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c20d8ecc-3469-4ba2-a06e-7d9762c7a9a5 | does-structure-matter-leveraging-data-to-text | 2112.04344 | null | https://arxiv.org/abs/2112.04344v1 | https://arxiv.org/pdf/2112.04344v1.pdf | Does Structure Matter? Leveraging Data-to-Text Generation for Answering Complex Information Needs | In this work, our aim is to provide a structured answer in natural language to a complex information need. Particularly, we envision using generative models from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating... | ['Lynda Tamine', 'Karen Pinel-Sauvagnat', 'Laure Soulier', 'Thomas Gerald', 'Hanane Djeddal'] | 2021-12-08 | null | null | null | null | ['data-to-text-generation'] | ['natural-language-processing'] | [ 3.44652325e-01 8.60465288e-01 2.64304489e-01 -4.27442312e-01
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2.02077940e-01 1.38917875e+00 5.32104909e-01 -5.96461654... | [11.553937911987305, 8.32755184173584] |
f0ecaf4f-7360-480f-9842-dcbaa40339f8 | customizing-knowledge-graph-embedding-to | 2212.14102 | null | https://arxiv.org/abs/2212.14102v1 | https://arxiv.org/pdf/2212.14102v1.pdf | Customizing Knowledge Graph Embedding to Improve Clinical Study Recommendation | Inferring knowledge from clinical trials using knowledge graph embedding is an emerging area. However, customizing graph embeddings for different use cases remains a significant challenge. We propose custom2vec, an algorithmic framework to customize graph embeddings by incorporating user preferences in training the emb... | ['Murthy Devarakonda', 'Iya Khalil', 'Xiong Liu'] | 2022-12-28 | null | null | null | null | ['knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'methodology'] | [-6.79140016e-02 4.88045692e-01 -9.37494278e-01 -3.21456701e-01
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-1.45837322e-01 8.08605194e-01 2.04380035e-01 -3.16262782... | [8.052042961120605, 7.37106466293335] |
8d597861-9376-42d8-80d4-c4fe0904217f | low-dose-ct-image-reconstruction-using-vector | null | null | https://rdcu.be/dajRa | https://rdcu.be/dajRa | Low-Dose CT Image Reconstruction using Vector Quantized Convolutional Autoencoder with Perceptual Loss | Computed Tomography (CT) has become a useful screening procedure to identify disease or injury within various regions of the human body. The human beings’ health issues caused by CT radiation have attracted the interest of the researchers and academic community. Reducing the radiation dose is the solution, but the CT i... | ['Mohan Ramasundaram', 'Shalini Ramanathan'] | 2023-03-28 | null | null | null | sadhana-2023-3 | ['image-reconstruction'] | ['computer-vision'] | [ 7.17448592e-02 -2.97142833e-01 -3.01859993e-02 -3.21232021e-01
-5.58811486e-01 2.09710807e-01 8.44277516e-02 3.66895080e-01
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-1.96560044e-02 2.35175073e-01 3.64295900e-01 -3.07957660... | [13.467547416687012, -2.5304830074310303] |
8aecf466-eb6f-4c1c-ae84-f6dd45ebead6 | sf-fsda-source-free-few-shot-domain-adaptive | 2306.04385 | null | https://arxiv.org/abs/2306.04385v1 | https://arxiv.org/pdf/2306.04385v1.pdf | SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory | Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain. Prior works typically require the access to the source domain data for adaptation, and the availability of sufficient data on the target domain. However, these a... | ['Luc van Gool', 'Konrad Schindler', 'Rui Gong', 'Han Sun'] | 2023-06-07 | null | null | null | null | ['robust-object-detection', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 4.49208081e-01 6.02343865e-02 -2.12985903e-01 -3.53969306e-01
-7.70700157e-01 -5.47245026e-01 4.89824951e-01 -9.45781544e-02
-3.65051299e-01 7.34742820e-01 -3.53078306e-01 1.92610443e-01
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4.95892018e-01 5.24458230e-01 5.81796587e-01 8.28967839... | [9.432158470153809, 1.5762228965759277] |
dbad2daf-8461-495b-9542-df0922c91874 | divide-and-conquer-based-large-scale-spectral-1 | 2104.15042 | null | https://arxiv.org/abs/2104.15042v2 | https://arxiv.org/pdf/2104.15042v2.pdf | Divide-and-conquer based Large-Scale Spectral Clustering | Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited computing resources has not been properly solved for a long time. In this paper, we propose a divide-and-conquer based large-scale spectral clus... | ['Tetsuya Sakurai', 'Akira Imakura', 'Xiucai Ye', 'Hongmin Li'] | 2021-04-30 | null | null | null | null | ['imagedocument-clustering'] | ['computer-vision'] | [-3.09017539e-01 -4.61265475e-01 -1.69513240e-01 -2.30744034e-01
-8.34110260e-01 -5.13991416e-01 8.08422714e-02 9.83225405e-02
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9.86373201e-02 4.94033486e-01 5.99415362e-01 3.68825734... | [7.5334625244140625, 4.747193813323975] |
c1f489c8-e2f6-4efa-bb08-980708ead90c | zero-shot-blind-audio-bandwidth-extension | 2306.01433 | null | https://arxiv.org/abs/2306.01433v1 | https://arxiv.org/pdf/2306.01433v1.pdf | Zero-Shot Blind Audio Bandwidth Extension | Audio bandwidth extension involves the realistic reconstruction of high-frequency spectra from bandlimited observations. In cases where the lowpass degradation is unknown, such as in restoring historical audio recordings, this becomes a blind problem. This paper introduces a novel method called BABE (Blind Audio Bandwi... | ['Vesa Välimäki', 'Filip Elvander', 'Eloi Moliner'] | 2023-06-02 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 1.72596037e-01 -2.88373113e-01 1.69339404e-01 1.92642882e-01
-1.34176922e+00 -5.87859571e-01 3.61815751e-01 -2.23881751e-01
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-4.63932991e-01 -2.61073977e-01 -6.34784937e-01 -9.43295896e-01
-1.21508598e-01 1.13633908e-01 1.29480273e-01 -1.01232067... | [15.310530662536621, 5.80245304107666] |
ebf617f3-839c-4e37-8a92-104951350b98 | covxnet-a-multi-dilation-convolutional-neural | null | null | https://www.sciencedirect.com/science/article/pii/S0010482520302250?via%3Dihub | https://www.sciencedirect.com/science/article/pii/S0010482520302250?via%3Dihub | CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization | With the recent outbreak of COVID-19, fast diagnostic testing has become one of the major challenges due to the critical shortage of test kit. Pneumonia, a major effect of COVID-19, needs to be urgently diagnosed along with its underlying reasons. In this paper, deep learning aided automated COVID-19 and other pneumoni... | ['Tanvir Mahmud', 'Shaikh Anowarul Fattah', 'Md Awsafur Rahman'] | 2020-07-01 | null | null | null | computers-in-biology-and-medicine-2020-7 | ['pneumonia-detection'] | ['medical'] | [ 1.35857090e-01 -6.83561802e-01 9.99331474e-02 -2.40209594e-01
-3.16886753e-01 -4.45677966e-01 8.95353854e-02 -3.17559391e-02
-4.60437238e-01 6.30697966e-01 -1.74436212e-01 -5.37425458e-01
-4.65030521e-01 -8.10868561e-01 -5.07941604e-01 -8.51756215e-01
-1.18260451e-01 8.94809723e-01 1.71297655e-01 2.37541705... | [15.567960739135742, -1.741341233253479] |
a3c35239-afdd-4541-b020-377b05106846 | application-of-data-engineering-approaches-to | 2307.00033 | null | https://arxiv.org/abs/2307.00033v2 | https://arxiv.org/pdf/2307.00033v2.pdf | Application of data engineering approaches to address challenges in microbiome data for optimal medical decision-making | The human gut microbiota is known to contribute to numerous physiological functions of the body and also implicated in a myriad of pathological conditions. Prolific research work in the past few decades have yielded valuable information regarding the relative taxonomic distribution of gut microbiota. Unfortunately, the... | ['Shyam Kumar Sudhakar', 'Pavan Kumar Perepu', 'Isha Thombre'] | 2023-06-30 | null | null | null | null | ['classification-1', 'decision-making'] | ['methodology', 'reasoning'] | [ 4.70002532e-01 -6.46919191e-01 -2.29311399e-02 -1.02370977e-01
2.60217190e-01 -3.54242295e-01 3.96270812e-01 5.42173505e-01
-1.15901884e-02 1.12048817e+00 1.28560327e-02 -6.23433590e-01
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-3.49929571e-01 1.17777482e-01 -2.11757809e-01 -3.61142039... | [5.123040199279785, 5.063233375549316] |
40d67bc5-bbdf-4371-9c49-0f9fb0e1a650 | robust-knowledge-adaptation-for-federated | 2301.07320 | null | https://arxiv.org/abs/2301.07320v1 | https://arxiv.org/pdf/2301.07320v1.pdf | Robust Knowledge Adaptation for Federated Unsupervised Person ReID | Person Re-identification (ReID) has been extensively studied in recent years due to the increasing demand in public security. However, collecting and dealing with sensitive personal data raises privacy concerns. Therefore, federated learning has been explored for Person ReID, which aims to share minimal sensitive data ... | ['Zhiyong Wang', 'Jingya Wang', 'Tingting Yao', 'Kun Hu', 'Jianfeng Weng'] | 2023-01-18 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-1.42299727e-01 -2.58048564e-01 -1.27643615e-01 -7.32506454e-01
-7.36686468e-01 -5.17215312e-01 7.38886416e-01 2.08466142e-01
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-3.45779359e-01 -6.40386939e-01 -4.65451568e-01 -8.41557801e-01
5.53569868e-02 4.22492176e-01 -5.53253368e-02 1.42574936... | [14.754451751708984, 1.0724077224731445] |
513fb0e4-8705-4689-8772-cdb2e7983b72 | distilling-relation-embeddings-from-pre | 2110.15705 | null | https://arxiv.org/abs/2110.15705v1 | https://arxiv.org/pdf/2110.15705v1.pdf | Distilling Relation Embeddings from Pre-trained Language Models | Pre-trained language models have been found to capture a surprisingly rich amount of lexical knowledge, ranging from commonsense properties of everyday concepts to detailed factual knowledge about named entities. Among others, this makes it possible to distill high-quality word vectors from pre-trained language models.... | ['Steven Schockaert', 'Jose Camacho-Collados', 'Asahi Ushio'] | 2021-09-21 | null | null | null | null | ['relation-classification'] | ['natural-language-processing'] | [-1.70399100e-01 2.60180295e-01 -4.74898070e-01 -3.67900699e-01
-4.25776452e-01 -8.27641249e-01 9.84425366e-01 7.74009526e-01
-4.55674440e-01 6.30485356e-01 5.86607158e-01 -5.50465584e-01
-1.61124021e-01 -1.17331791e+00 -4.43389088e-01 -2.71822691e-01
-2.67686937e-02 5.63321769e-01 1.19037829e-01 -5.07847607... | [10.07752799987793, 8.73450756072998] |
356d2b6f-da94-4d5a-9a29-58b0a5b6d95e | a-shared-multi-attention-framework-for-multi | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Huynh_A_Shared_Multi-Attention_Framework_for_Multi-Label_Zero-Shot_Learning_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Huynh_A_Shared_Multi-Attention_Framework_for_Multi-Label_Zero-Shot_Learning_CVPR_2020_paper.pdf | A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning | In this work, we develop a shared multi-attention model for multi-label zero-shot learning. We argue that designing attention mechanism for recognizing multiple seen and unseen labels in an image is a non-trivial task as there is no training signal to localize unseen labels and an image only contains a few present labe... | [' Ehsan Elhamifar', 'Dat Huynh'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['multi-label-zero-shot-learning'] | ['computer-vision'] | [ 5.22026956e-01 6.94236755e-02 -1.88167363e-01 -6.00564182e-01
-1.20264685e+00 -4.20348972e-01 4.36444968e-01 7.60423094e-02
-4.53374922e-01 6.77989364e-01 -1.80111259e-01 1.43887565e-01
3.95028256e-02 -5.40445507e-01 -8.56064677e-01 -7.99546719e-01
4.40265268e-01 4.75940883e-01 5.56469679e-01 2.56930947... | [9.799369812011719, 3.7769837379455566] |
cceea0ff-cfa4-48a7-8b31-73f4e9c73847 | lexical-query-modeling-in-session-search | 1608.06656 | null | http://arxiv.org/abs/1608.06656v1 | http://arxiv.org/pdf/1608.06656v1.pdf | Lexical Query Modeling in Session Search | Lexical query modeling has been the leading paradigm for session search. In
this paper, we analyze TREC session query logs and compare the performance of
different lexical matching approaches for session search. Naive methods based
on term frequency weighing perform on par with specialized session models. In
addition, ... | ['Maarten de Rijke', 'Evangelos Kanoulas', 'Christophe Van Gysel'] | 2016-08-23 | null | null | null | null | ['session-search'] | ['natural-language-processing'] | [ 8.06931853e-02 -3.39500695e-01 -8.32756519e-01 -4.71168548e-01
-1.28285360e+00 -6.48194671e-01 9.58195210e-01 5.43878675e-01
-9.36055064e-01 3.05782259e-01 4.96077865e-01 -6.40983045e-01
-5.96593738e-01 -4.24932837e-01 -1.75085768e-01 -4.42001075e-02
-1.31894559e-01 5.38469434e-01 8.05948079e-01 -4.74043369... | [11.743270874023438, 7.676333904266357] |
e58f8103-be4d-4ed0-b22d-91788a7f0ba0 | face-anti-spoofing-from-the-perspective-of | 2208.13164 | null | https://arxiv.org/abs/2208.13164v1 | https://arxiv.org/pdf/2208.13164v1.pdf | Face Anti-Spoofing from the Perspective of Data Sampling | Without deploying face anti-spoofing countermeasures, face recognition systems can be spoofed by presenting a printed photo, a video, or a silicon mask of a genuine user. Thus, face presentation attack detection (PAD) plays a vital role in providing secure facial access to digital devices. Most existing video-based PAD... | ['Mourad Oussalah', 'Usman Muhammad'] | 2022-08-28 | null | null | null | null | ['face-presentation-attack-detection', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision'] | [ 5.79930067e-01 -4.69942868e-01 -2.70088501e-02 -1.95899591e-01
-5.54646015e-01 -7.17893660e-01 5.27826428e-01 -1.71269938e-01
-2.43747041e-01 3.79772961e-01 -3.87664884e-01 -3.70806038e-01
1.25454599e-02 -6.57675028e-01 -7.30034173e-01 -9.60444868e-01
-1.45295471e-01 -4.15671736e-01 2.47362450e-01 -2.21755821... | [13.038737297058105, 1.1489773988723755] |
4fb252c8-2f1d-4449-8a2c-e402195031d2 | k-strip-a-novel-segmentation-algorithm-in-k | 2205.09706 | null | https://arxiv.org/abs/2205.09706v2 | https://arxiv.org/pdf/2205.09706v2.pdf | k-strip: A novel segmentation algorithm in k-space for the application of skull stripping | Objectives: Present a novel deep learning-based skull stripping algorithm for magnetic resonance imaging (MRI) that works directly in the information rich k-space. Materials and Methods: Using two datasets from different institutions with a total of 36,900 MRI slices, we trained a deep learning-based model to work dire... | ['Jens Kleesiek', 'Jan Egger', 'Kevin Kröninger', 'Felix Nensa', 'Johannes Haubold', 'Kelsey L. Pomykala', 'Florian Mentzel', 'Moritz Rempe'] | 2022-05-19 | null | null | null | null | ['skull-stripping'] | ['medical'] | [ 1.91349909e-01 3.08029115e-01 1.65833399e-01 -3.56370270e-01
-6.64102614e-01 -3.14470679e-01 2.43805081e-01 2.64399022e-01
-7.53941000e-01 6.32176995e-01 3.08128327e-01 -8.08984190e-02
-6.67112887e-01 -5.29568434e-01 -2.63945997e-01 -9.56866920e-01
-8.48160565e-01 4.02066737e-01 4.16803330e-01 2.05129310... | [14.038758277893066, -2.3446056842803955] |
f829f854-377e-4172-a60e-c9134401d85a | searching-for-legal-clauses-by-analogy-few | 1911.03911 | null | https://arxiv.org/abs/1911.03911v2 | https://arxiv.org/pdf/1911.03911v2.pdf | Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines | We propose a new shared task of semantic retrieval from legal texts, in which a so-called contract discovery is to be performed, where legal clauses are extracted from documents, given a few examples of similar clauses from other legal acts. The task differs substantially from conventional NLI and shared tasks on legal... | ['Filip Graliński', 'Gabriela Pałka', 'Łukasz Szałkiewicz', 'Dawid Wiśniewski', 'Łukasz Borchmann', 'Andrzej Gretkowski', 'Agnieszka Kaliska', 'Izabela Kosmala', 'Dawid Jurkiewicz', 'Karol Kaczmarek'] | 2019-11-10 | null | https://aclanthology.org/2020.findings-emnlp.380 | https://aclanthology.org/2020.findings-emnlp.380.pdf | findings-of-the-association-for-computational | ['semantic-retrieval'] | ['natural-language-processing'] | [ 6.59057617e-01 5.90459466e-01 -6.36667728e-01 -5.21690369e-01
-1.81218719e+00 -7.50433683e-01 9.10732508e-01 7.41358027e-02
-4.23127443e-01 9.66755986e-01 6.08868420e-01 -3.69976193e-01
-7.75884867e-01 -4.30107892e-01 -6.57885432e-01 -3.13718110e-01
2.17956692e-01 8.40336919e-01 1.52819067e-01 -2.36566082... | [9.954813957214355, 9.232892036437988] |
27419e7a-3333-457d-86ad-3e05e06b63c2 | unsupervised-translation-of-german-lower | 2109.12012 | null | https://arxiv.org/abs/2109.12012v1 | https://arxiv.org/pdf/2109.12012v1.pdf | Unsupervised Translation of German--Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language | This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2021 Unsupervised Machine Translation task for German--Lower Sorbian (DE--DSB): a high-resource language to a low-resource one. Our system uses a transformer encoder-decoder architecture in which we make three chang... | ['Gertjan van Noord', 'Antonio Toral', 'Ahmet Üstün', 'Lukas Edman'] | 2021-09-24 | null | null | null | null | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [-1.18580759e-01 2.23232910e-01 -3.32255483e-01 -4.21231091e-01
-1.36945152e+00 -8.17711234e-01 8.55257034e-01 -1.15856886e-01
-7.92178333e-01 1.31875002e+00 2.74467349e-01 -8.65064979e-01
2.42914334e-01 -5.86064696e-01 -7.52487183e-01 -2.12917462e-01
2.79914320e-01 1.15950584e+00 -1.47651091e-01 -8.97284150... | [11.504048347473145, 10.360682487487793] |
141905c5-01a6-4128-8685-bfbedaa40b70 | truveta-mapper-a-zero-shot-ontology-alignment | 2301.09767 | null | https://arxiv.org/abs/2301.09767v2 | https://arxiv.org/pdf/2301.09767v2.pdf | Truveta Mapper: A Zero-shot Ontology Alignment Framework | In this paper, a new perspective is suggested for unsupervised Ontology Matching (OM) or Ontology Alignment (OA) by treating it as a translation task. Ontologies are represented as graphs, and the translation is performed from a node in the source ontology graph to a path in the target ontology graph. The proposed fram... | ['Saman Zarandioon', 'Sadra Naddaf-sh', 'Alireza Bahramali', 'Sina Ehsani', 'Mahsa Eslamialishah', 'Murchana Baruah', 'Mariyam Amir'] | 2023-01-24 | null | null | null | null | ['ontology-matching'] | ['knowledge-base'] | [ 6.62286162e-01 3.65956217e-01 -1.67299509e-02 -4.32186395e-01
-7.98993945e-01 -5.15110672e-01 5.83891809e-01 5.14466107e-01
-4.48241532e-01 4.71311629e-01 -3.36087513e-04 -3.04941356e-01
-6.12703741e-01 -6.98147535e-01 -7.72520185e-01 -5.33027165e-02
1.79624371e-02 1.28211260e+00 1.91947386e-01 -3.60369802... | [9.195852279663086, 8.129453659057617] |
7e1b95cd-6a60-4160-ada2-2866a0ebefe0 | an-analysis-on-ensemble-learning-optimized | 2201.11440 | null | https://arxiv.org/abs/2201.11440v2 | https://arxiv.org/pdf/2201.11440v2.pdf | An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks | Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensembl... | ['Frank Kramer', 'Iñaki Soto-Rey', 'Dominik Müller'] | 2022-01-27 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.18765187e-01 5.88827245e-02 2.73358732e-01 -5.05342960e-01
-9.71060455e-01 -2.64310002e-01 3.40314388e-01 6.08460486e-01
-6.02746785e-01 5.70762753e-01 -1.06459312e-01 -4.11857843e-01
-2.60749549e-01 -5.46576798e-01 -4.97298479e-01 -9.91941094e-01
-3.47715229e-01 3.84416223e-01 1.37526676e-01 -2.32227862... | [15.17789077758789, -2.630194902420044] |
7f936abd-d952-48d9-b8e2-f920b2bb05e2 | towards-better-document-level-relation | 2211.14470 | null | https://arxiv.org/abs/2211.14470v1 | https://arxiv.org/pdf/2211.14470v1.pdf | Towards Better Document-level Relation Extraction via Iterative Inference | Document-level relation extraction (RE) aims to extract the relations between entities from the input document that usually containing many difficultly-predicted entity pairs whose relations can only be predicted through relational inference. Existing methods usually directly predict the relations of all entity pairs o... | ['Xiaodong Shi', 'Qingguo Hu', 'Zijun Min', 'Zhongjian Miao', 'Yidong Chen', 'Jinsong Su', 'Liang Zhang'] | 2022-11-26 | null | null | null | null | ['document-level-relation-extraction'] | ['natural-language-processing'] | [ 1.10114276e-01 7.28145361e-01 -3.43877107e-01 -3.69827658e-01
-7.09327757e-01 -3.43474805e-01 7.54022419e-01 3.61654580e-01
-4.15715903e-01 8.17446172e-01 2.90381424e-02 -3.74328226e-01
-1.67644303e-02 -1.24939275e+00 -9.57307816e-01 -2.75538206e-01
-4.83119889e-04 9.22309399e-01 4.54996079e-01 -3.34618628... | [9.235584259033203, 8.600212097167969] |
936a8817-de02-4157-b35e-1c4b3022c52c | assessing-the-effectiveness-of-syntactic | 2106.06110 | null | https://arxiv.org/abs/2106.06110v1 | https://arxiv.org/pdf/2106.06110v1.pdf | Assessing the Effectiveness of Syntactic Structure to Learn Code Edit Representations | In recent times, it has been shown that one can use code as data to aid various applications such as automatic commit message generation, automatic generation of pull request descriptions and automatic program repair. Take for instance the problem of commit message generation. Treating source code as a sequence of toke... | ['Rahul Kumar', 'Ranjita Bhagwan', 'Sonu Mehta', 'Syed Arbaaz Qureshi'] | 2021-06-11 | null | null | null | null | ['program-repair', 'program-repair'] | ['computer-code', 'reasoning'] | [ 4.22486901e-01 4.41400498e-01 -1.84784800e-01 -4.62521493e-01
-6.56658471e-01 -4.74343568e-01 5.77541053e-01 4.83000368e-01
-1.02604575e-01 4.22668785e-01 3.59288245e-01 -7.66487658e-01
3.90795529e-01 -8.88949096e-01 -8.84239435e-01 -9.60014984e-02
-1.13916457e-01 1.33719757e-01 4.91563290e-01 -3.93304944... | [7.697798728942871, 7.834532737731934] |
62d8b63a-d95f-4015-9c52-dc4168267657 | invariant-priors-for-bayesian-quadrature | 2112.01578 | null | https://arxiv.org/abs/2112.01578v1 | https://arxiv.org/pdf/2112.01578v1.pdf | Invariant Priors for Bayesian Quadrature | Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task at hand. In this paper, we explore priors that encode invariance of the integrand under a set of bijective transformations in the input dom... | ['Maren Mahsereci', 'Javier Gonzalez', 'Masha Naslidnyk'] | 2021-12-02 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [ 1.68016069e-02 -8.76059383e-02 -7.32718334e-02 -4.00607109e-01
-8.53113353e-01 -4.66687173e-01 1.00518978e+00 -2.49742597e-01
-3.65882128e-01 1.19726598e+00 7.16027468e-02 -2.17876464e-01
-3.65067542e-01 -8.70223284e-01 -5.29943466e-01 -6.02344871e-01
-1.48539960e-01 7.19862938e-01 2.67862529e-01 -1.52242184... | [6.812225341796875, 3.7793571949005127] |
21464efc-5eab-459a-b6a9-56c8f11631cd | matching-disparate-image-pairs-using-shape | 1811.09889 | null | http://arxiv.org/abs/1811.09889v1 | http://arxiv.org/pdf/1811.09889v1.pdf | Matching Disparate Image Pairs Using Shape-Aware ConvNets | An end-to-end trainable ConvNet architecture, that learns to harness the
power of shape representation for matching disparate image pairs, is proposed.
Disparate image pairs are deemed those that exhibit strong affine variations in
scale, viewpoint and projection parameters accompanied by the presence of
partial or com... | ['Suchendra M. Bhandarkar', 'Deepak Sharma', 'Abhimanyu Chopra', 'Arun CS Kumar', 'Shefali Srivastava'] | 2018-11-24 | null | null | null | null | ['matching-disparate-images'] | ['computer-vision'] | [ 2.00931832e-01 -2.61220366e-01 1.91686377e-01 -4.32501823e-01
-8.55525017e-01 -6.96740985e-01 6.32995725e-01 9.69148055e-02
-1.52872220e-01 1.74650148e-01 5.24244085e-03 2.27938712e-01
-3.62299711e-01 -7.42666483e-01 -8.28256130e-01 -8.24674726e-01
2.89127588e-01 3.62971544e-01 -9.79172587e-02 -2.78310269... | [8.367376327514648, -2.244828224182129] |
6c76c129-89a0-4ef6-8720-c4c0ef73aa11 | exploration-and-exploitation-two-ways-to | 2105.14813 | null | https://arxiv.org/abs/2105.14813v2 | https://arxiv.org/pdf/2105.14813v2.pdf | Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models | A sequence-to-sequence learning with neural networks has empirically proven to be an effective framework for Chinese Spelling Correction (CSC), which takes a sentence with some spelling errors as input and outputs the corrected one. However, CSC models may fail to correct spelling errors covered by the confusion sets, ... | ['Xuanjing Huang', 'Xiaoqing Zheng', 'Cenyuan Zhang', 'Chong Li'] | 2021-05-31 | null | https://aclanthology.org/2021.acl-short.56 | https://aclanthology.org/2021.acl-short.56.pdf | acl-2021-5 | ['spelling-correction'] | ['natural-language-processing'] | [ 7.46829867e-01 -3.58457476e-01 1.07373118e-01 -1.91842288e-01
-6.85827374e-01 -7.01641560e-01 5.02126515e-01 -7.90412910e-03
-5.65164804e-01 8.65123451e-01 9.01770666e-02 -4.96124625e-01
3.42582017e-01 -5.83462715e-01 -7.38052070e-01 -7.14814126e-01
3.15295339e-01 3.04550171e-01 3.55761647e-01 -3.90577227... | [10.949370384216309, 10.824515342712402] |
2e086f96-2ab4-4841-a4c5-2bc5d32d69c0 | features-compression-based-on-counterfactual | 2211.09894 | null | https://arxiv.org/abs/2211.09894v3 | https://arxiv.org/pdf/2211.09894v3.pdf | Supervised Feature Compression based on Counterfactual Analysis | Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work ... | ['Cecilia Salvatore', 'Dolores Romero Morales', 'Veronica Piccialli'] | 2022-11-17 | null | null | null | null | ['feature-compression', 'interpretable-machine-learning', 'counterfactual-explanation'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 5.41503012e-01 1.18915236e+00 -8.36324692e-01 -5.42106390e-01
-3.43881994e-01 -3.92550409e-01 5.43211401e-01 3.26735348e-01
8.55199397e-02 1.33340633e+00 2.36649990e-01 -5.55371881e-01
-4.16999847e-01 -9.10627961e-01 -8.96566391e-01 -6.06425583e-01
-4.32309866e-01 4.72763419e-01 -4.56638783e-01 3.03738028... | [8.668357849121094, 5.6085333824157715] |
c4f4a8d0-af9b-4a22-a7c7-0d4e70308c60 | med-unic-unifying-cross-lingual-medical | 2305.19894 | null | https://arxiv.org/abs/2305.19894v1 | https://arxiv.org/pdf/2305.19894v1.pdf | Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias | The scarcity of data presents a critical obstacle to the efficacy of medical visionlanguage pre-training (VLP). A potential solution lies in the combination of datasets from various language communities. Nevertheless, the main challenge stems from the complexity of integrating diverse syntax and semantics, language-spe... | ['Rossella Arcucci', 'César Quilodrán-Casas', 'Lei Ma', 'Sibo Cheng', 'Benyou Wang', 'Jie Fu', 'Mi Zhang', 'Che Liu', 'Zhongwei Wan'] | 2023-05-31 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 6.38124123e-02 -2.11447731e-01 -4.43856239e-01 -1.72746420e-01
-1.09294569e+00 -4.28958803e-01 5.42149663e-01 3.85255218e-01
-7.07148910e-01 4.85649645e-01 4.52353746e-01 -2.43286639e-01
4.57671620e-02 -3.89526337e-01 -3.01102757e-01 -7.71175206e-01
4.11705047e-01 3.54170412e-01 -3.19576353e-01 -4.39278670... | [10.908919334411621, 1.505889892578125] |
81492974-28d0-49a1-bcf6-62f467e8b0bf | instance-segmentation-by-deep-coloring | 1807.10007 | null | http://arxiv.org/abs/1807.10007v1 | http://arxiv.org/pdf/1807.10007v1.pdf | Instance Segmentation by Deep Coloring | We propose a new and, arguably, a very simple reduction of instance
segmentation to semantic segmentation. This reduction allows to train
feed-forward non-recurrent deep instance segmentation systems in an end-to-end
fashion using architectures that have been proposed for semantic segmentation.
Our approach proceeds by... | ['Victor Yurchenko', 'Victor Kulikov', 'Victor Lempitsky'] | 2018-07-26 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 7.27597415e-01 4.44927096e-01 5.66153489e-02 -5.38360238e-01
-7.47623265e-01 -1.12432635e+00 4.14591104e-01 1.43870130e-01
-1.41273513e-01 4.88411963e-01 -7.68476188e-01 -3.99724662e-01
-5.40782558e-03 -8.29887211e-01 -9.32237804e-01 -7.71111012e-01
6.30064905e-02 8.85978758e-01 2.69700706e-01 5.20732328... | [9.526178359985352, 0.14816851913928986] |
1090f896-00da-4379-9679-d3599adb561a | an-exploration-into-the-performance-of | 2305.05443 | null | https://arxiv.org/abs/2305.05443v1 | https://arxiv.org/pdf/2305.05443v1.pdf | An Exploration into the Performance of Unsupervised Cross-Task Speech Representations for "In the Wild'' Edge Applications | Unsupervised speech models are becoming ubiquitous in the speech and machine learning communities. Upstream models are responsible for learning meaningful representations from raw audio. Later, these representations serve as input to downstream models to solve a number of tasks, such as keyword spotting or emotion reco... | ['Tiago H. Falk', 'Mehdi Rezagholizadeh', 'Anderson Avila', 'Arthur Pimentel', 'Heitor Guimarães'] | 2023-05-09 | null | null | null | null | ['intent-classification', 'keyword-spotting'] | ['natural-language-processing', 'speech'] | [ 1.78315759e-01 -2.18527645e-01 1.45040661e-01 -2.63092309e-01
-1.06048155e+00 -5.62000811e-01 3.17989975e-01 2.71986932e-01
-1.53376281e-01 3.85816693e-01 6.32575929e-01 -3.51185828e-01
-2.02082932e-01 -2.26657122e-01 -3.46755058e-01 -5.18587112e-01
1.91955920e-02 -3.16538036e-01 -5.79701960e-02 -2.50750065... | [14.597381591796875, 6.185009956359863] |
b383fcf3-fd46-45c9-9d62-452003401d2b | unsupervised-intra-domain-adaptation-for | 2004.07703 | null | https://arxiv.org/abs/2004.07703v4 | https://arxiv.org/pdf/2004.07703v4.pdf | Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision | Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. However, these approaches heavily rely on annotated data which are labor intensive. To cope with this limitation, automatically annotated data generated from graphic engines are used to train segmentation models. H... | ['Inkyu Shin', 'Fei Pan', 'Seokju Lee', 'In So Kweon', 'Francois Rameau'] | 2020-04-16 | unsupervised-intra-domain-adaptation-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Pan_Unsupervised_Intra-Domain_Adaptation_for_Semantic_Segmentation_Through_Self-Supervision_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Unsupervised_Intra-Domain_Adaptation_for_Semantic_Segmentation_Through_Self-Supervision_CVPR_2020_paper.pdf | cvpr-2020-6 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 3.64100665e-01 1.15288273e-01 -2.44477019e-01 -5.00979364e-01
-6.75581157e-01 -4.80584413e-01 3.10373425e-01 2.18138788e-02
-5.53416610e-01 7.03049123e-01 -2.38536000e-01 -1.22115031e-01
6.01243600e-02 -8.96937847e-01 -6.78768635e-01 -5.35204232e-01
5.80977798e-01 5.58274806e-01 6.48042977e-01 4.12374176... | [9.658743858337402, 1.3513282537460327] |
51cb12dd-6a1d-45fc-86e3-c4e2be78df41 | simmatch-semi-supervised-learning-with | 2203.06915 | null | https://arxiv.org/abs/2203.06915v2 | https://arxiv.org/pdf/2203.06915v2.pdf | SimMatch: Semi-supervised Learning with Similarity Matching | Learning with few labeled data has been a longstanding problem in the computer vision and machine learning research community. In this paper, we introduced a new semi-supervised learning framework, SimMatch, which simultaneously considers semantic similarity and instance similarity. In SimMatch, the consistency regular... | ['Chang Xu', 'Chen Qian', 'Fei Wang', 'Lang Huang', 'Shan You', 'Mingkai Zheng'] | 2022-03-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zheng_SimMatch_Semi-Supervised_Learning_With_Similarity_Matching_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zheng_SimMatch_Semi-Supervised_Learning_With_Similarity_Matching_CVPR_2022_paper.pdf | cvpr-2022-1 | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 2.62195855e-01 1.88485205e-01 -5.66371858e-01 -8.84777486e-01
-7.61824310e-01 -2.50009179e-01 5.89055717e-01 2.19056681e-02
-1.96018651e-01 6.17262661e-01 -4.31743152e-02 2.40129277e-01
3.75846438e-02 -7.23244429e-01 -8.13894987e-01 -6.34243786e-01
3.42539877e-01 4.74840790e-01 3.70526463e-01 3.54436547... | [9.589088439941406, 2.994605302810669] |
c90032a0-d31f-427b-a5dc-5e693f1bbf73 | draft-and-revise-effective-image-generation | 2206.04452 | null | https://arxiv.org/abs/2206.04452v1 | https://arxiv.org/pdf/2206.04452v1.pdf | Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer | Although autoregressive models have achieved promising results on image generation, their unidirectional generation process prevents the resultant images from fully reflecting global contexts. To address the issue, we propose an effective image generation framework of Draft-and-Revise with Contextual RQ-transformer to ... | ['Wook-Shin Han', 'Minsu Cho', 'Saehoon Kim', 'Chiheon Kim', 'Doyup Lee'] | 2022-06-09 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 7.32743263e-01 -5.05440943e-02 9.51179564e-02 -1.38650864e-01
-9.28995013e-01 -4.70895529e-01 7.05524683e-01 -3.38922054e-01
-1.70255918e-02 6.02122068e-01 1.87525183e-01 -3.68809849e-01
2.38220185e-01 -1.00903845e+00 -8.61040950e-01 -9.27807689e-01
2.83289850e-01 -9.93895754e-02 8.50327462e-02 -3.42292562... | [11.278823852539062, -0.6749674081802368] |
b679c3ff-8db7-4e4e-b464-f5b0f87724c3 | rethinking-action-spaces-for-reinforcement | 1902.08858 | null | http://arxiv.org/abs/1902.08858v2 | http://arxiv.org/pdf/1902.08858v2.pdf | Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models | Defining action spaces for conversational agents and optimizing their
decision-making process with reinforcement learning is an enduring challenge.
Common practice has been to use handcrafted dialog acts, or the output
vocabulary, e.g. in neural encoder decoders, as the action spaces. Both have
their own limitations. T... | ['Tiancheng Zhao', 'Kaige Xie', 'Maxine Eskenazi'] | 2019-02-23 | rethinking-action-spaces-for-reinforcement-1 | https://aclanthology.org/N19-1123 | https://aclanthology.org/N19-1123.pdf | naacl-2019-6 | ['goal-oriented-dialogue-systems', 'dialogue-management'] | ['natural-language-processing', 'natural-language-processing'] | [-2.61065946e-03 4.72502679e-01 -7.34268010e-01 -4.40552235e-01
-9.44838643e-01 -6.55596554e-01 1.03991985e+00 -5.00013947e-01
-5.48693478e-01 1.12528062e+00 8.65687370e-01 -4.88050997e-01
1.41994104e-01 -4.92834836e-01 -1.10577606e-01 -8.00606728e-01
2.18026847e-01 9.76365626e-01 -2.11372644e-01 -3.16166788... | [12.946786880493164, 8.068065643310547] |
754e51e8-c4f3-4a95-83e8-bf8d4b231284 | robust-portfolio-design-and-stock-price | 2204.01850 | null | https://arxiv.org/abs/2204.01850v1 | https://arxiv.org/pdf/2204.01850v1.pdf | Robust Portfolio Design and Stock Price Prediction Using an Optimized LSTM Model | Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio with weights allocated to the stocks in a way that optimizes its return and the risk. This paper presents a systematic approach towards building two types of portfolios, optimum risk,... | ['Gourab Nath', 'Saikat Mondal', 'Jaydip Sen'] | 2022-03-02 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-5.17773092e-01 2.88376268e-02 -1.05190545e-01 -1.42560273e-01
-5.20875990e-01 -7.63714135e-01 6.58910573e-01 -2.36403704e-01
-3.15320373e-01 6.54921949e-01 5.37522554e-01 -6.84560657e-01
-4.61482853e-01 -1.13964021e+00 -3.55745941e-01 -4.78960276e-01
-3.36216360e-01 3.03816408e-01 -1.16729230e-01 -7.95076862... | [4.530671119689941, 4.164480686187744] |
48525220-87bc-40a1-a3c7-0955a98b9c8d | distributionally-robust-survival-analysis-a | 2211.10508 | null | https://arxiv.org/abs/2211.10508v1 | https://arxiv.org/pdf/2211.10508v1.pdf | Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics | We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a user-specified minimum probability). This approach uses a training loss function that does not know any demographic information to treat as sen... | ['George H. Chen', 'Shu Hu'] | 2022-11-18 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-1.32168621e-01 2.56396574e-03 -6.90371752e-01 -7.22128272e-01
-1.33302915e+00 -3.46713513e-01 4.11068618e-01 7.98146307e-01
-7.81778991e-01 1.30886793e+00 2.29933426e-01 -3.87411147e-01
-1.65954500e-01 -1.01277423e+00 -4.86640811e-01 -7.40390718e-01
-2.30591819e-01 6.23867035e-01 3.40216421e-02 -6.84912596... | [7.830067157745361, 5.511399745941162] |
4eacc4dd-9a84-43cd-8033-8f4f9a9a9374 | recycle-gan-unsupervised-video-retargeting | 1808.05174 | null | http://arxiv.org/abs/1808.05174v1 | http://arxiv.org/pdf/1808.05174v1.pdf | Recycle-GAN: Unsupervised Video Retargeting | We introduce a data-driven approach for unsupervised video retargeting that
translates content from one domain to another while preserving the style native
to a domain, i.e., if contents of John Oliver's speech were to be transferred
to Stephen Colbert, then the generated content/speech should be in Stephen
Colbert's s... | ['Deva Ramanan', 'Aayush Bansal', 'Yaser Sheikh', 'Shugao Ma'] | 2018-08-15 | recycle-gan-unsupervised-video-retargeting-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Aayush_Bansal_Recycle-GAN_Unsupervised_Video_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Aayush_Bansal_Recycle-GAN_Unsupervised_Video_ECCV_2018_paper.pdf | eccv-2018-9 | ['face-to-face-translation'] | ['computer-vision'] | [ 5.71660817e-01 -7.65516758e-02 -4.38527949e-02 -2.89563447e-01
-3.47403109e-01 -1.11359799e+00 6.30127430e-01 -3.54561090e-01
-4.74608764e-02 9.71934676e-01 1.33461326e-01 -1.43206969e-01
4.12949212e-02 -6.46043777e-01 -7.79576480e-01 -7.07939684e-01
2.39789039e-01 7.86802620e-02 3.09194952e-01 -2.69517004... | [11.662042617797852, -0.47789284586906433] |
9006d575-16cb-4bf0-97d9-a6d34aa0570b | skill-structured-knowledge-infusion-for-large | 2205.08184 | null | https://arxiv.org/abs/2205.08184v1 | https://arxiv.org/pdf/2205.08184v1.pdf | SKILL: Structured Knowledge Infusion for Large Language Models | Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. However, it is largely unexplored whether they can better internalize knowledge from a structured data, such as a knowledge graph, or from text. In this work, we propose a method to infuse structured kno... | ['Martin Jaggi', 'Enrique Alfonseca', 'Zhe Dong', 'Fedor Moiseev'] | 2022-05-17 | null | https://aclanthology.org/2022.naacl-main.113 | https://aclanthology.org/2022.naacl-main.113.pdf | naacl-2022-7 | ['triviaqa'] | ['miscellaneous'] | [-2.69941360e-01 9.65668321e-01 -2.31821835e-01 -3.18451315e-01
-1.14060640e+00 -8.49268377e-01 6.27085567e-01 2.93464124e-01
-4.92721796e-01 8.78289878e-01 2.64488429e-01 -4.76591438e-01
-1.69586226e-01 -1.26841271e+00 -1.36579335e+00 8.42572190e-03
6.28770366e-02 9.92186189e-01 6.01846814e-01 -7.00733423... | [10.445562362670898, 7.939227104187012] |
64bf6bbb-1ba0-44d8-8691-52fd443f2bb1 | understanding-worldwide-private-information | 2102.12869 | null | https://arxiv.org/abs/2102.12869v1 | https://arxiv.org/pdf/2102.12869v1.pdf | Understanding Worldwide Private Information Collection on Android | Mobile phones enable the collection of a wealth of private information, from unique identifiers (e.g., email addresses), to a user's location, to their text messages. This information can be harvested by apps and sent to third parties, which can use it for a variety of purposes. In this paper we perform the largest stu... | ['Gianluca Stringhini', 'Pierre-Antoine Vervier', 'Yun Shen'] | 2021-02-25 | null | null | null | null | ['mobile-security'] | ['miscellaneous'] | [ 5.23714609e-02 2.67410666e-01 -1.04014337e+00 -9.75032821e-02
-7.78454304e-01 -1.32722533e+00 6.07953191e-01 5.33475839e-02
-1.82649493e-01 7.45242953e-01 6.36630535e-01 -6.95564806e-01
2.09832683e-01 -7.79706180e-01 -4.38390911e-01 -1.26096264e-01
3.98040026e-01 6.28654659e-02 2.09894441e-02 1.59834817... | [6.311082363128662, 6.976050853729248] |
837034b6-10fb-443d-82e5-0ba0188c7556 | spectral-geometric-verification-re-ranking | 2210.04432 | null | https://arxiv.org/abs/2210.04432v2 | https://arxiv.org/pdf/2210.04432v2.pdf | Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization | In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account all the top retrieval candidates and re-order them to increase the likelihood of the top candidate being correct. However, state-of-the-art re... | ['Clinton Fookes', 'Sridha Sridharan', 'Peyman Moghadam', 'Kavisha Vidanapathirana'] | 2022-10-10 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.96732774e-01 -4.89427030e-01 3.90632637e-03 -1.52606696e-01
-1.47377241e+00 -7.94209003e-01 5.65154612e-01 5.70818126e-01
-3.15565556e-01 1.93054333e-01 -2.09806770e-01 2.53271237e-02
-4.24378812e-01 -6.41614795e-01 -1.08480883e+00 -4.04412746e-01
-2.00897008e-01 9.57938552e-01 5.75784922e-01 -1.30853266... | [7.590928077697754, -2.337731122970581] |
1c1db442-1304-4664-93b6-e8663b87275a | paper-abstract-writing-through-editing | 1805.06064 | null | http://arxiv.org/abs/1805.06064v1 | http://arxiv.org/pdf/1805.06064v1.pdf | Paper Abstract Writing through Editing Mechanism | We present a paper abstract writing system based on an attentive neural
sequence-to-sequence model that can take a title as input and automatically
generate an abstract. We design a novel Writing-editing Network that can attend
to both the title and the previously generated abstract drafts and then
iteratively revise a... | ['Zhi-Hao Zhou', 'Lifu Huang', 'Heng Ji', 'Boliang Zhang', 'Qingyun Wang', 'Kevin Knight', 'Spencer Whitehead'] | 2018-05-15 | paper-abstract-writing-through-editing-1 | https://aclanthology.org/P18-2042 | https://aclanthology.org/P18-2042.pdf | acl-2018-7 | ['paper-generation'] | ['natural-language-processing'] | [ 4.86570239e-01 6.36236906e-01 5.83551824e-02 -4.45363492e-01
-8.01724315e-01 -9.33776379e-01 5.38581908e-01 2.05696017e-01
-4.80306447e-01 9.27752674e-01 9.44949239e-02 -5.95953465e-01
1.61709458e-01 -5.37696421e-01 -6.87190175e-01 1.28386602e-01
6.04153872e-01 6.24303401e-01 2.18409836e-01 -1.82903409... | [11.978239059448242, 9.034523963928223] |
354f1a05-d5d1-4862-b777-715861162457 | exploiting-an-external-microphone-for | 2307.04460 | null | https://arxiv.org/abs/2307.04460v1 | https://arxiv.org/pdf/2307.04460v1.pdf | Exploiting an External Microphone for Binaural RTF-Vector-Based Direction of Arrival Estimation for Multiple Speakers | In hearing aid applications, an important objective is to accurately estimate the direction of arrival (DOA) of multiple speakers in noisy and reverberant environments. Recently, we proposed a binaural DOA estimation method, where the DOAs of the speakers are estimated by selecting the directions for which the so-calle... | ['Simon Doclo', 'Daniel Fejgin'] | 2023-07-10 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [-2.77692806e-02 -3.28220516e-01 9.81960237e-01 2.69621968e-01
-9.52952385e-01 -4.68845814e-01 1.03693038e-01 -1.12082675e-01
-2.23403379e-01 4.71672088e-01 7.24496901e-01 -2.91647255e-01
-3.20874482e-01 -3.07917178e-01 -3.09822142e-01 -1.05067790e+00
-2.73371518e-01 -2.69566596e-01 9.33985971e-03 -6.74966797... | [15.157198905944824, 5.7487382888793945] |
b9975938-9fdf-479f-8ecc-cfb03798692b | the-circor-digiscope-dataset-from-murmur | 2108.00813 | null | https://arxiv.org/abs/2108.00813v2 | https://arxiv.org/pdf/2108.00813v2.pdf | The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification | Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them o... | ['Miguel T. Coimbra', 'Gari D Clifford', 'Reza Sameni', 'Ali Bahrami Rad', 'Andoni Elola', 'Thiago Tavares', 'Thamine Hatem', 'Sandra Mattos', 'Alipio Jorge', 'Carlos Ferreira', 'Cristina Oliveira', 'Marcelo Nogueira', 'Paulo Dias Costa', 'Francesco Renna', 'Jorge Oliveira'] | 2021-08-02 | null | null | null | null | ['predict-clinical-outcome', 'classify-murmurs'] | ['time-series', 'time-series'] | [ 1.97945535e-01 1.00068405e-01 2.10783333e-02 -7.05313236e-02
-5.85247576e-01 -7.72553563e-01 -4.05401617e-01 5.86251259e-01
-1.10377856e-01 6.06060028e-01 8.34415760e-03 -5.38268089e-01
-2.95624703e-01 -5.27643502e-01 -9.94127840e-02 -7.80857325e-01
-2.66823202e-01 6.06119514e-01 2.20296741e-01 2.18523249... | [14.299718856811523, 3.313336133956909] |
bcf97bf1-8371-4d61-a02e-adb2e4299289 | multiple-object-tracking-in-recent-times-a | 2209.04796 | null | https://arxiv.org/abs/2209.04796v1 | https://arxiv.org/pdf/2209.04796v1.pdf | Multiple Object Tracking in Recent Times: A Literature Review | Multiple object tracking gained a lot of interest from researchers in recent years, and it has become one of the trending problems in computer vision, especially with the recent advancement of autonomous driving. MOT is one of the critical vision tasks for different issues like occlusion in crowded scenes, similar appe... | ['Md. Hasanul Kabir', 'A. B. M. Ashikur Rahman', 'Md. Bakhtiar Hasan', 'Kashifa Kawaakib Hussain', 'Samia Islam', 'Mk Bashar'] | 2022-09-11 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-4.54544090e-02 -5.16370952e-01 -1.64815173e-01 -4.40515503e-02
1.19621843e-01 -1.66732833e-01 5.30565023e-01 -1.05393521e-01
-6.84515476e-01 6.95409000e-01 -1.52262151e-01 1.52502000e-01
-4.01267320e-01 -4.77599859e-01 -7.22317517e-01 -6.61050081e-01
-1.09320223e-01 3.19557041e-01 9.32831109e-01 -4.90932286... | [6.508151531219482, -1.9875929355621338] |
bd1c2949-23b1-437c-8135-760911be7e09 | accurate-and-versatile-3d-segmentation-of | null | null | https://elifesciences.org/articles/57613 | https://elifesciences.org/articles/57613 | Accurate and versatile 3D segmentation of plant tissues at cellular resolution | Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. He... | ['Anna Kreshuk', 'Alexis Maizel', 'Kay Schneitz', 'Fred A Hamprecht', 'Miltos Tsiantis', 'Jan U Lohmann', 'George W Bassel', 'Salva Duran-Nebreda', 'Alberto Bailoni', 'Constantin Pape', 'Susanne S Steigleder', 'Rena Lymbouridou', 'David Wilson-Sánchez', 'Sören Strauss', 'Christian Wenzl', 'Marion Louveaux', 'Amaya Vilc... | 2020-07-29 | null | null | null | elife-2020-7 | ['graph-partitioning'] | ['graphs'] | [ 1.14044823e-01 5.50090522e-02 2.15954229e-01 -6.96863830e-02
-1.45764917e-01 -1.00784540e+00 8.87412354e-02 5.12840390e-01
-3.57069559e-02 4.36089426e-01 -7.26833701e-01 -4.66389626e-01
1.71195865e-01 -9.03786898e-01 -3.81681621e-01 -5.89462399e-01
-3.33055049e-01 7.93056309e-01 3.48250180e-01 1.53950512... | [14.305516242980957, -3.1190946102142334] |
2b8fdff9-e4c1-4434-8960-506fec310f86 | automated-pii-extraction-from-social-media | 2111.09415 | null | https://arxiv.org/abs/2111.09415v1 | https://arxiv.org/pdf/2111.09415v1.pdf | Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach | Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can cause severe losses to the users, and informing users of their PII exposure is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, a... | ['Hsinchun Chen', 'Weifeng Li', 'MohammadReza Ebrahimi', 'Fang Yu Lin', 'Yizhi Liu'] | 2021-11-11 | null | null | null | null | ['embeddings-evaluation'] | ['natural-language-processing'] | [ 3.09541374e-01 1.40943080e-01 -6.11391902e-01 -1.88049391e-01
-4.33352739e-01 -7.56859601e-01 5.05791187e-01 4.09239411e-01
-3.47197175e-01 3.93065393e-01 3.51521969e-01 -7.22661912e-01
-7.05943257e-02 -1.24137974e+00 -5.11996746e-01 -1.21376358e-01
-2.34089047e-01 2.93101110e-02 -6.26624897e-02 -2.24469304... | [6.05755615234375, 7.040432929992676] |
58cf7e6d-222f-4f61-8f4a-138f2d74d185 | unsupervised-person-re-identification-by-soft | 1903.06325 | null | http://arxiv.org/abs/1903.06325v2 | http://arxiv.org/pdf/1903.06325v2.pdf | Unsupervised Person Re-identification by Soft Multilabel Learning | Although unsupervised person re-identification (RE-ID) has drawn increasing
research attentions due to its potential to address the scalability problem of
supervised RE-ID models, it is very challenging to learn discriminative
information in the absence of pairwise labels across disjoint camera views. To
overcome this ... | ['An-Cong Wu', 'Jian-Huang Lai', 'Wei-Shi Zheng', 'Hong-Xing Yu', 'Xiaowei Guo', 'Shaogang Gong'] | 2019-03-15 | unsupervised-person-re-identification-by-soft-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Yu_Unsupervised_Person_Re-Identification_by_Soft_Multilabel_Learning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Yu_Unsupervised_Person_Re-Identification_by_Soft_Multilabel_Learning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-3.7110221e-01 -9.1752447e-02 -3.9248070e-01 -8.4366363e-01
-9.0923136e-01 -7.4645746e-01 9.9625582e-01 -1.0825861e-01
-5.9853351e-01 6.5087545e-01 4.3969771e-01 4.2832989e-01
1.5311673e-01 -1.3549976e-01 -5.5397326e-01 -6.5478885e-01
2.7639100e-01 9.3421388e-01 -3.3200413e-01 3.3690891e-01
-3.3639687e-01... | [14.780006408691406, 1.0562111139297485] |
90e5fabc-7ea4-42cb-9e68-d061eafd8d4a | robust-optimization-and-validation-of-echo | 2103.03174 | null | https://arxiv.org/abs/2103.03174v2 | https://arxiv.org/pdf/2103.03174v2.pdf | Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics | An approach to the time-accurate prediction of chaotic solutions is by learning temporal patterns from data. Echo State Networks (ESNs), which are a class of Reservoir Computing, can accurately predict the chaotic dynamics well beyond the predictability time. Existing studies, however, also showed that small changes in... | ['Luca Magri', 'Alberto Racca'] | 2021-02-09 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [-2.20212579e-01 -3.90656382e-01 2.81140655e-01 5.09886146e-02
-6.51254654e-02 -5.63822925e-01 7.81792581e-01 -2.98582911e-01
-2.08537340e-01 9.71292794e-01 -2.61738777e-01 -4.43184465e-01
-6.87716722e-01 -5.11003435e-01 -1.41112536e-01 -1.17055237e+00
-7.27269232e-01 5.57037413e-01 1.68912172e-01 -5.68819582... | [6.6200103759765625, 3.398724317550659] |
19872222-b9b6-4554-bb62-3c8852575b88 | addressing-the-confounds-of-accompaniments-in | 2002.06817 | null | https://arxiv.org/abs/2002.06817v1 | https://arxiv.org/pdf/2002.06817v1.pdf | Addressing the confounds of accompaniments in singer identification | Identifying singers is an important task with many applications. However, the task remains challenging due to many issues. One major issue is related to the confounding factors from the background instrumental music that is mixed with the vocals in music production. A singer identification model may learn to extract no... | ['Yi-Hsuan Yang', 'Yu-Ching Yang', 'Zhe-Cheng Fan', 'Kai-Hsiang Cheng', 'Tsung-Han Hsieh'] | 2020-02-17 | null | null | null | null | ['singer-identification'] | ['music'] | [ 3.73036683e-01 -4.79325950e-01 1.03377640e-01 -5.28614745e-02
-9.04888690e-01 -1.10159135e+00 2.37591207e-01 -2.46045321e-01
-1.78463176e-01 4.09137845e-01 1.87789381e-01 1.10464469e-01
-2.02680603e-01 -2.90793002e-01 -4.71298993e-01 -9.14350271e-01
-6.50315592e-03 1.60007268e-01 2.13772934e-02 -1.38837814... | [15.717881202697754, 5.527655601501465] |
36139ca9-063f-4cd0-b946-a79b77d94101 | memory-augmented-non-local-attention-for | 2108.11048 | null | https://arxiv.org/abs/2108.11048v1 | https://arxiv.org/pdf/2108.11048v1.pdf | Memory-Augmented Non-Local Attention for Video Super-Resolution | In this paper, we propose a novel video super-resolution method that aims at generating high-fidelity high-resolution (HR) videos from low-resolution (LR) ones. Previous methods predominantly leverage temporal neighbor frames to assist the super-resolution of the current frame. Those methods achieve limited performance... | ['Tao Mei', 'Liefeng Bo', 'Jingen Liu', 'Jiyang Yu'] | 2021-08-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yu_Memory-Augmented_Non-Local_Attention_for_Video_Super-Resolution_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution'] | ['computer-vision'] | [ 3.97180289e-01 -1.60457745e-01 -2.66773283e-01 -2.73429483e-01
-1.05494952e+00 -9.89139080e-02 4.57167059e-01 -5.22711277e-01
-2.26225778e-01 8.95152032e-01 5.46503067e-01 2.78142631e-01
1.02822125e-01 -6.78864241e-01 -8.43634844e-01 -6.59322023e-01
1.39850661e-01 -3.28520030e-01 5.82338750e-01 -2.49143660... | [11.027881622314453, -1.887801170349121] |
2e4dc6e0-6273-4d5b-9c4a-5498db646a09 | optimization-of-residential-demand-response | 2305.08077 | null | https://arxiv.org/abs/2305.08077v1 | https://arxiv.org/pdf/2305.08077v1.pdf | Optimization of Residential Demand Response Program Cost with Consideration for Occupants Thermal Comfort and Privacy | Residential consumers can use the demand response program (DRP) if they can utilize the home energy management system (HEMS), which reduces consumer costs by automatically adjusting air conditioning (AC) setpoints and shifting some appliances to off-peak hours. If HEMS knows occupancy status, consumers can gain more ec... | ['Amir Khorsandi', 'M. M. Ardehali', 'Reza Nematirad'] | 2023-05-14 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.76166907e-01 -1.20925987e-02 1.09401926e-01 -2.75194377e-01
-5.20323694e-01 -6.00647151e-01 1.71482369e-01 6.57327399e-02
2.81960100e-01 1.17948186e+00 2.08637759e-01 -1.51232421e-01
-5.32759309e-01 -1.09901822e+00 -1.71353534e-01 -1.17332065e+00
1.91487297e-01 5.54728925e-01 -3.22565287e-01 1.50933648... | [5.715348243713379, 2.483076572418213] |
daaa3a07-1154-45c4-8a52-6c8edd27620e | humandiffusion-a-coarse-to-fine-alignment | 2211.06235 | null | https://arxiv.org/abs/2211.06235v1 | https://arxiv.org/pdf/2211.06235v1.pdf | HumanDiffusion: a Coarse-to-Fine Alignment Diffusion Framework for Controllable Text-Driven Person Image Generation | Text-driven person image generation is an emerging and challenging task in cross-modality image generation. Controllable person image generation promotes a wide range of applications such as digital human interaction and virtual try-on. However, previous methods mostly employ single-modality information as the prior co... | ['Tieniu Tan', 'Zhenan Sun', 'Kunbo Zhang', 'Binghao Zhao', 'Jianxin Sun', 'Muyi Sun', 'Kaiduo Zhang'] | 2022-11-11 | null | null | null | null | ['virtual-try-on'] | ['computer-vision'] | [ 2.78562009e-01 -2.13783056e-01 3.55958462e-01 -3.93754691e-01
-7.16225028e-01 -3.23009312e-01 9.22076583e-01 -5.37140012e-01
-2.76271433e-01 6.43312633e-01 4.04932082e-01 4.42313880e-01
7.88740888e-02 -9.16969419e-01 -4.61228400e-01 -7.89979994e-01
6.69991851e-01 4.00069267e-01 -4.27360944e-02 -4.73794907... | [11.980626106262207, -0.8390780091285706] |
a886ad20-6422-455c-8eaf-a2bf79ccae6b | large-scale-news-classification-using-bert | 2107.06785 | null | https://arxiv.org/abs/2107.06785v2 | https://arxiv.org/pdf/2107.06785v2.pdf | Large-Scale News Classification using BERT Language Model: Spark NLP Approach | The rise of big data analytics on top of NLP increases the computational burden for text processing at scale. The problems faced in NLP are very high dimensional text, so it takes a high computation resource. The MapReduce allows parallelization of large computations and can improve the efficiency of text processing. T... | ['Anantha Yullian Sukmadewa', 'Kuncahyo Setyo Nugroho', 'Novanto Yudistira'] | 2021-07-14 | null | null | null | null | ['news-classification'] | ['natural-language-processing'] | [-1.11951303e+00 -1.05253704e-01 5.27954638e-01 -6.38755620e-01
-5.69134235e-01 -4.60218549e-01 5.92928588e-01 2.94668227e-01
-6.39800906e-01 5.27402580e-01 4.13737804e-01 -1.44682646e-01
3.43624912e-02 -1.35524893e+00 -7.24100292e-01 -6.21905327e-01
2.20133677e-01 1.05745196e+00 5.92950312e-03 -8.03245697... | [10.465505599975586, 8.122106552124023] |
b621ef41-2c98-4ffb-88e2-366fe42cdacc | atrial-septal-defect-detection-in-children | 2306.03835 | null | https://arxiv.org/abs/2306.03835v1 | https://arxiv.org/pdf/2306.03835v1.pdf | Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning | Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method... | ['Yuqi Zhang', 'Qingli Li', 'Jiangang Chen', 'Jionglong Su', 'Angelos Stefanidis', 'Jinfeng Wang', 'Lijun Chen', 'Zhifang Zhang', 'Tongtong Liang', 'Xiaoxiang Han', 'Qiming Huang', 'Yiman Liu'] | 2023-06-06 | null | null | null | null | ['defect-detection', 'specificity'] | ['computer-vision', 'natural-language-processing'] | [-1.43603042e-01 2.18569171e-02 -1.58955932e-01 -7.57870823e-02
-2.61803687e-01 -4.43093359e-01 -3.76228839e-01 1.72389019e-02
-1.56596810e-01 5.77047765e-01 -4.79577258e-02 -5.80252290e-01
4.51568607e-03 -7.13918209e-01 -4.60332990e-01 -5.26403308e-01
-3.90888363e-01 6.61849082e-01 2.56734669e-01 5.34545064... | [14.165300369262695, -2.3084464073181152] |
3c57fa0a-e3b6-4835-92df-de41bd1ed1ea | photofeeler-d3-a-neural-network-with-voter | 1904.07435 | null | https://arxiv.org/abs/1904.07435v3 | https://arxiv.org/pdf/1904.07435v3.pdf | Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction | In just a few years, online dating has become the dominant way that young people meet to date, making the deceptively error-prone task of picking good dating profile photos vital to a generation's ability to form romantic connections. Until now, artificial intelligence approaches to Dating Photo Impression Prediction (... | ['Agastya Kalra', 'Ben Peterson'] | 2019-04-16 | null | null | null | null | ['facial-beauty-prediction'] | ['computer-vision'] | [-1.86898842e-01 3.86059970e-01 -2.93776989e-01 -1.01896226e+00
-3.50654647e-02 -3.94998699e-01 9.75408673e-01 -5.51908389e-02
-2.29962856e-01 5.68348527e-01 -1.41432390e-01 -1.33488886e-02
3.02473009e-02 -9.47687984e-01 -6.98878229e-01 6.26073852e-02
7.08641186e-02 7.96078146e-01 -5.57318926e-01 -3.07691664... | [13.28288745880127, 1.044864296913147] |
edfbe0b1-40e2-4c3d-b654-cef50341c6b9 | multimodal-fusion-transformer-for-remote | 2203.16952 | null | https://arxiv.org/abs/2203.16952v2 | https://arxiv.org/pdf/2203.16952v2.pdf | Multimodal Fusion Transformer for Remote Sensing Image Classification | Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViTs in hyperspectral image (HSI) classification tasks. To achieve satisfactory performance, close... | ['Jocelyn Chanussot', 'Antonio Plaza', 'Behnood Rasti', 'Danfeng Hong', 'Ankur Deria', 'Swalpa Kumar Roy'] | 2022-03-31 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 4.57859457e-01 -3.71437937e-01 4.83816862e-02 -4.36082602e-01
-8.75953436e-01 -3.98356318e-01 5.90796411e-01 -9.85675752e-02
-3.12838167e-01 6.91160858e-01 -4.77274545e-02 -5.25695741e-01
-1.50586039e-01 -9.31872368e-01 -8.29657197e-01 -9.72955525e-01
3.28853458e-01 -7.33478963e-02 -2.97898669e-02 -3.54968488... | [9.888689994812012, -1.4925333261489868] |
91ea774c-fa1a-45eb-89af-f6e9b8c90946 | time-series-clustering-with-random | 2305.10457 | null | https://arxiv.org/abs/2305.10457v2 | https://arxiv.org/pdf/2305.10457v2.pdf | Time Series Clustering With Random Convolutional Kernels | Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insi... | ['Rubén Cuevas', 'Jorge Marco-Blanco'] | 2023-05-17 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.93373251e-01 -7.97363460e-01 -5.38195707e-02 -2.94770092e-01
-4.39850777e-01 -8.05889606e-01 2.92039216e-01 3.31452012e-01
-3.39682400e-01 2.98065037e-01 1.09842628e-01 -5.08962214e-01
-5.84021389e-01 -6.25421286e-01 -1.77941188e-01 -7.26504624e-01
-1.07303584e+00 2.13887289e-01 -4.32839185e-01 -3.32051784... | [7.145292282104492, 2.989281415939331] |
0f786c51-4b75-44d0-b6a9-3d2657a53b43 | araspot-arabic-spoken-command-spotting | 2303.16621 | null | https://arxiv.org/abs/2303.16621v1 | https://arxiv.org/pdf/2303.16621v1.pdf | AraSpot: Arabic Spoken Command Spotting | Spoken keyword spotting (KWS) is the task of identifying a keyword in an audio stream and is widely used in smart devices at the edge in order to activate voice assistants and perform hands-free tasks. The task is daunting as there is a need, on the one hand, to achieve high accuracy while at the same time ensuring tha... | ['Haidar Harmanani', 'Mahmoud Salhab'] | 2023-03-29 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation', 'keyword-spotting'] | ['medical', 'miscellaneous', 'speech'] | [ 2.87108302e-01 3.05611581e-01 1.40090391e-01 -1.86379746e-01
-1.14265239e+00 -6.24921739e-01 5.42715609e-01 -2.16664344e-01
-4.94240165e-01 4.37014520e-01 2.30272755e-01 -4.08215672e-01
2.45008841e-01 -2.46076763e-01 -3.38462561e-01 -4.46988255e-01
1.09809190e-01 7.30946898e-01 2.29928847e-02 -5.02583921... | [14.240655899047852, 6.568187713623047] |
d7d5e45d-0ec1-485d-94ab-fb2da879d881 | supervised-prediction-of-aging-related-genes | 1908.08135 | null | https://arxiv.org/abs/1908.08135v4 | https://arxiv.org/pdf/1908.08135v4.pdf | Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork | Background. Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other.... | ['Tijana Milenković', 'Qi Li'] | 2019-08-21 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [ 2.74944514e-01 1.66827440e-01 -4.98639971e-01 -1.11042887e-01
-1.80705279e-01 -1.00991838e-01 4.60628904e-02 4.78352100e-01
-2.81643897e-01 1.28748858e+00 1.95245832e-01 -1.52134597e-01
-4.20895159e-01 -8.74428511e-01 -4.34780687e-01 -7.96471000e-01
-4.72111374e-01 3.70947003e-01 2.55956829e-01 -3.89851421... | [6.529954433441162, 5.498507022857666] |
50409c6f-4911-4d1a-9797-14e677c6fe80 | learning-3d-aware-egocentric-spatial-temporal | 1909.09272 | null | https://arxiv.org/abs/1909.09272v3 | https://arxiv.org/pdf/1909.09272v3.pdf | Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks | To enable intelligent automated driving systems, a promising strategy is to understand how human drives and interacts with road users in complicated driving situations. In this paper, we propose a 3D-aware egocentric spatial-temporal interaction framework for automated driving applications. Graph convolution networks (... | ['Yi-Ting Chen', 'Chengxi Li', 'Yue Meng', 'Stanley H. Chan'] | 2019-09-20 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [-2.35820487e-01 -2.66926195e-02 4.74559031e-02 -6.89559102e-01
2.74712056e-01 -3.57193142e-01 7.54592180e-01 -3.79584104e-01
-3.93772095e-01 2.29976505e-01 4.25189674e-01 -4.13509399e-01
-3.18299532e-01 -7.47283638e-01 -6.59558654e-01 -6.62197590e-01
-5.48706017e-03 3.50277036e-01 3.36960644e-01 -5.96268296... | [6.41081428527832, 0.6455407738685608] |
f68151e9-edce-4904-be04-105aeab443da | solving-mathword-problems-automatically-with | 2208.05645 | null | https://arxiv.org/abs/2208.05645v2 | https://arxiv.org/pdf/2208.05645v2.pdf | Heterogeneous Line Graph Transformer for Math Word Problems | This paper describes the design and implementation of a new machine learning model for online learning systems. We aim at improving the intelligent level of the systems by enabling an automated math word problem solver which can support a wide range of functions such as homework correction, difficulty estimation, and p... | ['Meng Jiang', 'Zijian Hu'] | 2022-08-11 | null | null | null | null | ['semantic-role-labeling'] | ['natural-language-processing'] | [-1.82625040e-01 2.80065715e-01 -3.29416305e-01 -4.38913792e-01
-7.53870979e-02 -4.83376831e-01 1.89386889e-01 9.25624669e-01
-1.65764675e-01 7.71143973e-01 -2.07605567e-02 -9.74087417e-01
-4.17760521e-01 -1.47830999e+00 -5.59361160e-01 2.78030843e-01
-1.95616670e-02 5.80310762e-01 5.01710057e-01 -5.12611508... | [9.715221405029297, 7.45811653137207] |
e4bab3c2-9699-4a54-8169-5fc81b180e10 | sign-language-transformers-joint-end-to-end | 2003.13830 | null | https://arxiv.org/abs/2003.13830v1 | https://arxiv.org/pdf/2003.13830v1.pdf | Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation | Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performance drastically. In fact, the current state-of-the-art in translation requires gloss level tokenization in order to work. We introduce a nov... | ['Richard Bowden', 'Simon Hadfield', 'Oscar Koller', 'Necati Cihan Camgoz'] | 2020-03-30 | sign-language-transformers-joint-end-to-end-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Camgoz_Sign_Language_Transformers_Joint_End-to-End_Sign_Language_Recognition_and_Translation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Camgoz_Sign_Language_Transformers_Joint_End-to-End_Sign_Language_Recognition_and_Translation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['sign-language-translation'] | ['computer-vision'] | [ 4.18683976e-01 -3.32269907e-01 -2.87941962e-01 -4.31094527e-01
-1.38945997e+00 -7.56545305e-01 8.77005279e-01 -9.94104743e-01
-7.03535914e-01 4.92249101e-01 5.60638785e-01 -4.05010611e-01
4.25870895e-01 -2.36535162e-01 -8.88527572e-01 -6.40889108e-01
1.50087059e-01 8.56867373e-01 1.23302758e-01 -3.16680163... | [9.23388957977295, -6.559328556060791] |
1e962209-4713-4360-b1c4-f52568fdb20b | generalizing-gaze-estimation-with-outlier | 2107.13780 | null | https://arxiv.org/abs/2107.13780v2 | https://arxiv.org/pdf/2107.13780v2.pdf | Generalizing Gaze Estimation with Outlier-guided Collaborative Adaptation | Deep neural networks have significantly improved appearance-based gaze estimation accuracy. However, it still suffers from unsatisfactory performance when generalizing the trained model to new domains, e.g., unseen environments or persons. In this paper, we propose a plug-and-play gaze adaptation framework (PnP-GA), wh... | ['Feng Lu', 'Haofei Wang', 'Ruicong Liu', 'Yunfei Liu'] | 2021-07-29 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Generalizing_Gaze_Estimation_With_Outlier-Guided_Collaborative_Adaptation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Generalizing_Gaze_Estimation_With_Outlier-Guided_Collaborative_Adaptation_ICCV_2021_paper.pdf | iccv-2021-1 | ['gaze-estimation'] | ['computer-vision'] | [-1.77043974e-02 4.30876063e-03 1.28354564e-01 -5.64644814e-01
-2.51215398e-01 -1.66992873e-01 1.90694943e-01 -7.22621858e-01
-3.58159930e-01 9.39379692e-01 -2.76972890e-01 -7.86844734e-03
1.19161911e-01 -1.78964481e-01 -7.22121000e-01 -7.83951879e-01
3.56908768e-01 2.08558500e-01 1.91405535e-01 -1.48210451... | [14.167685508728027, 0.015059489756822586] |
248e16e1-b441-46d0-a8a8-6d6ea37b926a | topological-point-cloud-clustering | 2303.16716 | null | https://arxiv.org/abs/2303.16716v1 | https://arxiv.org/pdf/2303.16716v1.pdf | Topological Point Cloud Clustering | We present Topological Point Cloud Clustering (TPCC), a new method to cluster points in an arbitrary point cloud based on their contribution to global topological features. TPCC synthesizes desirable features from spectral clustering and topological data analysis and is based on considering the spectral properties of a... | ['Michael T. Schaub', 'Vincent P. Grande'] | 2023-03-29 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [ 1.80535130e-02 -3.91302332e-02 1.99729159e-01 3.35136771e-01
-3.73786360e-01 -7.79559493e-01 8.83314550e-01 6.78787827e-01
-3.31101678e-02 1.75147176e-01 4.70124632e-02 -7.17968345e-02
-7.57944703e-01 -8.52653921e-01 -5.34717083e-01 -7.69031048e-01
-4.84032393e-01 8.27301502e-01 2.21030608e-01 -2.25616261... | [7.408784866333008, 4.453433036804199] |
483ec355-3e74-4b49-8c25-0bbba00a1e7a | ct-bert-learning-better-tabular | 2307.04308 | null | https://arxiv.org/abs/2307.04308v1 | https://arxiv.org/pdf/2307.04308v1.pdf | CT-BERT: Learning Better Tabular Representations Through Cross-Table Pre-training | Tabular data -- also known as structured data -- is one of the most common data forms in existence, thanks to the stable development and scaled deployment of database systems in the last few decades. At present however, despite the blast brought by large pre-trained models in other domains such as ChatGPT or SAM, how c... | ['Junbo Zhao', 'Gang Chen', 'Sai Wu', 'Liyao Li', 'Haobo Wang', 'Guoshan Lu', 'Chao Ye'] | 2023-07-10 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 6.30960464e-02 2.28198245e-01 -6.04260564e-01 -4.59145606e-01
-1.10193717e+00 -7.58496761e-01 5.20732164e-01 4.33749139e-01
1.18003286e-01 9.05976772e-01 2.09001198e-01 -2.91182786e-01
-3.74322683e-01 -9.54678655e-01 -1.00572443e+00 -3.58496845e-01
-5.13849258e-02 9.72132206e-01 2.36172780e-01 -5.16200840... | [9.619219779968262, 7.893829822540283] |
7f1f636f-c57a-4a11-9a9d-5d4dbb2b3088 | slsdeep-skin-lesion-segmentation-based-on | 1805.10241 | null | http://arxiv.org/abs/1805.10241v2 | http://arxiv.org/pdf/1805.10241v2.pdf | SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks | Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for
automated diagnosis of melanoma. In this paper, we present a robust deep
learning SLS model, so-called SLSDeep, which is represented as an
encoder-decoder network. The encoder network is constructed by dilated residual
layers, in turn, a pyramid... | ['Saddam Abdulwahab', 'Petia Radeva', 'Md. Mostafa Kamal Sarker', 'Farhan Akram', 'Domenec Puig', 'Adel Saleh', 'Syeda Furruka Banu', 'Santiago Romani', 'Hatem A. Rashwan', 'Vivek Kumar Singh', 'Forhad U H Chowdhury'] | 2018-05-25 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.87631845e-01 1.93960413e-01 -8.49117860e-02 -2.30634391e-01
-1.06899142e+00 -2.93624282e-01 3.19655836e-01 1.92732349e-01
-7.90195286e-01 6.14883125e-01 -3.68290395e-01 -3.88962239e-01
2.50649095e-01 -6.48312509e-01 -5.90627193e-01 -8.66747618e-01
3.32957566e-01 -2.68597871e-01 5.04766822e-01 3.16261649... | [15.640033721923828, -2.955991268157959] |
97eee63c-cbbe-44ec-a5b5-a3656b2f7433 | the-value-of-semantic-parse-labeling-for | null | null | https://aclanthology.org/P16-2033 | https://aclanthology.org/P16-2033.pdf | The Value of Semantic Parse Labeling for Knowledge Base Question Answering | null | ['Ming-Wei Chang', 'Wen-tau Yih', 'Jina Suh', 'Matthew Richardson', 'Chris Meek'] | 2016-08-01 | null | null | null | acl-2016-8 | ['knowledge-base-question-answering'] | ['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... | [-7.173574924468994, 3.834340810775757] |
b22e7e8f-bb80-4e36-be40-16b5a13f1981 | a-study-of-variable-role-based-feature | 2303.04942 | null | https://arxiv.org/abs/2303.04942v2 | https://arxiv.org/pdf/2303.04942v2.pdf | A Study of Variable-Role-based Feature Enrichment in Neural Models of Code | Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on th... | ['Mohammad Amin Alipour', 'David Lo', 'Bowen Xu', 'Md Rafiqul Islam Rabin', 'Aftab Hussain'] | 2023-03-08 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 1.54645443e-01 3.71271104e-01 -1.62923485e-01 -4.58427310e-01
1.32646814e-01 -5.91242373e-01 1.97078228e-01 4.25754875e-01
-4.18337256e-01 4.43361551e-01 4.95216511e-02 -6.81545973e-01
-9.51747000e-02 -9.99300838e-01 -1.05790329e+00 -1.24608859e-01
-8.01814254e-03 -4.22790945e-02 8.20018947e-02 -4.54156160... | [7.664283275604248, 7.719470977783203] |
2401892b-394a-4b55-bf00-2c021566921c | marine-debris-detection-in-satellite | 2307.04128 | null | https://arxiv.org/abs/2307.04128v1 | https://arxiv.org/pdf/2307.04128v1.pdf | Marine Debris Detection in Satellite Surveillance using Attention Mechanisms | Marine debris is an important issue for environmental protection, but current methods for locating marine debris are yet limited. In order to achieve higher efficiency and wider applicability in the localization of Marine debris, this study tries to combine the instance segmentation of YOLOv7 with different attention m... | ['Richard Jiang', 'Yijie Zhu', 'Ao Shen'] | 2023-07-09 | null | null | null | null | ['semantic-segmentation', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [-2.67547488e-01 1.54710919e-01 4.81272012e-01 4.96475637e-01
-6.60352349e-01 -6.52623832e-01 6.48409903e-01 5.76883733e-01
-8.67929995e-01 4.31093961e-01 1.90722391e-01 -2.57529676e-01
-3.47144634e-01 -6.79977834e-01 -4.93210167e-01 -1.07216942e+00
-4.06731248e-01 1.62243620e-01 6.77613795e-01 2.32400242... | [8.921570777893066, -0.8580042123794556] |
230b6223-b3f6-4f46-a21a-3c9272d8ba1a | abanicco-a-new-color-space-for-multi-label | 2211.08460 | null | https://arxiv.org/abs/2211.08460v1 | https://arxiv.org/pdf/2211.08460v1.pdf | ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation | In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas. However, the development of methods able to successfully complete this task has proven challenging, mainly due to the gap between human color perception, linguistic color te... | ['Arrate Muñoz-Barrutia', 'Javier Pascau', 'Agapito Ledezma', 'Laura Nicolás-Sáenz'] | 2022-11-15 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 3.69616240e-01 -6.50728643e-01 4.43317043e-03 5.57063054e-03
-2.85567343e-01 -9.83920097e-01 2.98438638e-01 6.06385589e-01
-2.89035648e-01 6.67920351e-01 -4.63633984e-01 -5.81555307e-01
-1.92545190e-01 -7.57185936e-01 1.02519900e-01 -8.37451339e-01
4.43547964e-01 2.55677611e-01 -3.06997690e-02 -8.60568359... | [10.347616195678711, -2.458803415298462] |
7fdacf5c-a344-4d53-ac7c-53efa434cb94 | text-coherence-analysis-based-on-deep-neural | 1710.07770 | null | http://arxiv.org/abs/1710.07770v1 | http://arxiv.org/pdf/1710.07770v1.pdf | Text Coherence Analysis Based on Deep Neural Network | In this paper, we propose a novel deep coherence model (DCM) using a
convolutional neural network architecture to capture the text coherence. The
text coherence problem is investigated with a new perspective of learning
sentence distributional representation and text coherence modeling
simultaneously. In particular, th... | ['Zhongfei Zhang', 'Yingming Li', 'Baiyun Cui', 'Yaqing Zhang'] | 2017-10-21 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 5.78411184e-02 -1.30140170e-01 -1.83073003e-02 -7.95488894e-01
-8.34012568e-01 -1.89382315e-01 9.01280880e-01 3.66893083e-01
-4.30657536e-01 4.49016541e-01 9.71533716e-01 5.35787344e-02
-9.84542668e-02 -5.55010021e-01 -3.14486653e-01 -4.68963921e-01
3.64541784e-02 3.48894417e-01 -2.20407650e-01 -2.48181164... | [11.90483570098877, 9.294374465942383] |
772ed768-ba0b-40e5-81c7-ca6a992e3851 | ci-avsr-a-cantonese-audio-visual-speech | 2201.03804 | null | https://arxiv.org/abs/2201.03804v2 | https://arxiv.org/pdf/2201.03804v2.pdf | CI-AVSR: A Cantonese Audio-Visual Speech Dataset for In-car Command Recognition | With the rise of deep learning and intelligent vehicle, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving a... | ['Pascale Fung', 'Bertram E. Shi', 'Xiaojuan Ma', 'Qifeng Chen', 'Genta Indra Winata', 'Holy Lovenia', 'Rita Frieske', 'Cheuk Tung Shadow Yiu', 'Peng Xu', 'Elham J. Barezi', 'Tiezheng Yu', 'Samuel Cahyawijaya', 'Wenliang Dai'] | 2022-01-11 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [-2.90120631e-01 -3.31026345e-01 -3.50263789e-02 -4.63031739e-01
-1.21224308e+00 -4.34524924e-01 6.67563021e-01 -6.37017012e-01
-4.93769795e-01 3.71639818e-01 2.35841781e-01 -6.34787500e-01
5.06148517e-01 -1.98876664e-01 -7.14630544e-01 -8.12531233e-01
2.21298665e-01 1.97883353e-01 1.55536160e-01 -4.55269247... | [14.335892677307129, 5.112422943115234] |
94696cc7-0b3d-411a-aa82-22e9965771fd | a-spreader-ranking-algorithm-for-extremely | 2211.09657 | null | https://arxiv.org/abs/2211.09657v1 | https://arxiv.org/pdf/2211.09657v1.pdf | A Spreader Ranking Algorithm for Extremely Low-budget Influence Maximization in Social Networks using Community Bridge Nodes | In recent years, social networking platforms have gained significant popularity among the masses like connecting with people and propagating ones thoughts and opinions. This has opened the door to user-specific advertisements and recommendations on these platforms, bringing along a significant focus on Influence Maximi... | ['Mukesh Prasad', 'Dinesh Kumar Vishwakarma', 'Pranav Chandhok', 'Arjun Choudhry', 'Inder Khatri', 'Aaryan Gupta'] | 2022-11-17 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 5.42772235e-03 4.01811212e-01 -4.13597047e-01 -7.42524490e-02
2.76801102e-02 -6.41015351e-01 1.04255331e+00 3.95776093e-01
-3.40119392e-01 5.45131862e-01 4.81783718e-01 -2.92579949e-01
-2.45613560e-01 -1.10865736e+00 -2.61259317e-01 -3.76549721e-01
-3.83611590e-01 4.07830536e-01 7.02257633e-01 -4.46187705... | [6.930668354034424, 5.37814998626709] |
ee3edb4c-0cbf-47c9-965b-3205ce261f72 | point-voxel-absorbing-graph-representation | 2306.05239 | null | https://arxiv.org/abs/2306.05239v1 | https://arxiv.org/pdf/2306.05239v1.pdf | Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition | Considering the balance of performance and efficiency, sampled point and voxel methods are usually employed to down-sample dense events into sparse ones. After that, one popular way is to leverage a graph model which treats the sparse points/voxels as nodes and adopts graph neural networks (GNNs) to learn the represent... | ['Bin Luo', 'Lin Zhu', 'Zhimin Bao', 'Xiao Wang', 'Chengguo Yuan', 'Bo Jiang'] | 2023-06-08 | null | null | null | null | ['event-data-classification', 'graph-representation-learning'] | ['computer-vision', 'methodology'] | [ 1.17630050e-01 2.69800752e-01 -1.21875614e-01 -1.54751047e-01
-4.61991191e-01 -2.05416709e-01 6.90966785e-01 8.45452011e-01
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-5.87096848e-02 -1.40720308e+00 -8.90901566e-01 -7.21542478e-01
-2.43711978e-01 2.22718120e-01 4.42321956e-01 3.92051153... | [7.276482582092285, 6.221925735473633] |
bc339cac-d0c7-4194-bc24-2b38fbdc5583 | anomaly-detection-with-score-distribution | 2306.14403 | null | https://arxiv.org/abs/2306.14403v1 | https://arxiv.org/pdf/2306.14403v1.pdf | Anomaly Detection with Score Distribution Discrimination | Recent studies give more attention to the anomaly detection (AD) methods that can leverage a handful of labeled anomalies along with abundant unlabeled data. These existing anomaly-informed AD methods rely on manually predefined score target(s), e.g., prior constant or margin hyperparameter(s), to realize discriminatio... | ['Hailiang Huang', 'Songqiao Han', 'Minqi Jiang'] | 2023-06-26 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [-3.05935126e-02 -3.41035157e-01 -5.34453355e-02 -6.47579849e-01
-6.96019888e-01 -2.82444060e-01 2.38283738e-01 2.43145242e-01
-1.65677086e-01 4.50024277e-01 -2.24629194e-01 -9.97461304e-02
-2.20308945e-01 -7.83338845e-01 -2.39555031e-01 -8.56955886e-01
1.57993473e-02 2.88755506e-01 3.66698295e-01 -1.51100472... | [7.603653430938721, 2.3910908699035645] |
eb5cb522-dff3-43a2-811b-7de4e8f8923c | what-you-can-reconstruct-from-a-shadow | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_What_You_Can_Reconstruct_From_a_Shadow_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_What_You_Can_Reconstruct_From_a_Shadow_CVPR_2023_paper.pdf | What You Can Reconstruct From a Shadow | 3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a method that uses the shadows cast by an unobserved object in order to infer the possible 3D volumes under occlusion. We create a differe... | ['Carl Vondrick', 'Simon Stent', 'Dennis Park', 'Chengzhi Mao', 'Sachit Menon', 'Ruoshi Liu'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['3d-reconstruction'] | ['computer-vision'] | [ 3.08049440e-01 3.60772222e-01 4.32652503e-01 -1.64038971e-01
-2.86972046e-01 -7.30504096e-01 7.25164056e-01 -1.32067248e-01
-6.25777096e-02 4.24871027e-01 -2.64622480e-01 -1.33581519e-01
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3.86058033e-01 1.22446227e+00 5.36136925e-01 1.72209531... | [9.075675010681152, -3.031416177749634] |
47476eb7-75be-4f3d-97cb-88468d4d87ee | hierarchical-bilinear-pooling-for-fine | 1807.09915 | null | http://arxiv.org/abs/1807.09915v1 | http://arxiv.org/pdf/1807.09915v1.pdf | Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition | Fine-grained visual recognition is challenging because it highly relies on
the modeling of various semantic parts and fine-grained feature learning.
Bilinear pooling based models have been shown to be effective at fine-grained
recognition, while most previous approaches neglect the fact that inter-layer
part feature in... | ['Xinyi Zhao', 'Xinge You', 'Chaojian Yu', 'Qi Zheng', 'Peng Zhang'] | 2018-07-26 | hierarchical-bilinear-pooling-for-fine-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Chaojian_Yu_Hierarchical_Bilinear_Pooling_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Chaojian_Yu_Hierarchical_Bilinear_Pooling_ECCV_2018_paper.pdf | eccv-2018-9 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [-1.29020602e-01 -5.50938725e-01 -1.26454055e-01 -4.93201047e-01
-7.95741677e-01 -5.99640310e-01 8.01116586e-01 1.02658644e-01
-2.01870590e-01 7.03725576e-01 5.14152765e-01 3.71039867e-01
-4.17720824e-01 -9.16064620e-01 -6.06545150e-01 -7.39998937e-01
1.18282951e-01 -2.85789054e-02 4.60386276e-01 -2.04377901... | [9.593878746032715, 2.021925210952759] |
41ea5cd3-61d0-4f42-8a46-944608755fa7 | using-structured-events-to-predict-stock | null | null | https://aclanthology.org/D14-1148 | https://aclanthology.org/D14-1148.pdf | Using Structured Events to Predict Stock Price Movement: An Empirical Investigation | null | ['Yue Zhang', 'Ting Liu', 'Junwen Duan', 'Xiao Ding'] | 2014-10-01 | null | null | null | emnlp-2014-10 | ['stock-market-prediction', 'stock-prediction'] | ['time-series', 'time-series'] | [-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... | [-7.203945636749268, 3.779231309890747] |
d5147de8-929c-4bb4-9765-44f58fd9b758 | from-examples-to-rules-neural-guided-rule | 2202.00475 | null | https://arxiv.org/abs/2202.00475v1 | https://arxiv.org/pdf/2202.00475v1.pdf | From Examples to Rules: Neural Guided Rule Synthesis for Information Extraction | While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift. Rule-based methods, on the other hand, can be more easily modified. However, crafting rules requires expertise in linguistics and the domain of interest, making it infeasible fo... | ['Mihai Surdeanu', 'Rebecca Sharp', 'George C. G. Barbosa', 'Marco A. Valenzuela-Escarcega', 'Robert Vacareanu'] | 2022-01-16 | null | https://aclanthology.org/2022.lrec-1.665 | https://aclanthology.org/2022.lrec-1.665.pdf | lrec-2022-6 | ['enumerative-search', 'relation-classification'] | ['computer-code', 'natural-language-processing'] | [ 4.72419024e-01 2.44887948e-01 -4.07226175e-01 -3.46379042e-01
-8.27836931e-01 -4.80092347e-01 4.91700709e-01 4.68484342e-01
-2.26333663e-01 5.45637131e-01 6.79479837e-02 -8.40327024e-01
-4.57577333e-02 -9.50889409e-01 -6.58378601e-01 -1.59673095e-01
2.27389395e-01 3.47766221e-01 4.95992631e-01 -5.11254370... | [9.57179069519043, 7.779696941375732] |
3138f430-8e27-47cf-90df-2a38b0e441d6 | learning-reconstructability-for-drone-aerial | 2209.10174 | null | https://arxiv.org/abs/2209.10174v1 | https://arxiv.org/pdf/2209.10174v1.pdf | Learning Reconstructability for Drone Aerial Path Planning | We introduce the first learning-based reconstructability predictor to improve view and path planning for large-scale 3D urban scene acquisition using unmanned drones. In contrast to previous heuristic approaches, our method learns a model that explicitly predicts how well a 3D urban scene will be reconstructed from a s... | ['Hui Huang', 'Hao Zhang', 'Chi-Wing Fu', 'Ke Xie', 'Yue Hu', 'Liqiang Lin', 'Yilin Liu'] | 2022-09-21 | null | null | null | null | ['3d-scene-reconstruction'] | ['computer-vision'] | [ 8.68155658e-02 4.39638972e-01 -1.63144141e-01 -5.62967956e-01
-6.33788347e-01 -8.45175564e-01 4.76339340e-01 -2.08237931e-01
8.85188729e-02 7.07640350e-01 3.48979622e-01 -2.31549934e-01
-3.41625512e-01 -1.21660960e+00 -9.36939716e-01 -2.24915043e-01
-6.21719584e-02 9.93161261e-01 1.51633680e-01 -4.43479687... | [8.636751174926758, -2.8139805793762207] |
09e4bbbb-4e07-49b1-a0e5-9155cc1c6fd5 | impact-of-physical-activity-on-sleepa-deep | 1607.07034 | null | http://arxiv.org/abs/1607.07034v1 | http://arxiv.org/pdf/1607.07034v1.pdf | Impact of Physical Activity on Sleep:A Deep Learning Based Exploration | The importance of sleep is paramount for maintaining physical, emotional and
mental wellbeing. Though the relationship between sleep and physical activity
is known to be important, it is not yet fully understood. The explosion in
popularity of actigraphy and wearable devices, provides a unique opportunity to
understand... | ['Luis Fernandez-Luque', 'Ferda Ofli', 'Ahmed Elmagarmid', 'Shahrad Taheri', 'Jaideep Srivastava', 'Shafiq Joty', 'Aarti Sathyanarayana', 'Teresa Arora'] | 2016-07-24 | null | null | null | null | ['sleep-quality-prediction', 'sleep-quality-prediction-1'] | ['medical', 'medical'] | [-2.63800509e-02 -1.42432541e-01 -4.08799022e-01 -4.98839021e-01
-1.81988701e-01 -2.21253559e-02 1.60805881e-01 2.95177937e-01
-7.60589480e-01 9.06698585e-01 5.84615767e-01 -3.89068991e-01
-2.04799309e-01 -8.95147622e-01 -2.96313316e-01 -6.62072122e-01
-1.26771510e-01 2.49124914e-01 -2.59807587e-01 -1.25863040... | [13.60063648223877, 3.384312391281128] |
5bcc73a7-b88f-4416-8a79-19c7c2cb5519 | xastnn-improved-code-representations-for | 2303.07104 | null | https://arxiv.org/abs/2303.07104v1 | https://arxiv.org/pdf/2303.07104v1.pdf | xASTNN: Improved Code Representations for Industrial Practice | The application of deep learning techniques in software engineering becomes increasingly popular. One key problem is developing high-quality and easy-to-use source code representations for code-related tasks. The research community has acquired impressive results in recent years. However, due to the deployment difficul... | ['Hongyu Zhang', 'Xi Cheng', 'Yang Chen', 'Xibin Zhao', 'Min Zhou', 'Zhiwei Xu'] | 2023-03-13 | null | null | null | null | ['code-classification'] | ['computer-code'] | [ 1.58785388e-01 -3.42370361e-01 -4.08280015e-01 -3.72368366e-01
-5.02697825e-01 -5.86908385e-02 -1.39407665e-02 1.04110986e-01
-3.29274349e-02 -6.31906316e-02 1.15439156e-02 -8.47093582e-01
2.16894269e-01 -6.08317196e-01 -4.73231405e-01 -3.66928875e-01
-1.34182237e-02 -3.48236442e-01 3.20759326e-01 -2.12726906... | [7.547199726104736, 7.919604778289795] |
e65959a6-649e-44b9-8e13-d6e85149c7f0 | regularity-learning-via-explicit-distribution | 2112.03649 | null | https://arxiv.org/abs/2112.03649v2 | https://arxiv.org/pdf/2112.03649v2.pdf | Regularity Learning via Explicit Distribution Modeling for Skeletal Video Anomaly Detection | Anomaly detection in surveillance videos is challenging and important for ensuring public security. Different from pixel-based anomaly detection methods, pose-based methods utilize highly-structured skeleton data, which decreases the computational burden and also avoids the negative impact of background noise. However,... | ['Wei Wu', 'Cewu Lu', 'Weihao Gan', 'Dongliang Wang', 'Haisheng Su', 'Andong Deng', 'Haoshu Fang', 'Zhongyin Zhao', 'Shoubin Yu'] | 2021-12-07 | null | null | null | null | ['anomaly-detection-in-surveillance-videos', 'anomaly-detection-in-surveillance-videos'] | ['computer-vision', 'methodology'] | [ 3.39673549e-01 -1.87416777e-01 -1.71894088e-01 -1.02129221e-01
-6.80427730e-01 -2.63485044e-01 6.75246596e-01 8.23212266e-02
-3.87865573e-01 3.59127790e-01 1.40615597e-01 -1.91953987e-01
7.87844136e-02 -6.08038783e-01 -7.35983849e-01 -8.97457838e-01
6.34732246e-02 -2.34027505e-01 6.64484024e-01 1.74373239... | [8.188711166381836, 1.1622964143753052] |
a8a7ccf8-d968-4673-8ab6-274f9adef1af | va-learning-as-a-more-efficient-alternative | 2305.18161 | null | https://arxiv.org/abs/2305.18161v1 | https://arxiv.org/pdf/2305.18161v1.pdf | VA-learning as a more efficient alternative to Q-learning | In reinforcement learning, the advantage function is critical for policy improvement, but is often extracted from a learned Q-function. A natural question is: Why not learn the advantage function directly? In this work, we introduce VA-learning, which directly learns advantage function and value function using bootstra... | ['Michal Valko', 'Mark Rowland', 'Rémi Munos', 'Yunhao Tang'] | 2023-05-29 | null | null | null | null | ['q-learning'] | ['methodology'] | [-4.60919172e-01 2.63514549e-01 -5.52756727e-01 -1.17236659e-01
-9.64914858e-01 -9.02079940e-01 5.75889826e-01 3.05155776e-02
-7.67767072e-01 1.13860548e+00 1.41298771e-01 -7.96837389e-01
-3.81453037e-01 -8.16048861e-01 -8.32313597e-01 -6.90059006e-01
-4.53749448e-01 5.95867515e-01 2.26179823e-01 -6.14934087... | [4.069138050079346, 2.0927541255950928] |
0b9d7383-5f0f-4c6a-9269-7ac4145760be | building-low-resource-ner-models-using-non-1 | null | null | https://aclanthology.org/2021.dash-1.11 | https://aclanthology.org/2021.dash-1.11.pdf | Building Low-Resource NER Models Using Non-Speaker Annotations | In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it. Cross-lingual methods have had notable success in addressing these concerns, but in certain common circumstances, such as insufficient pre-training corpora or lang... | ['Dan Roth', 'Stephen Mayhew', 'Francesca Marini', 'Tatiana Tsygankova'] | null | null | null | null | naacl-dash-2021-6 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 1.76736355e-01 2.11390123e-01 -1.79398671e-01 -7.72594988e-01
-1.46224391e+00 -8.41202080e-01 5.50923645e-01 3.21500301e-01
-9.24842119e-01 8.35299730e-01 7.21647620e-01 -4.54209805e-01
2.89690971e-01 -1.12022012e-01 -2.82155097e-01 -2.27167606e-01
3.99281800e-01 5.24863183e-01 -4.46227342e-02 -2.67556638... | [10.109796524047852, 9.796167373657227] |
75fd769f-a245-414f-b9b7-9f0a469a822c | robust-human-identity-anonymization-using | 2301.04243 | null | https://arxiv.org/abs/2301.04243v1 | https://arxiv.org/pdf/2301.04243v1.pdf | Robust Human Identity Anonymization using Pose Estimation | Many outdoor autonomous mobile platforms require more human identity anonymized data to power their data-driven algorithms. The human identity anonymization should be robust so that less manual intervention is needed, which remains a challenge for current face detection and anonymization systems. In this paper, we prop... | ['Henrik I. Christensen', 'David Paz', 'Jing-Yan Liao', 'Hengyuan Zhang'] | 2023-01-10 | null | null | null | null | ['face-detection'] | ['computer-vision'] | [ 6.39252886e-02 1.83430091e-01 2.36744717e-01 -7.36837983e-01
-5.09722471e-01 -5.83862066e-01 3.96987498e-01 -3.39535117e-01
-6.90888822e-01 8.64192486e-01 1.06549338e-02 1.18663646e-01
2.97882378e-01 -8.00672054e-01 -6.02954566e-01 -6.83351398e-01
-3.22161615e-02 3.94398063e-01 4.52189386e-01 2.22157198... | [12.871909141540527, 0.7117544412612915] |
62f2fc82-0d66-442c-925e-5e81472036b8 | improving-sequence-to-sequence-semantic | null | null | https://aclanthology.org/2020.intexsempar-1.3 | https://aclanthology.org/2020.intexsempar-1.3.pdf | Improving Sequence-to-Sequence Semantic Parser for Task Oriented Dialog | Task Oriented Parsing (TOP) attempts to map utterances to compositional requests, including multiple intents and their slots. Previous work focus on a tree-based hierarchical meaning representation, and applying constituency parsing techniques to address TOP. In this paper, we propose a new format of meaning representa... | ['Chaoting Xuan'] | null | null | null | null | emnlp-intexsempar-2020-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 7.84006417e-01 7.66261280e-01 -4.03709352e-01 -9.20541227e-01
-1.26500225e+00 -7.02891827e-01 3.62713039e-01 3.46149147e-01
-1.14755402e-03 6.51167989e-01 1.22233653e+00 -5.19395828e-01
2.67513037e-01 -6.79239213e-01 -3.45760971e-01 -6.51604459e-02
1.27039522e-01 5.49934804e-01 2.50691354e-01 -6.90671980... | [10.435626029968262, 9.39526081085205] |
7e01d2c5-a173-4385-9f26-93b53fac7b98 | fault-detection-in-induction-motors-using | 2306.09365 | null | https://arxiv.org/abs/2306.09365v1 | https://arxiv.org/pdf/2306.09365v1.pdf | Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods | The implementation of strategies for fault detection and diagnosis on rotating electrical machines is crucial for the reliability and safety of modern industrial systems. The contribution of this work is a methodology that combines conventional strategy of Motor Current Signature Analysis with functional dimensionality... | ['Ángela Fernández', 'Carlos M. Alaíz', 'José M. Bossio', 'María Barroso'] | 2023-06-14 | null | null | null | null | ['dimensionality-reduction', 'fault-detection'] | ['methodology', 'miscellaneous'] | [ 2.75176793e-01 -3.69293004e-01 2.23407283e-01 2.76031613e-01
1.34529889e-01 -3.72003168e-01 3.94406855e-01 9.56603289e-02
1.78377166e-01 4.83161330e-01 -3.35184515e-01 -5.63935399e-01
-1.05696833e+00 -4.97687191e-01 -3.58539820e-02 -1.12018239e+00
-3.32762092e-01 4.80797023e-01 7.64080808e-02 -1.55324399... | [6.676941871643066, 2.414522171020508] |
9f4886e6-033a-46a9-86bc-a5a549c2b81f | autonomous-sputter-synthesis-of-thin-film | 2305.11122 | null | https://arxiv.org/abs/2305.11122v2 | https://arxiv.org/pdf/2305.11122v2.pdf | Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission | Autonomous experimentation has emerged as an efficient approach to accelerate the pace of materials discovery. Although instruments for autonomous synthesis have become popular in molecular and polymer science, solution processing of hybrid materials and nanoparticles, examples of autonomous tools for physical vapour d... | ['Andriy Zakutayev', 'Rebecca W. Smaha', 'John S. Mangum', 'Sage R. Bauers', 'Stephen Schaefer', 'Kendal Johnson', 'Kevin R. Talley', 'Davi M. Febba'] | 2023-05-18 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 5.12310505e-01 5.96836880e-02 -3.54632661e-02 -3.39422256e-01
-2.24692330e-01 -3.30461472e-01 3.57218295e-01 8.73749107e-02
-5.29099345e-01 1.01848233e+00 -8.59431684e-01 -1.40675351e-01
-1.76847398e-01 -8.37313771e-01 -6.96206272e-01 -1.13031638e+00
3.46794188e-01 1.19328105e+00 4.22267497e-01 -7.50968456... | [5.39114236831665, 4.942857265472412] |
9ca49e48-2542-4b2e-8f99-74f1f9f87850 | sub-policy-adaptation-for-hierarchical-1 | null | null | https://openreview.net/forum?id=rkfJB9Sj2V | https://openreview.net/pdf?id=rkfJB9Sj2V | Sub-policy Adaptation for Hierarchical Reinforcement Learning | Hierarchical Reinforcement Learning is a promising approach to long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Treating the skills as fixed can lead ... | ['Anonymous'] | 2019-05-16 | null | null | null | icml-workshop-amtl-2019-6 | ['hierarchical-reinforcement-learning'] | ['methodology'] | [ 1.99879751e-01 1.73469204e-02 -2.51122266e-01 -1.11813851e-01
-9.99460757e-01 -7.22258151e-01 3.79412919e-01 5.96939810e-02
-6.78394437e-01 1.07396722e+00 1.59654185e-01 -3.23859096e-01
-3.91179889e-01 -5.52127481e-01 -9.46541667e-01 -9.24984217e-01
-1.84489995e-01 6.07183456e-01 3.28644067e-01 -3.45236391... | [4.11032772064209, 1.7204103469848633] |
518cc747-78fb-4973-a28e-8e444c5f72f1 | discovering-and-explaining-the-non-causality | 2304.00668 | null | https://arxiv.org/abs/2304.00668v4 | https://arxiv.org/pdf/2304.00668v4.pdf | Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR | In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning... | ['Yongxiang Liu', 'Wenpeng Zhang', 'Li Liu', 'Wei Yang', 'Weijie Li'] | 2023-04-03 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-1.17618069e-01 -4.85329688e-01 -5.21126240e-02 -3.67683351e-01
-8.21026504e-01 -4.55987990e-01 2.70855010e-01 -3.55925977e-01
2.37243809e-02 6.89483523e-01 2.97092736e-01 -2.95525730e-01
-1.19678594e-01 -7.64332175e-01 -4.78428602e-01 -1.18021512e+00
-2.80249447e-01 3.85812633e-02 4.64106873e-02 -1.72584996... | [8.281984329223633, -0.9763543009757996] |
973a35fa-b320-45bd-86f4-f80766109ab7 | towards-standardizing-korean-grammatical | 2210.14389 | null | https://arxiv.org/abs/2210.14389v3 | https://arxiv.org/pdf/2210.14389v3.pdf | Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation | Research on Korean grammatical error correction (GEC) is limited, compared to other major languages such as English. We attribute this problematic circumstance to the lack of a carefully designed evaluation benchmark for Korean GEC. In this work, we collect three datasets from different sources (Kor-Lang8, Kor-Native, ... | ['Gyutae Kim', 'Alice Oh', 'Minjoon Seo', 'Kihyo Park', 'Junhee Cho', 'Gyuwan Kim', 'Sungjoon Park', 'Soyoung Yoon'] | 2022-10-25 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [-4.53065425e-01 -3.10653657e-01 1.55514464e-01 -4.72394228e-01
-1.22805929e+00 -5.10558546e-01 -2.56932080e-01 4.20216203e-01
-7.77786791e-01 1.03534567e+00 3.82031947e-01 -4.98822331e-01
3.57922703e-01 -5.07955074e-01 -6.34839296e-01 -3.80559713e-02
1.84839979e-01 3.54122370e-01 3.06619667e-02 -4.38974649... | [11.060962677001953, 10.733016967773438] |
d063a46c-1785-443b-849b-31fb3b23e5fc | dgfont-robust-deformable-generative-networks | 2212.14742 | null | https://arxiv.org/abs/2212.14742v1 | https://arxiv.org/pdf/2212.14742v1.pdf | DGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation | Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods for font generation are often in supervised learning. They require a large number of paired data, which are labor-intensive and expensive t... | ['Yue Lu', 'Li Sun', 'Yangchen Xie', 'Xinyuan Chen'] | 2022-12-30 | null | null | null | null | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 4.94743854e-01 -2.37539366e-01 2.81062573e-01 -3.88797224e-01
-4.19996023e-01 -9.73137021e-01 6.64598227e-01 -3.04958344e-01
-1.41557142e-01 6.64554179e-01 -1.94128662e-01 5.54347038e-03
2.32168317e-01 -1.17943418e+00 -9.97482717e-01 -8.51793349e-01
5.51346600e-01 4.23773319e-01 1.28237456e-01 -3.39984506... | [11.711909294128418, -0.4040853679180145] |
28f6af16-773d-4fdf-a3e1-deb12ee143ee | audio-driven-talking-face-generation-with | 2304.08945 | null | https://arxiv.org/abs/2304.08945v1 | https://arxiv.org/pdf/2304.08945v1.pdf | Audio-Driven Talking Face Generation with Diverse yet Realistic Facial Animations | Audio-driven talking face generation, which aims to synthesize talking faces with realistic facial animations (including accurate lip movements, vivid facial expression details and natural head poses) corresponding to the audio, has achieved rapid progress in recent years. However, most existing work focuses on generat... | ['Shijian Lu', 'Xiaoqin Zhang', 'Jiahui Zhang', 'Fangneng Zhan', 'Yingchen Yu', 'Rongliang Wu'] | 2023-04-18 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 7.24309832e-02 3.20428193e-01 2.76421398e-01 -5.63994050e-01
-8.21187437e-01 -2.85540700e-01 6.02323174e-01 -1.26963878e+00
3.39423031e-01 5.99212408e-01 4.09532070e-01 3.67949188e-01
3.98961335e-01 -5.05521715e-01 -6.33528233e-01 -9.32323873e-01
-4.05815467e-02 2.32424274e-01 -1.90777496e-01 -2.10167289... | [13.209757804870605, -0.44384264945983887] |
9eec67ed-0b75-4272-8d5f-cd9899565c40 | minding-language-models-lack-of-theory-of | 2306.00924 | null | https://arxiv.org/abs/2306.00924v1 | https://arxiv.org/pdf/2306.00924v1.pdf | Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker | Theory of Mind (ToM)$\unicode{x2014}$the ability to reason about the mental states of other people$\unicode{x2014}$is a key element of our social intelligence. Yet, despite their ever more impressive performance, large-scale neural language models still lack basic theory of mind capabilities out-of-the-box. We posit th... | ['Yulia Tsvetkov', 'Yejin Choi', 'Alane Suhr', 'Peter West', 'Sachin Kumar', 'Melanie Sclar'] | 2023-06-01 | null | null | null | null | ['reading-comprehension'] | ['natural-language-processing'] | [ 2.70093471e-01 6.41073763e-01 -7.83988535e-02 -5.23573637e-01
-3.55093271e-01 -2.51311690e-01 9.51239467e-01 3.85230571e-01
-4.33798701e-01 5.61791360e-01 2.93794423e-01 -5.95711887e-01
-2.49285355e-01 -9.69182849e-01 -9.27114964e-01 -2.51858830e-01
2.17574775e-01 1.04818606e+00 -4.61272709e-02 -3.85533690... | [9.543479919433594, 7.32581901550293] |
ad5cec0d-c772-4afc-a1e4-89373047d3d9 | malware-detection-and-prevention-using | 2206.12770 | null | https://arxiv.org/abs/2206.12770v1 | https://arxiv.org/pdf/2206.12770v1.pdf | Malware Detection and Prevention using Artificial Intelligence Techniques | With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders, particularly, end users security, protecting the data from fraudulent efforts is... | ['Fan Wu', 'Akond Rahman', 'Dan Lo', 'Alfredo Cuzzocreak', 'Michael Whitman', 'Md Abdullah Khan', 'Shahriar Sobhan', 'Farhat Lamia Barsha', 'Maria Valero', 'Hossain Shahriar', 'Md Jobair Hossain Faruk'] | 2022-06-26 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 3.41549248e-01 -4.49831128e-01 -5.63293099e-01 2.14649960e-01
9.96291935e-02 -8.32429230e-01 6.84412837e-01 6.39962479e-02
-6.24764711e-02 2.45715275e-01 -2.21974403e-01 -6.65601730e-01
2.57041484e-01 -9.52614784e-01 -2.56006271e-01 -5.50867438e-01
9.96342003e-02 -9.58913341e-02 3.39426875e-01 -1.90518320... | [14.413896560668945, 9.675056457519531] |
5f1ef12b-ee58-47dc-a9b3-7f060429b294 | a-hybrid-neuro-symbolic-approach-for-text | null | null | https://openreview.net/forum?id=NzjyY2Z9zJd | https://openreview.net/pdf?id=NzjyY2Z9zJd | A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming | Text-based games (TBGs) have emerged as an important test-bed, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents solving this task is to generalize across multiple games and shows good results on both seen and unseen objects. Currently, pur... | ['Gopal Gupta', 'Mrinmaya Sachan', 'Murray Campbell', 'Tim Klinger', 'Kartik Talamadupula', 'Pavan Kapanipathi', 'Mattia Atzeni', 'Keerthiram Murugesan', 'Kinjal Basu'] | 2021-11-21 | null | null | null | aaai-workshop-clear-2022-2 | ['inductive-logic-programming', 'text-based-games'] | ['methodology', 'playing-games'] | [-9.33727846e-02 5.52825332e-01 -8.10359195e-02 -1.50240004e-01
-1.54785082e-01 -5.70443928e-01 6.88564599e-01 -1.46505788e-01
-5.42991161e-01 1.00301921e+00 -1.13325574e-01 -4.94109333e-01
-2.71278381e-01 -1.57950509e+00 -9.24993813e-01 -2.59522676e-01
-1.25244990e-01 1.05958974e+00 6.64028466e-01 -1.03699350... | [3.869879722595215, 1.312391996383667] |
09c2b7e0-d525-4cb8-96dc-08d9ef6b620e | guided-transformer-leveraging-multiple | 2006.07548 | null | https://arxiv.org/abs/2006.07548v1 | https://arxiv.org/pdf/2006.07548v1.pdf | Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search | Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying questions have been studied recently but the accura... | ['W. Bruce Croft', 'Hamed Zamani', 'Helia Hashemi'] | 2020-06-13 | null | null | null | null | ['conversational-search', 'question-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.72279614e-01 3.55772823e-01 -3.41116130e-01 -4.35365081e-01
-1.27992678e+00 -6.01804316e-01 8.89025390e-01 -5.43977367e-03
-5.04674196e-01 9.30348814e-01 9.79000628e-01 -6.15053892e-01
-4.23986554e-01 -4.09922093e-01 -1.79726765e-01 -1.02108665e-01
6.39544129e-01 9.22333777e-01 2.50022382e-01 -6.71626151... | [12.149691581726074, 7.789054870605469] |
c0d5440a-fc9c-464b-8048-0ab81e2959ef | exclusive-autoencoder-xae-for-nucleus | 1811.11243 | null | http://arxiv.org/abs/1811.11243v1 | http://arxiv.org/pdf/1811.11243v1.pdf | eXclusive Autoencoder (XAE) for Nucleus Detection and Classification on Hematoxylin and Eosin (H&E) Stained Histopathological Images | In this paper, we introduced a novel feature extraction approach, named
exclusive autoencoder (XAE), which is a supervised version of autoencoder (AE),
able to largely improve the performance of nucleus detection and classification
on hematoxylin and eosin (H&E) histopathological images. The proposed XAE can
be used in... | ['Chao-Hui Huang', 'Daniel Racoceanu'] | 2018-11-27 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [ 1.07193477e-02 2.25527778e-01 3.22968394e-01 -1.18033020e-02
-2.83749759e-01 -2.69265443e-01 4.16672826e-01 5.89062274e-01
-9.67690825e-01 9.74522173e-01 -9.24717486e-02 -1.48818985e-01
-1.84541509e-01 -1.00764275e+00 -3.01103562e-01 -1.22543490e+00
9.88479145e-03 5.97308278e-01 4.12312858e-02 1.80080645... | [15.325451850891113, -2.856750726699829] |
d5b43177-2ec3-43f1-b05b-cce8147a485f | metaphora-kodikon-tes-matlab-se-programmata-c | null | null | http://ikee.lib.auth.gr/record/271194 | http://ikee.lib.auth.gr/record/271194/files/GRI-2015-15043.pdf | Μεταφορά κωδίκων της MATLAB σε προγράμματα C++ για την επίλυση Κανονικών Διαφορικών Εξισώσεων Porting MATLAB routines to C++ for the numerical integration of Ordinary Differential Equations | Στο πλαίσιο της διπλωματικής αυτής εργασίας επιχειρείται ο συγκερασμός της αξιοπιστίας και των δυνατοτήτων που παρέχει η ώριμη ρουτίνα ολοκλήρωσης προβλημάτων αρχικών συνθηκών ode15s του MATLAB με την ταχύτητα που προσφέρει η C++. Για το σκοπό αυτό δημιουργήθηκε σε γλώσσα C++ ένα αλγοριθμικά πιστό αντίγραφο της ode15s ... | ['Θεοδωρακόπουλος Αχιλλέας-Κωνσταντίνος Βασιλείου'] | 2015-10-01 | null | null | null | theodorakopoulos-akhilleas-elektrologos-mekh | ['numerical-integration'] | ['miscellaneous'] | [-0.06707186 0.71851075 -1.3376298 0.36580244 0.89169896 -0.7404142
0.76586497 -0.54263496 -0.7221984 1.4804866 1.058006 -1.7825217
-0.5664324 -0.9489754 0.4386211 -0.4071409 0.71024686 0.9571552
0.10713258 0.10720581 0.14636791 0.41291448 -1.5592191 0.23353395
0.533816 0.06062973 0.322... | [9.881379127502441, 9.199470520019531] |
fe9895ee-63f7-44dd-860c-45d688f4ab0f | demonstrating-the-risk-of-imbalanced-datasets | 2201.03559 | null | https://arxiv.org/abs/2201.03559v1 | https://arxiv.org/pdf/2201.03559v1.pdf | Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation | The recent trend of integrating multi-source Chest X-Ray datasets to improve automated diagnostics raises concerns that models learn to exploit source-specific correlations to improve performance by recognizing the source domain of an image rather than the medical pathology. We hypothesize that this effect is enforced ... | ['Michael Kampffmeyer', 'Robert Jenssen', 'Stine Hansen', 'Marina M. -C. Höhne', 'Srishti Gautam'] | 2022-01-10 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 6.28853798e-01 2.18488216e-01 -6.14644051e-01 -4.90743130e-01
-1.23484266e+00 -3.93795907e-01 3.64021331e-01 2.56135643e-01
-1.34570286e-01 7.25589037e-01 4.20993924e-01 -5.20359635e-01
-4.97922450e-01 -5.59152663e-01 -7.74914801e-01 -7.14952111e-01
1.36057153e-01 5.34806609e-01 -1.38838282e-02 3.94973934... | [15.034908294677734, -2.093337059020996] |
3b1b214b-3515-4a19-8ac2-5ad877660bd1 | rama-a-rapid-multicut-algorithm-on-gpu | 2109.01838 | null | https://arxiv.org/abs/2109.01838v3 | https://arxiv.org/pdf/2109.01838v3.pdf | RAMA: A Rapid Multicut Algorithm on GPU | We propose a highly parallel primal-dual algorithm for the multicut (a.k.a. correlation clustering) problem, a classical graph clustering problem widely used in machine learning and computer vision. Our algorithm consists of three steps executed recursively: (1) Finding conflicted cycles that correspond to violated ine... | ['Paul Swoboda', 'Ahmed Abbas'] | 2021-09-04 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Abbas_RAMA_A_Rapid_Multicut_Algorithm_on_GPU_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Abbas_RAMA_A_Rapid_Multicut_Algorithm_on_GPU_CVPR_2022_paper.pdf | cvpr-2022-1 | ['3d-instance-segmentation-1', 'graph-partitioning'] | ['computer-vision', 'graphs'] | [ 5.66047393e-02 1.66227780e-02 -2.41292551e-01 1.24255061e-01
-9.96803701e-01 -8.58163059e-01 2.22522944e-01 4.86964881e-01
-2.42535219e-01 7.81724870e-01 -1.91254362e-01 -5.74648082e-01
-3.04428250e-01 -7.97884822e-01 -6.78379476e-01 -9.52496827e-01
-5.18568575e-01 1.00258517e+00 1.09382771e-01 7.01606125... | [7.0462260246276855, 5.172204971313477] |
aba3a5d1-e7c9-4f89-a810-ccf8d0ae956f | differentially-private-adversarial-auto | 2307.02135 | null | https://arxiv.org/abs/2307.02135v1 | https://arxiv.org/pdf/2307.02135v1.pdf | Differentially Private Adversarial Auto-Encoder to Protect Gender in Voice Biometrics | Over the last decade, the use of Automatic Speaker Verification (ASV) systems has become increasingly widespread in response to the growing need for secure and efficient identity verification methods. The voice data encompasses a wealth of personal information, which includes but is not limited to gender, age, health c... | ['Melek Önen', 'Massimiliano Todisco', 'Imen Chihaoui', 'Ismet Kerenciler', 'Oualid Zari', 'Michele Panariello', 'Oubaïda Chouchane'] | 2023-07-05 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 1.62283748e-01 2.05291957e-01 -1.31678194e-01 -5.83379447e-01
-7.04738021e-01 -8.47369492e-01 4.57037300e-01 2.79390693e-01
-4.21611071e-01 4.52224731e-01 2.34485164e-01 -2.73814410e-01
4.60622050e-02 -6.38988078e-01 -3.20562899e-01 -8.66316855e-01
-6.40465915e-02 -1.43825442e-01 -2.41797313e-01 4.88494411... | [14.000777244567871, 5.860822677612305] |
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