | null | https://aclanthology.org/W19-4429 | https://aclanthology.org/W19-4429.pdf | Anglicized Words and Misspelled Cognates in Native Language Identification | In this paper, we present experiments that estimate the impact of specific lexical choices of people writing in a second language (L2). In particular, we look at misspelled words that indicate lexical uncertainty on the part of the author, and separate them into three categories: misspelled cognates, {``}L2-ed{''} (in ... | ['Carlo Strapparava', 'Vivi Nastase', 'Ilia Markov'] | 2019-08-01 | null | null | null | ws-2019-8 | ['native-language-identification'] | ['natural-language-processing'] | [-2.90697336e-01 2.74290126e-02 -3.36182803e-01 -2.78212070e-01
-8.06697786e-01 -1.15274346e+00 8.54053438e-01 3.17717433e-01
-6.30084634e-01 6.01452231e-01 5.08682013e-01 -7.38343358e-01
7.65112564e-02 -2.66550183e-01 -2.88612843e-01 -2.76867390e-01
7.40896702e-01 4.33721364e-01 -3.54980826e-02 -2.15773851... | [10.408025741577148, 10.388558387756348] |
efd3e24f-e651-4807-9f68-924c6f99a5f3 | quantifying-generalization-in-reinforcement | 1812.02341 | null | https://arxiv.org/abs/1812.02341v3 | https://arxiv.org/pdf/1812.02341v3.pdf | Quantifying Generalization in Reinforcement Learning | In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent's ability to generalize. We address this issue by usin... | ['Tae-hoon Kim', 'John Schulman', 'Karl Cobbe', 'Oleg Klimov', 'Chris Hesse'] | 2018-12-06 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.10886544e-01 6.38209358e-02 -2.47853827e-02 -3.27713966e-01
-5.68981469e-01 -8.39105129e-01 6.31571293e-01 1.15961647e-02
-9.52194512e-01 1.39921522e+00 -4.19292599e-02 -3.35441470e-01
5.34882024e-02 -8.84207547e-01 -8.26284766e-01 -6.04268551e-01
-1.85942650e-01 4.85337257e-01 -8.47246125e-03 -3.47894996... | [4.065098285675049, 1.7225666046142578] |
80b98daf-29a1-4cfb-8956-1e6101fedf5d | heuristics-based-mosaic-of-social-sensor | 2009.11663 | null | https://arxiv.org/abs/2009.11663v1 | https://arxiv.org/pdf/2009.11663v1.pdf | Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction | We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set... | ['Tooba Aamir', 'Hai Dong', 'Athman Bouguettaya'] | 2020-09-21 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 2.87256002e-01 -2.21321434e-01 2.68125385e-02 -4.99634027e-01
-7.95308948e-01 -5.26733816e-01 8.29406381e-01 2.09255457e-01
-1.75766498e-01 3.07351887e-01 2.45872006e-01 1.53837293e-01
-4.49513465e-01 -7.75392413e-01 -4.46487188e-01 -7.53540039e-01
9.89072304e-03 2.90192008e-01 3.05565536e-01 -2.10858732... | [7.9733734130859375, -1.5317692756652832] |
4cac64c6-3dd8-49f4-9b92-254fbc50a06e | granger-causality-based-hierarchical-time | 2104.04206 | null | https://arxiv.org/abs/2104.04206v1 | https://arxiv.org/pdf/2104.04206v1.pdf | Granger Causality Based Hierarchical Time Series Clustering for State Estimation | Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data. Complex dynamical systems in real life often entail data streaming from a large number of sources. Although it is desirable to use all source variables to form accurate state estimates, it is often imprac... | ['Soumik Sarkar', 'Gregor P. Henze', 'Margarite Jacoby', 'Homagni Saha', 'Sin Yong Tan'] | 2021-04-09 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [ 1.52719840e-01 -5.28874636e-01 -1.74012005e-01 -2.34169289e-01
-3.31623495e-01 -5.66398740e-01 3.81271690e-01 5.47076464e-01
-3.19734007e-01 7.41962314e-01 6.30917773e-02 -2.57503033e-01
-6.93652987e-01 -9.22138274e-01 -6.99647516e-02 -9.16823924e-01
-7.14277804e-01 6.13328636e-01 3.44048381e-01 -2.01902054... | [7.19122838973999, 3.3270130157470703] |
0c4eb3d3-3b1f-45d8-9218-d4c48e82555c | sample-size-in-arabic-authorship-verification | null | null | https://aclanthology.org/W19-7412 | https://aclanthology.org/W19-7412.pdf | Sample Size in Arabic Authorship Verification | null | ['Hossam Ahmed'] | 2019-09-01 | null | null | null | ws-2019-9 | ['authorship-verification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.344784736633301, 3.6997811794281006] |
23b40996-6a52-4ae9-a71a-86d7ea233298 | beyond-spectral-gap-the-role-of-the-topology | 2206.03093 | null | https://arxiv.org/abs/2206.03093v2 | https://arxiv.org/pdf/2206.03093v2.pdf | Beyond spectral gap: The role of the topology in decentralized learning | In data-parallel optimization of machine learning models, workers collaborate to improve their estimates of the model: more accurate gradients allow them to use larger learning rates and optimize faster. We consider the setting in which all workers sample from the same dataset, and communicate over a sparse graph (dece... | ['Martin Jaggi', 'Hadrien Hendrikx', 'Thijs Vogels'] | 2022-06-07 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-4.28085744e-01 3.87306660e-01 -1.49881050e-01 -2.64751643e-01
-4.43615079e-01 -4.38656986e-01 3.25719267e-01 5.41393340e-01
-5.85549176e-01 8.06197107e-01 1.08836465e-01 -2.79527962e-01
-4.92822558e-01 -6.55991733e-01 -1.02415395e+00 -7.32400715e-01
-5.96915185e-01 9.48121369e-01 -3.00976008e-01 3.80514562... | [6.400151252746582, 5.206604480743408] |
f90ea398-2b38-403b-a4bc-f240a9507e3d | opt-open-pre-trained-transformer-language | 2205.01068 | null | https://arxiv.org/abs/2205.01068v4 | https://arxiv.org/pdf/2205.01068v4.pdf | OPT: Open Pre-trained Transformer Language Models | Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is gra... | ['Luke Zettlemoyer', 'Tianlu Wang', 'Anjali Sridhar', 'Punit Singh Koura', 'Daniel Simig', 'Kurt Shuster', 'Sam Shleifer', 'Myle Ott', 'Todor Mihaylov', 'Xi Victoria Lin', 'Xian Li', 'Mona Diab', 'Christopher Dewan', 'Shuohui Chen', 'Moya Chen', 'Mikel Artetxe', 'Naman Goyal', 'Stephen Roller', 'Susan Zhang'] | 2022-05-02 | null | null | null | null | ['stereotypical-bias-analysis'] | ['natural-language-processing'] | [ 1.22392094e-02 1.22380987e-01 -2.22157866e-01 9.29794312e-02
-9.90349531e-01 -6.74740076e-01 7.62540400e-01 -2.39872724e-01
-3.00217837e-01 8.19722772e-01 1.64485327e-03 -8.75969112e-01
9.47590992e-02 -7.05010474e-01 -8.57592702e-01 -4.03906882e-01
-1.84459001e-01 6.65043294e-01 2.56826341e-01 -1.84383169... | [10.541096687316895, 8.18695068359375] |
e46f0a44-d437-4992-8538-e1f878a75672 | transcription-is-all-you-need-learning-to | 2010.11904 | null | https://arxiv.org/abs/2010.11904v1 | https://arxiv.org/pdf/2010.11904v1.pdf | Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision | Most music source separation systems require large collections of isolated sources for training, which can be difficult to obtain. In this work, we use musical scores, which are comparatively easy to obtain, as a weak label for training a source separation system. In contrast with previous score-informed separation app... | ['Jonathan Le Roux', 'Gordon Wichern', 'Yun-Ning Hung'] | 2020-10-22 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 3.95245492e-01 -9.15841237e-02 -1.55406699e-01 -2.80868173e-01
-1.22411156e+00 -1.11419308e+00 4.28638875e-01 -5.57342879e-02
-1.58669248e-01 6.04084551e-01 1.10223942e-01 6.46487484e-03
-1.26503155e-01 -2.77708471e-01 -6.91068172e-01 -1.00758100e+00
-1.15399472e-01 2.30466381e-01 1.68103516e-01 -3.19740996... | [15.458955764770508, 5.557227611541748] |
a614b6c4-a0f0-4d42-bc52-ab4d31a34e0e | augmentednet-a-roman-numeral-analysis-network | null | null | https://doi.org/10.5281/zenodo.5624533 | https://archives.ismir.net/ismir2021/paper/000050.pdf | AugmentedNet: A Roman Numeral Analysis Network with Synthetic Training Examples and Additional Tonal Tasks | AugmentedNet is a new convolutional recurrent neural network for predicting Roman numeral labels. The network architecture is characterized by a separate convolutional block for bass and chromagram inputs. This layout is further enhanced by using synthetic training examples for data augmentation, and a greater number o... | ['Ichiro Fujinaga', 'Mark Gotham', 'Nestor Napoles Lopez'] | 2021-11-07 | null | null | null | ismir-2021-11 | ['chord-recognition'] | ['audio'] | [ 1.79334760e-01 5.47673777e-02 -1.16889909e-01 -9.10324305e-02
-8.68893981e-01 -3.60755950e-01 6.08000100e-01 -5.47104001e-01
-3.37042481e-01 8.02914381e-01 4.53048736e-01 -7.99494088e-02
-1.95999473e-01 -7.06952572e-01 -6.80253565e-01 -6.12074196e-01
4.98905079e-03 6.05237305e-01 -3.25874388e-01 -9.13169026... | [15.829815864562988, 5.2507429122924805] |
d2394bdc-0828-42ac-a0ff-daeb0ffce7cc | high-perceptual-quality-jpeg-decoding-via | 2211.11827 | null | https://arxiv.org/abs/2211.11827v1 | https://arxiv.org/pdf/2211.11827v1.pdf | High-Perceptual Quality JPEG Decoding via Posterior Sampling | JPEG is arguably the most popular image coding format, achieving high compression ratios via lossy quantization that may create visual artifacts degradation. Numerous attempts to remove these artifacts were conceived over the years, and common to most of these is the use of deterministic post-processing algorithms that... | ['Michael Elad', 'Theo Adrai', 'Guy Ohayon', 'Sean Man'] | 2022-11-21 | null | null | null | null | ['jpeg-artifact-correction'] | ['computer-vision'] | [ 7.14825928e-01 1.38397776e-02 1.14518449e-01 -2.87094831e-01
-8.77863884e-01 -2.47920245e-01 4.75540429e-01 -1.39802266e-02
-3.26901108e-01 7.82630920e-01 3.64902765e-01 8.10248032e-02
-1.43104374e-01 -6.88661337e-01 -9.02292430e-01 -7.67554998e-01
-2.95644328e-02 9.05314907e-02 1.88828602e-01 -7.04860911... | [11.4775972366333, -1.893999457359314] |
9fa2ffd6-3fed-4f6a-b362-04f0f04a70a5 | ai-playground-unreal-engine-based-data | 2007.06153 | null | https://arxiv.org/abs/2007.06153v1 | https://arxiv.org/pdf/2007.06153v1.pdf | AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning | Machine learning requires data, but acquiring and labeling real-world data is challenging, expensive, and time-consuming. More importantly, it is nearly impossible to alter real data post-acquisition (e.g., change the illumination of a room), making it very difficult to measure how specific properties of the data affec... | ['Aashis Khanal', 'Mehdi Mousavi', 'Rolando Estrada'] | 2020-07-13 | null | null | null | null | ['data-ablation', 'indoor-monocular-depth-estimation', 'surface-normals-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.76541999e-01 -1.58816919e-01 2.62677938e-01 -5.39020181e-01
-4.95073736e-01 -9.32075679e-01 5.18628716e-01 1.40681997e-01
-5.34324765e-01 5.67419767e-01 -1.74104437e-01 -2.46166557e-01
1.07102461e-01 -1.00637555e+00 -1.04238105e+00 -5.36635935e-01
1.67254493e-01 3.95838559e-01 5.13688564e-01 1.11170702... | [9.169790267944336, -2.381455183029175] |
88b8c4a1-2f53-4792-9fdc-cafd0047ce56 | active-neural-localization | 1801.08214 | null | http://arxiv.org/abs/1801.08214v1 | http://arxiv.org/pdf/1801.08214v1.pdf | Active Neural Localization | Localization is the problem of estimating the location of an autonomous agent
from an observation and a map of the environment. Traditional methods of
localization, which filter the belief based on the observations, are
sub-optimal in the number of steps required, as they do not decide the actions
taken by the agent. W... | ['Emilio Parisotto', 'Ruslan Salakhutdinov', 'Devendra Singh Chaplot'] | 2018-01-24 | active-neural-localization-1 | https://openreview.net/forum?id=ry6-G_66b | https://openreview.net/pdf?id=ry6-G_66b | iclr-2018-1 | ['game-of-doom', 'fps-games'] | ['playing-games', 'playing-games'] | [-2.56068438e-01 2.23707259e-02 2.18997523e-01 -3.10806662e-01
-7.72342682e-01 -6.09921873e-01 4.58040595e-01 -1.07650291e-02
-1.00172520e+00 7.76600480e-01 -4.78930920e-02 -1.31109357e-01
-1.12599336e-01 -8.28521192e-01 -1.01645017e+00 -7.93478906e-01
-6.27862573e-01 3.58749628e-01 4.88499045e-01 -1.36220440... | [4.6164231300354, 0.7386215925216675] |
a369a9b0-22e4-4b59-8dd9-3b69527225f8 | comprehensive-evaluation-of-deep-and-graph | 2306.05257 | null | https://arxiv.org/abs/2306.05257v1 | https://arxiv.org/pdf/2306.05257v1.pdf | Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction | Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which ... | ['Xiangxiang Zeng', 'Philip S. Yu', 'Bosheng Song', 'Haowen Chen', 'Li Zeng', 'Dong-Sheng Cao', 'Jian-Yu Shi', 'Wen Zhang', 'Zu-Guo Yu', 'Yafang Zhou', 'Lichang Dai', 'Xuan Lin'] | 2023-06-08 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 2.41264239e-01 -1.94733769e-01 -6.02495790e-01 -6.16092607e-02
5.13392352e-02 -4.89375204e-01 3.21651191e-01 8.36543322e-01
1.73085555e-02 1.04703927e+00 -9.67375860e-02 -7.18682826e-01
-3.99286330e-01 -8.12479198e-01 -5.66746652e-01 -9.36908185e-01
-6.68846667e-01 8.94814134e-01 -2.18255281e-01 -2.96741784... | [5.2273077964782715, 5.848116397857666] |
c211e84b-d4cd-4bf4-b1c2-98e097382346 | pic-xai-post-hoc-image-captioning-explanation | null | null | https://ieeexplore.ieee.org/abstract/document/10158563 | https://ieeexplore.ieee.org/abstract/document/10158563 | PIC-XAI: Post-hoc Image Captioning Explanation using Segmentation | The rapid advancement in Deep Learning (DL) proposes viable solutions to various real-world problems. However, deploying DL-based models in some applications is hindered by their black-box nature and the inability to explain them. This has pushed Explainable Artificial Intelligence (XAI) research toward DL-based models... | ['Gábor Szűcs', 'Modafar Al-Shouha'] | 2023-05-23 | null | null | null | ieee-17th-international-symposium-on-applied | ['explainable-artificial-intelligence', 'image-captioning'] | ['computer-vision', 'computer-vision'] | [ 2.20702752e-01 6.43855929e-01 -2.28879988e-01 -5.41363895e-01
-7.26298094e-02 -3.19950461e-01 8.15896213e-01 -1.25480726e-01
5.21174341e-04 9.94333804e-01 1.80640996e-01 -3.73227149e-01
-3.17414582e-01 -4.00895327e-01 -1.00958669e+00 -4.71198112e-01
8.86545852e-02 7.21256435e-01 -1.59355640e-01 1.24959797... | [8.99783992767334, 5.479629039764404] |
66db4836-ccdf-49ea-8fd6-86f53a4ec117 | cltr-an-end-to-end-transformer-based-system-1 | null | null | https://aclanthology.org/2021.acl-demo.24 | https://aclanthology.org/2021.acl-demo.24.pdf | CLTR: An End-to-End, Transformer-Based System for Cell-Level Table Retrieval and Table Question Answering | We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables and locate the correct table cells to answer the question. Our system, CLTR, extends the current state-of-the-art QA over... | ['Peter Fox', 'Alfio Gliozzo', 'Michael Glass', 'Mustafa Canim', 'Feifei Pan'] | 2021-08-01 | null | null | null | acl-2021-5 | ['table-retrieval'] | ['natural-language-processing'] | [-1.76408350e-01 3.60275596e-01 6.67122705e-03 -4.50863481e-01
-2.29813123e+00 -1.16112721e+00 3.80162090e-01 6.50710821e-01
7.59094860e-03 8.59209776e-01 5.80022395e-01 -5.85447431e-01
-1.75277978e-01 -1.23425317e+00 -8.79523456e-01 1.94866419e-01
3.56654316e-01 1.73003018e+00 6.21445119e-01 -1.02932918... | [10.31632137298584, 7.836194038391113] |
8c662934-1f82-45e9-8f77-e71224c9d5a5 | long-term-leap-attention-short-term-periodic | 2207.05526 | null | https://arxiv.org/abs/2207.05526v2 | https://arxiv.org/pdf/2207.05526v2.pdf | Long-term Leap Attention, Short-term Periodic Shift for Video Classification | Video transformer naturally incurs a heavier computation burden than a static vision transformer, as the former processes $T$ times longer sequence than the latter under the current attention of quadratic complexity $(T^2N^2)$. The existing works treat the temporal axis as a simple extension of spatial axes, focusing o... | ['Chong-Wah Ngo', 'Yanbin Hao', 'Lechao Cheng', 'Hao Zhang'] | 2022-07-12 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [ 1.72024518e-01 -1.13825329e-01 8.53252336e-02 -5.19203916e-02
-6.69364691e-01 -6.11953795e-01 1.32582262e-01 -3.72762412e-01
-7.32395768e-01 5.13029933e-01 -1.49362162e-01 -4.75485682e-01
-3.95002142e-02 -7.32506990e-01 -1.05386901e+00 -7.68373609e-01
-2.15960041e-01 -2.66377032e-01 6.52832806e-01 -1.25210479... | [9.232989311218262, 0.02330116555094719] |
de3035c2-ec35-4e08-a171-066c80cac9d1 | multi-pooled-inception-features-for-no | 2011.05139 | null | https://arxiv.org/abs/2011.05139v1 | https://arxiv.org/pdf/2011.05139v1.pdf | Multi-pooled Inception features for no-reference image quality assessment | Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attach... | ['Domonkos Varga'] | 2020-11-10 | null | null | null | null | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 3.18910033e-01 -3.78286690e-01 1.69777498e-01 -4.06407326e-01
-7.68228173e-01 -3.63890052e-01 5.00131428e-01 2.34614432e-01
-7.67033160e-01 3.35165411e-01 -2.57924616e-01 -1.64422989e-01
-2.11229369e-01 -1.15903533e+00 -8.60332370e-01 -5.55522442e-01
-2.40472049e-01 -1.98237330e-01 4.44732785e-01 -2.93650180... | [11.748741149902344, -1.7810512781143188] |
85628063-f9c8-4fd2-a222-cb0afb23d54f | iterative-residual-refinement-for-joint | 1904.0529 | null | http://arxiv.org/abs/1904.05290v1 | http://arxiv.org/pdf/1904.05290v1.pdf | Iterative Residual Refinement for Joint Optical Flow and Occlusion Estimation | Deep learning approaches to optical flow estimation have seen rapid progress
over the recent years. One common trait of many networks is that they refine an
initial flow estimate either through multiple stages or across the levels of a
coarse-to-fine representation. While leading to more accurate results, the
downside ... | ['Junhwa Hur', 'Stefan Roth'] | 2019-04-10 | iterative-residual-refinement-for-joint-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Hur_Iterative_Residual_Refinement_for_Joint_Optical_Flow_and_Occlusion_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Hur_Iterative_Residual_Refinement_for_Joint_Optical_Flow_and_Occlusion_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['occlusion-estimation'] | ['computer-vision'] | [-1.01986438e-01 -2.91852683e-01 -2.09965810e-01 -3.32077742e-01
-3.48328650e-01 -4.13717508e-01 4.17676538e-01 -1.58047870e-01
-3.19503099e-01 8.67564023e-01 5.94284296e-01 6.35023266e-02
-3.39283608e-03 -7.39278197e-01 -4.08765793e-01 -3.98068935e-01
-1.49265109e-02 1.31584227e-01 5.38447261e-01 -1.41671821... | [8.81418514251709, -1.7429338693618774] |
72d166c2-c8fb-46f2-a8d6-96aea1485add | initial-explorations-of-ccg-supertagging-for | null | null | https://aclanthology.org/K17-3023 | https://aclanthology.org/K17-3023.pdf | Initial Explorations of CCG Supertagging for Universal Dependency Parsing | In this paper we describe the system by METU team for universal dependency parsing of multilingual text. We use a neural network-based dependency parser that has a greedy transition approach to dependency parsing. CCG supertags contain rich structural information that proves useful in certain NLP tasks. We experiment w... | ['Ruket Cakici', 'Heval Azizoglu', 'Burak Kerim Akkus'] | 2017-08-01 | null | null | null | conll-2017-8 | ['ccg-supertagging'] | ['natural-language-processing'] | [-6.95381880e-01 4.42746937e-01 -3.11816394e-01 -9.83142436e-01
-7.05910265e-01 -6.98068500e-01 3.95067990e-01 3.77423465e-01
-6.99692488e-01 1.13615811e+00 8.32329214e-01 -8.59146535e-01
3.64921033e-01 -6.45152450e-01 -3.77179474e-01 -3.70111525e-01
-7.28041291e-01 6.61557257e-01 2.87943929e-01 -7.72987902... | [10.355940818786621, 9.841753959655762] |
30637dcd-36b2-4d34-b0e3-c4087236aab6 | lit-former-linking-in-plane-and-through-plane | 2302.1063 | null | https://arxiv.org/abs/2302.10630v1 | https://arxiv.org/pdf/2302.10630v1.pdf | LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurring | This paper studies 3D low-dose computed tomography (CT) imaging. Although various deep learning methods were developed in this context, typically they perform denoising due to low-dose and deblurring for super-resolution separately. Up to date, little work was done for simultaneous in-plane denoising and through-plane ... | ['Hongming Shan', 'Ge Wang', 'Chuang Niu', 'Zhihao Chen'] | 2023-02-21 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [-1.72067985e-01 2.08419621e-01 2.36996114e-01 -3.84899080e-01
-1.05823052e+00 -8.43877643e-02 2.31761932e-01 -2.87486672e-01
-3.61244053e-01 3.73366356e-01 5.81529021e-01 -1.81493908e-01
-2.26375714e-01 -7.22906351e-01 -6.51474357e-01 -1.01287639e+00
-5.03865331e-02 3.78406674e-01 3.69171590e-01 -2.78746709... | [13.550154685974121, -2.485015869140625] |
1196a48f-73a9-4d78-92a8-2b0f74e7b568 | lordbert-embedding-long-text-by-segment | null | null | https://openreview.net/forum?id=b-064TCPoyB | https://openreview.net/pdf?id=b-064TCPoyB | LordBERT: Embedding Long Text by Segment Ordering with BERT | Although BERT has achieved significant improvements on many downstream NLP tasks, it has difficulty handling long text because of its quadratic computation complexity. A typical approach to this issue is splitting the input into shorter segments and utilizing order-independent attention mechanism to conduct inter-segme... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['implicit-relations'] | ['natural-language-processing'] | [ 1.56460375e-01 3.78528982e-01 -7.08016694e-01 -6.65234923e-01
-9.72909510e-01 -5.68008542e-01 3.64333421e-01 4.78280872e-01
-3.69966567e-01 5.68353534e-01 4.06932056e-01 -5.10734677e-01
-5.41994534e-02 -7.85861671e-01 -8.22118759e-01 -4.98152554e-01
3.36106360e-01 1.03578246e+00 3.67747784e-01 -1.84521779... | [10.94691276550293, 8.547266960144043] |
bf3d8a1c-5584-4cbc-a6eb-f9ccfd55e5ae | denoising-based-image-reconstruction-from | 2205.11202 | null | https://arxiv.org/abs/2205.11202v1 | https://arxiv.org/pdf/2205.11202v1.pdf | Denoising-based image reconstruction from pixels located at non-integer positions | Digital images are commonly represented as regular 2D arrays, so pixels are organized in form of a matrix addressed by integers. However, there are many image processing operations, such as rotation or motion compensation, that produce pixels at non-integer positions. Typically, image reconstruction techniques cannot h... | ['André Kaup', 'Jürgen Seiler', 'Ján Koloda'] | 2022-05-23 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 6.62406683e-01 -2.21881300e-01 1.89009383e-01 -1.77580535e-01
-4.87260848e-01 -2.91632801e-01 3.49702597e-01 2.03874782e-01
-5.22145212e-01 7.68601477e-01 1.33729994e-01 -7.73772225e-02
1.89268798e-01 -7.85666943e-01 -6.43574178e-01 -8.16020191e-01
-1.94598921e-02 -2.65625954e-01 1.78917959e-01 -6.56741187... | [11.208309173583984, -2.359672784805298] |
6fdddc82-5199-4f22-9b42-030ec7cfa8b3 | towards-understanding-pixel-vulnerability | 2010.06131 | null | https://arxiv.org/abs/2010.06131v2 | https://arxiv.org/pdf/2010.06131v2.pdf | Learning to Attack with Fewer Pixels: A Probabilistic Post-hoc Framework for Refining Arbitrary Dense Adversarial Attacks | Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Many adversarial attacks belong to the category of dense attacks, which generate adversarial examples by perturbing all the pixels of a natural image. ... | ['Dinh Phung', 'Tamas Abraham', 'Olivier De Vel', 'Paul Montague', 'Thanh Nguyen', 'Trung Le', 'He Zhao'] | 2020-10-13 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 7.09339917e-01 4.75191087e-01 2.85715431e-01 -4.98472117e-02
-5.63825727e-01 -9.11296189e-01 1.00447309e+00 -9.33327079e-02
-4.08834010e-01 7.24925637e-01 1.99285656e-01 -1.30135454e-02
1.16137397e-02 -1.03312552e+00 -1.07115936e+00 -1.07243121e+00
6.82848180e-03 1.05137371e-01 8.95939246e-02 -4.17494118... | [5.58610725402832, 7.912164688110352] |
3c0f54e1-3038-4942-88eb-1d1b05df0f4c | montage-based-3d-medical-image-retrieval-from | 1812.04118 | null | http://arxiv.org/abs/1812.04118v1 | http://arxiv.org/pdf/1812.04118v1.pdf | Montage based 3D Medical Image Retrieval from Traumatic Brain Injury Cohort using Deep Convolutional Neural Network | Brain imaging analysis on clinically acquired computed tomography (CT) is
essential for the diagnosis, risk prediction of progression, and treatment of
the structural phenotypes of traumatic brain injury (TBI). However, in real
clinical imaging scenarios, entire body CT images (e.g., neck, abdomen, chest,
pelvis) are t... | ['Cailey I. Kerley', 'Yuankai Huo', 'Shikha Chaganti', 'Mayur B. Patel', 'Shunxing Bao', 'Bennett A. Landman'] | 2018-12-10 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 1.85406983e-01 -2.67102718e-01 4.73087355e-02 -2.25017205e-01
-1.03505862e+00 -3.65814596e-01 2.71603286e-01 5.26784539e-01
-8.59201610e-01 5.39521396e-01 -4.97728074e-03 -1.74166277e-01
-2.97521263e-01 -8.41303110e-01 -4.16167200e-01 -6.83872283e-01
-1.49289727e-01 9.47091043e-01 1.91354692e-01 2.68113345... | [14.521520614624023, -2.0650343894958496] |
87945a34-aeae-4cd6-9bfb-3648a0f70b80 | apollo-a-simple-approach-for-adaptive | 2212.09282 | null | https://arxiv.org/abs/2212.09282v2 | https://arxiv.org/pdf/2212.09282v2.pdf | APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning | Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions. Prior works on improving the logical reasoning ability of language models require complex processing of training data (e.g.... | ['Xiang Ren', 'Chenguang Zhu', 'Wenhao Yu', 'Reid Pryzant', 'ZiYi Yang', 'Shuohang Wang', 'Yichong Xu', 'Soumya Sanyal'] | 2022-12-19 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 0.15934381 0.58999836 -0.1497784 -0.6457775 -0.4813157 -0.6592972
0.68285567 0.5053576 -0.62176114 0.56649154 0.25411007 -0.8752408
-0.12570207 -1.1020671 -1.2409528 0.016948 0.02220781 0.53842455
0.16830592 -0.43841046 0.23630533 0.24763513 -1.2229255 0.9581489
1.1689507 0.80378616 0.178... | [9.658500671386719, 7.485533237457275] |
48311cfc-c19a-4dd8-b7dc-03870781258f | membership-inference-attacks-on-lottery | 2108.03506 | null | https://arxiv.org/abs/2108.03506v1 | https://arxiv.org/pdf/2108.03506v1.pdf | Membership Inference Attacks on Lottery Ticket Networks | The vulnerability of the Lottery Ticket Hypothesis has not been studied from the purview of Membership Inference Attacks. Through this work, we are the first to empirically show that the lottery ticket networks are equally vulnerable to membership inference attacks. A Membership Inference Attack (MIA) is the process of... | ['Amol Deshpande', 'Shruti Bidwalka', 'Shishira R Maiya', 'Aadesh Bagmar'] | 2021-08-07 | null | https://openreview.net/forum?id=4lyXal2ZWB3 | https://openreview.net/pdf?id=4lyXal2ZWB3 | icml-workshop-aml-2021-7 | ['membership-inference-attack'] | ['computer-vision'] | [ 2.03057796e-01 2.20839426e-01 -1.87143892e-01 -3.99157196e-01
-2.55773932e-01 -7.38927186e-01 5.97526908e-01 1.33038789e-01
-4.86931801e-01 7.67776251e-01 -4.63761598e-01 -6.73957229e-01
-1.18618555e-01 -1.14728606e+00 -1.12745702e+00 -6.39036655e-01
-3.98926705e-01 6.93360329e-01 3.83230001e-01 1.14544094... | [5.863270282745361, 7.41719913482666] |
c3c3732a-fcff-4dd6-bbdb-795414bae2c9 | rethinking-image-super-resolution-from-long | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Gou_Rethinking_Image_Super_Resolution_From_Long-Tailed_Distribution_Learning_Perspective_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Gou_Rethinking_Image_Super_Resolution_From_Long-Tailed_Distribution_Learning_Perspective_CVPR_2023_paper.pdf | Rethinking Image Super Resolution From Long-Tailed Distribution Learning Perspective | Existing studies have empirically observed that the resolution of the low-frequency region is easier to enhance than that of the high-frequency one. Although plentiful works have been devoted to alleviating this problem, little understanding is given to explain it. In this paper, we try to give a feasible answer fr... | ['Xi Peng', 'Hongyuan Zhu', 'Jiancheng Lv', 'Peng Hu', 'Yuanbiao Gou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 2.90921450e-01 -1.04433171e-01 -2.81394452e-01 -4.08109725e-01
-6.96896911e-01 6.33618683e-02 3.87341291e-01 -3.81346047e-01
-1.93742767e-01 7.26080239e-01 3.59779656e-01 1.78604275e-01
-3.44887406e-01 -7.39880800e-01 -6.39098108e-01 -1.09457147e+00
3.27075154e-01 -8.38888809e-02 5.95978379e-01 -1.77892298... | [10.993551254272461, -2.1462206840515137] |
a0fc541d-80f6-4b1a-a930-824eedd21a63 | dialogue-response-selection-with-hierarchical | 2012.14756 | null | https://arxiv.org/abs/2012.14756v3 | https://arxiv.org/pdf/2012.14756v3.pdf | Dialogue Response Selection with Hierarchical Curriculum Learning | We study the learning of a matching model for dialogue response selection. Motivated by the recent finding that models trained with random negative samples are not ideal in real-world scenarios, we propose a hierarchical curriculum learning framework that trains the matching model in an "easy-to-difficult" scheme. Our ... | ['Yan Wang', 'Nigel Collier', 'Shuming Shi', 'Yunbo Cao', 'Simon Baker', 'Zibo Lin', 'Qingyu Zhou', 'Deng Cai', 'Yixuan Su'] | 2020-12-29 | null | https://aclanthology.org/2021.acl-long.137 | https://aclanthology.org/2021.acl-long.137.pdf | acl-2021-5 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 4.74661946e-01 1.37869492e-01 -2.66368747e-01 -5.71523249e-01
-1.20368576e+00 -4.82415974e-01 7.88395643e-01 5.17384410e-01
-4.58488822e-01 5.10719240e-01 2.68648446e-01 -4.52961564e-01
1.71599671e-01 -7.42404938e-01 -2.56939769e-01 -3.30221564e-01
3.98099214e-01 8.18162024e-01 6.60183549e-01 -8.26855421... | [12.511350631713867, 7.900093078613281] |
8969b938-736a-429e-ba63-09748097fd45 | understanding-uncertainty-sampling | 2307.02719 | null | https://arxiv.org/abs/2307.02719v1 | https://arxiv.org/pdf/2307.02719v1.pdf | Understanding Uncertainty Sampling | Uncertainty sampling is a prevalent active learning algorithm that queries sequentially the annotations of data samples which the current prediction model is uncertain about. However, the usage of uncertainty sampling has been largely heuristic: (i) There is no consensus on the proper definition of "uncertainty" for a ... | ['Xiaocheng Li', 'Shang Liu'] | 2023-07-06 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 2.17435688e-01 3.69689047e-01 -3.05166066e-01 -6.06647670e-01
-1.06605649e+00 -4.43739325e-01 4.73201424e-01 6.34953856e-01
-5.88039160e-01 1.13926792e+00 -1.84354812e-01 -1.02263153e-01
-6.22293532e-01 -1.13630867e+00 -8.40826988e-01 -6.80977523e-01
-1.47988662e-01 4.56119150e-01 7.61832371e-02 8.02773535... | [7.146929740905762, 4.062348365783691] |
418d0b32-9cee-46da-9940-ef99831d7cef | online-adaptation-through-meta-learning-for | 1904.08462 | null | http://arxiv.org/abs/1904.08462v1 | http://arxiv.org/pdf/1904.08462v1.pdf | Online Adaptation through Meta-Learning for Stereo Depth Estimation | In this work, we tackle the problem of online adaptation for stereo depth
estimation, that consists in continuously adapting a deep network to a target
video recordedin an environment different from that of the source training set.
To address this problem, we propose a novel Online Meta-Learning model with
Adaption (OM... | ['Zhen-Yu Zhang', 'Stéphane Lathuilière', 'Jian Yang', 'Andrea Pilzer', 'Elisa Ricci', 'Nicu Sebe'] | 2019-04-17 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.48565570e-01 1.76839251e-03 -6.95056021e-02 -4.89246935e-01
-6.11009598e-01 -3.28719199e-01 7.64018178e-01 -3.30765657e-02
-8.72643948e-01 5.67474484e-01 8.63923877e-02 2.64074355e-01
4.54365686e-02 -6.24895751e-01 -1.03286183e+00 -7.04455495e-01
1.72093213e-01 4.33854222e-01 4.94945735e-01 -1.15091801... | [8.672350883483887, -2.3266947269439697] |
3782594c-16f7-4020-ae37-60a0ffaabc55 | cross-document-coreference-resolution-over | 2106.0121 | null | https://arxiv.org/abs/2106.01210v1 | https://arxiv.org/pdf/2106.01210v1.pdf | Cross-document Coreference Resolution over Predicted Mentions | Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold... | ['Ido Dagan', 'Mandar Joshi', 'Gabriel Stanovsky', 'Alon Eirew', 'Arie Cattan'] | 2021-06-02 | null | https://aclanthology.org/2021.findings-acl.453 | https://aclanthology.org/2021.findings-acl.453.pdf | findings-acl-2021-8 | ['cross-document-coreference-resolution'] | ['natural-language-processing'] | [ 1.70214057e-01 6.84615493e-01 -5.76696694e-01 -2.92912126e-01
-1.64539695e+00 -1.01581120e+00 9.64911282e-01 2.26121083e-01
-5.71419537e-01 9.38984871e-01 1.05791914e+00 -3.73056643e-02
-4.01888013e-01 -2.50624806e-01 -4.59269106e-01 -2.34214827e-01
1.03216186e-01 1.43750012e+00 3.33466619e-01 -4.44605500... | [9.306904792785645, 9.555166244506836] |
977f36f3-b301-4813-b823-d46617d6b38d | volumetric-lung-nodule-segmentation-using | 1912.13335 | null | https://arxiv.org/abs/1912.13335v2 | https://arxiv.org/pdf/1912.13335v2.pdf | Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning | Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, which can enhance patient survival possibilities. A number of nodule segmentation techniques have been proposed, however, all of the existing techniques rely on radiologist 3-D volume of interest (VOI) input or use the c... | ['Byung-ilLee', 'Sung Hyun Kim', 'Byoung-Dai Lee', 'Muhammad Usman', 'Shi Sub Byon'] | 2019-12-31 | null | null | null | null | ['lung-nodule-segmentation'] | ['medical'] | [ 2.73633510e-01 2.79191136e-01 -9.05742049e-02 -3.55145074e-02
-4.21814978e-01 -2.82028019e-01 3.01768273e-01 -1.11299209e-01
-4.04727757e-01 4.48128104e-01 -1.71679556e-01 -3.89853716e-01
-2.93800890e-01 -7.84121096e-01 -2.10895360e-01 -7.48296797e-01
2.31919974e-01 7.37209201e-01 9.26508248e-01 2.23089144... | [15.309381484985352, -2.1599204540252686] |
1635af0d-7b27-4077-a9bd-9c09097cab04 | unsupervised-domain-adaptation-for-cardiac | 2204.09334 | null | https://arxiv.org/abs/2204.09334v3 | https://arxiv.org/pdf/2204.09334v3.pdf | Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization | Unsupervised domain adaptation approaches have recently succeeded in various medical image segmentation tasks. The reported works often tackle the domain shift problem by aligning the domain-invariant features and minimizing the domain-specific discrepancies. That strategy works well when the difference between a speci... | ['Gaurav Gupta', 'Shen Zheng', 'Changjie Lu'] | 2022-04-20 | null | null | null | null | ['cardiac-segmentation', 'mutual-information-estimation'] | ['medical', 'methodology'] | [ 2.17094392e-01 -1.57179050e-02 -3.06291401e-01 -5.71052313e-01
-1.26204598e+00 -3.75228405e-01 1.80351406e-01 -4.91144806e-02
-3.65173459e-01 7.32004941e-01 2.25610226e-01 1.32977039e-01
-2.55605608e-01 -3.93149287e-01 -4.01218474e-01 -9.49278235e-01
2.47000307e-01 5.94717860e-01 2.76401609e-01 2.28877947... | [14.515846252441406, -2.0306899547576904] |
3e5ad759-2565-43e9-8bed-02ca4a2eb975 | a-weakly-supervised-method-for-instance | 1908.09891 | null | https://arxiv.org/abs/1908.09891v1 | https://arxiv.org/pdf/1908.09891v1.pdf | A Weakly Supervised Method for Instance Segmentation of Biological Cells | We present a weakly supervised deep learning method to perform instance segmentation of cells present in microscopy images. Annotation of biomedical images in the lab can be scarce, incomplete, and inaccurate. This is of concern when supervised learning is used for image analysis as the discriminative power of a learni... | ['Pedro D. Marrero Fernandez', 'Fidel A. Guerrero-Peña', 'Tsang Ing Ren', 'Alexandre Cunha'] | 2019-08-26 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 5.54652214e-01 2.65148461e-01 1.11462928e-01 -2.93401152e-01
-4.94079858e-01 -5.60212314e-01 2.52350688e-01 7.70733654e-01
-9.61566508e-01 1.09256232e+00 -2.50495136e-01 -8.57302994e-02
-1.41941667e-01 -5.59225738e-01 -7.63451636e-01 -1.27744555e+00
-2.20854161e-03 6.93298340e-01 6.19741321e-01 4.11145501... | [14.598539352416992, -3.1119754314422607] |
d081d659-75c4-4222-b4dd-7161e0f2b961 | reenactnet-real-time-full-head-reenactment | 2006.105 | null | https://arxiv.org/abs/2006.10500v1 | https://arxiv.org/pdf/2006.10500v1.pdf | ReenactNet: Real-time Full Head Reenactment | Video-to-video synthesis is a challenging problem aiming at learning a translation function between a sequence of semantic maps and a photo-realistic video depicting the characteristics of a driving video. We propose a head-to-head system of our own implementation capable of fully transferring the human head 3D pose, f... | ['Anastasios Roussos', 'Mohammad Rami Koujan', 'Michail Christos Doukas', 'Stefanos Zafeiriou'] | 2020-05-22 | null | null | null | null | ['video-to-video-synthesis'] | ['computer-vision'] | [ 1.81827918e-01 3.44893813e-01 3.04626048e-01 -4.55992579e-01
-4.77103859e-01 -3.59582812e-01 6.41401947e-01 -3.60817313e-01
-2.71754116e-01 4.78820860e-01 1.75754055e-02 1.70863122e-01
5.26923716e-01 -2.78871596e-01 -9.36861753e-01 -5.18373072e-01
6.34534806e-02 3.29393774e-01 2.63778389e-01 -1.49983624... | [13.045905113220215, -0.4107978940010071] |
33b58ba4-d6fe-432c-b40e-ac71d0c83be4 | multi-level-multimodal-common-semantic-space | 1811.11683 | null | https://arxiv.org/abs/1811.11683v2 | https://arxiv.org/pdf/1811.11683v2.pdf | Multi-level Multimodal Common Semantic Space for Image-Phrase Grounding | We address the problem of phrase grounding by lear ing a multi-level common semantic space shared by the textual and visual modalities. We exploit multiple levels of feature maps of a Deep Convolutional Neural Network, as well as contextualized word and sentence embeddings extracted from a character-based language mode... | ['Shih-Fu Chang', 'Brian Chen', 'Svebor Karaman', 'Hassan Akbari', 'Carl Vondrick', 'Surabhi Bhargava'] | 2018-11-28 | multi-level-multimodal-common-semantic-space-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Akbari_Multi-Level_Multimodal_Common_Semantic_Space_for_Image-Phrase_Grounding_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Akbari_Multi-Level_Multimodal_Common_Semantic_Space_for_Image-Phrase_Grounding_CVPR_2019_paper.pdf | cvpr-2019-6 | ['phrase-grounding'] | ['natural-language-processing'] | [ 2.46072456e-01 -9.33331028e-02 -2.04863116e-01 -1.77840486e-01
-1.17286682e+00 -7.44667053e-01 7.00361609e-01 4.56587911e-01
-6.50833964e-01 3.38734031e-01 4.60206330e-01 -2.78889909e-02
1.35477796e-01 -5.78161001e-01 -8.65859032e-01 -5.18273532e-01
7.51603544e-02 1.39888972e-01 4.39415090e-02 -1.54807046... | [10.58759593963623, 1.6189011335372925] |
11306c66-27be-46eb-9d4c-f80417cf6173 | a-generalised-linear-model-framework-for | 2006.06267 | null | https://arxiv.org/abs/2006.06267v3 | https://arxiv.org/pdf/2006.06267v3.pdf | A Generalised Linear Model Framework for $β$-Variational Autoencoders based on Exponential Dispersion Families | Although variational autoencoders (VAE) are successfully used to obtain meaningful low-dimensional representations for high-dimensional data, the characterization of critical points of the loss function for general observation models is not fully understood. We introduce a theoretical framework that is based on a conne... | ['Stefanie Schwaar', 'Robert Sicks', 'Ralf Korn'] | 2020-06-11 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [-2.47534305e-01 2.31202245e-01 3.26772854e-02 -3.89597923e-01
-2.66154647e-01 -8.01784247e-02 3.98469716e-01 -2.10108561e-03
-4.38987613e-01 6.30969763e-01 -2.47050717e-01 -3.86531413e-01
-6.32202208e-01 -9.20242906e-01 -8.32716286e-01 -8.21780443e-01
-2.83121794e-01 4.55682427e-01 -1.22186013e-01 -1.50463998... | [7.494840145111084, 3.8390302658081055] |
119e2241-8810-48d2-ade7-3af6d4560376 | robust-machine-learning-pipelines-for-trading | 2301.0079 | null | https://arxiv.org/abs/2301.00790v2 | https://arxiv.org/pdf/2301.00790v2.pdf | Dynamic Feature Engineering and model selection methods for temporal tabular datasets with regime changes | The application of deep learning algorithms to temporal panel datasets is difficult due to heavy non-stationarities which can lead to over-fitted models that under-perform under regime changes. In this work we propose a new machine learning pipeline for ranking predictions on temporal panel datasets which is robust und... | ['Mauricio Barahona', 'Thomas Wong'] | 2022-12-30 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 2.53135502e-01 -3.08118194e-01 -2.83170849e-01 -6.20846629e-01
-7.76956558e-01 -9.26205277e-01 8.11808407e-01 2.54873931e-01
-2.74840951e-01 8.67290258e-01 3.06872666e-01 -6.66845143e-01
-6.32288635e-01 -6.39499962e-01 -9.70658481e-01 -6.65426433e-01
-5.86705580e-02 5.73478162e-01 3.51884305e-01 -2.11682603... | [7.121635437011719, 3.38749623298645] |
c1a96da3-d056-4d64-95a9-f37a464c4630 | graph-model-for-chinese-spell-checking | null | null | https://aclanthology.org/W13-4416 | https://aclanthology.org/W13-4416.pdf | Graph Model for Chinese Spell Checking | null | ['Zhongye Jia', 'Peilu Wang', 'Hai Zhao'] | 2013-10-01 | null | null | null | ws-2013-10 | ['chinese-spell-checking'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.286793231964111, 3.6747426986694336] |
f7d3ea00-d1ba-4ff4-b6a6-d7445ecab82f | multi-source-education-knowledge-graph | 2305.04567 | null | https://arxiv.org/abs/2305.04567v1 | https://arxiv.org/pdf/2305.04567v1.pdf | Multi-source Education Knowledge Graph Construction and Fusion for College Curricula | The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI). Among the various AI technologies, Knowledge Graphs (KGs) using Natural Language Processing (NLP) have emerged as powerful visualization tools for integrating multifaceted information. In the... | ['Hui Zhao', 'Xinning Zhu', 'Chunhong Zhang', 'Linya Cheng', 'Zeju Li'] | 2023-05-08 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 2.95441430e-02 5.71484193e-02 -1.45956337e-01 1.11325175e-01
7.25233257e-02 -6.80321932e-01 4.52757388e-01 1.12616372e+00
-8.65128115e-02 5.78759491e-01 -5.53546799e-03 -5.62788785e-01
-8.09358358e-01 -1.03612781e+00 -1.78792149e-01 -5.76541126e-01
-5.07315174e-02 3.12274583e-02 2.25631848e-01 -4.39067006... | [9.664751052856445, 7.790193557739258] |
ec144979-77dc-44f9-8b01-d3ad88c705c3 | sparse-representation-based-multi-sensor | 1702.03515 | null | http://arxiv.org/abs/1702.03515v1 | http://arxiv.org/pdf/1702.03515v1.pdf | Sparse Representation based Multi-sensor Image Fusion: A Review | As a result of several successful applications in computer vision and image
processing, sparse representation (SR) has attracted significant attention in
multi-sensor image fusion. Unlike the traditional multiscale transforms (MSTs)
that presume the basis functions, SR learns an over-complete dictionary from a
set of t... | ['DaCheng Tao', 'Yi Liu', 'Jungong Han', 'Qiang Zhang', 'Rick S. Blum'] | 2017-02-12 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 6.00400329e-01 -5.93776047e-01 -2.28818655e-01 -1.86222732e-01
-8.35931361e-01 -1.62142083e-01 3.25375825e-01 1.16959594e-01
-3.32313366e-02 5.05719304e-01 1.11835755e-01 1.40753284e-01
-2.56605536e-01 -7.63994277e-01 -2.68598467e-01 -1.02858663e+00
3.94341499e-02 -2.29748547e-01 1.09664783e-01 -3.66666555... | [10.592103958129883, -1.8633339405059814] |
aa3a2f3c-f705-44b6-a50a-ee15ee36acb9 | mastering-2048-with-delayed-temporal | 1604.05085 | null | http://arxiv.org/abs/1604.05085v3 | http://arxiv.org/pdf/1604.05085v3.pdf | Mastering 2048 with Delayed Temporal Coherence Learning, Multi-Stage Weight Promotion, Redundant Encoding and Carousel Shaping | 2048 is an engaging single-player, nondeterministic video puzzle game, which,
thanks to the simple rules and hard-to-master gameplay, has gained massive
popularity in recent years. As 2048 can be conveniently embedded into the
discrete-state Markov decision processes framework, we treat it as a testbed
for evaluating e... | ['Wojciech Jaśkowski'] | 2016-04-18 | null | null | null | null | ['2048'] | ['playing-games'] | [-5.66050150e-02 -5.44425324e-02 -2.62047559e-01 1.80769011e-01
-5.85676849e-01 -4.87147778e-01 6.14556849e-01 1.22742601e-01
-7.81858861e-01 8.97788167e-01 -2.04407349e-01 -6.63678110e-01
-4.06978548e-01 -8.23859632e-01 -4.97731745e-01 -8.02543223e-01
-8.10488582e-01 8.30300987e-01 8.61251235e-01 -3.62023294... | [3.6899991035461426, 1.584936261177063] |
137ca74b-49dc-4a30-9bd5-81c487686231 | recognizing-musical-entities-in-user | 1904.00648 | null | http://arxiv.org/abs/1904.00648v1 | http://arxiv.org/pdf/1904.00648v1.pdf | Recognizing Musical Entities in User-generated Content | Recognizing Musical Entities is important for Music Information Retrieval
(MIR) since it can improve the performance of several tasks such as music
recommendation, genre classification or artist similarity. However, most entity
recognition systems in the music domain have concentrated on formal texts (e.g.
artists' bio... | ['Lorenzo Porcaro', 'Horacio Saggion'] | 2019-04-01 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 1.71872243e-01 -3.04383576e-01 -2.09769413e-01 5.91475479e-02
-1.07522607e+00 -7.84711599e-01 9.06850219e-01 5.44585824e-01
-7.32204318e-01 6.27042413e-01 5.34187734e-01 2.36831248e-01
-5.03750741e-01 -9.33785737e-01 -4.99276370e-01 -2.70757705e-01
-5.49056865e-02 4.16309834e-01 1.71387538e-01 -2.33266801... | [15.889097213745117, 5.230820655822754] |
38d80f4e-ede5-4fe9-a3fb-7049c0e1c15d | graph-few-shot-class-incremental-learning | 2112.12819 | null | https://arxiv.org/abs/2112.12819v1 | https://arxiv.org/pdf/2112.12819v1.pdf | Graph Few-shot Class-incremental Learning | The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommendation systems, E-commerce platforms, etc. can be represented by graph models. In this paper, we investigate the challenging yet practical pro... | ['Huan Liu', 'Ruocheng Guo', 'Kaize Ding', 'Zhen Tan'] | 2021-12-23 | null | null | null | null | ['few-shot-class-incremental-learning'] | ['methodology'] | [ 2.47781977e-01 1.63950875e-01 -4.28809732e-01 -1.53980345e-01
-7.97981992e-02 -2.36782715e-01 4.43442613e-01 3.47065926e-01
-1.70439169e-01 6.33636236e-01 -1.40536234e-01 -1.59865588e-01
-1.86248437e-01 -1.18259549e+00 -7.41261482e-01 -4.40282404e-01
-1.38975129e-01 4.63692725e-01 7.00695693e-01 -1.70456603... | [9.611858367919922, 3.648683786392212] |
60a21a86-3314-45b4-90a6-eb62d5fff2a0 | mapping-chatgpt-in-mainstream-media-early | 2305.1834 | null | https://arxiv.org/abs/2305.18340v1 | https://arxiv.org/pdf/2305.18340v1.pdf | Mapping ChatGPT in Mainstream Media: Early Quantitative Insights through Sentiment Analysis and Word Frequency Analysis | The exponential growth in user acquisition and popularity of ChatGPT, an artificial intelligence(AI) powered chatbot, was accompanied by widespread mainstream media coverage. This article presents a quantitative data analysis of the early trends and sentiments revealed by conducting text mining and NLP methods onto a c... | ['Maya Karanouh'] | 2023-05-25 | null | null | null | null | ['chatbot', 'ethics', 'sentiment-analysis', 'chatbot'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing'] | [-3.42771262e-01 6.53681278e-01 -4.96334612e-01 2.06053749e-01
-2.22905770e-01 -6.84237897e-01 9.47707653e-01 4.86817598e-01
-3.63657683e-01 5.06049752e-01 9.27621722e-01 -3.58546376e-01
7.66291469e-03 -3.77399206e-01 -4.99506183e-02 -3.15376252e-01
4.72078353e-01 4.55178380e-01 -2.00806469e-01 -6.63483441... | [10.666936874389648, 7.2535271644592285] |
f2d5b877-1aa8-4059-9939-c58dbd5f23d7 | attribute-specific-manipulation-based-on | 2302.0926 | null | https://arxiv.org/abs/2302.09260v1 | https://arxiv.org/pdf/2302.09260v1.pdf | Attribute-Specific Manipulation Based on Layer-Wise Channels | Image manipulation on the latent space of the pre-trained StyleGAN can control the semantic attributes of the generated images. Recently, some studies have focused on detecting channels with specific properties to directly manipulate the latent code, which is limited by the entanglement of the latent space. To detect t... | ['Furao Shen', 'Jian Zhao', 'Yuanjie Yan'] | 2023-02-18 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 7.73929119e-01 5.14813364e-02 -2.68893450e-01 -4.42807794e-01
-4.84774917e-01 -8.89032066e-01 6.56969666e-01 -4.58184212e-01
-1.92565635e-01 5.20990312e-01 2.19445869e-01 2.22488478e-01
2.14515969e-01 -9.03958619e-01 -8.03359449e-01 -1.11963642e+00
-3.08355063e-01 -1.67968795e-01 -3.33384514e-01 7.55635500... | [11.972771644592285, -0.24241182208061218] |
cc1f186b-0068-4ac2-b7db-de7378b1fa16 | catch-you-and-i-can-revealing-source | 2302.12434 | null | https://arxiv.org/abs/2302.12434v1 | https://arxiv.org/pdf/2302.12434v1.pdf | Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion | Voice conversion (VC) techniques can be abused by malicious parties to transform their audios to sound like a target speaker, making it hard for a human being or a speaker verification/identification system to trace the source speaker. In this paper, we make the first attempt to restore the source voiceprint from audio... | ['Wenyuan Xu', 'Xueluan Gong', 'Qianhao Miao', 'Yinan Zhong', 'Yanjiao Chen', 'Jiangyi Deng'] | 2023-02-24 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'speaker-verification'] | ['audio', 'speech', 'speech'] | [ 3.88207972e-01 1.11039802e-01 2.07544893e-01 -5.40907048e-02
-9.51810360e-01 -1.04541492e+00 3.62456918e-01 -4.57034230e-01
-9.22208205e-02 4.71681476e-01 2.87512362e-01 -5.19396365e-01
2.71123767e-01 -3.48951906e-01 -6.76675498e-01 -6.82097912e-01
1.98587671e-01 2.00699493e-01 -1.55940294e-01 -1.97364181... | [14.076655387878418, 5.8930206298828125] |
87ecb998-6156-4fe0-922c-97c0a5005ff1 | kliep-based-density-ratio-estimation-for | 2105.12549 | null | https://arxiv.org/abs/2105.12549v1 | https://arxiv.org/pdf/2105.12549v1.pdf | KLIEP-based Density Ratio Estimation for Semantically Consistent Synthetic to Real Images Adaptation in Urban Traffic Scenes | Synthetic data has been applied in many deep learning based computer vision tasks. Limited performance of algorithms trained solely on synthetic data has been approached with domain adaptation techniques such as the ones based on generative adversarial framework. We demonstrate how adversarial training alone can introd... | ['Federico Tombari', 'Artem Savkin'] | 2021-05-26 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 3.96939427e-01 6.83967948e-01 2.66610265e-01 -4.14193869e-01
-8.48551989e-01 -5.94749629e-01 8.53058279e-01 -3.56855750e-01
-6.94151998e-01 1.20229483e+00 -2.97515899e-01 -2.31392041e-01
1.18377224e-01 -7.36748517e-01 -1.19629097e+00 -5.78891337e-01
6.10942066e-01 7.86612093e-01 4.57307875e-01 -3.25121373... | [9.839681625366211, 1.3023182153701782] |
238ee7cb-8dfb-480c-a514-c96ce831b0cd | lc-2-lidar-camera-loop-constraints-for-cross | 2304.0866 | null | https://arxiv.org/abs/2304.08660v1 | https://arxiv.org/pdf/2304.08660v1.pdf | (LC)$^2$: LiDAR-Camera Loop Constraints For Cross-Modal Place Recognition | Localization has been a challenging task for autonomous navigation. A loop detection algorithm must overcome environmental changes for the place recognition and re-localization of robots. Therefore, deep learning has been extensively studied for the consistent transformation of measurements into localization descriptor... | ['Hyun Myung', 'Woojoo Lee', 'Hyungtae Lim', 'Seungwon Song', 'Alex Junho Lee'] | 2023-04-17 | null | null | null | null | ['autonomous-navigation'] | ['computer-vision'] | [ 4.48354408e-02 -4.33701873e-01 -1.47652596e-01 -8.80398929e-01
-7.37914562e-01 -5.62948823e-01 4.35813934e-01 -9.97820869e-03
-7.01281726e-01 4.80914384e-01 -4.46968079e-01 -1.16606489e-01
-6.77132905e-02 -9.97949660e-01 -1.10565305e+00 -4.95148569e-01
1.56809583e-01 4.47142571e-01 1.94204152e-01 -2.44241655... | [7.535645961761475, -2.207530975341797] |
606e06ec-5a62-4601-87f6-dafc1c6b4baf | tmu-japanese-english-multimodal-machine | null | null | https://aclanthology.org/2020.wat-1.7 | https://aclanthology.org/2020.wat-1.7.pdf | TMU Japanese-English Multimodal Machine Translation System for WAT 2020 | We introduce our TMU system submitted to the Japanese<->English Multimodal Task (constrained) for WAT 2020 (Nakazawa et al., 2020). This task aims to improve translation performance with the help of another modality (images) associated with the input sentences. In a multimodal translation task, the dataset is, by its n... | ['Mamoru Komachi', 'Masahiro Kaneko', 'Tosho Hirasawa', 'Hiroto Tamura'] | null | null | null | null | aacl-wat-2020-12 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 7.95815825e-01 2.63932794e-01 1.35295004e-01 -4.73073572e-01
-1.30552685e+00 -5.64797163e-01 9.06358242e-01 -6.76737010e-01
-7.15246558e-01 9.76446688e-01 5.05752563e-01 -4.16634768e-01
8.38197589e-01 -3.04312348e-01 -9.80535746e-01 -7.53937542e-01
6.19236708e-01 7.65511751e-01 -5.89744262e-02 -3.04991722... | [11.462691307067871, 1.5247068405151367] |
f23c1b4b-a490-4c2c-bf3f-b99c0772222b | analysis-of-the-fed-s-communication-by-using | 2306.04277 | null | https://arxiv.org/abs/2306.04277v1 | https://arxiv.org/pdf/2306.04277v1.pdf | Analysis of the Fed's communication by using textual entailment model of Zero-Shot classification | In this study, we analyze documents published by central banks using text mining techniques and propose a method to evaluate the policy tone of central banks. Since the monetary policies of major central banks have a broad impact on financial market trends, the pricing of risky assets, and the real economy, market part... | ['Tomochika Sawaki', 'Yasuhiro Nakayama'] | 2023-06-07 | null | null | null | null | ['natural-language-inference', 'sentiment-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [-3.49999577e-01 3.17898914e-02 -4.23727393e-01 -2.18506977e-01
-3.62140208e-01 -7.51747429e-01 7.84022450e-01 5.90871513e-01
-5.40861845e-01 7.84512103e-01 9.57213521e-01 -1.05460191e+00
1.52794972e-01 -8.49671602e-01 -1.01461336e-01 -4.36273664e-01
2.15592980e-01 2.78541058e-01 -1.33510426e-01 -5.46122551... | [4.506572246551514, 4.366347312927246] |
beabc446-6198-4871-b2dc-5800a574f7d7 | learning-similarity-between-scene-graphs-and | 2304.0059 | null | https://arxiv.org/abs/2304.00590v1 | https://arxiv.org/pdf/2304.00590v1.pdf | Learning Similarity between Scene Graphs and Images with Transformers | Scene graph generation is conventionally evaluated by (mean) Recall@K, which measures the ratio of correctly predicted triplets that appear in the ground truth. However, such triplet-oriented metrics cannot capture the global semantic information of scene graphs, and measure the similarity between images and generated ... | ['Michael Ying Yang', 'Bodo Rosenhahn', 'Wentong Liao', 'Yuren Cong'] | 2023-04-02 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 6.76571190e-01 -1.07896902e-01 2.49753613e-02 -4.21229243e-01
-5.75232208e-01 -6.40390575e-01 6.92839146e-01 1.51592001e-01
-9.24639683e-03 3.45776498e-01 1.41107187e-01 -1.46982029e-01
-7.90943503e-02 -1.09528291e+00 -8.95379663e-01 -7.01061010e-01
3.09126914e-01 -1.99807193e-02 1.95577279e-01 -1.03311844... | [10.45053768157959, 1.5051616430282593] |
5112b365-1f0d-4ce1-96dd-e66233827b49 | semantic-scene-completion-combining-colour | 1802.04735 | null | http://arxiv.org/abs/1802.04735v1 | http://arxiv.org/pdf/1802.04735v1.pdf | Semantic Scene Completion Combining Colour and Depth: preliminary experiments | Semantic scene completion is the task of producing a complete 3D voxel
representation of volumetric occupancy with semantic labels for a scene from a
single-view observation. We built upon the recent work of Song et al. (CVPR
2017), who proposed SSCnet, a method that performs scene completion and
semantic labelling in ... | ['Teofilo Emidio de Campos', 'Andre Bernardes Soares Guedes', 'Adrian Hilton'] | 2018-02-13 | null | null | null | null | ['3d-semantic-scene-completion'] | ['computer-vision'] | [ 5.70352077e-01 3.75537127e-01 2.44709462e-01 -6.60245001e-01
-3.12037110e-01 -5.54945111e-01 5.96128166e-01 1.18079796e-01
-5.60440779e-01 3.64351034e-01 3.54975283e-01 -1.34399384e-01
3.10963899e-01 -8.82544935e-01 -7.71002293e-01 -1.02460772e-01
1.20430350e-01 4.97825146e-01 5.81535161e-01 1.05068646... | [8.452519416809082, -2.862555503845215] |
5ef6c791-6454-421d-9d4c-2c830afd065c | orthogonal-deep-features-decomposition-for | 1810.07599 | null | http://arxiv.org/abs/1810.07599v1 | http://arxiv.org/pdf/1810.07599v1.pdf | Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition | As facial appearance is subject to significant intra-class variations caused
by the aging process over time, age-invariant face recognition (AIFR) remains a
major challenge in face recognition community. To reduce the intra-class
discrepancy caused by the aging, in this paper we propose a novel approach
(namely, Orthog... | ['Yitong Wang', 'Zheng Zhou', 'Wei Liu', 'Hao Wang', 'Zhifeng Li', 'Xing Ji', 'Dihong Gong', 'Tong Zhang'] | 2018-10-17 | orthogonal-deep-features-decomposition-for-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/yitong_wang_Orthogonal_Deep_Features_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/yitong_wang_Orthogonal_Deep_Features_ECCV_2018_paper.pdf | eccv-2018-9 | ['age-invariant-face-recognition'] | ['computer-vision'] | [-1.47028759e-01 -2.27395073e-01 5.93006052e-02 -8.13408017e-01
-1.86810344e-01 4.85065803e-02 3.63464683e-01 -2.97177643e-01
-2.02446952e-01 6.94220185e-01 2.56050617e-01 2.62484998e-01
-1.62954912e-01 -7.61028647e-01 -4.44316626e-01 -7.99500942e-01
-4.09088016e-01 4.86085042e-02 -3.89218450e-01 -2.83161253... | [13.399888038635254, 0.6728320121765137] |
94a163b8-7a23-419c-a52f-f85dfae07c5b | hyperspectral-image-segmentation-a | 2303.08252 | null | https://arxiv.org/abs/2303.08252v1 | https://arxiv.org/pdf/2303.08252v1.pdf | Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB) | Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapsho... | ['Tom Vercauteren', 'Michael Ebner', 'Sebastien Ourselin', 'Conor Horgan', 'Luis C. Garcia-Peraza-Herrera'] | 2023-03-14 | null | null | null | null | ['hyperspectral-image-segmentation'] | ['computer-vision'] | [ 1.19355798e+00 -1.08797617e-01 -1.94338739e-01 -2.80542731e-01
-1.16290641e+00 -4.03813064e-01 6.75578089e-03 5.97699620e-02
-5.49358130e-01 4.11518127e-01 -1.42801434e-01 -4.76379693e-01
-1.41860500e-01 -4.70550328e-01 -1.87923431e-01 -1.32265139e+00
2.00739846e-01 2.30436146e-01 -6.08235113e-02 -5.19476682... | [15.312244415283203, -2.9162139892578125] |
f6489a74-8649-4a1d-b706-fbdbebe1d7f8 | learning-to-select-nodes-in-bounded | null | null | https://openreview.net/forum?id=ztEQOAzM1cN | https://openreview.net/pdf?id=ztEQOAzM1cN | Learning to Select Nodes in Bounded Suboptimal Conflict-Based Search for Multi-Agent Path Finding | Multi-Agent Path Finding is an NP-hard problem that is difficult for current approaches to solve optimally. Research has shown that bounded suboptimal solvers, such as Enhanced Conflict-Based Search (ECBS), are more efficient than optimal solvers in finding a feasible solution with suboptimality guarantees. ECBS is a t... | ['Sven Koenig', 'Bistra Dilkina', 'Taoan Huang'] | 2020-10-17 | null | null | null | neurips-workshop-lmca-2020-12 | ['multi-agent-path-finding'] | ['playing-games'] | [-1.32059976e-01 2.39835009e-01 -5.33960700e-01 1.95468292e-01
-1.06586385e+00 -8.84679377e-01 2.07460746e-01 -4.67717014e-02
-5.07102132e-01 1.45349789e+00 -1.42843038e-01 -4.78249669e-01
-4.63436782e-01 -9.31039810e-01 -8.78476560e-01 -7.00556993e-01
-5.82343280e-01 1.33321381e+00 3.74381304e-01 -1.80733338... | [4.962387561798096, 2.0564544200897217] |
5ddb7325-6b3b-4233-a62a-3cf01fd7255a | inductive-and-transductive-few-shot-video | 2207.10785 | null | https://arxiv.org/abs/2207.10785v1 | https://arxiv.org/pdf/2207.10785v1.pdf | Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments | We present a novel method for few-shot video classification, which performs appearance and temporal alignments. In particular, given a pair of query and support videos, we conduct appearance alignment via frame-level feature matching to achieve the appearance similarity score between the videos, while utilizing tempora... | ['Rang Nguyen', 'Binh-Son Hua', 'Khoi Nguyen', 'Quoc-Huy Tran', 'Khoi D. Nguyen'] | 2022-07-21 | null | null | null | null | ['video-classification', 'classification'] | ['computer-vision', 'methodology'] | [-1.18518593e-02 -6.93152726e-01 -5.61035395e-01 -5.00625014e-01
-9.15338516e-01 -5.76164305e-01 7.24435210e-01 5.53439707e-02
-2.14202031e-01 2.34436989e-01 3.81101258e-02 1.51656955e-01
-2.29021069e-02 -2.45224938e-01 -6.77264810e-01 -5.28509378e-01
-3.61141324e-01 1.60607606e-01 6.43249929e-01 -7.50859454... | [8.645445823669434, 0.7589871287345886] |
f5289c30-7385-47bd-a634-4bf07215c84e | slap-improving-physical-adversarial-examples | 2007.04137 | null | https://arxiv.org/abs/2007.04137v3 | https://arxiv.org/pdf/2007.04137v3.pdf | SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations | Research into adversarial examples (AE) has developed rapidly, yet static adversarial patches are still the main technique for conducting attacks in the real world, despite being obvious, semi-permanent and unmodifiable once deployed. In this paper, we propose Short-Lived Adversarial Perturbations (SLAP), a novel techn... | ['Martin Strohmeier', 'Ivan Martinovic', 'Ivo Sluganovic', 'Giulio Lovisotto', 'Henry Turner'] | 2020-07-08 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 5.43016732e-01 2.47366220e-01 4.96729225e-01 2.75257349e-01
-2.81658471e-01 -1.12220252e+00 8.96581650e-01 -4.01428461e-01
-4.51348454e-01 5.90728164e-01 -3.40150744e-01 -4.02895361e-01
4.37089838e-02 -9.24565673e-01 -1.07578242e+00 -1.04710960e+00
-4.44616616e-01 1.44024417e-01 7.33773828e-01 -5.04113793... | [5.469581127166748, 7.8476948738098145] |
b64ebed9-d89d-494a-ae6f-090f678b8e61 | the-nci-imaging-data-commons-as-a-platform | 2303.09354 | null | https://arxiv.org/abs/2303.09354v2 | https://arxiv.org/pdf/2303.09354v2.pdf | The NCI Imaging Data Commons as a platform for reproducible research in computational pathology | Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, w... | ['André Homeyer', 'Andrey Fedorov', 'Ron Kikinis', 'Steve Pieper', 'William J. R. Longabaugh', 'William Clifford', 'Henning Höfener', 'David A. Clunie', 'Markus D. Herrmann', 'Daniela P. Schacherer'] | 2023-03-16 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.46586148e-02 -3.95532399e-01 -5.17586648e-01 -1.63817436e-01
-1.23684776e+00 -6.74931347e-01 3.54599267e-01 5.02027035e-01
-7.16946423e-01 6.65761828e-01 5.82955368e-02 -7.44695961e-01
-2.25272164e-01 -3.70411575e-01 -5.05805254e-01 -9.47934508e-01
-6.56401962e-02 6.31254256e-01 3.24521929e-01 4.21083152... | [15.11402416229248, -2.9617979526519775] |
8a42a453-3d09-4ab8-95bb-1cdd37adcbd8 | ok-computer-analysis-an-audio-corpus-study-of | 2211.15834 | null | https://arxiv.org/abs/2211.15834v1 | https://arxiv.org/pdf/2211.15834v1.pdf | OK Computer Analysis: An Audio Corpus Study of Radiohead | The application of music information retrieval techniques in popular music studies has great promise. In the present work, a corpus of Radiohead songs across their career from 1992 to 2017 are subjected to automated audio analysis. We examine findings from a number of granularities and perspectives, including within so... | ['Nick Collins'] | 2022-11-29 | null | null | null | null | ['music-information-retrieval'] | ['music'] | [ 2.17478693e-01 -2.81432897e-01 -3.22842538e-01 1.81396022e-01
-1.27887499e+00 -8.44578207e-01 2.79464632e-01 3.66731703e-01
-3.36157709e-01 4.49216247e-01 8.66222560e-01 9.99196395e-02
-1.11316824e+00 -2.28447035e-01 -1.96096852e-01 -4.56486553e-01
-3.04630995e-01 7.09699169e-02 -8.27257410e-02 -2.99419940... | [15.940689086914062, 5.278630256652832] |
dfa91f21-74ee-45d6-98f4-fad3d2280ec0 | pointwavelet-learning-in-spectral-domain-for | 2302.05201 | null | https://arxiv.org/abs/2302.05201v1 | https://arxiv.org/pdf/2302.05201v1.pdf | PointWavelet: Learning in Spectral Domain for 3D Point Cloud Analysis | With recent success of deep learning in 2D visual recognition, deep learning-based 3D point cloud analysis has received increasing attention from the community, especially due to the rapid development of autonomous driving technologies. However, most existing methods directly learn point features in the spatial domain,... | ['DaCheng Tao', 'Baosheng Yu', 'Jianzhi Long', 'Cheng Wen'] | 2023-02-10 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-2.11496800e-01 -3.42623681e-01 -8.18235651e-02 -2.74061292e-01
-4.68896002e-01 -3.72026771e-01 2.51119316e-01 2.06921518e-01
4.51932587e-02 6.55778348e-02 -4.00957346e-01 -4.15499628e-01
-1.62376329e-01 -1.10052407e+00 -6.36261642e-01 -5.91424525e-01
-3.02265048e-01 2.48648643e-01 2.58065701e-01 -1.82160903... | [7.98133659362793, -3.633312940597534] |
a6db6bba-7b84-4563-a623-4385dee27d71 | m3er-multiplicative-multimodal-emotion | 1911.05659 | null | https://arxiv.org/abs/1911.05659v2 | https://arxiv.org/pdf/1911.05659v2.pdf | M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues | We present M3ER, a learning-based method for emotion recognition from multiple input modalities. Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities. M3ER models a novel, data-dri... | ['Uttaran Bhattacharya', 'Dinesh Manocha', 'Rohan Chandra', 'Trisha Mittal', 'Aniket Bera'] | 2019-11-09 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 1.64319724e-01 -1.91939697e-01 -1.37548856e-02 -4.11362827e-01
-1.33944941e+00 -3.55942219e-01 5.65466464e-01 -6.69027097e-04
-4.02149439e-01 5.62487602e-01 4.84609067e-01 6.01479113e-01
6.59328848e-02 -2.09715709e-01 -4.57463473e-01 -6.97397113e-01
-2.35569049e-02 -2.05445200e-01 -2.36268878e-01 -1.22855432... | [13.238875389099121, 5.100305557250977] |
1cc22ab3-ae8e-409a-ae88-d1b9275afc63 | road-barlow-twins-redundancy-reduction-for | 2306.1084 | null | https://arxiv.org/abs/2306.10840v1 | https://arxiv.org/pdf/2306.10840v1.pdf | Road Barlow Twins: Redundancy Reduction for Road Environment Descriptors and Motion Prediction | Anticipating the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation. We introduce a novel self-supervised pre-training method as well as a transformer model for motion prediction. Our method is based on Barlow Twins and applies the redundancy reduction principle to embeddi... | ['Carlos Fernandez Lopez', 'Marvin Klemp', 'Omer Sahin Tas', 'Royden Wagner'] | 2023-06-19 | null | null | null | null | ['motion-prediction', 'trajectory-prediction', 'trajectory-forecasting'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.98660329e-01 -6.80161417e-02 -2.86486566e-01 -4.74967718e-01
-7.06664681e-01 -2.62201339e-01 9.55547512e-01 -3.54480505e-01
-5.18813252e-01 3.37785840e-01 3.34172994e-01 -3.27950180e-01
3.96637395e-02 -9.38776791e-01 -8.01593602e-01 -6.25635147e-01
-2.18257576e-01 5.87928712e-01 7.96467423e-01 -4.13786381... | [6.162898540496826, 0.6310123205184937] |
6927ad29-9879-45cd-b860-dda900277d3e | deep-kernel-learning-for-mortality-prediction | 2212.00557 | null | https://arxiv.org/abs/2212.00557v1 | https://arxiv.org/pdf/2212.00557v1.pdf | Deep Kernel Learning for Mortality Prediction in the Face of Temporal Shift | Neural models, with their ability to provide novel representations, have shown promising results in prediction tasks in healthcare. However, patient demographics, medical technology, and quality of care change over time. This often leads to drop in the performance of neural models for prospective patients, especially i... | ['Ameen Abu-Hanna', 'Miguel Rios'] | 2022-12-01 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [-2.66741030e-02 4.97144699e-01 -3.77012715e-02 -5.71012974e-01
-8.22718799e-01 -2.07633018e-01 4.46130365e-01 4.78176326e-01
-5.08570850e-01 1.03055966e+00 4.14913714e-01 -4.76473033e-01
-4.16610271e-01 -7.28085756e-01 -8.91159177e-01 -6.58697724e-01
-2.62782812e-01 6.56735003e-01 -1.66185826e-01 2.28925645... | [7.95538854598999, 6.1280903816223145] |
eb9216cf-9632-44a7-b34a-19a68d888ea2 | privacy-preserving-data-synthetisation-for | 2212.00484 | null | https://arxiv.org/abs/2212.00484v1 | https://arxiv.org/pdf/2212.00484v1.pdf | Privacy-Preserving Data Synthetisation for Secure Information Sharing | We can protect user data privacy via many approaches, such as statistical transformation or generative models. However, each of them has critical drawbacks. On the one hand, creating a transformed data set using conventional techniques is highly time-consuming. On the other hand, in addition to long training phases, re... | ['Nitesh Chawla', 'Luís Antunes', 'Pedro Faria', 'Nuno Moniz', 'Tânia Carvalho'] | 2022-12-01 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [ 1.64705619e-01 7.83423483e-02 8.11882466e-02 -2.72917509e-01
-1.11240458e+00 -6.91983581e-01 4.92211312e-01 2.14815810e-01
-5.69871724e-01 8.04715812e-01 -2.01327316e-02 -3.49812329e-01
4.77674901e-02 -1.10248089e+00 -6.56307995e-01 -8.59029353e-01
2.08702669e-01 2.27538079e-01 -2.04889268e-01 -1.13244794... | [6.038959980010986, 6.938927173614502] |
95af91e2-fdfb-46ed-b226-b09839901bfe | audio-anti-spoofing-using-a-simple-attention | 2211.09898 | null | https://arxiv.org/abs/2211.09898v1 | https://arxiv.org/pdf/2211.09898v1.pdf | Audio Anti-spoofing Using a Simple Attention Module and Joint Optimization Based on Additive Angular Margin Loss and Meta-learning | Automatic speaker verification systems are vulnerable to a variety of access threats, prompting research into the formulation of effective spoofing detection systems to act as a gate to filter out such spoofing attacks. This study introduces a simple attention module to infer 3-dim attention weights for the feature map... | ['John H. L. Hansen', 'Zhenyu Wang'] | 2022-11-17 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'speaker-verification'] | ['audio', 'speech', 'speech'] | [ 3.59899163e-01 -2.15278044e-01 -5.97366020e-02 -2.50250101e-01
-5.08394301e-01 -5.44272244e-01 5.38683534e-01 1.06129825e-01
-4.79670823e-01 2.73699015e-01 1.04780734e-01 -7.80437589e-01
1.59418806e-01 -4.16364610e-01 -5.23023665e-01 -6.68393791e-01
-4.91347834e-02 -2.93852240e-01 8.80991854e-03 -3.68906170... | [14.079228401184082, 5.86139440536499] |
17d1c687-cfeb-4a13-b047-1f02ddbb7e41 | auto-focus-contrastive-learning-for-image | 2211.10922 | null | https://arxiv.org/abs/2211.10922v1 | https://arxiv.org/pdf/2211.10922v1.pdf | Auto-Focus Contrastive Learning for Image Manipulation Detection | Generally, current image manipulation detection models are simply built on manipulation traces. However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy information within the entire image, and 2) ignore the trace relations ... | ['Q. M. Jonathan Wu', 'Hongyang Yan', 'Teng Huang', 'Guangcan Liu', 'Zhili Zhou', 'Wenyan Pan'] | 2022-11-20 | null | null | null | null | ['image-manipulation-detection', 'image-manipulation'] | ['computer-vision', 'computer-vision'] | [ 5.24195611e-01 -2.48372108e-01 -2.20614746e-01 2.81385016e-02
-6.31523550e-01 -3.47486079e-01 4.83353406e-01 -4.78540882e-02
1.14337452e-01 6.67171255e-02 -9.00403038e-02 1.03176214e-01
-2.00749725e-01 -7.99481153e-01 -8.95338833e-01 -7.72420228e-01
7.98495784e-02 1.82088949e-02 2.91325897e-01 -1.96055114... | [12.134040832519531, 0.9041734933853149] |
9513fb18-d3f8-4a57-9a58-39ecb2b0f0be | invertible-rescaling-network-and-its | 2210.04188 | null | https://arxiv.org/abs/2210.04188v1 | https://arxiv.org/pdf/2210.04188v1.pdf | Invertible Rescaling Network and Its Extensions | Image rescaling is a commonly used bidirectional operation, which first downscales high-resolution images to fit various display screens or to be storage- and bandwidth-friendly, and afterward upscales the corresponding low-resolution images to recover the original resolution or the details in the zoom-in images. Howev... | ['Tie-Yan Liu', 'Zhouchen Lin', 'Chang Liu', 'Shuxin Zheng', 'Mingqing Xiao'] | 2022-10-09 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 8.70577514e-01 -2.51133144e-01 6.07226305e-02 -2.26649955e-01
-4.62277830e-01 -3.97385389e-01 4.09050375e-01 -6.68866336e-01
-1.68014586e-01 6.85681343e-01 5.43543160e-01 -2.30004132e-01
-7.82057792e-02 -9.51506436e-01 -7.90954053e-01 -9.49065387e-01
3.74045759e-01 -2.73076087e-01 -1.04841582e-01 -2.23101363... | [11.228896141052246, -2.045501947402954] |
702670cc-3468-470a-be47-74312aaa5f4c | overview-and-results-cl-scisumm-shared-task | 1907.09854 | null | https://arxiv.org/abs/1907.09854v1 | https://arxiv.org/pdf/1907.09854v1.pdf | Overview and Results: CL-SciSumm Shared Task 2019 | The CL-SciSumm Shared Task is the first medium-scale shared task on scientific document summarization in the computational linguistics~(CL) domain. In 2019, it comprised three tasks: (1A) identifying relationships between citing documents and the referred document, (1B) classifying the discourse facets, and (2) generat... | ['Min-Yen Kan', 'Dayne Freitag', 'Muthu Kumar Chandrasekaran', 'Dragomir Radev', 'Michihiro Yasunaga'] | 2019-07-23 | null | null | null | null | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 1.22133754e-01 2.98704356e-01 -3.77102524e-01 1.62529662e-01
-1.55591726e+00 -9.34155583e-01 1.13603723e+00 8.41317832e-01
-5.09999216e-01 9.93709147e-01 1.01353478e+00 -2.48954207e-01
-3.48429114e-01 -1.86605096e-01 -5.54893136e-01 -3.63835633e-01
1.44384906e-01 5.64135432e-01 7.03643635e-02 -1.93418384... | [12.384111404418945, 9.577133178710938] |
a674237e-943b-4fcc-ba66-e5f82de4bce7 | feature-robustness-and-sex-differences-in | 2204.01737 | null | https://arxiv.org/abs/2204.01737v3 | https://arxiv.org/pdf/2204.01737v3.pdf | Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detection | Convolutional neural networks have enabled significant improvements in medical image-based diagnosis. It is, however, increasingly clear that these models are susceptible to performance degradation when facing spurious correlations and dataset shift, leading, e.g., to underperformance on underrepresented patient groups... | ['Maria Luise da Costa Zemsch', 'Melanie Ganz', 'Oskar Eiler Wiese Christensen', 'Anders Henriksen', 'Aasa Feragen', 'Eike Petersen'] | 2022-04-04 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 3.19987208e-01 1.10651724e-01 -3.01703393e-01 -7.84343302e-01
-6.83270156e-01 -4.66590494e-01 5.78333557e-01 3.75879467e-01
-7.18410671e-01 6.07614100e-01 6.05089784e-01 -4.24494445e-01
-5.24500668e-01 -6.63760722e-01 -5.10986030e-01 -5.13714552e-01
-2.29520753e-01 6.23500228e-01 -2.20765501e-01 2.14392886... | [14.871918678283691, -2.1008336544036865] |
a2a63500-e524-4a6c-b020-5091082fda0b | creating-realistic-anterior-segment-optical | 2306.14058 | null | https://arxiv.org/abs/2306.14058v1 | https://arxiv.org/pdf/2306.14058v1.pdf | Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks | This paper presents the development and validation of a Generative Adversarial Network (GAN) purposed to create high-resolution, realistic Anterior Segment Optical Coherence Tomography (AS-OCT) images. We trained the Style and WAvelet based GAN (SWAGAN) on 142,628 AS-OCT B-scans. Three experienced refractive surgeons p... | ['Shady T. Awwad', 'Michèle Haykal', 'Elsa Lamah', 'Jeffrey Yammine', 'Timothy Archer', 'Cyril Zakka', 'Guillermo Amescua', 'Dan Z. Reinstein', 'Anthony Abou Mrad', 'Jad F. Assaf'] | 2023-06-24 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 6.05652332e-01 5.02652586e-01 2.90721208e-01 -2.67773539e-01
-1.37778819e+00 -4.46467817e-01 4.27996993e-01 -6.04671955e-01
-3.25153053e-01 1.11648822e+00 3.01529765e-01 -4.74393100e-01
2.61837512e-01 -7.12729633e-01 -6.83604717e-01 -6.99811399e-01
6.34517744e-02 3.35797399e-01 -5.37008792e-02 1.17651641... | [14.290935516357422, -1.9654408693313599] |
b34bfd02-e81b-4981-aa20-7aa18d568bf0 | end-to-end-estimation-of-multi-person-3d | 2004.06239 | null | https://arxiv.org/abs/2004.06239v4 | https://arxiv.org/pdf/2004.06239v4.pdf | VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment | We present an approach to estimate 3D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimations, we present an end-to-end solution which directly operates in the $3$D space, therefore av... | ['Wen-Jun Zeng', 'Hanyue Tu', 'Chunyu Wang'] | 2020-04-13 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/738_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123460188.pdf | eccv-2020-8 | ['3d-multi-person-pose-estimation'] | ['computer-vision'] | [-4.42958772e-01 -1.15197569e-01 1.58729777e-01 -4.65348214e-01
-9.36526060e-01 -6.99491501e-01 4.56046373e-01 -2.21701160e-01
-3.87124538e-01 5.74284375e-01 3.36039394e-01 2.70148695e-01
2.19282731e-01 -4.43692148e-01 -6.27974570e-01 -2.46598825e-01
5.08317053e-02 8.96451473e-01 -3.70729044e-02 1.21077053... | [7.049137592315674, -0.9834847450256348] |
c9981b65-fa26-49c2-a160-861b2a71ad15 | tsdf-a-multi-object-formulation-for-dynamic | 2105.07468 | null | https://arxiv.org/abs/2105.07468v1 | https://arxiv.org/pdf/2105.07468v1.pdf | TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction | The ability to simultaneously track and reconstruct multiple objects moving in the scene is of the utmost importance for robotic tasks such as autonomous navigation and interaction. Virtually all of the previous attempts to map multiple dynamic objects have evolved to store individual objects in separate reconstruction... | ['Juan Nieto', 'Roland Siegwart', 'Federico Tombari', 'Margarita Grinvald'] | 2021-05-16 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 4.26452845e-01 -1.03513353e-01 4.28007513e-01 -2.56182820e-01
-5.82238436e-01 -8.66996050e-01 8.75485897e-01 4.82447684e-01
-2.18673483e-01 5.97126544e-01 -2.85423040e-01 1.57665178e-01
-3.45971465e-01 -9.98043835e-01 -1.12831187e+00 -6.05699122e-01
2.47246877e-04 1.22394586e+00 1.08054829e+00 -4.43343073... | [7.353168964385986, -2.408747434616089] |
8a2d874a-95be-4e6a-aa8f-91d27c1e784b | crosskd-cross-head-knowledge-distillation-for | 2306.11369 | null | https://arxiv.org/abs/2306.11369v1 | https://arxiv.org/pdf/2306.11369v1.pdf | CrossKD: Cross-Head Knowledge Distillation for Dense Object Detection | Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation, which is generally observed to be better than prediction mimicking. In this paper, we show th... | ['Qibin Hou', 'Ming-Ming Cheng', 'Xiang Li', 'Zhaohui Zheng', 'Yuming Chen', 'Jiabao Wang'] | 2023-06-20 | null | null | null | null | ['dense-object-detection', 'model-compression'] | ['computer-vision', 'methodology'] | [-1.91441283e-01 2.42401183e-01 -3.32251728e-01 -8.31908435e-02
-8.16059291e-01 -2.58520484e-01 3.67034137e-01 2.29146317e-01
-5.73175192e-01 5.12047887e-01 -2.91106045e-01 -2.77606249e-01
8.35177824e-02 -5.69402218e-01 -9.94441450e-01 -6.84473932e-01
1.60058498e-01 4.49829489e-01 8.87500405e-01 9.88877043... | [9.267452239990234, 1.2898573875427246] |
17f5e634-08ae-4738-9d9d-5ad14c0d6958 | native-multi-band-audio-coding-within-hyper | 2303.08005 | null | https://arxiv.org/abs/2303.08005v1 | https://arxiv.org/pdf/2303.08005v1.pdf | Native Multi-Band Audio Coding within Hyper-Autoencoded Reconstruction Propagation Networks | Spectral sub-bands do not portray the same perceptual relevance. In audio coding, it is therefore desirable to have independent control over each of the constituent bands so that bitrate assignment and signal reconstruction can be achieved efficiently. In this work, we present a novel neural audio coding network that n... | ['Minje Kim', 'Inseon Jang', 'Darius Petermann'] | 2023-03-14 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 3.53025109e-01 1.06624521e-01 -5.12229443e-01 3.40643525e-02
-7.52588987e-01 -2.52876878e-01 -3.02098338e-02 -1.34694830e-01
-3.85860418e-04 6.34199381e-01 5.53761065e-01 -2.00608626e-01
-2.34563202e-01 -6.89829588e-01 -5.58341503e-01 -8.10509086e-01
-4.57988739e-01 -1.33176193e-01 4.79603708e-01 -1.56121165... | [15.490598678588867, 5.789776802062988] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.