paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7df3744b-aabc-4930-9257-5234074e0261 | knowledge-injected-prompt-based-fine-tuning | 2210.03304 | null | https://arxiv.org/abs/2210.03304v2 | https://arxiv.org/pdf/2210.03304v2.pdf | Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding | Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with average length of 3,000+ tokens. This task is challenging due to a high-dimensional space of multi-label assignment (tens of thousands of ICD codes) and the long-tail challenge: only a few codes (com... | ['Hong Yu', 'Avijit Mitra', 'Bhanu Pratap Singh Rawat', 'Shufan Wang', 'Zhichao Yang'] | 2022-10-07 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [ 2.62842327e-01 -4.08542827e-02 -4.97094572e-01 -2.53282130e-01
-1.06132829e+00 -3.99280429e-01 1.61267325e-01 4.83213335e-01
-3.97338957e-01 8.17784727e-01 4.98922646e-01 1.45731959e-02
-3.00339997e-01 -4.80107039e-01 -2.15495139e-01 -3.01606804e-01
-1.92630693e-01 7.23709941e-01 1.94499284e-01 -1.91457253... | [8.014490127563477, 6.8608832359313965] |
a0115296-1800-4476-9676-39bcdd81292c | prototype-based-counterfactual-explanation | 2105.00703 | null | https://arxiv.org/abs/2105.00703v3 | https://arxiv.org/pdf/2105.00703v3.pdf | Causality-based Counterfactual Explanation for Classification Models | Counterfactual explanation is one branch of interpretable machine learning that produces a perturbation sample to change the model's original decision. The generated samples can act as a recommendation for end-users to achieve their desired outputs. Most of the current counterfactual explanation approaches are the grad... | ['Guandong Xu', 'Qian Li', 'Tri Dung Duong'] | 2021-05-03 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 1.86155677e-01 4.65931475e-01 -6.68896079e-01 -4.67317611e-01
-5.34662008e-01 -9.37925428e-02 6.00201964e-01 -1.37541756e-01
3.06956992e-02 1.34113896e+00 3.39226663e-01 -7.91125655e-01
-5.14926076e-01 -7.72151828e-01 -8.18723857e-01 -6.72096908e-01
-1.50422603e-01 2.34843865e-01 -5.57663858e-01 -4.53508943... | [8.672701835632324, 5.620512962341309] |
8b63dedd-fcf0-432d-9071-6e2f69322fd1 | pixel-level-matching-for-video-object | 1708.05137 | null | http://arxiv.org/abs/1708.05137v1 | http://arxiv.org/pdf/1708.05137v1.pdf | Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks | We propose a novel video object segmentation algorithm based on pixel-level
matching using Convolutional Neural Networks (CNN). Our network aims to
distinguish the target area from the background on the basis of the pixel-level
similarity between two object units. The proposed network represents a target
object using f... | ['Seokju Lee', 'In So Kweon', 'Seunghak Shin', 'Jae Shin Yoon', 'Francois Rameau', 'Junsik Kim'] | 2017-08-17 | pixel-level-matching-for-video-object-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Yoon_Pixel-Level_Matching_for_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Yoon_Pixel-Level_Matching_for_ICCV_2017_paper.pdf | iccv-2017-10 | ['feature-compression'] | ['computer-vision'] | [ 4.58591193e-01 -3.19418579e-01 -2.74793088e-01 -4.11763042e-01
-2.47847512e-01 -3.43925923e-01 8.33279267e-02 2.94977650e-02
-4.60376412e-01 2.83718258e-01 -5.41273952e-01 -7.10626245e-02
-7.27241561e-02 -1.11284292e+00 -7.47636497e-01 -6.43713593e-01
1.49946398e-04 1.79247558e-01 7.34340727e-01 1.01415917... | [9.378649711608887, -0.024840237572789192] |
d2736b45-5109-421b-b0e4-7f18f82040b5 | how-many-answers-should-i-give-an-empirical | 2306.00435 | null | https://arxiv.org/abs/2306.00435v1 | https://arxiv.org/pdf/2306.00435v1.pdf | How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension | The multi-answer phenomenon, where a question may have multiple answers scattered in the document, can be well handled by humans but is challenging enough for machine reading comprehension (MRC) systems. Despite recent progress in multi-answer MRC, there lacks a systematic analysis of how this phenomenon arises and how... | ['Dongyan Zhao', 'Yansong Feng', 'Yuxuan Lai', 'Xiao Liu', 'Jiuheng Lin', 'Chen Zhang'] | 2023-06-01 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.46473870e-01 1.89964846e-01 7.36002810e-03 -3.13335389e-01
-9.89987552e-01 -1.02201056e+00 6.56312943e-01 5.69005370e-01
-1.21301889e-01 5.68435013e-01 5.23707628e-01 -6.93213522e-01
-5.79538465e-01 -7.15052187e-01 -4.64751124e-01 -2.83570588e-02
6.93406880e-01 6.09786332e-01 7.86203265e-01 -7.95479357... | [11.379459381103516, 8.125174522399902] |
9754e569-9dd6-41fe-bf96-68f24f5bdf4f | spin-simplifying-polar-invariance-for-neural | 2111.14507 | null | https://arxiv.org/abs/2111.14507v3 | https://arxiv.org/pdf/2111.14507v3.pdf | SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting | Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification. Yet to leverage rotational invariant tasks, convolutional architectures require specific rotational invariant layers or extensive data... | ['Joan Lasenby', 'Philippe Blanc', 'Guillaume Arbod', 'Anthony Hu', 'Quentin Paletta'] | 2021-11-29 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [ 6.15183830e-01 -2.78619617e-01 7.52034038e-02 -4.87254232e-01
-1.29385129e-01 -9.98130918e-01 8.08486462e-01 -2.32958004e-01
-3.98505241e-01 5.56603968e-01 2.26376772e-01 -3.85339826e-01
-3.61247733e-02 -6.83129728e-01 -8.29614758e-01 -1.02850938e+00
2.66126752e-01 -2.55086184e-01 -1.95011601e-01 -2.00149775... | [9.943340301513672, -2.6662745475769043] |
ec037092-5e91-4f02-b9b1-a6f05cb3056b | improvement-of-verbnet-like-resources-by | null | null | https://aclanthology.org/W16-3809 | https://aclanthology.org/W16-3809.pdf | Improvement of VerbNet-like resources by frame typing | Verbenet is a French lexicon developed by {``}translation{''} of its English counterpart {---} VerbNet (Kipper-Schuler, 2005){---}and treatment of the specificities of French syntax (Pradet et al., 2014; Danlos et al., 2016). One difficulty encountered in its development springs from the fact that the list of (potentia... | ['Matthieu Constant', 'Lucie Barque', 'Laurence Danlos'] | 2016-12-01 | null | null | null | ws-2016-12 | ['stock-market-prediction'] | ['time-series'] | [ 1.33616984e-01 2.60601968e-01 -1.34511992e-01 -3.01217109e-01
-4.37513173e-01 -1.05341196e+00 7.88361847e-01 1.65308014e-01
-1.73208177e-01 9.68841732e-01 3.38931739e-01 -7.03787088e-01
-2.16542423e-01 -8.64037871e-01 -4.29944396e-01 -8.61354321e-02
1.37193605e-01 2.20221266e-01 6.31431460e-01 -7.37451375... | [10.132232666015625, 9.340715408325195] |
263ec71b-7999-46c1-8865-458cf75f5c88 | persian-semantic-role-labeling-using-transfer | 2306.10339 | null | https://arxiv.org/abs/2306.10339v1 | https://arxiv.org/pdf/2306.10339v1.pdf | Persian Semantic Role Labeling Using Transfer Learning and BERT-Based Models | Semantic role labeling (SRL) is the process of detecting the predicate-argument structure of each predicate in a sentence. SRL plays a crucial role as a pre-processing step in many NLP applications such as topic and concept extraction, question answering, summarization, machine translation, sentiment analysis, and text... | ['Behrouz Minaei Bidgoli', 'Nasim Khozouei', 'Erfan Khedersolh Sadeh', 'Sayyed Ali Hossayni', 'Saeideh Niksirat Aghdam'] | 2023-06-17 | null | null | null | null | ['transfer-learning', 'question-answering', 'sentiment-analysis', 'machine-translation', 'semantic-role-labeling'] | ['miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 5.34580946e-01 6.76526353e-02 -2.16880694e-01 -2.93926448e-01
-8.13768387e-01 -6.56430304e-01 7.70230055e-01 8.67039800e-01
-7.01588035e-01 1.09904826e+00 4.12423372e-01 -2.95234382e-01
-1.22370712e-01 -6.86990857e-01 -1.21048011e-01 -5.50879121e-01
2.92425931e-01 3.10301512e-01 4.38644081e-01 -2.97400922... | [9.808002471923828, 8.76321792602539] |
7ccdb33f-ed26-4c25-a09d-9db45a8b922e | optimizing-cad-models-with-latent-space | 2303.12739 | null | https://arxiv.org/abs/2303.12739v1 | https://arxiv.org/pdf/2303.12739v1.pdf | Optimizing CAD Models with Latent Space Manipulation | When it comes to the optimization of CAD models in the automation domain, neural networks currently play only a minor role. Optimizing abstract features such as automation capability is challenging, since they can be very difficult to simulate, are too complex for rule-based systems, and also have little to no data ava... | ['Marco F Huber', 'Steffen Tauber', 'Raoul G. C. Schönhof', 'Jannes Elstner'] | 2023-03-09 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 1.77892864e-01 2.66498387e-01 -7.36520737e-02 -1.72685742e-01
-2.28245556e-01 -6.56768680e-01 5.61623394e-01 -2.12581068e-01
-1.68776348e-01 6.76375508e-01 -4.52644616e-01 -3.86616468e-01
-9.71582718e-03 -8.30972552e-01 -7.26564586e-01 -5.28365135e-01
1.35728404e-01 5.57755530e-01 -1.14210257e-02 -2.45292887... | [11.55858039855957, -0.531907856464386] |
886f5509-1eca-4a09-8506-0ce5cd39e3e0 | elo-system-for-skat-and-other-games-of-chance | 2104.05422 | null | https://arxiv.org/abs/2104.05422v1 | https://arxiv.org/pdf/2104.05422v1.pdf | ELO System for Skat and Other Games of Chance | Assessing the skill level of players to predict the outcome and to rank the players in a longer series of games is of critical importance for tournament play. Besides weaknesses, like an observed continuous inflation, through a steadily increasing playing body, the ELO ranking system, named after its creator Arpad Elo,... | ['Stefan Edelkamp'] | 2021-04-07 | null | null | null | null | ['card-games'] | ['playing-games'] | [-4.36845839e-01 1.11434951e-01 -8.46315697e-02 8.31263047e-03
-5.58600247e-01 -6.72999859e-01 3.59640986e-01 2.35427544e-01
-8.16610873e-01 8.07779074e-01 2.51155198e-01 -3.44534159e-01
-1.07426441e+00 -8.74108076e-01 -1.87646374e-01 -5.74810684e-01
-2.13988610e-02 7.67241299e-01 6.94999576e-01 -7.98375428... | [6.459214687347412, 0.4212234318256378] |
ce05f80a-690e-4820-965d-603b8fa68eb7 | cospgd-a-unified-white-box-adversarial-attack | 2302.02213 | null | https://arxiv.org/abs/2302.02213v2 | https://arxiv.org/pdf/2302.02213v2.pdf | CosPGD: a unified white-box adversarial attack for pixel-wise prediction tasks | While neural networks allow highly accurate predictions in many tasks, their lack of robustness towards even slight input perturbations hampers their deployment in many real-world applications. Recent research towards evaluating the robustness of neural networks such as the seminal projected gradient descent(PGD) attac... | ['Margret Keuper', 'Steffen Jung', 'Shashank Agnihotri'] | 2023-02-04 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 5.02848268e-01 2.06448473e-02 1.12690963e-03 -1.84048221e-01
-8.09306264e-01 -8.89097095e-01 6.35634601e-01 -3.77530813e-01
-5.50249398e-01 6.93795264e-01 -5.82557358e-02 -5.63522279e-01
2.05087513e-02 -7.28059709e-01 -1.00419343e+00 -8.66082132e-01
-8.91703889e-02 -1.54983804e-01 3.95864904e-01 -3.61316174... | [5.505273342132568, 7.942113876342773] |
602c963a-a36f-4e15-b7b2-b3b36b517cf0 | abolitionist-networks-modeling-language | 2103.07538 | null | https://arxiv.org/abs/2103.07538v1 | https://arxiv.org/pdf/2103.07538v1.pdf | Abolitionist Networks: Modeling Language Change in Nineteenth-Century Activist Newspapers | The abolitionist movement of the nineteenth-century United States remains among the most significant social and political movements in US history. Abolitionist newspapers played a crucial role in spreading information and shaping public opinion around a range of issues relating to the abolition of slavery. These newspa... | ['Jacob Eisenstein', 'Lauren Klein', 'Sandeep Soni'] | 2021-03-12 | null | null | null | null | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-2.16699857e-02 3.53364795e-01 -6.08439624e-01 -1.11923717e-01
-1.34417608e-01 -9.30877864e-01 1.43619835e+00 5.88291228e-01
-9.14415777e-01 5.62043190e-01 1.61089337e+00 -1.07906008e+00
-3.11451167e-01 -9.93709445e-01 -3.76105189e-01 -5.44765592e-01
2.59765267e-01 5.55767477e-01 -1.69285730e-01 -8.68266821... | [9.002531051635742, 10.006030082702637] |
f0883ad1-f457-4040-9097-7bdab1d76073 | scene-flow-to-action-map-a-new-representation | 1702.08652 | null | http://arxiv.org/abs/1702.08652v3 | http://arxiv.org/pdf/1702.08652v3.pdf | Scene Flow to Action Map: A New Representation for RGB-D based Action Recognition with Convolutional Neural Networks | Scene flow describes the motion of 3D objects in real world and potentially
could be the basis of a good feature for 3D action recognition. However, its
use for action recognition, especially in the context of convolutional neural
networks (ConvNets), has not been previously studied. In this paper, we propose
the extra... | ['Pichao Wang', 'Zhimin Gao', 'Philip Ogunbona', 'Chang Tang', 'Yuyao Zhang', 'Wanqing Li'] | 2017-02-28 | scene-flow-to-action-map-a-new-representation-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_Scene_Flow_to_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Scene_Flow_to_CVPR_2017_paper.pdf | cvpr-2017-7 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.60010237e-01 -6.23180807e-01 -1.44734517e-01 -2.88957328e-01
-5.69583662e-02 -4.40700233e-01 7.36342788e-01 -3.09379250e-01
-5.83448648e-01 4.55789298e-01 1.77571088e-01 5.51639907e-02
-1.75712302e-01 -7.88903356e-01 -4.22850817e-01 -8.86253953e-01
1.05646029e-01 -8.59588757e-02 4.62786764e-01 -5.55479415... | [7.9055399894714355, 0.3626278042793274] |
abb206c3-24c9-42ec-9526-bff55ee19fc8 | a-single-stream-network-for-robust-and-real | 2007.06811 | null | https://arxiv.org/abs/2007.06811v2 | https://arxiv.org/pdf/2007.06811v2.pdf | A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection | Existing RGB-D salient object detection (SOD) approaches concentrate on the cross-modal fusion between the RGB stream and the depth stream. They do not deeply explore the effect of the depth map itself. In this work, we design a single stream network to directly use the depth map to guide early fusion and middle fusion... | ['Huchuan Lu', 'Youwei Pang', 'Lihe Zhang', 'Lei Zhang', 'Xiaoqi Zhao'] | 2020-07-14 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4160_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670647.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection', 'thermal-image-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.55678147e-02 -1.03431195e-01 5.69240078e-02 -3.26847166e-01
-6.27796352e-01 -1.38769209e-01 4.26128000e-01 -7.60157183e-02
-6.36654735e-01 1.34694353e-01 1.78890362e-01 -1.00025699e-01
2.61705011e-01 -9.24693942e-01 -6.58854425e-01 -6.30436778e-01
9.57468003e-02 -2.74709851e-01 7.61829674e-01 -1.69584930... | [9.601926803588867, -0.8941361904144287] |
d163a666-a7c9-46f9-b51c-732bfa8a4fe8 | matching-with-transformers-in-melt | 2109.07401 | null | https://arxiv.org/abs/2109.07401v1 | https://arxiv.org/pdf/2109.07401v1.pdf | Matching with Transformers in MELT | One of the strongest signals for automated matching of ontologies and knowledge graphs are the textual descriptions of the concepts. The methods that are typically applied (such as character- or token-based comparisons) are relatively simple, and therefore do not capture the actual meaning of the texts. With the rise o... | ['Heiko Paulheim', 'Jan Portisch', 'Sven Hertling'] | 2021-09-15 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 2.08485961e-01 2.77202781e-02 -3.10006589e-01 -4.17242736e-01
-3.38193625e-01 -5.56158364e-01 1.02862930e+00 9.52533901e-01
-5.98988891e-01 1.95533499e-01 3.22058350e-01 -3.91386241e-01
-6.80295527e-01 -1.27122438e+00 -1.31520107e-01 -5.48124239e-02
2.35829204e-01 9.65538740e-01 5.83618402e-01 -6.20364249... | [9.312644958496094, 8.151619911193848] |
b59772dd-5b61-4f32-8a99-a63d8c289a03 | 190406726 | 1904.06726 | null | http://arxiv.org/abs/1904.06726v1 | http://arxiv.org/pdf/1904.06726v1.pdf | VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal | Video object removal is a challenging task in video processing that often
requires massive human efforts. Given the mask of the foreground object in each
frame, the goal is to complete (inpaint) the object region and generate a video
without the target object. While recently deep learning based methods have
achieved gr... | ['Ya-Liang Chang', 'Winston Hsu', 'Zhe Yu Liu'] | 2019-04-14 | null | null | null | null | ['one-shot-visual-object-segmentation', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 3.53644401e-01 -4.92710918e-01 -1.47903949e-01 8.07502121e-02
-4.75590348e-01 -3.77554297e-01 1.48540556e-01 -5.92047215e-01
-3.22795540e-01 7.93751121e-01 1.03064135e-01 1.11077003e-01
1.84609547e-01 -3.63538265e-01 -9.51246858e-01 -6.40100121e-01
1.11428142e-01 -8.82676467e-02 4.50361490e-01 2.03553692... | [10.776630401611328, -1.2917225360870361] |
f1d98b22-f073-46b9-af86-970c5c002c19 | from-clickbait-to-fake-news-detection-an | null | null | https://aclanthology.org/W17-4215 | https://aclanthology.org/W17-4215.pdf | From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles | We present a system for the detection of the stance of headlines with regard to their corresponding article bodies. The approach can be applied in fake news, especially clickbait detection scenarios. The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an in... | ['Peter Bourgonje', 'Georg Rehm', 'Julian Moreno Schneider'] | 2017-09-01 | null | null | null | ws-2017-9 | ['rumour-detection', 'clickbait-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-2.41707996e-01 4.22372013e-01 -4.08314914e-01 1.23295531e-01
-9.22796369e-01 -9.74363208e-01 9.01133060e-01 8.29136968e-01
-3.94368410e-01 7.11690009e-01 4.89570260e-01 -3.44700813e-01
1.37651607e-01 -7.36379862e-01 -8.24270904e-01 -2.56312937e-01
5.05627096e-01 5.52873731e-01 7.93224037e-01 -6.38937652... | [8.142444610595703, 10.164408683776855] |
6cdea61a-e06c-4e1a-bdaf-3125b2a7797c | fine-grained-opinion-summarization-with | 2110.08845 | null | https://arxiv.org/abs/2110.08845v1 | https://arxiv.org/pdf/2110.08845v1.pdf | Fine-Grained Opinion Summarization with Minimal Supervision | Opinion summarization aims to profile a target by extracting opinions from multiple documents. Most existing work approaches the task in a semi-supervised manner due to the difficulty of obtaining high-quality annotation from thousands of documents. Among them, some use aspect and sentiment analysis as a proxy for iden... | ['Jiawei Han', 'Sharon Wang', 'Yu Meng', 'Jiaxin Huang', 'Suyu Ge'] | 2021-10-17 | null | null | null | null | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 2.35365912e-01 2.46166319e-01 -6.25649154e-01 -4.31356877e-01
-1.08257639e+00 -7.94075489e-01 6.02081060e-01 9.66600001e-01
-3.57395083e-01 5.97494185e-01 1.01835477e+00 1.09614819e-01
1.10287927e-01 -8.40543330e-01 -2.66189605e-01 -8.44708502e-01
2.27011710e-01 5.00044167e-01 7.86245540e-02 -3.51563960... | [11.421252250671387, 6.69329833984375] |
b459c04b-0cb0-4419-b007-0644ccc93dd6 | flowtext-synthesizing-realistic-scene-text | 2305.03327 | null | https://arxiv.org/abs/2305.03327v1 | https://arxiv.org/pdf/2305.03327v1.pdf | FlowText: Synthesizing Realistic Scene Text Video with Optical Flow Estimation | Current video text spotting methods can achieve preferable performance, powered with sufficient labeled training data. However, labeling data manually is time-consuming and labor-intensive. To overcome this, using low-cost synthetic data is a promising alternative. This paper introduces a novel video text synthesis tec... | ['Weiqiang Wang', 'Jiahong Li', 'Zhuang Li', 'Weijia Wu', 'Yuzhong Zhao'] | 2023-05-05 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 2.33239934e-01 -5.89514911e-01 -1.96483150e-01 -9.68517661e-02
-3.91160399e-01 -5.63875198e-01 6.75287664e-01 -1.83168352e-01
-1.91703826e-01 6.80708289e-01 1.97410181e-01 -1.78315461e-01
4.36309755e-01 -3.95086437e-01 -6.45538449e-01 -5.90148866e-01
5.50227582e-01 5.91114573e-02 3.81825864e-01 1.12357967... | [10.826600074768066, -0.7795295119285583] |
cc79b5ca-ff8d-4544-80ba-8f988ea20fe8 | re2g-retrieve-rerank-generate-2 | 2207.06300 | null | https://arxiv.org/abs/2207.06300v1 | https://arxiv.org/pdf/2207.06300v1.pdf | Re2G: Retrieve, Rerank, Generate | As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models... | ['Alfio Gliozzo', 'Pengshan Cai', 'Ankita Rajaram Naik', 'Md Faisal Mahbub Chowdhury', 'Gaetano Rossiello', 'Michael Glass'] | 2022-07-13 | re2g-retrieve-rerank-generate-1 | https://aclanthology.org/2022.naacl-main.194 | https://aclanthology.org/2022.naacl-main.194.pdf | naacl-2022-7 | ['zero-shot-slot-filling', 'slot-filling', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.45288467e-01 2.56293684e-01 -2.82433182e-01 -8.43694136e-02
-1.71755219e+00 -7.26171970e-01 6.12552822e-01 2.89742053e-01
-4.62388694e-01 8.98091972e-01 4.59904432e-01 -5.50101221e-01
7.59710148e-02 -9.01112914e-01 -7.92741776e-01 4.55304086e-02
1.43201917e-01 1.22181237e+00 4.95412350e-01 -5.48282981... | [11.469141006469727, 8.141422271728516] |
ed005107-7d98-4d8c-8ab7-e66d93e96a56 | dataset-of-natural-language-queries-for-e | 2302.06355 | null | https://arxiv.org/abs/2302.06355v1 | https://arxiv.org/pdf/2302.06355v1.pdf | Dataset of Natural Language Queries for E-Commerce | Shopping online is more and more frequent in our everyday life. For e-commerce search systems, understanding natural language coming through voice assistants, chatbots or from conversational search is an essential ability to understand what the user really wants. However, evaluation datasets with natural and detailed i... | ['Norbert Fuhr', 'Ahmet Aker', 'Alfred Sliwa', 'Daniel Hienert', 'Dagmar Kern', 'Andrea Papenmeier'] | 2023-02-13 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [-1.50837600e-01 -1.95260234e-02 -6.94810331e-01 -7.43198335e-01
-5.78351855e-01 -9.18636143e-01 7.34159172e-01 5.62616944e-01
-5.89941204e-01 4.99367982e-01 1.67746991e-01 -4.88464981e-01
-2.68456608e-01 -7.33621418e-01 -1.46086663e-01 -7.39270896e-02
2.38706723e-01 8.81778300e-01 2.30907634e-01 -5.36170602... | [12.181660652160645, 7.810238361358643] |
2efaa7f5-330d-4633-b3c9-a20054431ee3 | machine-learning-applications-in-diagnosis | 2203.02794 | null | https://arxiv.org/abs/2203.02794v3 | https://arxiv.org/pdf/2203.02794v3.pdf | Machine Learning Applications in Lung Cancer Diagnosis, Treatment and Prognosis | The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in in... | ['Yuan Luo', 'Guoqian Jiang', 'Ping Yang', 'Xin Wu', 'Yawei Li'] | 2022-03-05 | null | null | null | null | ['lung-cancer-diagnosis'] | ['medical'] | [ 3.79813582e-01 -4.83225018e-01 -1.00978518e+00 1.49525225e-01
-1.05034649e+00 -4.96348143e-01 3.39576989e-01 5.46359122e-01
-4.08845842e-01 7.85657704e-01 2.40813434e-01 -7.21116662e-01
-2.66857356e-01 -6.07319176e-01 8.56929272e-02 -1.19203377e+00
1.97469950e-01 9.11986530e-01 2.53066242e-01 1.07076682... | [15.256296157836914, -2.640519618988037] |
630d8be5-47b1-4521-8bfd-3fd8b7081b2c | miriam-exploiting-elastic-kernels-for-real | 2307.04339 | null | https://arxiv.org/abs/2307.04339v1 | https://arxiv.org/pdf/2307.04339v1.pdf | Miriam: Exploiting Elastic Kernels for Real-time Multi-DNN Inference on Edge GPU | Many applications such as autonomous driving and augmented reality, require the concurrent running of multiple deep neural networks (DNN) that poses different levels of real-time performance requirements. However, coordinating multiple DNN tasks with varying levels of criticality on edge GPUs remains an area of limited... | ['Guoliang Xing', 'Nan Guan', 'Neiwen Ling', 'Zhihe Zhao'] | 2023-07-10 | null | null | null | null | ['autonomous-driving', 'management'] | ['computer-vision', 'miscellaneous'] | [-4.28878725e-01 -4.13755864e-01 -3.23214084e-01 -5.00290036e-01
-3.85417551e-01 -4.30711776e-01 4.34295148e-01 -2.03821093e-01
-7.77530074e-01 5.92629254e-01 -1.24295302e-01 -8.27687979e-01
3.40069830e-01 -7.49399722e-01 -7.66918361e-01 -5.35939574e-01
2.40810007e-01 5.98084390e-01 7.89423883e-01 2.87828714... | [8.451339721679688, 3.11051607131958] |
dfa766f6-0268-421f-923d-d4a32cc0917d | prequant-a-task-agnostic-quantization | 2306.00014 | null | https://arxiv.org/abs/2306.00014v1 | https://arxiv.org/pdf/2306.00014v1.pdf | PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models | While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use. Therefore, effectively compressing large-scale PLMs becomes an increasingly important problem. Quantization, which represents high-precision tensors with low-bit... | ['Rui Yan', 'Dongyan Zhao', 'Yunsen Xian', 'Wei Wu', 'Jingang Wang', 'Yang Yang', 'Qifan Wang', 'Jiahao Liu', 'Zhuocheng Gong'] | 2023-05-30 | null | null | null | null | ['quantization'] | ['methodology'] | [-5.20771518e-02 -3.50707054e-01 -2.82270461e-01 -4.03531969e-01
-1.15018749e+00 -6.39411747e-01 6.04295611e-01 3.15887332e-01
-5.85187078e-01 4.38372135e-01 1.05360016e-01 -6.27827108e-01
-2.34196410e-01 -5.85676491e-01 -9.32025373e-01 -5.94660103e-01
-5.25145829e-02 7.46630907e-01 2.97713429e-01 -1.79495871... | [8.70685863494873, 3.5061452388763428] |
c14989c8-d98d-45cf-b644-520beac80307 | ariann-low-interaction-privacy-preserving | 2006.04593 | null | https://arxiv.org/abs/2006.04593v4 | https://arxiv.org/pdf/2006.04593v4.pdf | ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing | We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data. Our semi-honest 2-party computation protocol (with a trusted dealer) leverages function secret sharing, a recent lightweight cryptographic protocol that allows us to achieve an efficien... | ['David Pointcheval', 'Pierre Tholoniat', 'Théo Ryffel', 'Francis Bach'] | 2020-06-08 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [-1.69552937e-01 2.66515613e-01 -4.49797697e-02 -1.04040658e+00
-7.55030751e-01 -1.08620775e+00 3.39309037e-01 3.21438685e-02
-1.17729580e+00 4.45513397e-01 -1.54943600e-01 -6.96294606e-01
2.40692407e-01 -9.57423091e-01 -1.22201538e+00 -9.86579657e-01
-4.22267348e-01 2.28662968e-01 2.40462095e-01 4.26277891... | [5.87473726272583, 6.831081390380859] |
f121ea04-5973-4365-afe1-a9288431270e | equivalent-transformation-and-dual-stream | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chao_Equivalent_Transformation_and_Dual_Stream_Network_Construction_for_Mobile_Image_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chao_Equivalent_Transformation_and_Dual_Stream_Network_Construction_for_Mobile_Image_CVPR_2023_paper.pdf | Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution | In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices. To address this issue, many lightweight super-resolution models have been proposed. However, these models still contain time-consuming components that increase inference latency, limiting their real-world... | ['Lydia Dehbi', 'Zhenbing Zeng', 'Zhengfeng Yang', 'Jiali Gong', 'Hongfan Gao', 'Zhou Zhou', 'Jiahao Chao'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-super-resolution'] | ['computer-vision'] | [ 3.59208792e-01 -1.94939122e-01 -2.04379827e-01 -2.93426007e-01
-4.65598345e-01 -8.78180042e-02 2.45956600e-01 -5.87446392e-01
-3.45706403e-01 5.94075918e-01 2.78932989e-01 -2.20924273e-01
-1.03602745e-02 -1.02250898e+00 -4.79266375e-01 -3.36379677e-01
1.92239061e-01 -1.79176614e-01 6.90828562e-01 -7.27134198... | [11.036898612976074, -1.8269202709197998] |
1eff5ebb-076b-435d-b5fa-68e52d48a33e | chemberta-2-towards-chemical-foundation | 2209.01712 | null | https://arxiv.org/abs/2209.01712v1 | https://arxiv.org/pdf/2209.01712v1.pdf | ChemBERTa-2: Towards Chemical Foundation Models | Large pretrained models such as GPT-3 have had tremendous impact on modern natural language processing by leveraging self-supervised learning to learn salient representations that can be used to readily finetune on a wide variety of downstream tasks. We investigate the possibility of transferring such advances to molec... | ['Bharath Ramsundar', 'Gabriel Grand', 'Seyone Chithrananda', 'Elana Simon', 'Walid Ahmad'] | 2022-09-05 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 5.98668635e-01 8.60658512e-02 -6.62275255e-01 -5.12824297e-01
-7.00107813e-01 -8.57627034e-01 7.35309362e-01 7.88225651e-01
-5.31318247e-01 1.19555414e+00 3.35332513e-01 -6.96430087e-01
8.76840204e-03 -7.12265730e-01 -1.17976654e+00 -5.58557093e-01
-2.27748662e-01 5.65067232e-01 1.35223055e-02 -3.64616126... | [5.004796504974365, 5.849133014678955] |
0eb67ea0-a1bd-47e2-b172-669e20ff77f2 | modelling-disease-impact-lifespan-reduction | 2305.06808 | null | https://arxiv.org/abs/2305.06808v1 | https://arxiv.org/pdf/2305.06808v1.pdf | Modelling disease impact: lifespan reduction is greatest for young adults in an exogenous damage model of disease | We model the effects of disease and other exogenous damage during human aging. While the exogenous damage is repaired at the end of acute disease, propagated secondary damage remains. We consider both short-term mortality effects due to (acute) exogenous damage and long-term mortality effects due to propagated damage w... | ['Andrew D. Rutenberg', 'Glen Pridham', 'Rebecca Tobin'] | 2023-05-11 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [-1.51011959e-01 1.55449480e-01 -3.51435155e-01 4.99877274e-01
1.97304979e-01 -2.68905967e-01 7.10237086e-01 6.39767230e-01
-5.88761210e-01 1.06311691e+00 6.41811967e-01 -2.75097519e-01
-4.93969947e-01 -9.09848392e-01 -2.02552572e-01 -5.51953495e-01
-7.24779069e-01 2.21721217e-01 8.43256861e-02 -3.44796330... | [6.106392860412598, 4.45125150680542] |
55379fc1-376e-4ab7-b683-588de41954e4 | unlocking-the-potential-of-chatgpt-a | 2304.02017 | null | https://arxiv.org/abs/2304.02017v5 | https://arxiv.org/pdf/2304.02017v5.pdf | Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing | Large language models have revolutionized the field of artificial intelligence and have been used in various applications. Among these models, ChatGPT (Chat Generative Pre-trained Transformer) has been developed by OpenAI, it stands out as a powerful tool that has been widely adopted. ChatGPT has been successfully appl... | ['Walid Hariri'] | 2023-03-27 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 3.20333570e-01 2.75578052e-01 -8.26484412e-02 -2.33440712e-01
-5.49464166e-01 -5.99765301e-01 7.28635907e-01 -3.95924672e-02
-2.65151083e-01 9.20112491e-01 3.50773275e-01 -2.37578433e-02
7.55137429e-02 -7.44961202e-01 -1.52528495e-01 -6.43073618e-01
2.95872182e-01 6.93108499e-01 -6.82700351e-02 -4.41894293... | [12.204853057861328, 8.49516487121582] |
f674bbf9-30f9-4abb-b577-7a7ccae3b9bc | athena-2-0-contextualized-dialogue-management-1 | 2111.02519 | null | https://arxiv.org/abs/2111.02519v1 | https://arxiv.org/pdf/2111.02519v1.pdf | Athena 2.0: Contextualized Dialogue Management for an Alexa Prize SocialBot | Athena 2.0 is an Alexa Prize SocialBot that has been a finalist in the last two Alexa Prize Grand Challenges. One reason for Athena's success is its novel dialogue management strategy, which allows it to dynamically construct dialogues and responses from component modules, leading to novel conversations with every inte... | ['Marilyn Walker', 'Adwait Ratnaparkhi', 'Rohan Pandey', 'Jeshwanth Bheemanpally', 'Phillip Lee', 'Eduardo Zamora', 'Cecilia Li', 'Angela Ramirez', 'Rishi Rajasekaran', 'Omkar Patil', 'Wen Cui', 'Vrindavan Harrison', 'Lena Reed', 'Kevin K. Bowden', 'Juraj Juraska'] | 2021-11-03 | athena-2-0-contextualized-dialogue-management | https://aclanthology.org/2021.emnlp-demo.15 | https://aclanthology.org/2021.emnlp-demo.15.pdf | emnlp-acl-2021-11 | ['dialogue-management'] | ['natural-language-processing'] | [-3.56656194e-01 7.87067413e-01 3.85843217e-01 -4.67821300e-01
-3.46405566e-01 -7.70616591e-01 1.09207714e+00 -4.78694402e-02
-1.26704067e-01 1.14007521e+00 7.36561716e-01 -6.72825798e-02
-1.89891160e-02 -5.38829029e-01 6.34652898e-02 3.24624814e-02
-1.94759727e-01 9.21005368e-01 2.93795526e-01 -1.34475815... | [12.748807907104492, 7.945132255554199] |
b3b8d60c-682a-4d9a-908a-e32542783668 | audio-declipping-performance-enhancement-via | 2104.03074 | null | https://arxiv.org/abs/2104.03074v1 | https://arxiv.org/pdf/2104.03074v1.pdf | Audio declipping performance enhancement via crossfading | Some audio declipping methods produce waveforms that do not fully respect the physical process of clipping, which is why we refer to them as inconsistent. This letter reports what effect on perception it has if the solution by inconsistent methods is forced consistent by postprocessing. We first propose a simple sample... | ['Ondřej Mokrý', 'Pavel Rajmic', 'Pavel Záviška'] | 2021-04-07 | null | null | null | null | ['audio-declipping'] | ['audio'] | [ 3.98530900e-01 -3.61519158e-02 6.85196891e-02 -4.43475172e-02
-9.43179607e-01 -6.04950070e-01 3.50176871e-01 -1.12330779e-01
-5.72203100e-02 8.29500318e-01 4.78551328e-01 1.88138857e-01
-6.43810749e-01 -3.78457844e-01 -6.89243615e-01 -7.04027772e-01
-1.73769072e-01 -3.49241555e-01 3.86626452e-01 -3.35259348... | [15.481310844421387, 5.5913405418396] |
777728c8-37c6-42ab-bc12-84718098709c | aircraft-environmental-impact-segmentation | 2306.13830 | null | https://arxiv.org/abs/2306.13830v1 | https://arxiv.org/pdf/2306.13830v1.pdf | Aircraft Environmental Impact Segmentation via Metric Learning | Metric learning is the process of learning a tailored distance metric for a particular task. This advanced subfield of machine learning is useful to any machine learning or data mining task that relies on the computation of distances or similarities over objects. In recently years, machine learning techniques have been... | ['Dimitri N. Mavris', 'Zhenyu Gao'] | 2023-06-24 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 5.50746977e-01 -3.65644336e-01 -1.51676759e-01 -6.36313438e-01
-6.35262668e-01 -4.91710633e-01 4.80247676e-01 4.85818535e-01
-3.43664855e-01 7.73646295e-01 -1.17495015e-01 -4.95666355e-01
-1.17641711e+00 -8.55476618e-01 -3.08332115e-01 -6.43007636e-01
-3.22980642e-01 3.89323384e-01 8.57058764e-02 -3.34664077... | [8.335380554199219, 4.2160491943359375] |
6a013663-b11e-4f5a-9654-24f9fc5ee0a8 | neural-scene-decoration-from-a-single | 2108.01806 | null | https://arxiv.org/abs/2108.01806v2 | https://arxiv.org/pdf/2108.01806v2.pdf | Neural Scene Decoration from a Single Photograph | Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce... | ['Duc Thanh Nguyen', 'Phuoc-Hieu Le', 'Sai-Kit Yeung', 'Binh-Son Hua', 'Yingshu Chen', 'Hong-Wing Pang'] | 2021-08-04 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 7.45607495e-01 5.06346673e-03 5.33416092e-01 -4.71733123e-01
-3.32625896e-01 -8.65641415e-01 6.74909890e-01 -3.28451246e-01
1.83065131e-01 6.30184054e-01 3.67773473e-01 -4.35341895e-01
1.09182067e-01 -1.07680809e+00 -9.74604666e-01 -4.05348629e-01
5.07214367e-01 4.27489989e-02 -3.29026371e-01 -2.31921047... | [9.362787246704102, -2.9688239097595215] |
91dfaf20-d2e2-49e4-a0db-35545b635c3e | solov2-dynamic-faster-and-stronger | 2003.10152 | null | https://arxiv.org/abs/2003.10152v3 | https://arxiv.org/pdf/2003.10152v3.pdf | SOLOv2: Dynamic and Fast Instance Segmentation | In this work, we aim at building a simple, direct, and fast instance segmentation framework with strong performance. We follow the principle of the SOLO method of Wang et al. "SOLO: segmenting objects by locations". Importantly, we take one step further by dynamically learning the mask head of the object segmenter such... | ['Chunhua Shen', 'Lei LI', 'Rufeng Zhang', 'Xinlong Wang', 'Tao Kong'] | 2020-03-23 | null | http://proceedings.neurips.cc/paper/2020/hash/cd3afef9b8b89558cd56638c3631868a-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/cd3afef9b8b89558cd56638c3631868a-Paper.pdf | neurips-2020-12 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 2.72093892e-01 -2.04445794e-01 -4.17687893e-02 -3.19116086e-01
-8.46409440e-01 -8.24808896e-01 3.87163937e-01 -4.76670116e-02
-5.90364039e-01 1.97734416e-01 -5.88871002e-01 -4.22677040e-01
3.16780537e-01 -9.06986117e-01 -1.00846195e+00 -8.41261625e-01
6.21157475e-02 6.33079708e-01 6.98989272e-01 1.33541077... | [9.351895332336426, 0.017526468262076378] |
e419f2bc-642b-4566-8e90-84ef7b5537fe | optimization-based-improvement-of-face-image | 2305.14856 | null | https://arxiv.org/abs/2305.14856v1 | https://arxiv.org/pdf/2305.14856v1.pdf | Optimization-Based Improvement of Face Image Quality Assessment Techniques | Contemporary face recognition (FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (realworld) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA... | ['Vitomir Štruc', 'Naser Damer', 'Žiga Babnik'] | 2023-05-24 | null | null | null | null | ['face-image-quality', 'face-recognition', 'image-quality-assessment', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 1.42445311e-01 -3.24560434e-01 2.07838416e-01 -8.13086450e-01
-8.77369940e-01 -4.28377628e-01 5.90133071e-01 -3.55702758e-01
-4.17803414e-02 6.05331719e-01 2.48480197e-02 7.49647245e-02
-5.41986465e-01 -6.75391853e-01 -4.11473036e-01 -5.16964734e-01
-1.35581315e-01 6.86411142e-01 -3.11923802e-01 -3.35210800... | [13.057344436645508, 0.752301812171936] |
7c1d67ff-8b0d-4fd0-abdb-49407ca7225c | differentiable-digital-signal-processing | 2202.00200 | null | https://arxiv.org/abs/2202.00200v1 | https://arxiv.org/pdf/2202.00200v1.pdf | Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds | A differentiable digital signal processing (DDSP) autoencoder is a musical sound synthesizer that combines a deep neural network (DNN) and spectral modeling synthesis. It allows us to flexibly edit sounds by changing the fundamental frequency, timbre feature, and loudness (synthesis parameters) extracted from an input ... | ['Kazunobu Kondo', 'Yu Takahashi', 'Hiroshi Saruwatari', 'Daichi Kitamura', 'Tomohiko Nakamura', 'Masaya Kawamura'] | 2022-02-01 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 3.78098041e-02 -2.50490248e-01 4.91898984e-01 9.28374827e-02
-4.13565308e-01 -7.10495412e-01 4.50438678e-01 -4.21540141e-01
-8.46173987e-03 2.87537545e-01 3.44286561e-01 3.37568782e-02
1.38389561e-02 -7.92123139e-01 -7.87967503e-01 -8.80718350e-01
3.67564112e-01 9.31682661e-02 -6.57525435e-02 -2.47482792... | [15.53277587890625, 5.937432765960693] |
44e2e515-cd42-4da8-af04-54175dfa8419 | astbert-enabling-language-model-for-code | 2201.07984 | null | https://arxiv.org/abs/2201.07984v4 | https://arxiv.org/pdf/2201.07984v4.pdf | AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees | Using the pre-trained language models to understand source codes has attracted increasing attention from financial institutions owing to the great potential to uncover financial risks. However, there are several challenges in applying these language models to solve programming language-related problems directly. For in... | ['Zhen Huang', 'Yuze Liu', 'Yujie Lu', 'Tiehua Zhang', 'Xin Chen', 'Rong Liang'] | 2022-01-20 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-1.84215024e-01 1.77850798e-01 -2.51972973e-01 -5.16800344e-01
-8.12651992e-01 -7.94454098e-01 1.25584111e-01 4.60972428e-01
1.20860830e-01 -5.01751155e-02 2.07266331e-01 -7.84478724e-01
2.33993918e-01 -6.85905337e-01 -7.19178617e-01 1.35452405e-01
5.83657287e-02 -2.57097065e-01 3.59213024e-01 -2.06185073... | [7.595324993133545, 7.936840534210205] |
3a07ea76-3a75-43b2-8030-327b380da709 | explainable-ai-for-time-series-via-virtual | 2303.06365 | null | https://arxiv.org/abs/2303.06365v1 | https://arxiv.org/pdf/2303.06365v1.pdf | Explainable AI for Time Series via Virtual Inspection Layers | The field of eXplainable Artificial Intelligence (XAI) has greatly advanced in recent years, but progress has mainly been made in computer vision and natural language processing. For time series, where the input is often not interpretable, only limited research on XAI is available. In this work, we put forward a virtua... | ['Wojciech Samek', 'Grégoire Montavon', 'Sebastian Lapuschkin', 'Johanna Vielhaben'] | 2023-03-11 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [ 7.09030688e-01 5.89975476e-01 1.13343112e-01 -4.14766490e-01
-3.41444850e-01 -5.77878296e-01 7.19089925e-01 3.50613922e-01
8.94650444e-02 5.68278491e-01 2.78634101e-01 -6.26478314e-01
-5.64853370e-01 -5.60694456e-01 -6.86976016e-01 -4.48134989e-01
-3.32207501e-01 1.90331563e-01 -3.04200351e-01 -2.24018067... | [7.291958808898926, 3.2109427452087402] |
933b2264-db38-4ce9-9d29-1b31db79da7a | empirical-analysis-of-indirect-internal | 2002.12274 | null | https://arxiv.org/abs/2002.12274v1 | https://arxiv.org/pdf/2002.12274v1.pdf | Empirical Analysis of Indirect Internal Conversions in Cryptocurrency Exchanges | Algorithmic trading is well studied in traditional financial markets. However, it has received less attention in centralized cryptocurrency exchanges. The Commodity Futures Trading Commission (CFTC) attributed the $2010$ flash crash, one of the most turbulent periods in the history of financial markets that saw the Dow... | ['Damon McCoy', 'Tobias Lauinger', 'Paz Grimberg'] | 2020-02-27 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-5.89958608e-01 3.25098448e-02 -1.33100785e-02 2.51798660e-01
-5.67321181e-01 -1.38706791e+00 8.14674675e-01 -1.54712111e-01
-4.40405518e-01 8.73312354e-01 -1.52995721e-01 -8.94165397e-01
2.94505246e-02 -9.71800566e-01 -4.74055976e-01 -4.98204321e-01
-4.21960145e-01 7.55459130e-01 2.23740533e-01 -3.86193961... | [4.698049545288086, 4.112616062164307] |
a2c1a3a5-4aa9-415f-a235-d2d28b8ca1b5 | sibylvariant-transformations-for-robust-text | 2205.05137 | null | https://arxiv.org/abs/2205.05137v1 | https://arxiv.org/pdf/2205.05137v1.pdf | Sibylvariant Transformations for Robust Text Classification | The vast majority of text transformation techniques in NLP are inherently limited in their ability to expand input space coverage due to an implicit constraint to preserve the original class label. In this work, we propose the notion of sibylvariance (SIB) to describe the broader set of transforms that relax the label-... | ['Miryung Kim', 'Nanyun Peng', 'Muhammad Ali Gulzar', 'Fabrice Harel-Canada'] | 2022-05-10 | null | https://aclanthology.org/2022.findings-acl.140 | https://aclanthology.org/2022.findings-acl.140.pdf | findings-acl-2022-5 | ['defect-detection', 'classification'] | ['computer-vision', 'methodology'] | [ 7.30792940e-01 1.81043133e-01 -1.27931386e-01 -4.16903198e-01
-6.12636745e-01 -1.08573210e+00 7.48367310e-01 1.22488715e-01
-2.43922830e-01 9.20670986e-01 -6.87889084e-02 -3.94185871e-01
-1.43613979e-01 -8.38367522e-01 -7.19711423e-01 -8.70962083e-01
4.59523112e-01 6.39169097e-01 1.98576242e-01 -2.28252858... | [10.223586082458496, 3.2241275310516357] |
e0636bc0-9ba1-4589-a929-09e6ceb232cd | amortized-synthesis-of-constrained | 2106.09019 | null | https://arxiv.org/abs/2106.09019v2 | https://arxiv.org/pdf/2106.09019v2.pdf | Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate | In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraints while maximizing one or more objective functions. The synthesis problem is typically characterized by a physical process in which many dif... | ['Szymon Rusinkiewicz', 'Ryan P. Adams', 'Tianju Xue', 'Xingyuan Sun'] | 2021-06-16 | null | http://proceedings.neurips.cc/paper/2021/hash/9d38e6eab92b2aeb0a83b570188d5a1a-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/9d38e6eab92b2aeb0a83b570188d5a1a-Paper.pdf | neurips-2021-12 | ['physical-simulations'] | ['miscellaneous'] | [ 2.85846055e-01 1.94434240e-01 4.48050769e-03 -3.01153541e-01
-6.32650077e-01 -4.47677225e-01 5.33997476e-01 -1.37397274e-01
8.16023722e-02 7.50594735e-01 -6.53241798e-02 -1.61911249e-01
-5.84977448e-01 -8.78126025e-01 -1.13142192e+00 -7.17629552e-01
2.07582712e-01 9.12051857e-01 -5.36097705e-01 -2.89633304... | [5.8661112785339355, 3.303075075149536] |
7ef65090-3f8a-4c01-80d3-e5756744979f | a-hierarchical-interactive-network-for-joint | 2208.11283 | null | https://arxiv.org/abs/2208.11283v1 | https://arxiv.org/pdf/2208.11283v1.pdf | A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis | Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches either sequentially extra... | ['Zhongshi He', 'Fuzhen Zhuang', 'Zhao Zhang', 'Jinglong Du', 'Wei Chen'] | 2022-08-24 | null | https://aclanthology.org/2022.coling-1.611 | https://aclanthology.org/2022.coling-1.611.pdf | coling-2022-10 | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.48062938e-01 -6.09118380e-02 -1.93273231e-01 -6.68452680e-01
-5.28678179e-01 -4.97889280e-01 6.43858075e-01 -5.32904342e-02
-4.21540827e-01 5.19796550e-01 2.68367022e-01 -1.59303144e-01
-2.99525913e-02 -6.87896729e-01 -4.80445832e-01 -7.92327642e-01
2.14406654e-01 1.94125459e-01 3.45945776e-01 -2.82497764... | [11.46137809753418, 6.601786136627197] |
df17dc00-da3a-4aae-b9a0-2d87ab647007 | embrace-evaluation-and-modifications-for | 2305.08433 | null | https://arxiv.org/abs/2305.08433v1 | https://arxiv.org/pdf/2305.08433v1.pdf | EMBRACE: Evaluation and Modifications for Boosting RACE | When training and evaluating machine reading comprehension models, it is very important to work with high-quality datasets that are also representative of real-world reading comprehension tasks. This requirement includes, for instance, having questions that are based on texts of different genres and require generating ... | ['Johan Boye', 'Dmytro Kalpakchi', 'Mariia Zyrianova'] | 2023-05-15 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.57213074e-01 3.38529378e-01 1.27125248e-01 -4.83799726e-01
-1.01539016e+00 -7.61159778e-01 5.56092501e-01 8.14690590e-01
-5.07427454e-01 7.10995376e-01 4.03210223e-01 -1.04368532e+00
-4.36888546e-01 -9.93676066e-01 -8.07695389e-01 -1.53171331e-01
4.98788923e-01 5.57478786e-01 2.50037432e-01 -5.70586324... | [11.461197853088379, 8.184136390686035] |
6cb1a8df-3eb7-412c-be95-6389c098d6aa | clenshaw-graph-neural-networks | 2210.16508 | null | https://arxiv.org/abs/2210.16508v2 | https://arxiv.org/pdf/2210.16508v2.pdf | Clenshaw Graph Neural Networks | Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. E... | ['Zhewei Wei', 'Yuhe Guo'] | 2022-10-29 | null | null | null | null | ['node-classification-on-non-homophilic'] | ['graphs'] | [-2.11001956e-03 3.73843014e-01 1.19265549e-01 1.73795462e-01
2.05404595e-01 -4.08196658e-01 6.71970606e-01 9.86624658e-02
-4.28030528e-02 4.09318686e-01 -1.10932611e-01 -6.67075455e-01
-1.04500294e-01 -1.18538916e+00 -7.88644493e-01 -6.90328717e-01
-5.56445777e-01 -1.13223597e-01 2.70326406e-01 -5.92078447... | [6.894405364990234, 6.1327290534973145] |
3cb908cb-322c-42b2-8bd0-e4f364bf229f | classifier-calibration-how-to-assess-and | 2112.10327 | null | https://arxiv.org/abs/2112.10327v2 | https://arxiv.org/pdf/2112.10327v2.pdf | Classifier Calibration: A survey on how to assess and improve predicted class probabilities | This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of uncertainty or confidence associated with its instance-wise predictions. This is essential for critical applications, optimal decis... | ['Peter Flach', 'Meelis Kull', 'Raul Santos-Rodriguez', 'Miquel Perello-Nieto', 'Hao Song', 'Telmo Silva Filho'] | 2021-12-20 | null | null | null | null | ['classifier-calibration', 'classifier-calibration'] | ['computer-vision', 'miscellaneous'] | [ 4.73556042e-01 -1.72047362e-01 -6.19619071e-01 -1.02911127e+00
-8.81563127e-01 -7.14430332e-01 4.90627199e-01 6.64443314e-01
-1.81618467e-01 9.26458776e-01 -2.18130037e-01 -6.33382618e-01
-6.00739777e-01 -5.23212492e-01 -8.58886242e-02 -7.39563763e-01
-1.83788851e-01 6.64960921e-01 6.70031309e-02 -4.36501503... | [8.451921463012695, 4.255292892456055] |
215df617-ab35-4708-b3fc-6d5bc6785b59 | fexgan-meta-facial-expression-generation-with | 2203.05975 | null | https://arxiv.org/abs/2203.05975v1 | https://arxiv.org/pdf/2203.05975v1.pdf | FExGAN-Meta: Facial Expression Generation with Meta Humans | The subtleness of human facial expressions and a large degree of variation in the level of intensity to which a human expresses them is what makes it challenging to robustly classify and generate images of facial expressions. Lack of good quality data can hinder the performance of a deep learning model. In this article... | ['J. Rafid Siddiqui'] | 2022-02-17 | null | null | null | null | ['facial-expression-generation'] | ['computer-vision'] | [-2.60157622e-02 4.52186950e-02 3.32426637e-01 -8.40289950e-01
-1.01825267e-01 -2.63142854e-01 6.55442894e-01 -9.65509713e-01
-1.66791752e-01 6.44906580e-01 2.13564411e-02 4.05986339e-01
4.02213395e-01 -5.68183243e-01 -3.04326087e-01 -7.94016302e-01
-1.12756290e-01 1.67984248e-03 -6.04109049e-01 -6.19116008... | [13.49331283569336, 1.7358776330947876] |
661b69e4-c36b-4896-b203-7fc3b73efc2c | heterogeneous-target-speech-separation | 2204.03594 | null | https://arxiv.org/abs/2204.03594v1 | https://arxiv.org/pdf/2204.03594v1.pdf | Heterogeneous Target Speech Separation | We introduce a new paradigm for single-channel target source separation where the sources of interest can be distinguished using non-mutually exclusive concepts (e.g., loudness, gender, language, spatial location, etc). Our proposed heterogeneous separation framework can seamlessly leverage datasets with large distribu... | ['Jonathan Le Roux', 'Paris Smaragdis', 'Aswin Subramanian', 'Gordon Wichern', 'Efthymios Tzinis'] | 2022-04-07 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 5.60109198e-01 -3.00634116e-01 -3.70503545e-01 -4.24448013e-01
-1.69776797e+00 -1.18087757e+00 7.54045308e-01 1.78531423e-01
-3.48022617e-02 7.51244068e-01 4.81274277e-01 -2.60441191e-02
-5.74690580e-01 -3.40691328e-01 -5.87981880e-01 -9.51680243e-01
-1.75807104e-01 5.25987089e-01 9.97228827e-03 6.49716258... | [15.342333793640137, 5.494719505310059] |
1723b7e9-3ee1-49c4-8d4c-d5404e76289a | mobile-authentication-of-copy-detection-1 | 2203.02397 | null | https://arxiv.org/abs/2203.02397v2 | https://arxiv.org/pdf/2203.02397v2.pdf | Mobile authentication of copy detection patterns | In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonab... | ['Slava Voloshynovskiy', 'Slavi Bonev', 'Roman Chaban', 'Taras Holotyak', 'Joakim Tutt', 'Olga Taran'] | 2022-03-04 | null | null | null | null | ['one-class-classification'] | ['miscellaneous'] | [ 4.18739051e-01 -9.37208682e-02 -4.70469892e-01 2.46289149e-01
-5.28304636e-01 -6.82794213e-01 1.06261539e+00 2.00179681e-01
-1.49497926e-01 6.21922731e-01 -6.11785233e-01 -6.55314445e-01
-3.33158493e-01 -8.86118054e-01 -6.31952405e-01 -7.85608888e-01
-3.84479575e-02 3.88667136e-01 2.03705475e-01 -2.40815103... | [12.421416282653809, 1.0402101278305054] |
8e57472a-d4af-4bcc-b3ac-e71ea6471ec5 | dce-offline-reinforcement-learning-with | 2209.13132 | null | https://arxiv.org/abs/2209.13132v1 | https://arxiv.org/pdf/2209.13132v1.pdf | DCE: Offline Reinforcement Learning With Double Conservative Estimates | Offline Reinforcement Learning has attracted much interest in solving the application challenge for traditional reinforcement learning. Offline reinforcement learning uses previously-collected datasets to train agents without any interaction. For addressing the overestimation of OOD (out-of-distribution) actions, conse... | ['Chun Yuan', 'Kai Xing Huang', 'Chen Zhao'] | 2022-09-27 | null | null | null | null | ['d4rl'] | ['robots'] | [-3.43941778e-01 3.82705152e-01 -5.94210267e-01 -1.94730729e-01
-7.03517258e-01 -4.99331862e-01 4.16068166e-01 -3.27843241e-02
-7.74131954e-01 1.27595699e+00 -1.97537363e-01 -4.33757246e-01
1.62267108e-02 -5.19468307e-01 -9.39526498e-01 -5.33972859e-01
-3.68073672e-01 2.42945999e-01 3.69451225e-01 -2.70255744... | [4.108262538909912, 2.253573417663574] |
3ea6dd16-2bf7-4e02-aafd-1153680f7912 | facial-age-estimation-using-convolutional | 2105.06746 | null | https://arxiv.org/abs/2105.06746v1 | https://arxiv.org/pdf/2105.06746v1.pdf | Facial Age Estimation using Convolutional Neural Networks | This paper is a part of a student project in Machine Learning at the Norwegian University of Science and Technology. In this paper, a deep convolutional neural network with five convolutional layers and three fully-connected layers is presented to estimate the ages of individuals based on images. The model is in its en... | ['Erling Stray Bugge', 'Christian Bakke Vennerød', 'Adrian Kjærran'] | 2021-05-14 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.13894114e-01 3.58585119e-01 -1.58913620e-02 -7.71901071e-01
-3.81333888e-01 -3.06032449e-01 6.93295419e-01 3.26253921e-02
-7.75793433e-01 4.77947623e-01 -9.80354622e-02 -1.15341358e-01
4.29912034e-04 -8.92301083e-01 -4.64866012e-01 -4.46511775e-01
-8.99595469e-02 6.03864491e-01 -1.72721714e-01 1.39973760... | [13.48942756652832, 1.0402238368988037] |
490c2d35-76de-4a5a-9d89-530a99900117 | sketch-less-face-image-retrieval-a-new | 2302.05576 | null | https://arxiv.org/abs/2302.05576v1 | https://arxiv.org/pdf/2302.05576v1.pdf | Sketch Less Face Image Retrieval: A New Challenge | In some specific scenarios, face sketch was used to identify a person. However, drawing a complete face sketch often needs skills and takes time, which hinder its widespread applicability in the practice. In this study, we proposed a new task named sketch less face image retrieval (SLFIR), in which the retrieval was ca... | ['Guoyin Wang', 'Shuyin Xia', 'Shiyu Fu', 'Liang Wang', 'Yutang Li', 'Dawei Dai'] | 2023-02-11 | null | null | null | null | ['face-image-retrieval'] | ['computer-vision'] | [ 1.17051817e-01 -1.10975228e-01 5.84363379e-02 -4.04014468e-01
-4.52424705e-01 -4.19071883e-01 9.33765352e-01 -7.80772507e-01
-1.04669563e-01 3.81568521e-01 1.02527447e-01 2.37854391e-01
-8.19497108e-02 -6.66861117e-01 -3.94292235e-01 -6.26386523e-01
5.92995226e-01 5.24678469e-01 -3.20506752e-01 2.30631322... | [11.830499649047852, 0.4508243203163147] |
e13f9f56-32e7-4d92-8604-26966096258e | comparison-of-interactive-knowledge-base | 2010.10472 | null | https://arxiv.org/abs/2010.10472v1 | https://arxiv.org/pdf/2010.10472v1.pdf | Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages | Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict and large corpora are usually required to collect enough examples. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenari... | ['Alan W Black', 'Antonios Anastasopoulos', 'Yiyuan Li'] | 2020-10-20 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 7.39589930e-02 5.62265702e-02 6.88100830e-02 -5.37711918e-01
-6.77880704e-01 -4.96061742e-01 6.73311293e-01 2.81976074e-01
-8.75483990e-01 7.84123302e-01 2.80133814e-01 -2.75624514e-01
4.10885751e-01 -4.28761870e-01 -6.02447033e-01 6.33494521e-04
5.20673464e-04 8.45141232e-01 4.72206116e-01 -6.54194832... | [10.879021644592285, 10.295392036437988] |
9414a548-5554-4457-a4ae-ba377aadddd3 | multimodal-dialogue-state-tracking-by-qa | 2007.09903 | null | https://arxiv.org/abs/2007.09903v1 | https://arxiv.org/pdf/2007.09903v1.pdf | Multimodal Dialogue State Tracking By QA Approach with Data Augmentation | Recently, a more challenging state tracking task, Audio-Video Scene-Aware Dialogue (AVSD), is catching an increasing amount of attention among researchers. Different from purely text-based dialogue state tracking, the dialogue in AVSD contains a sequence of question-answer pairs about a video and the final answer to th... | ['Ian Steenstra', 'Brandyn Sigouin', 'Xiangyang Mou', 'Hui Su'] | 2020-07-20 | null | null | null | null | ['scene-aware-dialogue'] | ['computer-vision'] | [ 3.62058431e-01 3.91388029e-01 1.96359932e-01 -6.89255178e-01
-1.39779377e+00 -6.68374062e-01 1.01618767e+00 -9.36579555e-02
-2.71140486e-01 6.43015325e-01 8.39749038e-01 -3.29448879e-01
3.19729477e-01 -1.08913198e-01 -4.50519264e-01 -3.62752169e-01
2.65946031e-01 6.18291140e-01 4.70750481e-01 -5.65057814... | [10.786334037780762, 1.1628167629241943] |
ad8eb825-3f70-4d4f-b1f1-1b2b266fc2ab | a-pilot-study-of-query-free-adversarial | 2303.16378 | null | https://arxiv.org/abs/2303.16378v2 | https://arxiv.org/pdf/2303.16378v2.pdf | A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion | Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable Diffusion, less research attention is paid to its adversarial robustness. In this work, we study the problem of adversarial attack generation for Stable Diffusion and ask if an adversarial text prompt can be obtained even in the absence... | ['Sijia Liu', 'Yihua Zhang', 'Haomin Zhuang'] | 2023-03-29 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 5.23470461e-01 -5.90405334e-03 8.56434479e-02 2.54342526e-01
-1.03489232e+00 -1.27453065e+00 6.11594379e-01 -2.08894819e-01
-1.12073362e-01 2.85557687e-01 3.39382529e-01 -5.82669735e-01
6.43952042e-02 -6.37932479e-01 -9.75790918e-01 -7.21341133e-01
9.57230777e-02 -2.55292028e-01 3.51368517e-01 -2.91785598... | [5.6962480545043945, 7.867449760437012] |
eef3be21-625d-4685-816d-c53d59551bf0 | messy-estimation-maximum-entropy-based | 2306.04120 | null | https://arxiv.org/abs/2306.04120v1 | https://arxiv.org/pdf/2306.04120v1.pdf | MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation | We introduce MESSY estimation, a Maximum-Entropy based Stochastic and Symbolic densitY estimation method. The proposed approach recovers probability density functions symbolically from samples using moments of a Gradient flow in which the ansatz serves as the driving force. In particular, we construct a gradient-based ... | ['Nicolas G. Hadjiconstantinou', 'Kamal Youcef-Toumi', 'Mohsen Sadr', 'Tony Tohme'] | 2023-06-07 | null | null | null | null | ['symbolic-regression', 'density-estimation'] | ['knowledge-base', 'methodology'] | [-2.11060062e-01 6.76349327e-02 -1.46757707e-01 4.14545536e-02
-9.32421088e-01 -2.01227367e-01 6.48219645e-01 3.67094457e-01
-3.90168369e-01 1.23380339e+00 -5.21448731e-01 -1.79866195e-01
-2.42288351e-01 -8.36299539e-01 -8.52387667e-01 -9.87906635e-01
5.27101569e-02 9.51162696e-01 1.51346281e-01 -1.38912261... | [6.5835137367248535, 3.874798059463501] |
99fbacbf-1033-4be8-b2ac-71b36d2da054 | selfme-self-supervised-motion-learning-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Fan_SelfME_Self-Supervised_Motion_Learning_for_Micro-Expression_Recognition_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Fan_SelfME_Self-Supervised_Motion_Learning_for_Micro-Expression_Recognition_CVPR_2023_paper.pdf | SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition | Facial micro-expressions (MEs) refer to brief spontaneous facial movements that can reveal a person's genuine emotion. They are valuable in lie detection, criminal analysis, and other areas. While deep learning-based ME recognition (MER) methods achieved impressive success, these methods typically require pre-proce... | ['Hong Yan', 'Ali Raza Shahid', 'Mingjie Jiang', 'Xueli Chen', 'Xinqi Fan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['micro-expression-recognition'] | ['computer-vision'] | [-5.42624630e-02 -6.71120435e-02 -1.18217513e-01 -5.71651340e-01
-3.35718840e-01 -2.68048465e-01 6.46840990e-01 -9.96262014e-01
-4.78299022e-01 4.32564914e-01 2.49168947e-01 1.64277792e-01
3.02705139e-01 -2.17970312e-01 -3.37515593e-01 -6.83895290e-01
-2.89748628e-02 -2.41884053e-01 3.46681885e-02 -1.63602740... | [13.574618339538574, 1.7211593389511108] |
2a7c0b4b-b8ff-4aa0-a1ad-b0aabc0574a8 | gan-based-image-compression-with-improved-rdo | 2306.10461 | null | https://arxiv.org/abs/2306.10461v1 | https://arxiv.org/pdf/2306.10461v1.pdf | GAN-based Image Compression with Improved RDO Process | GAN-based image compression schemes have shown remarkable progress lately due to their high perceptual quality at low bit rates. However, there are two main issues, including 1) the reconstructed image perceptual degeneration in color, texture, and structure as well as 2) the inaccurate entropy model. In this paper, we... | ['Huaxiang Zhang', 'Feng Ding', 'Lili Meng', 'Jian Jin', 'Fanxin Xia'] | 2023-06-18 | null | null | null | null | ['image-compression', 'ms-ssim'] | ['computer-vision', 'computer-vision'] | [ 3.59903425e-01 -3.45106959e-01 7.81273991e-02 2.63989158e-02
-6.79434240e-01 -5.09870052e-02 8.15054998e-02 -1.64064944e-01
-9.15835276e-02 5.36806643e-01 4.41326946e-01 -1.42800435e-02
1.81307688e-01 -6.36735439e-01 -3.34739655e-01 -9.26416516e-01
2.71158040e-01 -2.32797325e-01 5.29645942e-02 1.45618632... | [11.404329299926758, -1.7599643468856812] |
e0f013a1-5b0d-4701-a656-6bb12d4a91a4 | handwritten-digit-recognition-by-elastic | 1807.09324 | null | http://arxiv.org/abs/1807.09324v1 | http://arxiv.org/pdf/1807.09324v1.pdf | Handwritten Digit Recognition by Elastic Matching | A simple model of MNIST handwritten digit recognition is presented here. The
model is an adaptation of a previous theory of face recognition. It realizes
translation and rotation invariance in a principled way instead of being based
on extensive learning from large masses of sample data. The presented
recognition rates... | ['C. von der Malsburg', 'Sagnik Majumder', 'Aashish Richhariya', 'Surekha Bhanot'] | 2018-07-24 | null | null | null | null | ['handwritten-digit-recognition'] | ['computer-vision'] | [ 1.08643606e-01 8.94557498e-03 -4.44250286e-01 -8.51286590e-01
3.17134634e-02 -4.13604558e-01 1.14809120e+00 -4.72082794e-01
-6.60528243e-01 6.14639461e-01 -2.02935144e-01 -4.40276295e-01
-3.75152975e-01 -4.95711058e-01 -2.73506463e-01 -7.16749072e-01
-1.99932650e-01 7.43320167e-01 1.23021662e-01 -1.19822949... | [9.999953269958496, 2.0656611919403076] |
6d94a53a-7213-41a6-a4fe-6d8241e08c36 | a-modified-ctgan-plus-features-based-method | 2302.02269 | null | https://arxiv.org/abs/2302.02269v2 | https://arxiv.org/pdf/2302.02269v2.pdf | A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation | We propose a new approach to portfolio optimization that utilizes a unique combination of synthetic data generation and a CVaR-constraint. We formulate the portfolio optimization problem as an asset allocation problem in which each asset class is accessed through a passive (index) fund. The asset-class weights are dete... | ['Arturo Cifuentes', 'Domingo Ramírez', 'Omar Larré', 'Fernando Suárez', 'José-Manuel Peña'] | 2023-02-05 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'portfolio-optimization'] | ['medical', 'miscellaneous', 'time-series'] | [ 2.60282569e-02 1.36635959e-01 -5.74770160e-02 -2.72454053e-01
-7.79368341e-01 -8.79386067e-01 1.03283966e+00 -5.68104647e-02
-3.51336062e-01 1.00148439e+00 1.70195848e-01 -4.35392380e-01
-6.28724217e-01 -1.42923546e+00 -3.92834961e-01 -6.42353892e-01
-1.40294641e-01 7.67808616e-01 -2.90558428e-01 -3.83763164... | [4.90438175201416, 4.011074066162109] |
bcfd3eab-3f7a-45ed-8b27-d926fcf40f1b | query-driven-knowledge-base-completion-using | 2212.01923 | null | https://arxiv.org/abs/2212.01923v3 | https://arxiv.org/pdf/2212.01923v3.pdf | Query-Driven Knowledge Base Completion using Multimodal Path Fusion over Multimodal Knowledge Graph | Over the past few years, large knowledge bases have been constructed to store massive amounts of knowledge. However, these knowledge bases are highly incomplete, for example, over 70% of people in Freebase have no known place of birth. To solve this problem, we propose a query-driven knowledge base completion system wi... | ['Daisy Zhe Wang', 'Yang Peng'] | 2022-12-04 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-2.24665716e-01 1.14121653e-01 -3.80736232e-01 -4.94210511e-01
-1.08057594e+00 -7.50927806e-01 3.05631965e-01 5.18034279e-01
-3.00336450e-01 8.80705953e-01 4.88633990e-01 -1.37447879e-01
-4.01088476e-01 -1.22307527e+00 -6.23897672e-01 -1.00929596e-01
1.69992104e-01 7.06788778e-01 6.06257975e-01 -5.57022154... | [10.449646949768066, 7.879647254943848] |
e3e859d2-3039-4fa1-86fc-2cb509adac45 | knowledge-based-paranoia-search-in-trick | 2104.05423 | null | https://arxiv.org/abs/2104.05423v1 | https://arxiv.org/pdf/2104.05423v1.pdf | Knowledge-Based Paranoia Search in Trick-Taking | This paper proposes \emph{knowledge-based paraonoia search} (KBPS) to find forced wins during trick-taking in the card game Skat; for some one of the most interesting card games for three players. It combines efficient partial information game-tree search with knowledge representation and reasoning. This worst-case ana... | ['Stefan Edelkamp'] | 2021-04-07 | null | null | null | null | ['card-games'] | ['playing-games'] | [-3.78318906e-01 3.27531368e-01 -3.44953500e-02 1.90841570e-01
-9.63800073e-01 -1.08551252e+00 2.80132443e-01 4.22761776e-02
-8.07552159e-01 1.00225604e+00 -8.21574852e-02 -6.60596609e-01
-9.46745574e-01 -9.95960057e-01 -2.58109897e-01 -2.41878703e-01
-1.19031124e-01 1.45666456e+00 1.13299465e+00 -1.03652132... | [3.419816255569458, 1.5107909440994263] |
975c377d-06ed-46c7-a631-051affaa9555 | alignment-free-cross-lingual-semantic-role | null | null | https://aclanthology.org/2020.emnlp-main.319 | https://aclanthology.org/2020.emnlp-main.319.pdf | Alignment-free Cross-lingual Semantic Role Labeling | Cross-lingual semantic role labeling (SRL) aims at leveraging resources in a source language to minimize the effort required to construct annotations or models for a new target language. Recent approaches rely on word alignments, machine translation engines, or preprocessing tools such as parsers or taggers. We propose... | ['Mirella Lapata', 'Rui Cai'] | null | null | null | null | emnlp-2020-11 | ['multilingual-word-embeddings'] | ['methodology'] | [ 4.67597634e-01 5.68695664e-01 -9.42699671e-01 -6.58433795e-01
-1.15288579e+00 -8.97281468e-01 5.82940042e-01 6.21731639e-01
-9.11803067e-01 8.08920264e-01 6.53184950e-01 -3.05391431e-01
2.30171025e-01 -5.05873978e-01 -7.02135146e-01 -4.23003823e-01
2.72615969e-01 8.01145434e-01 4.40279879e-02 -3.20199817... | [10.421709060668945, 9.502047538757324] |
da08bcaa-01b6-4edf-8a85-2b0ffa722d57 | neural-sentence-ordering-based-on-constraint | 2101.11178 | null | https://arxiv.org/abs/2101.11178v2 | https://arxiv.org/pdf/2101.11178v2.pdf | Neural Sentence Ordering Based on Constraint Graphs | Sentence ordering aims at arranging a list of sentences in the correct order. Based on the observation that sentence order at different distances may rely on different types of information, we devise a new approach based on multi-granular orders between sentences. These orders form multiple constraint graphs, which are... | ['Zhicheng Dou', 'Shengchao Liu', 'Jian-Yun Nie', 'Kun Zhou', 'Yutao Zhu'] | 2021-01-27 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 1.20442532e-01 -1.62875298e-02 -2.92768776e-01 -7.00286388e-01
-3.44755948e-01 -6.07680321e-01 3.78738433e-01 7.44703710e-01
-2.73133248e-01 4.21148688e-01 7.25887716e-01 -4.65512395e-01
-2.64796197e-01 -8.28696072e-01 -5.80689669e-01 -5.19712865e-02
-2.14886785e-01 5.32577991e-01 2.23413438e-01 -4.37803388... | [11.119565963745117, 8.78867244720459] |
2e53ef37-dba4-4f5e-aef4-f91b466ac54b | stau-a-spatiotemporal-aware-unit-for-video | 2204.09456 | null | https://arxiv.org/abs/2204.09456v1 | https://arxiv.org/pdf/2204.09456v1.pdf | STAU: A SpatioTemporal-Aware Unit for Video Prediction and Beyond | Video prediction aims to predict future frames by modeling the complex spatiotemporal dynamics in videos. However, most of the existing methods only model the temporal information and the spatial information for videos in an independent manner but haven't fully explored the correlations between both terms. In this pape... | ['Wen Gao', 'Siwei Ma', 'Shanshe Wang', 'Xinfeng Zhang', 'Zheng Chang'] | 2022-04-20 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [-7.23673552e-02 -4.52071548e-01 -4.76425231e-01 -2.75607347e-01
8.61946680e-03 -6.85967803e-02 3.42527896e-01 -3.20792407e-01
-1.34369388e-01 4.56727594e-01 5.40008187e-01 6.84068128e-02
3.39433588e-02 -4.64702159e-01 -6.47729456e-01 -9.99382794e-01
-2.93240875e-01 -3.14799964e-01 1.03265965e+00 7.68871792... | [8.641632080078125, 0.4760168194770813] |
1283242b-4afe-412f-aa45-19ce8508d97b | mt-quality-estimation-the-cmu-system-for | null | null | https://aclanthology.info/papers/W13-2246/w13-2246 | https://www.aclweb.org/anthology/W13-2246 | MT Quality Estimation: The CMU System for WMT’13 | null | ['Stephan Vogel', 'Silja Hildebrand'] | 2013-08-01 | null | null | null | ws-2013-8 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391778945922852, 15.869178771972656] |
f46e51c1-e368-4840-b8d9-77a94ba98d5e | dynamic-survival-transformers-for-causal | 2210.15417 | null | https://arxiv.org/abs/2210.15417v1 | https://arxiv.org/pdf/2210.15417v1.pdf | Dynamic Survival Transformers for Causal Inference with Electronic Health Records | In medicine, researchers often seek to infer the effects of a given treatment on patients' outcomes. However, the standard methods for causal survival analysis make simplistic assumptions about the data-generating process and cannot capture complex interactions among patient covariates. We introduce the Dynamic Surviva... | ['Jeffrey Regier', 'Zhenke Wu', 'Yixin Wang', 'Prayag Chatha'] | 2022-10-25 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 5.13238087e-02 7.36877024e-02 -4.54456389e-01 -5.12207508e-01
-8.53109837e-01 -2.44402781e-01 5.69522023e-01 5.17034650e-01
-1.47548720e-01 1.18594134e+00 8.84390593e-01 -8.15922558e-01
-3.73156607e-01 -9.01192904e-01 -5.11345387e-01 -6.94101930e-01
-6.78350210e-01 7.07100570e-01 -2.80186951e-01 2.91984200... | [7.918551445007324, 5.616941928863525] |
3a71083f-720f-4180-b1d5-c34f7e59b3d7 | optimal-planning-of-hybrid-energy-storage | 2212.05662 | null | https://arxiv.org/abs/2212.05662v1 | https://arxiv.org/pdf/2212.05662v1.pdf | Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning | Energy management systems (EMS) are becoming increasingly important in order to utilize the continuously growing curtailed renewable energy. Promising energy storage systems (ESS), such as batteries and green hydrogen should be employed to maximize the efficiency of energy stakeholders. However, optimal decision-making... | ['Jonggeol Na', 'J. Jay Liu', 'Won Bo Lee', 'Haider Niaz', 'Sumin Hwangbo', 'Doeun Kang', 'Dongju Kang'] | 2022-12-12 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.91018444e-01 -8.27623606e-02 8.93022344e-02 1.32407218e-01
-5.17628014e-01 -4.88348305e-01 7.17221677e-01 1.29046589e-01
-3.99902165e-01 1.39956605e+00 -7.31041655e-02 -3.23611826e-01
-6.95142627e-01 -9.63501096e-01 -5.49100995e-01 -1.29343760e+00
-1.28716201e-01 4.34522361e-01 -2.83168256e-01 -9.07152295... | [5.595440864562988, 2.4994571208953857] |
a86053ec-4863-4898-ba51-c04ac0cc6e15 | knowledge-transfer-for-surgical-activity | 1711.05848 | null | http://arxiv.org/abs/1711.05848v1 | http://arxiv.org/pdf/1711.05848v1.pdf | Knowledge transfer for surgical activity prediction | Lack of training data hinders automatic recognition and prediction of
surgical activities necessary for situation-aware operating rooms. We propose
using knowledge transfer to compensate for data deficit and improve prediction.
We used two approaches to extract and transfer surgical process knowledge.
First, we encoded... | ['Xavier Morandi', 'Pierre Jannin', 'Olga Dergachyova'] | 2017-11-15 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 3.31800103e-01 6.95804238e-01 -5.61814070e-01 -4.68799949e-01
-7.12284803e-01 -2.56940275e-01 4.62586850e-01 5.26990891e-01
-9.76189315e-01 9.76888597e-01 1.05731142e+00 -7.52723515e-01
-6.05057240e-01 -8.45040321e-01 -6.37075305e-01 -4.81121719e-01
-8.92655253e-02 4.18001235e-01 3.66573930e-02 -2.69673198... | [14.124971389770508, -3.3795742988586426] |
90cbdf69-8abb-47ea-ab4a-dd5734390bd8 | towards-stability-of-autoregressive-neural | 2306.10619 | null | https://arxiv.org/abs/2306.10619v1 | https://arxiv.org/pdf/2306.10619v1.pdf | Towards Stability of Autoregressive Neural Operators | Neural operators have proven to be a promising approach for modeling spatiotemporal systems in the physical sciences. However, training these models for large systems can be quite challenging as they incur significant computational and memory expense -- these systems are often forced to rely on autoregressive time-step... | ['Jed Brown', 'Shashank Subramanian', 'Peter Harrington', 'Michael McCabe'] | 2023-06-18 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-1.79482013e-01 -2.44079635e-01 5.14048159e-01 -1.59634605e-01
-2.74956226e-01 -5.33510804e-01 4.07499671e-01 -4.97660339e-02
-2.58152992e-01 1.01833451e+00 -2.30585039e-02 -8.95037115e-01
-2.42715627e-01 -7.36622870e-01 -6.12562180e-01 -8.19749296e-01
-7.07412422e-01 1.89738676e-01 8.34483206e-02 -5.46271026... | [6.5612688064575195, 3.309206485748291] |
4f0c5b50-4e23-4510-abb3-b7555e67cb9a | learning-multi-view-aggregation-in-the-wild | 2204.07548 | null | https://arxiv.org/abs/2204.07548v2 | https://arxiv.org/pdf/2204.07548v2.pdf | Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation | Recent works on 3D semantic segmentation propose to exploit the synergy between images and point clouds by processing each modality with a dedicated network and projecting learned 2D features onto 3D points. Merging large-scale point clouds and images raises several challenges, such as constructing a mapping between po... | ['Loic Landrieu', 'Bruno Vallet', 'Damien Robert'] | 2022-04-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Robert_Learning_Multi-View_Aggregation_in_the_Wild_for_Large-Scale_3D_Semantic_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Robert_Learning_Multi-View_Aggregation_in_the_Wild_for_Large-Scale_3D_Semantic_CVPR_2022_paper.pdf | cvpr-2022-1 | ['colorization'] | ['computer-vision'] | [ 1.30070925e-01 -2.73633711e-02 -1.38336392e-02 -5.52559137e-01
-1.07653069e+00 -9.91540372e-01 2.73166865e-01 -2.95565405e-04
-2.67683983e-01 9.54853147e-02 -3.21862310e-01 -7.79556111e-02
1.63837031e-01 -9.88105595e-01 -1.21528649e+00 -1.21017173e-01
-4.98908106e-03 9.08231080e-01 5.72733521e-01 -1.15852952... | [8.36036491394043, -2.960167407989502] |
a214e67d-7d63-463b-b5c0-7a7c7fc1ae07 | how-search-engine-marketing-influences-user | 2301.10086 | null | https://arxiv.org/abs/2301.10086v1 | https://arxiv.org/pdf/2301.10086v1.pdf | How search engine marketing influences user knowledge gain: Development and empirical testing of an information search behavior model | People use search engines to find answers to questions related to their health, finances, or other socially relevant issues. However, most users are unaware that search results are considerably influenced by search engine marketing (SEM). SEM measures are driven by commercial, political, or other motives. Due to these ... | ['Sebastian Schultheiß'] | 2023-01-24 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-2.52501637e-01 4.05863136e-01 -9.78669941e-01 1.83644384e-01
-1.75199613e-01 -3.06031734e-01 5.28424680e-01 6.15087807e-01
-7.56465137e-01 2.32018247e-01 5.84386826e-01 -9.66415823e-01
-6.29394829e-01 -6.65255368e-01 -4.56321061e-01 2.36850306e-01
7.61708677e-01 4.01428044e-02 3.34796250e-01 -1.37237117... | [10.039582252502441, 6.324141502380371] |
44d53e6d-942e-47e7-bc8f-e559b1972a75 | multimodal-chain-of-thought-reasoning-in | 2302.00923 | null | https://arxiv.org/abs/2302.00923v4 | https://arxiv.org/pdf/2302.00923v4.pdf | Multimodal Chain-of-Thought Reasoning in Language Models | Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies have focused on the language modality. We propose Multimodal-CoT that incorpora... | ['Alex Smola', 'George Karypis', 'Hai Zhao', 'Mu Li', 'Aston Zhang', 'Zhuosheng Zhang'] | 2023-02-02 | null | null | null | null | ['science-question-answering'] | ['miscellaneous'] | [-3.95114943e-02 4.88067508e-01 -1.94217891e-01 -4.55118924e-01
-1.43119955e+00 -8.24563324e-01 9.04567719e-01 1.72134787e-01
-1.84215039e-01 5.92825890e-01 7.22719073e-01 -7.89588869e-01
3.03729802e-01 -5.00301600e-01 -8.46816123e-01 -1.52973175e-01
5.58218420e-01 5.97456872e-01 -2.14361050e-03 -1.73033014... | [10.847780227661133, 1.8965524435043335] |
1e4392ba-c457-427a-ba5c-681416478861 | mixed-norm-regularization-for-brain-decoding | 1403.3628 | null | http://arxiv.org/abs/1403.3628v1 | http://arxiv.org/pdf/1403.3628v1.pdf | Mixed-norm Regularization for Brain Decoding | This work investigates the use of mixed-norm regularization for sensor
selection in Event-Related Potential (ERP) based Brain-Computer Interfaces
(BCI). The classification problem is cast as a discriminative optimization
framework where sensor selection is induced through the use of mixed-norms.
This framework is exten... | ['Rémi Flamary', 'Marco Congedo', 'Ronald Phlypo', 'Nisrine Jrad', 'Alain Rakotomamonjy'] | 2014-03-14 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 6.00804031e-01 -6.14603721e-02 6.35234118e-02 -5.56689382e-01
-1.15386534e+00 -1.30135134e-01 4.33241874e-01 2.46316880e-01
-7.84950197e-01 1.06181777e+00 1.60015464e-01 4.05503660e-01
-6.99862361e-01 -9.94031355e-02 -4.36899990e-01 -8.50206316e-01
-1.87328428e-01 -3.51346955e-02 -2.25369707e-01 -1.24532111... | [13.045869827270508, 3.4375088214874268] |
66770c1e-3387-444d-88bd-fefd1586fbdb | key-information-extraction-in-purchase | 2210.03453 | null | https://arxiv.org/abs/2210.03453v1 | https://arxiv.org/pdf/2210.03453v1.pdf | Key Information Extraction in Purchase Documents using Deep Learning and Rule-based Corrections | Deep Learning (DL) is dominating the fields of Natural Language Processing (NLP) and Computer Vision (CV) in the recent times. However, DL commonly relies on the availability of large data annotations, so other alternative or complementary pattern-based techniques can help to improve results. In this paper, we build up... | ['Javier Lorenzo', 'Héctor Corrales', 'Elena Martínez', 'Javier Yebes', 'Roberto Arroyo'] | 2022-10-07 | null | https://aclanthology.org/2022.pandl-1.2 | https://aclanthology.org/2022.pandl-1.2.pdf | pandl-coling-2022-10 | ['line-detection', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing'] | [-6.00700732e-03 5.58077022e-02 -3.05023611e-01 -5.09796143e-01
-5.69007695e-01 -7.43493080e-01 8.33316565e-01 1.04867399e+00
-6.08135581e-01 6.44784808e-01 1.80065736e-01 -2.98300624e-01
-5.58690988e-02 -8.95313859e-01 -8.75053167e-01 -2.51516163e-01
3.17300111e-02 4.13362026e-01 1.88672051e-01 -1.92075055... | [11.642945289611816, 2.931990623474121] |
48416ca3-fd25-4627-90a5-46ddc304aa37 | doubly-stochastic-matrix-models-for | 2304.02458 | null | https://arxiv.org/abs/2304.02458v1 | https://arxiv.org/pdf/2304.02458v1.pdf | Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms | Problems with solutions represented by permutations are very prominent in combinatorial optimization. Thus, in recent decades, a number of evolutionary algorithms have been proposed to solve them, and among them, those based on probability models have received much attention. In that sense, most efforts have focused on... | ['Josu Ceberio', 'Valentino Santucci'] | 2023-04-05 | null | null | null | null | ['combinatorial-optimization'] | ['methodology'] | [ 4.02883202e-01 -2.59602726e-01 -1.98677987e-01 -5.73223174e-01
-5.42293370e-01 -3.67761791e-01 3.16684157e-01 -7.71960691e-02
-2.03603461e-01 9.74745095e-01 -8.86536837e-02 -1.67044967e-01
-1.02410555e+00 -8.40200961e-01 -4.65613812e-01 -8.30020487e-01
-2.20766038e-01 9.32739675e-01 5.22124246e-02 -2.20128745... | [6.0711588859558105, 3.9594013690948486] |
adb50ecb-4668-4d68-9a9d-8ba47c023bb0 | single-shot-implicit-morphable-faces-with | 2305.03043 | null | https://arxiv.org/abs/2305.03043v1 | https://arxiv.org/pdf/2305.03043v1.pdf | Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization | There is a growing demand for the accessible creation of high-quality 3D avatars that are animatable and customizable. Although 3D morphable models provide intuitive control for editing and animation, and robustness for single-view face reconstruction, they cannot easily capture geometric and appearance details. Method... | ['Sameh Khamis', 'Gordon Wetzstein', 'Leonidas Guibas', 'Umar Iqbal', 'Eric R. Chan', 'Jan Kautz', 'Koki Nagano', 'Connor Z. Lin'] | 2023-05-04 | null | null | null | null | ['face-model', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 3.40588272e-01 4.05427307e-01 1.42758980e-01 -7.05823839e-01
-3.17148060e-01 -6.55664802e-01 6.13150537e-01 -6.31894231e-01
3.04079950e-01 3.86829376e-01 1.15354575e-01 2.74480641e-01
2.46562168e-01 -1.03434658e+00 -8.66271853e-01 -4.27459151e-01
1.67757392e-01 6.95240796e-01 -4.08311367e-01 -2.42940530... | [12.694307327270508, -0.3731805086135864] |
d083ca98-0940-47b4-b172-87d98ac56c23 | antbo-towards-real-world-automated-antibody | 2201.12570 | null | https://arxiv.org/abs/2201.12570v4 | https://arxiv.org/pdf/2201.12570v4.pdf | AntBO: Towards Real-World Automated Antibody Design with Combinatorial Bayesian Optimisation | Antibodies are canonically Y-shaped multimeric proteins capable of highly specific molecular recognition. The CDRH3 region located at the tip of variable chains of an antibody dominates antigen-binding specificity. Therefore, it is a priority to design optimal antigen-specific CDRH3 regions to develop therapeutic antib... | ['Amos Storkey', 'Rahmad Akbar', 'Puneet Rawat', 'Eva Smorodina', 'Philippe A. Robert', 'Antoine Grosnit', 'Haitham Bou-Ammar', 'Jun Wang', 'Dany Bou-Ammar', 'Rasul Tutunov', 'Victor Greiff', 'Kamil Dreczkowski', 'Derrick-Goh-Xin Deik', 'Alexander I. Cowen-Rivers', 'Asif Khan'] | 2022-01-29 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.54680890e-01 -4.58501140e-03 5.32972720e-03 -4.11611021e-01
-8.14720690e-01 -8.82072031e-01 1.39653683e-01 1.95011988e-01
-4.74654734e-01 1.34267592e+00 -3.05144221e-01 -9.58496988e-01
-3.82172436e-01 -4.71161693e-01 -9.83791590e-01 -9.29674864e-01
1.71365574e-01 9.67834413e-01 1.98503584e-01 -3.46222550... | [4.752732276916504, 5.5995564460754395] |
6033a932-2ae8-4d39-8154-a5b3f6caaa62 | adaptively-lighting-up-facial-expression | 2203.14045 | null | https://arxiv.org/abs/2203.14045v1 | https://arxiv.org/pdf/2203.14045v1.pdf | Adaptively Lighting up Facial Expression Crucial Regions via Local Non-Local Joint Network | Facial expression recognition (FER) is still one challenging research due to the small inter-class discrepancy in the facial expression data. In view of the significance of facial crucial regions for FER, many existing researches utilize the prior information from some annotated crucial points to improve the performanc... | ['Lin Xiong', 'Licheng Jiao', 'Dandan Yan', 'Shuiping Gou', 'GuangHui Shi', 'Shasha Mao'] | 2022-03-26 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 9.58265141e-02 -1.25590429e-01 -1.58346847e-01 -6.45334959e-01
-4.60991472e-01 1.56986475e-01 1.72034770e-01 -2.31812984e-01
-4.82679784e-01 5.52386880e-01 2.75840431e-01 5.76871753e-01
-1.99652717e-01 -6.32075191e-01 -4.89306003e-01 -1.06713080e+00
1.10018775e-01 -1.72883853e-01 6.20622300e-02 -5.42970359... | [13.626585006713867, 1.5921097993850708] |
23852dc1-7e98-4a91-8b67-8d8fcf0df3a4 | bayesian-nonparametric-estimation-of-coverage | 2209.02135 | null | https://arxiv.org/abs/2209.02135v1 | https://arxiv.org/pdf/2209.02135v1.pdf | Bayesian nonparametric estimation of coverage probabilities and distinct counts from sketched data | The estimation of coverage probabilities, and in particular of the missing mass, is a classical statistical problem with applications in numerous scientific fields. In this paper, we study this problem in relation to randomized data compression, or sketching. This is a novel but practically relevant perspective, and it... | ['Matteo Sesia', 'Stefano Favaro'] | 2022-09-05 | null | null | null | null | ['data-compression'] | ['time-series'] | [ 8.32887948e-01 -1.61857277e-01 -3.56455564e-01 -1.03485622e-01
-8.82162392e-01 -3.37969095e-01 5.49103200e-01 3.01130325e-01
-5.31378746e-01 1.27674079e+00 -8.25584531e-02 -2.15415269e-01
-3.51105243e-01 -8.59610736e-01 -7.69556940e-01 -1.18600321e+00
-1.05316043e-01 1.20334995e+00 1.10744119e-01 2.95187384... | [7.091291904449463, 4.2426228523254395] |
76b64a46-2f2a-4442-9d44-4b1c819dea13 | zero-shot-action-recognition-with-transformer | 2203.05156 | null | https://arxiv.org/abs/2203.05156v2 | https://arxiv.org/pdf/2203.05156v2.pdf | End-to-End Semantic Video Transformer for Zero-Shot Action Recognition | While video action recognition has been an active area of research for several years, zero-shot action recognition has only recently started gaining traction. In this work, we propose a novel end-to-end trained transformer model which is capable of capturing long range spatiotemporal dependencies efficiently, contrary ... | ['Yasin Yilmaz', 'Keval Doshi'] | 2022-03-10 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 2.93484241e-01 -5.92815802e-02 -4.52325881e-01 -3.26752603e-01
-5.32393515e-01 -7.25697950e-02 6.97855949e-01 -3.79112035e-01
-4.47456956e-01 5.96479297e-01 4.64697152e-01 -2.73598228e-02
-5.88652259e-03 -5.04527152e-01 -5.79393685e-01 -7.34215319e-01
1.26338035e-01 3.12513262e-01 5.81444740e-01 7.49472231... | [8.345847129821777, 0.6749891042709351] |
460dfd02-4267-4f79-8df2-65c9758568ae | development-of-personalized-sleep-induction | 2212.05669 | null | https://arxiv.org/abs/2212.05669v1 | https://arxiv.org/pdf/2212.05669v1.pdf | Development of Personalized Sleep Induction System based on Mental States | Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim... | ['Heon-Gyu Kwak', 'Gi-Hwan Shin', 'Young-Seok Kweon'] | 2022-12-12 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [-3.62438411e-01 -4.47885275e-01 1.58578083e-01 -2.18827099e-01
1.85311690e-01 -2.69710124e-01 -3.07155758e-01 -1.46943629e-01
-6.40672982e-01 1.08405936e+00 3.91112417e-01 -3.46916407e-01
1.29403263e-01 -5.33099353e-01 1.46812961e-01 -5.41892171e-01
2.11530283e-01 -6.58529326e-02 3.18374127e-01 -3.59217554... | [13.501275062561035, 3.4647045135498047] |
4c5d4877-df08-443c-8c60-33e7afc55b96 | semi-automatic-definite-description | 1712.08933 | null | http://arxiv.org/abs/1712.08933v1 | http://arxiv.org/pdf/1712.08933v1.pdf | Semi-automatic definite description annotation: a first report | Studies in Referring Expression Generation (REG) often make use of corpora of
definite descriptions produced by human subjects in controlled experiments.
Experiments of this kind, which are essential for the study of reference
phenomena and many others, may however include a considerable amount of noise.
Human subjects... | ['Ivandre Paraboni', 'Alex Gwo Jen Lan', 'Danillo da Silva Rocha'] | 2017-12-24 | null | null | null | null | ['referring-expression-generation'] | ['computer-vision'] | [ 3.16717386e-01 1.24377176e-01 7.53631815e-03 -5.45943022e-01
-8.07756484e-01 -7.72217989e-01 7.34757602e-01 5.99854708e-01
-5.57555914e-01 1.07132900e+00 3.13601732e-01 -2.42378980e-01
1.47629231e-02 -6.50591791e-01 -3.14151853e-01 -4.40674305e-01
3.58028233e-01 7.47017026e-01 3.23898315e-01 -3.37828517... | [10.324409484863281, 9.16994571685791] |
9fede14e-0865-45f2-be42-19a414961048 | bent-broken-bicycles-leveraging-synthetic | 2304.07883 | null | https://arxiv.org/abs/2304.07883v1 | https://arxiv.org/pdf/2304.07883v1.pdf | Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification | Instance-level object re-identification is a fundamental computer vision task, with applications from image retrieval to intelligent monitoring and fraud detection. In this work, we propose the novel task of damaged object re-identification, which aims at distinguishing changes in visual appearance due to deformations ... | ['Fabrizio Lamberti', 'Lia Morra', 'Lorenzo Lanari', 'Alessandro Sebastian Russo', 'Filippo Gabriele Pratticò', 'Luca Piano'] | 2023-04-16 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [ 2.82109916e-01 -4.22143042e-01 -6.64767623e-02 -2.19938681e-01
-8.31837833e-01 -6.07045650e-01 6.41045153e-01 -3.37875150e-02
1.02998633e-02 4.14456874e-01 2.41180286e-01 2.93050051e-01
1.33627607e-02 -6.28386497e-01 -9.80941534e-01 -6.50434911e-01
1.45599440e-01 4.85556126e-01 1.00726493e-01 -8.44634417... | [14.654884338378906, 0.9732877016067505] |
acf23301-687c-4297-a356-711c2dd0ac87 | people-talking-and-ai-listening-how | 2305.10201 | null | https://arxiv.org/abs/2305.10201v4 | https://arxiv.org/pdf/2305.10201v4.pdf | Echoes of Biases: How Stigmatizing Language Affects AI Performance | Electronic health records (EHRs) serve as an essential data source for the envisioned artificial intelligence (AI)-driven transformation in healthcare. However, clinician biases reflected in EHR notes can lead to AI models inheriting and amplifying these biases, perpetuating health disparities. This study investigates ... | ['Ritu Agarwal', 'Guodong Gordon Gao', 'Weiguang Wang', 'Yizhi Liu'] | 2023-05-17 | null | null | null | null | ['mortality-prediction'] | ['medical'] | [ 2.84867138e-01 7.93017447e-01 -1.75973386e-01 -3.38522851e-01
-7.45366752e-01 -3.21528733e-01 3.75260979e-01 5.40110707e-01
-3.53871882e-01 3.86016876e-01 1.39356399e+00 -1.01478457e+00
-9.07719582e-02 -5.08462608e-01 -6.24541104e-01 -3.46426666e-01
3.76081288e-01 5.38692534e-01 -9.93180633e-01 8.49485844... | [7.969786643981934, 6.172063827514648] |
b47327e3-6bee-4897-bfff-32d993c30e37 | unsupervised-3d-human-mesh-recovery-from | 2107.07539 | null | https://arxiv.org/abs/2107.07539v2 | https://arxiv.org/pdf/2107.07539v2.pdf | Self-supervised 3D Human Mesh Recovery from Noisy Point Clouds | This paper presents a novel self-supervised approach to reconstruct human shape and pose from noisy point cloud data. Relying on large amount of dataset with ground-truth annotations, recent learning-based approaches predict correspondences for every vertice on the point cloud; Chamfer distance is usually used to minim... | ['Li Cheng', 'Minglun Gong', 'Qiang Sun', 'Sen Wang', 'Xinxin Zuo'] | 2021-07-15 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-2.26233900e-02 1.17708474e-01 2.20079333e-01 -2.44380385e-01
-9.66446579e-01 -2.17522696e-01 5.66397965e-01 1.99983373e-01
-3.25251877e-01 4.79989231e-01 -3.27537626e-01 4.69811410e-01
-1.00932360e-01 -8.79782081e-01 -1.00773144e+00 -5.63426733e-01
2.32263252e-01 1.42656994e+00 4.81117964e-01 -4.53350646... | [8.063830375671387, -3.0441715717315674] |
c200d20e-386b-4309-9eb8-ade6cda5686b | denoising-relation-extraction-from-document | 2011.03888 | null | https://arxiv.org/abs/2011.03888v1 | https://arxiv.org/pdf/2011.03888v1.pdf | Denoising Relation Extraction from Document-level Distant Supervision | Distant supervision (DS) has been widely used to generate auto-labeled data for sentence-level relation extraction (RE), which improves RE performance. However, the existing success of DS cannot be directly transferred to the more challenging document-level relation extraction (DocRE), since the inherent noise in DS ma... | ['Leyu Lin', 'Fen Lin', 'Maosong Sun', 'Zhiyuan Liu', 'Xu Han', 'Ruobing Xie', 'Yuan YAO', 'Chaojun Xiao'] | 2020-11-08 | null | https://aclanthology.org/2020.emnlp-main.300 | https://aclanthology.org/2020.emnlp-main.300.pdf | emnlp-2020-11 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 3.44583511e-01 5.33580482e-01 -1.92980856e-01 -3.03089797e-01
-1.15474939e+00 -3.53759676e-01 5.70599377e-01 -5.08302525e-02
-3.65091920e-01 1.04096293e+00 6.74641132e-01 -2.75820255e-01
1.09533727e-01 -7.06191659e-01 -5.32320619e-01 -3.20798725e-01
2.34463409e-01 5.22774637e-01 1.22469723e-01 -5.50620615... | [9.34582233428955, 8.707720756530762] |
c430d9a7-2428-457e-a0ae-62ba510ac853 | the-age-of-synthetic-realities-challenges-and | 2306.11503 | null | https://arxiv.org/abs/2306.11503v1 | https://arxiv.org/pdf/2306.11503v1.pdf | The Age of Synthetic Realities: Challenges and Opportunities | Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive. In this paper, we delve into the concept of synthetic re... | ['Anderson Rocha', 'Sébastien Marcel', 'Fernanda Andaló', 'Shiqi Wang', 'Haoliang Li', 'Daniel Moreira', 'Renjie Wan', 'Rafael Padilha', 'Jing Yang', 'João Phillipe Cardenuto'] | 2023-06-09 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 7.15959489e-01 2.80109257e-01 -7.95922894e-03 3.25832188e-01
-5.50242007e-01 -8.76723826e-01 1.31266367e+00 2.06779584e-01
-7.64896423e-02 7.22508848e-01 6.65710747e-01 -5.39079070e-01
1.68810964e-01 -8.62835586e-01 -5.90920627e-01 -4.20879394e-01
-1.19374044e-01 9.38356668e-02 -1.81225434e-01 -2.60095507... | [12.427656173706055, 1.1120774745941162] |
95dc75b9-cb1f-4506-abff-09e09b442d39 | transeditor-transformer-based-dual-space-gan | 2203.17266 | null | https://arxiv.org/abs/2203.17266v1 | https://arxiv.org/pdf/2203.17266v1.pdf | TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing | Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. Nonetheless, these methods are still inc... | ['Wayne Wu', 'Bo Dai', 'Chen Change Loy', 'Chengyao Zheng', 'Qianyi Wu', 'Liming Jiang', 'Yueqin Yin', 'Yanbo Xu'] | 2022-03-31 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Xu_TransEditor_Transformer-Based_Dual-Space_GAN_for_Highly_Controllable_Facial_Editing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Xu_TransEditor_Transformer-Based_Dual-Space_GAN_for_Highly_Controllable_Facial_Editing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['facial-editing'] | ['computer-vision'] | [ 4.27979559e-01 3.54999155e-02 -1.70844495e-02 -3.06131393e-01
-5.06261349e-01 -6.04509532e-01 7.55242646e-01 -6.76780581e-01
2.22460002e-01 7.61141181e-01 4.15873289e-01 1.33714631e-01
-2.21685320e-01 -7.63016522e-01 -4.91612107e-01 -7.79770195e-01
6.99460089e-01 -1.15799353e-01 -5.01121223e-01 -3.68268251... | [12.489534378051758, -0.26609405875205994] |
ff585670-b302-4bca-b404-7e2d3baa68b9 | pixelrl-fully-convolutional-network-with | 1912.07190 | null | https://arxiv.org/abs/1912.07190v1 | https://arxiv.org/pdf/1912.07190v1.pdf | PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing | This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep reinforcement learning (RL) for image processing are still limited. Therefore... | ['Ryosuke Furuta', 'Naoto Inoue', 'Toshihiko Yamasaki'] | 2019-12-16 | null | null | null | null | ['local-color-enhancement'] | ['computer-vision'] | [ 3.51683050e-01 -8.80819485e-02 -1.34458005e-01 -1.57613188e-01
-1.60540164e-01 -1.24577224e-01 2.11645499e-01 -2.80340277e-02
-6.41911805e-01 7.03777850e-01 -3.06670278e-01 -3.63668203e-01
2.91359518e-02 -9.38580632e-01 -9.00300682e-01 -1.02642024e+00
1.72238350e-01 -1.82523906e-01 2.16953799e-01 -2.88595259... | [11.224810600280762, -1.3185428380966187] |
7e5e5242-72d1-4b72-a9ab-e5696e311342 | hprnet-hierarchical-point-regression-for | 2106.04269 | null | https://arxiv.org/abs/2106.04269v2 | https://arxiv.org/pdf/2106.04269v2.pdf | HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation | In this paper, we present a new bottom-up one-stage method for whole-body pose estimation, which we call "hierarchical point regression," or HPRNet for short. In standard body pose estimation, the locations of $\sim 17$ major joints on the human body are estimated. Differently, in whole-body pose estimation, the locati... | ['Emre Akbas', 'Nermin Samet'] | 2021-06-08 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-5.12550473e-01 4.91147861e-02 -1.49141803e-01 3.11009632e-03
-8.13554406e-01 -3.79047215e-01 2.50506699e-01 -2.22738355e-01
-3.66792023e-01 2.64056355e-01 1.79966137e-01 6.15525961e-01
2.76485860e-01 -4.51042086e-01 -7.42269754e-01 -5.74520826e-01
1.75571479e-02 8.72470140e-01 3.93810749e-01 -3.94452780... | [7.057460784912109, -0.8918129205703735] |
3bdd8e1d-e2a9-4ad3-b8d5-ebf725f73693 | a-novel-brain-decoding-method-a-correlation | 1712.01668 | null | http://arxiv.org/abs/1712.01668v1 | http://arxiv.org/pdf/1712.01668v1.pdf | A Novel Brain Decoding Method: a Correlation Network Framework for Revealing Brain Connections | Brain decoding is a hot spot in cognitive science, which focuses on
reconstructing perceptual images from brain activities. Analyzing the
correlations of collected data from human brain activities and representing
activity patterns are two problems in brain decoding based on functional
magnetic resonance imaging (fMRI)... | ['Badong Chen', 'Hao Wu', 'Yongqiang Ma', 'Siyu Yu', 'Nanning Zheng'] | 2017-12-01 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 3.80934894e-01 -3.24380189e-01 1.15904003e-01 -2.74011403e-01
4.70403016e-01 -4.49943513e-01 6.68011665e-01 -9.53332782e-02
-1.76180601e-01 7.41751432e-01 1.40168846e-01 -8.65569711e-02
-5.47473848e-01 -7.78573036e-01 -4.52855855e-01 -9.55773473e-01
-3.81156564e-01 -5.49574532e-02 2.13806242e-01 -1.35313064... | [12.620474815368652, 3.4133052825927734] |
319f5bd7-2d85-418d-82af-d2d7fe265292 | neural-implicit-dense-semantic-slam | 2304.14560 | null | https://arxiv.org/abs/2304.14560v2 | https://arxiv.org/pdf/2304.14560v2.pdf | Neural Implicit Dense Semantic SLAM | Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment using a camera sensor while simultaneously tracking its position over time. In this paper, we propose a novel RGBD vSLAM algorithm that can le... | ['Jean-Philippe Thiran', 'Luc van Gool', 'Suryansh Kumar', 'Yasaman Haghighi'] | 2023-04-27 | null | null | null | null | ['simultaneous-localization-and-mapping', 'semantic-slam'] | ['computer-vision', 'computer-vision'] | [ 1.91662282e-01 -1.91404462e-01 7.19075371e-03 -4.33629632e-01
-3.68444264e-01 -9.90227878e-01 3.67158234e-01 2.76149005e-01
-5.77244222e-01 2.38618195e-01 -4.94736999e-01 -2.45011806e-01
1.47461772e-01 -6.97146118e-01 -1.16227782e+00 -3.01279038e-01
2.72985488e-01 7.38190591e-01 7.01923847e-01 -6.91895559... | [7.643080234527588, -2.383608102798462] |
5e0d5358-8efa-4344-83fa-a8adcd39a830 | distilbert-a-distilled-version-of-bert | 1910.01108 | null | https://arxiv.org/abs/1910.01108v4 | https://arxiv.org/pdf/1910.01108v4.pdf | DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter | As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-pur... | ['Julien Chaumond', 'Lysandre Debut', 'Victor Sanh', 'Thomas Wolf'] | 2019-10-02 | null | null | null | neurips-2019-12 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 1.40172884e-01 2.85400659e-01 -4.45098639e-01 -5.56550801e-01
-1.15701246e+00 -6.03245735e-01 4.17661607e-01 3.02613318e-01
-7.79283166e-01 6.95409358e-01 2.02738836e-01 -7.33019650e-01
1.60132229e-01 -8.10228646e-01 -9.67556655e-01 -9.27296951e-02
6.59394339e-02 7.09481478e-01 8.02074652e-03 -1.09421626... | [10.62611198425293, 8.453210830688477] |
55e8eb62-eb3a-4182-bb83-f798e2b84409 | coherence-based-frequency-subset-selection | 2205.08985 | null | https://arxiv.org/abs/2205.08985v1 | https://arxiv.org/pdf/2205.08985v1.pdf | Coherence-Based Frequency Subset Selection For Binaural RTF-Vector-Based Direction of Arrival Estimation for Multiple Speakers | Recently, a method has been proposed to estimate the direction of arrival (DOA) of a single speaker by minimizing the frequency-averaged Hermitian angle between an estimated relative transfer function (RTF) vector and a database of prototype anechoic RTF vectors. In this paper, we extend this method to multi-speaker lo... | ['Simon Doclo', 'Daniel Fejgin'] | 2022-05-18 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-1.09876625e-01 -5.30420482e-01 8.80380273e-01 -2.11524755e-01
-1.24542415e+00 -6.16768301e-01 2.36454591e-01 4.12681401e-02
-2.02831253e-01 5.03951371e-01 5.50887942e-01 -1.50731206e-01
-3.45422417e-01 -4.32955056e-01 -3.66165072e-01 -9.62909222e-01
-4.86624092e-01 -3.61443102e-01 1.35239661e-01 1.92719344... | [15.152446746826172, 5.762768268585205] |
b89f754a-e3fc-4f24-99c6-1eacf8d23674 | lay-text-summarisation-using-natural-language | 2303.14222 | null | https://arxiv.org/abs/2303.14222v1 | https://arxiv.org/pdf/2303.14222v1.pdf | Lay Text Summarisation Using Natural Language Processing: A Narrative Literature Review | Summarisation of research results in plain language is crucial for promoting public understanding of research findings. The use of Natural Language Processing to generate lay summaries has the potential to relieve researchers' workload and bridge the gap between science and society. The aim of this narrative literature... | ['Zoe Tieges', 'David McMinn', 'Gordon Morison', 'Mark David Jenkins', 'Oliver Vinzelberg'] | 2023-03-24 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [ 7.82398701e-01 6.03441417e-01 -5.72031558e-01 1.17917443e-02
-1.27930856e+00 -6.91183746e-01 8.12158227e-01 8.90003920e-01
-5.27016878e-01 1.12789118e+00 1.33965492e+00 -6.02214456e-01
-4.10741150e-01 -4.85178590e-01 -4.46769536e-01 -1.42804697e-01
3.47293109e-01 2.23114580e-01 -1.64625496e-01 -7.33835697... | [12.348066329956055, 9.589519500732422] |
3cc17aa6-29de-48bc-8a56-38ce744b3f38 | least-to-most-prompting-enables-complex | 2205.10625 | null | https://arxiv.org/abs/2205.10625v3 | https://arxiv.org/pdf/2205.10625v3.pdf | Least-to-Most Prompting Enables Complex Reasoning in Large Language Models | Chain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars shown in the prompts. To overcome this challenge of easy-to-hard generalization, we propose a novel promp... | ['Olivier Bousquet', 'Claire Cui', 'Ed Chi', 'Quoc Le', 'Dale Schuurmans', 'Xuezhi Wang', 'Nathan Scales', 'Jason Wei', 'Le Hou', 'Nathanael Schärli', 'Denny Zhou'] | 2022-05-21 | null | null | null | null | ['arithmetic-reasoning'] | ['reasoning'] | [ 3.60670239e-01 2.85689205e-01 2.80681159e-02 -4.19102460e-01
-7.29773462e-01 -8.56858671e-01 4.63590890e-01 3.67834061e-01
-1.59340113e-01 7.16988921e-01 -2.47412622e-01 -8.72749627e-01
-5.60447395e-01 -7.47276247e-01 -7.11553395e-01 -2.90552318e-01
-1.59995705e-01 7.87345111e-01 1.45176291e-01 -6.12226605... | [9.406899452209473, 7.279869556427002] |
74253061-17f5-42d9-bf48-8bccceeb6591 | substructure-aware-graph-neural-networks | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/26318 | https://ojs.aaai.org/index.php/AAAI/article/view/26318/26090 | Substructure Aware Graph Neural Networks | Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL.Based on th... | ['H', '& Qu', 'M.', 'Zhang', 'L.', 'Zhou', 'Chen', 'W.', 'Liu', 'D.', 'Zeng'] | 2023-06-26 | null | null | null | proceedings-of-the-aaai-conference-on-6 | ['graph-learning', 'graph-regression'] | ['graphs', 'graphs'] | [ 2.70103216e-01 4.52917784e-01 -3.04215610e-01 -2.25797556e-02
-4.53157313e-02 -4.76689726e-01 3.12729120e-01 -9.75189544e-03
-8.61492008e-02 4.72901106e-01 -1.63029820e-01 -7.28404760e-01
-5.18720686e-01 -1.38622284e+00 -9.11737561e-01 -6.04173362e-01
-5.00993252e-01 3.92369837e-01 3.88772875e-01 -3.44410777... | [6.982449531555176, 6.222543716430664] |
48f50e1a-719f-4d6b-a9f3-96bab99a44a2 | confidence-intervals-for-error-rates-in | 2306.01198 | null | https://arxiv.org/abs/2306.01198v1 | https://arxiv.org/pdf/2306.01198v1.pdf | Confidence Intervals for Error Rates in Matching Tasks: Critical Review and Recommendations | Matching algorithms are commonly used to predict matches between items in a collection. For example, in 1:1 face verification, a matching algorithm predicts whether two face images depict the same person. Accurately assessing the uncertainty of the error rates of such algorithms can be challenging when data are depende... | ['Pietro Perona', 'Pratik Patil', 'Riccardo Fogliato'] | 2023-06-01 | null | null | null | null | ['face-verification'] | ['computer-vision'] | [ 3.99373889e-01 -3.89093235e-02 -4.67080891e-01 -9.12712455e-01
-7.72629142e-01 -6.83085263e-01 4.46281254e-01 5.15804589e-01
-4.15544659e-01 6.65403485e-01 8.83204788e-02 -3.56138945e-01
-3.12651098e-01 -6.36693358e-01 -8.26199532e-01 7.95310289e-02
-2.71715254e-01 3.14589143e-01 -3.00380915e-01 5.47958612... | [13.061836242675781, 1.2022353410720825] |
fca69d28-cdca-4e43-bec1-6b4a97675c19 | class-incremental-learning-with-repetition | 2301.11396 | null | https://arxiv.org/abs/2301.11396v2 | https://arxiv.org/pdf/2301.11396v2.pdf | Class-Incremental Learning with Repetition | Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike replay, cannot be controlled by the agent. Nowadays, the Class-Incremental (CI) scenario represents the leading test-bed for assessing and co... | ['Damian Borth', 'Vincenzo Lomonaco', 'Davide Bacciu', 'Lorenzo Pellegrini', 'Julio Hurtado', 'Antonio Carta', 'Andrea Cossu', 'Hamed Hemati'] | 2023-01-26 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [ 1.66118696e-01 -3.33375394e-01 -1.03817925e-01 -7.13382214e-02
-3.11812311e-01 -6.26145899e-01 1.03066576e+00 6.21132135e-01
-6.04723871e-01 8.22484910e-01 4.72827628e-02 -9.78244692e-02
-2.31747240e-01 -8.46056521e-01 -9.06766176e-01 -7.48972714e-01
-7.02557445e-01 4.84563202e-01 4.87680495e-01 -3.87805402... | [7.6926727294921875, 3.060680866241455] |
53520bb8-7688-491b-b04c-c78b102208b7 | re-attention-transformer-for-weakly | 2208.01838 | null | https://arxiv.org/abs/2208.01838v2 | https://arxiv.org/pdf/2208.01838v2.pdf | Re-Attention Transformer for Weakly Supervised Object Localization | Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories. Existing deep network approaches are mainly based on class activation map, which focuses on highlighting discriminative local region while ignoring the full object. In addition, t... | ['Lechao Cheng', 'Mingli Song', 'Zhiwei Chen', 'Yue Ye', 'Hui Su'] | 2022-08-03 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 9.54810679e-02 1.40208021e-01 -2.23519549e-01 -4.49148417e-01
-6.87102437e-01 -2.73365378e-01 5.79137564e-01 2.06617385e-01
-3.74099910e-01 3.94173265e-01 3.27140540e-01 6.22303039e-02
8.41563642e-02 -5.09919286e-01 -7.09727407e-01 -7.42183208e-01
2.23226994e-01 -2.36189887e-02 6.22848630e-01 9.20293108... | [9.594327926635742, 0.8424345254898071] |
f1d2c376-18f0-4390-8c11-eb684b7ebcfa | 3d-hand-pose-detection-in-egocentric-rgb-d | 1412.0065 | null | http://arxiv.org/abs/1412.0065v1 | http://arxiv.org/pdf/1412.0065v1.pdf | 3D Hand Pose Detection in Egocentric RGB-D Images | We focus on the task of everyday hand pose estimation from egocentric
viewpoints. For this task, we show that depth sensors are particularly
informative for extracting near-field interactions of the camera wearer with
his/her environment. Despite the recent advances in full-body pose estimation
using Kinect-like sensor... | ['Maryam Khademi', 'Deva Ramanan', 'Jose Maria Martinez Montiel', 'James S. Supancic III', 'Gregory Rogez'] | 2014-11-29 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 8.76614824e-02 -3.24328035e-01 -8.78574103e-02 -8.98186788e-02
-5.50804079e-01 -5.31248808e-01 3.58888984e-01 -5.69967031e-01
-4.66215938e-01 5.52334189e-01 3.39819074e-01 3.67691755e-01
-7.77210519e-02 -1.49255648e-01 -5.65280139e-01 -4.39684063e-01
1.42549545e-01 7.85546601e-01 1.64323598e-01 -1.21704705... | [6.548900604248047, -0.8780843019485474] |
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