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
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
752f795a-0de6-4a7f-bd6d-d4f214dace3c | dynamic-low-resolution-distillation-for-cost | 2207.06694 | null | https://arxiv.org/abs/2207.06694v2 | https://arxiv.org/pdf/2207.06694v2.pdf | Dynamic Low-Resolution Distillation for Cost-Efficient End-to-End Text Spotting | End-to-end text spotting has attached great attention recently due to its benefits on global optimization and high maintainability for real applications. However, the input scale has always been a tough trade-off since recognizing a small text instance usually requires enlarging the whole image, which brings high compu... | ['Liang Qiao', 'Xi Li', 'Yi Niu', 'ShiLiang Pu', 'Zhanzhan Cheng', 'Ying Chen'] | 2022-07-14 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.46542561e-01 -5.47482491e-01 -6.45219088e-02 -3.01467419e-01
-8.60354245e-01 -3.74621004e-01 3.94927323e-01 -2.65334129e-01
-4.97746438e-01 3.06194633e-01 -6.57809377e-02 -2.85279930e-01
-1.48270339e-01 -6.99399710e-01 -4.21847194e-01 -7.72593856e-01
8.38311434e-01 4.44394648e-01 3.77788037e-01 -7.95664918... | [12.006536483764648, 2.2228524684906006] |
12de32e3-b071-4171-8bbb-9de2a14385c6 | fuseformer-fusing-fine-grained-information-in | 2109.02974 | null | https://arxiv.org/abs/2109.02974v1 | https://arxiv.org/pdf/2109.02974v1.pdf | FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting | Transformer, as a strong and flexible architecture for modelling long-range relations, has been widely explored in vision tasks. However, when used in video inpainting that requires fine-grained representation, existed method still suffers from yielding blurry edges in detail due to the hard patch splitting. Here we ai... | ['Hongsheng Li', 'Jifeng Dai', 'Xiaogang Wang', 'Wenxiu Sun', 'Lewei Lu', 'Xiaoyu Shi', 'Yangyi Huang', 'Hanming Deng', 'Rui Liu'] | 2021-09-07 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_FuseFormer_Fusing_Fine-Grained_Information_in_Transformers_for_Video_Inpainting_ICCV_2021_paper.pdf | iccv-2021-1 | ['seeing-beyond-the-visible', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 2.11821526e-01 -8.79789740e-02 1.11304849e-01 -1.73402771e-01
-3.38925987e-01 -6.62536994e-02 4.83471215e-01 -1.07855357e-01
-1.15962401e-01 7.10427463e-01 2.17802301e-01 4.37247008e-01
-1.90555349e-01 -9.52161968e-01 -8.61333966e-01 -9.66828346e-01
4.45568971e-02 -2.70318419e-01 3.76339555e-01 -2.18944862... | [10.801051139831543, -1.5072343349456787] |
6511673d-c67e-46ef-85bb-d9e904a340ce | artifact-identification-in-x-ray-diffraction | 2207.14804 | null | https://arxiv.org/abs/2207.14804v1 | https://arxiv.org/pdf/2207.14804v1.pdf | Artifact Identification in X-ray Diffraction Data using Machine Learning Methods | The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in complex sample environments (e.g., diamond anvil cells or syntheses reactors). An atomic structure ... | ['Nicholas Schwarz', 'Uta Ruett', 'Wenqian Xu', 'Hannah Parraga', 'James Weng', 'Howard Yanxon'] | 2022-07-29 | null | null | null | null | ['machine-learning', 'machine-learning'] | ['methodology', 'miscellaneous'] | [ 3.38185728e-01 -4.41037536e-01 -3.95997524e-01 -2.41586894e-01
-3.24853957e-01 -1.38100177e-01 5.49163222e-01 4.00539637e-01
-3.53661597e-01 7.68028736e-01 -4.40492600e-01 -8.67818296e-02
-8.85456130e-02 -8.85097384e-01 -6.67737424e-01 -1.33083177e+00
2.88391113e-01 1.03993368e+00 2.88295269e-01 2.36129761... | [5.251382827758789, 5.173381805419922] |
0755d911-ffd4-46ec-9e24-fddf8386dce1 | identifying-suspicious-regions-of-covid-19-by | 2303.14901 | null | https://arxiv.org/abs/2303.14901v1 | https://arxiv.org/pdf/2303.14901v1.pdf | Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping | This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists th... | ['Kensaku MORI', 'Shigeki Aoki', 'Toshiaki Akashi', 'Masahiro Hashimoto', 'Yoshito Otake', 'Yuichiro Hayashi', 'Masahiro Oda', 'Hayato Itoh', 'Ryo Toda'] | 2023-03-27 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 1.97853744e-01 -8.06574225e-02 2.41652712e-01 -3.40957493e-01
-5.92853129e-01 -4.52424496e-01 1.56753421e-01 4.26718265e-01
-5.61703146e-01 4.77998585e-01 -2.84350187e-01 -4.84270900e-01
-2.36149624e-01 -6.64167047e-01 -5.18019438e-01 -6.88956082e-01
-2.69782275e-01 7.85699785e-01 1.13757111e-01 2.50586182... | [15.56490421295166, -1.7048465013504028] |
bd94b0a1-b346-4e36-8548-9baf6d49d86d | face-hallucination-revisited-an-exploratory | 1812.0901 | null | http://arxiv.org/abs/1812.09010v1 | http://arxiv.org/pdf/1812.09010v1.pdf | Face Hallucination Revisited: An Exploratory Study on Dataset Bias | Contemporary face hallucination (FH) models exhibit considerable ability to
reconstruct high-resolution (HR) details from low-resolution (LR) face images.
This ability is commonly learned from examples of corresponding HR-LR image
pairs, created by artificially down-sampling the HR ground truth data. This
down-sampling... | ['Vitomir Štruc', 'Simon Dobrišek', 'Martin Pernuš', 'Leo Cluzel', 'Walter Scheirer', 'Klemen Grm'] | 2018-12-21 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 5.29990375e-01 2.54711419e-01 8.96332487e-02 -3.87578845e-01
-7.13027835e-01 -1.70667186e-01 5.91824234e-01 -5.20557582e-01
1.16494179e-01 9.07753468e-01 1.93359673e-01 2.73776710e-01
-8.19009766e-02 -7.78937459e-01 -8.42242062e-01 -8.88575256e-01
-1.52542114e-01 2.20385343e-01 -2.94093072e-01 -2.42507026... | [12.782113075256348, -0.1718812733888626] |
a0d426c8-0dfb-4461-b637-b824b840db58 | multiplex-heterogeneous-graph-convolutional | 2208.06129 | null | https://arxiv.org/abs/2208.06129v1 | https://arxiv.org/pdf/2208.06129v1.pdf | Multiplex Heterogeneous Graph Convolutional Network | Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and dif... | ['Junyu Dong', 'Zhongying Zhao', 'Chao Huang', 'Yanwei Yu', 'Chaofan Fu', 'Pengyang Yu'] | 2022-08-12 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-2.00836107e-01 3.45847279e-01 -6.92391217e-01 -2.94378489e-01
6.49880022e-02 -4.57427591e-01 6.09718263e-01 3.88370544e-01
8.34895745e-02 5.72997808e-01 3.44873101e-01 -3.14026028e-01
-4.18169707e-01 -1.42226279e+00 -2.44799212e-01 -4.66349751e-01
-5.38473606e-01 6.60246491e-01 4.87883776e-01 -3.40918213... | [7.215280055999756, 6.2893548011779785] |
a5406fd9-4719-448a-aeaa-9be334481dab | sparse-representer-theorems-for-learning-in | 2305.12584 | null | https://arxiv.org/abs/2305.12584v1 | https://arxiv.org/pdf/2305.12584v1.pdf | Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces | Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions. We consider two typical lear... | ['Mingsong Yan', 'Yuesheng Xu', 'Rui Wang'] | 2023-05-21 | null | null | null | null | ['sparse-learning'] | ['methodology'] | [ 9.79366750e-02 4.13153023e-01 -1.23925544e-01 -5.88631630e-02
-3.56119990e-01 -6.40143752e-02 2.00057658e-03 -3.20293427e-01
-1.12440310e-01 8.86350393e-01 1.82978913e-01 -5.48833720e-02
-4.68981028e-01 -5.53636074e-01 -8.59265149e-01 -1.02480721e+00
-1.61225468e-01 -1.50932148e-01 -4.28615242e-01 -3.01869899... | [7.241813659667969, 4.195682048797607] |
e336b211-f6ef-44b6-b42e-4995e406f02b | zero-pronoun-resolution-with-attention-based | null | null | https://aclanthology.org/C18-1002 | https://aclanthology.org/C18-1002.pdf | Zero Pronoun Resolution with Attention-based Neural Network | Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents. Typically, contextual information is utilized to encode the zero pronouns since they are simply gaps that contain no actual content. To better utiliz... | ['Wei-Nan Zhang', 'Yu Zhang', 'William Yang Wang', 'Ting Liu', 'Qingyu Yin'] | 2018-08-01 | zero-pronoun-resolution-with-attention-based-1 | https://aclanthology.org/C18-1002 | https://aclanthology.org/C18-1002.pdf | coling-2018-8 | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [ 2.87765324e-01 4.99225557e-01 -3.78106117e-01 -1.59940675e-01
-1.14010775e+00 -2.77998149e-01 5.77945054e-01 -1.01242736e-02
-6.57142162e-01 9.34803486e-01 9.02319491e-01 -1.70694441e-01
1.40262261e-01 -9.06548738e-01 -5.67410529e-01 -5.88023424e-01
1.84852257e-01 5.82097828e-01 7.20998496e-02 -7.28689551... | [10.346763610839844, 9.266100883483887] |
96896f29-f905-4b6a-b93d-e6a4efa0965c | feature-based-decipherment-for-large | 1508.02142 | null | http://arxiv.org/abs/1508.02142v1 | http://arxiv.org/pdf/1508.02142v1.pdf | Feature-based Decipherment for Large Vocabulary Machine Translation | Orthographic similarities across languages provide a strong signal for
probabilistic decipherment, especially for closely related language pairs. The
existing decipherment models, however, are not well-suited for exploiting these
orthographic similarities. We propose a log-linear model with latent variables
that incorp... | ['Iftekhar Naim', 'Daniel Gildea'] | 2015-08-10 | null | null | null | null | ['decipherment'] | ['natural-language-processing'] | [-3.04094821e-01 -5.79367816e-01 -3.01185012e-01 -4.17645514e-01
-6.29484057e-01 -6.99219763e-01 1.03348231e+00 2.19364315e-01
-5.19923866e-01 7.53043234e-01 5.26704609e-01 -3.13948452e-01
-1.56133741e-01 -8.40142548e-01 -7.20879972e-01 -4.73904341e-01
8.46367702e-02 7.81311274e-01 -1.76045597e-01 6.21353388... | [11.085809707641602, 9.555103302001953] |
3221a1dd-b446-4c74-9ff9-02361c03671e | cross-lingual-dialogue-dataset-creation-via | 2201.13405 | null | https://arxiv.org/abs/2201.13405v1 | https://arxiv.org/pdf/2201.13405v1.pdf | Cross-Lingual Dialogue Dataset Creation via Outline-Based Generation | Multilingual task-oriented dialogue (ToD) facilitates access to services and information for many (communities of) speakers. Nevertheless, the potential of this technology is not fully realised, as current datasets for multilingual ToD - both for modular and end-to-end modelling - suffer from severe limitations. 1) Whe... | ['Anna Korhonen', 'Ivan Vulić', 'Edoardo Maria Ponti', 'Evgeniia Razumovskaia', 'Olga Majewska'] | 2022-01-31 | null | null | null | null | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [-3.36239785e-01 5.14888406e-01 -1.32985935e-01 -4.93862152e-01
-9.45729911e-01 -1.08907461e+00 1.07647228e+00 8.02269429e-02
-4.10358876e-01 9.80510235e-01 7.30752468e-01 -5.26803493e-01
1.54661924e-01 -2.53755450e-01 -8.53639692e-02 -8.80990457e-03
2.40223393e-01 1.23552406e+00 6.06410503e-02 -1.02771592... | [12.65090274810791, 8.215751647949219] |
4967a996-ef60-41bc-8c84-efe0c1fb9bc0 | retrosynthesis-with-attention-based-nmt-model | 1908.00727 | null | https://arxiv.org/abs/1908.00727v1 | https://arxiv.org/pdf/1908.00727v1.pdf | Retrosynthesis with Attention-Based NMT Model and Chemical Analysis of the "Wrong" Predictions | We cast retrosynthesis as a machine translation problem by introducing a special Tensor2Tensor, an entire attention-based and fully data-driven model. Given a data set comprising about 50,000 diverse reactions extracted from USPTO patents, the model significantly outperforms seq2seq model (34.7%) on a top-1 accuracy by... | ['Ling Wang', 'Hongliang Duan', 'Jianjun Li', 'Chengyun Zhang'] | 2019-08-02 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 3.65396976e-01 2.74478614e-01 -4.89444345e-01 -9.68593732e-02
-1.24233747e+00 -9.09010708e-01 5.59250295e-01 9.72475260e-02
-2.17208907e-01 1.29888308e+00 2.41408691e-01 -7.38834977e-01
3.21744502e-01 -5.15303373e-01 -1.18921018e+00 -9.50258255e-01
3.58550400e-01 3.43380332e-01 -1.96536228e-01 -3.28443855... | [4.5033278465271, 6.101730823516846] |
5cf7891b-0909-4442-be0d-543dc5faf873 | improving-sequence-to-sequence-pre-training | 2101.00416 | null | https://arxiv.org/abs/2101.00416v2 | https://arxiv.org/pdf/2101.00416v2.pdf | Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting | In this paper, we generalize text infilling (e.g., masked language models) by proposing Sequence Span Rewriting (SSR) as a self-supervised sequence-to-sequence (seq2seq) pre-training objective. SSR provides more fine-grained learning signals for text representations by supervising the model to rewrite imperfect spans t... | ['Ke Xu', 'Canwen Xu', 'Furu Wei', 'Tao Ge', 'Wangchunshu Zhou'] | 2021-01-02 | null | https://aclanthology.org/2021.emnlp-main.45 | https://aclanthology.org/2021.emnlp-main.45.pdf | emnlp-2021-11 | ['text-infilling'] | ['natural-language-processing'] | [ 9.83040273e-01 6.58474088e-01 -5.75084463e-02 -4.39732313e-01
-1.08123171e+00 -8.69834840e-01 6.11483037e-01 -1.10007808e-01
-2.22058803e-01 9.07631636e-01 8.68568659e-01 -7.11000323e-01
7.02214003e-01 -6.82252705e-01 -1.10410738e+00 -2.36303017e-01
2.75900602e-01 2.73120970e-01 -5.46903573e-02 -6.62943840... | [11.660324096679688, 9.031847953796387] |
76d6728a-3995-45b6-8155-c794c96406db | automatically-summarizing-evidence-from | 2303.05392 | null | https://arxiv.org/abs/2303.05392v1 | https://arxiv.org/pdf/2303.05392v1.pdf | Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges | We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work, the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s)... | ['Byron C. Wallace', 'Iain J. Marshal', 'Denis Jered McInerney', 'Sanjana Ramprasad'] | 2023-03-07 | null | null | null | null | ['multi-document-summarization', 'document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.88053906e-01 1.15878463e-01 -1.04805064e+00 -3.67791086e-01
-1.39059746e+00 -7.51432359e-01 7.05218732e-01 9.81779695e-01
-4.59630817e-01 1.08907342e+00 9.49808419e-01 -8.21036041e-01
-3.78519654e-01 -4.01159853e-01 -6.05855227e-01 -2.10893407e-01
-4.45839427e-02 3.28106552e-01 -4.30469923e-02 3.27952355... | [12.29055404663086, 9.607202529907227] |
8cd03f56-211d-46fa-9fd6-8b89c5b8faaf | maximum-class-separation-as-inductive-bias-in | 2206.08704 | null | https://arxiv.org/abs/2206.08704v2 | https://arxiv.org/pdf/2206.08704v2.pdf | Maximum Class Separation as Inductive Bias in One Matrix | Maximizing the separation between classes constitutes a well-known inductive bias in machine learning and a pillar of many traditional algorithms. By default, deep networks are not equipped with this inductive bias and therefore many alternative solutions have been proposed through differential optimization. Current ap... | ['Pascal Mettes', 'Rita Cucchiara', 'Elise van der Pol', 'Max van Spengler', 'Gertjan J. Burghouts', 'Tejaswi Kasarla'] | 2022-06-17 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 1.89022884e-01 1.22667447e-01 -2.62212723e-01 -8.36257398e-01
-6.20390892e-01 -8.02727938e-01 5.39705098e-01 2.50270039e-01
-8.48664284e-01 6.47753417e-01 -2.79163450e-01 -3.64450753e-01
-2.95409083e-01 -5.46842098e-01 -7.58190930e-01 -9.34804022e-01
-1.64669231e-01 5.24954021e-01 5.35078980e-02 -6.77853748... | [8.794456481933594, 3.289363145828247] |
19071d06-d090-41b6-b481-efe7efc628bc | towards-more-realistic-generation-of | 2205.12609 | null | https://arxiv.org/abs/2205.12609v2 | https://arxiv.org/pdf/2205.12609v2.pdf | Generating Information-Seeking Conversations from Unlabeled Documents | In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants. In our baseline SIMSEEK-SYM, a questioner generates follow-up questions upon the predetermined answer by an answerer. On the contrary, SIMSEEK-ASYM first generate... | ['Jaewoo Kang', 'Kang Min Yoo', 'Sungdong Kim', 'Gangwoo Kim'] | 2022-05-25 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.02057420e-01 4.04846758e-01 1.57765284e-01 -4.03710365e-01
-1.52154410e+00 -1.00007689e+00 1.10930920e+00 -2.95827556e-02
-4.07708108e-01 7.85421014e-01 7.13362694e-01 -4.88010526e-01
1.37154341e-01 -6.48450196e-01 -3.35970134e-01 -3.90737444e-01
3.30295235e-01 1.07552397e+00 3.17151308e-01 -6.69259727... | [12.017929077148438, 7.928141117095947] |
a89aed86-9b30-47f7-a392-ef8ff2c76955 | conversational-question-answering-over | 2004.13117 | null | https://arxiv.org/abs/2004.13117v3 | https://arxiv.org/pdf/2004.13117v3.pdf | Conversational Question Answering over Passages by Leveraging Word Proximity Networks | Question answering (QA) over text passages is a problem of long-standing interest in information retrieval. Recently, the conversational setting has attracted attention, where a user asks a sequence of questions to satisfy her information needs around a topic. While this setup is a natural one and similar to humans con... | ['Magdalena Kaiser', 'Rishiraj Saha Roy', 'Gerhard Weikum'] | 2020-04-27 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 3.39071363e-01 1.62033349e-01 1.68562189e-01 -4.88452792e-01
-1.17599404e+00 -7.73091674e-01 6.51503980e-01 6.88150644e-01
-5.56257784e-01 5.81678748e-01 7.84691393e-01 -5.56576490e-01
-5.30762315e-01 -7.14704037e-01 -4.20091331e-01 -3.84105027e-01
-3.47048670e-01 7.85406590e-01 4.83087689e-01 -9.46071863... | [11.994791030883789, 7.862964630126953] |
478ad947-803b-49dd-b261-cdbbb6e1a95a | imagenet-training-in-minutes | 1709.05011 | null | http://arxiv.org/abs/1709.05011v10 | http://arxiv.org/pdf/1709.05011v10.pdf | ImageNet Training in Minutes | Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 GPU
takes 14 days. This training requires 10^18 single precision operations in
total. On the other hand, the world's current fastest supercomputer can finish
2 * 10^17 single precision operations per second (Dongarra et al 2017,
https://www.top500.o... | ['Cho-Jui Hsieh', 'Zhao Zhang', 'Yang You', 'Kurt Keutzer', 'James Demmel'] | 2017-09-14 | null | null | null | null | ['2048'] | ['playing-games'] | [-5.41332066e-01 -2.77062178e-01 -5.59190810e-02 -1.77471921e-01
-5.27484715e-01 -4.43876088e-01 1.46081924e-01 -2.51704872e-01
-1.08129048e+00 7.41865695e-01 -3.82920921e-01 -9.03983355e-01
3.69511694e-01 -1.02416921e+00 -9.49994564e-01 -5.48810184e-01
-6.89018145e-02 4.35904622e-01 2.36197516e-01 -1.35224834... | [8.534956932067871, 3.051907777786255] |
86336f23-f06b-4c9e-b328-c319feaa9918 | long-term-stability-and-generalization-of | 2205.04601 | null | https://arxiv.org/abs/2205.04601v1 | https://arxiv.org/pdf/2205.04601v1.pdf | Long-term stability and generalization of observationally-constrained stochastic data-driven models for geophysical turbulence | Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale proc... | ['Pedram Hassanzadeh', 'Wahid Bhimji', 'Ebrahim Nabizadeh', 'Jaideep Pathak', 'Ashesh Chattopadhyay'] | 2022-05-09 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-4.56175327e-01 -3.49492520e-01 4.72798228e-01 -6.40219271e-01
-5.34779251e-01 -5.41834950e-01 8.57941806e-01 -2.65862532e-02
-2.39560097e-01 1.17069066e+00 4.22984153e-01 -8.41850996e-01
1.49792343e-01 -1.18715858e+00 -7.75745690e-01 -1.01071072e+00
-3.68417561e-01 5.50505042e-01 -2.31355324e-01 -7.44760752... | [6.565045356750488, 2.9575955867767334] |
35aaeb7a-a4a8-431e-a3f5-d0a7c7928b0b | uit-saviors-at-medvqa-gi-2023-improving | 2307.02783 | null | https://arxiv.org/abs/2307.02783v1 | https://arxiv.org/pdf/2307.02783v1.pdf | UIT-Saviors at MEDVQA-GI 2023: Improving Multimodal Learning with Image Enhancement for Gastrointestinal Visual Question Answering | In recent years, artificial intelligence has played an important role in medicine and disease diagnosis, with many applications to be mentioned, one of which is Medical Visual Question Answering (MedVQA). By combining computer vision and natural language processing, MedVQA systems can assist experts in extracting relev... | ['Thien T. B. Nguyen', 'Linh N. P. Bui', 'Hao K. Tieu', 'Anh T. Vo', 'Triet M. Thai'] | 2023-07-06 | null | null | null | null | ['visual-question-answering', 'visual-question-answering-1', 'image-enhancement', 'question-answering'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [-6.69754818e-02 3.79850626e-01 1.03276901e-01 -1.20599285e-01
-8.51611733e-01 -5.91163218e-01 2.85843104e-01 4.14484978e-01
-5.76345801e-01 1.90925345e-01 3.02390695e-01 -4.56148833e-01
-7.22806109e-03 -6.03829265e-01 -4.96611834e-01 -5.62330425e-01
-4.20609936e-02 2.81856149e-01 1.03392832e-01 -4.03519899... | [11.072051048278809, 1.6182829141616821] |
d6ba4d33-6b44-4f1c-b7f2-3956107774bb | muxconv-information-multiplexing-in | 2003.1388 | null | https://arxiv.org/abs/2003.13880v2 | https://arxiv.org/pdf/2003.13880v2.pdf | MUXConv: Information Multiplexing in Convolutional Neural Networks | Convolutional neural networks have witnessed remarkable improvements in computational efficiency in recent years. A key driving force has been the idea of trading-off model expressivity and efficiency through a combination of $1\times 1$ and depth-wise separable convolutions in lieu of a standard convolutional layer. T... | ['Zhichao Lu', 'Kalyanmoy Deb', 'Vishnu Naresh Boddeti'] | 2020-03-31 | muxconv-information-multiplexing-in-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Lu_MUXConv_Information_Multiplexing_in_Convolutional_Neural_Networks_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lu_MUXConv_Information_Multiplexing_in_Convolutional_Neural_Networks_CVPR_2020_paper.pdf | cvpr-2020-6 | ['pneumonia-detection'] | ['medical'] | [ 6.06849380e-02 -4.53091860e-02 -1.91333205e-01 -1.97540686e-01
-6.11919403e-01 -4.23766196e-01 1.13011159e-01 -1.35006189e-01
-9.74990249e-01 4.46763396e-01 -3.71343642e-01 -6.56431735e-01
-2.77998984e-01 -7.08359718e-01 -8.45202029e-01 -5.28304338e-01
-1.45248875e-01 -9.56721231e-02 7.75996521e-02 3.32960486... | [8.594230651855469, 2.859525442123413] |
815d8a52-cb16-46b8-9c64-61dd4901c8b3 | efficient-deep-models-for-real-time-4k-image | null | null | https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/html/Conde_Efficient_Deep_Models_for_Real-Time_4K_Image_Super-Resolution._NTIRE_2023_CVPRW_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023W/NTIRE/papers/Conde_Efficient_Deep_Models_for_Real-Time_4K_Image_Super-Resolution._NTIRE_2023_CVPRW_2023_paper.pdf | Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report | This paper introduces a novel benchmark for efficient upscaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (x2 and x3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4... | ['and others', 'Daniel Motilla', 'Radu Timofte', 'Eduard Zamfir', 'Marcos V. Conde'] | 2023-06-01 | null | null | null | cvprw-2023-6 | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 4.71520364e-01 -4.76009727e-01 1.73009381e-01 -1.59398079e-01
-1.05365539e+00 -3.99013937e-01 4.06281620e-01 -5.44851005e-01
-4.04148936e-01 7.10610569e-01 3.01617831e-01 -1.34570792e-01
6.69862628e-02 -5.63798487e-01 -6.53708756e-01 -3.05290014e-01
-3.37000579e-01 -6.30792975e-02 4.74375278e-01 -4.07021850... | [10.98399543762207, -2.0754001140594482] |
d697c76c-dc8b-473b-bc6b-a302c7980b96 | hdmapnet-an-online-hd-map-construction-and | 2107.06307 | null | https://arxiv.org/abs/2107.06307v4 | https://arxiv.org/pdf/2107.06307v4.pdf | HDMapNet: An Online HD Map Construction and Evaluation Framework | Constructing HD semantic maps is a central component of autonomous driving. However, traditional pipelines require a vast amount of human efforts and resources in annotating and maintaining the semantics in the map, which limits its scalability. In this paper, we introduce the problem of HD semantic map learning, which... | ['Hang Zhao', 'Yilun Wang', 'Yue Wang', 'Qi Li'] | 2021-07-13 | null | null | null | null | ['hd-semantic-map-learning'] | ['computer-vision'] | [-1.24813244e-01 -2.56033335e-02 -2.48328283e-01 -7.74836540e-01
-7.42184579e-01 -6.63728058e-01 7.49133229e-01 3.73487741e-01
-3.71304244e-01 4.96596962e-01 1.25194058e-01 1.01656569e-02
-1.41632155e-01 -1.08812678e+00 -1.14853799e+00 -4.11549032e-01
1.07248186e-03 3.69702280e-01 8.47595811e-01 -2.59773761... | [7.934122085571289, -1.996225357055664] |
175dab04-bf67-4e85-8bf2-cf0031998046 | seeing-the-wood-for-the-trees-a-contrastive | null | null | https://aclanthology.org/2022.findings-naacl.82 | https://aclanthology.org/2022.findings-naacl.82.pdf | Seeing the wood for the trees: a contrastive regularization method for the low-resource Knowledge Base Question Answering | Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB. Despite sophisticated retrieval algorithms, the impact of the low-resource (incomplete) KB is not fully exploited, where contributing components (. key enti... | ['Yi Guo', 'Jack Yang', 'Xun Yao', 'Xinrong Hu', 'Shijie Mei', 'Jpliu@wtu.edu.cn Jpliu@wtu.edu.cn'] | null | null | null | null | findings-naacl-2022-7 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 8.92854035e-02 5.29697776e-01 -2.81337023e-01 -1.23976804e-01
-1.17619383e+00 -4.77219969e-01 3.84136111e-01 5.44899583e-01
-4.72622484e-01 1.19875264e+00 3.59277010e-01 -1.24692470e-01
-4.29615170e-01 -9.80383039e-01 -7.89331794e-01 -3.72402608e-01
2.09468260e-01 5.94910443e-01 6.90370023e-01 -4.18060303... | [10.778676986694336, 7.965538501739502] |
06325e7b-8ce5-4d6f-bd61-b57070b413cb | polarity-based-sarcasm-detection-using | 2304.01424 | null | https://arxiv.org/abs/2304.01424v1 | https://arxiv.org/pdf/2304.01424v1.pdf | Polarity based Sarcasm Detection using Semigraph | Sarcasm is an advanced linguistic expression often found on various online platforms. Sarcasm detection is challenging in natural language processing tasks that affect sentiment analysis. This article presents the inventive method of the semigraph, including semigraph construction and sarcasm detection processes. A var... | ['Vaibhav Khatavkar', 'Swapnil Mane'] | 2023-04-04 | null | null | null | null | ['sarcasm-detection'] | ['natural-language-processing'] | [-6.16132915e-02 5.60427070e-01 -2.35800818e-01 -5.14546633e-01
-9.41637531e-02 -6.95945263e-01 6.23508036e-01 2.96995968e-01
3.51307392e-02 3.06389421e-01 7.33425975e-01 2.37969816e-01
5.18604457e-01 -3.96317631e-01 7.01558068e-02 -4.07890111e-01
5.96442461e-01 1.51040420e-01 -1.36177972e-01 -4.56403047... | [9.108311653137207, 10.56945514678955] |
70286a42-4d57-44a0-8871-6c14831449a8 | dblface-domain-based-labels-for-nir-vis | 2010.03771 | null | https://arxiv.org/abs/2010.03771v1 | https://arxiv.org/pdf/2010.03771v1.pdf | DBLFace: Domain-Based Labels for NIR-VIS Heterogeneous Face Recognition | Deep learning-based domain-invariant feature learning methods are advancing in near-infrared and visible (NIR-VIS) heterogeneous face recognition. However, these methods are prone to overfitting due to the large intra-class variation and the lack of NIR images for training. In this paper, we introduce Domain-Based Labe... | ['Ioannis A. Kakadiaris', 'Ha Le'] | 2020-10-08 | null | null | null | null | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 2.42383495e-01 -1.73041210e-01 -3.46246541e-01 -7.46046245e-01
-7.55205393e-01 -4.09244657e-01 3.07256222e-01 -1.69808328e-01
-1.65068194e-01 5.42608202e-01 -1.24026604e-01 9.27361026e-02
-3.19617331e-01 -6.21484458e-01 -5.65466583e-01 -1.19717479e+00
3.37479889e-01 3.85825783e-01 -3.43806475e-01 -1.22846058... | [13.160568237304688, 0.5396853685379028] |
ace3e43e-e364-4915-93b4-1eada0208f9d | knowledge-graph-empowered-entity-description | 2004.14813 | null | https://arxiv.org/abs/2004.14813v2 | https://arxiv.org/pdf/2004.14813v2.pdf | ENT-DESC: Entity Description Generation by Exploring Knowledge Graph | Previous works on knowledge-to-text generation take as input a few RDF triples or key-value pairs conveying the knowledge of some entities to generate a natural language description. Existing datasets, such as WIKIBIO, WebNLG, and E2E, basically have a good alignment between an input triple/pair set and its output text... | ['Zhanming Jie', 'Liying Cheng', 'Dekun Wu', 'Yan Zhang', 'Luo Si', 'Wei Lu', 'Lidong Bing'] | 2020-04-30 | null | https://aclanthology.org/2020.emnlp-main.90 | https://aclanthology.org/2020.emnlp-main.90.pdf | emnlp-2020-11 | ['graph-to-sequence', 'kg-to-text'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.71521500e-02 7.80426025e-01 -4.57962036e-01 -3.08501780e-01
-7.95218885e-01 -7.33952820e-01 7.23292649e-01 6.12317741e-01
6.31450862e-02 1.26503265e+00 3.35475832e-01 -2.92591844e-02
-1.68954775e-01 -1.57536864e+00 -9.73339915e-01 -1.14794977e-01
1.18292093e-01 7.97237456e-01 3.68063062e-01 -5.87881923... | [9.453425407409668, 8.118066787719727] |
aad0cc10-fd97-440c-8325-459c44c910a7 | walking-on-thin-air-environment-free-physics | 1812.01203 | null | http://arxiv.org/abs/1812.01203v1 | http://arxiv.org/pdf/1812.01203v1.pdf | Walking on Thin Air: Environment-Free Physics-based Markerless Motion Capture | We propose a generative approach to physics-based motion capture. Unlike
prior attempts to incorporate physics into tracking that assume the subject and
scene geometry are calibrated and known a priori, our approach is automatic and
online. This distinction is important since calibration of the environment is
often dif... | ['Marcus A. Brubaker', 'Micha Livne', 'Leonid Sigal', 'David J. Fleet'] | 2018-12-04 | null | null | null | null | ['markerless-motion-capture'] | ['computer-vision'] | [ 8.85555968e-02 1.15640968e-01 9.64123979e-02 -7.27894977e-02
-4.97830987e-01 -6.63351536e-01 7.77647555e-01 -1.56605616e-02
-5.10427535e-01 7.36685514e-01 1.73042506e-01 2.53076792e-01
-1.43136352e-01 -5.82128048e-01 -1.00272822e+00 -5.31527579e-01
3.93558480e-03 1.01242709e+00 5.24461508e-01 -2.59255975... | [7.093757629394531, -1.023489236831665] |
69d2d5aa-b9c5-4ad9-ae88-855d1df64bfb | spelling-correction-using-phonetics-in-e | null | null | https://aclanthology.org/2022.ecnlp-1.9 | https://aclanthology.org/2022.ecnlp-1.9.pdf | Spelling Correction using Phonetics in E-commerce Search | In E-commerce search, spelling correction plays an important role to find desired products for customers in processing user-typed search queries. However, resolving phonetic errors is a critical but much overlooked area. The query with phonetic spelling errors tends to appear correct based on pronunciation but is nonet... | ['Yi Sun', 'Jingyuan Deng', 'Jia Liu', 'Yan Gao', 'Yifei Teng', 'Alireza Bagheri Garakani', 'Fan Yang'] | null | null | null | null | ecnlp-acl-2022-5 | ['spelling-correction'] | ['natural-language-processing'] | [-2.41678115e-02 -7.83908427e-01 -1.01899952e-01 -5.26849508e-01
-1.28745592e+00 -9.73346293e-01 -9.09228399e-02 3.97669852e-01
-3.18626404e-01 2.93723226e-01 -5.05873226e-02 -6.04512751e-01
-3.63272041e-01 -4.27868545e-01 -5.70870161e-01 -2.28063032e-01
6.30366445e-01 7.73356318e-01 2.04382047e-01 -3.24304432... | [10.890271186828613, 10.645601272583008] |
f9c8d0ae-7c30-47b2-b79a-4220d2bd09af | analysis-of-risk-factor-domains-in-psychosis | 1809.05752 | null | http://arxiv.org/abs/1809.05752v1 | http://arxiv.org/pdf/1809.05752v1.pdf | Analysis of Risk Factor Domains in Psychosis Patient Health Records | Readmission after discharge from a hospital is disruptive and costly,
regardless of the reason. However, it can be particularly problematic for
psychiatric patients, so predicting which patients may be readmitted is
critically important but also very difficult. Clinical narratives in
psychiatric electronic health recor... | ['Mei-Hua Hall', 'Philip Cawkwell', 'Marie Meteer', 'Eben Holderness', 'Nicholas Miller', 'Kirsten Bolton', 'James Pustejovsky'] | 2018-09-15 | analysis-of-risk-factor-domains-in-psychosis-1 | https://aclanthology.org/W18-5615 | https://aclanthology.org/W18-5615.pdf | ws-2018-10 | ['readmission-prediction'] | ['medical'] | [ 2.13366196e-01 3.82455736e-01 2.18100883e-02 -5.29477179e-01
-1.14266491e+00 -2.66495109e-01 4.65405285e-01 1.18984783e+00
-3.76757473e-01 6.83232129e-01 1.09905410e+00 -4.43867832e-01
-5.13276637e-01 -5.28909147e-01 3.24029207e-01 -1.27321258e-01
-2.47273400e-01 1.00979161e+00 -5.47230005e-01 2.01676100... | [8.46728515625, 8.434021949768066] |
5e981eae-27f1-4916-80cf-746f8fe70c4c | compm-context-modeling-with-speakers-pre | null | null | https://aclanthology.org/2022.naacl-main.416 | https://aclanthology.org/2022.naacl-main.416.pdf | CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation | As the use of interactive machines grow, the task of Emotion Recognition in Conversation (ERC) became more important. If the machine-generated sentences reflect emotion, more human-like sympathetic conversations are possible. Since emotion recognition in conversation is inaccurate if the previous utterances are not tak... | ['Wooin Lee', 'Joosung Lee'] | null | null | null | null | naacl-2022-7 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.21626697e-01 2.90690422e-01 -2.07074419e-01 -6.34575069e-01
-6.07635260e-01 -6.07259929e-01 5.81806839e-01 -1.49061128e-01
-4.63993251e-01 6.74026668e-01 6.65979922e-01 -7.60308430e-02
6.63886726e-01 -5.47972322e-01 -3.57904911e-01 -3.13948989e-01
1.05096266e-01 3.57086390e-01 -3.07444558e-02 -5.48021674... | [12.98874568939209, 6.257452487945557] |
08a96edf-642e-4c25-bcdf-cfc1b735147c | poisoning-behavioral-malware-clustering | 1811.09985 | null | http://arxiv.org/abs/1811.09985v1 | http://arxiv.org/pdf/1811.09985v1.pdf | Poisoning Behavioral Malware Clustering | Clustering algorithms have become a popular tool in computer security to
analyze the behavior of malware variants, identify novel malware families, and
generate signatures for antivirus systems. However, the suitability of
clustering algorithms for security-sensitive settings has been recently
questioned by showing tha... | ['Davide Ariu', 'Battista Biggio', 'Igino Corona', 'Giorgio Giacinto', 'Fabio Roli', 'Konrad Rieck', 'Christian Wressnegger'] | 2018-11-25 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 2.45288044e-01 -4.98114794e-01 7.43248500e-03 1.79761380e-01
-2.76484281e-01 -1.50879967e+00 7.06289589e-01 4.30026114e-01
-2.76810586e-01 3.81884426e-01 -4.54328656e-01 -8.96743596e-01
6.63962886e-02 -8.61088574e-01 -4.40239847e-01 -9.30790663e-01
-2.92483479e-01 4.66803640e-01 6.28969073e-01 1.25292782... | [5.620843887329102, 7.550308704376221] |
7d5bde7a-c804-47ba-a62b-444df863108d | redeeming-intrinsic-rewards-via-constrained | 2211.07627 | null | https://arxiv.org/abs/2211.07627v2 | https://arxiv.org/pdf/2211.07627v2.pdf | Redeeming Intrinsic Rewards via Constrained Optimization | State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (e.g., $\epsilon$-greedy) for exploration, but this method fails on hard exploration tasks like Montezuma's Revenge. To address the challenge of exploration, prior works incentivize exploration by rewarding the agent when it visits no... | ['Pulkit Agrawal', 'Joni Pajarinen', 'Zhang-Wei Hong', 'Eric Chen'] | 2022-11-14 | null | null | null | null | ['montezumas-revenge', 'atari-games'] | ['playing-games', 'playing-games'] | [-1.29020184e-01 2.78115124e-01 -5.61751187e-01 4.78191376e-02
-7.51379490e-01 -5.49236178e-01 5.11718869e-01 -2.17635080e-01
-9.89645422e-01 1.21189249e+00 1.27680004e-01 -4.76200759e-01
-2.32396394e-01 -4.56035525e-01 -6.68448687e-01 -8.17323983e-01
-1.10500105e-01 5.05161464e-01 -2.21867144e-01 -2.82216221... | [3.951296329498291, 1.7730259895324707] |
af744c6d-bff3-42ab-93ba-41a3e398b84b | learning-to-deblur | 1406.7444 | null | http://arxiv.org/abs/1406.7444v1 | http://arxiv.org/pdf/1406.7444v1.pdf | Learning to Deblur | We describe a learning-based approach to blind image deconvolution. It uses a
deep layered architecture, parts of which are borrowed from recent work on
neural network learning, and parts of which incorporate computations that are
specific to image deconvolution. The system is trained end-to-end on a set of
artificiall... | ['Bernhard Schölkopf', 'Stefan Harmeling', 'Michael Hirsch', 'Christian J. Schuler'] | 2014-06-28 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [ 1.54311150e-01 -1.10689148e-01 3.99883449e-01 -3.81214142e-01
-6.91283703e-01 -5.57400763e-01 6.34162724e-01 -5.31596363e-01
-5.50122976e-01 4.65309322e-01 5.67989707e-01 -5.47958791e-01
1.28920332e-01 -3.56755048e-01 -8.51981997e-01 -5.20176709e-01
-1.60022661e-01 3.96097153e-01 9.03563350e-02 -5.88495284... | [11.687443733215332, -2.6717329025268555] |
74f7e5a8-cbde-4a9f-ab02-633820b1c428 | internal-contrastive-learning-for-generalized | 2306.15266 | null | https://arxiv.org/abs/2306.15266v1 | https://arxiv.org/pdf/2306.15266v1.pdf | Internal Contrastive Learning for Generalized Out-of-distribution Fault Diagnosis (GOOFD) Framework | Fault diagnosis is essential in industrial processes for monitoring the conditions of important machines. With the ever-increasing complexity of working conditions and demand for safety during production and operation, different diagnosis methods are required, and more importantly, an integrated fault diagnosis system ... | ['Hongwei Wang', 'Peng Peng', 'Shuting Tao', 'Ke Ma', 'Hanrong Zhang', 'Xingyue Wang'] | 2023-06-27 | null | null | null | null | ['contrastive-learning', 'contrastive-learning', 'fault-detection'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 2.01973096e-01 -4.44380462e-01 2.45944902e-01 -1.67100027e-01
-2.64143139e-01 -2.04737082e-01 3.27573121e-01 2.67803371e-01
2.48162240e-01 3.40740502e-01 -3.14771861e-01 -1.79124787e-01
-6.86416566e-01 -5.05469382e-01 -1.43506899e-01 -9.80770707e-01
6.51608184e-02 3.24861944e-01 2.58228183e-01 1.29561350... | [7.001981735229492, 2.326874256134033] |
4fdd50ba-546c-4376-b186-cd8e1b4f5cda | kiut-knowledge-injected-u-transformer-for-1 | 2306.11345 | null | https://arxiv.org/abs/2306.11345v1 | https://arxiv.org/pdf/2306.11345v1.pdf | KiUT: Knowledge-injected U-Transformer for Radiology Report Generation | Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing. Although various image caption methods have shown remarkable performance in the natural image field, generating accurate... | ['Shaoting Zhang', 'Xiaofan Zhang', 'Zhongzhen Huang'] | 2023-06-20 | kiut-knowledge-injected-u-transformer-for | http://openaccess.thecvf.com//content/CVPR2023/html/Huang_KiUT_Knowledge-Injected_U-Transformer_for_Radiology_Report_Generation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Huang_KiUT_Knowledge-Injected_U-Transformer_for_Radiology_Report_Generation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.46464330e-01 7.24223673e-01 -4.49517250e-01 -4.43614364e-01
-1.38140237e+00 -1.52714163e-01 5.23577750e-01 2.20499054e-01
1.11443333e-01 8.17871332e-01 7.90111899e-01 -5.23873806e-01
1.21084321e-03 -6.31367862e-01 -6.84622526e-01 -3.47575307e-01
9.75467712e-02 3.58425975e-01 -3.71123850e-02 2.33238712... | [15.038041114807129, -1.422161340713501] |
aa1d5f9a-6480-443c-a79a-814ce2130494 | partial-least-square-regression-via-three | 2208.04324 | null | https://arxiv.org/abs/2208.04324v2 | https://arxiv.org/pdf/2208.04324v2.pdf | Partial Least Square Regression via Three-factor SVD-type Manifold Optimization for EEG Decoding | Partial least square regression (PLSR) is a widely-used statistical model to reveal the linear relationships of latent factors that comes from the independent variables and dependent variables. However, traditional methods to solve PLSR models are usually based on the Euclidean space, and easily getting stuck into a lo... | ['Quanying Liu', 'JianGuo Zhang', 'Zhichao Liang', 'Wanguang Yin'] | 2022-08-09 | null | null | null | null | ['eeg-decoding', 'eeg-decoding'] | ['medical', 'time-series'] | [ 7.92219117e-02 -5.69744706e-01 2.14089364e-01 -4.58932608e-01
-7.53297031e-01 -3.95483524e-01 7.11941570e-02 -7.88780451e-01
-3.11539739e-01 4.55328256e-01 2.44575590e-01 -2.65566528e-01
-4.34906155e-01 3.81326117e-02 -6.06370747e-01 -8.48349035e-01
-1.50759339e-01 -2.54061818e-01 -5.34044683e-01 -1.10268623... | [7.922762870788574, 4.438176155090332] |
764ad0d1-dbd2-43d2-a9ed-fd029c0b3be2 | panopticfusion-online-volumetric-semantic | 1903.01177 | null | https://arxiv.org/abs/1903.01177v2 | https://arxiv.org/pdf/1903.01177v2.pdf | PanopticFusion: Online Volumetric Semantic Mapping at the Level of Stuff and Things | We propose PanopticFusion, a novel online volumetric semantic mapping system at the level of stuff and things. In contrast to previous semantic mapping systems, PanopticFusion is able to densely predict class labels of a background region (stuff) and individually segment arbitrary foreground objects (things). In additi... | ['Takashi Seno', 'Yohsuke Kaji', 'Gaku Narita', 'Tomoya Ishikawa'] | 2019-03-04 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 2.56149232e-01 5.40171325e-01 -9.77559537e-02 -6.27824247e-01
-5.81750929e-01 -3.89342576e-01 6.29905522e-01 1.56073317e-01
-3.13756883e-01 5.50801277e-01 -2.52383798e-01 1.51266903e-01
2.04491675e-01 -1.37516308e+00 -1.10679328e+00 -5.64619064e-01
3.89855325e-01 1.25768173e+00 7.17912734e-01 2.12609783... | [8.63723373413086, -2.9797134399414062] |
0509458c-b685-41de-9886-c4be13228ab2 | ser-fiq-unsupervised-estimation-of-face-image | 2003.09373 | null | https://arxiv.org/abs/2003.09373v1 | https://arxiv.org/pdf/2003.09373v1.pdf | SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness | Face image quality is an important factor to enable high performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mecha... | ['Naser Damer', 'Philipp Terhörst', 'Arjan Kuijper', 'Jan Niklas Kolf', 'Florian Kirchbuchner'] | 2020-03-20 | null | null | null | null | ['face-quality-assessement', 'robust-face-recognition', 'image-quality-estimation', 'face-image-quality', 'no-reference-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 6.96807057e-02 -1.32673949e-01 6.77891448e-03 -6.82621062e-01
-6.71815574e-01 -3.90605181e-01 7.49721408e-01 -3.72435659e-01
-2.58799762e-01 4.78412300e-01 -1.50847688e-01 2.14087293e-01
-4.78569627e-01 -7.49477863e-01 -4.88903493e-01 -8.36313605e-01
1.77427262e-01 3.16207588e-01 -2.76250452e-01 8.49030763... | [13.102400779724121, 0.8447502851486206] |
b52f0db7-8eda-4c32-8076-ea216227d371 | aleda-a-free-large-scale-entity-database-for | null | null | https://aclanthology.org/L12-1664 | https://aclanthology.org/L12-1664.pdf | Aleda, a free large-scale entity database for French | Named entity recognition, which focuses on the identification of the span and type of named entity mentions in texts, has drawn the attention of the NLP community for a long time. However, many real-life applications need to know which real entity each mention refers to. For such a purpose, often refered to as entity r... | ['Beno{\\^\\i}t Sagot', 'Rosa Stern'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.59463388e-01 2.49861613e-01 -9.40601304e-02 -2.87172824e-01
-7.09011674e-01 -1.00325191e+00 7.97757745e-01 8.52086544e-01
-8.42592537e-01 1.08541274e+00 4.63656515e-01 -1.11553699e-01
5.97924590e-02 -8.80926311e-01 -4.92278814e-01 -1.88548267e-01
3.25424708e-02 7.32961893e-01 3.60254616e-01 -1.23786040... | [9.571098327636719, 9.309906959533691] |
cbaf2e55-e811-432b-8bb3-74fd3386d4ae | clip-for-all-things-zero-shot-sketch-based | 2303.1344 | null | https://arxiv.org/abs/2303.13440v3 | https://arxiv.org/pdf/2303.13440v3.pdf | CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not | In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best... | ['Yi-Zhe Song', 'Tao Xiang', 'Subhadeep Koley', 'Pinaki Nath Chowdhury', 'Ayan Kumar Bhunia', 'Aneeshan Sain'] | 2023-03-23 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.pdf | cvpr-2023-1 | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.61679733e-01 -1.21761858e-01 -1.83944851e-01 -1.07290909e-01
-1.10115039e+00 -8.48336279e-01 8.26926410e-01 -2.12621808e-01
-1.07264102e-01 3.05275172e-01 2.73865998e-01 -2.13818908e-01
-5.08415401e-01 -5.22926688e-01 -6.52480304e-01 -6.47809446e-01
-1.18926698e-02 4.53063935e-01 1.45294100e-01 -5.36383569... | [11.619354248046875, 0.6086273193359375] |
df73a89a-fbf2-4365-822a-b187e2b9f949 | survival-instinct-in-offline-reinforcement | 2306.03286 | null | https://arxiv.org/abs/2306.03286v1 | https://arxiv.org/pdf/2306.03286v1.pdf | Survival Instinct in Offline Reinforcement Learning | We present a novel observation about the behavior of offline reinforcement learning (RL) algorithms: on many benchmark datasets, offline RL can produce well-performing and safe policies even when trained with "wrong" reward labels, such as those that are zero everywhere or are negatives of the true rewards. This phenom... | ['Ching-An Cheng', 'Andrey Kolobov', 'Dipendra Misra', 'Anqi Li'] | 2023-06-05 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-1.83655590e-01 4.72454578e-01 -7.61136949e-01 -1.96860239e-01
-4.55296338e-01 -8.23531628e-01 3.51896375e-01 2.84927607e-01
-7.33236551e-01 1.06287515e+00 1.71251521e-02 -5.45058131e-01
-2.70603508e-01 -7.36544549e-01 -1.02411246e+00 -1.00385737e+00
-5.88043272e-01 3.96084726e-01 -2.32149616e-01 -2.39385605... | [4.1658782958984375, 2.393610715866089] |
5eaaec3a-61a7-4719-ab36-9fdd41c496f2 | the-analysis-of-synonymy-and-antonymy-in | 2208.04479 | null | https://arxiv.org/abs/2208.04479v1 | https://arxiv.org/pdf/2208.04479v1.pdf | The Analysis of Synonymy and Antonymy in Discourse Relations: An interpretable Modeling Approach | The idea that discourse relations are construed through explicit content and shared, or implicit, knowledge between producer and interpreter is ubiquitous in discourse research and linguistics. However, the actual contribution of the lexical semantics of arguments is unclear. We propose a computational approach to the ... | ['J. Hermosillo-Valadez', 'A. Taroni', 'M. Toledo-Acosta', 'E. Morales-González', 'D. Torres-Moreno', 'A. Reig-Alamillo'] | 2022-08-09 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 2.50618428e-01 5.72099686e-01 -7.40888476e-01 -3.35708708e-01
3.48030552e-02 -7.96989739e-01 1.13815486e+00 8.12690794e-01
-1.47886053e-01 6.16082430e-01 1.14450252e+00 -9.47678030e-01
-4.44491297e-01 -7.02926636e-01 -2.09516704e-01 -3.38142186e-01
3.02307963e-01 2.21924961e-01 4.30927098e-01 -7.36440778... | [10.691957473754883, 9.465415000915527] |
687819f5-2c9d-4a39-9490-7ba8cef6f675 | coadnet-collaborative-aggregation-and | 2011.04887 | null | https://arxiv.org/abs/2011.04887v1 | https://arxiv.org/pdf/2011.04887v1.pdf | CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection | Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. In this paper, we present an end-to-end... | ['Yao Zhao', 'Chongyi Li', 'Junhui Hou', 'Runmin Cong', 'Qijian Zhang'] | 2020-11-10 | null | http://proceedings.neurips.cc/paper/2020/hash/4dc3ed26a29c9c3df3ec373524377a5b-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/4dc3ed26a29c9c3df3ec373524377a5b-Paper.pdf | neurips-2020-12 | ['co-saliency-detection'] | ['computer-vision'] | [ 4.35891390e-01 -5.31072281e-02 -1.91925898e-01 -4.47715938e-01
-4.76197571e-01 7.44273886e-02 4.30201054e-01 3.13720465e-01
-2.41805121e-01 3.68175745e-01 4.56249118e-01 4.21478957e-01
-7.18964711e-02 -4.36909765e-01 -6.49414957e-01 -5.90790570e-01
6.17281944e-02 -6.33882657e-02 1.06384027e+00 -2.58227736... | [9.818048477172852, -0.31079959869384766] |
edd8cf36-e9ad-4a8f-82e7-94ece3486e25 | semantic-enhanced-differentiable-search-index | 2305.15115 | null | https://arxiv.org/abs/2305.15115v1 | https://arxiv.org/pdf/2305.15115v1.pdf | Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies | Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single ... | ['Xueqi Cheng', 'Dawei Yin', 'Shuaiqiang Wang', 'Zuowei Zhu', 'Jiangui Chen', 'Jiafeng Guo', 'Ruqing Zhang', 'Yubao Tang'] | 2023-05-24 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 5.62029362e-01 -1.26231581e-01 -1.75421223e-01 -3.14135820e-01
-7.03356922e-01 -6.57960415e-01 9.03311789e-01 4.82811183e-01
-6.87661707e-01 3.62817168e-01 3.81274045e-01 -1.47957146e-01
-5.50317943e-01 -7.57822275e-01 -6.50442779e-01 -2.52037644e-01
2.27548033e-01 6.53880060e-01 2.73575842e-01 -4.59997356... | [11.417061805725098, 7.743794918060303] |
888614c9-6ebe-4283-9bff-41b6c11766d7 | leaper-modeling-cloud-fpga-based-systems-via | 2208.10606 | null | https://arxiv.org/abs/2208.10606v2 | https://arxiv.org/pdf/2208.10606v2.pdf | LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning | Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and resource usage models that enable fast early-stage design space exploration. First, training requires large amounts of data (features extracte... | ['Onur Mutlu', 'Henk Corporaal', 'Sander Stuijk', 'Juan Gómez-Luna', 'Dionysios Diamantopoulos', 'Gagandeep Singh'] | 2022-08-22 | null | null | null | null | ['design-synthesis'] | ['adversarial'] | [-1.19305745e-01 -6.60301507e-01 -4.78041172e-01 -3.31895262e-01
-5.60262263e-01 -2.04606220e-01 2.17479646e-01 3.44770849e-01
-1.68271095e-01 4.63974983e-01 -3.15388799e-01 -8.34380448e-01
3.46127860e-02 -8.85781765e-01 -5.27098477e-01 -3.44879538e-01
-2.81727295e-02 2.48070046e-01 1.81622416e-01 -1.26520038... | [6.048603057861328, 3.4113962650299072] |
418d0252-5361-4584-859b-d8e91eb9212c | gesture-based-arabic-sign-language | 2203.05602 | null | https://arxiv.org/abs/2203.05602v1 | https://arxiv.org/pdf/2203.05602v1.pdf | Gesture based Arabic Sign Language Recognition for Impaired People based on Convolution Neural Network | The Arabic Sign Language has endorsed outstanding research achievements for identifying gestures and hand signs using the deep learning methodology. The term "forms of communication" refers to the actions used by hearing-impaired people to communicate. These actions are difficult for ordinary people to comprehend. The ... | ['Ahmed I. Taloba', 'Osama R. Shahin', 'Rady El Rwelli'] | 2022-03-10 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [-2.62400389e-01 -4.19168085e-01 -5.06544709e-02 -3.13000917e-01
-6.76933676e-02 -5.70390165e-01 4.41497833e-01 -9.87516224e-01
-7.43477583e-01 4.52182978e-01 4.22357619e-01 -2.22663909e-01
-3.79891843e-01 -4.27765250e-01 9.31228697e-02 -9.29341197e-01
1.31320786e-02 1.70520991e-01 -3.24402303e-02 -4.73833144... | [9.066426277160645, -6.3672614097595215] |
b1c9def0-a3ca-4286-9b8a-1eae5fc12f0b | dom-lm-learning-generalizable-representations | 2201.10608 | null | https://arxiv.org/abs/2201.10608v1 | https://arxiv.org/pdf/2201.10608v1.pdf | DOM-LM: Learning Generalizable Representations for HTML Documents | HTML documents are an important medium for disseminating information on the Web for human consumption. An HTML document presents information in multiple text formats including unstructured text, structured key-value pairs, and tables. Effective representation of these documents is essential for machine understanding to... | ['Huan Sun', 'Binxuan Huang', 'Colin Lockard', 'Prashant Shiralkar', 'Xiang Deng'] | 2022-01-25 | null | null | null | null | ['open-information-extraction'] | ['natural-language-processing'] | [ 3.81336957e-01 2.33854726e-01 -6.98157191e-01 -2.01480329e-01
-1.17690825e+00 -7.37874448e-01 8.10216725e-01 5.93580127e-01
-4.01229113e-02 5.22413552e-01 5.89786768e-01 -6.16651177e-01
-9.93293002e-02 -9.65708673e-01 -7.21614838e-01 -1.94495544e-01
-6.16425797e-02 3.86625707e-01 4.13239211e-01 -1.18436076... | [9.84322452545166, 7.9509429931640625] |
977ea9f0-1e58-4579-8c00-205f0dfa436b | emg-wrist-hand-motion-recognition-system-for | 1903.06764 | null | http://arxiv.org/abs/1903.06764v1 | http://arxiv.org/pdf/1903.06764v1.pdf | EMG wrist-hand motion recognition system for real-time Embedded platform | Electromyography (EMG) signal analysis is a popular method for controlling
prosthetic and gesture control equipment. For portable systems, such as
prosthetic limbs, real-time low-power operation on embedded processors is
critical, but to date, there has been no record of how existing EMG analysis
approaches support suc... | [] | 2019-03-15 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 5.86708963e-01 -1.78635821e-01 -2.08529696e-01 6.31472319e-02
-7.48648524e-01 -3.48601907e-01 -4.10287902e-02 -4.21350509e-01
-7.64489293e-01 7.81035841e-01 -1.21841289e-01 -3.89473081e-01
-5.13535261e-01 -3.33282053e-02 -3.17262918e-01 -4.86400187e-01
-4.52420503e-01 1.46803886e-01 1.47186741e-01 4.55581554... | [6.847260475158691, 0.1832403987646103] |
ee7f2c30-fe0b-4cca-bba9-d72f5b9c35f9 | constructing-a-visual-relationship | 2010.05185 | null | https://arxiv.org/abs/2010.05185v1 | https://arxiv.org/pdf/2010.05185v1.pdf | Constructing a Visual Relationship Authenticity Dataset | A visual relationship denotes a relationship between two objects in an image, which can be represented as a triplet of (subject; predicate; object). Visual relationship detection is crucial for scene understanding in images. Existing visual relationship detection datasets only contain true relationships that correctly ... | ['Yuta Nakashima', 'Mishra Vipul', 'Yuto Takebayashi', 'Chenhui Chu'] | 2020-10-11 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.58630505e-01 1.69889092e-01 -2.78692305e-01 -7.41738975e-01
-3.09411794e-01 -9.52207804e-01 7.77182460e-01 3.20324421e-01
-2.21344829e-02 5.84689975e-01 4.07345712e-01 -2.31520355e-01
2.42214665e-01 -6.72969103e-01 -9.84586239e-01 -3.63705933e-01
2.83085078e-01 -4.26143520e-02 3.03368181e-01 -8.83678049... | [10.368066787719727, 1.6159604787826538] |
e1520652-8559-467f-bd9d-dc39f147097f | network-resource-allocation-strategy-based-on | 2202.03193 | null | https://arxiv.org/abs/2202.03193v1 | https://arxiv.org/pdf/2202.03193v1.pdf | Network Resource Allocation Strategy Based on Deep Reinforcement Learning | The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined ... | ['Peiying Zhang', 'Xinhong You', 'Youxiang Duan', 'Junsan Zhang', 'Chao Wang', 'Shidong Zhang'] | 2022-02-03 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-3.42417359e-01 -2.12212071e-01 -6.49578691e-01 3.00832808e-01
3.56600106e-01 -2.05831289e-01 5.34469448e-02 -6.07074380e-01
-7.91534632e-02 1.13668299e+00 -2.89129615e-01 -7.85504997e-01
-5.48694909e-01 -8.31022263e-01 -4.75855142e-01 -5.71521461e-01
-3.39247644e-01 6.90678418e-01 -1.41655114e-02 -3.63143414... | [5.842926502227783, 1.7336089611053467] |
ccea8d9b-6599-4ac4-a485-c11b923a98b7 | hgt-a-hierarchical-gcn-based-transformer-for | 2305.18022 | null | https://arxiv.org/abs/2305.18022v1 | https://arxiv.org/pdf/2305.18022v1.pdf | HGT: A Hierarchical GCN-Based Transformer for Multimodal Periprosthetic Joint Infection Diagnosis Using CT Images and Text | Prosthetic Joint Infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disp... | ['Hongwei Shi', 'Xianjie Liu', 'Fujun Yang', 'Ruiyang Li'] | 2023-05-29 | null | null | null | null | ['computed-tomography-ct'] | ['methodology'] | [ 3.52193266e-01 7.44696110e-02 -4.10042591e-02 -5.96946850e-03
-1.10746646e+00 -9.70467180e-02 5.23265898e-01 4.42329049e-01
-4.46377039e-01 6.58571362e-01 2.22770959e-01 -3.93579990e-01
-4.65927631e-01 -5.73498547e-01 -1.86422646e-01 -7.16084182e-01
-4.13188398e-01 8.45364928e-01 6.41167015e-02 1.13072067... | [15.028552055358887, -2.049055814743042] |
c4491e3d-0f7a-44c1-b839-bfddc2032ff5 | keypoint-graspnet-keypoint-based-6-dof-grasp | 2209.08752 | null | https://arxiv.org/abs/2209.08752v4 | https://arxiv.org/pdf/2209.08752v4.pdf | Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input | Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern. Alternatively, we explore the grasp generation from the RGB-D input in this paper. The proposed solution, Keypoint-GraspNet, detects the projection of ... | ['Patricio Vela', 'Ruinian Xu', 'Yunzhi Lin', 'Yiye Chen'] | 2022-09-19 | null | null | null | null | ['grasp-generation'] | ['computer-vision'] | [-2.51040936e-01 -2.25522831e-01 2.05634758e-02 1.80671606e-02
-7.60227799e-01 -6.36303008e-01 1.96946025e-01 -1.67420924e-01
-1.92726284e-01 3.22709084e-01 -1.07237972e-01 1.73210680e-01
-4.47708994e-01 -6.28422916e-01 -8.85659695e-01 -8.26409817e-01
-5.56741059e-01 7.30134726e-01 2.89702654e-01 -3.34710896... | [5.85009241104126, -0.9018558263778687] |
00194a02-715d-4838-aa66-59283fba0d8b | bi-directional-domain-adaptation-for-sim2real | 2011.12421 | null | https://arxiv.org/abs/2011.12421v2 | https://arxiv.org/pdf/2011.12421v2.pdf | Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents | Deep reinforcement learning models are notoriously data hungry, yet real-world data is expensive and time consuming to obtain. The solution that many have turned to is to use simulation for training before deploying the robot in a real environment. Simulation offers the ability to train large numbers of robots in paral... | ['Dhruv Batra', 'Sonia Chernova', 'Joanne Truong'] | 2020-11-24 | null | null | null | null | ['pointgoal-navigation'] | ['robots'] | [-3.21024716e-01 -1.72688439e-01 1.75943404e-01 -1.83867678e-01
-6.21185958e-01 -5.45457184e-01 5.96309900e-01 1.58492662e-02
-9.87199664e-01 9.98615503e-01 -3.29510868e-01 -6.83752477e-01
-9.04976577e-02 -7.24048793e-01 -9.23973262e-01 -5.76071858e-01
-3.40690196e-01 7.76042581e-01 4.34274048e-01 -6.71739161... | [4.573051452636719, 1.1913447380065918] |
1619af34-02d5-442e-8a4c-17f473ce6f7d | nonparametric-causal-discovery-with | 2306.1652 | null | https://arxiv.org/abs/2306.16520v1 | https://arxiv.org/pdf/2306.16520v1.pdf | Nonparametric causal discovery with applications to cancer bioinformatics | Many natural phenomena are intrinsically causal. The discovery of the cause-effect relationships implicit in these processes can help us to understand and describe them more effectively, which boils down to causal discovery about the data and variables that describe them. However, causal discovery is not an easy task. ... | ['Jean Pierre Gomez'] | 2023-06-28 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 4.74959642e-01 8.83392543e-02 -5.07769704e-01 -3.70302290e-01
-1.36012241e-01 -3.98649096e-01 7.18550920e-01 6.58613026e-01
4.44547273e-02 9.99908090e-01 3.74919772e-01 -5.39573193e-01
-1.08139479e+00 -1.02832472e+00 -6.38529778e-01 -1.00942695e+00
-7.20554948e-01 6.15800321e-01 -2.25896299e-01 2.00647078... | [7.853438377380371, 5.351152420043945] |
a728e72c-0025-40a2-a5a6-10d499934105 | latent-compositional-representations-improve | 2007.00266 | null | https://arxiv.org/abs/2007.00266v3 | https://arxiv.org/pdf/2007.00266v3.pdf | Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering | Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer. However, state-of-the-art models in grounded question answering often do not explicitly perform decomposition, leading to difficulties in generalization to out-of-distri... | ['Jonathan Berant', 'Ben Bogin', 'Matt Gardner', 'Sanjay Subramanian'] | 2020-07-01 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 1.95510983e-01 7.68829942e-01 -7.27618262e-02 -6.27037227e-01
-1.70659077e+00 -1.00724030e+00 4.66860682e-01 3.52495730e-01
-3.30434330e-02 5.81210136e-01 5.45660853e-01 -7.75131166e-01
1.00384638e-01 -1.24889874e+00 -8.76429558e-01 -8.97628888e-02
2.77108103e-01 8.74400795e-01 2.34610528e-01 -4.04155552... | [10.767043113708496, 7.885897636413574] |
2b14ae4f-93ab-425d-9eca-34380a3ce2b3 | understanding-performance-of-long-document | 2207.01262 | null | https://arxiv.org/abs/2207.01262v1 | https://arxiv.org/pdf/2207.01262v1.pdf | Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding | We carry out a comprehensive evaluation of 13 recent models for ranking of long documents using two popular collections (MS MARCO documents and Robust04). Our model zoo includes two specialized Transformer models (such as Longformer) that can process long documents without the need to split them. Along the way, we docu... | ['Eric Nyberg', 'Jeffrey Huang', 'Yutian Zhao', 'Fangwei Gao', 'Tianyi Lin', 'Leonid Boytsov'] | 2022-07-04 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [-4.38115671e-02 -2.96750486e-01 -3.73858213e-02 -2.11390600e-01
-1.53599191e+00 -1.28275621e+00 1.33546698e+00 5.20127356e-01
-6.17894828e-01 7.81161845e-01 6.32240057e-01 -5.87616205e-01
-5.12770236e-01 -3.05652589e-01 -5.97513795e-01 -5.26577353e-01
-9.95654538e-02 8.87606621e-01 6.71232402e-01 -4.50775415... | [11.564288139343262, 7.7965779304504395] |
1c500577-d877-4b36-8d9a-05b1e01c9f1c | an-efficient-drug-drug-interactions | 2212.094 | null | https://arxiv.org/abs/2212.09400v2 | https://arxiv.org/pdf/2212.09400v2.pdf | An Efficient Drug-Drug Interactions Prediction Technology for Molecularly Intelligent Manufacturing | Drug-Drug Interactions (DDIs) prediction is an essential issue in the molecular field. Traditional methods of observing DDIs in medical experiments require plenty of resources and labor. In this paper, we present a computational model dubbed MedKGQA based on Graph Neural Networks to automatically predict the DDIs after... | ['Jian-Cheng Ni', 'Feng Gao', 'Peng Gao'] | 2022-12-19 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 3.10010701e-01 5.97002327e-01 -4.26937610e-01 -2.85364807e-01
-5.70889831e-01 -3.76871288e-01 4.53791767e-01 1.00279391e+00
2.27331683e-01 1.31750989e+00 3.72242294e-02 -8.44212711e-01
-7.28347778e-01 -1.02557540e+00 -9.99034941e-01 -5.02820790e-01
-2.70169735e-01 1.13318443e+00 1.58119723e-01 -2.52263069... | [5.296639442443848, 5.954298496246338] |
2139090d-b320-4012-a178-af6f25f43de7 | reinforcement-learning-for-personalized | 1908.00286 | null | https://arxiv.org/abs/1908.00286v1 | https://arxiv.org/pdf/1908.00286v1.pdf | Reinforcement Learning for Personalized Dialogue Management | Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize ... | ['Frank van Harmelen', 'Mark Hoogendoorn', 'Floris den Hengst', 'Joost Bosman'] | 2019-08-01 | null | null | null | null | ['product-recommendation', 'dialogue-management'] | ['miscellaneous', 'natural-language-processing'] | [ 1.04048394e-01 4.33273166e-01 -4.67331827e-01 -5.31873405e-01
-5.50314426e-01 -8.46075892e-01 1.13899732e+00 5.28229952e-01
-7.56433725e-01 9.31930184e-01 4.91277874e-01 -3.11386317e-01
-2.55821973e-01 -7.54932523e-01 -1.15744613e-01 -5.68157017e-01
1.17783897e-01 9.43793654e-01 5.20831466e-01 -9.51002121... | [13.039886474609375, 7.977753639221191] |
022f2c6e-1ece-4e27-ae46-1e06dd8ba313 | injecting-relational-structural | 1806.08009 | null | http://arxiv.org/abs/1806.08009v1 | http://arxiv.org/pdf/1806.08009v1.pdf | Injecting Relational Structural Representation in Neural Networks for Question Similarity | Effectively using full syntactic parsing information in Neural Networks (NNs)
to solve relational tasks, e.g., question similarity, is still an open problem.
In this paper, we propose to inject structural representations in NNs by (i)
learning an SVM model using Tree Kernels (TKs) on relatively few pairs of
questions (... | ['Alessandro Moschitti', 'Antonio Uva', 'Daniele Bonadiman'] | 2018-06-20 | injecting-relational-structural-1 | https://aclanthology.org/P18-2046 | https://aclanthology.org/P18-2046.pdf | acl-2018-7 | ['question-similarity'] | ['natural-language-processing'] | [ 3.03085685e-01 3.86559129e-01 1.71869248e-01 -6.34646058e-01
-1.00830436e+00 -6.71351254e-01 4.06878501e-01 4.21846211e-01
-5.47579348e-01 4.91167277e-01 1.74377598e-02 -8.02763462e-01
-5.20583689e-02 -1.11157012e+00 -8.50479245e-01 -6.29502386e-02
4.72744495e-01 4.77026105e-01 6.54679477e-01 -4.54662770... | [11.205436706542969, 8.06191349029541] |
fe7b7e92-95d4-4947-a2fb-694ae477e817 | generative-flow-networks-a-markov-chain | 2307.01422 | null | https://arxiv.org/abs/2307.01422v1 | https://arxiv.org/pdf/2307.01422v1.pdf | Generative Flow Networks: a Markov Chain Perspective | While Markov chain Monte Carlo methods (MCMC) provide a general framework to sample from a probability distribution defined up to normalization, they often suffer from slow convergence to the target distribution when the latter is highly multi-modal. Recently, Generative Flow Networks (GFlowNets) have been proposed as ... | ['Yoshua Bengio', 'Tristan Deleu'] | 2023-07-04 | null | null | null | null | ['decision-making'] | ['reasoning'] | [ 1.09678574e-01 1.11041464e-01 -5.47871351e-01 1.06000900e-02
-1.57427356e-01 -7.59101331e-01 1.06969178e+00 -2.08495319e-01
-1.54890478e-01 7.67388523e-01 3.08342397e-01 -5.02110183e-01
-3.51582527e-01 -1.16752219e+00 -2.43764326e-01 -7.95349300e-01
-1.00564279e-01 7.83102155e-01 2.68740654e-01 1.99199438... | [6.903027534484863, 3.967801094055176] |
fe21bea9-845e-4934-ab72-b97e4e562a52 | pricing-football-players-using-neural | 1711.05865 | null | http://arxiv.org/abs/1711.05865v2 | http://arxiv.org/pdf/1711.05865v2.pdf | Pricing Football Players using Neural Networks | We designed a multilayer perceptron neural network to predict the price of a
football (soccer) player using data on more than 15,000 players from the
football simulation video game FIFA 2017. The network was optimized by
experimenting with different activation functions, number of neurons and
layers, learning rate and ... | ['Sourya Dey'] | 2017-11-16 | null | null | null | null | ['l2-regularization', 'game-of-football'] | ['methodology', 'playing-games'] | [-5.49325049e-01 -2.26569802e-01 -6.00935161e-01 -2.94426084e-01
-1.37615234e-01 -1.78742319e-01 4.88117151e-02 3.65155973e-02
-1.29115307e+00 7.21641839e-01 -1.43052340e-01 -4.10150617e-01
-4.05246586e-01 -7.14001715e-01 -8.76935601e-01 -2.82111883e-01
-6.01096392e-01 5.81537426e-01 4.61600542e-01 -4.23788577... | [3.498016834259033, 1.4335614442825317] |
6d64f269-4331-4942-97cb-ecf778b0a2af | learning-to-reason-with-relational | 2210.02615 | null | https://arxiv.org/abs/2210.02615v2 | https://arxiv.org/pdf/2210.02615v2.pdf | Learning to Reason With Relational Abstractions | Large language models have recently shown promising progress in mathematical reasoning when fine-tuned with human-generated sequences walking through a sequence of solution steps. However, the solution sequences are not formally structured and the resulting model-generated sequences may not reflect the kind of systemat... | ['James L. McClelland', 'Chelsea Finn', 'Mengye Ren', 'Andrew J. Nam'] | 2022-10-06 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 3.25518638e-01 6.12502158e-01 -9.62768793e-02 -3.85003895e-01
-7.63727188e-01 -8.24694574e-01 8.59007001e-01 3.30862641e-01
-1.78560033e-01 5.92366457e-01 3.20386410e-01 -8.78245115e-01
-1.57013640e-01 -1.19207990e+00 -7.77992070e-01 -2.13378463e-02
-1.72614437e-02 7.67868519e-01 2.05586344e-01 -5.93140900... | [9.32018756866455, 7.335941314697266] |
3be03751-4df8-4e3f-9b2f-afeabe1dd28c | stl-cqa-structure-based-transformers-with | null | null | https://aclanthology.org/2020.emnlp-main.264 | https://aclanthology.org/2020.emnlp-main.264.pdf | STL-CQA: Structure-based Transformers with Localization and Encoding for Chart Question Answering | Chart Question Answering (CQA) is the task of answering natural language questions about visualisations in the chart image. Recent solutions, inspired by VQA approaches, rely on image-based attention for question/answering while ignoring the inherent chart structure. We propose STL-CQA which improves the question/answe... | ['Sumit Shekhar', 'Hrituraj Singh'] | null | null | null | null | emnlp-2020-11 | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 2.94176549e-01 1.61613211e-01 2.54578680e-01 -2.76812553e-01
-1.31169641e+00 -1.07901037e+00 7.07787573e-01 5.65617144e-01
1.87703773e-01 3.86966646e-01 6.07199192e-01 -8.39109838e-01
-1.59797639e-01 -8.04184139e-01 -8.95043433e-01 -1.71174541e-01
1.39827490e-01 6.47149801e-01 4.25133139e-01 -1.89416111... | [11.145679473876953, 2.0340678691864014] |
ea4a96c4-36a3-4231-a4d1-e01e2247c379 | longer-version-for-deep-context-encoding | 2105.14538 | null | https://arxiv.org/abs/2105.14538v1 | https://arxiv.org/pdf/2105.14538v1.pdf | Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning" | Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency. In this work, we propose a new context-driven encoding network to automatically generate medical reports for retinal images. The proposed model is mainly c... | ['Marcel Worring', 'Chao-Han Huck Yang', 'Ting-Wei Wu', 'Jia-Hong Huang'] | 2021-05-30 | null | null | null | null | ['medical-report-generation'] | ['medical'] | [ 4.11772549e-01 6.03957735e-02 1.77360103e-02 -3.78646493e-01
-1.23731899e+00 -1.50144801e-01 5.09352684e-01 1.83685407e-01
-3.99047077e-01 7.86179602e-01 5.20862937e-01 -6.05557337e-02
9.24213231e-02 -5.45394242e-01 -4.00589645e-01 -4.42920476e-01
2.26546034e-01 4.16959226e-02 3.48677114e-02 1.22687429... | [15.05052375793457, -1.3886631727218628] |
ced60550-9f5b-4e24-bf65-d309d62737c7 | image-enhancement-for-remote | 2303.09336 | null | https://arxiv.org/abs/2303.09336v1 | https://arxiv.org/pdf/2303.09336v1.pdf | Image Enhancement for Remote Photoplethysmography in a Low-Light Environment | With the improvement of sensor technology and significant algorithmic advances, the accuracy of remote heart rate monitoring technology has been significantly improved. Despite of the significant algorithmic advances, the performance of rPPG algorithm can degrade in the long-term, high-intensity continuous work occurre... | ['Jianhua Wang', 'Xingming Wu', 'Changchen Zhao', 'Weihai Chen', 'Lin Xi'] | 2023-03-16 | null | null | null | null | ['face-detection', 'image-enhancement', 'heart-rate-estimation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 3.06181997e-01 -4.49531376e-01 2.50709981e-01 1.77388564e-02
1.47804618e-01 -2.48211384e-01 -1.31300241e-01 -3.91172498e-01
-1.56045303e-01 8.58181238e-01 -8.41497406e-02 2.81290621e-01
1.12731298e-02 -5.44683397e-01 -2.89458949e-02 -9.78105962e-01
1.20401278e-01 -6.19185627e-01 -6.82073683e-02 1.51793644... | [13.873282432556152, 2.8006670475006104] |
a2729b3a-c54e-40c9-8075-5ef08e9ee9dc | avformer-injecting-vision-into-frozen-speech | 2303.16501 | null | https://arxiv.org/abs/2303.16501v1 | https://arxiv.org/pdf/2303.16501v1.pdf | AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR | Audiovisual automatic speech recognition (AV-ASR) aims to improve the robustness of a speech recognition system by incorporating visual information. Training fully supervised multimodal models for this task from scratch, however is limited by the need for large labelled audiovisual datasets (in each downstream domain o... | ['Cordelia Schmid', 'Arsha Nagrani', 'Paul Hongsuck Seo'] | 2023-03-29 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Seo_AVFormer_Injecting_Vision_Into_Frozen_Speech_Models_for_Zero-Shot_AV-ASR_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Seo_AVFormer_Injecting_Vision_Into_Frozen_Speech_Models_for_Zero-Shot_AV-ASR_CVPR_2023_paper.pdf | cvpr-2023-1 | ['robust-speech-recognition'] | ['speech'] | [ 2.30129912e-01 2.64908582e-01 -8.83738101e-02 -1.82731003e-01
-1.36544907e+00 -9.47873771e-01 1.00080311e+00 -1.61228806e-01
-4.90638703e-01 2.19796166e-01 5.95904827e-01 -5.80151975e-01
4.81113076e-01 5.94067201e-02 -8.70015204e-01 -7.44784892e-01
1.21209532e-01 4.62004930e-01 1.76076517e-01 -2.35876352... | [14.370651245117188, 5.082764148712158] |
462047da-3fb7-4309-8009-296e8ed840b3 | blind-image-deconvolution-using-variational | 2202.00179 | null | https://arxiv.org/abs/2202.00179v3 | https://arxiv.org/pdf/2202.00179v3.pdf | Blind Image Deconvolution Using Variational Deep Image Prior | Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, they fail to generalize well to blurs unseen in the training dataset. Thus, training image-specific models is important for highe... | ['Yee-Hong Yang', 'Rafsanjany Kushol', 'Abbas Masoumzadeh', 'Dong Huo'] | 2022-02-01 | null | null | null | null | ['image-deconvolution'] | ['computer-vision'] | [-3.34930383e-02 -4.24878523e-02 8.44391286e-02 -3.74641329e-01
-4.03354079e-01 -3.69240046e-01 3.73438001e-01 -8.60086918e-01
-2.75084794e-01 6.20027184e-01 2.59084851e-01 -7.18321353e-02
-1.07758380e-01 -4.09447283e-01 -9.59673584e-01 -1.10127056e+00
4.83314425e-01 4.03448269e-02 2.04827130e-01 -1.07945234... | [11.592201232910156, -2.6627037525177] |
be9aa421-0b3a-4922-98b5-b95e24ca2349 | tess-text-to-text-self-conditioned-simplex | 2305.08379 | null | https://arxiv.org/abs/2305.08379v1 | https://arxiv.org/pdf/2305.08379v1.pdf | TESS: Text-to-Text Self-Conditioned Simplex Diffusion | Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive text generation, applying diffusion models to natural language remains challenging due to its discrete nature. In this work... | ['Arman Cohan', 'Matthew E. Peters', 'Iz Beltagy', 'James Henderson', 'Hamish Ivison', 'Jaesung Tae', 'Rabeeh Karimi Mahabadi'] | 2023-05-15 | null | null | null | null | ['paraphrase-generation', 'question-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing', 'natural-language-processing'] | [ 5.63605964e-01 4.49397892e-01 -1.26826361e-01 -2.52499759e-01
-9.65372086e-01 -4.82517391e-01 1.21863794e+00 -1.67394914e-02
-3.00312459e-01 8.60166728e-01 1.09190059e+00 -6.26180291e-01
6.99274987e-02 -8.91841233e-01 -7.81935215e-01 -4.02078331e-01
4.39471781e-01 1.01573241e+00 -3.52878809e-01 -5.41700304... | [11.889513969421387, 9.123397827148438] |
9821f863-cd5e-4521-9014-6118f5188c1c | greek-sign-language-recognition-for-the-sl | null | null | https://aclanthology.org/2022.sltat-1.12 | https://aclanthology.org/2022.sltat-1.12.pdf | Greek Sign Language Recognition for the SL-ReDu Learning Platform | There has been increasing interest lately in developing education tools for sign language (SL) learning that enable self-assessment and objective evaluation of learners’ SL productions, assisting both students and their instructors. Crucially, such tools require the automatic recognition of SL videos, while operating i... | ['Petros Maragos', 'Stavroula-Evita Fotinea', 'Eleni Efthimiou', 'Theodore Goulas', 'Galini Sapountzaki', 'Gerasimos Potamianos', 'Katerina Papadimitriou'] | null | null | null | null | sltat-lrec-2022-6 | ['sign-language-recognition'] | ['computer-vision'] | [ 1.02953538e-01 -4.52174664e-01 -3.80489305e-02 -2.56438285e-01
-9.51409221e-01 -9.14954364e-01 4.49578047e-01 -6.18911564e-01
-7.51676321e-01 2.20860898e-01 1.72213167e-01 -4.29486990e-01
-7.37815052e-02 -2.47252241e-01 -5.45086801e-01 -5.59646785e-01
2.63041884e-01 1.30789027e-01 7.23411620e-01 -2.72895902... | [9.121271133422852, -6.437844276428223] |
88f2d147-e520-4b06-9d2d-553db999388d | training-time-adversarial-attack-aiming-the | 2211.15875 | null | https://arxiv.org/abs/2211.15875v2 | https://arxiv.org/pdf/2211.15875v2.pdf | Data Poisoning Attack Aiming the Vulnerability of Continual Learning | Generally, regularization-based continual learning models limit access to the previous task data to imitate the real-world constraints related to memory and privacy. However, this introduces a problem in these models by not being able to track the performance on each task. In essence, current continual learning methods... | ['Junmo Kim', 'Hyeong Gwon Hong', 'Jaehyun Choi', 'Gyojin Han'] | 2022-11-29 | null | null | null | null | ['data-poisoning'] | ['adversarial'] | [ 2.16549888e-01 -5.67419194e-02 2.77261198e-01 -2.62834430e-01
-3.45115125e-01 -8.83396089e-01 7.89104283e-01 1.91544726e-01
-9.75337207e-01 8.18904281e-01 -4.73430097e-01 -3.57976317e-01
-3.50943714e-01 -6.30224228e-01 -1.07414877e+00 -6.67692006e-01
-2.32312888e-01 2.34424233e-01 4.25553948e-01 1.75454710... | [5.795494556427002, 7.626213550567627] |
2207f660-467b-44b5-b028-954f62b7edf3 | voxceleb-enrichment-for-age-and-gender | 2109.1351 | null | https://arxiv.org/abs/2109.13510v2 | https://arxiv.org/pdf/2109.13510v2.pdf | VoxCeleb Enrichment for Age and Gender Recognition | VoxCeleb datasets are widely used in speaker recognition studies. Our work serves two purposes. First, we provide speaker age labels and (an alternative) annotation of speaker gender. Second, we demonstrate the use of this metadata by constructing age and gender recognition models with different features and classifier... | ['Tomi Kinnunen', 'Ville Hautamaki', 'Trung Ngo Trong', 'Khaled Hechmi'] | 2021-09-28 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-3.34251314e-01 2.23015338e-01 -9.33586210e-02 -1.03856874e+00
-1.12862349e+00 -4.66119647e-01 7.99251020e-01 2.15518683e-01
-6.30200446e-01 6.97576225e-01 2.60437727e-01 3.97711769e-02
2.27275223e-01 -3.85771215e-01 -2.70704359e-01 -7.97044873e-01
9.85810012e-02 6.48565590e-01 -2.66905993e-01 8.16686600... | [14.161341667175293, 6.115533351898193] |
02fa5b16-fb15-47b6-8d12-23bdcae2f180 | variational-bayesian-filtering-with-subspace | 2201.08307 | null | https://arxiv.org/abs/2201.08307v1 | https://arxiv.org/pdf/2201.08307v1.pdf | Variational Bayesian Filtering with Subspace Information for Extreme Spatio-Temporal Matrix Completion | Missing data is a common problem in real-world sensor data collection. The performance of various approaches to impute data degrade rapidly in the extreme scenarios of low data sampling and noisy sampling, a case present in many real-world problems in the field of traffic sensing and environment monitoring, etc. Howeve... | ['Ketan Rajawat', 'Pravesh Biyani', 'Charul Paliwal'] | 2022-01-20 | null | null | null | null | ['low-rank-matrix-completion'] | ['methodology'] | [ 3.87412518e-01 -6.13790810e-01 1.63121507e-01 -2.30172113e-01
-9.50243473e-01 -3.54120046e-01 4.51788694e-01 -4.19193581e-02
-3.73061895e-01 9.80679750e-01 4.13729906e-01 -1.64235719e-02
-6.99575901e-01 -5.93333066e-01 -9.59237337e-01 -1.06165767e+00
1.94215789e-01 2.87552536e-01 -1.57029048e-01 -2.15088561... | [6.599430084228516, 2.2086541652679443] |
c5846cfd-49cd-4275-8ef6-f4b71a421ef2 | the-role-of-context-in-neural-morphological | null | null | https://aclanthology.org/C16-1018 | https://aclanthology.org/C16-1018.pdf | The Role of Context in Neural Morphological Disambiguation | Languages with rich morphology often introduce sparsity in language processing tasks. While morphological analyzers can reduce this sparsity by providing morpheme-level analyses for words, they will often introduce ambiguity by returning multiple analyses for the same surface form. The problem of disambiguating between... | ['Daniel Clothiaux', 'Qinlan Shen', 'Patrick Littell', 'Emily Tagtow', 'Chris Dyer'] | 2016-12-01 | the-role-of-context-in-neural-morphological-1 | https://aclanthology.org/C16-1018 | https://aclanthology.org/C16-1018.pdf | coling-2016-12 | ['morphological-disambiguation'] | ['natural-language-processing'] | [ 3.01836580e-01 -2.17208326e-01 1.50632476e-02 -4.13329154e-01
-7.44957387e-01 -1.10708237e+00 2.26718888e-01 8.46984565e-01
-8.82260919e-01 4.65226650e-01 2.50514537e-01 -8.66928935e-01
6.40483573e-02 -9.57464635e-01 -3.60014707e-01 -3.73129040e-01
-1.87874213e-01 4.66004997e-01 1.58141665e-02 -4.63929296... | [10.415495872497559, 10.03642463684082] |
32459846-ce05-4fd7-82dd-a82c2840564f | irgan-a-minimax-game-for-unifying-generative | 1705.10513 | null | http://arxiv.org/abs/1705.10513v2 | http://arxiv.org/pdf/1705.10513v2.pdf | IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models | This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game... | ['Wei-Nan Zhang', 'Lantao Yu', 'Yinghui Xu', 'Dell Zhang', 'Benyou Wang', 'Jun Wang', 'Yu Gong', 'Peng Zhang'] | 2017-05-30 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [ 2.47682586e-01 2.48077676e-01 -1.69699073e-01 -9.11700577e-02
-1.44925010e+00 -7.27813184e-01 8.65302801e-01 -3.93046474e-04
-3.14604759e-01 2.77106851e-01 1.61301941e-01 -1.33105487e-01
-5.31226873e-01 -8.67883921e-01 -7.26152301e-01 -9.13428664e-01
-1.46117911e-01 7.55176485e-01 4.50813845e-02 -3.36473107... | [11.491297721862793, 7.57740592956543] |
fa2dd3f2-253d-44ad-82fd-435314feb4b9 | recurrent-models-for-auditory-attention-in | 1511.06407 | null | http://arxiv.org/abs/1511.06407v2 | http://arxiv.org/pdf/1511.06407v2.pdf | Recurrent Models for Auditory Attention in Multi-Microphone Distance Speech Recognition | Integration of multiple microphone data is one of the key ways to achieve
robust speech recognition in noisy environments or when the speaker is located
at some distance from the input device. Signal processing techniques such as
beamforming are widely used to extract a speech signal of interest from
background noise. ... | ['Suyoun Kim', 'Ian Lane'] | 2015-11-19 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 6.10855937e-01 -2.02467799e-01 5.39899766e-01 -3.66798222e-01
-1.58011889e+00 -6.51829779e-01 3.75334650e-01 -3.26407813e-02
-5.75788915e-01 2.57709920e-01 4.78713214e-01 -5.31995356e-01
4.75581065e-02 -2.59641767e-01 -8.81831169e-01 -8.10267985e-01
8.89044702e-02 2.37309024e-01 1.77488953e-01 -2.01975733... | [14.914254188537598, 5.981829643249512] |
b853bd23-929d-426a-863a-f8b0e77720f4 | image-and-texture-independent-deep-learning | 2207.07604 | null | https://arxiv.org/abs/2207.07604v1 | https://arxiv.org/pdf/2207.07604v1.pdf | Image and Texture Independent Deep Learning Noise Estimation using Multiple Frames | In this study, a novel multiple-frame based image and texture independent convolutional Neural Network (CNN) noise estimator is introduced. The estimator works. | ['Nurettin Besli', 'Hikmet Kirmizitas'] | 2022-07-15 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 4.25161459e-02 -4.43577319e-01 -1.51298940e-02 -5.25583386e-01
-2.78705508e-01 4.58674341e-01 1.05068639e-01 -5.29847205e-01
-8.48108709e-01 1.03366137e+00 -1.07937656e-01 2.47355267e-01
2.14287460e-01 -4.87812787e-01 -6.06683016e-01 -7.70580471e-01
4.36495662e-01 -5.22150576e-01 3.31336886e-01 6.12054579... | [11.459898948669434, -2.3086636066436768] |
23e259dd-f627-421c-bc0c-341d088fb39c | transalign-fully-automatic-and-effective | 2210.0854 | null | https://arxiv.org/abs/2210.08540v1 | https://arxiv.org/pdf/2210.08540v1.pdf | TransAlign: Fully Automatic and Effective Entity Alignment for Knowledge Graphs | The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of entities from two different KGs that represent the same entity. Many machine learning-based methods have been proposed for this task. However, to our best knowledge, existing methods all require manually crafted seed alignments, ... | ['Jianzhong Qi', 'Hong Cheng', 'Min Yang', 'Bayu Distiawan Trisedya', 'Xiaoyan Zhao', 'Rui Zhang'] | 2022-10-16 | null | null | null | null | ['entity-alignment', 'entity-embeddings', 'entity-alignment'] | ['knowledge-base', 'methodology', 'natural-language-processing'] | [-7.07156211e-02 1.89333484e-01 -3.36382180e-01 -3.95952046e-01
-4.79677796e-01 -5.44530690e-01 3.78308654e-01 8.86062145e-01
-4.31715995e-01 3.50731254e-01 8.81643891e-02 -1.85606793e-01
-7.20287561e-02 -1.21431088e+00 -6.75949931e-01 -4.21207994e-01
1.33073136e-01 5.96509695e-01 3.74968559e-01 -2.42779851... | [8.73729419708252, 7.962116241455078] |
8aaf2790-36fb-4971-bb5f-1fb8c15bef28 | large-scale-autonomous-driving-scenarios | 2103.16101 | null | https://arxiv.org/abs/2103.16101v1 | https://arxiv.org/pdf/2103.16101v1.pdf | Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction | The clustering of autonomous driving scenario data can substantially benefit the autonomous driving validation and simulation systems by improving the simulation tests' completeness and fidelity. This article proposes a comprehensive data clustering framework for a large set of vehicle driving data. Existing algorithms... | ['Liangjun Zhang', 'Zhixian Ye', 'Jin Fang', 'Jinxin Zhao'] | 2021-03-30 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [-3.07546675e-01 -8.53546858e-02 -2.00972378e-01 -9.60442066e-01
-5.80963731e-01 -2.34297290e-01 6.13708496e-01 -1.48485750e-02
-6.84320152e-01 5.11926711e-01 -6.91048279e-02 -3.61353517e-01
-3.34045112e-01 -9.49172795e-01 -6.38388932e-01 -7.44268835e-01
-2.13768244e-01 4.98616725e-01 3.34858268e-01 -5.13668716... | [8.054669380187988, -1.4387166500091553] |
32175bcb-480e-4134-94dc-9602984acb7d | factual-consistency-of-multilingual | 2203.11552 | null | https://arxiv.org/abs/2203.11552v1 | https://arxiv.org/pdf/2203.11552v1.pdf | Factual Consistency of Multilingual Pretrained Language Models | Pretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference. However, for that, we need to know how reliable this knowledge is, and recent work has shown that monolingual English language models lack consistency when predict... | ['Anders Søgaard', 'Constanza Fierro'] | 2022-03-22 | null | https://aclanthology.org/2022.findings-acl.240 | https://aclanthology.org/2022.findings-acl.240.pdf | findings-acl-2022-5 | ['xlm-r'] | ['natural-language-processing'] | [-6.90396190e-01 1.87679842e-01 -8.16729665e-01 -3.56201291e-01
-8.48308563e-01 -9.61014509e-01 8.54061127e-01 3.02259326e-01
-5.37487686e-01 1.33802831e+00 4.73968029e-01 -6.20483696e-01
-3.33604753e-01 -9.09450114e-01 -1.10207868e+00 -2.18552306e-01
4.02307272e-01 6.62554920e-01 6.75232103e-03 -4.78694022... | [9.968100547790527, 8.312031745910645] |
1cd426f9-311a-4fa3-b444-677923ade194 | unsupervised-deep-feature-extraction-for | 1511.08131 | null | http://arxiv.org/abs/1511.08131v1 | http://arxiv.org/pdf/1511.08131v1.pdf | Unsupervised Deep Feature Extraction for Remote Sensing Image Classification | This paper introduces the use of single layer and deep convolutional networks
for remote sensing data analysis. Direct application to multi- and
hyper-spectral imagery of supervised (shallow or deep) convolutional networks
is very challenging given the high input data dimensionality and the relatively
small amount of a... | ['Gustau Camps-Valls', 'Carlo Gatta', 'Adriana Romero'] | 2015-11-25 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 4.30038691e-01 -1.87708437e-01 -1.63471773e-01 -4.05230969e-01
-5.28948605e-01 -6.73717380e-01 5.93564332e-01 5.99167272e-02
-4.12982970e-01 5.54660201e-01 1.20728016e-01 -3.29217613e-01
-7.53396630e-01 -1.10881734e+00 -4.32301879e-01 -1.04401910e+00
-7.77603149e-01 9.28687081e-02 -1.63807347e-01 -1.28148168... | [9.68156623840332, -1.478415846824646] |
0aee43fc-b63c-454a-80d6-b27e515f3a9f | group-contextualization-for-video-recognition | 2203.09694 | null | https://arxiv.org/abs/2203.09694v1 | https://arxiv.org/pdf/2203.09694v1.pdf | Group Contextualization for Video Recognition | Learning discriminative representation from the complex spatio-temporal dynamic space is essential for video recognition. On top of those stylized spatio-temporal computational units, further refining the learnt feature with axial contexts is demonstrated to be promising in achieving this goal. However, previous works ... | ['Xiangnan He', 'Chong-Wah Ngo', 'Hao Zhang', 'Yanbin Hao'] | 2022-03-18 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Hao_Group_Contextualization_for_Video_Recognition_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Hao_Group_Contextualization_for_Video_Recognition_CVPR_2022_paper.pdf | cvpr-2022-1 | ['egocentric-activity-recognition'] | ['computer-vision'] | [ 3.04673854e-02 -5.12154579e-01 -6.33182228e-02 -3.77476960e-01
-4.39667225e-01 -6.13938987e-01 6.74066663e-01 -3.28515023e-01
-4.07027632e-01 3.63592058e-01 2.26085216e-01 -6.91745877e-02
-2.57974237e-01 -6.52639568e-01 -9.30843234e-01 -7.83577859e-01
-2.37492010e-01 -1.24566518e-01 3.68936986e-01 -2.34167084... | [8.993006706237793, 0.4429289400577545] |
118a75e5-cba8-41dc-b275-45c99b77dbfb | relative-geometry-aware-siamese-neural | 1901.01049 | null | http://arxiv.org/abs/1901.01049v2 | http://arxiv.org/pdf/1901.01049v2.pdf | Relative Geometry-Aware Siamese Neural Network for 6DOF Camera Relocalization | 6DOF camera relocalization is an important component of autonomous driving
and navigation. Deep learning has recently emerged as a promising technique to
tackle this problem. In this paper, we present a novel relative geometry-aware
Siamese neural network to enhance the performance of deep learning-based
methods throug... | ['Ke Sun', 'Rui Cao', 'Bozhi Liu', 'Qingquan Li', 'Guoping Qiu', 'Qing Li', 'Jonathan M. Garibaldi', 'Jiasong Zhu'] | 2019-01-04 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [-2.17866212e-01 -4.63461667e-01 -1.86081141e-01 -8.01322997e-01
-9.94509280e-01 -3.70440871e-01 5.99495530e-01 -1.68599263e-01
-8.80027115e-01 4.86793309e-01 1.65642276e-02 1.53070865e-02
-3.71786207e-01 -5.05093396e-01 -9.95770454e-01 -6.03174806e-01
-8.99048075e-02 1.82255000e-01 1.80206820e-01 -1.77386746... | [7.644552707672119, -2.090426206588745] |
10bc7abe-9786-4a77-bc75-87bfb7545260 | assurance-monitoring-of-learning-enabled | 2110.0312 | null | https://arxiv.org/abs/2110.03120v1 | https://arxiv.org/pdf/2110.03120v1.pdf | Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning | Machine learning components such as deep neural networks are used extensively in Cyber-Physical Systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capa... | ['Xenofon Koutsoukos', 'Dimitrios Boursinos'] | 2021-10-07 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [-9.57895890e-02 5.18123686e-01 3.26318294e-02 -4.07605886e-01
-6.59525514e-01 -2.54948348e-01 5.09997547e-01 6.26225546e-02
-1.69678882e-01 6.09465718e-01 -4.85875458e-01 -5.99765837e-01
-3.40361089e-01 -9.87835944e-01 -9.39790547e-01 -7.37796724e-01
-4.35623527e-01 3.49122472e-02 3.21888238e-01 2.11013015... | [5.480321407318115, 7.401754379272461] |
2b4b1217-83c1-47f6-bfff-51c218ef8231 | action-priors-for-large-action-spaces-in | 2101.04178 | null | https://arxiv.org/abs/2101.04178v2 | https://arxiv.org/pdf/2101.04178v2.pdf | Action Priors for Large Action Spaces in Robotics | In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a consequence, many researchers rely on expert demonstrations to guide learning. However, acquiring expert demonstrations can be expensive. This paper... | ['Lawson L. S. Wong', 'Jan-Willem van de Meent', 'Robert Platt', 'Dian Wang', 'Ondrej Biza'] | 2021-01-11 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [-3.19084823e-02 3.15690041e-01 -2.67039746e-01 -1.32078961e-01
-6.12047315e-01 -8.17427635e-01 6.19257569e-01 -1.08728506e-01
-6.52785480e-01 1.48058093e+00 -1.35768309e-01 -4.89535630e-01
-2.73162097e-01 -4.14259136e-01 -8.88386309e-01 -6.04910493e-01
-6.29369467e-02 4.12975669e-01 2.31420770e-01 -1.77991733... | [4.286700248718262, 1.4351084232330322] |
995cc098-eccb-422c-8b35-927f9e298bd1 | the-geometry-of-deep-generative-image-models | 2101.06006 | null | https://arxiv.org/abs/2101.06006v2 | https://arxiv.org/pdf/2101.06006v2.pdf | The Geometry of Deep Generative Image Models and its Applications | Generative adversarial networks (GANs) have emerged as a powerful unsupervised method to model the statistical patterns of real-world data sets, such as natural images. These networks are trained to map random inputs in their latent space to new samples representative of the learned data. However, the structure of the ... | ['Carlos R. Ponce', 'Binxu Wang'] | 2021-01-15 | null | null | null | null | ['image-variation'] | ['computer-vision'] | [ 4.91452008e-01 3.94669533e-01 1.04892431e-02 -2.69843459e-01
-2.75052488e-01 -1.10848975e+00 8.97376239e-01 -6.24190688e-01
1.53883606e-01 3.51945430e-01 4.71397072e-01 -1.46646038e-01
-1.75315648e-01 -8.08343053e-01 -8.22402477e-01 -1.00731409e+00
1.32423133e-01 6.69715822e-01 -4.47858781e-01 -2.96758145... | [11.749839782714844, -0.0346292182803154] |
725ce130-9309-44fc-aab2-c227629344e6 | planes-vs-chairs-category-guided-3d-shape | 2204.10235 | null | https://arxiv.org/abs/2204.10235v1 | https://arxiv.org/pdf/2204.10235v1.pdf | Planes vs. Chairs: Category-guided 3D shape learning without any 3D cues | We present a novel 3D shape reconstruction method which learns to predict an implicit 3D shape representation from a single RGB image. Our approach uses a set of single-view images of multiple object categories without viewpoint annotation, forcing the model to learn across multiple object categories without 3D supervi... | ['James M. Rehg', 'Varun Jampani', 'Anh Thai', 'Stefan Stojanov', 'Zixuan Huang'] | 2022-04-21 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-2.42716223e-02 2.56552398e-01 -1.09312367e-02 -7.68697500e-01
-7.22507715e-01 -9.19131458e-01 7.58813739e-01 -2.56095052e-01
-5.94598576e-02 1.17617019e-01 9.63284150e-02 -1.75598189e-01
3.57540905e-01 -6.48080468e-01 -9.26580369e-01 -3.34605336e-01
3.08138639e-01 9.58695173e-01 4.22914684e-01 6.77703181... | [8.3859281539917, -3.263258218765259] |
2fa8c6ae-8237-49ce-a07b-bc023b067954 | hypergraph-pre-training-with-graph-neural | 2105.10862 | null | https://arxiv.org/abs/2105.10862v1 | https://arxiv.org/pdf/2105.10862v1.pdf | Hypergraph Pre-training with Graph Neural Networks | Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting. Among others, a major hurdle for effective hypergraph repres... | ['Hanghang Tong', 'Tal Neiman', 'Robert Barton', 'Changhe Yuan', 'Boxin Du'] | 2021-05-23 | null | null | null | null | ['hyperedge-classification'] | ['graphs'] | [ 3.89474541e-01 4.53603506e-01 -7.26693809e-01 -8.41107219e-02
-4.37069267e-01 -6.32372677e-01 5.59299469e-01 2.40536064e-01
2.94015743e-02 7.06469774e-01 1.06260933e-01 -6.39958382e-01
-2.94577241e-01 -1.20238364e+00 -7.03208923e-01 -9.36390817e-01
-8.61755535e-02 3.07261735e-01 1.36987254e-01 -2.88365722... | [7.362224102020264, 6.283175468444824] |
39c1771d-f615-4952-bf87-f5f82ff3d3b4 | model-agnostic-high-dimensional-error-in | 1902.1092 | null | https://arxiv.org/abs/1902.10920v9 | https://arxiv.org/pdf/1902.10920v9.pdf | On Robustness of Principal Component Regression | Principal Component Regression (PCR) is a simple, but powerful and ubiquitously utilized method. Its effectiveness is well established when the covariates exhibit low-rank structure. However, its ability to handle settings with noisy, missing, and mixed-valued covariates is not understood and remains an important open ... | ['Dennis Shen', 'Dogyoon Song', 'Anish Agarwal', 'Devavrat Shah'] | 2019-02-28 | on-robustness-of-principal-component | http://papers.nips.cc/paper/9181-on-robustness-of-principal-component-regression | http://papers.nips.cc/paper/9181-on-robustness-of-principal-component-regression.pdf | neurips-2019-12 | ['art-analysis'] | ['computer-vision'] | [ 4.76025611e-01 1.70918420e-01 -1.86585441e-01 2.78698765e-02
-7.52915144e-01 -6.03964686e-01 5.78668296e-01 2.18164325e-01
-4.90037203e-01 7.69682527e-01 2.79365569e-01 -3.74631375e-01
-5.29843748e-01 -6.21211648e-01 -9.79093134e-01 -1.06295884e+00
-1.76836506e-01 -2.32233722e-02 -3.90183896e-01 -7.83835799... | [7.088939666748047, 4.523853302001953] |
7d1891e4-6203-45ed-b33a-5a3b27707869 | a-unified-framework-for-multi-sensor-hdr | 1308.4908 | null | http://arxiv.org/abs/1308.4908v1 | http://arxiv.org/pdf/1308.4908v1.pdf | A Unified Framework for Multi-Sensor HDR Video Reconstruction | One of the most successful approaches to modern high quality HDR-video
capture is to use camera setups with multiple sensors imaging the scene through
a common optical system. However, such systems pose several challenges for HDR
reconstruction algorithms. Previous reconstruction techniques have considered
debayering, ... | ['Anders Ynnerman', 'Stefan Gustavson', 'Gerhard Bonnet', 'Jonas Unger', 'Joel Kronander'] | 2013-08-22 | null | null | null | null | ['video-reconstruction', 'hdr-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.42926139e-01 -5.14062881e-01 5.42811215e-01 -2.66992062e-01
-9.71287310e-01 -3.67866337e-01 2.96444505e-01 -2.36478135e-01
-4.27053392e-01 5.76296210e-01 1.36093989e-01 1.24810308e-01
1.51943654e-01 -5.93097150e-01 -8.21367264e-01 -6.36773527e-01
2.44928628e-01 4.56680208e-01 6.72758937e-01 -1.60923913... | [10.488283157348633, -2.269416570663452] |
a923fa99-298a-4e08-8e86-651697de07bb | millimeter-wave-wireless-communication | 2303.02617 | null | https://arxiv.org/abs/2303.02617v1 | https://arxiv.org/pdf/2303.02617v1.pdf | Millimeter Wave Wireless Communication Assisted Three-Dimensional Simultaneous Localization and Mapping | In this paper, we study the three-dimensional (3D) simultaneous localization and mapping (SLAM) problem in complex outdoor and indoor environments based only on millimeter-wave (mmWave) wireless communication signals. Firstly, we propose a deep-learning based mapping (DLM) algorithm that can leverage the reflections po... | ['Feifei Gao', 'Zhiyu Mou'] | 2023-03-05 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [-2.25767255e-01 -3.05280864e-01 3.36393028e-01 -4.21952695e-01
-6.63710713e-01 -3.91164839e-01 4.62057173e-01 -3.52879651e-02
-6.41038656e-01 8.74939919e-01 -2.67464966e-01 -4.68339950e-01
-6.64031148e-01 -1.16330528e+00 -7.75561392e-01 -7.52416551e-01
-4.76503998e-01 4.92155343e-01 -3.48175943e-01 -2.20031410... | [6.273915767669678, 1.0735268592834473] |
071832cb-905d-4eae-850e-7df2974b94eb | llama-adapter-v2-parameter-efficient-visual | 2304.1501 | null | https://arxiv.org/abs/2304.15010v1 | https://arxiv.org/pdf/2304.15010v1.pdf | LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model | How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains less explored. Although the recent LLaMA-Adapter demonstrates the potential to handle visual inputs with LLMs, it still cannot generalize we... | ['Yu Qiao', 'Hongsheng Li', 'Xiangyu Yue', 'Conghui He', 'Pan Lu', 'Wei zhang', 'Aojun Zhou', 'Shijie Geng', 'Ziyi Lin', 'Renrui Zhang', 'Jiaming Han', 'Peng Gao'] | 2023-04-28 | null | null | null | null | ['optical-character-recognition', 'instruction-following'] | ['computer-vision', 'natural-language-processing'] | [ 7.46149123e-02 2.36170709e-01 -4.87693965e-01 -4.02765185e-01
-8.58633041e-01 -8.51127684e-01 6.77406132e-01 -2.19549999e-01
-5.13569713e-01 1.38295785e-01 2.72217914e-02 -8.78416717e-01
3.60928774e-01 -5.46206355e-01 -1.24245584e+00 -5.17079711e-01
6.20483816e-01 5.41438520e-01 8.88065100e-02 -1.87581092... | [10.910120964050293, 1.5943434238433838] |
3130e2f1-6b3e-41ea-825d-ed022f642abf | trocr-transformer-based-optical-character | 2109.10282 | null | https://arxiv.org/abs/2109.10282v5 | https://arxiv.org/pdf/2109.10282v5.pdf | TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models | Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this pa... | ['Yijuan Lu', 'Lei Cui', 'Jingye Chen', 'Furu Wei', 'Zhoujun Li', 'Cha Zhang', 'Dinei Florencio', 'Tengchao Lv', 'Minghao Li'] | 2021-09-21 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 6.57428503e-01 -2.90790945e-01 9.41659790e-03 -3.75068694e-01
-7.58487046e-01 -5.07031322e-01 9.63724852e-01 -4.21235323e-01
-1.64571568e-01 1.83732405e-01 1.37520254e-01 -6.27387285e-01
4.07025695e-01 -8.13421130e-01 -9.81158018e-01 -5.07814527e-01
9.93551910e-01 5.20251989e-01 -1.79402679e-01 -1.83170661... | [11.822325706481934, 2.154616594314575] |
a676e746-eb0a-48e9-8287-55891c926698 | which-and-where-to-focus-a-simple-yet | 2109.03451 | null | https://arxiv.org/abs/2109.03451v1 | https://arxiv.org/pdf/2109.03451v1.pdf | Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images | Scene text detection has drawn the close attention of researchers. Though many methods have been proposed for horizontal and oriented texts, previous methods may not perform well when dealing with arbitrary-shaped texts such as curved texts. In particular, confusion problem arises in the case of nearby text instances. ... | ['Weiping Wang', 'Xugong Qin', 'Yu Zhou', 'Youhui Guo'] | 2021-09-08 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [-6.18220307e-02 -2.25919098e-01 1.24740206e-01 -2.27656603e-01
-7.76421726e-01 -2.09455058e-01 7.18864739e-01 1.49100855e-01
-3.43991011e-01 -6.22315109e-02 2.10103199e-01 -2.12628588e-01
2.48723969e-01 -6.97973132e-01 -6.07943594e-01 -6.59672558e-01
5.40826559e-01 4.60887492e-01 8.77605140e-01 -3.18352371... | [12.043935775756836, 2.216667652130127] |
b3799523-3932-485f-ac15-f77b7e2f5dac | exploring-the-efficacy-of-pre-trained | 2211.11216 | null | https://arxiv.org/abs/2211.11216v2 | https://arxiv.org/pdf/2211.11216v2.pdf | Exploring the Efficacy of Pre-trained Checkpoints in Text-to-Music Generation Task | Benefiting from large-scale datasets and pre-trained models, the field of generative models has recently gained significant momentum. However, most datasets for symbolic music are very small, which potentially limits the performance of data-driven multimodal models. An intuitive solution to this problem is to leverage ... | ['Maosong Sun', 'Shangda Wu'] | 2022-11-21 | null | null | null | null | ['text-to-music-generation', 'music-generation', 'music-generation', 'text-to-music-generation'] | ['audio', 'audio', 'music', 'music'] | [ 3.45095694e-01 1.26735866e-01 2.30825245e-02 -2.58997858e-01
-1.30793715e+00 -7.12417781e-01 9.61544335e-01 1.08239248e-01
-1.72501534e-01 5.32816947e-01 6.19044662e-01 1.49898350e-01
-2.34401509e-01 -5.03964484e-01 -6.59668565e-01 -3.26749176e-01
9.33186784e-02 8.54043007e-01 -1.29242659e-01 -3.49740297... | [15.724515914916992, 5.255472183227539] |
7d1ef13b-ee3d-4615-9580-64ea8eae11f4 | lidar-based-localization-using-universal | null | null | https://www.sciencedirect.com/science/article/pii/S0031320322001662 | https://www.sciencedirect.com/science/article/pii/S0031320322001662 | LiDAR-based localization using universal encoding and memory-aware regression | Visual localization is critical to many robotics and computer vision applications. Absolute pose regression performs localization by encoding scene features followed by pose regression, which has achieved impressive results in localization. It recovers 6-DoF poses from captured scene data alone. However, current method... | ['Shangshu Yu'] | 2022-08-01 | null | null | null | pattern-recognition-2022-8 | ['visual-localization', 'memorization'] | ['computer-vision', 'natural-language-processing'] | [ 1.13238297e-01 -4.36641306e-01 -1.05583012e-01 -5.96662581e-01
-5.66706002e-01 -4.30266440e-01 1.90647304e-01 1.29706115e-01
-8.35882962e-01 9.58249807e-01 -1.58152863e-01 -8.03254843e-02
-1.89017326e-01 -6.78233087e-01 -9.29127634e-01 -8.89877737e-01
3.46292220e-02 -9.11708474e-02 1.29298910e-01 2.31232554... | [7.654376029968262, -2.17293643951416] |
8c8a8e61-5d2c-40c3-b3b4-c1c6b8ebe96a | improving-knowledge-graph-representation | 2112.04087 | null | https://arxiv.org/abs/2112.04087v1 | https://arxiv.org/pdf/2112.04087v1.pdf | Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training | Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose a novel pre-training-then-fine-tuning framework for knowledge graph representation learning, in which a KG model is firstly pre-trained wit... | ['Huajun Chen', 'Chen Hui', 'Chi Man Wong', 'Zhen Bi', 'Wen Zhang', 'Ganqiang Ye'] | 2021-12-08 | null | null | null | null | ['type-prediction', 'triple-classification'] | ['computer-code', 'graphs'] | [ 2.27364734e-01 6.89768434e-01 -6.50490403e-01 -3.61601353e-01
-8.24059606e-01 -5.21762252e-01 6.16640985e-01 6.37923002e-01
-2.71421045e-01 6.01713300e-01 6.12685800e-01 -5.18351912e-01
-5.78917861e-02 -1.26348579e+00 -1.03223860e+00 -1.35927767e-01
-1.38342112e-01 7.39629626e-01 7.41245300e-02 -3.42801809... | [8.914484024047852, 7.939960479736328] |
5fac4e0e-0b88-48fd-8a28-49e0d67af4eb | using-u-net-network-for-efficient-brain-tumor | 2211.01885 | null | https://arxiv.org/abs/2211.01885v1 | https://arxiv.org/pdf/2211.01885v1.pdf | Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images | Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes... | ['Soumyabrata Dev', 'Mayank Jain', 'Alice Othmani', 'Jason Walsh'] | 2022-11-03 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [ 2.60495424e-01 5.07993639e-01 2.92819291e-02 -5.77123106e-01
-8.72921407e-01 -1.80257231e-01 3.83602470e-01 2.18995839e-01
-1.01062131e+00 5.79033494e-01 -2.23094702e-01 -6.03038967e-01
1.98606518e-03 -6.70951426e-01 -4.00170654e-01 -8.14003408e-01
-1.65857330e-01 8.65293026e-01 4.57034022e-01 2.01325983... | [14.452470779418945, -2.452648639678955] |
c8f5ffff-c934-4f1c-befb-410ea5f566cf | deep-level-sets-implicit-surface | 1901.06802 | null | http://arxiv.org/abs/1901.06802v1 | http://arxiv.org/pdf/1901.06802v1.pdf | Deep Level Sets: Implicit Surface Representations for 3D Shape Inference | This repository contains the code for the paper "Occupancy Networks - Learning 3D Reconstruction in Function Space" | ['Jhony K. Pontes', 'Mateusz Michalkiewicz', 'Anders Eriksson', 'Mahsa Baktashmotlagh', 'Dominic Jack'] | 2019-01-21 | null | null | null | null | ['3d-shape-representation'] | ['computer-vision'] | [-6.16616011e-02 1.86106071e-01 -4.51382250e-01 -4.91268665e-01
-2.89477915e-01 1.49087712e-01 1.24470294e-02 1.06946014e-01
-4.22214597e-01 8.71576130e-01 6.57509387e-01 -6.16593897e-01
-3.25171977e-01 -6.80252492e-01 -8.46629500e-01 -9.61724102e-01
-6.38078153e-02 7.37581968e-01 -4.48068827e-02 9.45083350... | [8.329948425292969, -3.58337664604187] |
356c649a-6ea2-4daf-92f2-083de25daab9 | bayesian-optimization-based-beam-alignment | 2207.14174 | null | https://arxiv.org/abs/2207.14174v1 | https://arxiv.org/pdf/2207.14174v1.pdf | Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems | Due to the very narrow beam used in millimeter wave communication (mmWave), beam alignment (BA) is a critical issue. In this work, we investigate the issue of mmWave BA and present a novel beam alignment scheme on the basis of a machine learning strategy, Bayesian optimization (BO). In this context, we consider the bea... | ['Zhongpei Zhang', 'Zhiqin Hong', 'Baojuan Liu', 'Songjie Yang'] | 2022-07-28 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 1.90760717e-01 -2.88711905e-01 -8.17660019e-02 -2.61128485e-01
-8.56815398e-01 -8.92429128e-02 2.18590498e-01 -3.06705981e-01
-1.96713105e-01 1.05735719e+00 3.38947117e-01 -5.42769670e-01
-8.85652900e-01 -8.50864708e-01 -3.80678475e-01 -1.19482744e+00
1.13496818e-01 2.56939709e-01 -3.54452312e-01 3.78911346... | [6.402356147766113, 1.3409779071807861] |
256b5933-a182-4a50-95e0-69c4acf1db40 | fine-tuning-bert-with-character-level-noise | 2303.17683 | null | https://arxiv.org/abs/2303.17683v1 | https://arxiv.org/pdf/2303.17683v1.pdf | Fine-Tuning BERT with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languages | In this work, we induce character-level noise in various forms when fine-tuning BERT to enable zero-shot cross-lingual transfer to unseen dialects and languages. We fine-tune BERT on three sentence-level classification tasks and evaluate our approach on an assortment of unseen dialects and languages. We find that chara... | ['David Chiang', 'Aarohi Srivastava'] | 2023-03-30 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.28554846e-02 -3.36246222e-01 -1.53018251e-01 -4.27047163e-01
-1.20060873e+00 -1.07163620e+00 6.44143701e-01 3.66952360e-01
-9.74622548e-01 8.04724872e-01 5.12599528e-01 -3.70858610e-01
1.07438043e-01 -7.27230132e-01 -7.18476713e-01 -3.85184526e-01
2.03666508e-01 6.86816037e-01 2.39594281e-01 -6.91467047... | [10.864577293395996, 9.998495101928711] |
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