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a1b0be94-109a-42d6-8369-aa7b1d612b98 | exploring-automatically-perturbed-natural | 2305.15520 | null | https://arxiv.org/abs/2305.15520v1 | https://arxiv.org/pdf/2305.15520v1.pdf | Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction | Previous research has demonstrated that natural language explanations provide valuable inductive biases that guide models, thereby improving the generalization ability and data efficiency. In this paper, we undertake a systematic examination of the effectiveness of these explanations. Remarkably, we find that corrupted... | ['Xingran Chen', 'Wanyun Cui'] | 2023-05-24 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 4.92474645e-01 6.32936120e-01 -5.45110762e-01 -4.90362614e-01
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8.17634091e-02 2.67967939e-01 -9.81215239e-02 -2.68968880... | [9.184073448181152, 6.253123760223389] |
9dbc0d95-f7a1-4acb-ac9f-a2f55d8143fb | incentive-theoretic-bayesian-inference-for | 2307.03748 | null | https://arxiv.org/abs/2307.03748v1 | https://arxiv.org/pdf/2307.03748v1.pdf | Incentive-Theoretic Bayesian Inference for Collaborative Science | Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing different incentives. To maintain scientific rigor, statistical methods should... | ['Jake A. Soloff', 'Michael Sklar', 'Michael I. Jordan', 'Stephen Bates'] | 2023-07-07 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 3.96634847e-01 3.56685728e-01 -7.70134330e-01 -1.45219296e-01
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2.26793483e-01 4.77546543e-01 -3.36000741e-01 6.18802667... | [7.965751647949219, 5.249485969543457] |
f2d71fee-1010-416f-bc16-72ebb6750845 | words-as-gatekeepers-measuring-discipline | 2212.09676 | null | https://arxiv.org/abs/2212.09676v2 | https://arxiv.org/pdf/2212.09676v2.pdf | Words as Gatekeepers: Measuring Discipline-specific Terms and Meanings in Scholarly Publications | Scholarly text is often laden with jargon, or specialized language that can facilitate efficient in-group communication within fields but hinder understanding for out-groups. In this work, we develop and validate an interpretable approach for measuring scholarly jargon from text. Expanding the scope of prior work which... | ['Katherine A. Keith', 'David Bamman', 'Jesse Dodge', 'Li Lucy'] | 2022-12-19 | null | null | null | null | ['word-sense-induction'] | ['natural-language-processing'] | [-1.55640200e-01 5.42731099e-02 -5.51344633e-01 2.78807670e-01
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b8ec5e75-cd4e-4452-b846-649b8e23e674 | infillmore-neural-frame-lexicalization-for | 2103.04941 | null | https://arxiv.org/abs/2103.04941v3 | https://arxiv.org/pdf/2103.04941v3.pdf | InFillmore: Frame-Guided Language Generation with Bidirectional Context | We propose a structured extension to bidirectional-context conditional language generation, or "infilling," inspired by Frame Semantic theory (Fillmore, 1976). Guidance is provided through two approaches: (1) model fine-tuning, conditioning directly on observed symbolic frames, and (2) a novel extension to disjunctive ... | ['Benjamin Van Durme', 'Felix Yu', 'Anton Belyy', 'Nathaniel Weir', 'Jiefu Ou'] | 2021-03-08 | null | https://aclanthology.org/2021.starsem-1.12 | https://aclanthology.org/2021.starsem-1.12.pdf | joint-conference-on-lexical-and-computational-1 | ['text-infilling'] | ['natural-language-processing'] | [ 5.85448205e-01 5.27094185e-01 -5.33006251e-01 -5.84356785e-01
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7.98246451e-03 2.30474830e-01 1.69316292e-01 -3.93332720... | [10.922453880310059, 9.033588409423828] |
f2bb6e35-a509-4c36-a7a4-21b07c316db6 | flare-aware-cross-modal-enhancement-network | 2305.13659 | null | https://arxiv.org/abs/2305.13659v1 | https://arxiv.org/pdf/2305.13659v1.pdf | Flare-Aware Cross-modal Enhancement Network for Multi-spectral Vehicle Re-identification | Multi-spectral vehicle re-identification aims to address the challenge of identifying vehicles in complex lighting conditions by incorporating complementary visible and infrared information. However, in harsh environments, the discriminative cues in RGB and NIR modalities are often lost due to strong flares from vehicl... | ['Chenglong Li', 'Zi Wang', 'Zhiqi Ma', 'Aihua Zheng'] | 2023-05-23 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [ 4.51803625e-01 -9.28209245e-01 8.71562399e-03 -4.96765524e-01
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573b8b6d-2250-4ec3-a30e-be7589e4f196 | secrets-of-3d-implicit-object-shape | 2101.06860 | null | https://arxiv.org/abs/2101.06860v2 | https://arxiv.org/pdf/2101.06860v2.pdf | Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild | Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics. While recent neural implicit modeling methods show promising results on synthetic or dense data, they perform poorly on sparse and noi... | ['Raquel Urtasun', 'Shenlong Wang', 'Justin Liang', 'Sivabalan Manivasagam', 'Wei-Chiu Ma', 'ZiHao Wang', 'Shivam Duggal'] | 2021-01-18 | null | null | null | null | ['3d-object-reconstruction'] | ['computer-vision'] | [ 1.82374522e-01 1.53102949e-01 -2.02580437e-01 -4.31460023e-01
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1.91312477e-01 1.17139578e+00 2.63628155e-01 1.02552488... | [8.70626163482666, -3.3521416187286377] |
87eea680-c95f-4ec4-82b8-e863f33b337a | chromatic-learning-for-sparse-datasets | 2006.03779 | null | https://arxiv.org/abs/2006.03779v1 | https://arxiv.org/pdf/2006.03779v1.pdf | Chromatic Learning for Sparse Datasets | Learning over sparse, high-dimensional data frequently necessitates the use of specialized methods such as the hashing trick. In this work, we design a highly scalable alternative approach that leverages the low degree of feature co-occurrences present in many practical settings. This approach, which we call Chromatic ... | ['Vladimir Feinberg', 'Peter Bailis'] | 2020-06-06 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [-2.30142400e-01 -5.25356568e-02 -4.34649944e-01 -1.72995955e-01
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-7.64395118e-01 6.58686101e-01 -1.00157641e-01 1.75971672... | [7.08128023147583, 4.959749221801758] |
b9019ec0-a34e-48ce-9fb9-0eaf01de7728 | contrast-with-major-classifier-vectors-for | 2301.05376 | null | https://arxiv.org/abs/2301.05376v1 | https://arxiv.org/pdf/2301.05376v1.pdf | Contrast with Major Classifier Vectors for Federated Medical Relation Extraction with Heterogeneous Label Distribution | Federated medical relation extraction enables multiple clients to train a deep network collaboratively without sharing their raw medical data. In order to handle the heterogeneous label distribution across clients, most of the existing works only involve enforcing regularization between local and global models during o... | ['Yaohui Jin', 'Hao He', 'Chunhui Du'] | 2023-01-13 | null | null | null | null | ['medical-relation-extraction'] | ['medical'] | [ 7.54410774e-02 2.97198355e-01 -4.23213214e-01 -8.53500724e-01
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-7.42398668e-03 8.83375406e-01 1.45560101e-01 2.45742947... | [6.047317028045654, 6.448250770568848] |
114b8fee-309d-4b4e-b050-ac3af8f23a54 | timestamp-supervised-action-segmentation-in | 2212.11694 | null | https://arxiv.org/abs/2212.11694v2 | https://arxiv.org/pdf/2212.11694v2.pdf | Timestamp-Supervised Action Segmentation from the Perspective of Clustering | Video action segmentation under timestamp supervision has recently received much attention due to lower annotation costs. Most existing methods generate pseudo-labels for all frames in each video to train the segmentation model. However, these methods suffer from incorrect pseudo-labels, especially for the semantically... | ['Fuchun Sun', 'Fanjiang Xu', 'Lingyu Si', 'Enhan Li', 'Dazhao Du'] | 2022-12-22 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 5.20522058e-01 1.25104412e-01 -4.15032655e-01 -6.54455364e-01
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2.45543122e-01 3.21376204e-01 7.40207851e-01 3.74550283... | [8.523780822753906, 0.55406254529953] |
7c075d85-23ed-435a-9d28-13d0d3d1639d | attend-and-guide-ag-net-a-keypoints-driven | 2110.12183 | null | https://arxiv.org/abs/2110.12183v1 | https://arxiv.org/pdf/2110.12183v1.pdf | Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition | This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distinctive classes have shown great success, but their performance in discriminating fine-grained changes is not at the same level. We address th... | ['Ardhendu Behera', 'Nik Bessis', 'Yonghuai Liu', 'Zachary Wharton', 'Asish Bera'] | 2021-10-23 | null | null | null | null | ['fine-grained-image-recognition'] | ['computer-vision'] | [-5.16677313e-02 -1.75873533e-01 -1.82559475e-01 -3.69176865e-01
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-5.44853993e-02 -2.53789667e-02 6.53674722e-01 -4.84436184... | [9.599624633789062, 2.009425163269043] |
ef565d20-05dc-4171-95ee-4b6c118d8d01 | unified-open-domain-question-answering-with | 2012.14610 | null | https://arxiv.org/abs/2012.14610v3 | https://arxiv.org/pdf/2012.14610v3.pdf | UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering | We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior work, we propose a unifying approach that homogenizes all sources by reducing them to text and applies the retriever-reader model which has... | ['Scott Yih', 'Yashar Mehdad', 'Sonal Gupta', 'Michael Schlichtkrull', 'Dmytro Okhonko', 'Stan Peshterliev', 'Vladimir Karpukhin', 'Xilun Chen', 'Barlas Oguz'] | 2020-12-29 | null | https://aclanthology.org/2022.findings-naacl.115 | https://aclanthology.org/2022.findings-naacl.115.pdf | findings-naacl-2022-7 | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-2.29473352e-01 4.65742201e-01 -2.87489146e-01 1.50397616e-02
-1.53614902e+00 -1.10376632e+00 7.61829317e-01 6.22193694e-01
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4.32504326e-01 1.00075138e+00 8.83687556e-01 -7.88689077... | [10.699372291564941, 7.954188346862793] |
8e4c6ca7-af17-4a54-8c76-26da59a6f4b7 | self-supervised-learning-of-multi-object | 2205.08316 | null | https://arxiv.org/abs/2205.08316v2 | https://arxiv.org/pdf/2205.08316v2.pdf | Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation | In recent years, policy learning methods using either reinforcement or imitation have made significant progress. However, both techniques still suffer from being computationally expensive and requiring large amounts of training data. This problem is especially prevalent in real-world robotic manipulation tasks, where a... | ['Abhinav Valada', 'Tim Welschehold', 'Eugenio Chisari', 'Jan Ole von Hartz'] | 2022-05-17 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 3.45676333e-01 -6.79404885e-02 -7.01275408e-01 -1.69010554e-02
-7.35052228e-01 -8.22163165e-01 8.39286447e-01 1.89019546e-01
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-2.51830548e-01 -3.98646683e-01 -8.96888793e-01 -6.32096231e-01
-6.45252168e-02 7.93535411e-01 2.13813499e-01 -1.49923578... | [4.6399736404418945, 0.7080104947090149] |
5c640414-964a-4186-9f46-bab263624f80 | 1st-place-solution-to-icdar-2021-rrc-ictext | 2104.03544 | null | https://arxiv.org/abs/2104.03544v1 | https://arxiv.org/pdf/2104.03544v1.pdf | 1st Place Solution to ICDAR 2021 RRC-ICTEXT End-to-end Text Spotting and Aesthetic Assessment on Integrated Circuit | This paper presents our proposed methods to ICDAR 2021 Robust Reading Challenge - Integrated Circuit Text Spotting and Aesthetic Assessment (ICDAR RRC-ICTEXT 2021). For the text spotting task, we detect the characters on integrated circuit and classify them based on yolov5 detection model. We balance the lowercase and ... | ['Yi Niu', 'Li Zhu', 'Pengfei Li', 'Qiyao Wang'] | 2021-04-08 | null | null | null | null | ['text-spotting'] | ['computer-vision'] | [ 4.32647854e-01 -1.39405876e-01 9.39088836e-02 -3.80510837e-01
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5.04282475e-01 4.19529945e-01 6.26593679e-02 3.33337300... | [11.864747047424316, 2.2575185298919678] |
00e6be64-9ee8-4e11-8ed8-4acbb00e4cc7 | analyzing-how-bert-performs-entity-matching | null | null | https://dl.acm.org/doi/10.14778/3529337.3529356 | https://dl.acm.org/doi/pdf/10.14778/3529337.3529356 | Analyzing how BERT performs entity matching | State-of-the-art Entity Matching (EM) approaches rely on transformer architectures, such as BERT, for generating highly contex-tualized embeddings of terms. The embeddings are then used to predict whether pairs of entity descriptions refer to the same real-world entity. BERT-based EM models demonstrated to be effective... | ['Francesco Guerra', 'Andrea Baraldi', 'Francesco Del Buono', 'Matteo Paganelli'] | 2022-04-01 | null | null | null | proceedings-of-the-vldb-endowment-2022-4 | ['entity-resolution'] | ['natural-language-processing'] | [-4.17230874e-01 2.97103077e-01 -1.57147199e-02 -4.65191722e-01
-5.07996023e-01 -5.09161830e-01 1.06493390e+00 5.42878449e-01
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-9.83996466e-02 9.50086594e-01 2.98870146e-01 -4.70081687... | [9.471957206726074, 8.47412395477295] |
29b32180-d0ca-490d-804f-89b1a829cf50 | conversational-implicatures-in-english | 1911.10704 | null | https://arxiv.org/abs/1911.10704v1 | https://arxiv.org/pdf/1911.10704v1.pdf | Conversational implicatures in English dialogue: Annotated dataset | Human dialogue often contains utterances having meanings entirely different from the sentences used and are clearly understood by the interlocutors. But in human-computer interactions, the machine fails to understand the implicated meaning unless it is trained with a dataset containing the implicated meaning of an utte... | ['Radhika Mamidi', 'Elizabeth Jasmi George'] | 2019-11-25 | null | null | null | null | ['implicatures'] | ['natural-language-processing'] | [ 3.61970484e-01 7.46786356e-01 1.88192561e-01 -8.84908140e-01
-3.91254187e-01 -1.07605195e+00 7.68756807e-01 1.21056683e-01
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6.73631504e-02 5.81876874e-01 2.98663806e-02 -6.63432002... | [12.567962646484375, 7.895253658294678] |
c462ab2f-b142-4fe4-aa8a-de553bd1b9ae | k-radar-4d-radar-object-detection-dataset-and | 2206.08171 | null | https://arxiv.org/abs/2206.08171v3 | https://arxiv.org/pdf/2206.08171v3.pdf | K-Radar: 4D Radar Object Detection for Autonomous Driving in Various Weather Conditions | Unlike RGB cameras that use visible light bands (384$\sim$769 THz) and Lidars that use infrared bands (361$\sim$331 THz), Radars use relatively longer wavelength radio bands (77$\sim$81 GHz), resulting in robust measurements in adverse weathers. Unfortunately, existing Radar datasets only contain a relatively small num... | ['Kevin Tirta Wijaya', 'Seung-Hyun Kong', 'Dong-Hee Paek'] | 2022-06-16 | null | null | null | null | ['radar-object-detection'] | ['robots'] | [ 1.15338609e-01 -2.87490517e-01 1.12184085e-01 -5.09213924e-01
-7.46351480e-01 -6.83775246e-01 4.00370985e-01 -4.75417316e-01
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-3.31358045e-01 4.19983745e-01 7.25790262e-02 -3.08761716... | [7.848667621612549, -1.5012108087539673] |
4957c1c3-c8ef-40f7-9b46-8356ddd5af58 | scrolls-standardized-comparison-over-long | 2201.03533 | null | https://arxiv.org/abs/2201.03533v2 | https://arxiv.org/pdf/2201.03533v2.pdf | SCROLLS: Standardized CompaRison Over Long Language Sequences | NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild. We introduce SCROLLS, a suite of tasks that require reasoning over long texts. We examine existing long-text datasets, and handpick ones where the ... | ['Omer Levy', 'Jonathan Berant', 'Mor Geva', 'Wenhan Xiong', 'Ankit Gupta', 'Adi Haviv', 'Ori Yoran', 'Avia Efrat', 'Maor Ivgi', 'Elad Segal', 'Uri Shaham'] | 2022-01-10 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 4.78540063e-01 4.07607406e-01 -4.57846463e-01 -4.44115400e-01
-1.24884057e+00 -8.22927952e-01 8.53888810e-01 4.86383259e-01
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3.53398174e-01 5.03627598e-01 -7.08628446e-02 -3.83774936... | [11.651307106018066, 8.900276184082031] |
2a1e156d-5999-4168-a21d-ed40adc4a16a | open-cykg-an-open-cyber-threat-intelligence | null | null | https://www-sciencedirect-com.proxy.library.uu.nl/science/article/pii/S0950705121007863?dgcid=rss_sd_all | https://www-sciencedirect-com.proxy.library.uu.nl/science/article/pii/S0950705121007863/pdfft?md5=526ecd890d0da054e1084d452a43ec78&pid=1-s2.0-S0950705121007863-main.pdf | Open-CyKG: An Open Cyber Threat Intelligence Knowledge Graph | Instant analysis of cybersecurity reports is a fundamental challenge for security experts as an immeasurable amount of cyber information is generated on a daily basis, which necessitates automated information extraction tools to facilitate querying and retrieval of data. Hence, we present Open-CyKG: an Open Cyber Threa... | ['MarcoSpruit', 'InjySarhan'] | 2021-12-05 | null | null | null | knowledge-based-systems-2021-12 | ['deep-attention', 'deep-attention', 'open-information-extraction'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [-1.70328002e-02 3.09931666e-01 -1.64799735e-01 2.80281663e-01
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-3.45273852e-01 4.54883456e-01 4.07598108e-01 -6.68185472e-01
-4.05720592e-01 -1.29580200e+00 -4.25744087e-01 -4.73131193e-03
-3.51569951e-01 1.60421267e-01 2.77630263e-03 -2.88322210... | [6.628945350646973, 7.508791446685791] |
3e7d6330-1797-43f3-8655-b7a7cd1b3e47 | generative-plug-and-play-posterior-sampling | 2306.07233 | null | https://arxiv.org/abs/2306.07233v1 | https://arxiv.org/pdf/2306.07233v1.pdf | Generative Plug and Play: Posterior Sampling for Inverse Problems | Over the past decade, Plug-and-Play (PnP) has become a popular method for reconstructing images using a modular framework consisting of a forward and prior model. The great strength of PnP is that an image denoiser can be used as a prior model while the forward model can be implemented using more traditional physics-ba... | ['Gregery T. Buzzard', 'Charles A. Bouman'] | 2023-06-12 | null | null | null | null | ['image-denoising'] | ['computer-vision'] | [ 1.94964215e-01 -1.38141245e-01 2.45454088e-01 -9.24118757e-02
-7.12286711e-01 -2.55441368e-01 7.38166392e-01 -4.33994472e-01
6.30357414e-02 8.80249143e-01 1.07037522e-01 -1.09757625e-01
-1.21003643e-01 -9.83144164e-01 -8.63592505e-01 -1.05689204e+00
1.95731729e-01 5.68225205e-01 4.44096118e-01 -1.69561088... | [11.760435104370117, -2.3961968421936035] |
3aedbf06-41d3-4e70-984e-a399c9c27a9b | big-gans-are-watching-you-towards | 2006.04988 | null | https://arxiv.org/abs/2006.04988v2 | https://arxiv.org/pdf/2006.04988v2.pdf | Object Segmentation Without Labels with Large-Scale Generative Models | The recent rise of unsupervised and self-supervised learning has dramatically reduced the dependency on labeled data, providing effective image representations for transfer to downstream vision tasks. Furthermore, recent works employed these representations in a fully unsupervised setup for image classification, reduci... | ['Stanislav Morozov', 'Artem Babenko', 'Andrey Voynov'] | 2020-06-08 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 9.31315541e-01 4.19765055e-01 -4.69250143e-01 -4.67868865e-01
-7.55620003e-01 -5.42916834e-01 7.05054283e-01 9.94483382e-02
-5.28184235e-01 7.20210016e-01 -8.56356919e-02 1.33316382e-03
2.66308159e-01 -6.61914706e-01 -8.45109522e-01 -8.69788289e-01
2.80445129e-01 4.54564989e-01 6.64180160e-01 4.74231094... | [9.606121063232422, 0.6644976735115051] |
adbdd2bd-270b-4bb7-ba05-80758bad2f49 | learning-controls-using-cross-modal | 1909.06993 | null | https://arxiv.org/abs/1909.06993v2 | https://arxiv.org/pdf/1909.06993v2.pdf | Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations | Machines are a long way from robustly solving open-world perception-control tasks, such as first-person view (FPV) aerial navigation. While recent advances in end-to-end Machine Learning, especially Imitation and Reinforcement Learning appear promising, they are constrained by the need of large amounts of difficult-to-... | ['Vibhav Vineet', 'Sebastian Scherer', 'Ratnesh Madaan', 'Rogerio Bonatti', 'Ashish Kapoor'] | 2019-09-16 | null | null | null | null | ['drone-navigation'] | ['computer-vision'] | [ 2.56225280e-02 1.26906306e-01 -9.99902412e-02 -9.32854936e-02
-6.05947256e-01 -9.04425263e-01 7.22719193e-01 -2.57346213e-01
-5.35691857e-01 8.68540466e-01 1.35043412e-01 -2.76875407e-01
-2.39380538e-01 -5.54662406e-01 -1.16806567e+00 -6.89050436e-01
-2.85076588e-01 3.92269850e-01 5.33041693e-02 -6.17355168... | [4.580878734588623, 1.0460129976272583] |
6af09cc7-c227-42c1-b224-b492638194b4 | sound-event-localization-based-on-sound | 2002.05994 | null | http://arxiv.org/abs/2002.05994v1 | http://arxiv.org/pdf/2002.05994v1.pdf | Sound Event Localization based on Sound Intensity Vector Refined By DNN-Based Denoising and Source Separation | We propose a direction-of-arrival (DOA) estimation method for Sound Event
Localization and Detection (SELD). Direct estimation of DOA using a deep neural
network (DNN), i.e. completely-datadriven approach, achieves high accuracy.
However, there is a gap in the accuracy between DOA estimation for single and
overlapping ... | [] | 2020-02-14 | null | null | null | null | ['sound-event-localization-and-detection'] | ['audio'] | [-4.80353266e-01 -8.29895377e-01 6.42851591e-01 -9.61297899e-02
-1.23077166e+00 -4.86988753e-01 3.25757712e-01 1.64748877e-01
-1.41941547e-01 4.75570172e-01 6.22537792e-01 5.42998612e-02
-4.05864269e-01 -9.44876254e-01 -5.08453310e-01 -1.17864215e+00
-1.15508966e-01 -1.02366716e-01 2.49449074e-01 1.49259448... | [15.18129825592041, 5.704602241516113] |
d9927513-96d0-416e-9e81-696356f2533d | reconstructive-sparse-code-transfer-for | 1410.4521 | null | http://arxiv.org/abs/1410.4521v1 | http://arxiv.org/pdf/1410.4521v1.pdf | Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling | We frame the task of predicting a semantic labeling as a sparse
reconstruction procedure that applies a target-specific learned transfer
function to a generic deep sparse code representation of an image. This
strategy partitions training into two distinct stages. First, in an
unsupervised manner, we learn a set of gene... | ['Pietro Perona', 'Stella X. Yu', 'Michael Maire'] | 2014-10-16 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [ 6.31732762e-01 4.89514202e-01 -2.91132480e-01 -7.03755736e-01
-7.08684385e-01 -6.01075590e-01 5.00096262e-01 -1.68593183e-01
-3.38898529e-03 3.97774756e-01 2.61070669e-01 7.63560683e-02
3.05369139e-01 -8.95094037e-01 -9.87959504e-01 -7.20046580e-01
6.20830022e-02 6.64376855e-01 8.92431363e-02 3.61984298... | [9.842270851135254, 1.4905693531036377] |
fbb55201-b020-4a25-ae34-32867d001e7d | image-inpainting-by-patch-propagation-using | null | null | https://ieeexplore.ieee.org/document/5404308 | https://ieeexplore.ieee.org/document/5404308 | Image Inpainting by Patch Propagation Using Patch Sparsity | This paper introduces a novel examplar-based inpainting algorithm through investigating the sparsity of natural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priority and patch representation, which are two crucial steps for patch propagation in the examplar-based ... | ['Zongben Xu', 'Jian Sun'] | 2010-05-01 | null | null | null | journal-2010-5 | ['novel-concepts'] | ['reasoning'] | [ 3.41584355e-01 2.50225455e-01 -5.31851113e-01 7.10905939e-02
-5.91497779e-01 5.81799410e-02 1.01870216e-01 1.37697846e-01
4.42694217e-01 8.55165958e-01 2.55414784e-01 4.32131797e-01
-5.63401952e-02 -8.33211303e-01 -8.53954494e-01 -9.09280419e-01
1.46375328e-01 -2.75574550e-02 2.50253946e-01 -6.26566857... | [11.075202941894531, -2.0358738899230957] |
0e1aae63-b750-488a-a35c-f64373f74b33 | refining-low-resource-unsupervised | 2205.15544 | null | https://arxiv.org/abs/2205.15544v3 | https://arxiv.org/pdf/2205.15544v3.pdf | Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Model | Numerous recent work on unsupervised machine translation (UMT) implies that competent unsupervised translations of low-resource and unrelated languages, such as Nepali or Sinhala, are only possible if the model is trained in a massive multilingual environment, where these low-resource languages are mixed with high-reso... | ['Ai Ti Aw', 'Wu Kui', 'Shafiq Joty', 'Xuan-Phi Nguyen'] | 2022-05-31 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.51951492e-01 -1.14354484e-01 -3.88935208e-01 -2.72648931e-01
-1.57107496e+00 -9.90433753e-01 7.40708649e-01 -1.57618821e-01
-5.20725727e-01 1.39268756e+00 2.89815575e-01 -9.32455897e-01
3.48556787e-01 -4.56759274e-01 -6.41825080e-01 -5.33098400e-01
5.09053588e-01 1.02992845e+00 -2.57232130e-01 -5.16273916... | [11.619808197021484, 10.422966957092285] |
401b24df-11b7-4e48-98bf-08cedf5f41df | method-to-classify-skin-lesions-using | 2008.09418 | null | https://arxiv.org/abs/2008.09418v2 | https://arxiv.org/pdf/2008.09418v2.pdf | Method to Classify Skin Lesions using Dermoscopic images | Skin cancer is the most common cancer in the existing world constituting one-third of the cancer cases. Benign skin cancers are not fatal, can be cured with proper medication. But it is not the same as the malignant skin cancers. In the case of malignant melanoma, in its peak stage, the maximum life expectancy is less ... | ['Umarani Jayaraman', 'Subin Sahayam', 'Hemanth Nadipineni', 'Dusa Sai Charan'] | 2020-08-21 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 3.15975517e-01 2.04369858e-01 -2.34541267e-01 -1.39930427e-01
8.61010030e-02 -2.04359114e-01 3.75868618e-01 2.62397021e-01
-4.87697303e-01 5.79978585e-01 -1.44384384e-01 -3.98554236e-01
-2.71683484e-01 -9.32134151e-01 -2.21764907e-01 -9.18128967e-01
2.28907749e-01 1.26541048e-01 2.06568971e-01 -2.78782606... | [15.611842155456543, -2.983447790145874] |
09a32a4b-b4be-4f68-86e9-1ac258d6f0ff | joint-hand-detection-and-rotation-estimation | 1612.02742 | null | http://arxiv.org/abs/1612.02742v1 | http://arxiv.org/pdf/1612.02742v1.pdf | Joint Hand Detection and Rotation Estimation by Using CNN | Hand detection is essential for many hand related tasks, e.g. parsing hand
pose, understanding gesture, which are extremely useful for robotics and
human-computer interaction. However, hand detection in uncontrolled
environments is challenging due to the flexibility of wrist joint and cluttered
background. We propose a... | ['Shuo Yang', 'Yinda Zhang', 'Ye Yuan', 'Ping Tan', 'Hongan Wang', 'Xiaoming Deng', 'Liang Chang'] | 2016-12-08 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [-2.60320365e-01 -4.74422812e-01 -4.32459682e-01 -1.09279059e-01
-3.66727889e-01 -5.85178137e-01 3.13472003e-01 -6.76092625e-01
-5.32963037e-01 3.73203814e-01 1.26756057e-01 -2.80699104e-01
1.10117890e-01 -4.36998188e-01 -6.59810185e-01 -6.41156495e-01
1.71389967e-01 6.23864949e-01 5.37746727e-01 6.23845272... | [6.609283447265625, -0.6935527920722961] |
44dda5f7-55c6-4fc6-83cb-0c9e17500984 | accented-speech-recognition-under-the-indian | 2209.03787 | null | https://arxiv.org/abs/2209.03787v4 | https://arxiv.org/pdf/2209.03787v4.pdf | Goodness of Pronunciation Pipelines for OOV Problem | In the following report we propose pipelines for Goodness of Pronunciation (GoP) computation solving OOV problem at testing time using Vocab/Lexicon expansion techniques. The pipeline uses different components of ASR system to quantify accent and automatically evaluate them as scores. We use the posteriors of an ASR mo... | ['Ankit Grover'] | 2022-09-08 | null | null | null | null | ['accented-speech-recognition'] | ['speech'] | [ 2.89410166e-02 3.86710137e-01 4.39359486e-01 -4.82080936e-01
-1.28234291e+00 -1.01781404e+00 1.21248491e-01 6.67129681e-02
-3.92602086e-01 6.42904937e-01 7.75541365e-01 -3.62131655e-01
4.71305288e-02 -5.85617959e-01 -4.30263489e-01 -3.00232291e-01
2.07013085e-01 7.30690241e-01 3.06364924e-01 -4.41135585... | [14.327410697937012, 6.811738014221191] |
8dee4503-4efb-485a-8be4-51fffc49f8fc | link-prediction-with-contextualized-self | 2201.10069 | null | https://arxiv.org/abs/2201.10069v2 | https://arxiv.org/pdf/2201.10069v2.pdf | Link Prediction with Contextualized Self-Supervision | Link prediction aims to infer the link existence between pairs of nodes in networks/graphs. Despite their wide application, the success of traditional link prediction algorithms is hindered by three major challenges -- link sparsity, node attribute noise and dynamic changes -- that are faced by many real-world networks... | ['Philip S. Yu', 'Jie Yin', 'Daokun Zhang'] | 2022-01-25 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [ 3.67462933e-01 4.04595941e-01 -1.02114820e+00 -5.31119883e-01
-1.39055803e-01 -3.61679316e-01 6.06953382e-01 3.93966973e-01
3.50702927e-02 8.24419856e-01 1.60980850e-01 -4.16193575e-01
-4.60701346e-01 -1.16942108e+00 -7.49761045e-01 -4.90422398e-01
-6.02242708e-01 5.70720971e-01 3.96900773e-01 -1.33189902... | [7.293572902679443, 6.36279821395874] |
3be9e53a-a634-4ecf-866b-0933b8abe661 | hum3dil-semi-supervised-multi-modal-3d-human | 2212.07729 | null | https://arxiv.org/abs/2212.07729v1 | https://arxiv.org/pdf/2212.07729v1.pdf | HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving | Autonomous driving is an exciting new industry, posing important research questions. Within the perception module, 3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. While hardware systems and sensors ha... | ['Cristian Sminchisescu', 'Dragomir Anguelov', 'Yin Zhou', 'Jingwei Ji', 'Alexander Gorban', 'Mihai Zanfir', 'Andrei Zanfir'] | 2022-12-15 | null | null | null | null | ['3d-pose-estimation', '3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.57716936e-03 -1.34570196e-01 -2.00324878e-01 -6.12072766e-01
-5.94051540e-01 -4.75802779e-01 5.81535697e-01 -2.51232713e-01
-5.69185913e-01 3.34372908e-01 1.02212615e-01 -2.67749727e-01
2.91877508e-01 -6.03552878e-01 -7.70909131e-01 -3.31785977e-01
-8.15909579e-02 7.63475180e-01 6.22235298e-01 -5.26835203... | [7.838972568511963, -2.4173150062561035] |
e45a0e05-4f98-4645-868b-6430c31b1498 | all-fragments-count-in-parser-evaluation | null | null | https://aclanthology.org/L14-1324 | https://aclanthology.org/L14-1324.pdf | All Fragments Count in Parser Evaluation | PARSEVAL, the default paradigm for evaluating constituency parsers, calculates parsing success (Precision/Recall) as a function of the number of matching labeled brackets across the test set. Nodes in constituency trees, however, are connected together to reflect important linguistic relations such as predicate-argumen... | ["Khalil Sima{'}an", 'Jasmijn Bastings'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['human-parsing'] | ['computer-vision'] | [ 4.76391762e-02 3.82243186e-01 -2.28154719e-01 -6.96648479e-01
-1.19707668e+00 -1.39173269e+00 5.39245009e-01 7.19546676e-01
-3.88401151e-01 7.42426634e-01 5.35511851e-01 -8.14945161e-01
-1.62763879e-01 -9.37925875e-01 -3.37887377e-01 -1.97747037e-01
-1.35598630e-01 3.83254677e-01 6.06154025e-01 -4.31753486... | [10.384833335876465, 9.602071762084961] |
969bba10-ac22-4a7c-9355-fd3a5f018619 | who-s-waldo-linking-people-across-text-and | 2108.07253 | null | https://arxiv.org/abs/2108.07253v2 | https://arxiv.org/pdf/2108.07253v2.pdf | Who's Waldo? Linking People Across Text and Images | We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image. In contrast to prior work in visual grounding, which is predominantly object-based, our new task masks out the names of people in captions in order to en... | ['Hadar Averbuch-Elor', 'Noah Snavely', 'Yoav Artzi', 'Apoorv Khandelwal', 'Claire Yuqing Cui'] | 2021-08-16 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Cui_Whos_Waldo_Linking_People_Across_Text_and_Images_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Cui_Whos_Waldo_Linking_People_Across_Text_and_Images_ICCV_2021_paper.pdf | iccv-2021-1 | ['person-centric-visual-grounding'] | ['computer-vision'] | [ 2.22241908e-01 2.66586483e-01 -7.23140389e-02 -6.61908984e-01
-5.67125916e-01 -5.73744535e-01 1.12466359e+00 1.19416364e-01
-4.78156269e-01 6.39141619e-01 9.05042112e-01 -1.48130208e-01
4.17836577e-01 -3.99221659e-01 -1.02356398e+00 -8.29804912e-02
2.47905433e-01 6.97722435e-01 5.67244403e-02 -6.57081679... | [10.729677200317383, 1.5873280763626099] |
2ead8087-e1f6-45b0-bbeb-f0cf3a906ef8 | learning-from-context-agnostic-synthetic-data | 2005.14707 | null | https://arxiv.org/abs/2005.14707v3 | https://arxiv.org/pdf/2005.14707v3.pdf | Towards Context-Agnostic Learning Using Synthetic Data | We propose a novel setting for learning, where the input domain is the image of a map defined on the product of two sets, one of which completely determines the labels. We derive a new risk bound for this setting that decomposes into a bias and an error term, and exhibits a surprisingly weak dependence on the true labe... | ['Martin Rinard', 'Charles Jin'] | 2020-05-29 | towards-context-agnostic-learning-using | http://proceedings.neurips.cc/paper/2021/hash/dccb1c3a558c50d389c24d69a9856730-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/dccb1c3a558c50d389c24d69a9856730-Paper.pdf | neurips-2021-12 | ['traffic-sign-recognition'] | ['computer-vision'] | [ 6.08279407e-01 1.68489128e-01 -5.34582622e-02 -3.85438561e-01
-8.65519881e-01 -9.43848968e-01 8.76301408e-01 8.75456184e-02
-5.33041656e-01 4.68295008e-01 -2.97127128e-01 -2.01762274e-01
1.94193393e-01 -7.87688196e-01 -1.20591938e+00 -9.39845979e-01
-8.14866200e-02 5.31782568e-01 4.48320985e-01 8.74776989... | [9.770793914794922, 2.7599313259124756] |
b95f519c-2a23-44a5-a795-743dbb593259 | hate-alert-dravidianlangtech-acl2022 | 2204.12587 | null | https://arxiv.org/abs/2204.12587v1 | https://arxiv.org/pdf/2204.12587v1.pdf | hate-alert@DravidianLangTech-ACL2022: Ensembling Multi-Modalities for Tamil TrollMeme Classification | Social media platforms often act as breeding grounds for various forms of trolling or malicious content targeting users or communities. One way of trolling users is by creating memes, which in most cases unites an image with a short piece of text embedded on top of it. The situation is more complex for multilingual(e.g... | ['Animesh Mukherjee', 'Somnath Banerjee', 'Mithun Das'] | 2022-03-25 | null | https://aclanthology.org/2022.dravidianlangtech-1.8 | https://aclanthology.org/2022.dravidianlangtech-1.8.pdf | dravidianlangtech-acl-2022-5 | ['meme-classification'] | ['natural-language-processing'] | [-3.85233536e-02 -3.00625771e-01 -5.00245579e-02 4.40119714e-01
-8.44415963e-01 -8.41290295e-01 1.38250732e+00 2.94830084e-01
-7.74388373e-01 5.56643844e-01 2.19899118e-01 -1.97283000e-01
5.36772609e-01 -7.27475941e-01 -6.17025793e-01 -6.68450713e-01
8.00044760e-02 2.43366539e-01 5.04357517e-01 -4.58339930... | [8.474621772766113, 10.698049545288086] |
4c13782c-45ae-4582-83be-3418ab03a8bc | de-risking-carbon-capture-and-sequestration | 2212.08596 | null | https://arxiv.org/abs/2212.08596v1 | https://arxiv.org/pdf/2212.08596v1.pdf | De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images | With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO2... | ['Felix J. Herrmann', 'Mathias Louboutin', 'Ziyi Yin', 'Abhinav Prakash Gahlot', 'Huseyin Tuna Erdinc'] | 2022-12-16 | null | null | null | null | ['seismic-imaging'] | ['miscellaneous'] | [ 2.56422192e-01 -3.04906130e-01 4.07973155e-02 3.48723024e-01
-1.04113233e+00 -7.15535700e-01 9.52776968e-01 2.23986015e-01
-2.43486360e-01 6.06360495e-01 2.82552183e-01 -9.85239625e-01
3.44259478e-03 -8.32489669e-01 -7.21860588e-01 -9.97870088e-01
-5.16860843e-01 1.49229094e-01 2.98217803e-01 6.07644580... | [6.823764324188232, 2.62823486328125] |
f5e96ba3-a6b4-4831-9993-89987db0cc71 | on-the-direct-maximization-of-quadratic | 1509.07107 | null | http://arxiv.org/abs/1509.07107v3 | http://arxiv.org/pdf/1509.07107v3.pdf | On The Direct Maximization of Quadratic Weighted Kappa | In recent years, quadratic weighted kappa has been growing in popularity in
the machine learning community as an evaluation metric in domains where the
target labels to be predicted are drawn from integer ratings, usually obtained
from human experts. For example, it was the metric of choice in several recent,
high prof... | ['Derek Justice', 'David Vaughn'] | 2015-09-23 | null | null | null | null | ['diabetic-retinopathy-detection'] | ['medical'] | [-1.48813784e-01 -2.35753935e-02 -3.43830764e-01 -6.19929969e-01
-1.05453479e+00 -7.39740908e-01 1.25664309e-01 5.96340775e-01
-5.60248852e-01 9.24348116e-01 1.42822564e-01 -3.72802854e-01
-5.10656118e-01 -3.73995662e-01 -3.22716266e-01 -8.59823883e-01
-1.40413389e-01 5.35884798e-01 2.17973411e-01 4.49952520... | [8.542142868041992, 4.427086353302002] |
90c76ddd-dcc7-42eb-a5c6-3b81c2adb43a | personalised-recommendations-of-sleep | 2208.00033 | null | https://arxiv.org/abs/2208.00033v1 | https://arxiv.org/pdf/2208.00033v1.pdf | Personalised recommendations of sleep behaviour with neural networks using sleep diaries captured in Sleepio | SleepioTM is a digital mobile phone and web platform that uses techniques from cognitive behavioural therapy (CBT) to improve sleep in people with sleep difficulty. As part of this process, Sleepio captures data about the sleep behaviour of the users that have consented to such data being processed. For neural networks... | ['Chris Miller', 'Tom Walker', 'Kate Saunders', 'Niall Taylor', 'Jenny Gu', 'Alasdair Henry', 'Maria Liakata', 'Colin Espie', 'Alejo Nevado-Holgado'] | 2022-07-29 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [ 2.46699959e-01 2.96428949e-01 -5.16589940e-01 -6.49657428e-01
-1.80314764e-01 -3.95920351e-02 -1.15238965e-01 1.55038506e-01
-7.09228575e-01 8.22404742e-01 7.80219913e-01 -6.81823075e-01
-4.13293540e-01 -4.43312883e-01 -6.09775819e-03 -6.17182791e-01
3.47118676e-02 6.67760789e-01 -2.02210903e-01 5.16176969... | [13.53902816772461, 3.486248731613159] |
cd34ad54-8f62-4200-910a-af8a2f738286 | consistent-structural-relation-learning-for | null | null | http://proceedings.neurips.cc/paper/2020/hash/7504adad8bb96320eb3afdd4df6e1f60-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/7504adad8bb96320eb3afdd4df6e1f60-Paper.pdf | Consistent Structural Relation Learning for Zero-Shot Segmentation | Zero-shot semantic segmentation aims to recognize the semantics of pixels from unseen categories with zero training samples. Previous practice [1] proposed to train the classifiers for unseen categories using the visual features generated from semantic word embeddings. However, the generator is merely learned on the se... | ['Yi Yang', 'Yunchao Wei', 'Peike Li'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['zero-shot-segmentation'] | ['computer-vision'] | [ 4.40379977e-01 1.40439555e-01 -1.63250864e-01 -8.07891607e-01
-2.66645044e-01 -6.07870460e-01 7.27033257e-01 1.50792137e-01
-3.58493090e-01 3.73371869e-01 1.94000408e-01 -4.23243567e-02
9.49488059e-02 -8.49992633e-01 -7.63521016e-01 -7.76062191e-01
2.87231773e-01 -4.64999191e-02 3.85677844e-01 -2.14889646... | [9.913046836853027, 2.0159912109375] |
fee65664-500d-40d6-9767-0afd3944f884 | extractive-summarization-and-dialogue-act | null | null | https://aclanthology.org/W14-4318 | https://aclanthology.org/W14-4318.pdf | Extractive Summarization and Dialogue Act Modeling on Email Threads: An Integrated Probabilistic Approach | null | ['Giuseppe Carenini', 'Tatsuro Oya'] | 2014-06-01 | null | null | null | ws-2014-6 | ['meeting-summarization'] | ['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... | [-7.268677234649658, 3.6840670108795166] |
f0cb3a78-83a2-45e4-9938-7e7b0f5b507b | photogrammetric-point-cloud-segmentation-and | null | null | https://ascelibrary.org/doi/abs/10.1061/(ASCE)ME.1943-5479.0000737 | https://www.researchgate.net/profile/Meida-Chen/publication/339605064_Photogrammetric_Point_Cloud_Segmentation_and_Object_Information_Extraction_for_Creating_Virtual_Environments_and_Simulations/links/5ee1e4d1299bf1faac4aee4e/Photogrammetric-Point-Cloud-Segmentation-and-Object-Information-Extraction-for-Creating-Virtua... | Photogrammetric point cloud segmentation and object information extraction for creating virtual environments and simulations | Photogrammetric techniques have dramatically improved over the last few years, enabling the creation of visually compelling three-dimensional (3D) meshes using unmanned aerial vehicle imagery. These high-quality 3D meshes have attracted notice from both academicians and industry practitioners in developing virtual envi... | ['Lucio Soibelman', 'Ryan McAlinden', 'Andrew Feng', 'Meida Chen'] | 2020-03-01 | null | null | null | journal-of-management-in-engineering-2020-3 | ['point-cloud-segmentation'] | ['computer-vision'] | [ 2.62344927e-01 -3.83241475e-01 -3.42609957e-02 -3.18203270e-01
-4.71118957e-01 -5.61342061e-01 5.78359663e-01 7.64083326e-01
-1.70687571e-01 4.86494124e-01 -4.46385205e-01 -2.99049139e-01
-4.17452484e-01 -1.32240760e+00 -5.61506450e-01 -2.19504043e-01
-5.11228204e-01 6.71383142e-01 3.60363573e-01 -3.19744915... | [8.404561042785645, -2.6539113521575928] |
4e4ab0fc-ec76-442b-9a4a-9e5b6ae2da64 | lightcts-a-lightweight-framework-for | 2302.11974 | null | https://arxiv.org/abs/2302.11974v2 | https://arxiv.org/pdf/2302.11974v2.pdf | LightCTS: A Lightweight Framework for Correlated Time Series Forecasting | Correlated time series (CTS) forecasting plays an essential role in many practical applications, such as traffic management and server load control. Many deep learning models have been proposed to improve the accuracy of CTS forecasting. However, while models have become increasingly complex and computationally intensi... | ['Yan Zhao', 'Hua Lu', 'Christian S. Jensen', 'Huan Li', 'Dalin Zhang', 'Zhichen Lai'] | 2023-02-23 | null | null | null | null | ['correlated-time-series-forecasting', 'univariate-time-series-forecasting'] | ['time-series', 'time-series'] | [ 1.21790338e-02 -7.26858675e-01 -3.14633995e-01 -5.65471888e-01
-5.67126274e-01 -1.53337985e-01 6.74556613e-01 -2.06505239e-01
-1.10623598e-01 4.47445452e-01 -7.05294609e-02 -7.52320111e-01
-2.45918453e-01 -6.69099152e-01 -4.09308374e-01 -9.14075971e-01
-2.27162212e-01 1.85298219e-01 3.67169261e-01 -1.77311450... | [7.007943153381348, 2.845611095428467] |
7e4d51a4-2980-4b14-b56c-232d68b7e432 | vision-transformer-equipped-with-neural | 2204.02181 | null | https://arxiv.org/abs/2204.02181v1 | https://arxiv.org/pdf/2204.02181v1.pdf | Vision Transformer Equipped with Neural Resizer on Facial Expression Recognition Task | When it comes to wild conditions, Facial Expression Recognition is often challenged with low-quality data and imbalanced, ambiguous labels. This field has much benefited from CNN based approaches; however, CNN models have structural limitation to see the facial regions in distant. As a remedy, Transformer has been intr... | ['Hyeon Yeo', 'Kyungtae Ko', 'Jiho Seo', 'Wei-Jin Park', 'Soyeon Kim', 'Hyeonbin Hwang'] | 2022-04-05 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.87828749e-01 1.92679331e-01 -1.60685137e-01 -7.95219123e-01
-4.78998095e-01 6.79001957e-02 3.19453657e-01 -6.20561659e-01
-2.35932603e-01 6.99443102e-01 2.11633772e-01 1.33748189e-01
1.06655598e-01 -7.68022954e-01 -6.94133461e-01 -8.69487643e-01
4.12705272e-01 5.93307950e-02 -2.54129227e-02 -4.53908116... | [13.550697326660156, 1.5586473941802979] |
d042d0a5-f9a9-4d72-9ccc-87fa07bd8949 | an-empirical-study-of-quantum-dynamics-as-a | 2206.09241 | null | https://arxiv.org/abs/2206.09241v2 | https://arxiv.org/pdf/2206.09241v2.pdf | An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States | We consider the Feynman-Kitaev formalism applied to a spin chain described by the transverse field Ising model. This formalism consists of building a Hamiltonian whose ground state encodes the time evolution of the spin chain at discrete time steps. To find this ground state, variational wave functions parameterised by... | ['Fabio A. González', 'Herbert Vinck-Posada', 'Vladimir Vargas-Calderón'] | 2022-06-18 | null | null | null | null | ['variational-monte-carlo'] | ['miscellaneous'] | [ 1.49507254e-01 2.13528037e-01 -1.48048326e-02 -4.55198586e-02
-3.95247668e-01 -5.41925430e-01 6.55482531e-01 -3.24101478e-01
-4.80084926e-01 1.05305898e+00 -3.31515074e-01 -3.22338611e-01
-3.53911847e-01 -1.04633784e+00 -4.60282177e-01 -1.26968157e+00
-2.08510116e-01 8.11500669e-01 1.35454208e-01 -5.31702578... | [5.557745456695557, 4.9569854736328125] |
d271938c-896f-4407-ae01-2c748676c1bc | sagc-a68-a-space-access-graph-dataset-for-the | 2307.04515 | null | https://arxiv.org/abs/2307.04515v1 | https://arxiv.org/pdf/2307.04515v1.pdf | SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings | The analysis of building models for usable area, building safety, and energy use requires accurate classification data of spaces and space elements. To reduce input model preparation effort and errors, automated classification of spaces and space elements is desirable. A barrier hindering the utilization of Graph Deep ... | ['Georg Suter', 'Amir Ziaee'] | 2023-07-10 | null | null | null | null | ['graph-attention', 'classification-1'] | ['graphs', 'methodology'] | [-4.71875072e-02 1.16072923e-01 5.07762358e-02 -4.06512141e-01
-3.93595546e-01 -6.44105673e-01 4.37850654e-01 5.72185695e-01
1.72226742e-01 5.36372483e-01 2.97537833e-01 -1.01771331e+00
-4.04789269e-01 -1.43891633e+00 -7.19625950e-01 -1.87507540e-01
-1.32000670e-01 3.58745873e-01 -1.41927943e-01 -3.75260085... | [8.373861312866211, -1.8904662132263184] |
f9262c39-d1d5-47d0-8429-cfcc741aa736 | black-box-adversarial-attacks-in-autonomous | 2101.06092 | null | https://arxiv.org/abs/2101.06092v1 | https://arxiv.org/pdf/2101.06092v1.pdf | Black-box Adversarial Attacks in Autonomous Vehicle Technology | Despite the high quality performance of the deep neural network in real-world applications, they are susceptible to minor perturbations of adversarial attacks. This is mostly undetectable to human vision. The impact of such attacks has become extremely detrimental in autonomous vehicles with real-time "safety" concerns... | ['C Krishna Mohan', 'Reshmi Mitra', 'C Vishnu', 'K Naveen Kumar'] | 2021-01-15 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 3.36887762e-02 1.81001499e-01 3.62858742e-01 -2.72743613e-01
-6.36953592e-01 -6.88779473e-01 7.80847669e-01 -3.78318787e-01
-6.96826756e-01 9.66646791e-01 -5.14868736e-01 -7.27483094e-01
1.70865059e-01 -8.84934366e-01 -1.16893935e+00 -9.62480843e-01
1.59766197e-01 2.84703314e-01 8.63532782e-01 -3.27662051... | [5.414913654327393, 7.826550006866455] |
5bd92525-92ef-4170-8889-ab4b28e3ca3c | topic-propagation-in-conversational-search | 2004.14054 | null | https://arxiv.org/abs/2004.14054v1 | https://arxiv.org/pdf/2004.14054v1.pdf | Topic Propagation in Conversational Search | In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the conversation evolves through user utterances and system replies. The retrieval of documents relevant to a given utterance in a conversation is ... | ['C. I. Muntean', 'R. Perego', 'I. Mele', 'F. M. Nardini', 'O. Frieder', 'N. Tonellotto'] | 2020-04-29 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 5.60214281e-01 5.17497003e-01 8.99937302e-02 -7.03310311e-01
-1.39777911e+00 -8.48329484e-01 1.16814780e+00 3.21804762e-01
-4.94942516e-01 6.80061996e-01 8.94689202e-01 -1.66922241e-01
-8.24555606e-02 -2.42788255e-01 -3.58467877e-01 -3.18895340e-01
-1.11331113e-01 1.08081651e+00 3.91433477e-01 -5.99080384... | [12.208694458007812, 7.881113529205322] |
7a0602a7-689b-40d5-acef-0a30b71de452 | unifying-clustered-and-non-stationary-bandits | 2009.02463 | null | https://arxiv.org/abs/2009.02463v1 | https://arxiv.org/pdf/2009.02463v1.pdf | Unifying Clustered and Non-stationary Bandits | Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though the essence in solving these two problems overlaps considerably, they have been studied independently. In this paper, we connect these two s... | ['Chuanhao Li', 'Qingyun Wu', 'Hongning Wang'] | 2020-09-05 | null | null | null | null | ['online-clustering'] | ['computer-vision'] | [ 9.27764848e-02 -2.16825858e-01 -1.21694219e+00 -2.91012228e-01
-9.14316654e-01 -1.10114920e+00 2.23030910e-01 -3.33233625e-01
-2.12185644e-02 1.22418547e+00 2.03026682e-02 -9.02326405e-01
-9.45373416e-01 -4.81498927e-01 -9.90803003e-01 -9.22652185e-01
-1.07267387e-01 7.15290844e-01 5.74453957e-02 3.55833709... | [4.5447869300842285, 3.3008763790130615] |
27aa76b1-da97-41ff-9090-d56e64790076 | mmptrack-large-scale-densely-annotated-multi | 2111.15157 | null | https://arxiv.org/abs/2111.15157v1 | https://arxiv.org/pdf/2111.15157v1.pdf | MMPTRACK: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark | Multi-camera tracking systems are gaining popularity in applications that demand high-quality tracking results, such as frictionless checkout because monocular multi-object tracking (MOT) systems often fail in cluttered and crowded environments due to occlusion. Multiple highly overlapped cameras can significantly alle... | ['Zicheng Liu', 'Jiang Wang', 'Houdong Hu', 'Peng Chu', 'Zhizheng Zhang', 'Chunyu Wang', 'Quanzeng You', 'Xiaotian Han'] | 2021-11-30 | null | null | null | null | ['multiple-people-tracking'] | ['computer-vision'] | [-2.63200402e-01 -8.35862219e-01 2.52850890e-01 -1.40099004e-01
-5.47824502e-01 -9.43264902e-01 1.45072371e-01 -1.92981720e-01
-5.93246460e-01 5.97221911e-01 -8.80782977e-02 1.57239631e-01
5.07825732e-01 -3.52262318e-01 -5.50425172e-01 -6.09097540e-01
4.78111178e-01 6.25034153e-01 6.91113770e-01 2.08433464... | [6.7153496742248535, -1.618273377418518] |
18badebe-4c9a-4328-ab6d-6bf5aa5e7c5a | robust-localized-multi-view-subspace | 1705.07777 | null | http://arxiv.org/abs/1705.07777v1 | http://arxiv.org/pdf/1705.07777v1.pdf | Robust Localized Multi-view Subspace Clustering | In multi-view clustering, different views may have different confidence
levels when learning a consensus representation. Existing methods usually
address this by assigning distinctive weights to different views. However, due
to noisy nature of real-world applications, the confidence levels of samples in
the same view m... | ['Bao-Gang Hu', 'Ran He', 'Siwei Lyu', 'Yanbo Fan', 'Jian Liang'] | 2017-05-22 | null | null | null | null | ['multi-view-subspace-clustering'] | ['computer-vision'] | [-2.23923460e-01 -5.66226065e-01 -9.42980275e-02 -3.59898508e-01
-6.65625095e-01 -7.22956061e-01 1.95126921e-01 -2.02611983e-01
-5.12057263e-03 3.51302177e-01 3.63308638e-01 3.88584107e-01
-3.36266518e-01 -4.13152516e-01 -4.14440781e-01 -1.10443866e+00
4.04782861e-01 3.34260106e-01 8.24030638e-02 3.16217035... | [8.211053848266602, 4.61764669418335] |
ab0d9158-3fc4-4093-bcd5-c23c3f3dbf1a | verifying-safety-of-neural-networks-from | 2306.15403 | null | https://arxiv.org/abs/2306.15403v1 | https://arxiv.org/pdf/2306.15403v1.pdf | Verifying Safety of Neural Networks from Topological Perspectives | Neural networks (NNs) are increasingly applied in safety-critical systems such as autonomous vehicles. However, they are fragile and are often ill-behaved. Consequently, their behaviors should undergo rigorous guarantees before deployment in practice. In this paper, we propose a set-boundary reachability method to inve... | ['Wanwei Liu', 'Wenjing Yang', 'Ji Wang', 'Bai Xue', 'Dejin Ren', 'Zhen Liang'] | 2023-06-27 | null | null | null | null | ['autonomous-vehicles'] | ['computer-vision'] | [ 1.92934334e-01 4.65356171e-01 -1.57715783e-01 -1.22298477e-02
2.01006606e-01 -6.98541939e-01 3.26903552e-01 -1.54925972e-01
-1.67717546e-01 7.48376608e-01 -4.27509040e-01 -8.29660416e-01
-4.91239101e-01 -1.00151205e+00 -1.03590238e+00 -9.33308780e-01
-1.51056796e-01 -1.28122687e-01 4.82017666e-01 -4.00387496... | [6.09644079208374, 7.645134925842285] |
8d80db8f-cf3a-4780-ac07-592332bc52f2 | change-detection-in-vhr-imagery-with-severe | null | null | https://ieeexplore.ieee.org/document/9740657 | https://ieeexplore.ieee.org/document/9740657 | Change Detection in VHR Imagery With Severe Co-Registration Errors Using Deep Learning: A Comparative Study | Change detection (CD) through Earth observation techniques can offer very significant information for monitoring tasks in a time-efficient manner. Very high-resolution (VHR) images can display objects in fine detail, thus making it possible to rapidly perceive isolated changes. However, this is a challenging task becau... | ['Vassilia Karathanassi', 'Viktoria Kristollari'] | 2022-03-24 | null | null | null | ieee-access-2022-3 | ['change-detection-for-remote-sensing-images'] | ['miscellaneous'] | [-3.61233577e-02 -5.06369889e-01 3.74375165e-01 -2.40210593e-01
-6.57242119e-01 -2.01244831e-01 9.32018101e-01 3.15097868e-01
-6.61314487e-01 6.03415668e-01 -1.64652139e-01 9.03883204e-02
-4.35960531e-01 -1.15392208e+00 -1.95762679e-01 -8.86385798e-01
-5.07580340e-01 5.54967344e-01 2.91381419e-01 -6.72955573... | [9.781009674072266, -1.8983839750289917] |
e4f5689b-f0c0-40d7-afe4-3b4cb867ea00 | verifying-fairness-in-quantum-machine | 2207.11173 | null | https://arxiv.org/abs/2207.11173v1 | https://arxiv.org/pdf/2207.11173v1.pdf | Verifying Fairness in Quantum Machine Learning | Due to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making. In this work, we define a forma... | ['Mingsheng Ying', 'Wang Fang', 'Ji Guan'] | 2022-07-22 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [-5.10529941e-03 1.96146563e-01 -1.43631101e-01 -6.12438202e-01
-3.03879797e-01 -3.27518970e-01 2.52005965e-01 3.91733050e-01
-6.01965725e-01 9.03585970e-01 -3.80284965e-01 -6.27138078e-01
-3.51696849e-01 -1.25501370e+00 -4.55360264e-01 -8.17150354e-01
-2.48731464e-01 5.64376414e-01 -3.43544066e-01 -4.12525296... | [5.593581199645996, 5.019985198974609] |
9286e713-4c6f-4fc6-9830-8b65df1a4b4d | a-joint-sentiment-target-stance-model-for | null | null | https://aclanthology.org/C16-1250 | https://aclanthology.org/C16-1250.pdf | A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets | Classifying the stance expressed in online microblogging social media is an emerging problem in opinion mining. We propose a probabilistic approach to stance classification in tweets, which models stance, target of stance, and sentiment of tweet, jointly. Instead of simply conjoining the sentiment or target variables a... | ['Javid Ebrahimi', 'Dejing Dou', 'Daniel Lowd'] | 2016-12-01 | a-joint-sentiment-target-stance-model-for-1 | https://aclanthology.org/C16-1250 | https://aclanthology.org/C16-1250.pdf | coling-2016-12 | ['subjectivity-analysis'] | ['natural-language-processing'] | [ 2.83059269e-01 2.31721938e-01 -5.96684039e-01 -8.94527853e-01
-1.00418532e+00 -5.19168258e-01 9.52985227e-01 2.66045809e-01
-3.68418664e-01 6.29458547e-01 4.14119840e-01 -1.61991924e-01
3.68348777e-01 -1.10566413e+00 -5.78336298e-01 -9.56646740e-01
1.70255482e-01 6.57682300e-01 7.44899884e-02 -5.29206038... | [8.816258430480957, 9.641390800476074] |
a72a2696-a7d8-4d0e-b6b7-a767d91e0266 | otseq2set-an-optimal-transport-enhanced | 2210.14523 | null | https://arxiv.org/abs/2210.14523v2 | https://arxiv.org/pdf/2210.14523v2.pdf | OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification | Extreme multi-label text classification (XMTC) is the task of finding the most relevant subset labels from an extremely large-scale label collection. Recently, some deep learning models have achieved state-of-the-art results in XMTC tasks. These models commonly predict scores for all labels by a fully connected layer a... | ['Yin Zhang', 'Jie Cao'] | 2022-10-26 | null | null | null | null | ['multi-label-text-classification', 'multi-label-text-classification'] | ['methodology', 'natural-language-processing'] | [ 5.65023661e-01 -2.84958720e-01 -4.16480660e-01 -7.07279146e-01
-1.15854371e+00 -7.10289359e-01 2.49932304e-01 -2.31478095e-01
-5.48397720e-01 8.35963249e-01 8.93623531e-02 -2.50779450e-01
-1.08565643e-01 -6.54581368e-01 -6.46018982e-01 -8.67452383e-01
4.41888779e-01 8.81026685e-01 1.75727338e-01 -1.41918287... | [9.603764533996582, 4.413486957550049] |
e2d1a57c-f7a1-4049-b0b3-a9a8767a1bcd | nilc-at-sr20-exploring-pre-trained-models-in | null | null | https://aclanthology.org/2020.msr-1.6 | https://aclanthology.org/2020.msr-1.6.pdf | NILC at SR’20: Exploring Pre-Trained Models in Surface Realisation | This paper describes the submission by the NILC Computational Linguistics research group of the University of S ̃ao Paulo/Brazil to the English Track 2 (closed sub-track) at the Surface Realisation Shared Task 2020. The success of the current pre-trained models like BERT or GPT-2 in several tasks is well-known, however... | ['Thiago Pardo', 'Marco Antonio Sobrevilla Cabezudo'] | null | null | null | null | msr-coling-2020-12 | ['data-to-text-generation'] | ['natural-language-processing'] | [ 1.82083011e-01 1.10501528e+00 -1.19966224e-01 -2.30167419e-01
-7.46741951e-01 -3.65518689e-01 1.25443590e+00 1.38359785e-01
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1.26624927e-01 -7.85722792e-01 -4.46962208e-01 -3.88097942e-01
1.81685418e-01 1.09017670e+00 5.34200259e-02 -7.51758754... | [11.369269371032715, 9.177227973937988] |
dd019e5b-8e3e-4282-8a8e-c6177e8abdc3 | ghq-grouped-hybrid-q-learning-for | 2303.01070 | null | https://arxiv.org/abs/2303.01070v1 | https://arxiv.org/pdf/2303.01070v1.pdf | GHQ: Grouped Hybrid Q Learning for Heterogeneous Cooperative Multi-agent Reinforcement Learning | Previous deep multi-agent reinforcement learning (MARL) algorithms have achieved impressive results, typically in homogeneous scenarios. However, heterogeneous scenarios are also very common and usually harder to solve. In this paper, we mainly discuss cooperative heterogeneous MARL problems in Starcraft Multi-Agent Ch... | ['Kai Lv', 'Sheng Han', 'Xiangsen Wang', 'Youfang Lin', 'Xiaoyang Yu'] | 2023-03-02 | null | null | null | null | ['smac-1', 'starcraft', 'smac'] | ['playing-games', 'playing-games', 'playing-games'] | [-6.56167746e-01 -1.94647044e-01 -1.99373409e-01 2.61648446e-02
-8.38785410e-01 -6.37820542e-01 5.76359272e-01 2.82527566e-01
-4.29530293e-01 1.11235058e+00 1.82975963e-01 1.98401749e-01
-6.82420135e-01 -6.84606373e-01 -6.54991806e-01 -1.01051664e+00
-6.90878868e-01 8.46049666e-01 3.36767286e-01 -8.19249332... | [3.723856210708618, 1.9335036277770996] |
f90818fd-6612-4ffc-b53e-d0bdcb42e501 | semantic-clustering-of-pivot-paraphrases | null | null | https://aclanthology.org/L14-1401 | https://aclanthology.org/L14-1401.pdf | Semantic Clustering of Pivot Paraphrases | Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications. In this study, we analyse the semantics of unigram pivot paraphrases and use a graph-based sense induction approach to unveil hidden sense distinctions in ... | ['Emilia Verzeni', 'Diana McCarthy', 'Marianna Apidianaki'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['multilingual-nlp'] | ['natural-language-processing'] | [ 2.08167389e-01 -1.61341894e-02 -6.12859070e-01 -2.19166905e-01
-5.55778563e-01 -1.14067650e+00 8.08789313e-01 8.65910232e-01
-5.28596640e-01 9.89794791e-01 6.24689579e-01 -6.27014935e-01
-5.33849120e-01 -5.92487514e-01 -3.00679952e-01 -2.64168948e-01
4.39799219e-01 6.99493170e-01 2.24055946e-01 -9.42394018... | [10.71280288696289, 9.524370193481445] |
60c082dd-6110-478b-997f-97754d8ed46a | end-to-end-automatic-speech-translation-of | 1802.04200 | null | http://arxiv.org/abs/1802.04200v1 | http://arxiv.org/pdf/1802.04200v1.pdf | End-to-End Automatic Speech Translation of Audiobooks | We investigate end-to-end speech-to-text translation on a corpus of
audiobooks specifically augmented for this task. Previous works investigated
the extreme case where source language transcription is not available during
learning nor decoding, but we also study a midway case where source language
transcription is avai... | ['Alexandre Bérard', 'Laurent Besacier', 'Ali Can Kocabiyikoglu', 'Olivier Pietquin'] | 2018-02-12 | null | null | null | null | ['speech-to-text-translation'] | ['natural-language-processing'] | [ 3.98794800e-01 4.18387949e-01 9.84089524e-02 -3.22793901e-01
-1.53484392e+00 -7.52440691e-01 5.45614898e-01 -2.43377805e-01
-3.61580372e-01 8.54815423e-01 4.11831468e-01 -7.44828463e-01
5.51863134e-01 -1.84803993e-01 -7.70977974e-01 -4.01154548e-01
2.28554577e-01 7.42200434e-01 9.81556401e-02 -3.15671921... | [14.542235374450684, 7.043604850769043] |
e89b62f2-278f-4031-bc49-59853718d71c | progressively-parsing-interactional-objects | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Ni_Progressively_Parsing_Interactional_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Ni_Progressively_Parsing_Interactional_CVPR_2016_paper.pdf | Progressively Parsing Interactional Objects for Fine Grained Action Detection | Fine grained video action analysis often requires reliable detection and tracking of various interacting objects and human body parts, denoted as interactional object parsing. However, most of the previous methods based on either independent or joint object detection might suffer from high model complexity and challeng... | ['Bingbing Ni', 'Shenghua Gao', 'Xiaokang Yang'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['fine-grained-action-detection', 'action-analysis'] | ['computer-vision', 'computer-vision'] | [ 3.53314489e-01 -1.35131210e-01 6.30211905e-02 -2.98553795e-01
-5.59325755e-01 -3.38610768e-01 4.13911968e-01 -9.22369584e-02
-5.02344549e-01 6.81647003e-01 9.77322236e-02 2.50768006e-01
1.22737288e-01 -4.49921131e-01 -8.29700947e-01 -7.18749583e-01
1.53469786e-01 1.48185700e-01 8.72715950e-01 2.29814202... | [8.091127395629883, 0.3083561360836029] |
57ba6eda-ba64-46a1-8519-a7df16b4c9ac | generalization-in-adaptive-data-analysis-and | 1506.02629 | null | http://arxiv.org/abs/1506.02629v2 | http://arxiv.org/pdf/1506.02629v2.pdf | Generalization in Adaptive Data Analysis and Holdout Reuse | Overfitting is the bane of data analysts, even when data are plentiful.
Formal approaches to understanding this problem focus on statistical inference
and generalization of individual analysis procedures. Yet the practice of data
analysis is an inherently interactive and adaptive process: new analyses and
hypotheses ar... | ['Moritz Hardt', 'Cynthia Dwork', 'Vitaly Feldman', 'Toniann Pitassi', 'Aaron Roth', 'Omer Reingold'] | 2015-06-08 | generalization-in-adaptive-data-analysis-and-1 | http://papers.nips.cc/paper/5993-generalization-in-adaptive-data-analysis-and-holdout-reuse | http://papers.nips.cc/paper/5993-generalization-in-adaptive-data-analysis-and-holdout-reuse.pdf | neurips-2015-12 | ['holdout-set'] | ['computer-vision'] | [ 4.07011151e-01 6.78533539e-02 -1.24751389e-01 -3.78357023e-01
-7.57020354e-01 -8.65030944e-01 3.37133616e-01 4.47255552e-01
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-5.16845226e-01 -7.89238274e-01 -7.78551996e-01 -8.56976986e-01
-3.89452010e-01 3.12758476e-01 6.92865327e-02 8.44665840... | [7.552873134613037, 4.570426940917969] |
059204a7-9b5e-480d-b296-68bfafb4cbf6 | monitoring-and-improving-personalized-sleep | 2211.12778 | null | https://arxiv.org/abs/2211.12778v1 | https://arxiv.org/pdf/2211.12778v1.pdf | Monitoring and Improving Personalized Sleep Quality from Long-Term Lifelogs | Sleep plays a vital role in our physical, cognitive, and psychological well-being. Despite its importance, long-term monitoring of personalized sleep quality (SQ) in real-world contexts is still challenging. Many sleep researches are still developing clinically and far from accessible to the general public. Fortunately... | ['Koji Zettsu', 'Minh-Son Dao', 'Wenbin Gan'] | 2022-11-23 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [-2.06867427e-01 -1.87126338e-01 -4.68747735e-01 -5.67839026e-01
-3.23918313e-01 2.27401257e-01 -9.75317359e-02 3.60681623e-01
-3.84998202e-01 1.03996980e+00 6.04037642e-01 1.37883931e-01
-4.51989144e-01 -9.28153574e-01 2.26827506e-02 -7.27726340e-01
3.52644362e-02 1.03564754e-01 4.37342599e-02 -3.19101483... | [13.585501670837402, 3.4223227500915527] |
f5034b72-5ef7-41ee-a24c-b933d25f5011 | revisiting-lstm-networks-for-semi-supervised-1 | 2009.04007 | null | https://arxiv.org/abs/2009.04007v1 | https://arxiv.org/pdf/2009.04007v1.pdf | Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function | In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches. Several prior works have suggested that either complex pretraining schemes using unsupervised methods such as language modeling (Dai and Le 2015; Miyato, Dai, and Goodfellow 2016)... | ['Manzil Zaheer', 'Ruslan Salakhutdinov', 'Devendra Singh Sachan'] | 2020-09-08 | revisiting-lstm-networks-for-semi-supervised | https://www.semanticscholar.org/paper/Revisiting-LSTM-Networks-for-Semi-Supervised-Text-Sachan-Petuum/c3f89364aecd661eb032840d2fe3efd0f6d1698c | https://www.aaai.org/Papers/AAAI/2019/AAAI-SachanD.7236.pdf | aaai-2019-2019-2 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 1.19204640e-01 3.65337193e-01 -3.78085554e-01 -6.32250905e-01
-9.14822936e-01 -3.65321606e-01 7.81426847e-01 3.22247654e-01
-6.41238034e-01 8.77319455e-01 1.07258342e-01 -4.64982599e-01
2.39616230e-01 -7.00351417e-01 -6.42521799e-01 -4.28986460e-01
2.11188197e-01 4.02926087e-01 -1.36859016e-02 -2.44121522... | [10.628454208374023, 8.137564659118652] |
d3757d30-2efd-45e2-89b0-2340048e588f | pixor-real-time-3d-object-detection-from | 1902.06326 | null | http://arxiv.org/abs/1902.06326v3 | http://arxiv.org/pdf/1902.06326v3.pdf | PIXOR: Real-time 3D Object Detection from Point Clouds | We address the problem of real-time 3D object detection from point clouds in
the context of autonomous driving. Computation speed is critical as detection
is a necessary component for safety. Existing approaches are, however,
expensive in computation due to high dimensionality of point clouds. We utilize
the 3D data mo... | ['Bin Yang', 'Raquel Urtasun', 'Wenjie Luo'] | 2019-02-17 | pixor-real-time-3d-object-detection-from-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Yang_PIXOR_Real-Time_3D_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_PIXOR_Real-Time_3D_CVPR_2018_paper.pdf | cvpr-2018-6 | ['birds-eye-view-object-detection'] | ['computer-vision'] | [-3.18215191e-02 -2.57118702e-01 6.58419132e-02 -4.00004297e-01
-6.31916106e-01 -5.70665836e-01 5.99920332e-01 1.18487336e-01
-7.74446130e-01 -1.56537622e-01 -5.76292157e-01 -6.71926200e-01
4.04389709e-01 -6.42145038e-01 -1.03259361e+00 -3.20823789e-01
6.37894049e-02 5.48676670e-01 8.97582650e-01 -2.02158839... | [7.73106575012207, -2.5710713863372803] |
f8003472-2f78-4dd0-8bb7-9ed90003d0d3 | multimodal-emotion-recognition-on-ravdess | null | null | https://www.mdpi.com/1424-8220/21/22/7665/htm | https://www.mdpi.com/1424-8220/21/22/7665/pdf | Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning | Emotion Recognition is attracting the attention of the research community due to the multiple areas where it can be applied, such as in healthcare or in road safety systems. In this paper, we propose a multimodal emotion recognition system that relies on speech and facial information. For the speech-based modality, we ... | ['Fernando Fernández-Martínez', 'Juan M. Montero', 'Ricardo Kleinlein', 'Zoraida Callejas', 'David Griol', 'Cristina Luna-Jiménez'] | 2021-11-18 | null | null | null | sensors-2021-11 | ['facial-emotion-recognition', 'multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'computer-vision', 'speech'] | [ 1.59453437e-01 4.30372134e-02 1.67973742e-01 -3.53712201e-01
-4.57624793e-01 -6.36903644e-02 6.01551056e-01 -1.54241338e-01
-6.95351779e-01 5.86217642e-01 9.11740586e-02 1.63695827e-01
-3.08845639e-02 -5.16202152e-01 -6.93812609e-01 -7.43574798e-01
1.49458677e-01 -1.97846934e-01 2.49362633e-01 -5.45946956... | [13.352835655212402, 5.053073883056641] |
9abfc183-840a-4992-8d94-38c43e8290e6 | abusive-language-detection-in-online | 1905.07894 | null | https://arxiv.org/abs/1905.07894v1 | https://arxiv.org/pdf/1905.07894v1.pdf | Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features | In recent years, online social networks have allowed worldwide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing ... | ['Georges Linarès', 'Noé Cecillon', 'Vincent Labatut', 'Richard Dufour'] | 2019-05-20 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-2.76719462e-02 1.93001151e-01 -1.69121116e-01 -3.77019167e-01
-4.04388338e-01 -6.38063908e-01 9.90681171e-01 6.88328505e-01
-1.91879869e-01 5.96718907e-01 4.28811103e-01 -2.07742646e-01
3.62807475e-02 -7.46787727e-01 1.49027064e-01 -2.67656296e-01
-2.44717777e-01 2.71698982e-01 4.25303698e-01 -4.93943930... | [8.41421127319336, 10.241193771362305] |
30cce135-762b-4a6a-9a05-bcca35d40c03 | attributed-grammars-for-joint-estimation-of | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Park_Attributed_Grammars_for_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Park_Attributed_Grammars_for_ICCV_2015_paper.pdf | Attributed Grammars for Joint Estimation of Human Attributes, Part and Pose | In this paper, we are interested in developing compositional models to explicit representing pose, parts and attributes and tackling the tasks of attribute recognition, pose estimation and part localization jointly. This is different from the recent trend of using CNN-based approaches for training and testing on these... | ['Se-Young Park', 'Song-Chun Zhu'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['human-parsing'] | ['computer-vision'] | [ 4.00655001e-01 4.63274658e-01 -2.08807006e-01 -8.74646604e-01
-1.01480675e+00 -5.37156343e-01 5.81770420e-01 3.10564220e-01
-1.53378397e-01 2.85306364e-01 1.40433922e-01 1.85923427e-01
1.25412598e-01 -5.61990440e-01 -1.04307437e+00 -3.34154755e-01
-7.20092505e-02 1.47552907e+00 3.20424408e-01 9.32369083... | [8.227133750915527, -0.21894672513008118] |
0f0daff5-8434-448d-a00f-77e1b665555e | interval-load-forecasting-for-individual | 2306.03010 | null | https://arxiv.org/abs/2306.03010v1 | https://arxiv.org/pdf/2306.03010v1.pdf | Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging | The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distri... | ['Syed Mir', 'Katarina Grolinger', 'Mohamed Ahmed T. A. Elgalhud', 'Raiden Skala'] | 2023-06-05 | null | null | null | null | ['bayesian-inference', 'load-forecasting', 'prediction-intervals'] | ['methodology', 'miscellaneous', 'miscellaneous'] | [-2.57708848e-01 8.01298991e-02 -2.33811930e-01 -3.91334265e-01
-3.69908988e-01 -2.54484385e-01 8.77966881e-01 2.24061206e-01
-3.07750732e-01 9.16936398e-01 1.57808349e-01 -6.63968384e-01
-4.68538314e-01 -1.10176349e+00 -7.30134130e-01 -9.03787792e-01
-8.47504586e-02 8.66474867e-01 -2.41601244e-01 -7.96799455... | [6.175178050994873, 2.820765733718872] |
ec05b45d-4a71-4322-8892-e6620942696c | topology-aware-correlations-between-relations | 2103.03642 | null | https://arxiv.org/abs/2103.03642v1 | https://arxiv.org/pdf/2103.03642v1.pdf | Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs | Inductive link prediction -- where entities during training and inference stages can be different -- has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing app... | ['Jie Wang', 'Feng Wu', 'Huarui He', 'Jiajun Chen'] | 2021-03-05 | null | null | null | null | ['inductive-link-prediction'] | ['graphs'] | [-5.79395220e-02 5.08128762e-01 -7.55935788e-01 -2.17809647e-01
2.22758397e-01 -5.24635971e-01 6.34061873e-01 5.83763540e-01
3.38146865e-01 7.61748374e-01 2.44604498e-01 -7.05363512e-01
-8.85410368e-01 -1.52289069e+00 -7.59430528e-01 1.28298290e-02
-6.40335917e-01 9.01914418e-01 6.56904697e-01 -4.40370649... | [8.914011001586914, 7.936952114105225] |
7d035292-7210-454b-8cf6-b111b8ab55ec | multi-task-sub-band-network-for-deep-residual | 2303.06404 | null | https://arxiv.org/abs/2303.06404v1 | https://arxiv.org/pdf/2303.06404v1.pdf | Multi-Task Sub-Band Network For Deep Residual Echo Suppression | This paper introduces the SWANT team entry to the ICASSP 2023 AEC Challenge. We submit a system that cascades a linear filter with a neural post-filter. Particularly, we adopt sub-band processing to handle full-band signals and shape the network with multi-task learning, where dual signal voice activity detection (DSVA... | ['Yang Li', 'Yukai Ju', 'Jindong Li', 'Zhaoxia Li', 'Dawei Luo', 'Jiayao Sun'] | 2023-03-11 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [-1.98401421e-01 -4.69292641e-01 3.25216979e-01 -2.93767691e-01
-1.17507279e+00 -7.17536509e-01 5.00592291e-01 -6.09152019e-01
-6.79989338e-01 4.16220605e-01 6.57818556e-01 -1.89850330e-01
-1.68836266e-01 -1.43058561e-02 -6.30908132e-01 -4.76233393e-01
-3.69447649e-01 -2.53759682e-01 1.53910816e-01 -4.61750180... | [14.963545799255371, 5.912715911865234] |
faaf8a4c-5473-4d8c-9dc2-9482a204205c | depac-a-corpus-for-depression-and-anxiety-1 | 2306.12443 | null | https://arxiv.org/abs/2306.12443v1 | https://arxiv.org/pdf/2306.12443v1.pdf | DEPAC: a Corpus for Depression and Anxiety Detection from Speech | Mental distress like depression and anxiety contribute to the largest proportion of the global burden of diseases. Automated diagnosis systems of such disorders, empowered by recent innovations in Artificial Intelligence, can pave the way to reduce the sufferings of the affected individuals. Development of such systems... | ['Jekaterina Novikova', 'Brian Diep', 'Malikeh Ehghaghi', 'Mashrura Tasnim'] | 2023-06-20 | depac-a-corpus-for-depression-and-anxiety | https://aclanthology.org/2022.clpsych-1.1 | https://aclanthology.org/2022.clpsych-1.1.pdf | naacl-clpsych-2022-7 | ['anxiety-detection'] | ['medical'] | [ 1.53569892e-01 3.74769956e-01 1.99306443e-01 -5.78879893e-01
-1.29943609e+00 -1.50354400e-01 3.03454965e-01 4.99415666e-01
-3.05232137e-01 4.57540691e-01 6.65929496e-01 2.93333173e-01
-3.72257054e-01 -5.50555587e-01 3.63929957e-01 -5.04783213e-01
-7.43338987e-02 5.52712262e-01 -2.26511553e-01 -2.60066241... | [13.734728813171387, 4.928059101104736] |
6f5c2bf7-f932-4366-931d-e1fd851c5aa3 | the-devil-is-in-the-channels-mutual-channel | 2002.04264 | null | https://arxiv.org/abs/2002.04264v3 | https://arxiv.org/pdf/2002.04264v3.pdf | The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification | Key for solving fine-grained image categorization is finding discriminate and local regions that correspond to subtle visual traits. Great strides have been made, with complex networks designed specifically to learn part-level discriminate feature representations. In this paper, we show it is possible to cultivate subt... | ['Yi-Zhe Song', 'Yifeng Ding', 'Jiyang Xie', 'Ayan Kumar Bhunia', 'Zhanyu Ma', 'Xiaoxu Li', 'Dongliang Chang', 'Ming Wu', 'Jun Guo'] | 2020-02-11 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 1.54977337e-01 -2.61647433e-01 -1.91145942e-01 -6.48953199e-01
-7.02190220e-01 -6.87362850e-01 4.78254616e-01 1.26220852e-01
-4.41955298e-01 6.20531023e-01 2.33194344e-02 -1.88335568e-01
-1.80750623e-01 -6.67775154e-01 -7.54136264e-01 -7.25340664e-01
-1.83202505e-01 2.39442587e-02 2.24078983e-01 -7.46816257... | [9.56346321105957, 2.0897889137268066] |
77e67339-bf4f-4eb9-8a2c-97c88f8efaaa | active-learning-for-interactive-relation | null | null | https://aclanthology.org/2021.ranlp-main.101 | https://aclanthology.org/2021.ranlp-main.101.pdf | Active Learning for Interactive Relation Extraction in a French Newspaper’s Articles | Relation extraction is a subtask of natural langage processing that has seen many improvements in recent years, with the advent of complex pre-trained architectures. Many of these state-of-the-art approaches are tested against benchmarks with labelled sentences containing tagged entities, and require important pre-trai... | ['Pascale Sébillot', 'Guillaume Gravier', 'Michel Le Nouy', 'Cyrielle Mallart'] | null | null | https://aclanthology.org/2021.ranlp-1.101 | https://aclanthology.org/2021.ranlp-1.101.pdf | ranlp-2021-9 | ['relation-classification'] | ['natural-language-processing'] | [ 2.22199187e-01 8.13865423e-01 -3.81730050e-01 -3.39626998e-01
-5.97471654e-01 -4.14030463e-01 8.32939386e-01 9.91079628e-01
-8.76467586e-01 1.01419938e+00 1.95662111e-01 -4.23879653e-01
-3.01622540e-01 -1.15556300e+00 -4.41387117e-01 -3.22998196e-01
-5.05557775e-01 9.57710624e-01 5.23345053e-01 -6.10290825... | [9.310953140258789, 8.75106143951416] |
301a4aa1-42bb-40cb-bf6f-3f914a8f91a7 | reinforcement-learning-for-predicting-traffic | 2212.04677 | null | https://arxiv.org/abs/2212.04677v1 | https://arxiv.org/pdf/2212.04677v1.pdf | Reinforcement Learning for Predicting Traffic Accidents | As the demand for autonomous driving increases, it is paramount to ensure safety. Early accident prediction using deep learning methods for driving safety has recently gained much attention. In this task, early accident prediction and a point prediction of where the drivers should look are determined, with the dashcam ... | ['Dongsoo Har', 'TaeYoung Kim', 'Praveen Kumar Rajendran', 'Injoon Cho'] | 2022-12-09 | null | null | null | null | ['accident-anticipation'] | ['computer-vision'] | [-9.77775455e-02 5.38129508e-01 -3.32792372e-01 -2.69773901e-01
-9.76292610e-01 7.31903389e-02 5.82611978e-01 3.30231078e-02
-7.72364974e-01 5.86481988e-01 2.20414400e-01 -6.85923398e-01
-3.17343771e-01 -5.82980454e-01 -7.14677513e-01 -4.33169246e-01
-1.83468938e-01 1.76724747e-01 5.15759051e-01 -6.61978066... | [5.673739910125732, 1.0263299942016602] |
7d52c281-a3f7-4ce4-b97c-7b193cf00857 | a-processing-framework-to-access-large | 2303.07442 | null | https://arxiv.org/abs/2303.07442v1 | https://arxiv.org/pdf/2303.07442v1.pdf | A processing framework to access large quantities of whispered speech found in ASMR | Whispering is a ubiquitous mode of communication that humans use daily. Despite this, whispered speech has been poorly served by existing speech technology due to a shortage of resources and processing methodology. To remedy this, this paper provides a processing framework that enables access to large and unique data o... | ['Zofia Malisz', 'Gustav Eje Henter', 'Pablo Perez Zarazaga'] | 2023-03-13 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 3.22308034e-01 1.58852175e-01 5.92199147e-01 -2.10391477e-01
-1.26877213e+00 -5.87044716e-01 1.33604899e-01 2.85874993e-01
-2.77493268e-01 4.41643268e-01 5.38260341e-01 1.77784473e-01
1.22693390e-01 -1.01745613e-01 -3.93181980e-01 -6.30594850e-01
-3.66503932e-02 -8.22504684e-02 4.87493217e-01 -4.77694720... | [15.084329605102539, 5.292364597320557] |
f6518b3d-76c3-481c-9843-0f8dc80c7824 | diagnostic-benchmark-and-iterative-inpainting | 2304.06671 | null | https://arxiv.org/abs/2304.06671v2 | https://arxiv.org/pdf/2304.06671v2.pdf | Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation | Spatial control is a core capability in controllable image generation. Advancements in layout-guided image generation have shown promising results on in-distribution (ID) datasets with similar spatial configurations. However, it is unclear how these models perform when facing out-of-distribution (OOD) samples with arbi... | ['Mohit Bansal', 'Lijuan Wang', 'Zhe Gan', 'Zhengyuan Yang', 'Linjie Li', 'Jaemin Cho'] | 2023-04-13 | null | null | null | null | ['layout-to-image-generation'] | ['computer-vision'] | [ 4.22113717e-01 4.60444055e-02 3.42276767e-02 -6.98753148e-02
-6.93692565e-01 -9.78536427e-01 8.16194475e-01 -3.76310758e-02
5.40551618e-02 7.20492363e-01 2.81142980e-01 -5.23242235e-01
2.15535588e-03 -7.43820012e-01 -9.31533515e-01 -5.02517939e-01
7.93687925e-02 3.79626125e-01 1.17198460e-01 -1.62063688... | [11.369099617004395, -0.2305523306131363] |
cb9eade9-ae22-4751-9426-a779c1429523 | instanceformer-an-online-video-instance | 2208.10547 | null | https://arxiv.org/abs/2208.10547v1 | https://arxiv.org/pdf/2208.10547v1.pdf | InstanceFormer: An Online Video Instance Segmentation Framework | Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on the whole video and the immense computational complexity caused by full Spatio-temporal attention limit them in real-life applications such... | ['Volker Tresp', 'Thomas Seidl', 'Matthias Schubert', 'Sahand Sharifzadeh', 'Suprosanna Shit', 'Tanveer Hannan', 'Rajat Koner'] | 2022-08-22 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [-7.81214014e-02 -3.07544589e-01 -3.99280518e-01 -3.44956130e-01
-7.49047279e-01 -4.44452226e-01 4.47378308e-01 2.08018851e-02
-5.44481874e-01 5.13506532e-01 2.38736704e-01 -1.20170685e-02
-2.39656419e-02 -4.62381840e-01 -1.00032556e+00 -4.47481632e-01
-1.90053582e-01 8.95371586e-02 6.35611832e-01 9.10036191... | [9.287270545959473, 0.10547833144664764] |
6e5406ce-9755-4753-8af3-74244f82d021 | deca-deep-viewpoint-equivariant-human-pose | 2108.08557 | null | https://arxiv.org/abs/2108.08557v1 | https://arxiv.org/pdf/2108.08557v1.pdf | DECA: Deep viewpoint-Equivariant human pose estimation using Capsule Autoencoders | Human Pose Estimation (HPE) aims at retrieving the 3D position of human joints from images or videos. We show that current 3D HPE methods suffer a lack of viewpoint equivariance, namely they tend to fail or perform poorly when dealing with viewpoints unseen at training time. Deep learning methods often rely on either s... | ['Nicola Conci', 'Piotr Bródka', 'Niccolò Bisagno', 'Nicola Garau'] | 2021-08-19 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Garau_DECA_Deep_Viewpoint-Equivariant_Human_Pose_Estimation_Using_Capsule_Autoencoders_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Garau_DECA_Deep_Viewpoint-Equivariant_Human_Pose_Estimation_Using_Capsule_Autoencoders_ICCV_2021_paper.pdf | iccv-2021-1 | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-4.08536822e-01 -1.74858257e-01 1.17986582e-01 -2.11864948e-01
-6.62335575e-01 -4.99863416e-01 3.94013435e-01 -3.72981608e-01
-4.23335761e-01 4.24708068e-01 1.81119129e-01 2.91131020e-01
-1.10139094e-01 -5.91326118e-01 -8.70243371e-01 -8.14337611e-01
9.58347544e-02 4.55158770e-01 1.48025602e-01 -3.31761576... | [6.961808204650879, -1.0654551982879639] |
e3783ef2-5d55-4658-8874-dceed9566434 | graph-graph-similarity-network | null | null | https://openreview.net/forum?id=R3a2G2tSf3c | https://openreview.net/pdf?id=R3a2G2tSf3c | Graph-Graph Similarity Network | Graph classification aims to predict the class label for an entire graph. Recently, Graph Neural Networks (GNNs)-based approaches become an essential strand to learn low-dimensional continuous embeddings of the entire graphs for graph label prediction. While GNNs explicitly aggregate the neighborhood information and im... | ['Hongfu Liu', 'Pengyu Hong', 'Han Yue'] | 2021-01-01 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [-6.95722848e-02 4.64104950e-01 -2.47447267e-01 -5.11122048e-01
2.11101964e-01 -5.83930552e-01 3.73208553e-01 3.87222886e-01
8.61105025e-02 1.42023712e-01 1.87844709e-02 -2.07729146e-01
-7.11750314e-02 -1.42341459e+00 -6.02726400e-01 -6.80799425e-01
-1.60945848e-01 3.74320328e-01 -3.25902477e-02 -1.05293477... | [7.189666271209717, 6.2856950759887695] |
31ae3d0e-568b-41c8-9aab-56f14eee14e7 | skin-deep-unlearning-artefact-and-instrument | 2109.09818 | null | https://arxiv.org/abs/2109.09818v7 | https://arxiv.org/pdf/2109.09818v7.pdf | Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification | Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible. In this work, we robustly r... | ['Peter J. Bevan', 'Amir Atapour-Abarghouei'] | 2021-09-20 | null | null | null | null | ['skin-cancer-classification'] | ['medical'] | [ 8.26781213e-01 9.35202464e-02 -9.91405025e-02 -5.23616552e-01
-8.95527482e-01 -5.25806129e-01 5.81545293e-01 1.19320333e-01
-7.70109475e-01 7.98112035e-01 2.80098796e-01 -3.91675949e-01
-3.51498604e-01 -3.70268703e-01 -5.75782418e-01 -9.40843642e-01
3.65914702e-01 -8.70474428e-02 -7.57041425e-02 2.77901273... | [15.59247875213623, -2.924461841583252] |
9d17f11c-7917-4581-938e-7b12ac7956f3 | emotion-recognition-in-speech-using-cross | 1808.05561 | null | http://arxiv.org/abs/1808.05561v1 | http://arxiv.org/pdf/1808.05561v1.pdf | Emotion Recognition in Speech using Cross-Modal Transfer in the Wild | Obtaining large, human labelled speech datasets to train models for emotion
recognition is a notoriously challenging task, hindered by annotation cost and
label ambiguity. In this work, we consider the task of learning embeddings for
speech classification without access to any form of labelled audio. We base our
approa... | ['Arsha Nagrani', 'Andrew Zisserman', 'Andrea Vedaldi', 'Samuel Albanie'] | 2018-08-16 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 3.54467630e-01 4.87739503e-01 1.01823568e-01 -7.40072727e-01
-7.88405180e-01 -5.69255650e-01 5.48526645e-01 -2.06096917e-01
-3.64634097e-01 4.44010675e-01 1.95410222e-01 -1.20564796e-01
3.37382823e-01 -1.57357231e-01 -6.20639205e-01 -6.30732536e-01
-1.29148141e-01 3.68455976e-01 -3.88601243e-01 -2.19256163... | [13.550518989562988, 5.5705766677856445] |
f88e372c-af3a-4923-a923-cfe1cf99d341 | improving-pre-trained-weights-through-meta | 2212.09447 | null | https://arxiv.org/abs/2212.09447v1 | https://arxiv.org/pdf/2212.09447v1.pdf | Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning | Machine Learning algorithms have been extensively researched throughout the last decade, leading to unprecedented advances in a broad range of applications, such as image classification and reconstruction, object recognition, and text categorization. Nonetheless, most Machine Learning algorithms are trained via derivat... | ['Claudio F. G. dos Santos', 'João Paulo Papa', 'Mateus Roder', 'Gustavo H. de Rosa'] | 2022-12-19 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [ 3.25170428e-01 -1.35353431e-01 -2.86861449e-01 -1.78221881e-01
-7.89477900e-02 -9.49689224e-02 4.69585776e-01 2.72357911e-01
-7.11301684e-01 7.49691784e-01 -3.26581746e-02 -3.58157098e-01
-5.42686760e-01 -5.95350206e-01 -3.54217380e-01 -8.74783754e-01
4.81843725e-02 1.51312038e-01 2.24846359e-02 -2.78993577... | [8.275918006896973, 3.291749954223633] |
b711cafb-936c-4cc9-9fb0-650eca32259f | linear-cross-lingual-mapping-of-sentence | 2305.14256 | null | https://arxiv.org/abs/2305.14256v1 | https://arxiv.org/pdf/2305.14256v1.pdf | Linear Cross-Lingual Mapping of Sentence Embeddings | Semantics of a sentence is defined with much less ambiguity than semantics of a single word, and it should be better preserved by translation to another language. If multilingual sentence embeddings intend to represent sentence semantics, then the similarity between embeddings of any two sentences must be invariant wit... | ['John Bohannon', 'Fumika Isono', 'Oleg Vasilyev'] | 2023-05-23 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [-2.05092758e-01 1.70120150e-01 -2.44411066e-01 -6.82791233e-01
-2.76001781e-01 -7.95623124e-01 9.32111919e-01 4.97707039e-01
-6.68230236e-01 8.68689001e-01 8.29166889e-01 -3.63456637e-01
4.18899842e-02 -7.07861125e-01 -5.48035502e-01 -3.56776506e-01
2.59190351e-01 2.87950248e-01 1.82451308e-01 -5.95210195... | [11.006617546081543, 9.90994930267334] |
da7b47d7-a4ac-4792-bd0c-82514a2dc0c7 | clulex-at-semeval-2021-task-1-a-simple-system | null | null | https://aclanthology.org/2021.semeval-1.81 | https://aclanthology.org/2021.semeval-1.81.pdf | CLULEX at SemEval-2021 Task 1: A Simple System Goes a Long Way | This paper presents the system we submitted to the first Lexical Complexity Prediction (LCP) Shared Task 2021. The Shared Task provides participants with a new English dataset that includes context of the target word. We participate in the single-word complexity prediction sub-task and focus on feature engineering. Our... | ['Elin Askl{\\"o}v', "H{\\'e}ctor Hern{\\'a}ndez", 'Mironas Bitinis', 'Sinan Tang', 'Peter Kolb', 'Greta Smolenska'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-3.42899412e-01 1.25531539e-01 -1.98997214e-01 -3.85139227e-01
-9.58420157e-01 -4.59013492e-01 4.73416299e-01 6.26704454e-01
-1.14233470e+00 5.48737526e-01 5.15253127e-01 -4.15293217e-01
2.35288593e-04 -4.22315925e-01 -2.03677252e-01 1.75803602e-01
-2.81370997e-01 4.20569420e-01 1.95222050e-01 -4.64109808... | [10.629242897033691, 10.442846298217773] |
b3bd0a92-7d17-470e-9e27-81b446b669a6 | hybrid-model-for-single-stage-multi-person | 2305.01167 | null | https://arxiv.org/abs/2305.01167v2 | https://arxiv.org/pdf/2305.01167v2.pdf | Hybrid model for Single-Stage Multi-Person Pose Estimation | In general, human pose estimation methods are categorized into two approaches according to their architectures: regression (i.e., heatmap-free) and heatmap-based methods. The former one directly estimates precise coordinates of each keypoint using convolutional and fully-connected layers. Although this approach is able... | ['Jungho Lee', 'Dowoo Kwon', 'Lanying Jin', 'Wonhyeok Im', 'Jungpyo Kim', 'Hyotae Lee', 'Bosang Kim', 'Jonghyun Kim'] | 2023-05-02 | null | null | null | null | ['multi-person-pose-estimation'] | ['computer-vision'] | [-3.38761687e-01 -1.76178187e-01 2.30298638e-01 -1.32205546e-01
-5.04028320e-01 -2.89501429e-01 4.15577173e-01 3.87339503e-01
-5.84954023e-01 6.15747988e-01 -2.10520044e-01 4.41280812e-01
-1.29917994e-01 -9.74042296e-01 -7.16568112e-01 -6.20539844e-01
1.10786706e-01 4.06987369e-01 5.59863627e-01 -2.10734382... | [7.096185684204102, -0.8242926597595215] |
8b6c800d-8835-4f16-86ec-a4b7b35e984b | gesture-recognition-with-keypoint-and-radar | 2302.09998 | null | https://arxiv.org/abs/2302.09998v1 | https://arxiv.org/pdf/2302.09998v1.pdf | Gesture Recognition with Keypoint and Radar Stream Fusion for Automated Vehicles | We present a joint camera and radar approach to enable autonomous vehicles to understand and react to human gestures in everyday traffic. Initially, we process the radar data with a PointNet followed by a spatio-temporal multilayer perceptron (stMLP). Independently, the human body pose is extracted from the camera fram... | ['Vasileios Belagiannis', 'Klaus Dietmayer', 'Christian Waldschmidt', 'Nicolai Kern', 'Adrian Holzbock'] | 2023-02-20 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 4.57410008e-01 -8.66121799e-02 -1.75928071e-01 -6.30453110e-01
-4.53325599e-01 -2.58154094e-01 1.03746092e+00 -6.92602575e-01
-9.35459614e-01 4.05855626e-01 -6.60646558e-02 -1.54052198e-01
1.86053574e-01 -5.49278617e-01 -7.63562560e-01 -6.05839431e-01
2.15827953e-02 3.56287569e-01 4.48875993e-01 -6.88208640... | [7.259193420410156, -0.15876542031764984] |
0464bc6b-6a75-4120-ae6a-70368ec9f9b6 | less-is-more-surgical-phase-recognition-from | 2202.08199 | null | https://arxiv.org/abs/2202.08199v2 | https://arxiv.org/pdf/2202.08199v2.pdf | Less is More: Surgical Phase Recognition from Timestamp Supervision | Surgical phase recognition is a fundamental task in computer-assisted surgery systems. Most existing works are under the supervision of expensive and time-consuming full annotations, which require the surgeons to repeat watching videos to find the precise start and end time for a surgical phase. In this paper, we intro... | ['Xiaowei Xu', 'Jian Zhuang', 'Xinjian Yan', 'Xinpeng Ding', 'Xiaomeng Li', 'Wei Zhao', 'Zixun Wang'] | 2022-02-16 | null | null | null | null | ['surgical-phase-recognition'] | ['computer-vision'] | [ 4.05269057e-01 2.98135638e-01 -6.74502909e-01 -3.51630419e-01
-9.43055391e-01 -6.22639716e-01 1.40850827e-01 3.40651840e-01
-4.87013817e-01 4.76761103e-01 2.38297805e-01 -2.97048867e-01
-3.24281931e-01 -2.87448496e-01 -6.05100572e-01 -1.10352993e+00
-1.60154730e-01 3.95749897e-01 3.46598804e-01 2.78044432... | [14.126240730285645, -3.2573118209838867] |
c389c000-5ec0-46d0-a714-1915e2a1abd3 | a-comprehensive-survey-on-pretrained | 2302.09419 | null | https://arxiv.org/abs/2302.09419v3 | https://arxiv.org/pdf/2302.09419v3.pdf | A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT | Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on large-scale data which provides a reasonable parameter initialization for a wide range of downstream applications. BERT learns bidirectiona... | ['Lichao Sun', 'Philip S. Yu', 'Jian Pei', 'Caiming Xiong', 'Pengtao Xie', 'Ziwei Liu', 'Jia Wu', 'JianXin Li', 'Hao Peng', 'Lifang He', 'Qiben Yan', 'Cheng Ji', 'Kai Zhang', 'Guangjing Wang', 'Yixin Liu', 'Jun Yu', 'Chen Li', 'Qian Li', 'Ce Zhou'] | 2023-02-18 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 3.30604911e-01 1.25361338e-01 -5.89849889e-01 -2.41858289e-01
-2.66292870e-01 -2.50094175e-01 5.79022288e-01 2.37642210e-02
-1.40386447e-01 2.90672958e-01 2.52756894e-01 -3.42483729e-01
-1.40157312e-01 -1.25101197e+00 -6.52008295e-01 -6.83222294e-01
-4.21434045e-02 5.24642527e-01 -1.03355564e-01 -1.55998379... | [10.535195350646973, 1.7084239721298218] |
5cb3f3e2-62c9-47cc-8d54-013439d8671c | graph-based-blind-image-deblurring-from-a | 1802.07929 | null | http://arxiv.org/abs/1802.07929v1 | http://arxiv.org/pdf/1802.07929v1.pdf | Graph-Based Blind Image Deblurring From a Single Photograph | Blind image deblurring, i.e., deblurring without knowledge of the blur
kernel, is a highly ill-posed problem. The problem can be solved in two parts:
i) estimate a blur kernel from the blurry image, and ii) given estimated blur
kernel, de-convolve blurry input to restore the target image. In this paper, we
propose a gr... | ['Xian-Ming Liu', 'Yuanchao Bai', 'Wen Gao', 'Gene Cheung'] | 2018-02-22 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 4.17453736e-01 -2.57018864e-01 1.69202685e-01 1.03409335e-01
-4.99093294e-01 -5.27980804e-01 7.63608590e-02 -7.40347207e-01
1.19740196e-01 6.97697043e-01 5.99968493e-01 -6.02102317e-02
-3.88182133e-01 -3.68504107e-01 -7.39229262e-01 -9.55646038e-01
1.38369575e-01 -3.27758759e-01 -1.44618422e-01 5.76778613... | [11.613428115844727, -2.7813332080841064] |
54736440-3be4-47ba-a1f6-a7d766efa935 | a-survey-of-label-efficient-deep-learning-for | 2305.19812 | null | https://arxiv.org/abs/2305.19812v1 | https://arxiv.org/pdf/2305.19812v1.pdf | A Survey of Label-Efficient Deep Learning for 3D Point Clouds | In the past decade, deep neural networks have achieved significant progress in point cloud learning. However, collecting large-scale precisely-annotated training data is extremely laborious and expensive, which hinders the scalability of existing point cloud datasets and poses a bottleneck for efficient exploration of ... | ['Shijian Lu', 'Ling Shao', 'Xiaoqin Zhang', 'Aoran Xiao'] | 2023-05-31 | null | null | null | null | ['efficient-exploration'] | ['methodology'] | [ 4.06354805e-03 6.77761436e-03 -5.73499739e-01 -5.23806334e-01
-9.59393919e-01 -6.64373219e-01 2.68983364e-01 3.22785228e-01
-3.31644088e-01 4.60050672e-01 -3.46834302e-01 -3.00765663e-01
-1.25484064e-01 -7.08590269e-01 -9.68553543e-01 -4.27430183e-01
-2.36589760e-01 9.24021602e-01 3.23201194e-02 2.42247522... | [8.030072212219238, -3.2365293502807617] |
cb09133f-bb4f-4d6f-8b5d-05d8add1f130 | bottom-up-parsing-via-sequence-labeling | null | null | https://openreview.net/forum?id=CMXc3nK27q1 | https://openreview.net/pdf?id=CMXc3nK27q1 | Bottom Up Parsing via Sequence Labeling | We translate the sequence labeling framework, first introduced for top-down discourse parsing by Koto et al. (2021), to bottom-up discourse parsing. We introduce a novel parser that is not constrained by parsing direction (left-to-right or otherwise), and is conditioned on previous parsing decisions. We describe the un... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['discourse-parsing'] | ['natural-language-processing'] | [ 6.81917369e-01 1.17826593e+00 -1.97053865e-01 -6.03934407e-01
-1.21742690e+00 -1.07202637e+00 6.95305049e-01 1.93011791e-01
-2.41983473e-01 8.92449260e-01 7.06118882e-01 -9.97275531e-01
3.50846052e-01 -8.91859293e-01 -7.19324470e-01 -2.89096802e-01
-4.25295904e-02 4.79099989e-01 6.43186569e-01 -3.54467005... | [10.702310562133789, 9.490985870361328] |
1b2b775a-4985-4a7b-87c9-de887b553f89 | runtime-construction-of-large-scale-spiking | 2306.09855 | null | https://arxiv.org/abs/2306.09855v1 | https://arxiv.org/pdf/2306.09855v1.pdf | Runtime Construction of Large-Scale Spiking Neuronal Network Models on GPU Devices | Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being in... | ['Johanna Senk', 'Abigail Morrison', 'Pier Stanislao Paolucci', 'Viviana Fanti', 'Jonas Stapmanns', 'Elena Pastorelli', 'Gianmarco Tiddia', 'Jose Villamar', 'Bruno Golosio'] | 2023-06-16 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-3.56871751e-03 -1.59005493e-01 6.70560241e-01 1.11554250e-01
4.48014438e-01 -7.29200900e-01 5.72186530e-01 1.81275666e-01
-9.07747209e-01 8.36496234e-01 -5.88504493e-01 -5.21983504e-01
2.59552784e-02 -9.69904304e-01 -6.18440032e-01 -6.35523915e-01
-3.83492768e-01 8.17321181e-01 6.66404307e-01 -4.39604580... | [8.134110450744629, 2.6895735263824463] |
aec43131-86d8-44f7-989d-7d0844f0a53c | combining-graph-degeneracy-and-submodularity | null | null | https://aclanthology.org/W17-4507 | https://aclanthology.org/W17-4507.pdf | Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization | We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research. The framework allows summaries to be generated in a greedy way while preserving near-optimal performance guarantees. Our main contribution is the novel coverage reward term of the ... | ['Antoine Tixier', 'Polykarpos Meladianos', 'Michalis Vazirgiannis'] | 2017-09-01 | null | null | null | ws-2017-9 | ['unsupervised-extractive-summarization', 'extractive-document-summarization'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.85605061e-01 5.10376871e-01 -4.45658714e-01 -2.42935687e-01
-1.37315547e+00 -7.84950912e-01 6.12969995e-01 4.84947473e-01
-3.28578532e-01 7.52418756e-01 1.20671678e+00 -2.40953565e-02
-2.61937052e-01 -2.87131846e-01 -4.69370246e-01 -5.58743000e-01
-2.40194649e-01 6.91766620e-01 -1.57056659e-01 -2.99549133... | [12.512043952941895, 9.501816749572754] |
79fa3610-3fa4-4d15-8f86-ec117c2107a7 | mathematical-model-for-transmission-dynamics | 2304.01975 | null | https://arxiv.org/abs/2304.01975v1 | https://arxiv.org/pdf/2304.01975v1.pdf | Mathematical Model for Transmission Dynamics of Tuberculosis in Burundi | Tuberculosis (TB) is among the main public health challenges in Burundi. The literature lacks mathematical models for key parameter estimates of TB transmission dynamics in Burundi. In this paper, the supectible-exposed-infected-recovered (SEIR) model is used to investigate the transmission dynamics of tuberculosis in ... | ['David Niyukuri', 'Paterne Gahungu', 'Kelly Joelle Gatore Sinigirira', 'Steve Sibomanaa'] | 2023-04-04 | null | null | null | null | ['unity'] | ['computer-vision'] | [ 2.66212583e-01 -2.67532200e-01 -2.47050494e-01 3.94588441e-01
-6.74252287e-02 -1.03722796e-01 5.67247510e-01 -8.70841295e-02
-1.27548471e-01 1.18983734e+00 -1.50583178e-01 -5.74788570e-01
-6.70791805e-01 -7.50864744e-01 -1.30581006e-01 -1.30069005e+00
-4.10869658e-01 8.44608486e-01 2.34530807e-01 -3.13966069... | [5.924240589141846, 4.413984775543213] |
0c19ccd5-4bed-4be9-8765-4581c5ee29df | deciphering-the-interaction-of-genetic-and | 2210.11323 | null | https://arxiv.org/abs/2210.11323v2 | https://arxiv.org/pdf/2210.11323v2.pdf | Bottom-up data integration in polymer models of chromatin organisation | Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large data sets probing this process from multiple angles, we still lack a bottom-up framework which can in... | ['Guido Sanguinetti', 'Angelo Rosa', 'Alex Chen Yi Zhang'] | 2022-10-20 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 4.81273502e-01 -3.44163179e-02 -3.64839770e-02 -1.12668268e-01
-3.80779743e-01 -8.33593607e-01 7.09181786e-01 3.49549711e-01
-4.66172636e-01 8.37343931e-01 4.73341912e-01 -4.22152966e-01
-3.20866466e-01 -6.58577383e-01 -9.40702975e-01 -1.24319446e+00
2.36125588e-01 9.84916985e-01 5.09061933e-01 4.14408781... | [5.0018792152404785, 5.193822860717773] |
0b1b39c0-884b-432c-b713-e34f9b59c27a | how-to-address-monotonicity-for-model-risk | 2305.00799 | null | https://arxiv.org/abs/2305.00799v1 | https://arxiv.org/pdf/2305.00799v1.pdf | How to address monotonicity for model risk management? | In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity. Although there have been numerous studies on individual monotonicity, pairwise monotonicity is often overlooked in the existing literature. This paper studies transparent neur... | ['Weicheng Ye', 'Dangxing Chen'] | 2023-04-28 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.60733029e-01 4.41548139e-01 -5.59423804e-01 -8.25715721e-01
-4.46074493e-02 -5.37941098e-01 2.71138847e-01 -3.36167872e-01
-4.79296535e-01 1.16406345e+00 2.48936757e-01 -6.08435154e-01
-4.59098995e-01 -4.87816632e-01 -8.89011919e-01 -4.08928931e-01
1.25483144e-02 -3.33405770e-02 -2.60540009e-01 1.23089448... | [8.876046180725098, 5.3567423820495605] |
99ef5eca-d50d-4bf1-910e-80a301b430ce | climart-a-benchmark-dataset-for-emulating | 2111.14671 | null | https://arxiv.org/abs/2111.14671v1 | https://arxiv.org/pdf/2111.14671v1.pdf | ClimART: A Benchmark Dataset for Emulating Atmospheric Radiative Transfer in Weather and Climate Models | Numerical simulations of Earth's weather and climate require substantial amounts of computation. This has led to a growing interest in replacing subroutines that explicitly compute physical processes with approximate machine learning (ML) methods that are fast at inference time. Within weather and climate models, atmos... | ['David Rolnick', 'Howard Barker', 'Jason N. S. Cole', 'Venkatesh Ramesh', 'Salva Rühling Cachay'] | 2021-11-29 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [-4.14622277e-01 -5.36354125e-01 -4.94292267e-02 -4.50833678e-01
-3.53677005e-01 -7.12129593e-01 1.00115037e+00 1.27652004e-01
-2.67406166e-01 1.20039463e+00 -1.65222064e-02 -8.56827199e-01
2.01991901e-01 -1.06953454e+00 -7.70210505e-01 -5.57232738e-01
-1.89059392e-01 5.16486645e-01 -9.08458084e-02 -6.05009645... | [6.553930282592773, 3.0260226726531982] |
f0e0ea05-7cb2-4468-a148-61093f2d159d | a-deep-learning-accelerated-data-assimilation | 2105.09468 | null | https://arxiv.org/abs/2105.09468v2 | https://arxiv.org/pdf/2105.09468v2.pdf | A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage | Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage. The high computational cost of data assimilation with a high-dimensional parameter space impedes fast decision-making fo... | ['Joseph P. Morris', 'Matthew Burton-Kelly', 'Nicholas A. Azzolina', 'François Hamon', 'Xin Ju', 'Jize Zhang', 'Christopher S. Sherman', 'Pengcheng Fu', 'Hewei Tang'] | 2021-05-09 | null | null | null | null | ['seismic-inversion'] | ['miscellaneous'] | [-4.04733062e-01 -4.55487549e-01 3.13517898e-01 2.43313715e-01
-6.46487117e-01 -6.07158363e-01 7.40960956e-01 3.93587768e-01
-2.59871125e-01 1.09544444e+00 3.15197766e-01 -8.23286951e-01
1.34144247e-01 -1.12499297e+00 -6.18524790e-01 -6.30872428e-01
-6.94889665e-01 6.79713607e-01 2.13266071e-02 -5.59252501... | [6.482611656188965, 3.0959951877593994] |
e5727e22-1ce5-4d59-8722-3fca105df6f8 | prompt-engineering-for-transformer-based | 2305.16330 | null | https://arxiv.org/abs/2305.16330v1 | https://arxiv.org/pdf/2305.16330v1.pdf | Prompt Engineering for Transformer-based Chemical Similarity Search Identifies Structurally Distinct Functional Analogues | Chemical similarity searches are widely used in-silico methods for identifying new drug-like molecules. These methods have historically relied on structure-based comparisons to compute molecular similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend implementations by cre... | ['Andrew D. Ellington', 'Claus O. Wilke', 'Aaron L. Feller', 'Clayton W. Kosonocky'] | 2023-05-17 | null | null | null | null | ['prompt-engineering'] | ['natural-language-processing'] | [ 6.23754978e-01 -1.78468332e-01 -4.32920009e-01 4.00276445e-02
-7.75885403e-01 -1.39059079e+00 5.54152966e-01 7.36120582e-01
-2.64413297e-01 1.26274788e+00 -1.43675217e-02 -1.01361763e+00
-1.46231994e-01 -6.13738954e-01 -2.76426286e-01 -5.32069325e-01
-1.73441898e-02 3.33438367e-01 4.03165460e-01 -3.15173179... | [4.9503889083862305, 5.748427867889404] |
2572c1b2-c828-492a-b72d-c05f8047c15e | differentially-private-sliced-inverse | 2306.06324 | null | https://arxiv.org/abs/2306.06324v1 | https://arxiv.org/pdf/2306.06324v1.pdf | Differentially private sliced inverse regression in the federated paradigm | We extend the celebrated sliced inverse regression to address the challenges of decentralized data, prioritizing privacy and communication efficiency. Our approach, federated sliced inverse regression (FSIR), facilitates collaborative estimation of the sufficient dimension reduction subspace among multiple clients, sol... | ['Xin Chen', 'Jiarui Zhang', 'Shuaida He'] | 2023-06-10 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [-1.18893810e-01 -9.05322134e-02 -3.86632383e-01 -2.81898528e-01
-1.16687155e+00 -9.72568035e-01 1.96859062e-01 -1.02160200e-01
-2.44139746e-01 8.19040000e-01 1.62663177e-01 -4.33728039e-01
-4.35638517e-01 -6.80750847e-01 -5.46227813e-01 -1.21064579e+00
-5.15415609e-01 3.52382362e-01 -4.70488042e-01 3.85012925... | [5.929462909698486, 6.471562385559082] |
9f18a663-b8a4-429c-9983-42a6b224d21a | upb-at-semeval-2020-task-6-pretrained | 2009.05603 | null | https://arxiv.org/abs/2009.05603v2 | https://arxiv.org/pdf/2009.05603v2.pdf | UPB at SemEval-2020 Task 6: Pretrained Language Models for Definition Extraction | This work presents our contribution in the context of the 6th task of SemEval-2020: Extracting Definitions from Free Text in Textbooks (DeftEval). This competition consists of three subtasks with different levels of granularity: (1) classification of sentences as definitional or non-definitional,(2) labeling of definit... | ['Costin-Gabriel Chiru', 'Dumitru-Clementin Cercel', 'Andrei-Marius Avram'] | 2020-09-11 | null | https://aclanthology.org/2020.semeval-1.97 | https://aclanthology.org/2020.semeval-1.97.pdf | semeval-2020 | ['definition-extraction'] | ['natural-language-processing'] | [ 6.22411482e-02 3.93910199e-01 -1.62091270e-01 -4.58797514e-01
-8.29753995e-01 -7.91532815e-01 1.11023176e+00 1.92224830e-01
-5.89602113e-01 1.04596996e+00 8.44672397e-02 -7.68382847e-01
-1.01546757e-01 -6.83579028e-01 -6.69365704e-01 -2.73228496e-01
1.80369034e-01 6.53976738e-01 1.42744839e-01 -2.85035133... | [9.8046875, 9.01154613494873] |
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