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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a7c93832-1460-4f43-a4d2-e0f50538ebcc | dip-differentiable-interreflection-aware | 2212.04705 | null | https://arxiv.org/abs/2212.04705v1 | https://arxiv.org/pdf/2212.04705v1.pdf | DIP: Differentiable Interreflection-aware Physics-based Inverse Rendering | We present a physics-based inverse rendering method that learns the illumination, geometry, and materials of a scene from posed multi-view RGB images. To model the illumination of a scene, existing inverse rendering works either completely ignore the indirect illumination or model it by coarse approximations, leading t... | ['Ming-Hsuan Yang', 'Sifei Liu', 'Xueting Li', 'Youming Deng'] | 2022-12-09 | null | null | null | null | ['inverse-rendering'] | ['computer-vision'] | [ 5.98746240e-01 -1.02386773e-01 5.67684233e-01 -5.92500329e-01
-3.02897215e-01 -6.11956358e-01 5.15541911e-01 -3.84817243e-01
1.19119458e-01 5.84927440e-01 1.48621649e-01 6.69410378e-02
3.89706306e-02 -9.28955674e-01 -1.04841292e+00 -7.26410329e-01
6.71498537e-01 3.71611476e-01 -1.17678247e-01 -2.66854554... | [9.69948959350586, -3.0710480213165283] |
bfaefe29-f4fe-4527-97e7-6a8771201be0 | recallm-an-architecture-for-temporal-context | 2307.02738 | null | https://arxiv.org/abs/2307.02738v2 | https://arxiv.org/pdf/2307.02738v2.pdf | RecallM: An Architecture for Temporal Context Understanding and Question Answering | The ideal long-term memory mechanism for Large Language Model (LLM) based chatbots, would lay the foundation for continual learning, complex reasoning and allow sequential and temporal dependencies to be learnt. Creating this type of memory mechanism is an extremely challenging problem. In this paper we explore differe... | ['Hugo Latapie', 'Brandon Kynoch'] | 2023-07-06 | null | null | null | null | ['continual-learning', 'question-answering'] | ['methodology', 'natural-language-processing'] | [-8.34920228e-01 2.64378071e-01 -3.40205103e-01 -3.34961236e-01
-2.98993975e-01 -5.96146941e-01 9.32867467e-01 -6.56722635e-02
-4.46986794e-01 1.05240417e+00 1.72904283e-01 -4.99100119e-01
-3.20136815e-01 -1.04802835e+00 -4.65356022e-01 -2.28073686e-01
-6.41832650e-01 8.98688078e-01 8.76387119e-01 -5.49971223... | [12.5944242477417, 7.869400501251221] |
fbd1c6e7-67ef-475c-9351-661760284694 | beyond-human-parts-dual-part-aligned | 1910.10111 | null | https://arxiv.org/abs/1910.10111v1 | https://arxiv.org/pdf/1910.10111v1.pdf | Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification | Person re-identification is a challenging task due to various complex factors. Recent studies have attempted to integrate human parsing results or externally defined attributes to help capture human parts or important object regions. On the other hand, there still exist many useful contextual cues that do not fall into... | ['Jinge Yao', 'Yuhui Yuan', 'Kai Han', 'Lang Huang', 'Jianyuan Guo', 'Chao Zhang'] | 2019-10-22 | beyond-human-parts-dual-part-aligned-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Guo_Beyond_Human_Parts_Dual_Part-Aligned_Representations_for_Person_Re-Identification_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Guo_Beyond_Human_Parts_Dual_Part-Aligned_Representations_for_Person_Re-Identification_ICCV_2019_paper.pdf | iccv-2019-10 | ['human-parsing'] | ['computer-vision'] | [-5.24107553e-02 8.41873735e-02 2.20893591e-04 -5.89071691e-01
-7.85783410e-01 -3.92766923e-01 5.59346318e-01 -1.26339048e-01
-6.04722738e-01 8.54985833e-01 3.04111511e-01 2.26337433e-01
3.91689807e-01 -4.03302997e-01 -5.01052976e-01 -4.25224394e-01
3.68671209e-01 4.74553287e-01 4.50696409e-01 -2.04754621... | [14.712675094604492, 0.8450649380683899] |
27139a42-4ac7-439f-8c31-ed43115ab08d | phase-only-image-based-kernel-estimation-for | 1811.10185 | null | http://arxiv.org/abs/1811.10185v3 | http://arxiv.org/pdf/1811.10185v3.pdf | Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring | The image blurring process is generally modelled as the convolution of a blur
kernel with a latent image. Therefore, the estimation of the blur kernel is
essentially important for blind image deblurring. Unlike existing approaches
which focus on approaching the problem by enforcing various priors on the blur
kernel and... | ['Miaomiao Liu', 'Richard Hartley', 'Liyuan Pan', 'Yuchao Dai'] | 2018-11-26 | null | null | null | null | ['single-image-blind-deblurring', 'blind-image-deblurring'] | ['computer-vision', 'computer-vision'] | [ 3.06235790e-01 -5.38178086e-01 4.08876091e-01 -8.63880962e-02
-3.75726014e-01 -6.02387547e-01 6.13192081e-01 -5.43513238e-01
-3.25963646e-01 8.65919232e-01 5.67762852e-01 6.88217729e-02
-2.91774571e-01 -2.52531260e-01 -5.67170799e-01 -1.03339207e+00
6.89424342e-03 -2.01746330e-01 9.64785367e-02 2.10513547... | [11.616364479064941, -2.7486751079559326] |
eca5fc9b-e6bd-410f-968b-d7a748f9647a | graph-neural-networks-for-molecules | 2209.05582 | null | https://arxiv.org/abs/2209.05582v2 | https://arxiv.org/pdf/2209.05582v2.pdf | Graph Neural Networks for Molecules | Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to up... | ['Amir Barati Farimani', 'Zijie Li', 'Yuyang Wang'] | 2022-09-12 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 1.77901149e-01 -2.01709613e-01 -7.00109720e-01 -1.45310029e-01
3.70798200e-01 -2.50956357e-01 3.55627030e-01 9.75120962e-01
-1.54329538e-01 1.08389318e+00 -3.87197793e-01 -9.64879930e-01
-4.20173258e-01 -1.25729156e+00 -6.85666203e-01 -8.02547634e-01
-9.72549736e-01 4.04109687e-01 9.40722600e-02 -3.96580130... | [5.158682823181152, 5.819759368896484] |
39f00452-c522-49f1-8b7c-ec6d14da2faf | short-term-and-long-term-context-aggregation-1 | 2009.05721 | null | https://arxiv.org/abs/2009.05721v1 | https://arxiv.org/pdf/2009.05721v1.pdf | Short-Term and Long-Term Context Aggregation Network for Video Inpainting | Video inpainting aims to restore missing regions of a video and has many applications such as video editing and object removal. However, existing methods either suffer from inaccurate short-term context aggregation or rarely explore long-term frame information. In this work, we present a novel context aggregation netwo... | ['DaCheng Tao', 'Mingming Gong', 'Ang Li', 'Ramamohanarao Kotagiri', 'Rui Zhang', 'Jianzhong Qi', 'Xingjun Ma', 'Shanshan Zhao'] | 2020-09-12 | short-term-and-long-term-context-aggregation | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2723_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490698.pdf | eccv-2020-8 | ['video-inpainting'] | ['computer-vision'] | [ 2.93520927e-01 -4.93885845e-01 -2.38019437e-01 -3.86111587e-01
-4.95334208e-01 -2.68003047e-01 2.29676142e-01 2.18442261e-01
-2.93400228e-01 9.56366718e-01 5.23528397e-01 2.04100087e-01
5.28896265e-02 -7.64370382e-01 -8.21125865e-01 -5.22627711e-01
6.33358434e-02 -1.10729583e-01 5.02879083e-01 -1.17820129... | [10.815571784973145, -1.3959252834320068] |
0918ce95-0fa2-42f3-a0b5-a9b4f8be85bb | broaden-your-views-for-self-supervised-video | 2103.16559 | null | https://arxiv.org/abs/2103.16559v3 | https://arxiv.org/pdf/2103.16559v3.pdf | Broaden Your Views for Self-Supervised Video Learning | Most successful self-supervised learning methods are trained to align the representations of two independent views from the data. State-of-the-art methods in video are inspired by image techniques, where these two views are similarly extracted by cropping and augmenting the resulting crop. However, these methods miss a... | ['Corentin Tallec', 'Florian Strub', 'Ross Hemsley', 'Andrew Zisserman', 'Aäron van den Oord', 'Jean-bastien Grill', 'Michal Valko', 'Florent Altché', 'Viorica Patraucean', 'Mateusz Malinowski', 'Luyu Wang', 'Jean-Baptiste Alayrac', 'Pauline Luc', 'Adrià Recasens'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Recasens_Broaden_Your_Views_for_Self-Supervised_Video_Learning_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Recasens_Broaden_Your_Views_for_Self-Supervised_Video_Learning_ICCV_2021_paper.pdf | iccv-2021-1 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 2.44530931e-01 -1.46128535e-01 -3.72880071e-01 -1.23345166e-01
-5.30049086e-01 -6.91153586e-01 6.55076206e-01 -2.53887504e-01
-2.28127599e-01 4.52984124e-01 4.71246958e-01 2.86644340e-01
1.30075961e-01 -5.25150657e-01 -9.31353748e-01 -8.01470935e-01
-2.04579502e-01 1.68864951e-01 2.29468092e-01 -1.16979562... | [9.322710990905762, 0.8865151405334473] |
29f32eb7-f100-4f9f-90d4-01712305292c | towards-linked-hypernyms-dataset-20 | null | null | https://aclanthology.org/L14-1552 | https://aclanthology.org/L14-1552.pdf | Towards Linked Hypernyms Dataset 2.0: complementing DBpedia with hypernym discovery | This paper presents a statistical type inference algorithm for ontology alignment, which assigns DBpedia entities with a new type (class). To infer types for a specific entity, the algorithm first identifies types that co-occur with the type the entity already has, and subsequently prunes the set of candidates for the ... | ['Ond{\\v{r}}ej Zamazal', "Tom{\\'a}{\\v{s}} Kliegr"] | 2014-05-01 | null | null | null | lrec-2014-5 | ['hypernym-discovery'] | ['natural-language-processing'] | [-1.40924662e-01 7.46929049e-01 -2.60930151e-01 -2.71258384e-01
-9.72146019e-02 -6.45236254e-01 6.18782282e-01 8.93610537e-01
-8.26602817e-01 1.52303290e+00 -1.49215013e-01 -1.62100583e-01
-4.80555862e-01 -1.61689854e+00 -8.22554290e-01 -3.03422719e-01
-9.38549414e-02 1.23366153e+00 5.09150684e-01 -2.81752855... | [9.211627006530762, 8.086844444274902] |
482b895e-0b9a-4407-817d-0231d9056041 | black-box-node-injection-attack-for-graph | 2202.09389 | null | https://arxiv.org/abs/2202.09389v1 | https://arxiv.org/pdf/2202.09389v1.pdf | Black-box Node Injection Attack for Graph Neural Networks | Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommendation and traffic forecasting. Under such scenarios, exploiting GNN's vulnerabilities and further downgrade its classification performance b... | ['Liang Zhao', 'Yanfang Ye', 'Yujie Fan', 'Mingxuan Ju'] | 2022-02-18 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 2.50147998e-01 4.37300563e-01 -4.04874980e-01 1.76541746e-01
-8.39438215e-02 -8.08446348e-01 5.33044219e-01 -4.61951643e-03
-8.13774168e-02 6.26098275e-01 -3.88724715e-01 -1.04019833e+00
-2.45794244e-02 -1.08688557e+00 -8.86729956e-01 -6.80086613e-01
-2.11581171e-01 -1.33791063e-02 2.16066644e-01 -3.49467158... | [6.102791786193848, 7.328357219696045] |
8ade0c9c-bdb8-4cdd-8256-fa659a78a1d4 | a-novel-deep-learning-based-approach-for | 2208.03408 | null | https://arxiv.org/abs/2208.03408v2 | https://arxiv.org/pdf/2208.03408v2.pdf | A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals | Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, and cardiomyopathy (enlargement of the muscle tissue of the heart). The electrocardiogram (ECG) plays a critical role in identifying SA since ... | ['Cuong Do', 'Huy-Hieu Pham', 'Huy-Khiem Le', 'Thao Nguyen', 'Anh-Tu Nguyen'] | 2022-08-05 | null | null | null | null | ['sleep-apnea-detection'] | ['medical'] | [ 4.31847125e-01 -4.05945748e-01 -5.25552556e-02 -1.42519444e-01
-3.74743074e-01 -2.44782090e-01 -3.64491343e-01 2.86708862e-01
-3.30187917e-01 6.87141776e-01 -2.74177164e-01 -2.82485783e-01
-3.04373261e-02 -6.07451737e-01 1.30992264e-01 -8.96137416e-01
-1.52039811e-01 -1.42989084e-01 1.55322319e-02 3.98140512... | [14.085192680358887, 3.2255005836486816] |
6fc655d6-48c4-4c79-bed4-e9075e7e1a20 | winning-the-lottery-with-continuous-1 | 1912.04427 | null | https://arxiv.org/abs/1912.04427v4 | https://arxiv.org/pdf/1912.04427v4.pdf | Winning the Lottery with Continuous Sparsification | The search for efficient, sparse deep neural network models is most prominently performed by pruning: training a dense, overparameterized network and removing parameters, usually via following a manually-crafted heuristic. Additionally, the recent Lottery Ticket Hypothesis conjectures that, for a typically-sized neural... | ['Pedro Savarese', 'Michael Maire', 'Hugo Silva'] | 2019-12-10 | null | http://proceedings.neurips.cc/paper/2020/hash/83004190b1793d7aa15f8d0d49a13eba-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/83004190b1793d7aa15f8d0d49a13eba-Paper.pdf | neurips-2020-12 | ['ticket-search'] | ['methodology'] | [ 3.16202074e-01 7.29576290e-01 -3.92228812e-01 -3.51906031e-01
-4.27763402e-01 -1.80832054e-02 3.63234073e-01 -4.54032689e-01
-6.44223928e-01 9.30958390e-01 1.05847158e-01 -2.25041375e-01
-5.51738918e-01 -7.64464319e-01 -9.63648915e-01 -5.58778882e-01
-2.82829612e-01 8.32101345e-01 1.18767560e-01 9.29029584... | [8.547319412231445, 3.3171210289001465] |
6cf09fe1-29ac-47ec-b83e-60931c17ab31 | 190408338 | 1904.08338 | null | http://arxiv.org/abs/1904.08338v1 | http://arxiv.org/pdf/1904.08338v1.pdf | OCKELM+: Kernel Extreme Learning Machine based One-class Classification using Privileged Information (or KOC+: Kernel Ridge Regression or Least Square SVM with zero bias based One-class Classification using Privileged Information) | Kernel method-based one-class classifier is mainly used for outlier or
novelty detection. In this letter, kernel ridge regression (KRR) based
one-class classifier (KOC) has been extended for learning using privileged
information (LUPI). LUPI-based KOC method is referred to as KOC+. This
privileged information is availa... | ['Chandan Gautam', 'M. Tanveer', 'Aruna Tiwari'] | 2019-04-13 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-6.11417517e-02 -3.79516572e-01 -4.15695906e-01 -3.68492812e-01
-2.91666657e-01 -2.72165745e-01 6.33676112e-01 4.28131968e-01
-5.28523266e-01 1.05075228e+00 -1.91289008e-01 -3.05977434e-01
-4.81592536e-01 -6.35349810e-01 -4.22576189e-01 -8.70393515e-01
-4.66924071e-01 -1.18934833e-01 1.21347144e-01 -1.51440769... | [8.189534187316895, 3.883599281311035] |
1310afd8-ced7-4b0f-8cb1-0e151f1fd885 | bidirectional-copy-paste-for-semi-supervised | 2305.00673 | null | https://arxiv.org/abs/2305.00673v1 | https://arxiv.org/pdf/2305.00673v1.pdf | Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation | In semi-supervised medical image segmentation, there exist empirical mismatch problems between labeled and unlabeled data distribution. The knowledge learned from the labeled data may be largely discarded if treating labeled and unlabeled data separately or in an inconsistent manner. We propose a straightforward method... | ['Yan Wang', 'Wei Shen', 'Qingli Li', 'Duowen Chen', 'Yunhao Bai'] | 2023-05-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bai_Bidirectional_Copy-Paste_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bai_Bidirectional_Copy-Paste_for_Semi-Supervised_Medical_Image_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 3.97674739e-01 6.12154126e-01 -6.61888301e-01 -7.93322325e-01
-1.02791488e+00 -6.81902587e-01 1.19734518e-01 -3.13987911e-01
-3.26131016e-01 8.70911956e-01 -6.64380565e-02 -2.73220003e-01
2.01324269e-01 -3.90682817e-01 -8.94644320e-01 -1.15122759e+00
3.73289675e-01 6.68299973e-01 1.12352163e-01 3.03243279... | [14.62393569946289, -1.9951493740081787] |
14679da2-3931-4755-a064-d92eb988f48b | an-efficient-multilinear-optimization | 1511.02667 | null | http://arxiv.org/abs/1511.02667v2 | http://arxiv.org/pdf/1511.02667v2.pdf | An Efficient Multilinear Optimization Framework for Hypergraph Matching | Hypergraph matching has recently become a popular approach for solving
correspondence problems in computer vision as it allows to integrate
higher-order geometric information. Hypergraph matching can be formulated as a
third-order optimization problem subject to the assignment constraints which
turns out to be NP-hard.... | ['Francesco Tudisco', 'Antoine Gautier', 'Quynh Nguyen', 'Matthias Hein'] | 2015-11-09 | null | null | null | null | ['hypergraph-matching'] | ['graphs'] | [ 2.76761174e-01 3.94612193e-01 9.47029516e-02 1.28112137e-01
-8.49481404e-01 -5.09711981e-01 4.59804237e-01 4.62471366e-01
-4.26316738e-01 2.98930436e-01 -1.40263304e-01 -4.28460181e-01
-3.44599694e-01 -8.47654223e-01 -8.04108918e-01 -6.21278465e-01
-7.68189281e-02 8.01183581e-01 5.08777142e-01 -2.57099986... | [8.099360466003418, -2.190068244934082] |
9190ab08-8b27-43e9-86d6-2d5de9f9ab4a | improving-object-counting-with-heatmap | 1803.05494 | null | http://arxiv.org/abs/1803.05494v2 | http://arxiv.org/pdf/1803.05494v2.pdf | Improving Object Counting with Heatmap Regulation | In this paper, we propose a simple and effective way to improve one-look
regression models for object counting from images. We use class activation map
visualizations to illustrate the drawbacks of learning a pure one-look
regression model for a counting task. Based on these insights, we enhance
one-look regression cou... | ['Ian Stavness', 'Shubhra Aich'] | 2018-03-14 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [-4.38311845e-02 -6.37714043e-02 4.01804596e-01 -3.61520022e-01
-3.70018661e-01 -3.52517337e-01 8.37844074e-01 4.41282004e-01
-1.06455517e+00 7.88954914e-01 -1.54498473e-01 -3.86075288e-01
5.61236501e-01 -9.18840706e-01 -8.44524443e-01 -5.65842688e-01
1.68059781e-01 4.19686109e-01 6.45081997e-01 -3.29337493... | [8.713135719299316, 0.006962099578231573] |
3f7936fa-2bb4-490d-bb29-1772cec28da0 | incorporating-linguistic-constraints-into | null | null | https://aclanthology.org/P19-1515 | https://aclanthology.org/P19-1515.pdf | Incorporating Linguistic Constraints into Keyphrase Generation | Keyphrases, that concisely describe the high-level topics discussed in a document, are very useful for a wide range of natural language processing tasks. Though existing keyphrase generation methods have achieved remarkable performance on this task, they generate many overlapping phrases (including sub-phrases or super... | ['Yuxiang Zhang', 'Jing Zhao'] | 2019-07-01 | null | null | null | acl-2019-7 | ['keyphrase-generation'] | ['natural-language-processing'] | [ 1.40772134e-01 2.73786243e-02 -2.19913065e-01 -2.66411398e-02
-1.04955196e+00 -5.90768874e-01 6.49044275e-01 3.42000365e-01
-5.31580031e-01 1.18471873e+00 9.71389472e-01 -3.33172321e-01
1.35946527e-01 -8.66856098e-01 -9.19651508e-01 -7.81539559e-01
1.40212342e-01 2.56062627e-01 3.34221035e-01 -3.42740089... | [12.323901176452637, 9.02120590209961] |
00256ce1-0d6a-4062-a96d-bd7f7db42bc1 | maniskill2-a-unified-benchmark-for | 2302.04659 | null | https://arxiv.org/abs/2302.04659v1 | https://arxiv.org/pdf/2302.04659v1.pdf | ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills | Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks, mostly composed of a suite of simulatable environments, are insufficient to push cutting-edge research works because they lack object-level ... | ['Hao Su', 'Rui Chen', 'Zhiao Huang', 'Pengwei Xie', 'Xiaodi Yuan', 'Yunchao Yao', 'Xinyue Wei', 'Stone Tao', 'Yihe Tang', 'Tongzhou Mu', 'Xiqiang Liu', 'Zhan Ling', 'Xuanlin Li', 'Fanbo Xiang', 'Jiayuan Gu'] | 2023-02-09 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [-2.25888208e-01 -3.10562730e-01 -1.54897735e-01 2.86268204e-01
-8.03228915e-02 -8.83077264e-01 5.79994380e-01 -2.29004800e-01
-4.71561223e-01 6.61234736e-01 -2.32413441e-01 -3.55052471e-01
-1.88097715e-01 -6.76551640e-01 -8.95628512e-01 -4.91242319e-01
-4.54569459e-01 8.32352102e-01 4.81640130e-01 -8.04040194... | [4.623555660247803, 0.7832876443862915] |
2c7cabd7-4831-4384-8e9d-a7333085d71e | exploiting-unsupervised-data-for-emotion | 2010.01908 | null | https://arxiv.org/abs/2010.01908v2 | https://arxiv.org/pdf/2010.01908v2.pdf | Exploiting Unsupervised Data for Emotion Recognition in Conversations | Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task. Unlike the sentence-level text classification problem, the available supervised data for the ERC task is limited, which potentially prevents the models from playi... | ['Irwin King', 'Michael R. Lyu', 'Wenxiang Jiao'] | 2020-10-02 | null | https://aclanthology.org/2020.findings-emnlp.435 | https://aclanthology.org/2020.findings-emnlp.435.pdf | findings-of-the-association-for-computational | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.78095922e-01 2.25146934e-01 5.67368232e-02 -1.01659656e+00
-9.97049212e-01 -2.32583106e-01 3.12296540e-01 -5.05594769e-03
-2.81394213e-01 5.72358668e-01 6.92607999e-01 -2.57086337e-01
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1.33815527e-01 7.54321739e-02 -6.00452304e-01 -2.13726461... | [13.130273818969727, 6.0322184562683105] |
d8fb8b25-404e-4541-bbf8-e965a4c14a36 | ranking-in-contextual-multi-armed-bandits | 2207.00109 | null | https://arxiv.org/abs/2207.00109v1 | https://arxiv.org/pdf/2207.00109v1.pdf | Ranking in Contextual Multi-Armed Bandits | We study a ranking problem in the contextual multi-armed bandit setting. A learning agent selects an ordered list of items at each time step and observes stochastic outcomes for each position. In online recommendation systems, showing an ordered list of the most attractive items would not be the best choice since both ... | ['Arnaud Doucet', 'George Deligiannidis', 'Amitis Shidani'] | 2022-06-30 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [-1.27326682e-01 -1.49320632e-01 -8.30901802e-01 -4.54659313e-01
-9.66153443e-01 -9.80083287e-01 -9.54173207e-02 3.84869993e-01
-7.86379039e-01 1.15703022e+00 1.16190396e-03 -5.06583333e-01
-9.55258846e-01 -8.73131752e-01 -1.11877882e+00 -8.29097092e-01
-3.67256463e-01 9.60323989e-01 1.34035975e-01 -1.13848493... | [4.6173577308654785, 3.366774797439575] |
ffdca9ee-6d15-4245-96d9-71f456e0cc20 | dynamic-atomic-column-detection-in | 2302.00816 | null | https://arxiv.org/abs/2302.00816v1 | https://arxiv.org/pdf/2302.00816v1.pdf | Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation | Ridge detection is a classical tool to extract curvilinear features in image processing. As such, it has great promise in applications to material science problems; specifically, for trend filtering relatively stable atom-shaped objects in image sequences, such as Transmission Electron Microscopy (TEM) videos. Standard... | ['David S. Matteson', 'Peter A. Crozier', 'Andrew M. Thomas', 'Yuchen Xu'] | 2023-02-02 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 3.19876164e-01 -8.00028443e-01 3.40122581e-01 5.25175594e-02
-7.87534535e-01 -5.71998000e-01 6.26076519e-01 2.52524823e-01
-7.52506137e-01 5.71677327e-01 -5.22539198e-01 -1.97931379e-01
-1.21940069e-01 -3.91954601e-01 -5.84512293e-01 -1.29084933e+00
-2.95743525e-01 3.99205595e-01 3.89525950e-01 1.83999240... | [12.214000701904297, -2.6167595386505127] |
4264f803-2ca2-4529-9703-5ef4402d876b | a-useful-criterion-on-studying-consistent | 2109.14950 | null | https://arxiv.org/abs/2109.14950v2 | https://arxiv.org/pdf/2109.14950v2.pdf | A useful criterion on studying consistent estimation in community detection | In network analysis, developing a unified theoretical framework that can compare methods under different models is an interesting problem. This paper proposes a partial solution to this problem. We summarize the idea of using separation condition for a standard network and sharp threshold of Erd\"os-R\'enyi random grap... | ['Huan Qing'] | 2021-09-30 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.38704228e-01 1.53978735e-01 -4.17494923e-01 1.09413955e-02
1.10867210e-02 -5.35794973e-01 1.72056481e-01 -2.99541861e-01
-1.14735030e-01 8.59979033e-01 -1.82642072e-01 -2.30656072e-01
-7.40531802e-01 -7.59741008e-01 -3.19128662e-01 -8.57872784e-01
-2.79635757e-01 5.02160490e-01 3.69053036e-01 -1.22432381... | [6.976063251495361, 5.200781345367432] |
08e5f8aa-f4ee-41af-af4c-137ada9108c9 | mvp-unified-motion-and-visual-self-supervised | 2003.00667 | null | https://arxiv.org/abs/2003.00667v1 | https://arxiv.org/pdf/2003.00667v1.pdf | MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navigation | Autonomous navigation emerges from both motion and local visual perception in real-world environments. However, most successful robotic motion estimation methods (e.g. VO, SLAM, SfM) and vision systems (e.g. CNN, visual place recognition-VPR) are often separately used for mapping and localization tasks. Conversely, rec... | ['Marvin Chancán', 'Michael Milford'] | 2020-03-02 | null | null | null | null | ['radar-odometry'] | ['robots'] | [-2.61680514e-01 -3.28235626e-01 -2.28444368e-01 -2.24595293e-01
-7.42642105e-01 -7.50539362e-01 8.02336991e-01 -9.68634859e-02
-1.07957029e+00 1.01440084e+00 -1.71610907e-01 -3.42906237e-01
-7.12843612e-02 -9.88021791e-01 -1.07734430e+00 -7.17541456e-01
-3.27995002e-01 5.57543814e-01 5.25150299e-01 -5.58136046... | [7.427687168121338, -1.9276379346847534] |
f4386f99-57f8-4dd7-8b7d-d156cd209be3 | performance-analysis-of-empirical-open-1 | 2306.16547 | null | https://arxiv.org/abs/2306.16547v1 | https://arxiv.org/pdf/2306.16547v1.pdf | Performance Analysis of Empirical Open-Circuit Voltage Modeling in Lithium Ion Batteries, Part-2: Data Collection Procedure | This paper is the second part of a series of papers about empirical approaches to open circuit voltage (OCV) modeling and its performance comparison in lithium-ion batteries. The first part of the series introduced various sources of uncertainties in the OCV models and established a theoretical relationship between unc... | ['Balakumar Balasingam', 'James Nguyen', 'Prarthana Pillai'] | 2023-06-28 | null | null | null | null | ['management'] | ['miscellaneous'] | [-3.81186932e-01 -7.09343553e-01 -4.77112412e-01 -2.47663364e-01
-2.39798412e-01 -6.57561600e-01 5.69255590e-01 6.83890402e-01
-4.89207447e-01 1.30324340e+00 -4.77634102e-01 -4.90023375e-01
-5.17372668e-01 -7.59326994e-01 -6.17936671e-01 -6.76735520e-01
3.63738462e-02 6.68764710e-01 2.72465706e-01 -2.76982099... | [6.289844512939453, 2.749812602996826] |
01b68053-4282-4f0a-a62a-1fde70221cfc | a-demand-driven-perspective-on-generative | 2307.04292 | null | https://arxiv.org/abs/2307.04292v1 | https://arxiv.org/pdf/2307.04292v1.pdf | A Demand-Driven Perspective on Generative Audio AI | To achieve successful deployment of AI research, it is crucial to understand the demands of the industry. In this paper, we present the results of a survey conducted with professional audio engineers, in order to determine research priorities and define various research tasks. We also summarize the current challenges i... | ['Ben Sangbae Chon', 'Keunwoo Choi', 'Hyeongi Moon', 'Minsung Kang', 'Sangshin Oh'] | 2023-07-10 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.26745981e-01 -3.10111754e-02 -1.17552161e-01 -1.04381748e-01
-9.41222310e-01 -5.49244046e-01 8.92690942e-02 -2.03176737e-01
-1.43218726e-01 6.53828621e-01 4.93538052e-01 -2.38639116e-01
-5.01949787e-01 -2.20472857e-01 -3.97028565e-01 -1.43494725e-01
2.95641134e-03 9.53580812e-02 -1.11832224e-01 -2.90452570... | [15.47815990447998, 5.804722309112549] |
0790d9e7-d530-4edc-be41-28b061e0e29a | top-down-rst-parsing-utilizing-granularity | null | null | https://doi.org/10.1609/aaai.v34i05.6321 | https://ojs.aaai.org/index.php/AAAI/article/view/6321/6177 | Top-Down RST Parsing Utilizing Granularity Levels in Documents | Some downstream NLP tasks exploit discourse dependency trees converted from RST trees. To obtain better discourse dependency trees, we need to improve the accuracy of RST trees at the upper parts of the structures. Thus, we propose a novel neural top-down RST parsing method. Then, we exploit three levels of granularity... | ['Masaaki Nagata', 'Manabu Okumura', 'Hidetaka Kamigaito', 'Tsutomu Hirao', 'Naoki Kobayashi'] | 2020-04-03 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [-4.83788177e-02 9.86795306e-01 -5.87586939e-01 -3.37305725e-01
-1.01469922e+00 -6.40596807e-01 4.45131063e-01 3.86174530e-01
-1.16189957e-01 1.22593522e+00 8.25328112e-01 -4.43710268e-01
3.11501473e-01 -9.76216674e-01 -5.03138363e-01 -4.79813099e-01
-1.33379400e-01 6.85059130e-01 4.90795642e-01 -3.01530480... | [10.735918998718262, 9.417052268981934] |
f8c83014-4fff-4d32-94df-57eeaea39aac | valuation-of-public-bus-electrification-with | 2209.12107 | null | https://arxiv.org/abs/2209.12107v1 | https://arxiv.org/pdf/2209.12107v1.pdf | Valuation of Public Bus Electrification with Open Data | This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for the environmental and social benefits. However, the stat... | ['Carlo Papa', 'Erika Mellekas', 'Christian Zulberti', 'Luigi Lanuzza', 'Giuseppe Ferrara', 'Sergio Gambacorta', 'David Rodriguez', 'Akshat Jain', 'Scott J. Moura', 'Soomin Woo', 'Upadhi Vijay'] | 2022-09-25 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [-8.56066525e-01 -7.28928521e-02 -4.80618477e-01 -9.14007351e-02
-1.11121500e+00 -3.71268749e-01 4.37753201e-01 3.40206057e-01
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-4.32540178e-01 -1.44515932e+00 -3.44747692e-01 -6.27807558e-01
-2.23667268e-02 5.70536554e-01 -7.71172047e-02 -3.11507940... | [5.905526638031006, 2.1311838626861572] |
1abb5674-b3d0-428b-bba4-407d5c5489f1 | three-dimensional-generative-adversarial-nets | 1911.08105 | null | https://arxiv.org/abs/1911.08105v3 | https://arxiv.org/pdf/1911.08105v3.pdf | Three-dimensional Generative Adversarial Nets for Unsupervised Metal Artifact Reduction | The reduction of metal artifacts in computed tomography (CT) images, specifically for strong artifacts generated from multiple metal objects, is a challenging issue in medical imaging research. Although there have been some studies on supervised metal artifact reduction through the learning of synthesized artifacts, it... | ['Yuichiro Imai', 'Megumi Nakao', 'Keiho Imanishi', 'Nobuhiro Ueda', 'Tadaaki Kirita', 'Tetsuya Matsuda'] | 2019-11-19 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 5.33395946e-01 3.47939044e-01 5.28889835e-01 -3.38038892e-01
-1.17927408e+00 1.20302476e-01 4.70706671e-02 -1.25922978e-01
-1.27289742e-01 8.03544462e-01 1.65248603e-01 1.05978604e-02
-2.70795554e-01 -7.46638000e-01 -8.45949769e-01 -9.74009514e-01
-2.87406147e-01 5.49855649e-01 1.25370607e-01 1.35670587... | [13.46764087677002, -2.546825408935547] |
7417e00a-e61d-433a-866e-411bd9168172 | residue-based-natural-language-adversarial-1 | 2204.10192 | null | https://arxiv.org/abs/2204.10192v2 | https://arxiv.org/pdf/2204.10192v2.pdf | Residue-Based Natural Language Adversarial Attack Detection | Deep learning based systems are susceptible to adversarial attacks, where a small, imperceptible change at the input alters the model prediction. However, to date the majority of the approaches to detect these attacks have been designed for image processing systems. Many popular image adversarial detection approaches a... | ['Mark Gales', 'Vyas Raina'] | 2022-04-17 | null | https://aclanthology.org/2022.naacl-main.281 | https://aclanthology.org/2022.naacl-main.281.pdf | naacl-2022-7 | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 6.75067484e-01 1.26455277e-01 2.28491679e-01 -4.30107936e-02
-6.06089890e-01 -1.09874845e+00 1.17434466e+00 1.02922134e-01
-4.05682445e-01 1.89230144e-01 -3.92568037e-02 -5.14867067e-01
4.04846132e-01 -8.13461304e-01 -8.22892964e-01 -4.86767650e-01
4.95560430e-02 1.06608838e-01 4.49853212e-01 -4.82138008... | [5.878303050994873, 8.059139251708984] |
a44102e2-0bba-4374-8b97-979e0c3775ad | qaf-frame-semantics-based-question | null | null | https://aclanthology.org/W16-4412 | https://aclanthology.org/W16-4412.pdf | QAF: Frame Semantics-based Question Interpretation | Natural language questions are interpreted to a sequence of patterns to be matched with instances of patterns in a knowledge base (KB) for answering. A natural language (NL) question answering (QA) system utilizes meaningful patterns matching the syntac-tic/lexical features between the NL questions and KB. In the most ... | ['Key-Sun Choi', 'Younggyun Hahm', 'Sangha Nam'] | 2016-12-01 | null | null | null | ws-2016-12 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 1.25340730e-01 6.04276836e-01 -1.13342963e-01 -8.33710372e-01
-4.02832270e-01 -5.09720266e-01 4.07592803e-01 4.74252701e-01
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-5.83040118e-01 -1.57337689e+00 -5.90114951e-01 2.87834555e-01
6.57186866e-01 5.65212905e-01 1.27792859e+00 -1.09739220... | [10.415282249450684, 8.042506217956543] |
e21af7d8-49e0-4a3e-aedc-2457494fabfb | the-overview-of-the-nlm-chem-biocreative-vii | null | null | https://biocreative.bioinformatics.udel.edu/resources/publications/bc-vii-workshop-proceedings/ | https://biocreative.bioinformatics.udel.edu/media/store/files/2021/TRACK2_pos_01_BC7_submission_223.pdf | The overview of the NLM-Chem BioCreative VII track: full-text chemical identification and indexing in PubMed articles | The BioCreative NLM-Chem track calls for a community effort to fine-tune automated recognition of chemical names in biomedical literature. Chemical names are one of the most searched biomedical entities in PubMed and – as highlighted during the COVID-19 pandemic – their identification may significantly advance research... | ['Zhiyong Lu', 'Rezarta Islamaj', 'Robert Leaman'] | 2021-11-08 | null | null | null | biocreative-vii-challenge-evaluation-workshop | ['chemical-entity-recognition', 'chemical-indexing'] | ['medical', 'natural-language-processing'] | [ 3.86247426e-01 2.66712129e-01 -6.92600310e-01 1.01229055e-02
-9.56561685e-01 -9.49132979e-01 4.56324875e-01 1.06112564e+00
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7.33790025e-02 7.16099918e-01 -3.11493039e-01 4.23927367... | [8.486650466918945, 8.729165077209473] |
5b180a27-c231-4b18-aaa2-91c220d66f22 | mirrornet-bio-inspired-adversarial-attack-for-1 | 2007.12881 | null | https://arxiv.org/abs/2007.12881v3 | https://arxiv.org/pdf/2007.12881v3.pdf | MirrorNet: Bio-Inspired Camouflaged Object Segmentation | Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing netwo... | ['Tam V. Nguyen', 'Thanh-Toan Do', 'Minh-Triet Tran', 'Khanh-Duy Nguyen', 'Trung-Nghia Le', 'Jinnan Yan'] | 2020-07-25 | mirrornet-bio-inspired-adversarial-attack-for | null | null | pattern-recognition-journal-2020-7 | ['camouflage-segmentation', 'camouflaged-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.92977083e-01 1.60697788e-01 -2.07672983e-01 7.16653392e-02
-2.65114158e-01 -6.48012459e-01 4.16453272e-01 -3.82486135e-01
-3.37522268e-01 5.55005848e-01 -9.23150256e-02 -2.00929418e-01
5.37003994e-01 -7.90327251e-01 -8.12146723e-01 -5.67479312e-01
4.06866044e-01 6.65431693e-02 6.24202907e-01 4.56784479... | [9.615591049194336, -0.13234348595142365] |
b9744d7b-c648-41d1-8f70-3abb73befb5d | unsupervised-document-embedding-via | 2103.14542 | null | https://arxiv.org/abs/2103.14542v1 | https://arxiv.org/pdf/2103.14542v1.pdf | Unsupervised Document Embedding via Contrastive Augmentation | We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner. Inspired by recent contrastive self-supervised learning algorithms used for image and NLP pretraining, we hypothesize that high-quality document embedding should be invariant to dive... | ['Xiang Zhang', 'Haifeng Chen', 'Dongjin Song', 'Zhengzhang Chen', 'Yanchi Liu', 'Bo Zong', 'Xuchao Zhang', 'Wenchao Yu', 'Jingchao Ni', 'Wei Cheng', 'Dongsheng Luo'] | 2021-03-26 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 7.45299995e-01 1.29932821e-01 -6.81321979e-01 -3.88963103e-01
-8.64424586e-01 -7.74928808e-01 1.27412868e+00 5.15273511e-01
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1.04929604e-01 -6.19172037e-01 -6.88656509e-01 -6.93436742e-01
7.86258280e-02 5.29532611e-01 -1.47644833e-01 -3.07397544... | [9.577845573425293, 2.6745615005493164] |
72403dc7-a0a0-42c0-bcb8-de250ac720b3 | neural-micro-planning-for-data-to-text | null | null | https://aclanthology.org/2020.dt4tp-1.2 | https://aclanthology.org/2020.dt4tp-1.2.pdf | Neural Micro-Planning for Data to Text Generation Produces more Cohesive Text | null | ['Michael Elhadad', 'Roy Eisenstadt'] | null | null | null | null | dt4tp-2020-12 | ['data-to-text-generation'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.400879859924316, 3.705815076828003] |
b7970e15-9351-42ee-b996-c6add9adea0a | ream-sharp-an-enhancement-approach-to-1 | null | null | https://aclanthology.org/2021.findings-acl.220 | https://aclanthology.org/2021.findings-acl.220.pdf | REAM\sharp: An Enhancement Approach to Reference-based Evaluation Metrics for Open-domain Dialog Generation | null | ['Shuming Shi', 'Ruifeng Xu', 'Wei Bi', 'Jun Gao'] | null | null | null | null | findings-acl-2021-8 | ['open-domain-dialog'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.357307434082031, 3.636298418045044] |
384b44d6-b1c7-4e22-8d98-fe8e4a1c9dbc | pseudo-session-based-recommendation-with | 2306.10029 | null | https://arxiv.org/abs/2306.10029v1 | https://arxiv.org/pdf/2306.10029v1.pdf | Pseudo session-based recommendation with hierarchical embedding and session attributes | Recently, electronic commerce (EC) websites have been unable to provide an identification number (user ID) for each transaction data entry because of privacy issues. Because most recommendation methods assume that all data are assigned a user ID, they cannot be applied to the data without user IDs. Recently, session-ba... | ['Satoshi Takahashi', 'Ryusei Numata', 'Yuta Sumiya'] | 2023-06-06 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [-2.75044620e-01 -2.15851292e-01 -7.66862988e-01 -4.26949203e-01
1.73793156e-02 -7.38758504e-01 2.33006924e-01 4.58018005e-01
-3.17892909e-01 4.13512409e-01 2.26397157e-01 -3.37369084e-01
-3.25681776e-01 -1.28412437e+00 -3.32257330e-01 -4.61272031e-01
-4.15611751e-02 5.54778457e-01 4.14100915e-01 -3.74123186... | [10.10521125793457, 5.661802291870117] |
d43cc455-dab2-4785-8fd9-1744dc94fb71 | pose-forecasting-in-industrial-human-robot | 2208.07308 | null | https://arxiv.org/abs/2208.07308v1 | https://arxiv.org/pdf/2208.07308v1.pdf | Pose Forecasting in Industrial Human-Robot Collaboration | Pushing back the frontiers of collaborative robots in industrial environments, we propose a new Separable-Sparse Graph Convolutional Network (SeS-GCN) for pose forecasting. For the first time, SeS-GCN bottlenecks the interaction of the spatial, temporal and channel-wise dimensions in GCNs, and it learns sparse adjacenc... | ['Fabio Galasso', 'Marco Cristani', 'Francesco Setti', 'Geri Skenderi', 'Federico Cunico', 'Andrea Avogaro', "Guido D'Amely", 'Alessio Sampieri'] | 2022-07-24 | null | null | null | null | ['human-pose-forecasting'] | ['computer-vision'] | [-1.74953938e-01 5.30882418e-01 5.58832228e-01 1.53612137e-01
-4.16413963e-01 -4.77295756e-01 8.00804347e-02 -1.63486853e-01
-3.71922761e-01 2.92029053e-01 -4.04752970e-01 -1.42709926e-01
-3.86435628e-01 -4.44356501e-01 -9.34505463e-01 -5.35199702e-01
-7.74111152e-01 9.47305679e-01 4.10177678e-01 -4.41795051... | [5.007174015045166, 0.4563662111759186] |
6279ee51-0926-470b-9ef7-45454dbfa923 | prin-pointwise-rotation-invariant-network | 1811.09361 | null | https://arxiv.org/abs/1811.09361v5 | https://arxiv.org/pdf/1811.09361v5.pdf | Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution | Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clou... | ['Lizhuang Ma', 'Yu-Wing Tai', 'Yang You', 'Cewu Lu', 'Yujing Lou', 'Weiming Wang', 'Qi Liu'] | 2018-11-23 | null | null | null | null | ['3d-feature-matching'] | ['computer-vision'] | [ 1.66074052e-01 -2.74970420e-02 -5.45698181e-02 -4.43184346e-01
-6.98517025e-01 -5.12167156e-01 4.23382282e-01 -8.37912634e-02
-3.19325686e-01 1.86485335e-01 -2.77204126e-01 1.31156191e-01
-3.23661089e-01 -5.85757911e-01 -1.17055237e+00 -6.24747872e-01
1.43009543e-01 1.03238130e+00 1.29974619e-01 6.32707626... | [7.961167812347412, -3.4737086296081543] |
8283d072-88b3-4a8a-91e2-de68a5eacb2f | cross-language-learning-with-adversarial | null | null | https://aclanthology.org/K17-1024 | https://aclanthology.org/K17-1024.pdf | Cross-language Learning with Adversarial Neural Networks | We address the problem of cross-language adaptation for question-question similarity reranking in community question answering, with the objective to port a system trained on one input language to another input language given labeled training data for the first language and only unlabeled data for the second language. ... | ["Llu{\\'\\i}s M{\\`a}rquez", 'Preslav Nakov', 'Shafiq Joty', 'Israa Jaradat'] | 2017-08-01 | null | null | null | conll-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [ 2.77895570e-01 1.91533178e-01 2.18656778e-01 -4.36876327e-01
-1.33171487e+00 -8.60007286e-01 7.12461710e-01 2.68528789e-01
-7.39540815e-01 4.53711450e-01 3.75353187e-01 -5.26295424e-01
2.31227770e-01 -7.49990940e-01 -5.94134450e-01 -1.47838384e-01
1.54947877e-01 8.34268332e-01 5.00905395e-01 -5.04260898... | [11.301753044128418, 8.182782173156738] |
2227f9d7-935f-411f-b77f-4ae14431419b | a-framework-for-adapting-offline-algorithms | 2301.13326 | null | https://arxiv.org/abs/2301.13326v1 | https://arxiv.org/pdf/2301.13326v1.pdf | A Framework for Adapting Offline Algorithms to Solve Combinatorial Multi-Armed Bandit Problems with Bandit Feedback | We investigate the problem of stochastic, combinatorial multi-armed bandits where the learner only has access to bandit feedback and the reward function can be non-linear. We provide a general framework for adapting discrete offline approximation algorithms into sublinear $\alpha$-regret methods that only require bandi... | ['Christopher John Quinn', 'Vaneet Aggarwal', 'Yanhui Zhu', 'Yididiya Y Nadew', 'Guanyu Nie'] | 2023-01-30 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.01217486e-01 2.94511288e-01 -5.29397190e-01 -3.06909770e-01
-1.35678220e+00 -1.15979898e+00 -2.90413082e-01 1.47738039e-01
-7.63245106e-01 1.58503330e+00 -4.65583801e-01 -6.25095963e-01
-7.36808777e-01 -8.39024603e-01 -1.43436420e+00 -1.08874357e+00
-2.31798872e-01 6.09854519e-01 -3.47764105e-01 -1.15989439... | [4.576333522796631, 3.3663978576660156] |
cebd6b77-4912-4ce8-90ca-e4823ee6c28c | exploring-stroke-level-modifications-for | 2212.01982 | null | https://arxiv.org/abs/2212.01982v1 | https://arxiv.org/pdf/2212.01982v1.pdf | Exploring Stroke-Level Modifications for Scene Text Editing | Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. However, due to the complicated background textures and various text styles, existing methods fall short in generating clear and legible edited text images. In this study, we attribute the poo... | ['Yongdong Zhang', 'Yuxin Wang', 'Jianjun Xu', 'Hongtao Xie', 'Qingfeng Tan', 'Yadong Qu'] | 2022-12-05 | null | null | null | null | ['scene-text-editing'] | ['computer-vision'] | [ 6.96251035e-01 -2.36829564e-01 1.72576830e-01 -3.53440613e-01
-3.17512095e-01 -5.13570309e-01 5.77457249e-01 -5.53461730e-01
-4.44682539e-01 7.60610402e-01 5.73308654e-02 -1.86753497e-01
4.02934045e-01 -6.92586303e-01 -8.56953084e-01 -6.32187307e-01
7.97330797e-01 2.07390711e-01 4.16843504e-01 -2.46619523... | [11.516582489013672, -0.20081087946891785] |
76a43e22-e85f-407a-a129-60da27b873b0 | cherrypicker-semantic-skeletonization-and | 2304.04708 | null | https://arxiv.org/abs/2304.04708v1 | https://arxiv.org/pdf/2304.04708v1.pdf | CherryPicker: Semantic Skeletonization and Topological Reconstruction of Cherry Trees | In plant phenotyping, accurate trait extraction from 3D point clouds of trees is still an open problem. For automatic modeling and trait extraction of tree organs such as blossoms and fruits, the semantically segmented point cloud of a tree and the tree skeleton are necessary. Therefore, we present CherryPicker, an aut... | ['Marc Stamminger', 'Oliver Scholz', 'Andreas Gilson', 'Lukas Meyer'] | 2023-04-10 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 4.61207122e-01 2.11549178e-02 1.22153483e-01 -2.32226342e-01
-4.17427540e-01 -1.04518509e+00 -1.21019976e-02 5.65816164e-01
3.40816617e-01 1.75309896e-01 -5.95849633e-01 -3.68474036e-01
-2.98569351e-01 -1.00306177e+00 -5.12586534e-01 -3.08614612e-01
-9.04284976e-03 9.25805509e-01 6.53102636e-01 9.33894962... | [8.973226547241211, -1.7773329019546509] |
0deba036-9561-4b77-abc8-3f212980aec7 | a-natural-upper-bound-to-the-accuracy-of | 1809.10389 | null | http://arxiv.org/abs/1809.10389v1 | http://arxiv.org/pdf/1809.10389v1.pdf | A natural upper bound to the accuracy of predicting protein stability changes upon mutations | Accurate prediction of protein stability changes upon single-site variations
(DDG) is important for protein design, as well as our understanding of the
mechanism of genetic diseases. The performance of high-throughput computational
methods to this end is evaluated mostly based on the Pearson correlation
coefficient bet... | [] | 2018-09-27 | null | null | null | null | ['protein-design'] | ['medical'] | [ 2.31610447e-01 -1.20627537e-01 -6.18013255e-02 -4.15078908e-01
-6.80774093e-01 -6.22817218e-01 3.04274052e-01 7.47405171e-01
-4.18207377e-01 1.02160537e+00 -7.58548155e-02 -4.48389679e-01
-2.56545126e-01 -4.70225573e-01 -7.84272730e-01 -9.76025581e-01
-1.65559016e-02 4.97801185e-01 5.25454044e-01 -8.57064575... | [4.88426399230957, 5.357780456542969] |
224f7291-3759-430b-99df-43bb24b1c473 | image-based-navigation-using-visual-features | 1812.03795 | null | https://arxiv.org/abs/1812.03795v2 | https://arxiv.org/pdf/1812.03795v2.pdf | Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map | Building on progress in feature representations for image retrieval, image-based localization has seen a surge of research interest. Image-based localization has the advantage of being inexpensive and efficient, often avoiding the use of 3D metric maps altogether. That said, the need to maintain a large number of refer... | ['Luc van Gool', 'Danda Pani Paudel', 'Thomas Probst', 'Ajad Chhatkuli', 'Janine Thoma'] | 2018-12-10 | mapping-localization-and-path-planning-for | http://openaccess.thecvf.com/content_CVPR_2019/html/Thoma_Mapping_Localization_and_Path_Planning_for_Image-Based_Navigation_Using_Visual_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Thoma_Mapping_Localization_and_Path_Planning_for_Image-Based_Navigation_Using_Visual_CVPR_2019_paper.pdf | cvpr-2019-6 | ['image-based-localization'] | ['computer-vision'] | [ 3.93153988e-02 -2.43155196e-01 -2.41934940e-01 -5.05481958e-01
-9.29704666e-01 -7.97340214e-01 5.95744669e-01 4.25626785e-01
-5.76494932e-01 5.76312840e-01 -4.50977720e-02 -2.42893130e-01
-4.60613400e-01 -7.79747188e-01 -8.25356662e-01 -5.36944211e-01
-1.63227804e-02 1.94821924e-01 1.22128539e-01 -1.92856178... | [7.662446975708008, -2.175915241241455] |
2dee3d9a-ae21-4686-92f1-6e8c5f1a8b7d | unsupervised-document-embedding-with-cnns | 1711.04168 | null | http://arxiv.org/abs/1711.04168v3 | http://arxiv.org/pdf/1711.04168v3.pdf | Unsupervised Document Embedding With CNNs | We propose a new model for unsupervised document embedding. Leading existing
approaches either require complex inference or use recurrent neural networks
(RNN) that are difficult to parallelize. We take a different route and develop
a convolutional neural network (CNN) embedding model. Our CNN architecture is
fully par... | ['Maksims Volkovs', 'Shunan Zhao', 'Chundi Liu'] | 2017-11-11 | null | null | null | null | ['document-embedding'] | ['methodology'] | [ 1.63690254e-01 3.27446401e-01 -5.81220567e-01 -3.52740943e-01
-6.13153100e-01 -4.09859449e-01 7.14722216e-01 2.20789611e-01
-6.93065226e-01 3.15962285e-01 6.20310664e-01 -5.73340654e-01
1.97239712e-01 -9.87319529e-01 -7.55664885e-01 -4.41330224e-01
5.65156937e-02 4.22538012e-01 1.62560910e-01 -7.12877661... | [10.749000549316406, 7.705173015594482] |
d379ec2e-f548-4141-a71b-44e0c849fcfe | a-data-driven-strategy-to-combine-word | 2105.12788 | null | https://arxiv.org/abs/2105.12788v1 | https://arxiv.org/pdf/2105.12788v1.pdf | A data-driven strategy to combine word embeddings in information retrieval | Word embeddings are vital descriptors of words in unigram representations of documents for many tasks in natural language processing and information retrieval. The representation of queries has been one of the most critical challenges in this area because it consists of a few terms and has little descriptive capacity. ... | ['Marcelo Mendoza', 'Alfredo Silva'] | 2021-05-26 | null | null | null | null | ['ad-hoc-information-retrieval'] | ['natural-language-processing'] | [-2.47341082e-01 -1.79013520e-01 -6.83696389e-01 -1.47475764e-01
-6.38370514e-01 -5.20158529e-01 1.22032034e+00 8.74399960e-01
-9.16702569e-01 1.47220954e-01 5.77590406e-01 -1.72915593e-01
-6.22927248e-01 -8.61945868e-01 -3.83341424e-02 -4.49184775e-01
-2.31493950e-01 7.14027405e-01 4.35032398e-01 -8.05175126... | [10.609394073486328, 8.505160331726074] |
75404176-0560-443d-a73b-2ea9b77e015b | automatic-personality-prediction-an-enhanced | 2007.04571 | null | https://arxiv.org/abs/2007.04571v3 | https://arxiv.org/pdf/2007.04571v3.pdf | Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling | Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Perso... | ['Taymaz Rahkar-Farshi', 'Elnaz Zafarani-Moattar', 'Zoleikha Jahanbakhsh-Nagadeh', 'Mehrdad Ranjbar-Khadivi', 'Narjes Nikzad-Khasmakhi', 'Ali-Reza Feizi-Derakhshi', 'Meysam Asgari-Chenaghlu', 'Mohammad-Ali Balafar', 'Mohammad-Reza Feizi-Derakhshi', 'Majid Ramezani'] | 2020-07-09 | null | null | null | null | ['personality-trait-recognition'] | ['computer-vision'] | [-8.19532350e-02 3.74186337e-02 1.57399207e-01 -2.58947909e-01
7.57917613e-02 2.74145812e-01 8.39960217e-01 8.95268172e-02
-2.11148351e-01 7.80037045e-01 6.76533997e-01 3.33895773e-01
-3.31693888e-01 -7.83114791e-01 -2.14806735e-03 -6.35266900e-01
4.14758474e-02 2.64949709e-01 -1.13009505e-01 -4.25636858... | [12.992971420288086, 5.909172058105469] |
8ab89246-1c91-4339-b7b1-18da02c868f4 | search-based-task-and-motion-planning-for | 2301.10384 | null | https://arxiv.org/abs/2301.10384v1 | https://arxiv.org/pdf/2301.10384v1.pdf | Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles | To achieve optimal robot behavior in dynamic scenarios we need to consider complex dynamics in a predictive manner. In the vehicle dynamics community, it is well know that to achieve time-optimal driving on low surface, the vehicle should utilize drifting. Hence many authors have devised rules to split circuits and emp... | ['Antonella Ferrara', 'Martin Horn', 'Hana Ćatić', 'Barys Shyrokau', 'Enrico Regolin', 'Zlatan Ajanović'] | 2023-01-25 | null | null | null | null | ['community-search', 'motion-planning'] | ['graphs', 'robots'] | [-9.18485224e-03 -6.01364952e-03 -5.57783306e-01 3.81107703e-02
-2.73850530e-01 -8.62157226e-01 5.42534888e-01 -2.80448496e-01
-2.00552255e-01 7.07782507e-01 -4.54633623e-01 -7.82372773e-01
-3.70634377e-01 -9.09609020e-01 -7.69897938e-01 -8.17367554e-01
6.03300817e-02 7.74054945e-01 5.99121928e-01 -8.64270866... | [5.155618190765381, 1.5857784748077393] |
3d17d01e-f516-41f5-99a5-1387b0705a2b | to-drop-or-not-to-drop-robustness-consistency | 1503.02031 | null | http://arxiv.org/abs/1503.02031v1 | http://arxiv.org/pdf/1503.02031v1.pdf | To Drop or Not to Drop: Robustness, Consistency and Differential Privacy Properties of Dropout | Training deep belief networks (DBNs) requires optimizing a non-convex
function with an extremely large number of parameters. Naturally, existing
gradient descent (GD) based methods are prone to arbitrarily poor local minima.
In this paper, we rigorously show that such local minima can be avoided (upto
an approximation ... | ['Oliver Williams', 'Vivek Kulkarni', 'Abhradeep Thakurta', 'Prateek Jain'] | 2015-03-06 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [-1.77604944e-01 4.63531494e-01 -2.70064592e-01 -3.91268015e-01
-1.05204666e+00 -3.98631752e-01 1.08756468e-01 3.00163746e-01
-6.85421288e-01 1.07930934e+00 -2.25967973e-01 -1.49597988e-01
-1.52626531e-02 -8.43885601e-01 -1.36541224e+00 -1.11196625e+00
-5.57730570e-02 3.78909707e-01 -1.81737259e-01 1.19564056... | [7.767922401428223, 3.9903976917266846] |
e1b3b444-8424-47a9-b45f-4a6e835914ac | deep-metric-learning-based-feature-embedding | null | null | https://doi.org/10.1109/TGRS.2019.2946318 | https://doi.org/10.1109/TGRS.2019.2946318 | Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification | Learning from a limited number of labeled samples (pixels) remains a key challenge in the hyperspectral image (HSI) classification. To address this issue, we propose a deep metric learning-based feature embedding model, which can meet the tasks both for same- and cross-scene HSI classifications. In the first task, when... | ['Daming Shi', 'Sen Jia', 'Bin Deng'] | 2019-10-30 | null | null | null | ieee-transactions-on-geoscience-and-remote-15 | ['metric-learning', 'scene-classification', 'few-shot-image-classification', 'metric-learning'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 5.38271546e-01 -2.73802549e-01 7.01561049e-02 -5.75476408e-01
-5.53465068e-01 -4.98251051e-01 4.27359074e-01 6.00230731e-02
-3.54180098e-01 5.97981989e-01 -2.30735600e-01 5.56075089e-02
-1.94030792e-01 -1.06319642e+00 -3.53025615e-01 -1.20303035e+00
2.34709397e-01 8.06275085e-02 2.25321889e-01 -7.33678341... | [9.961955070495605, -1.4404453039169312] |
0c7ed5fd-44af-40f7-a3a2-3aad7d64de07 | a-little-pretraining-goes-a-long-way-a-case | 2102.06551 | null | https://arxiv.org/abs/2102.06551v2 | https://arxiv.org/pdf/2102.06551v2.pdf | A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages | Neural dependency parsing has achieved remarkable performance for many domains and languages. The bottleneck of massive labeled data limits the effectiveness of these approaches for low resource languages. In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting. Al... | ['Pawan Goyal', 'Laxmidhar Behera', 'Ashim Gupta', 'Amrith Krishna', 'Jivnesh Sandhan'] | 2021-02-12 | null | https://aclanthology.org/2021.eacl-srw.16 | https://aclanthology.org/2021.eacl-srw.16.pdf | eacl-2021-2 | ['morphological-disambiguation'] | ['natural-language-processing'] | [-1.26227960e-01 3.90903987e-02 -1.16597436e-01 -5.25928795e-01
-1.08604193e+00 -7.41107643e-01 3.04523915e-01 2.43775308e-01
-9.44435954e-01 7.44322062e-01 2.04429924e-01 -7.51479447e-01
4.33321029e-01 -5.49999952e-01 -6.45784616e-01 -2.95207977e-01
7.75329322e-02 3.10098737e-01 2.29330093e-01 -7.33013824... | [10.467031478881836, 9.869367599487305] |
f1fb1dbe-8b45-42a0-a43f-6c0c07c45270 | schooling-to-exploit-foolish-contracts | 2304.10737 | null | https://arxiv.org/abs/2304.10737v1 | https://arxiv.org/pdf/2304.10737v1.pdf | Schooling to Exploit Foolish Contracts | We introduce SCooLS, our Smart Contract Learning (Semi-supervised) engine. SCooLS uses neural networks to analyze Ethereum contract bytecode and identifies specific vulnerable functions. SCooLS incorporates two key elements: semi-supervised learning and graph neural networks (GNNs). Semi-supervised learning produces mo... | ['Aquinas Hobor', 'Tamer Abdelaziz'] | 2023-04-21 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [-3.87791246e-02 4.92891550e-01 -7.07489848e-01 -2.58142233e-01
-6.52046263e-01 -1.01532650e+00 4.67164487e-01 5.05869985e-02
-6.92717955e-02 3.84078741e-01 -1.09752730e-01 -1.45627248e+00
2.08442792e-01 -1.13712883e+00 -5.96197844e-01 -2.62428463e-01
-4.98479992e-01 6.63000345e-01 4.38453436e-01 -1.73239738... | [6.871028423309326, 7.426784515380859] |
30cec8ff-c663-481b-b376-f66dfd35856f | variational-autoencoding-molecular-graphs | 2307.00623 | null | https://arxiv.org/abs/2307.00623v1 | https://arxiv.org/pdf/2307.00623v1.pdf | Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model | In data-driven drug discovery, designing molecular descriptors is a very important task. Deep generative models such as variational autoencoders (VAEs) offer a potential solution by designing descriptors as probabilistic latent vectors derived from molecular structures. These models can be trained on large datasets, wh... | ['Shigehiko Kanaya', 'Naoaki Ono', 'Daiki Koge'] | 2023-07-02 | null | null | null | null | ['drug-discovery', 'property-prediction', 'transfer-learning', 'molecular-property-prediction'] | ['medical', 'medical', 'miscellaneous', 'miscellaneous'] | [ 1.39519006e-01 -1.87535748e-01 -5.36832809e-01 -1.90040529e-01
-6.53798699e-01 -3.00929248e-01 6.29797995e-01 4.56414893e-02
-6.79032058e-02 9.94145572e-01 4.05929148e-01 -2.06402406e-01
-2.75018662e-01 -1.02294922e+00 -8.40115309e-01 -1.31018448e+00
6.75452352e-02 3.59357536e-01 8.00849274e-02 4.04111482... | [5.129680156707764, 5.830612659454346] |
a692ac37-e873-4547-a222-df507dfbff66 | multi-view-human-body-mesh-translator | 2210.01886 | null | https://arxiv.org/abs/2210.01886v1 | https://arxiv.org/pdf/2210.01886v1.pdf | Multi-view Human Body Mesh Translator | Existing methods for human mesh recovery mainly focus on single-view frameworks, but they often fail to produce accurate results due to the ill-posed setup. Considering the maturity of the multi-view motion capture system, in this paper, we propose to solve the prior ill-posed problem by leveraging multiple images from... | ['Si Liu', 'Luoqi Liu', 'Zitian Wang', 'Xuecheng Nie', 'Xiangjian Jiang'] | 2022-10-04 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 1.85315624e-01 -8.55860412e-02 5.02624810e-02 4.51853499e-02
-8.13530743e-01 -3.93937886e-01 1.54448912e-01 -3.82859230e-01
-6.80822209e-02 3.59891683e-01 3.76946360e-01 5.47063589e-01
-4.01816033e-02 -6.61935389e-01 -7.18361855e-01 -5.18493474e-01
3.43978465e-01 6.39419377e-01 6.43559322e-02 -2.79837608... | [7.067468643188477, -1.1307491064071655] |
9e75e0e8-72c7-4013-8389-b70012a3836e | mdpgt-momentum-based-decentralized-policy | 2112.02813 | null | https://arxiv.org/abs/2112.02813v1 | https://arxiv.org/pdf/2112.02813v1.pdf | MDPGT: Momentum-based Decentralized Policy Gradient Tracking | We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations. Specifically, we propose a momentum-based decentralized policy gradient tracking (MDPGT) where a new momentum-based variance r... | ['Soumik Sarkar', 'Chinmay Hegde', 'Young M. Lee', 'Aditya Balu', 'Kai Liang Tan', 'Sin Yong Tan', 'Xian Yeow Lee', 'Zhanhong Jiang'] | 2021-12-06 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-4.91341472e-01 3.41961533e-02 -3.37958664e-01 2.70521432e-01
-1.03023851e+00 -4.55903530e-01 3.98035586e-01 5.40833652e-01
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-5.43286502e-01 -6.28739119e-01 -9.14556086e-01 -9.38090980e-01
-6.99949861e-01 5.27843475e-01 1.07585669e-01 -3.29249293... | [4.126123905181885, 2.5302019119262695] |
389b4b1d-1b10-40c2-942b-3a5e461b902b | improving-scheduled-sampling-for-neural | 2305.15958 | null | https://arxiv.org/abs/2305.15958v1 | https://arxiv.org/pdf/2305.15958v1.pdf | Improving Scheduled Sampling for Neural Transducer-based ASR | The recurrent neural network-transducer (RNNT) is a promising approach for automatic speech recognition (ASR) with the introduction of a prediction network that autoregressively considers linguistic aspects. To train the autoregressive part, the ground-truth tokens are used as substitutions for the previous output toke... | ['Ryo Masumura', 'Tomohiro Tanaka', 'Kohei Matsuura', 'Hiroshi Sato', 'Takanori Ashihara', 'Takafumi Moriya'] | 2023-05-25 | null | null | null | null | ['automatic-speech-recognition'] | ['speech'] | [ 4.56083894e-01 3.68529767e-01 6.22501448e-02 -1.80353269e-01
-7.51917243e-01 -1.36931702e-01 6.55388653e-01 -2.65267700e-01
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3.19349915e-01 4.16902214e-01 1.32072836e-01 -2.89833933... | [14.465428352355957, 6.774567127227783] |
dee54fff-dc60-4e82-9fc6-4d166a13583f | designing-rotationally-invariant-neural | 2108.13993 | null | https://arxiv.org/abs/2108.13993v2 | https://arxiv.org/pdf/2108.13993v2.pdf | Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods | Partial differential equation (PDE) models and their associated variational energy formulations are often rotationally invariant by design. This ensures that a rotation of the input results in a corresponding rotation of the output, which is desirable in applications such as image analysis. Convolutional neural network... | ['Matthias Augustin', 'Pascal Peter', 'Joachim Weickert', 'Karl Schrader', 'Tobias Alt'] | 2021-08-31 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 8.12792182e-02 -1.48995236e-01 -2.34744355e-01 -2.13575602e-01
3.43422025e-01 -7.53626049e-01 7.51384020e-01 -3.95513654e-01
-4.96513993e-01 4.60920095e-01 1.23625405e-01 -1.82391763e-01
-4.27743971e-01 -9.28082049e-01 -6.73586130e-01 -9.15800929e-01
1.48895010e-01 -2.53893286e-01 3.21254045e-01 -3.55243087... | [9.063260078430176, 2.3216493129730225] |
ff99f73f-ccc4-4bd5-8678-19ded7a07f91 | creativity-of-ai-automatic-symbolic-option | 2112.09836 | null | https://arxiv.org/abs/2112.09836v2 | https://arxiv.org/pdf/2112.09836v2.pdf | Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning | Despite of achieving great success in real-world applications, Deep Reinforcement Learning (DRL) is still suffering from three critical issues, i.e., data efficiency, lack of the interpretability and transferability. Recent research shows that embedding symbolic knowledge into DRL is promising in addressing those chall... | ['Shuting Deng', 'Hankz Hankui Zhuo', 'Chao Yu', 'Chen Chen', 'Kebing Jin', 'Zhihao Ma', 'Mu Jin'] | 2021-12-18 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-6.54291585e-02 3.53548974e-01 -5.33676624e-01 -3.28296751e-01
-2.15408102e-01 -4.79597241e-01 5.50612807e-01 -1.31491557e-01
-3.06021243e-01 1.10614657e+00 4.01977152e-01 -5.64250350e-01
-6.54447675e-01 -8.14800978e-01 -6.66523755e-01 -3.80748361e-01
-2.43973851e-01 5.08828163e-01 1.40254453e-01 -2.76389688... | [4.2476959228515625, 1.6844491958618164] |
ad1f1356-7f25-4668-ae09-808c471deba0 | morphological-change-forecasting-for-prostate | 2101.06425 | null | https://arxiv.org/abs/2101.06425v1 | https://arxiv.org/pdf/2101.06425v1.pdf | Morphological Change Forecasting for Prostate Glands using Feature-based Registration and Kernel Density Extrapolation | Organ morphology is a key indicator for prostate disease diagnosis and prognosis. For instance, In longitudinal study of prostate cancer patients under active surveillance, the volume, boundary smoothness and their changes are closely monitored on time-series MR image data. In this paper, we describe a new framework fo... | ['Yipeng Hu', 'Dean Barratt', 'Matt Clarkson', 'Caroline Moore', 'Vasilis Stavrinides', 'Nooshin Ghavami', 'Francesco Giganti', 'Yunguan Fu', 'Tom Vercauteren', 'Qianye Yang'] | 2021-01-16 | null | null | null | null | ['holdout-set'] | ['computer-vision'] | [ 3.83249074e-01 4.19321865e-01 -8.78812000e-02 -7.51452565e-01
-6.48561239e-01 -5.14657080e-01 8.29302609e-01 6.01505041e-01
-7.32592165e-01 8.15860808e-01 1.84115842e-01 1.65640548e-01
-7.39790797e-01 -7.98179865e-01 -4.09800142e-01 -8.74783516e-01
-9.99117494e-01 9.03421104e-01 1.75851434e-01 2.61628747... | [14.41563892364502, -2.530344009399414] |
4f5205e8-6812-45a7-a6c7-ec76fa6a0a7e | correspondence-learning-via-linearly | 2010.13136 | null | https://arxiv.org/abs/2010.13136v1 | https://arxiv.org/pdf/2010.13136v1.pdf | Correspondence Learning via Linearly-invariant Embedding | In this paper, we propose a fully differentiable pipeline for estimating accurate dense correspondences between 3D point clouds. The proposed pipeline is an extension and a generalization of the functional maps framework. However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous wo... | ['Maks Ovsjanikov', 'Simone Melzi', 'Marie-Julie Rakotosaona', 'Riccardo Marin'] | 2020-10-25 | null | http://proceedings.neurips.cc/paper/2020/hash/11953163dd7fb12669b41a48f78a29b6-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/11953163dd7fb12669b41a48f78a29b6-Paper.pdf | neurips-2020-12 | ['3d-dense-shape-correspondence'] | ['computer-vision'] | [-2.86419243e-02 2.27791831e-01 1.19630180e-01 -4.36923862e-01
-8.02316308e-01 -6.93443954e-01 8.89388740e-01 8.29206686e-03
-3.76513392e-01 1.95734933e-01 1.28492326e-01 -2.79671163e-03
-2.02345520e-01 -6.16225481e-01 -1.04583371e+00 -5.93563259e-01
-6.78089112e-02 7.74284899e-01 1.34759665e-01 -1.51485860... | [8.214181900024414, -3.181184768676758] |
666d2faf-24d0-4791-b325-a8268c35deda | kprnet-improving-projection-based-lidar | 2007.12668 | null | https://arxiv.org/abs/2007.12668v2 | https://arxiv.org/pdf/2007.12668v2.pdf | KPRNet: Improving projection-based LiDAR semantic segmentation | Semantic segmentation is an important component in the perception systems of autonomous vehicles. In this work, we adopt recent advances in both image and point cloud segmentation to achieve a better accuracy in the task of segmenting LiDAR scans. KPRNet improves the convolutional neural network architecture of 2D proj... | ['Olaf Booij', 'Deyvid Kochanov', 'Fatemeh Karimi Nejadasl'] | 2020-07-24 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 3.48540731e-02 1.33775681e-01 -3.23275238e-01 -8.12684774e-01
-4.06600863e-01 -4.31072235e-01 6.10437572e-01 -2.26464495e-01
-6.74643993e-01 8.08568522e-02 -5.62485158e-01 -5.51137269e-01
2.54440904e-01 -8.97241116e-01 -1.08623910e+00 -2.46214405e-01
2.52891779e-01 1.13685346e+00 8.60233009e-01 -2.61044592... | [8.07308578491211, -2.830575704574585] |
94d0ffed-0a21-403b-a3b7-c4dd0cb1b88a | a-new-pattern-recognition-method-for | null | null | http://dx.doi.org/10.4236/jbise.2014.710081 | https://pdfs.semanticscholar.org/7fcb/6e4f06394bfc671165946dadb5b9b80add38.pdf | A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal | In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for t... | ['Naser Safdarian', 'Gholamreza Attarodi', 'Nader Jafarnia Dabanloo'] | 2014-08-01 | null | null | null | jbise-vol7-no10-august-2014-2014-8 | ['myocardial-infarction-detection'] | ['medical'] | [ 6.55721277e-02 -2.55280674e-01 2.44057804e-01 -3.48545939e-01
-2.03852698e-01 -3.41281891e-01 -1.59226239e-01 2.78132021e-01
-6.59910440e-01 8.60447049e-01 -1.09506086e-01 -3.76693040e-01
-5.47198892e-01 -1.01170611e+00 -2.21869752e-01 -5.95080972e-01
-5.21038532e-01 4.01334435e-01 5.41022420e-01 -5.17958729... | [14.186064720153809, 3.221101760864258] |
5d1c9287-cf14-4311-b9ce-e8ba585b3bbb | microstructural-segmentation-using-a-union-of | null | null | https://www.nature.com/articles/s41598-023-32318-9#Abs1 | https://www.nature.com/articles/s41598-023-32318-9 | Microstructural segmentation using a union of attention guided U-Net models with different color transformed images | Metallographic images or often called the microstructures contain important information about metals, such as strength, toughness, ductility, corrosion resistance, which are used to choose the proper materials for various engineering applications. Thus by understanding the microstructures, one can determine the behavio... | ['Ram Sarkar', 'Dmitry Kaplun', 'Aleksandr Sinitca', 'Shibaprasad Sen', 'Rishav Pramanik', 'Momojit Biswas'] | 2023-04-07 | null | null | null | scientific-reports-2023-4 | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 2.03390837e-01 -2.65111536e-01 5.30458391e-02 -2.95053601e-01
-4.33540314e-01 -1.51832789e-01 1.77967384e-01 1.00783324e-02
-1.22165881e-01 5.57207882e-01 -5.03362477e-01 -2.92926013e-01
-2.17246369e-01 -1.18602490e+00 -8.71001124e-01 -1.02714717e+00
2.89856344e-01 4.64798123e-01 3.15296888e-01 -1.37560278... | [7.502292156219482, 1.8192315101623535] |
b7635af1-d4f7-4160-aac0-ce643d836bcb | mimetics-towards-understanding-human-actions | 1912.07249 | null | https://arxiv.org/abs/1912.07249v3 | https://arxiv.org/pdf/1912.07249v3.pdf | Mimetics: Towards Understanding Human Actions Out of Context | Recent methods for video action recognition have reached outstanding performances on existing benchmarks. However, they tend to leverage context such as scenes or objects instead of focusing on understanding the human action itself. For instance, a tennis field leads to the prediction playing tennis irrespectively of t... | ['Grégory Rogez', 'Philippe Weinzaepfel'] | 2019-12-16 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 4.38543737e-01 -7.89155886e-02 -2.08840936e-01 -3.56173426e-01
-2.43627846e-01 -4.10530329e-01 9.35272932e-01 -2.97505230e-01
-5.65896332e-01 4.44338918e-01 6.93892002e-01 8.17118436e-02
1.83297560e-01 -4.13481086e-01 -1.05066180e+00 -5.56052446e-01
-2.08017126e-01 3.74696285e-01 2.50054419e-01 -4.43145186... | [8.163435935974121, 0.4954693019390106] |
d113bae8-59a9-4834-880a-c7fd0e1f42c6 | compressing-deep-neural-networks-via-layer | 2007.14917 | null | https://arxiv.org/abs/2007.14917v1 | https://arxiv.org/pdf/2007.14917v1.pdf | Compressing Deep Neural Networks via Layer Fusion | This paper proposes \textit{layer fusion} - a model compression technique that discovers which weights to combine and then fuses weights of similar fully-connected, convolutional and attention layers. Layer fusion can significantly reduce the number of layers of the original network with little additional computation o... | ["James O' Neill", 'Aram Galstyan', 'Greg Ver Steeg'] | 2020-07-29 | null | null | null | null | ['exponential-degradation'] | ['time-series'] | [ 3.20275664e-01 4.27985489e-01 -1.04076982e-01 -4.75270331e-01
-5.43353260e-01 -2.36326724e-01 4.50798273e-01 1.27175033e-01
-9.92651463e-01 4.98409152e-01 6.97327703e-02 -6.26107931e-01
-1.52512670e-01 -4.92804706e-01 -9.43269432e-01 -4.02168065e-01
-1.10094972e-01 5.33449531e-01 2.38865048e-01 3.02584339... | [8.597057342529297, 3.2147228717803955] |
d30b7c6e-0647-4a2a-89a2-0ab6a8873afd | on-games-and-simulators-as-a-platform-for | 2110.11305 | null | https://arxiv.org/abs/2110.11305v1 | https://arxiv.org/pdf/2110.11305v1.pdf | On games and simulators as a platform for development of artificial intelligence for command and control | Games and simulators can be a valuable platform to execute complex multi-agent, multiplayer, imperfect information scenarios with significant parallels to military applications: multiple participants manage resources and make decisions that command assets to secure specific areas of a map or neutralize opposing forces.... | ['Alexander Kott', 'Priya Narayanan', 'Theron Trout', 'Mark Dennison', 'Anne Logie', 'Manuel Vindiola', 'John Richardson', 'Mark Mittrick', 'Song Jun Park', 'Derrik E. Asher', 'Nicholas Waytowich', 'Vinicius G. Goecks'] | 2021-10-21 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.66152948e-01 6.91155493e-02 2.97740191e-01 1.31650254e-01
4.26652789e-01 -8.37274194e-01 7.50069141e-01 -7.38030300e-02
-7.88581014e-01 9.50081050e-01 -5.38292043e-02 -5.60396254e-01
-4.00305480e-01 -1.04861987e+00 6.91108778e-02 -1.73105702e-01
-8.29343796e-01 1.07583547e+00 3.16089511e-01 -1.50404215... | [3.5176894664764404, 1.5237020254135132] |
4f75808b-90cc-4c76-92a4-8b9b1ffc261e | rethinking-range-view-representation-for | 2303.05367 | null | https://arxiv.org/abs/2303.05367v2 | https://arxiv.org/pdf/2303.05367v2.pdf | Rethinking Range View Representation for LiDAR Segmentation | LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point- or voxel-based methods as they often yield better performance than the traditional range view representation. In this work, we unveil several key factors in building powerful range view models. We observe that the "many-to-one" ... | ['Ziwei Liu', 'Yu Qiao', 'Yuenan Hou', 'Yikang Li', 'Xinge Zhu', 'Yuexin Ma', 'Runnan Chen', 'Youquan Liu', 'Lingdong Kong'] | 2023-03-09 | null | null | null | null | ['panoptic-segmentation', 'lidar-semantic-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.94177622e-01 -1.96969613e-01 -1.31872728e-01 -9.79639709e-01
-7.71997273e-01 -8.95085812e-01 8.44220757e-01 -1.94300830e-01
-3.14300239e-01 3.37853372e-01 -2.33450159e-01 -6.21231735e-01
-3.07196259e-01 -9.82658565e-01 -8.69990468e-01 -4.78729874e-01
3.92076582e-01 1.03848481e+00 4.07146037e-01 -5.29755235... | [8.184155464172363, -2.814225196838379] |
f844b8fe-eac5-4f01-b145-ea5b082d95bb | what-if-we-only-use-real-datasets-for-scene | 2103.04400 | null | https://arxiv.org/abs/2103.04400v2 | https://arxiv.org/pdf/2103.04400v2.pdf | What If We Only Use Real Datasets for Scene Text Recognition? Toward Scene Text Recognition With Fewer Labels | Scene text recognition (STR) task has a common practice: All state-of-the-art STR models are trained on large synthetic data. In contrast to this practice, training STR models only on fewer real labels (STR with fewer labels) is important when we have to train STR models without synthetic data: for handwritten or artis... | ['Kiyoharu Aizawa', 'Yusuke Matsui', 'Jeonghun Baek'] | 2021-03-07 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Baek_What_if_We_Only_Use_Real_Datasets_for_Scene_Text_CVPR_2021_paper.pdf | cvpr-2021-1 | ['scene-text-recognition'] | ['computer-vision'] | [ 3.19696039e-01 2.92092025e-01 -1.51928738e-01 -3.15247029e-01
-7.22201765e-01 -7.03183115e-01 5.62284231e-01 -5.88553250e-02
-4.05785650e-01 8.94841433e-01 -7.71969333e-02 -4.86265779e-01
4.23745990e-01 -7.05002666e-01 -1.02105069e+00 -2.91701853e-01
5.54082334e-01 7.17746019e-01 2.30340824e-01 -1.74685568... | [11.646038055419922, 1.9566041231155396] |
c5db257a-37be-478b-babe-afca75494523 | vandermonde-trajectory-bounds-for-linear | 2302.10995 | null | https://arxiv.org/abs/2302.10995v1 | https://arxiv.org/pdf/2302.10995v1.pdf | Vandermonde Trajectory Bounds for Linear Companion Systems | Fast and accurate safety assessment and collision checking are essential for motion planning and control of highly dynamic autonomous robotic systems. Informative, intuitive, and explicit motion trajectory bounds enable explainable and time-critical safety verification of autonomous robot motion. In this paper, we cons... | ['Aykut İşleyen', 'Ömür Arslan'] | 2023-02-21 | null | null | null | null | ['motion-prediction', 'motion-planning'] | ['computer-vision', 'robots'] | [-3.88464898e-01 6.12710059e-01 -1.29576385e-01 3.56848031e-01
-4.34961051e-01 -7.57565618e-01 5.39255619e-01 1.30273044e-01
-4.54844058e-01 9.08868790e-01 -3.30477387e-01 -6.45416915e-01
-5.22031546e-01 -2.63658553e-01 -9.78431761e-01 -9.49247003e-01
-6.23961687e-01 3.29683870e-01 1.24437429e-01 -6.75287485... | [5.140517711639404, 2.1776010990142822] |
4c4fdb7e-1f7b-44a2-ac56-c0c4f75930e9 | on-the-global-convergence-of-risk-averse | 2301.10932 | null | https://arxiv.org/abs/2301.10932v2 | https://arxiv.org/pdf/2301.10932v2.pdf | On the Global Convergence of Risk-Averse Policy Gradient Methods with Expected Conditional Risk Measures | Risk-sensitive reinforcement learning (RL) has become a popular tool to control the risk of uncertain outcomes and ensure reliable performance in various sequential decision-making problems. While policy gradient methods have been developed for risk-sensitive RL, it remains unclear if these methods enjoy the same globa... | ['Lei Ying', 'Xian Yu'] | 2023-01-26 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [ 1.25352162e-04 2.75936127e-01 -4.67362911e-01 -2.59092033e-01
-1.20716882e+00 -4.52192843e-01 4.38188016e-01 2.67170668e-01
-8.91505957e-01 1.30232751e+00 2.00131044e-01 -5.92792869e-01
-5.78993797e-01 -6.37493968e-01 -4.34909672e-01 -7.69442379e-01
-5.87728500e-01 1.83240488e-01 -7.95981511e-02 -1.03152588... | [4.244218826293945, 2.5323643684387207] |
56be8afb-5cdc-42f7-85d3-b0c4565dce01 | open-problems-in-applied-deep-learning | 2301.11316 | null | https://arxiv.org/abs/2301.11316v1 | https://arxiv.org/pdf/2301.11316v1.pdf | Open Problems in Applied Deep Learning | This work formulates the machine learning mechanism as a bi-level optimization problem. The inner level optimization loop entails minimizing a properly chosen loss function evaluated on the training data. This is nothing but the well-studied training process in pursuit of optimal model parameters. The outer level optim... | ['Maziar Raissi'] | 2023-01-26 | null | null | null | null | ['automl'] | ['methodology'] | [ 4.01752681e-01 2.05334529e-01 -2.43006662e-01 -1.60902098e-01
-2.65789330e-01 -5.84809482e-01 5.54385900e-01 3.51797491e-01
-5.33926249e-01 7.09767580e-01 -4.80104357e-01 -4.21531230e-01
-6.01047516e-01 -7.59469211e-01 -7.41243660e-01 -8.59773397e-01
1.56306416e-01 7.00476527e-01 -2.05717623e-01 -9.70077738... | [6.23753547668457, 3.8093063831329346] |
c6612749-4e19-4e27-a981-55f7809cf4b9 | scene-change-detection-using-multiscale | 2212.10417 | null | https://arxiv.org/abs/2212.10417v1 | https://arxiv.org/pdf/2212.10417v1.pdf | Scene Change Detection Using Multiscale Cascade Residual Convolutional Neural Networks | Scene change detection is an image processing problem related to partitioning pixels of a digital image into foreground and background regions. Mostly, visual knowledge-based computer intelligent systems, like traffic monitoring, video surveillance, and anomaly detection, need to use change detection techniques. Amongs... | ['João P. Papa', 'Danilo Colombo', 'Rafael G. Pires', 'Daniel F. S. Santos'] | 2022-12-20 | null | null | null | null | ['scene-change-detection', 'change-detection'] | ['computer-vision', 'computer-vision'] | [ 0.5428773 -0.4535503 0.15348406 -0.2696291 -0.22159994 -0.31756383
0.41812146 0.367448 -0.6485766 0.62974524 -0.44530663 -0.4497982
-0.08358749 -0.94411564 -0.69691324 -0.69663334 0.01026187 -0.20699242
0.9229331 -0.09023795 0.4998962 0.5511075 -1.8537141 0.01286676
0.82279176 1.3001313 0.0... | [8.75394344329834, -0.6865972280502319] |
4a5d1c3f-7055-4ea0-9b1c-deaa8eeabcf7 | atlantanet-inferring-the-3d-indoor-layout | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/604_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530426.pdf | AtlantaNet: Inferring the 3D Indoor Layout from a Single 360(∘) Image beyond the Manhattan World Assumption | We introduce a novel end-to-end approach to predict a 3D room layout from a single panoramic image. Compared to recent state-of-the-art works, our method is not limited to Manhattan World environments, and can reconstruct rooms bounded by vertical walls that do not form right angles or are curved -- i.e., Atlanta World... | ['Marco Agus', 'Giovanni Pintore', 'Enrico Gobbetti'] | null | null | null | null | eccv-2020-8 | ['3d-room-layouts-from-a-single-rgb-panorama'] | ['computer-vision'] | [ 4.46456701e-01 2.22674951e-01 3.71894300e-01 -5.78448772e-01
-4.56734687e-01 -3.66421163e-01 4.20607358e-01 -2.09985718e-01
-1.44647202e-02 1.35636613e-01 5.11385441e-01 -5.01971304e-01
-1.53620154e-01 -1.07222342e+00 -1.03237021e+00 -3.33019555e-01
1.00190975e-01 4.89694566e-01 6.21222937e-03 -2.13979647... | [8.719344139099121, -2.866748809814453] |
7852e464-8891-4e16-8227-9dd42e4a38ac | amrita-lt-edi-eacl2021-hope-speech-detection | null | null | https://aclanthology.org/2021.ltedi-1.22 | https://aclanthology.org/2021.ltedi-1.22.pdf | Amrita@LT-EDI-EACL2021: Hope Speech Detection on Multilingual Text | Analysis and deciphering code-mixed data is imperative in academia and industry, in a multilingual country like India, in order to solve problems apropos Natural Language Processing. This paper proposes a bidirectional long short-term memory (BiLSTM) with the attention-based approach, in solving the hope speech detecti... | ['Kothamasu Sai rahul', 'Ravi teja Tasubilli', 'Thara S'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-1.56934097e-01 -3.76702882e-02 2.79130518e-01 -7.81238005e-02
-1.03071308e+00 -2.96178192e-01 4.37822312e-01 1.68187737e-01
-7.48201907e-01 6.85962617e-01 4.10311967e-01 -5.72814107e-01
1.24688484e-01 -9.09815878e-02 -5.59762359e-01 -3.68259043e-01
-1.49780124e-01 5.11002019e-02 -5.90799041e-02 -3.40912670... | [14.172866821289062, 6.728785037994385] |
b252ae6d-758d-402a-921c-cc3c934795c5 | diverse-audio-captioning-via-adversarial | 2110.06691 | null | https://arxiv.org/abs/2110.06691v2 | https://arxiv.org/pdf/2110.06691v2.pdf | Diverse Audio Captioning via Adversarial Training | Audio captioning aims at generating natural language descriptions for audio clips automatically. Existing audio captioning models have shown promising improvement in recent years. However, these models are mostly trained via maximum likelihood estimation (MLE),which tends to make captions generic, simple and determinis... | ['Wenwu Wang', 'Mark D. Plumbley', 'Jianyuan Sun', 'Xubo Liu', 'Xinhao Mei'] | 2021-10-13 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.12980795e-01 4.88545656e-01 1.04319617e-01 -3.30208331e-01
-1.45649314e+00 -6.10788763e-01 5.23082793e-01 -2.71651030e-01
2.17709064e-01 1.19472432e+00 5.53559065e-01 2.46498257e-01
5.20249009e-01 -6.52188480e-01 -9.48343098e-01 -5.42086899e-01
5.44608496e-02 5.77069938e-01 -2.25530133e-01 -9.39466283... | [15.271735191345215, 4.922688961029053] |
9eaf10ac-5630-47c2-91c8-a01261371c87 | deep-lifelong-cross-modal-hashing | 2304.13357 | null | https://arxiv.org/abs/2304.13357v1 | https://arxiv.org/pdf/2304.13357v1.pdf | Deep Lifelong Cross-modal Hashing | Hashing methods have made significant progress in cross-modal retrieval tasks with fast query speed and low storage cost. Among them, deep learning-based hashing achieves better performance on large-scale data due to its excellent extraction and representation ability for nonlinear heterogeneous features. However, ther... | ['Jiancheng Lv', 'Weisheng Li', 'Bochuan Zheng', 'Hanqi Li', 'Liming Xu'] | 2023-04-26 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [-4.23990726e-01 -6.40046358e-01 -4.18943375e-01 -2.40409374e-01
-1.31076491e+00 -4.92314160e-01 2.55606174e-01 5.39807916e-01
-6.58234358e-01 6.52608097e-01 1.65182009e-01 2.86550611e-01
-3.26771349e-01 -9.34271872e-01 -6.69760823e-01 -9.50062692e-01
-2.39825130e-01 7.24052429e-01 5.93847990e-01 -1.72886893... | [11.299572944641113, 0.9745357036590576] |
b7519900-b823-4dc8-9f1b-271d3917a394 | knowledge-extraction-from-texts-based-on | null | null | https://aclanthology.org/2022.naacl-industry.33 | https://aclanthology.org/2022.naacl-industry.33.pdf | Knowledge Extraction From Texts Based on Wikidata | This paper presents an effort within our company of developing knowledge extraction pipeline for English, which can be further used for constructing an entreprise-specific knowledge base. We present a system consisting of entity detection and linking, coreference resolution, and relation extraction based on the Wikidat... | ['Frédéric Herledan', 'Johannes Heinecke', 'Anastasia Shimorina'] | null | null | null | null | naacl-acl-2022-7 | ['coreference-resolution'] | ['natural-language-processing'] | [-9.01236087e-02 9.78891850e-01 -4.67582643e-01 -3.14482033e-01
-5.80431104e-01 -6.48800194e-01 7.71589160e-01 8.08146119e-01
-7.07485437e-01 1.18573105e+00 5.58863997e-01 -2.34542847e-01
-5.40228248e-01 -9.44471836e-01 -4.16186601e-01 1.27868712e-01
-2.16166690e-01 1.14539909e+00 5.94109654e-01 -7.03235865... | [9.345810890197754, 8.725204467773438] |
23b10eeb-cfe7-4efc-92b6-3574522f0cfa | graphsha-synthesizing-harder-samples-for | 2306.09612 | null | https://arxiv.org/abs/2306.09612v1 | https://arxiv.org/pdf/2306.09612v1.pdf | GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification | Class imbalance is the phenomenon that some classes have much fewer instances than others, which is ubiquitous in real-world graph-structured scenarios. Recent studies find that off-the-shelf Graph Neural Networks (GNNs) would under-represent minor class samples. We investigate this phenomenon and discover that the sub... | ['Jian-Huang Lai', 'Hui Xiong', 'Chang-Dong Wang', 'Wen-Zhi Li'] | 2023-06-16 | null | null | null | null | ['node-classification', 'blocking'] | ['graphs', 'natural-language-processing'] | [ 6.77925125e-02 5.61903596e-01 -6.84663117e-01 -3.27738047e-01
6.28616586e-02 -5.63089132e-01 3.00512075e-01 1.76538289e-01
5.61941974e-02 7.93303907e-01 1.40917644e-01 -5.17458260e-01
-1.16038002e-01 -1.26168251e+00 -7.37973154e-01 -8.08822453e-01
-4.11180332e-02 6.06001258e-01 1.33931026e-01 -2.25528955... | [7.315227508544922, 5.999572277069092] |
15993325-1032-4f7f-942c-5df6f3e50893 | ai-bind-improving-binding-predictions-for | 2112.13168 | null | https://arxiv.org/abs/2112.13168v5 | https://arxiv.org/pdf/2112.13168v5.pdf | AI-Bind: Improving Binding Predictions for Novel Protein Targets and Ligands | Identifying novel drug-target interactions (DTI) is a critical and rate limiting step in drug discovery. While deep learning models have been proposed to accelerate the identification process, we show that state-of-the-art models fail to generalize to novel (i.e., never-before-seen) structures. We first unveil the mech... | ['Michael Sebek', 'Omair Shafi Ahmed', 'Zohair Shafi', 'Robin Walters', 'Giulia Menichetti', 'Albert-László Barabási', 'Tina Eliassi-Rad', 'Rose Yu', 'Deisy Gysi', 'Ayan Chatterjee'] | 2021-12-25 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 5.46483636e-01 -4.21934538e-02 -6.42839909e-01 -2.83915550e-01
-5.73412418e-01 -8.63936841e-01 2.60918498e-01 4.19897646e-01
-2.40873516e-01 1.32498443e+00 -1.82481632e-02 -8.70142341e-01
-3.71770680e-01 -3.62436175e-01 -8.49062979e-01 -7.80266523e-01
-4.74271566e-01 9.60138440e-01 3.59751917e-02 -4.09491025... | [4.949052810668945, 5.664579391479492] |
7d11fbb5-8350-4fe1-8b3c-701c392d2a3a | a-two-stage-method-for-text-line-detection-in | 1802.03345 | null | https://arxiv.org/abs/1802.03345v2 | https://arxiv.org/pdf/1802.03345v2.pdf | A Two-Stage Method for Text Line Detection in Historical Documents | This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator or other. The separator class marks beginning and en... | ['Tobias Strauß', 'Gundram Leifert', 'Tobias Grüning', 'Johannes Michael', 'Roger Labahn'] | 2018-02-09 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.13599074e-01 1.02175340e-01 -1.04141608e-01 -2.72951901e-01
-7.89000154e-01 -6.66021645e-01 7.69009888e-01 4.98339742e-01
-3.68073195e-01 4.40138727e-01 4.14175950e-02 -5.02155066e-01
3.47124845e-01 -6.55551374e-01 -7.59954154e-01 -4.44669545e-01
2.79521465e-01 7.05433786e-01 4.72229570e-01 1.54051691... | [11.825384140014648, 2.5458343029022217] |
f25b57fc-cbb7-4f0e-9666-14ba4d8ac517 | clearing-the-skies-a-deep-network | 1609.02087 | null | http://arxiv.org/abs/1609.02087v2 | http://arxiv.org/pdf/1609.02087v2.pdf | Clearing the Skies: A deep network architecture for single-image rain removal | We introduce a deep network architecture called DerainNet for removing rain
streaks from an image. Based on the deep convolutional neural network (CNN), we
directly learn the mapping relationship between rainy and clean image detail
layers from data. Because we do not possess the ground truth corresponding to
real-worl... | ['Jia-Bin Huang', 'Xueyang Fu', 'Xinghao Ding', 'Yinghao Liao', 'John Paisley'] | 2016-09-07 | null | null | null | null | ['single-image-deraining'] | ['computer-vision'] | [ 1.79803863e-01 1.44984499e-01 6.63147628e-01 -6.43019259e-01
-4.11230952e-01 -3.65241259e-01 8.14413652e-02 -5.72738111e-01
-4.14465368e-01 8.95062029e-01 -5.84599189e-02 -3.56250137e-01
4.86814886e-01 -1.14732897e+00 -1.03768337e+00 -8.21602046e-01
2.88967658e-02 -1.27564758e-01 8.73069316e-02 -3.98848861... | [10.918107986450195, -3.2303073406219482] |
ef65e7c5-41db-4e0f-b1ed-be3e8dd3a36c | increasing-performance-and-sample-efficiency | 2306.16431 | null | https://arxiv.org/abs/2306.16431v1 | https://arxiv.org/pdf/2306.16431v1.pdf | Increasing Performance And Sample Efficiency With Model-agnostic Interactive Feature Attributions | Model-agnostic feature attributions can provide local insights in complex ML models. If the explanation is correct, a domain expert can validate and trust the model's decision. However, if it contradicts the expert's knowledge, related work only corrects irrelevant features to improve the model. To allow for unlimited ... | ['Johan Suykens', 'Maarten De Vos', 'Joran Michiels'] | 2023-06-28 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 2.48058155e-01 9.00096059e-01 -5.75651646e-01 -7.34405160e-01
-5.53028166e-01 -5.47342420e-01 3.94742310e-01 2.90016413e-01
-1.22557558e-01 9.71635044e-01 -3.38827483e-02 -4.75073934e-01
-2.25996897e-01 -4.74246651e-01 -6.81115746e-01 -5.09657264e-01
1.05295755e-01 7.80930281e-01 2.72340745e-01 8.86378959... | [8.83051586151123, 5.723832607269287] |
cf6fbabf-7d37-4a80-a864-57950c9a7701 | more-behind-your-electricity-bill-a-dual-dnn | 2106.00297 | null | https://arxiv.org/abs/2106.00297v1 | https://arxiv.org/pdf/2106.00297v1.pdf | More Behind Your Electricity Bill: a Dual-DNN Approach to Non-Intrusive Load Monitoring | Non-intrusive load monitoring (NILM) is a well-known single-channel blind source separation problem that aims to decompose the household energy consumption into itemised energy usage of individual appliances. In this way, considerable energy savings could be achieved by enhancing household's awareness of energy usage. ... | ['Hong Xu', 'Yi Wang', 'Qianyi Huang', 'Guoming Tang', 'Yu Zhang'] | 2021-06-01 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [ 1.46488979e-01 -1.83452234e-01 -4.17681426e-01 -4.47329134e-01
-4.04692799e-01 -3.96891594e-01 3.98962110e-01 -3.85680139e-01
-6.08261824e-02 5.18827379e-01 2.61899054e-01 -2.99051106e-01
-8.93670842e-02 -7.78864920e-01 -4.77749825e-01 -1.28589213e+00
8.72308686e-02 9.99508500e-02 -5.88107288e-01 2.78777957... | [16.066070556640625, 7.580445766448975] |
366b1dd5-0966-422b-9f5f-78c27e35344d | on-the-importance-of-signer-overlap-for-sign | 2303.10782 | null | https://arxiv.org/abs/2303.10782v1 | https://arxiv.org/pdf/2303.10782v1.pdf | On the Importance of Signer Overlap for Sign Language Detection | Sign language detection, identifying if someone is signing or not, is becoming crucially important for its applications in remote conferencing software and for selecting useful sign data for training sign language recognition or translation tasks. We argue that the current benchmark data sets for sign language detectio... | ['Oscar Koller', 'Alessandro Manzotti', 'Cyrine Chaabani', 'Stephan Huber', 'Abhilash Pal'] | 2023-03-19 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 5.16729236e-01 -2.03483105e-01 -9.11188200e-02 -3.74674857e-01
-1.03516126e+00 -7.33325899e-01 6.65464580e-01 -5.02688706e-01
-6.63608313e-01 5.32949388e-01 4.61073160e-01 -4.31565911e-01
-2.00511720e-02 -3.25209409e-01 -2.17207819e-01 -6.51952505e-01
-1.96646407e-01 4.61627185e-01 5.12587786e-01 -7.87083507... | [9.13172435760498, -6.446225643157959] |
8fd1f449-c902-4785-9f01-a5bdd450125d | sparse-relational-reasoning-with-object | 2207.07512 | null | https://arxiv.org/abs/2207.07512v1 | https://arxiv.org/pdf/2207.07512v1.pdf | Sparse Relational Reasoning with Object-Centric Representations | We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric (slot-based) representations, under a variety of sparsity-inducing constraints. We find that increasing sparsity, especially on features, improves the performance of some models and leads to simp... | ['Murray Shanahan', 'Alessandra Russo', 'Alex F. Spies'] | 2022-07-15 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 2.34935626e-01 6.02248728e-01 -6.74206197e-01 -4.66479033e-01
-4.46591862e-02 -4.55308646e-01 8.32554221e-01 2.43534446e-01
-3.75462472e-02 4.61664319e-01 5.63639939e-01 -4.12686229e-01
-5.61476886e-01 -8.85002971e-01 -8.42568815e-01 -1.55425921e-01
-1.39254272e-01 5.80825269e-01 -3.00582759e-02 -3.49020779... | [9.429798126220703, 7.048830986022949] |
27c3c85d-e504-4a1c-a82f-be672f28bea5 | spectral-analysis-network-for-deep | 2009.05235 | null | https://arxiv.org/abs/2009.05235v1 | https://arxiv.org/pdf/2009.05235v1.pdf | Spectral Analysis Network for Deep Representation Learning and Image Clustering | Deep representation learning is a crucial procedure in multimedia analysis and attracts increasing attention. Most of the popular techniques rely on convolutional neural network and require a large amount of labeled data in the training procedure. However, it is time consuming or even impossible to obtain the label inf... | ['Jinghua Wang', 'Jianmin Jiang', 'Adrian Hilton'] | 2020-09-11 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 1.83801144e-01 -6.26254261e-01 -2.06736177e-02 -1.84092566e-01
-4.77305233e-01 -5.33159859e-02 2.01857284e-01 1.97629645e-01
-2.26319999e-01 1.96700349e-01 -1.05675772e-01 1.25683984e-02
-4.45011616e-01 -9.21386600e-01 -3.30643058e-01 -1.16825724e+00
8.73733908e-02 1.03805438e-01 1.00480765e-01 -2.85199601... | [9.088578224182129, 3.2151010036468506] |
93870087-7d6f-4880-86d3-ca566fd329d5 | deep-lexical-segmentation-and-syntactic | null | null | https://aclanthology.org/N16-1127 | https://aclanthology.org/N16-1127.pdf | Deep Lexical Segmentation and Syntactic Parsing in the Easy-First Dependency Framework | null | ['Matthieu Constant', 'Nadi Tomeh', 'Joseph Le Roux'] | 2016-06-01 | null | null | null | naacl-2016-6 | ['lexical-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.445680141448975, 3.6778674125671387] |
95a2f13b-1a47-4e97-99df-34a2447020fc | regression-transformer-concurrent-conditional | 2202.01338 | null | https://arxiv.org/abs/2202.01338v3 | https://arxiv.org/pdf/2202.01338v3.pdf | Regression Transformer: Concurrent sequence regression and generation for molecular language modeling | Despite significant progress of generative models in the natural sciences, their controllability remains challenging. One fundamentally missing aspect of molecular or protein generative models is an inductive bias that can reflect continuous properties of interest. To that end, we propose the Regression Transformer (RT... | ['Matteo Manica', 'Jannis Born'] | 2022-02-01 | null | null | null | null | ['conditional-text-generation'] | ['natural-language-processing'] | [ 7.68482804e-01 5.60943149e-02 -3.26218247e-01 -1.89886808e-01
-1.00189602e+00 -8.54833126e-01 8.35189283e-01 1.04581572e-01
-1.78166866e-01 1.38383210e+00 1.33559238e-02 -7.47994006e-01
-8.72002020e-02 -6.43037140e-01 -1.21120095e+00 -1.14966035e+00
1.52740717e-01 7.20982909e-01 -6.12353198e-02 -4.09996629... | [4.710371017456055, 5.797785758972168] |
a2cf4a70-9163-4ba7-a52c-3effe3b5f5f9 | sparsealign-a-super-resolution-algorithm-for | 2201.08706 | null | https://arxiv.org/abs/2201.08706v1 | https://arxiv.org/pdf/2201.08706v1.pdf | SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography | Tilt-series alignment is crucial to obtaining high-resolution reconstructions in cryo-electron tomography. Beam-induced local deformation of the sample is hard to estimate from the low-contrast sample alone, and often requires fiducial gold bead markers. The state-of-the-art approach for deformation estimation uses (se... | ['K Joost Batenburg', 'Hermen Jan Hupkes', 'Erik Franken', 'Holger Kohr', 'Felix Lucka', 'Poulami Somanya Ganguly'] | 2022-01-21 | null | null | null | null | ['electron-tomography'] | ['medical'] | [ 6.42188668e-01 -1.83764145e-01 4.49019045e-01 -2.21016496e-01
-1.21925914e+00 -4.67806786e-01 4.12041336e-01 1.57787591e-01
-8.13673139e-01 8.83445740e-01 -4.40212250e-01 1.41339362e-01
-6.69662952e-02 -4.26038325e-01 -8.15653086e-01 -9.78774786e-01
2.05043674e-01 1.24442911e+00 4.56067085e-01 2.23566994... | [13.188070297241211, -2.9690043926239014] |
d853d8c6-54b1-4d66-9523-92efe2fc845e | metasci-scalable-and-adaptive-reconstruction | 2103.01786 | null | https://arxiv.org/abs/2103.01786v1 | https://arxiv.org/pdf/2103.01786v1.pdf | MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing | To capture high-speed videos using a two-dimensional detector, video snapshot compressive imaging (SCI) is a promising system, where the video frames are coded by different masks and then compressed to a snapshot measurement. Following this, efficient algorithms are desired to reconstruct the high-speed frames, where t... | ['Xin Yuan', 'Bo Chen', 'Ziheng Cheng', 'Hao Zhang', 'Zhengjue Wang'] | 2021-03-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wang_MetaSCI_Scalable_and_Adaptive_Reconstruction_for_Video_Compressive_Sensing_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wang_MetaSCI_Scalable_and_Adaptive_Reconstruction_for_Video_Compressive_Sensing_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-compressive-sensing'] | ['computer-vision'] | [ 4.85890716e-01 -2.69287616e-01 -4.88160811e-02 1.23933963e-02
-7.47879803e-01 -2.03250006e-01 3.44620496e-01 -8.01700592e-01
-3.18530053e-01 4.93487537e-01 -1.32687807e-01 -3.04793686e-01
4.39658016e-02 -5.63667238e-01 -9.62104857e-01 -8.54310513e-01
-4.04342175e-01 -3.58881243e-02 2.63366580e-01 1.44817248... | [11.071147918701172, -2.0255329608917236] |
38d91348-31b1-4342-b691-3273d8bf556a | correlation-networks-for-extreme-multi-label | null | null | https://dl.acm.org/doi/pdf/10.1145/3394486.3403151 | https://dl.acm.org/doi/pdf/10.1145/3394486.3403151 | Correlation Networks for Extreme Multi-label Text Classification | This paper develops the Correlation Networks (CorNet) architecture for the extreme multi-label text classification (XMTC) task, where the objective is to tag an input text sequence with the most relevant subset of labels from an extremely large label set. XMTC can be found in many real-world applications, such as docum... | ['Aidong Zhang', 'Jianhui Sun', 'Kishlay Jha', 'Guangxu Xun'] | 2022-08-23 | null | null | null | proceedings-of-the-26th-acm-sigkdd-1 | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 3.27459693e-01 -1.86238661e-01 -2.48616755e-01 -6.30014062e-01
-5.45376122e-01 -4.54641193e-01 3.90378594e-01 1.30494937e-01
-1.66493446e-01 4.16148305e-01 -9.48567390e-02 -2.42525846e-01
-1.71152562e-01 -5.14073491e-01 -2.48928100e-01 -6.09765768e-01
3.34184796e-01 8.20967793e-01 -1.07699580e-01 -3.89188454... | [9.619002342224121, 4.451057434082031] |
5eb25b5a-b285-447a-95f5-a8698046a7b3 | exploiting-neural-query-translation-into | 2010.13659 | null | https://arxiv.org/abs/2010.13659v1 | https://arxiv.org/pdf/2010.13659v1.pdf | Exploiting Neural Query Translation into Cross Lingual Information Retrieval | As a crucial role in cross-language information retrieval (CLIR), query translation has three main challenges: 1) the adequacy of translation; 2) the lack of in-domain parallel training data; and 3) the requisite of low latency. To this end, existing CLIR systems mainly exploit statistical-based machine translation (SM... | ['Boxing Chen', 'Weihua Luo', 'Haibo Zhang', 'Baosong Yang', 'Liang Yao'] | 2020-10-26 | null | null | null | null | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [ 5.18062934e-02 -5.46246469e-01 -5.73628843e-01 6.85357228e-02
-1.50557840e+00 -7.12498307e-01 8.04648340e-01 1.96575690e-02
-7.22715497e-01 7.71299481e-01 1.64128438e-01 -7.24012315e-01
-5.66079468e-02 -5.57674170e-01 -7.02502310e-01 -2.91732371e-01
5.01521707e-01 7.25733161e-01 1.33136660e-01 -6.42786264... | [11.599230766296387, 10.040308952331543] |
c49a6b4c-3916-42ff-983c-49c398114bd7 | implicit-neural-networks-with-fourier-feature | 2305.06822 | null | https://arxiv.org/abs/2305.06822v1 | https://arxiv.org/pdf/2305.06822v1.pdf | Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction | In this paper, we propose an approach for cardiac magnetic resonance imaging (MRI), which aims to reconstruct a real-time video of a beating heart from continuous highly under-sampled measurements. This task is challenging since the object to be reconstructed (the heart) is continuously changing during signal acquisiti... | ['Reinhard Heckel', 'Stefan Ruschke', 'Johannes F. Kunz'] | 2023-05-11 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 6.64241970e-01 1.93246573e-01 2.31430292e-01 -1.28395468e-01
-3.93316984e-01 -2.30045572e-01 1.10888956e-02 -8.48695338e-02
-6.81624711e-01 5.89568615e-01 -1.90878406e-01 -1.58288956e-01
-2.62455970e-01 -4.14196134e-01 -6.58132672e-01 -9.08667684e-01
-1.64325729e-01 3.81409585e-01 5.09362258e-02 1.53228164... | [13.57983112335205, -2.477501392364502] |
d62488aa-f3d3-4e66-9772-9badbb9398dd | secure-multiparty-computation-for-synthetic | 2210.07332 | null | https://arxiv.org/abs/2210.07332v2 | https://arxiv.org/pdf/2210.07332v2.pdf | Secure Multiparty Computation for Synthetic Data Generation from Distributed Data | Legal and ethical restrictions on accessing relevant data inhibit data science research in critical domains such as health, finance, and education. Synthetic data generation algorithms with privacy guarantees are emerging as a paradigm to break this data logjam. Existing approaches, however, assume that the data holder... | ['Martine De Cock', 'Rafael T. de Sousa Jr.', 'Anderson Nascimento', 'Sikha Pentyala', 'Mayana Pereira'] | 2022-10-13 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-1.05070714e-02 6.12635016e-01 -1.06419139e-01 -3.59400064e-01
-7.37172544e-01 -1.39919031e+00 6.30092323e-01 9.02992487e-01
-7.73415387e-01 1.08864236e+00 -1.05812913e-02 -3.31953079e-01
1.05168559e-01 -1.36863279e+00 -7.48156428e-01 -9.51507449e-01
2.11799085e-01 4.75596011e-01 1.74103796e-01 -1.46038398... | [5.8922438621521, 6.665071487426758] |
6f36344b-d70e-4c6a-9855-170cf4aa7cb7 | keyphrase-extraction-using-neighborhood | 2111.07198 | null | https://arxiv.org/abs/2111.07198v1 | https://arxiv.org/pdf/2111.07198v1.pdf | Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings | Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document. Most existing graph-based models use co-occurrence links as cohesion indicators to model the relationship of syntactic elements. However, a word may have different for... | ['Mohammed J. Zaki', 'Yuchen Liang'] | 2021-11-13 | null | null | null | null | ['keyphrase-extraction'] | ['natural-language-processing'] | [-3.45492095e-01 -1.53044194e-01 -8.11228454e-01 -2.81139910e-02
-4.05694544e-02 -5.77815950e-01 8.03353727e-01 1.07401359e+00
-4.83140439e-01 4.73225445e-01 1.00848448e+00 -1.61525488e-01
-4.05551910e-01 -1.12836528e+00 -1.75918877e-01 -3.46661359e-01
-1.51733503e-01 4.43155169e-02 5.63650846e-01 -5.14262021... | [10.395464897155762, 8.429976463317871] |
9843b515-3a52-4459-87a2-6025192596e9 | wipin-operation-free-person-identification | 1810.04106 | null | https://arxiv.org/abs/1810.04106v2 | https://arxiv.org/pdf/1810.04106v2.pdf | WiPIN: Operation-free Passive Person Identification Using Wi-Fi Signals | Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a p... | ['Feng Lin', 'Fei Wang', 'Kui Ren', 'Jinsong Han'] | 2018-10-06 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 2.22358599e-01 -4.20851141e-01 -9.10027996e-02 -1.05119124e-01
-2.69298434e-01 -4.20935452e-01 -6.41495064e-02 -2.71266103e-01
-3.74553084e-01 7.50356257e-01 -1.68080069e-02 2.60940474e-02
-4.52465892e-01 -9.99205410e-01 -9.84286815e-02 -5.87535024e-01
-8.16118792e-02 1.10885061e-01 1.88909590e-01 4.53129113... | [6.726963043212891, 0.6799643039703369] |
efbe5472-18cd-47a3-b9fb-83771352565c | learning-the-distribution-of-errors-in-stereo | 2304.00152 | null | https://arxiv.org/abs/2304.00152v1 | https://arxiv.org/pdf/2304.00152v1.pdf | Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation | We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the dis... | ['Philippos Mordohai', 'Weihan Wang', 'Liyan Chen'] | 2023-03-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Chen_Learning_the_Distribution_of_Errors_in_Stereo_Matching_for_Joint_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Chen_Learning_the_Distribution_of_Errors_in_Stereo_Matching_for_Joint_CVPR_2023_paper.pdf | cvpr-2023-1 | ['stereo-matching-1'] | ['computer-vision'] | [-1.33805975e-01 1.06246984e-02 -7.65863210e-02 -8.40902328e-01
-1.38699114e+00 -3.28609109e-01 4.24848020e-01 2.02542111e-01
-6.45462632e-01 1.08919513e+00 4.48023558e-01 -8.82522203e-03
-7.28661418e-02 -4.73503351e-01 -1.13897395e+00 -4.05075848e-01
2.19369322e-01 4.39612269e-01 3.34150165e-01 2.62948602... | [8.30721664428711, -2.2283220291137695] |
7182d828-5ea2-4928-9ec1-1f4dc0e447d7 | electronics-and-sensor-subsystem-design-for | 2211.02870 | null | https://arxiv.org/abs/2211.02870v1 | https://arxiv.org/pdf/2211.02870v1.pdf | Electronics and Sensor Subsystem Design for Daedalus 2 on REXUS 29: An Autorotation Probe for Sub-Orbital Re-Entry | The Daedalus 2 mission aboard REXUS 29 is a technology demonstrator for an alternative descent mechanism for very high altitude drops based on auto-rotation. It consists of two probes that are ejected from a sounding rocket at an altitude of about 80 km and decelerate to a soft landing using only a passive rotor with p... | ['Frederik Dunschen', 'Clemens Riegler', 'Philip Bergmann', 'Lennart Werner', 'Jan M. Wolf'] | 2022-11-05 | null | null | null | null | ['pitch-control'] | ['audio'] | [-4.43537802e-01 1.42324820e-01 1.64667889e-01 1.08770147e-01
4.92368370e-01 -1.04473543e+00 -4.55616899e-02 -2.73412287e-01
-6.10696562e-02 8.27391088e-01 -5.57842135e-01 -3.28379095e-01
-7.12563038e-01 -4.86523777e-01 -4.16624606e-01 -3.50888729e-01
-3.92286748e-01 3.64300638e-01 2.51627803e-01 -6.19576573... | [5.427677154541016, 2.3380684852600098] |
b654a539-574a-4e4c-9585-6d51f31a1589 | implicit-bilevel-optimization-differentiating | 2302.14473 | null | https://arxiv.org/abs/2302.14473v1 | https://arxiv.org/pdf/2302.14473v1.pdf | Implicit Bilevel Optimization: Differentiating through Bilevel Optimization Programming | Bilevel Optimization Programming is used to model complex and conflicting interactions between agents, for example in Robust AI or Privacy-preserving AI. Integrating bilevel mathematical programming within deep learning is thus an essential objective for the Machine Learning community. Previously proposed approaches on... | ['Francesco Alesiani'] | 2023-02-28 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-3.70509475e-01 -1.11663684e-01 -3.73336494e-01 -2.49591887e-01
-7.91417897e-01 -5.57434738e-01 7.43155956e-01 4.22589481e-01
-7.82425225e-01 7.23101616e-01 -3.85529101e-01 -4.11374480e-01
-5.88834047e-01 -7.36379087e-01 -1.07059538e+00 -8.07483852e-01
-3.19463491e-01 9.07932818e-01 -2.79259264e-01 -1.09982930... | [6.6822075843811035, 4.3949432373046875] |
a1188305-a616-4b7c-a015-15f4bee8bc6d | multi-task-regression-based-learning-for | 1907.08320 | null | https://arxiv.org/abs/1907.08320v1 | https://arxiv.org/pdf/1907.08320v1.pdf | Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments | Increased growth in the global Unmanned Aerial Vehicles (UAV) (drone) industry has expanded possibilities for fully autonomous UAV applications. A particular application which has in part motivated this research is the use of UAV in wide area search and surveillance operations in unstructured outdoor environments. The ... | ['Toby P. Breckon', 'Amir Atapour-Abarghouei', 'Bruna G. Maciel-Pearson', 'Samet Akcay', 'Christopher Holder'] | 2019-07-18 | null | null | null | null | ['autonomous-flight-dense-forest'] | ['computer-vision'] | [ 3.99016827e-01 -2.69486785e-01 1.81066468e-01 -2.01343015e-01
-2.95616090e-01 -1.07295787e+00 5.31612396e-01 1.33206114e-01
-5.82105637e-01 9.00377452e-01 -5.02229214e-01 -4.54187810e-01
-6.79807365e-01 -8.67329001e-01 -5.29034019e-01 -2.60028034e-01
-7.85177290e-01 7.52094686e-01 7.82943487e-01 -8.35936725... | [7.329524993896484, -1.9234261512756348] |
2af2903a-9cd0-43a7-97dc-3e4b8c6ecb20 | zero-shot-learning-for-code-education-rubric | 1809.01357 | null | http://arxiv.org/abs/1809.01357v2 | http://arxiv.org/pdf/1809.01357v2.pdf | Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference | In modern computer science education, massive open online courses (MOOCs) log
thousands of hours of data about how students solve coding challenges. Being so
rich in data, these platforms have garnered the interest of the machine
learning community, with many new algorithms attempting to autonomously provide
feedback t... | ['Noah Goodman', 'Milan Mosse', 'Mike Wu', 'Chris Piech'] | 2018-09-05 | null | null | null | null | ['misconceptions'] | ['miscellaneous'] | [-5.57524078e-02 1.54623806e-01 -2.32752010e-01 -4.38148826e-01
-7.11541295e-01 -9.32741582e-01 2.77940810e-01 7.96488643e-01
-1.23833492e-01 4.06038523e-01 -2.88869925e-02 -8.02090228e-01
-1.11062095e-01 -8.64879966e-01 -7.49987185e-01 -5.52785993e-02
3.34840566e-01 4.20308739e-01 2.35074759e-01 -7.34096467... | [9.776865005493164, 7.364322185516357] |
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