paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
20d448d7-37bf-4092-a7ab-2768c9b7b997 | trajectory-flow-map-graph-based-approach-to | 2212.02927 | null | https://arxiv.org/abs/2212.02927v1 | https://arxiv.org/pdf/2212.02927v1.pdf | Trajectory Flow Map: Graph-based Approach to Analysing Temporal Evolution of Aggregated Traffic Flows in Large-scale Urban Networks | This paper proposes a graph-based approach to representing spatio-temporal trajectory data that allows an effective visualization and characterization of city-wide traffic dynamics. With the advance of sensor, mobile, and Internet of Things (IoT) technologies, vehicle and passenger trajectories are being increasingly c... | ['Marty Papamanolis', 'Sanghyung Ahn', 'Jonathan Corcoran', 'Kai Zheng', 'Jiwon Kim'] | 2022-12-06 | null | null | null | null | ['graph-mining'] | ['graphs'] | [-2.58732438e-01 -2.07581565e-01 -4.04112279e-01 2.86685582e-02
-3.15044299e-02 -7.45414317e-01 6.12934947e-01 7.31901228e-01
1.27472296e-01 5.16305327e-01 3.10995698e-01 -7.39001811e-01
-6.33424640e-01 -1.63196397e+00 -2.94380724e-01 -4.11631554e-01
-7.43048131e-01 4.73995090e-01 6.89099252e-01 -1.98993325... | [6.406552791595459, 1.99032461643219] |
779f2f3a-ea44-4ca4-9019-3377d606c1ea | po-elic-perception-oriented-efficient-learned | 2205.14501 | null | https://arxiv.org/abs/2205.14501v1 | https://arxiv.org/pdf/2205.14501v1.pdf | PO-ELIC: Perception-Oriented Efficient Learned Image Coding | In the past years, learned image compression (LIC) has achieved remarkable performance. The recent LIC methods outperform VVC in both PSNR and MS-SSIM. However, the low bit-rate reconstructions of LIC suffer from artifacts such as blurring, color drifting and texture missing. Moreover, those varied artifacts make image... | ['Yan Wang', 'Hongwei Qin', 'Xinjie Shi', 'Chenjian Gao', 'Yuan Chen', 'Jixiang Luo', 'Tongda Xu', 'Hongjiu Yu', 'Ziming Yang', 'Dailan He'] | 2022-05-28 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 5.26008248e-01 -3.80607128e-01 -8.62520635e-02 -1.55987486e-01
-7.64027357e-01 -3.61321390e-01 2.61294156e-01 -5.30696154e-01
-1.56177729e-01 8.70369494e-01 1.93883732e-01 2.50073522e-02
7.87307546e-02 -6.60048008e-01 -9.16818261e-01 -7.66933680e-01
-3.77805810e-03 -3.86698157e-01 1.10059336e-01 2.24252623... | [11.334895133972168, -1.7851194143295288] |
f3eb8591-f178-4662-863d-912b5b674725 | unconditional-image-text-pair-generation-with | 2204.07537 | null | https://arxiv.org/abs/2204.07537v2 | https://arxiv.org/pdf/2204.07537v2.pdf | Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer | Although deep generative models have gained a lot of attention, most of the existing works are designed for unimodal generation. In this paper, we explore a new method for unconditional image-text pair generation. We design Multimodal Cross-Quantization VAE (MXQ-VAE), a novel vector quantizer for joint image-text repre... | ['Edward Choi', 'Joonseok Lee', 'Sungjin Park', 'Hyungyung Lee'] | 2022-04-15 | null | null | null | null | ['multimodal-generation'] | ['natural-language-processing'] | [ 3.00575554e-01 -1.56826019e-01 -1.19286239e-01 -2.39724547e-01
-1.17564976e+00 -4.81676638e-01 8.36863279e-01 -4.48613286e-01
3.32583179e-04 7.00895190e-01 4.22096640e-01 -2.27133468e-01
3.46262872e-01 -9.04399812e-01 -7.71169305e-01 -8.34668875e-01
5.36108613e-01 1.32676139e-01 -1.09763168e-01 -2.36959502... | [11.237918853759766, 0.47684305906295776] |
51b6feed-1b81-4eb5-bdf6-46487e079d52 | bayesian-models-for-unit-discovery-on-a-very | 1802.06053 | null | http://arxiv.org/abs/1802.06053v2 | http://arxiv.org/pdf/1802.06053v2.pdf | Bayesian Models for Unit Discovery on a Very Low Resource Language | Developing speech technologies for low-resource languages has become a very
active research field over the last decade. Among others, Bayesian models have
shown some promising results on artificial examples but still lack of in situ
experiments. Our work applies state-of-the-art Bayesian models to unsupervised
Acoustic... | ['François Yvon', 'Emmanuel Dupoux', 'Mark Hasegawa-Johnson', 'Laurent Besacier', 'Sanjeev Khudanpur', 'Odette Scharenborg', 'Lucas Ondel', 'Elin Larsen', 'Pierre Godard', 'Lukas Burget'] | 2018-02-16 | null | null | null | null | ['acoustic-unit-discovery'] | ['speech'] | [ 1.02895260e-01 2.31857479e-01 -1.26185209e-01 -6.24184847e-01
-1.29835975e+00 -3.79103571e-01 6.63209260e-01 -1.68444067e-01
-7.17216671e-01 6.60287261e-01 5.17392576e-01 -3.72395277e-01
1.62448540e-01 -4.60200280e-01 -6.03890240e-01 -5.97093701e-01
1.60812274e-01 1.01559305e+00 8.62262249e-01 -6.68793097... | [14.468788146972656, 6.720249176025391] |
c6e5124b-a05d-44fb-82ac-cf5ee6deea17 | multi-head-attention-neural-network-for | 2205.08069 | null | https://arxiv.org/abs/2205.08069v1 | https://arxiv.org/pdf/2205.08069v1.pdf | Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization | Smartphones together with RSSI fingerprinting serve as an efficient approach for delivering a low-cost and high-accuracy indoor localization solution. However, a few critical challenges have prevented the wide-spread proliferation of this technology in the public domain. One such critical challenge is device heterogene... | ['Sudeep Pasricha', 'Danish Gufran', 'Saideep Tiku'] | 2022-05-17 | null | null | null | null | ['indoor-localization'] | ['computer-vision'] | [-1.78555325e-02 -7.84502506e-01 -4.37944084e-02 -5.82472265e-01
-1.14041460e+00 -5.88618338e-01 7.25774318e-02 1.20534688e-01
-1.54274851e-01 7.23309577e-01 2.02764601e-01 -4.05860275e-01
-3.53678674e-01 -7.17866659e-01 -9.22418594e-01 -5.67923844e-01
9.99719836e-03 -5.38003072e-02 -5.57784401e-02 2.14889899... | [6.409774303436279, 0.9103546142578125] |
94f4dc22-7891-4b92-87b7-e86a9a18c894 | bicubic-slim-slimmer-slimmest-designing-an | 2305.02126 | null | https://arxiv.org/abs/2305.02126v1 | https://arxiv.org/pdf/2305.02126v1.pdf | Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network | We propose a real-time and lightweight single-image super-resolution (SR) network named Bicubic++. Despite using spatial dimensions of the input image across the whole network, Bicubic++ first learns quick reversible downgraded and lower resolution features of the image in order to decrease the number of computations. ... | ['Mustafa Ayazoglu', 'Bahri Batuhan Bilecen'] | 2023-05-03 | null | null | null | null | ['image-super-resolution'] | ['computer-vision'] | [ 3.58758688e-01 -3.24281771e-03 -1.05421670e-01 -4.14484501e-01
-8.25896144e-01 -2.16507539e-01 2.61633635e-01 -4.89954621e-01
-7.38936663e-01 7.68648386e-01 2.81765938e-01 -1.70561939e-01
1.73807055e-01 -7.25732684e-01 -1.06946290e+00 -3.51370960e-01
-1.66988626e-01 -1.95751444e-01 5.76468527e-01 -3.18895727... | [10.99025821685791, -1.906261682510376] |
4ba37db7-6be9-48f9-afb6-f54a5e051341 | stepwise-extractive-summarization-and | 2010.02744 | null | https://arxiv.org/abs/2010.02744v1 | https://arxiv.org/pdf/2010.02744v1.pdf | Stepwise Extractive Summarization and Planning with Structured Transformers | We propose encoder-centric stepwise models for extractive summarization using structured transformers -- HiBERT and Extended Transformers. We enable stepwise summarization by injecting the previously generated summary into the structured transformer as an auxiliary sub-structure. Our models are not only efficient in mo... | ['Ryan Mcdonald', 'Blaž Bratanič', 'Daniele Pighin', 'Jakub Adamek', 'Joshua Maynez', 'Shashi Narayan'] | 2020-10-06 | null | https://aclanthology.org/2020.emnlp-main.339 | https://aclanthology.org/2020.emnlp-main.339.pdf | emnlp-2020-11 | ['table-to-text-generation'] | ['natural-language-processing'] | [ 5.44018447e-01 6.40053511e-01 -2.75927186e-01 -1.70299057e-02
-1.10886109e+00 -7.55497217e-01 1.01222408e+00 4.82578009e-01
-4.87758666e-01 8.99986088e-01 1.09867144e+00 -1.41924515e-01
-4.06888835e-02 -6.12313330e-01 -8.89627159e-01 -1.20279945e-01
1.00158058e-01 8.38486135e-01 1.83978513e-01 -5.83649874... | [12.379104614257812, 9.354162216186523] |
9b5ce76c-2716-499b-9f72-99c214bf32d1 | object-topological-character-acquisition-by | 2306.10664 | null | https://arxiv.org/abs/2306.10664v1 | https://arxiv.org/pdf/2306.10664v1.pdf | Object Topological Character Acquisition by Inductive Learning | Understanding the shape and structure of objects is undoubtedly extremely important for object recognition, but the most common pattern recognition method currently used is machine learning, which often requires a large number of training data. The problem is that this kind of object-oriented learning lacks a priori kn... | ['Yiran Wei', 'Liping Yu', 'Wei Hui'] | 2023-06-19 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 2.45973960e-01 1.25026718e-01 -1.96936965e-01 -4.38479602e-01
1.82396725e-01 -3.53543460e-01 4.99837667e-01 3.88698190e-01
-1.43371612e-01 7.04093695e-01 -3.27241004e-01 -5.18291056e-01
-6.97673559e-01 -1.15576994e+00 -5.09291947e-01 -6.61996186e-01
-1.01226479e-01 5.44281542e-01 2.80802339e-01 -3.05000961... | [10.123737335205078, -0.5827431082725525] |
42e2c0a5-b124-4533-971c-97e7e02891a3 | treepiece-faster-semantic-parsing-via-tree | 2303.17161 | null | https://arxiv.org/abs/2303.17161v1 | https://arxiv.org/pdf/2303.17161v1.pdf | TreePiece: Faster Semantic Parsing via Tree Tokenization | Autoregressive (AR) encoder-decoder neural networks have proved successful in many NLP problems, including Semantic Parsing -- a task that translates natural language to machine-readable parse trees. However, the sequential prediction process of AR models can be slow. To accelerate AR for semantic parsing, we introduce... | ['Sasha Livshits', 'Akshat Shrivastava', 'Sid Wang'] | 2023-03-30 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 4.04092252e-01 6.26402915e-01 -7.98556507e-02 -7.53084481e-01
-1.49735272e+00 -6.17407739e-01 2.47552752e-01 3.89911793e-02
-1.38584360e-01 4.03051257e-01 5.24362206e-01 -9.73975062e-01
7.54230201e-01 -1.01343465e+00 -8.90487790e-01 -1.81611091e-01
2.06406981e-01 8.21586609e-01 -2.92912349e-02 -9.80717242... | [10.4653959274292, 9.18059253692627] |
0e2153ac-90b0-4017-830b-915a8c89da1f | honestbait-forward-references-for-attractive | 2306.14828 | null | https://arxiv.org/abs/2306.14828v1 | https://arxiv.org/pdf/2306.14828v1.pdf | HonestBait: Forward References for Attractive but Faithful Headline Generation | Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views. Although clicks or views do reflect user interest, they can fail to reveal how much interest is raised by the writing style and how much is due to the event or topic its... | ['Lun-Wei Ku', 'Dennis Wu', 'Chih-Yao Chen'] | 2023-06-26 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 9.15876999e-02 3.75650734e-01 -3.79130244e-01 -3.06946874e-01
-8.03091824e-01 -6.39548421e-01 9.87910211e-01 3.69075574e-02
-1.84983119e-01 1.05109656e+00 4.65798706e-01 -1.47852644e-01
3.38111192e-01 -8.71412575e-01 -9.00692701e-01 5.43219130e-03
3.27369958e-01 1.64110824e-01 3.22625041e-01 -6.02698028... | [12.033865928649902, 9.04726791381836] |
6abb2762-5f6a-41a1-9c0e-d292a6286113 | large-language-models-are-effective-table-to | 2305.14987 | null | https://arxiv.org/abs/2305.14987v1 | https://arxiv.org/pdf/2305.14987v1.pdf | Large Language Models are Effective Table-to-Text Generators, Evaluators, and Feedback Providers | Large language models (LLMs) have shown remarkable ability on controllable text generation. However, the potential of LLMs in generating text from structured tables remains largely under-explored. In this paper, we study the capabilities of LLMs for table-to-text generation tasks, particularly aiming to investigate the... | ['Arman Cohan', 'Xiangru Tang', 'Linyong Nan', 'Shengyun Si', 'Haowei Zhang', 'Yilun Zhao'] | 2023-05-24 | null | null | null | null | ['table-to-text-generation'] | ['natural-language-processing'] | [ 2.71523654e-01 1.05225825e+00 -8.62456337e-02 -3.15803796e-01
-1.03294063e+00 -5.30322254e-01 1.09230614e+00 5.46509206e-01
1.93822280e-01 1.20624328e+00 7.83412695e-01 -4.06600922e-01
2.28291214e-01 -1.19968545e+00 -6.96095049e-01 2.04981774e-01
2.17454195e-01 7.78934777e-01 -6.58779219e-02 -6.47100687... | [11.519046783447266, 8.80410099029541] |
85831ec2-a59f-4a5c-8ddb-c62b0c16db4d | an-empirical-survey-of-data-augmentation-for-2 | null | null | https://openreview.net/forum?id=n3MFoq1WOXU | https://openreview.net/pdf?id=n3MFoq1WOXU | An Empirical Survey of Data Augmentation \\for Limited Data Learning in NLP |
NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money, or expertise is required to label massive amounts of textual data... | ['Anonymous'] | 2021-08-17 | null | null | null | acl-arr-august-2021-8 | ['news-classification'] | ['natural-language-processing'] | [ 6.05846882e-01 2.73870528e-01 -7.28715837e-01 -5.30770242e-01
-9.61687624e-01 -7.34906137e-01 6.25329852e-01 4.18790758e-01
-6.69875622e-01 9.64610338e-01 5.92269182e-01 -5.28123498e-01
2.73173511e-01 -5.45161307e-01 -5.71652353e-01 -4.11680281e-01
3.42235833e-01 7.64210165e-01 -4.53435302e-01 -3.31775546... | [10.77418041229248, 8.266281127929688] |
06b35f40-dc78-4b75-a3a0-cbc290ed6cfd | one-class-support-measure-machines-for-group-1 | 1408.2064 | null | http://arxiv.org/abs/1408.2064v1 | http://arxiv.org/pdf/1408.2064v1.pdf | One-Class Support Measure Machines for Group Anomaly Detection | We propose one-class support measure machines (OCSMMs) for group anomaly
detection which aims at recognizing anomalous aggregate behaviors of data
points. The OCSMMs generalize well-known one-class support vector machines
(OCSVMs) to a space of probability measures. By formulating the problem as
quantile estimation on ... | ['Krikamol Muandet', 'Bernhard Schoelkopf'] | 2014-08-09 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [-1.32317305e-01 1.17729023e-01 -5.62465966e-01 -6.26447558e-01
-5.32280564e-01 -3.11528355e-01 4.36969370e-01 4.98378813e-01
-2.60766566e-01 8.07050467e-01 -3.08432430e-01 -6.17630303e-01
-3.60951692e-01 -7.97881126e-01 -5.61749518e-01 -8.11533332e-01
-2.97752649e-01 4.30954069e-01 3.89498264e-01 1.37750283... | [7.637380599975586, 2.4987175464630127] |
37309ea0-8e25-4e98-93ad-940e2e41384d | deep-learning-algorithms-for-coronary-artery | 1912.06417 | null | https://arxiv.org/abs/1912.06417v1 | https://arxiv.org/pdf/1912.06417v1.pdf | Deep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans | Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research. In this work we compare and improve three deep learning algorithms for this task: A 3D recurrent convolutional ... | ['Andreas Maier', 'Axel Schmermund', 'Michael Sühling', 'Anika Reidelshöfer', 'Katharina Breininger', 'Michael Wels', 'Joachim Eckert', 'Felix Denzinger'] | 2019-12-13 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-6.14807084e-02 -1.05832852e-01 6.12779558e-02 -1.03862204e-01
-8.83410752e-01 -5.52423120e-01 4.44368124e-01 3.33615035e-01
-3.08687568e-01 8.83784950e-01 3.09756994e-01 -7.23444760e-01
-3.98521274e-01 -8.11584532e-01 -1.88998595e-01 -7.50375509e-01
-4.16145802e-01 5.86482525e-01 5.22083104e-01 -1.66977897... | [14.163189888000488, -2.4597086906433105] |
502c656e-3566-425c-98aa-bf650274accc | few-shot-adversarial-learning-of-realistic | 1905.08233 | null | https://arxiv.org/abs/1905.08233v2 | https://arxiv.org/pdf/1905.08233v2.pdf | Few-Shot Adversarial Learning of Realistic Neural Talking Head Models | Several recent works have shown how highly realistic human head images can be obtained by training convolutional neural networks to generate them. In order to create a personalized talking head model, these works require training on a large dataset of images of a single person. However, in many practical scenarios, suc... | ['Victor Lempitsky', 'Aliaksandra Shysheya', 'Egor Zakharov', 'Egor Burkov'] | 2019-05-20 | few-shot-adversarial-learning-of-realistic-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Zakharov_Few-Shot_Adversarial_Learning_of_Realistic_Neural_Talking_Head_Models_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zakharov_Few-Shot_Adversarial_Learning_of_Realistic_Neural_Talking_Head_Models_ICCV_2019_paper.pdf | iccv-2019-10 | ['talking-head-generation'] | ['computer-vision'] | [ 2.74935395e-01 5.74705005e-01 5.26240766e-01 -5.28077543e-01
-7.18691289e-01 -3.27634424e-01 7.25592911e-01 -6.59325421e-01
-4.35592383e-01 7.15647936e-01 2.14031637e-01 3.34925890e-01
4.01553631e-01 -6.77736938e-01 -9.64613557e-01 -7.57845283e-01
1.46310970e-01 1.09041107e+00 3.20707023e-01 -2.57855147... | [12.920309066772461, -0.2884344458580017] |
3e4c5447-2436-4cc3-a6f3-96ab1bb050cf | local-geometric-indexing-of-high-resolution | 1903.00119 | null | https://arxiv.org/abs/1903.00119v2 | https://arxiv.org/pdf/1903.00119v2.pdf | Local Geometric Indexing of High Resolution Data for Facial Reconstruction from Sparse Markers | When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints. With the current trend towards using more and more data, our aim is not to fit the motion capture markers with a parameter... | ['Ronald Fedkiw', 'Matthew Cong', 'Lana Lan'] | 2019-03-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 5.48173822e-02 5.40664345e-02 7.12003484e-02 2.02700615e-01
-8.88082266e-01 -3.46169621e-01 5.88997364e-01 1.09869391e-01
-2.96206594e-01 6.81461632e-01 -3.56432423e-02 -2.33793594e-02
-2.86339074e-01 -6.90933585e-01 -5.42268217e-01 -6.41280055e-01
-1.38393208e-01 7.55789578e-01 3.34553510e-01 -3.35215889... | [8.092839241027832, -2.6308419704437256] |
2419904a-a313-48bc-b87e-53570c87f61e | scmhl5-at-trac-2-shared-task-on-aggression | null | null | https://aclanthology.org/2020.trac-1.10 | https://aclanthology.org/2020.trac-1.10.pdf | Scmhl5 at TRAC-2 Shared Task on Aggression Identification: Bert Based Ensemble Learning Approach | This paper presents a system developed during our participation (team name: scmhl5) in the TRAC-2 Shared Task on aggression identification. In particular, we participated in English Sub-task A on three-class classification ({`}Overtly Aggressive{'}, {`}Covertly Aggressive{'} and {`}Non-aggressive{'}) and English Sub-ta... | ['Pete Burnap', 'Matthew Williams', 'Wafa Alorainy', 'Han Liu'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['aggression-identification'] | ['natural-language-processing'] | [-3.45890939e-01 1.87372014e-01 1.13400914e-01 -2.06896260e-01
-6.86130166e-01 -3.03888708e-01 5.15318871e-01 4.88312334e-01
-1.05437708e+00 7.61583090e-01 2.26971984e-01 -1.47219226e-01
-8.38291764e-01 -5.17971158e-01 1.31765679e-01 -5.36948919e-01
-2.59658635e-01 7.91291177e-01 9.73993167e-02 -4.12285119... | [8.809158325195312, 10.758642196655273] |
2c707316-e46e-4a36-9070-efda0764a716 | bayesian-inference-for-the-mixed-conditional | null | null | https://academic.oup.com/ectj/article-abstract/10/2/408/5062603?login=false | https://academic.oup.com/ectj/article-abstract/10/2/408/5062603?login=false | Bayesian inference for the mixed conditional heteroskedasticity model | We estimate by Bayesian inference the mixed conditional heteroskedasticity model of Haas et al. (2004a Journal of Financial Econometrics 2, 211–50). We construct a Gibbs sampler algorithm to compute posterior and predictive densities. The number of mixture components is selected by the marginal likelihood criterion.We ... | ['L. BAUWENS and J.V.K. ROMBOUTS'] | 2007-02-01 | null | null | null | econometrics-journal-2007-2 | ['econometrics'] | ['miscellaneous'] | [-5.43682814e-01 -6.45091459e-02 -2.06379369e-01 -3.37042630e-01
-8.96172106e-01 -4.80894893e-01 1.00094140e+00 -5.85944235e-01
-2.64486849e-01 1.10522592e+00 8.37177038e-02 -7.47015774e-01
-9.78154615e-02 -9.31336522e-01 -2.73515910e-01 -6.64588511e-01
-2.33019561e-01 5.92901111e-01 3.79634239e-02 5.69326520... | [6.26272439956665, 4.0137104988098145] |
f323661b-cfd3-4a3f-a78f-241d83cf6a93 | distributed-cpu-scheduling-subject-to | 2208.14059 | null | https://arxiv.org/abs/2208.14059v1 | https://arxiv.org/pdf/2208.14059v1.pdf | Distributed CPU Scheduling Subject to Nonlinear Constraints | This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on ... | ['Themistoklis Charalambous', 'Karl H. Johansson', 'Christoforos N. Hadjicostis', 'Evangelia Kalyvianaki', 'Andreas Grammenos', 'Apostolos I. Rikos', 'Alireza Aghasi', 'Mohammadreza Doostmohammadian'] | 2022-08-30 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-2.47024968e-01 2.31049061e-01 -1.76814139e-01 -1.94128662e-01
-3.78700316e-01 -6.65856183e-01 -2.30608672e-01 1.10783130e-01
-1.61977693e-01 1.07458365e+00 -3.69037449e-01 -1.36941597e-01
-9.99770403e-01 -6.48027182e-01 -2.45726094e-01 -1.18916571e+00
-2.80930042e-01 9.83294129e-01 -2.76878953e-01 -1.48383588... | [6.180289268493652, 4.917590141296387] |
76c5c793-7fc6-46c6-886e-1816ea52e422 | camelira-an-arabic-multi-dialect | 2211.16807 | null | https://arxiv.org/abs/2211.16807v1 | https://arxiv.org/pdf/2211.16807v1.pdf | Camelira: An Arabic Multi-Dialect Morphological Disambiguator | We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine. Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information,... | ['Nizar Habash', 'Go Inoue', 'Ossama Obeid'] | 2022-11-30 | null | null | null | null | ['dialect-identification', 'morphological-disambiguation'] | ['natural-language-processing', 'natural-language-processing'] | [-7.81279564e-01 -4.90805477e-01 -2.57781427e-02 -4.29075301e-01
-9.61223066e-01 -1.34225237e+00 3.73803347e-01 5.48648298e-01
-2.70622730e-01 5.82013249e-01 8.89787227e-02 -7.17939019e-01
2.18558963e-02 -8.95028234e-01 1.19214617e-01 -5.39634943e-01
-8.73689950e-02 8.04670334e-01 -9.36257094e-03 -1.39083529... | [10.329154968261719, 10.467994689941406] |
5e4c7aee-d580-4e3e-b3af-500f5340464e | textless-speech-to-speech-translation-on-real | 2112.08352 | null | https://arxiv.org/abs/2112.08352v2 | https://arxiv.org/pdf/2112.08352v2.pdf | Textless Speech-to-Speech Translation on Real Data | We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language and can be built without the need of any text data. Different from existing work in the literature, we tackle the challenge in modeling multi-speaker target speech and train the systems wit... | ['Wei-Ning Hsu', 'Juan Pino', 'Yossi Adi', 'Jiatao Gu', 'Sravya Popuri', 'Changhan Wang', 'Peng-Jen Chen', 'Holger Schwenk', 'Paul-Ambroise Duquenne', 'Hongyu Gong', 'Ann Lee'] | 2021-12-15 | null | https://aclanthology.org/2022.naacl-main.63 | https://aclanthology.org/2022.naacl-main.63.pdf | naacl-2022-7 | ['speech-to-speech-translation'] | ['speech'] | [ 4.44983393e-01 4.04465556e-01 -5.17261997e-02 -4.45154130e-01
-1.56174386e+00 -6.64775133e-01 6.34865344e-01 -1.47795558e-01
-4.13564265e-01 5.53765953e-01 5.84245145e-01 -5.52733600e-01
5.83999336e-01 -2.24685282e-01 -7.67631531e-01 -4.55782145e-01
6.02813244e-01 7.61339426e-01 2.26018608e-01 -6.54282272... | [14.562471389770508, 6.977108478546143] |
fd97a7a0-dd71-49ba-9dd6-2b46a326849a | interpreting-a-recurrent-neural-network-model | 1905.09865 | null | https://arxiv.org/abs/1905.09865v4 | https://arxiv.org/pdf/1905.09865v4.pdf | Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk | Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions. Algorithms such as Recurrent Neural Networks (RNNs) when applied to Electronic Medical Records (EMR) introduce additional barriers to transpa... | ['Melissa D. Aczon', 'David Ledbetter', 'Randall Wetzel', 'Long V. Ho'] | 2019-05-23 | null | null | null | null | ['icu-mortality'] | ['medical'] | [ 4.65168446e-01 4.44072038e-01 4.21838425e-02 -4.32122678e-01
-5.67479312e-01 -2.50385821e-01 3.84467304e-01 4.02729064e-01
-3.16870004e-01 6.77652478e-01 8.87712598e-01 -8.01966071e-01
-4.20925707e-01 -5.62923372e-01 -4.15636599e-01 -4.47212905e-01
-6.68773949e-02 5.80076873e-01 -6.07648194e-01 1.37511641... | [8.031935691833496, 6.146010398864746] |
03b3dc2d-4f14-4772-959d-07a65ba69b35 | a-geometrically-constrained-point-matching | 2211.03007 | null | https://arxiv.org/abs/2211.03007v1 | https://arxiv.org/pdf/2211.03007v1.pdf | A Geometrically Constrained Point Matching based on View-invariant Cross-ratios, and Homography | In computer vision, finding point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research worksfocus on the matching of local feature before a sampling method is employed, such as RANSAC, to verify initial matchin... | ['Jen-Hui Chuang', 'Chen-Tao Hsu', 'Ching-Huai Yang', 'Yueh-Cheng Huang'] | 2022-11-06 | null | null | null | null | ['image-stitching', 'visual-localization'] | ['computer-vision', 'computer-vision'] | [ 2.08913743e-01 -6.97964489e-01 -9.18867141e-02 -1.49076238e-01
-5.78653157e-01 -7.55837500e-01 6.39425397e-01 1.83489904e-01
-3.15153480e-01 2.74735212e-01 -3.37627262e-01 -1.85846686e-01
-3.98250401e-01 -6.57209516e-01 -6.31693721e-01 -6.35312259e-01
1.47183135e-01 5.09809315e-01 3.81169438e-01 5.83680440... | [8.130913734436035, -2.3181512355804443] |
4c60a79c-2736-41c6-b02b-dd6a04878bd7 | 4d-millimeter-wave-radar-in-autonomous | 2306.04242 | null | https://arxiv.org/abs/2306.04242v2 | https://arxiv.org/pdf/2306.04242v2.pdf | 4D Millimeter-Wave Radar in Autonomous Driving: A Survey | The 4D millimeter-wave (mmWave) radar, capable of measuring the range, azimuth, elevation, and velocity of targets, has attracted considerable interest in the autonomous driving community. This is attributed to its robustness in extreme environments and outstanding velocity and elevation measurement capabilities. Howev... | ['Jianqiang Wang', 'Shaobing Xu', 'Lei He', 'Shuocheng Yang', 'Zikun Xu', 'Jiahao Wang', 'Zeyu Han'] | 2023-06-07 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 1.98013276e-01 -3.07774395e-01 2.60152936e-01 -6.39386773e-01
-4.49778110e-01 -5.65977991e-01 5.25522232e-01 -4.86524850e-01
-2.86104530e-01 5.74458957e-01 -1.90075755e-01 -4.04721588e-01
-3.78023148e-01 -1.12976825e+00 6.51063956e-03 -8.21659982e-01
-3.13310415e-01 3.57113093e-01 -1.27071798e-01 -3.00767928... | [6.679361820220947, 0.7396187782287598] |
70db2538-e90f-4891-8092-131bffe4fed6 | tanet-a-new-paradigm-for-global-face-super | 2109.08174 | null | https://arxiv.org/abs/2109.08174v1 | https://arxiv.org/pdf/2109.08174v1.pdf | TANet: A new Paradigm for Global Face Super-resolution via Transformer-CNN Aggregation Network | Recently, face super-resolution (FSR) methods either feed whole face image into convolutional neural networks (CNNs) or utilize extra facial priors (e.g., facial parsing maps, facial landmarks) to focus on facial structure, thereby maintaining the consistency of the facial structure while restoring facial details. Howe... | ['Jiayi Ma', 'Zhongyuan Wang', 'JiaMing Wang', 'Junjun Jiang', 'Yanduo Zhang', 'Tao Lu', 'Yuanzhi Wang'] | 2021-09-16 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [-1.68367587e-02 1.33783579e-01 -2.07511205e-02 -6.21602178e-01
-3.63311052e-01 4.93096709e-02 3.76071155e-01 -6.60803378e-01
1.02432445e-01 4.78727251e-01 5.88302076e-01 3.88737977e-01
-1.05116211e-01 -1.03808892e+00 -7.61912525e-01 -8.05598617e-01
5.28401494e-01 -2.19855636e-01 1.54339105e-01 -3.39194864... | [12.87299919128418, 0.014749184250831604] |
f648a6a2-f83d-43fa-9e3d-f28b6bdd40f0 | parameter-free-dynamic-graph-embedding-for | 2210.08189 | null | https://arxiv.org/abs/2210.08189v2 | https://arxiv.org/pdf/2210.08189v2.pdf | Parameter-free Dynamic Graph Embedding for Link Prediction | Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interaction patterns. Existi... | ['Ning Gu', 'Peng Zhang', 'Tun Lu', 'Hansu Gu', 'Dongsheng Li', 'Jiahao Liu'] | 2022-10-15 | null | null | null | null | ['dynamic-graph-embedding'] | ['graphs'] | [-5.07388532e-01 -1.25990167e-01 -3.93193036e-01 -1.97806418e-01
-5.83235435e-02 -3.73331875e-01 1.70091376e-01 2.97874302e-01
-1.39342353e-01 2.78761655e-01 2.30515152e-01 -5.12036324e-01
-3.59964848e-01 -9.30213153e-01 -3.82513434e-01 -4.14941818e-01
-5.10208428e-01 6.02064550e-01 2.39342868e-01 -3.05157602... | [10.184409141540527, 5.620010852813721] |
ce88de6b-9ba5-4d8d-85f4-2f8f2ea8d536 | argument-component-classification-for-1 | 1909.03022 | null | https://arxiv.org/abs/1909.03022v1 | https://arxiv.org/pdf/1909.03022v1.pdf | Argument Component Classification for Classroom Discussions | This paper focuses on argument component classification for transcribed spoken classroom discussions, with the goal of automatically classifying student utterances into claims, evidence, and warrants. We show that an existing method for argument component classification developed for another educationally-oriented doma... | ['Luca Lugini', 'Diane Litman'] | 2019-09-06 | argument-component-classification-for | https://aclanthology.org/W18-5208 | https://aclanthology.org/W18-5208.pdf | ws-2018-11 | ['component-classification'] | ['natural-language-processing'] | [ 3.17192435e-01 5.10830939e-01 -3.32985878e-01 -3.76575738e-01
-1.10245144e+00 -7.09191263e-01 7.56958902e-01 7.47811675e-01
-2.43583441e-01 7.87390113e-01 7.83233821e-01 -1.13726771e+00
-3.80256563e-01 -5.70373416e-01 -5.10349095e-01 -2.46730849e-01
5.50851703e-01 2.75979370e-01 1.56483412e-01 -3.92663866... | [10.516480445861816, 9.42950439453125] |
76969844-9780-4aea-b068-50e7fd5793e8 | voicebox-text-guided-multilingual-universal | 2306.15687 | null | https://arxiv.org/abs/2306.15687v1 | https://arxiv.org/pdf/2306.15687v1.pdf | Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale | Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still pri... | ['Wei-Ning Hsu', 'Jay Mahadeokar', 'Yossi Adi', 'Vimal Manohar', 'Mary Williamson', 'Rashel Moritz', 'Leda Sari', 'Brian Karrer', 'Bowen Shi', 'Apoorv Vyas', 'Matthew Le'] | 2023-06-23 | null | null | null | null | ['text-to-speech-synthesis', 'speech-synthesis'] | ['speech', 'speech'] | [ 2.39686981e-01 1.20774530e-01 2.67005056e-01 -1.12105042e-01
-1.11189771e+00 -5.31276286e-01 8.43663156e-01 -4.55110461e-01
-1.43343538e-01 5.03750265e-01 5.96148312e-01 -5.07830322e-01
1.72816321e-01 -6.48301125e-01 -7.18847811e-01 -5.23381889e-01
4.01101440e-01 3.96664768e-01 -2.84435302e-02 -3.85706961... | [15.189194679260254, 6.366114139556885] |
08bf005d-b4fb-4366-8779-f8f8afa38e67 | capturing-the-motion-of-every-joint-3d-human | 2303.00298 | null | https://arxiv.org/abs/2303.00298v1 | https://arxiv.org/pdf/2303.00298v1.pdf | Capturing the motion of every joint: 3D human pose and shape estimation with independent tokens | In this paper we present a novel method to estimate 3D human pose and shape from monocular videos. This task requires directly recovering pixel-alignment 3D human pose and body shape from monocular images or videos, which is challenging due to its inherent ambiguity. To improve precision, existing methods highly rely o... | ['Gang Yu', 'Wankou Yang', 'Guozhong Luo', 'Gang Liu', 'Wen Heng', 'Sen yang'] | 2023-03-01 | null | null | null | null | ['3d-human-pose-estimation', '3d-human-pose-and-shape-estimation'] | ['computer-vision', 'computer-vision'] | [-5.67909367e-02 -2.29736418e-01 -1.37937576e-01 -2.62645006e-01
-5.27006030e-01 -2.69060612e-01 3.76748860e-01 -4.65281665e-01
-5.40892124e-01 4.73870963e-01 2.22847834e-01 4.25809443e-01
1.78538933e-01 -2.68042117e-01 -8.50095510e-01 -7.12361634e-01
-8.79923925e-02 3.35411280e-01 1.93695933e-01 -9.12254527... | [7.191433906555176, -0.7443550229072571] |
e4edcd7e-75e5-4c09-a07e-75abc1475039 | xlda-cross-lingual-data-augmentation-for | 1905.11471 | null | https://arxiv.org/abs/1905.11471v1 | https://arxiv.org/pdf/1905.11471v1.pdf | XLDA: Cross-Lingual Data Augmentation for Natural Language Inference and Question Answering | While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in anoth... | ['Caiming Xiong', 'Nitish Shirish Keskar', 'Richard Socher', 'Jasdeep Singh', 'Bryan McCann'] | 2019-05-27 | xlda-cross-lingual-data-augmentation-for-1 | https://openreview.net/forum?id=BJgAf6Etwr | https://openreview.net/pdf?id=BJgAf6Etwr | iclr-2020-1 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [-2.47732952e-01 7.36137852e-02 -3.98621053e-01 -3.82556558e-01
-1.52613044e+00 -7.82867670e-01 7.15545595e-01 3.23806018e-01
-9.11964476e-01 1.00657201e+00 3.37085187e-01 -8.69796097e-01
3.68744165e-01 -6.73986912e-01 -8.90457690e-01 -1.69682801e-01
2.04473153e-01 8.14762890e-01 -2.09595457e-01 -4.87738848... | [11.032797813415527, 9.805609703063965] |
9af58851-6507-4f08-84cf-22de706492ce | coverhunter-cover-song-identification-with | 2306.09025 | null | https://arxiv.org/abs/2306.09025v1 | https://arxiv.org/pdf/2306.09025v1.pdf | CoverHunter: Cover Song Identification with Refined Attention and Alignments | Abstract: Cover song identification (CSI) focuses on finding the same music with different versions in reference anchors given a query track. In this paper, we propose a novel system named CoverHunter that overcomes the shortcomings of existing detection schemes by exploring richer features with refined attention and a... | ['Xintong Han', 'Yinan Xu', 'Deyi Tuo', 'Feng Liu'] | 2023-06-15 | null | null | null | null | ['cover-song-identification'] | ['music'] | [ 1.76197827e-01 -4.15439904e-01 -3.29875231e-01 -1.19006649e-01
-1.10916531e+00 -7.15604067e-01 2.60275126e-01 -5.17124534e-02
-2.55424768e-01 3.63771290e-01 4.52728689e-01 5.39857261e-02
-2.47676954e-01 -6.55910611e-01 -8.07967186e-01 -6.71970725e-01
-4.12396073e-01 1.91266432e-01 2.93483317e-01 -3.40041965... | [15.737329483032227, 5.215534210205078] |
d5c6b729-ed3f-4d5c-96cf-03b723e2913f | earthnet2021-a-large-scale-dataset-and | 2104.10066 | null | https://arxiv.org/abs/2104.10066v1 | https://arxiv.org/pdf/2104.10066v1.pdf | EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task | Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. It contains Sentinel 2 satellite imagery... | ['Joachim Denzler', 'Jakob Runge', 'Markus Reichstein', 'Vitus Benson', 'Christian Requena-Mesa'] | 2021-04-16 | null | null | null | null | ['video-forensics', 'crop-yield-prediction', 'crop-yield-prediction', 'earth-surface-forecasting'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'time-series'] | [-1.83888618e-02 7.89947137e-02 -2.44524524e-01 -4.37793374e-01
-3.06202620e-01 -6.04200721e-01 9.13011968e-01 -9.72338095e-02
-8.50251466e-02 9.48514223e-01 3.91674757e-01 -9.39910531e-01
2.33962968e-01 -1.30210209e+00 -6.62408769e-01 -6.20383263e-01
-9.52112496e-01 7.32130408e-02 -1.46047398e-01 -7.03338027... | [9.48739242553711, -1.5305424928665161] |
5c3c8d76-9e56-410e-8fc7-10986e8fa678 | label-aware-hyperbolic-embeddings-for-fine | 2306.14822 | null | https://arxiv.org/abs/2306.14822v1 | https://arxiv.org/pdf/2306.14822v1.pdf | Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification | Fine-grained emotion classification (FEC) is a challenging task. Specifically, FEC needs to handle subtle nuance between labels, which can be complex and confusing. Most existing models only address text classification problem in the euclidean space, which we believe may not be the optimal solution as labels of close s... | ['Lun-Wei Ku', 'Yi-Li Hsu', 'Tun-Min Hung', 'Chih-Yao Chen'] | 2023-06-26 | null | null | null | null | ['emotion-classification', 'classification-1', 'text-classification', 'emotion-classification'] | ['computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.96250424e-01 -1.90392435e-01 -8.71623214e-03 -6.12647176e-01
-7.48932183e-01 -4.78038847e-01 1.39398575e-01 4.55760717e-01
-4.12642270e-01 3.94148439e-01 3.15839201e-01 1.49627747e-02
1.18656931e-02 -6.15472198e-01 -3.85487191e-02 -7.27047384e-01
1.49706692e-01 1.61843598e-01 -3.70640792e-02 -2.73933169... | [10.42637825012207, 6.687601089477539] |
8251b69e-4dc0-4bc8-b017-b6f178ac93b9 | medai-at-semeval-2021-task-10-negation-aware | null | null | https://aclanthology.org/2021.semeval-1.183 | https://aclanthology.org/2021.semeval-1.183.pdf | MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation | Due to the increasing concerns for data privacy, source-free unsupervised domain adaptation attracts more and more research attention, where only a trained source model is assumed to be available, while the labeled source data remain private. To get promising adaptation results, we need to find effective ways to transf... | ['Lei Zhang', 'Yu Wang', 'Qi Zhang', 'Jinquan Sun'] | 2021-08-01 | null | null | null | semeval-2021 | ['source-free-domain-adaptation', 'negation-detection'] | ['computer-vision', 'natural-language-processing'] | [ 3.02573770e-01 4.32790101e-01 -5.44875026e-01 -8.64744484e-01
-9.61010039e-01 -8.55289876e-01 5.35858274e-01 1.01660796e-01
-7.04154551e-01 1.10049427e+00 3.28535110e-01 -3.88507508e-02
3.67538661e-01 -7.21158028e-01 -7.97900558e-01 -3.49722654e-01
5.99051476e-01 4.91838068e-01 1.32843703e-01 -2.62588322... | [10.38086986541748, 3.158299684524536] |
9d042afb-aa98-41e0-94ef-0ac79c0ff547 | affinity-aware-compression-and-expansion | 2008.10191 | null | https://arxiv.org/abs/2008.10191v1 | https://arxiv.org/pdf/2008.10191v1.pdf | Affinity-aware Compression and Expansion Network for Human Parsing | As a fine-grained segmentation task, human parsing is still faced with two challenges: inter-part indistinction and intra-part inconsistency, due to the ambiguous definitions and confusing relationships between similar human parts. To tackle these two problems, this paper proposes a novel \textit{Affinity-aware Compres... | ['Pengfei Xiong', 'Yunfeng Wang', 'Xinyan Zhang'] | 2020-08-24 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 3.30428064e-01 2.55154818e-01 -3.28576148e-01 -4.94252652e-01
-5.98121464e-01 -3.78625929e-01 -1.85547508e-02 3.14214230e-02
-3.49361092e-01 4.63837951e-01 4.78644818e-01 2.00622484e-01
5.61912842e-02 -5.66281378e-01 -6.12023592e-01 -5.35265148e-01
5.49939632e-01 5.76813459e-01 7.14387715e-01 -3.88572030... | [8.884822845458984, 0.032328050583601] |
27a9adc9-eedf-48ed-894d-4f7591b1f620 | tiefake-title-text-similarity-and-emotion | 2304.09421 | null | https://arxiv.org/abs/2304.09421v1 | https://arxiv.org/pdf/2304.09421v1.pdf | TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection | Fake news detection aims to detect fake news widely spreading on social media platforms, which can negatively influence the public and the government. Many approaches have been developed to exploit relevant information from news images, text, or videos. However, these methods may suffer from the following limitations: ... | ['Zhouguo Chen', 'Ling Tian', 'Zhao Kang', 'Quanjiang Guo'] | 2023-04-19 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-0.29155734 -0.36409757 -0.406553 -0.15857275 -0.7026211 -0.34732613
0.708653 0.03691232 -0.28321293 0.3874972 0.6339349 0.14087166
0.5142661 -0.42868313 -0.664934 -0.5407293 0.5076869 -0.36954352
0.07032479 -0.47545505 0.60475296 -0.02064411 -1.2793458 0.5383605
0.9063099 1.3131981 -0.0... | [8.163406372070312, 10.288440704345703] |
c60d1b58-95e0-41dd-8ca4-64a15a72001d | sg-net-spatial-granularity-network-for-one | 2103.10284 | null | https://arxiv.org/abs/2103.10284v2 | https://arxiv.org/pdf/2103.10284v2.pdf | SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation | Video instance segmentation (VIS) is a new and critical task in computer vision. To date, top-performing VIS methods extend the two-stage Mask R-CNN by adding a tracking branch, leaving plenty of room for improvement. In contrast, we approach the VIS task from a new perspective and propose a one-stage spatial granulari... | ['Yingjie Chen', 'Wenbo Tan', 'Yiming Cui', 'Dongfang Liu'] | 2021-03-18 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_SG-Net_Spatial_Granularity_Network_for_One-Stage_Video_Instance_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_SG-Net_Spatial_Granularity_Network_for_One-Stage_Video_Instance_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['head-detection', 'video-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [-2.45381743e-02 6.66377619e-02 -4.97635692e-01 -2.24959403e-01
-7.62682140e-01 -4.41236407e-01 3.64224911e-01 -3.05870086e-01
-4.99494165e-01 3.20666462e-01 2.89310347e-02 -4.64357771e-02
4.35090959e-01 -3.38615417e-01 -9.29425597e-01 -5.50205767e-01
2.99737528e-02 4.06846076e-01 1.20702970e+00 1.71203390... | [9.036554336547852, -0.14720898866653442] |
dd545144-14fd-4b72-bc6a-024bd62662f4 | collaborative-metric-learning-recommendation | 1803.00202 | null | http://arxiv.org/abs/1803.00202v1 | http://arxiv.org/pdf/1803.00202v1.pdf | Collaborative Metric Learning Recommendation System: Application to Theatrical Movie Releases | Product recommendation systems are important for major movie studios during
the movie greenlight process and as part of machine learning personalization
pipelines. Collaborative Filtering (CF) models have proved to be effective at
powering recommender systems for online streaming services with explicit
customer feedbac... | ['Miguel Campo', 'Abhinav Taliyan', 'Julie Rieger', 'JJ Espinoza'] | 2018-03-01 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [-5.98125458e-02 -2.59769976e-01 -9.54327062e-02 -8.60653281e-01
-6.56996727e-01 -8.38144600e-01 6.28918409e-01 4.74932224e-01
-3.70810598e-01 7.26541653e-02 6.00679994e-01 -3.19310188e-01
-3.42624098e-01 -8.28662992e-01 -4.13838536e-01 -2.36923873e-01
-2.74100691e-01 7.80530035e-01 -9.96636078e-02 -5.27073264... | [10.107243537902832, 5.7809157371521] |
388d9a5e-41b4-4e52-8a81-d14bdfa3e8ca | capitalization-cues-improve-dependency | null | null | https://aclanthology.org/W12-1903 | https://aclanthology.org/W12-1903.pdf | Capitalization Cues Improve Dependency Grammar Induction | null | ['Hiyan Alshawi', 'Valentin I. Spitkovsky', 'Daniel Jurafsky'] | 2012-06-01 | null | null | null | ws-2012-6 | ['dependency-grammar-induction'] | ['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... | [-7.398244857788086, 3.6925456523895264] |
d70dc534-8e85-44a2-96fd-5f779b6d9958 | cadet-fully-self-supervised-anomaly-detection | 2210.01742 | null | https://arxiv.org/abs/2210.01742v3 | https://arxiv.org/pdf/2210.01742v3.pdf | CADet: Fully Self-Supervised Out-Of-Distribution Detection With Contrastive Learning | Handling out-of-distribution (OOD) samples has become a major stake in the real-world deployment of machine learning systems. This work explores the use of self-supervised contrastive learning to the simultaneous detection of two types of OOD samples: unseen classes and adversarial perturbations. First, we pair self-su... | ['Joao Monteiro', 'Ioannis Mitliagkas', 'David Vazquez', 'Pau Rodriguez', 'Charles Guille-Escuret'] | 2022-10-04 | null | null | null | null | ['self-supervised-anomaly-detection', 'supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [ 3.58443469e-01 2.49134842e-02 4.12201881e-02 -2.11376861e-01
-1.06526089e+00 -1.14289129e+00 9.83246863e-01 3.27381700e-01
-3.52536470e-01 4.33852494e-01 -1.13418207e-01 -3.50105196e-01
4.38169867e-01 -4.99102265e-01 -8.99634719e-01 -5.21206260e-01
-3.07420492e-01 4.73678350e-01 2.75863469e-01 1.54931527... | [8.275948524475098, 2.59401273727417] |
638d0a4f-bd16-4763-95ce-29256f604b96 | duluthnlp-at-semeval-2021-task-7-fine-tuning | null | null | https://aclanthology.org/2021.semeval-1.169 | https://aclanthology.org/2021.semeval-1.169.pdf | DuluthNLP at SemEval-2021 Task 7: Fine-Tuning RoBERTa Model for Humor Detection and Offense Rating | This paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense. In it, we explain the approach used to train the model together with the process of fine-tuning our model in getting the results. We focus on humor detection, rating, and of-fense rating, re... | ['Samuel Akrah'] | 2021-08-01 | null | null | null | semeval-2021 | ['humor-detection'] | ['natural-language-processing'] | [-3.90019089e-01 8.72058049e-03 -5.78330830e-03 -3.52681041e-01
-3.46750915e-01 -4.94396001e-01 6.33894026e-01 1.25350535e-01
-5.29491365e-01 6.01199925e-01 8.73054385e-01 -1.74889956e-02
2.75735259e-01 -3.30866188e-01 -1.72004893e-01 -6.07819073e-02
1.97033495e-01 3.11390966e-01 6.05725832e-02 -4.65430558... | [8.8696870803833, 11.08786392211914] |
0e238e27-dd06-4571-bde9-4f556d325d15 | image-seam-carving-by-controlling-positional | 1912.13214 | null | https://arxiv.org/abs/1912.13214v1 | https://arxiv.org/pdf/1912.13214v1.pdf | Image Seam-Carving by Controlling Positional Distribution of Seams | Image retargeting is a new image processing task that renders the change of aspect ratio in images. One of the most famous image-retargeting algorithms is seam-carving. Although seam-carving is fast and straightforward, it usually distorts the images. In this paper, we introduce a new seam-carving algorithm that not on... | ['Shadrokh Samavi', 'Nader Karimi', 'Mahdi Ahmadi'] | 2019-12-31 | null | null | null | null | ['image-retargeting'] | ['computer-vision'] | [ 3.51221859e-01 -1.09610714e-01 2.46204466e-01 -1.13794476e-01
-3.88507575e-01 -4.26817924e-01 5.02020061e-01 -1.11833975e-01
-4.24761921e-01 6.54178679e-01 1.15216784e-01 -2.29956239e-01
9.37498286e-02 -7.10011780e-01 -6.46719337e-01 -7.27121711e-01
3.81658316e-01 -1.75476953e-01 7.66012788e-01 -6.43817604... | [11.107629776000977, -1.202759027481079] |
38444511-3480-42b5-ae13-e508d849e072 | a-highly-effective-low-rank-compression-of | 2111.15179 | null | https://arxiv.org/abs/2111.15179v2 | https://arxiv.org/pdf/2111.15179v2.pdf | A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank | Compression has emerged as one of the essential deep learning research topics, especially for the edge devices that have limited computation power and storage capacity. Among the main compression techniques, low-rank compression via matrix factorization has been known to have two problems. First, an extensive tuning is... | ['Wonjong Rhee', 'Suhyun Kang', 'Moonjung Eo'] | 2021-11-30 | null | null | null | null | ['low-rank-compression'] | ['computer-code'] | [ 2.45768324e-01 -1.71079203e-01 -3.56214046e-01 -2.42927566e-01
-8.91568601e-01 -1.74737364e-01 3.38871062e-01 5.31360507e-01
-5.34870982e-01 6.82507753e-01 6.65674135e-02 -3.54643732e-01
-6.16605639e-01 -8.78965199e-01 -6.01025283e-01 -7.26134896e-01
-1.20335363e-01 6.81431293e-01 4.78463322e-01 -8.59773234... | [8.511474609375, 3.243786334991455] |
5ac06e6e-faa0-429e-9bf3-e28bcd35685b | causal-discovery-from-temporal-data-an | 2303.10112 | null | https://arxiv.org/abs/2303.10112v2 | https://arxiv.org/pdf/2303.10112v2.pdf | Causal Discovery from Temporal Data: An Overview and New Perspectives | Temporal data, representing chronological observations of complex systems, has always been a typical data structure that can be widely generated by many domains, such as industry, medicine and finance. Analyzing this type of data is extremely valuable for various applications. Thus, different temporal data analysis tas... | ['Jingping Bi', 'Wenbin Li', 'Chuzhe Zhang', 'Di Yao', 'Chang Gong'] | 2023-03-17 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 1.22912891e-01 -4.70867425e-01 -6.35634899e-01 -2.80546278e-01
-1.94020212e-01 -5.31213164e-01 9.84826982e-01 5.35387933e-01
2.94014625e-02 9.34300065e-01 4.44316626e-01 -5.46230495e-01
-1.00652313e+00 -6.88491523e-01 -2.78207362e-01 -9.04539943e-01
-6.58628106e-01 3.59234631e-01 4.59167480e-01 -3.48686911... | [7.671210765838623, 5.13963508605957] |
b4fade84-d6d2-4465-be12-f5f2f20486fa | multi-criterion-evolutionary-design-of-deep | 1912.01369 | null | https://arxiv.org/abs/1912.01369v3 | https://arxiv.org/pdf/1912.01369v3.pdf | Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification | Early advancements in convolutional neural networks (CNNs) architectures are primarily driven by human expertise and by elaborate design processes. Recently, neural architecture search was proposed with the aim of automating the network design process and generating task-dependent architectures. While existing approach... | ['Vishnu Naresh Boddeti', 'Erik Goodman', 'Zhichao Lu', 'Yashesh Dhebar', 'Wolfgang Banzhaf', 'Kalyanmoy Deb', 'Ian Whalen'] | 2019-12-03 | null | null | null | null | ['pneumonia-detection'] | ['medical'] | [ 1.85254544e-01 -6.48577288e-02 -1.87655568e-01 -3.75983357e-01
-2.72673875e-01 -4.63268429e-01 3.98494482e-01 -2.85583902e-02
-6.44277573e-01 6.65004909e-01 -3.38150203e-01 -4.43068802e-01
-5.95357835e-01 -7.18481600e-01 -5.26641786e-01 -7.25107491e-01
2.06706285e-01 5.44365227e-01 1.27239570e-01 -1.01211905... | [8.360098838806152, 3.233081340789795] |
f3a24285-979f-4b8a-bf8e-cbaff06ba023 | development-and-evaluation-of-automated | 2211.02760 | null | https://arxiv.org/abs/2211.02760v1 | https://arxiv.org/pdf/2211.02760v1.pdf | Development and evaluation of automated localization and reconstruction of all fruits on tomato plants in a greenhouse based on multi-view perception and 3D multi-object tracking | Accurate representation and localization of relevant objects is important for robots to perform tasks. Building a generic representation that can be used across different environments and tasks is not easy, as the relevant objects vary depending on the environment and the task. Furthermore, another challenge arises in ... | ['Gert Kootstra', 'Eldert J. van Henten', 'David Rapado Rincon'] | 2022-11-04 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 7.69152492e-02 -2.70855308e-01 3.93113077e-01 5.89911751e-02
-3.21570158e-01 -9.87791777e-01 2.12959453e-01 1.04405630e+00
-7.74317458e-02 1.63467363e-01 -6.33858204e-01 1.08589754e-01
-3.32422405e-02 -9.15944815e-01 -8.84493113e-01 -5.05126834e-01
-2.95820415e-01 8.60109210e-01 8.99725497e-01 -2.83650011... | [7.5482659339904785, -2.0398292541503906] |
41d9f5a2-d045-4db6-8f79-c2782f3a2031 | are-deep-neural-networks-adequate-behavioural | 2305.17023 | null | https://arxiv.org/abs/2305.17023v1 | https://arxiv.org/pdf/2305.17023v1.pdf | Are Deep Neural Networks Adequate Behavioural Models of Human Visual Perception? | Deep neural networks (DNNs) are machine learning algorithms that have revolutionised computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perce... | ['Robert Geirhos', 'Felix A. Wichmann'] | 2023-05-26 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 3.72889370e-01 -1.85316727e-01 -3.68537568e-02 -3.71431082e-01
1.12103738e-01 -4.03549969e-01 7.02175200e-01 -6.89687505e-02
-1.01259363e+00 7.59278387e-02 1.15163699e-01 -6.38352156e-01
-3.61596912e-01 -3.43677670e-01 -3.72405350e-01 -7.46913910e-01
2.28931785e-01 2.98202246e-01 2.77630687e-01 -8.90783295... | [9.861238479614258, 2.3656978607177734] |
e4f8b32c-90c2-4a21-83d1-7f000cbb2de2 | ncsu-sas-ning-candidate-generation-and | null | null | https://aclanthology.org/W15-4313 | https://aclanthology.org/W15-4313.pdf | NCSU-SAS-Ning: Candidate Generation and Feature Engineering for Supervised Lexical Normalization | null | ['Ning Jin'] | 2015-07-01 | null | null | null | ws-2015-7 | ['lexical-normalization'] | ['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... | [-7.419112205505371, 3.731546640396118] |
c7311233-1a02-4c4f-9e46-7b8c4691eac8 | simfbo-towards-simple-flexible-and | 2305.19442 | null | https://arxiv.org/abs/2305.19442v2 | https://arxiv.org/pdf/2305.19442v2.pdf | SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning | Federated bilevel optimization (FBO) has shown great potential recently in machine learning and edge computing due to the emerging nested optimization structure in meta-learning, fine-tuning, hyperparameter tuning, etc. However, existing FBO algorithms often involve complicated computations and require multiple sub-loo... | ['Kaiyi Ji', 'Peiyao Xiao', 'Yifan Yang'] | 2023-05-30 | null | null | null | null | ['bilevel-optimization', 'edge-computing'] | ['methodology', 'time-series'] | [-5.43389618e-01 -4.87578154e-01 -3.84598881e-01 -1.91216737e-01
-8.70927513e-01 -4.19395596e-01 2.64688045e-01 2.91877985e-01
-7.65473545e-02 7.85201788e-01 3.42267752e-01 -3.41393173e-01
-5.65565765e-01 -8.48382592e-01 -7.01121032e-01 -1.03491163e+00
-3.18285495e-01 6.33056343e-01 3.14497888e-01 -1.65252350... | [6.229273319244385, 5.083626747131348] |
089a310a-2a3d-4741-9e73-b6aa7de07e07 | generating-dispatching-rules-for-the | 2302.02506 | null | https://arxiv.org/abs/2302.02506v1 | https://arxiv.org/pdf/2302.02506v1.pdf | Generating Dispatching Rules for the Interrupting Swap-Allowed Blocking Job Shop Problem Using Graph Neural Network and Reinforcement Learning | The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a complex scheduling problem that is able to model many manufacturing planning and logistics applications realistically by addressing both the lack of storage capacity and unforeseen production interruptions. Subjected to random disruptions due to mac... | ['Kincho H. Law', 'Jinkyoo Park', 'Junyoung Park', 'Sang Hun Kim', 'Vivian W. H. Wong'] | 2023-02-05 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [ 5.64041495e-01 9.49260667e-02 -4.44771081e-01 -2.41651759e-01
-1.41461268e-01 -3.66350710e-01 2.71230102e-01 3.42319429e-01
-1.26781836e-01 1.08077002e+00 -3.69895786e-01 -8.51142645e-01
-8.80940199e-01 -6.14255071e-01 -8.23083401e-01 -7.62767971e-01
-5.14187157e-01 1.36977160e+00 -5.54078780e-02 -2.23512352... | [4.809714317321777, 2.4279680252075195] |
c560a10a-57bc-4960-97bf-934d2787007e | dan-net-dual-domain-adaptive-scaling-non | 2102.08003 | null | https://arxiv.org/abs/2102.08003v1 | https://arxiv.org/pdf/2102.08003v1.pdf | DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction | Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and negatively impact subsequent diagnoses and treatment planning. With the rapid development of deep learning in the field of medical imaging,... | ['Yi Zhang', 'Jiliu Zhou', 'Hu Chen', 'Yan Liu', 'Huaiqiang Sun', 'Yongqiang Huang', 'Wenjun Xia', 'Tao Wang'] | 2021-02-16 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 4.06525135e-01 8.52997079e-02 1.52033418e-01 -3.35725456e-01
-9.51931894e-01 2.15156674e-01 1.31329238e-01 -8.34434014e-03
-3.07295024e-01 6.90453947e-01 3.63782197e-01 -5.05772494e-02
-2.86658913e-01 -7.61040151e-01 -5.08066356e-01 -9.85868931e-01
1.63487628e-01 2.27514759e-01 5.32250226e-01 8.42073187... | [13.497515678405762, -2.5445101261138916] |
e1e0953d-49d8-46ca-bfa3-555b3aabdc37 | continuous-ppg-based-blood-pressure | 2011.02231 | null | https://arxiv.org/abs/2011.02231v2 | https://arxiv.org/pdf/2011.02231v2.pdf | Continuous PPG-Based Blood Pressure Monitoring Using Multi-Linear Regression | In this work, we present the Senbiosys blood pressure monitoring algorithm (SB-BPM) that solely requires a photoplethysmography (PPG) signal. The technology is based on pulse wave analysis (PWA) of PPG signals retrieved from different body locations to continuously estimate the systolic blood pressure (SBP) and the dia... | ['Antonino Caizzone', 'Assim Boukhayma', 'Serj Haddad'] | 2020-11-04 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 1.11057006e-01 -9.31058824e-02 1.97709143e-01 -3.68828654e-01
1.40973762e-01 -3.52337629e-01 -1.51897579e-01 -4.34767485e-01
-1.50967479e-01 1.14682412e+00 2.18693435e-01 -2.85409153e-01
2.35184714e-01 -6.86529934e-01 1.84334561e-01 -7.53898799e-01
-3.86565059e-01 7.77778402e-02 1.42425656e-01 2.23064974... | [14.020322799682617, 2.94793701171875] |
f7ca995a-cc3d-4faf-8f9b-60a221bb78e8 | sequential-optimization-for-efficient-high | 1511.04511 | null | http://arxiv.org/abs/1511.04511v3 | http://arxiv.org/pdf/1511.04511v3.pdf | Sequential Optimization for Efficient High-Quality Object Proposal Generation | We are motivated by the need for a generic object proposal generation
algorithm which achieves good balance between object detection recall, proposal
localization quality and computational efficiency. We propose a novel object
proposal algorithm, BING++, which inherits the virtue of good computational
efficiency of BIN... | ['Philip H. S. Torr', 'Ming-Ming Cheng', 'Ziming Zhang', 'Venkatesh Saligrama', 'Yun Liu', 'Yanjun Zhu', 'Xi Chen'] | 2015-11-14 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [-1.48407802e-01 1.01080351e-01 -2.34459400e-01 -1.58913895e-01
-1.25612199e+00 -4.06049758e-01 3.90804827e-01 1.88160628e-01
-4.59160358e-01 3.34645182e-01 -2.47934669e-01 1.20852731e-01
-1.96331199e-02 -7.80652940e-01 -7.28856623e-01 -4.72721636e-01
1.80920474e-02 6.93495035e-01 1.15679312e+00 3.34246904... | [8.757713317871094, -0.22126996517181396] |
6d4159b3-cfe4-4252-9db6-b960c4da43b1 | fusing-heterogeneous-factors-with-triaffine-1 | null | null | https://openreview.net/forum?id=OXXX_dfeH7v | https://openreview.net/pdf?id=OXXX_dfeH7v | Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition | Nested entities are observed in many domains due to their compositionality, which cannot be easily recognized by the widely-used sequence labeling framework.
A natural solution is to treat the task as a span classification problem.
To learn better span representation and increase classification performance, it is cruci... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-2.55426288e-01 -1.96714908e-01 -2.33707041e-01 -4.38616842e-01
-7.16790557e-01 -8.58812392e-01 3.25021923e-01 5.12464881e-01
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-1.58211738e-01 2.99400032e-01 3.95194054e-01 -2.43010312... | [9.549274444580078, 9.31125259399414] |
a0d85b0b-1092-43f2-884f-24f2e22ddd5f | 3d-part-based-sparse-tracker-with-automatic | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Bibi_3D_Part-Based_Sparse_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Bibi_3D_Part-Based_Sparse_CVPR_2016_paper.pdf | 3D Part-Based Sparse Tracker With Automatic Synchronization and Registration | In this paper, we present a part-based sparse tracker in a particle filter framework where both the motion and appearance model are formulated in 3D. The motion model is adaptive and directed according to a simple yet powerful occlusion handling paradigm, which is intrinsically fused in the motion model. Also, since 3... | ['Tianzhu Zhang', 'Adel Bibi', 'Bernard Ghanem'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['occlusion-handling'] | ['computer-vision'] | [-2.50561953e-01 -5.14987946e-01 -1.65706038e-01 -3.16424221e-02
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1.34985700e-01 2.65956581e-01 7.29435980e-01 2.60478202... | [6.689198970794678, -2.183455228805542] |
7e3f3616-ada6-4e6a-b42e-1184277de6a9 | efficiently-maintaining-next-basket | 2201.13313 | null | https://arxiv.org/abs/2201.13313v1 | https://arxiv.org/pdf/2201.13313v1.pdf | Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items | Recommender systems play an important role in helping people find information and make decisions in today's increasingly digitalized societies. However, the wide adoption of such machine learning applications also causes concerns in terms of data privacy. These concerns are addressed by the recent "General Data Protect... | ['Sebastian Schelter', 'Benjamin Longxiang Wang'] | 2022-01-27 | null | null | null | null | ['next-basket-recommendation'] | ['miscellaneous'] | [ 1.23515446e-02 -3.87917429e-01 -2.72718251e-01 -6.07325852e-01
-2.68525064e-01 -6.90595806e-01 2.13547930e-01 8.65856647e-01
-8.38278472e-01 4.57506001e-01 2.45495699e-02 -7.15288103e-01
-3.04555655e-01 -1.13349104e+00 -6.56222224e-01 -2.41843805e-01
-9.20979381e-02 5.70490777e-01 3.14301461e-01 -3.63342822... | [6.113073825836182, 6.393284797668457] |
3c76d07c-11f3-44b9-bf92-df698ace0896 | learning-a-compressed-sensing-measurement | 1806.10175 | null | https://arxiv.org/abs/1806.10175v4 | https://arxiv.org/pdf/1806.10175v4.pdf | Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling | Linear encoding of sparse vectors is widely popular, but is commonly data-independent -- missing any possible extra (but a priori unknown) structure beyond sparsity. In this paper we present a new method to learn linear encoders that adapt to data, while still performing well with the widely used $\ell_1$ decoder. The ... | ['Dmitry Storcheus', 'Daniel Holtmann-Rice', 'Sujay Sanghavi', 'Afshin Rostamizadeh', 'Shanshan Wu', 'Felix X. Yu', 'Alexandros G. Dimakis', 'Sanjiv Kumar'] | 2018-06-26 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 3.38257700e-01 3.13012302e-01 -4.01264578e-01 -3.66338491e-01
-1.21628153e+00 -3.04865748e-01 3.15829992e-01 1.00610867e-01
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-3.68108392e-01 5.40313303e-01 -1.08193576e-01 -1.44486025... | [7.29800271987915, 4.427329063415527] |
87dba07e-fc07-464d-b7b8-ffaaab223e1d | efficient-and-interpretable-infrared-and | 2005.05896 | null | https://arxiv.org/abs/2005.05896v2 | https://arxiv.org/pdf/2005.05896v2.pdf | Efficient and Model-Based Infrared and Visible Image Fusion Via Algorithm Unrolling | Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as Algorithm Unrolling Image Fusion (AUIF), is proposed to overcome... | ['Junmin Liu', 'Chunxia Zhang', 'Chengyang Liang', 'Shuang Xu', 'Jiangshe Zhang', 'Zixiang Zhao'] | 2020-05-12 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 6.09895647e-01 -1.98262855e-01 1.06641725e-01 -8.73730797e-03
-4.38497812e-01 -6.79659322e-02 4.44525033e-01 -2.83368468e-01
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-5.54841980e-02 -8.95181060e-01 -6.68855786e-01 -1.01721942e+00
5.14508963e-01 -2.07016766e-01 -6.09224774e-02 -2.61862725... | [10.547784805297852, -2.003321886062622] |
441c48d5-088c-4c69-9a0e-c79999fa073c | making-images-undiscoverable-from-co-saliency | 2009.09258 | null | https://arxiv.org/abs/2009.09258v5 | https://arxiv.org/pdf/2009.09258v5.pdf | Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection | Co-salient object detection (CoSOD) has recently achieved significant progress and played a key role in retrieval-related tasks. However, it inevitably poses an entirely new safety and security issue, i.e., highly personal and sensitive content can potentially be extracting by powerful CoSOD methods. In this paper, we ... | ['Song Wang', 'Yang Liu', 'Huazhu Fu', 'Felix Juefei-Xu', 'Hongkai Yu', 'Qing Guo', 'Wei Feng', 'Ruijun Gao'] | 2020-09-19 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gao_Can_You_Spot_the_Chameleon_Adversarially_Camouflaging_Images_From_Co-Salient_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gao_Can_You_Spot_the_Chameleon_Adversarially_Camouflaging_Images_From_Co-Salient_CVPR_2022_paper.pdf | cvpr-2022-1 | ['co-saliency-detection'] | ['computer-vision'] | [ 5.61984718e-01 -1.96003392e-01 3.12911242e-01 1.62409917e-01
-9.51466084e-01 -8.70246589e-01 7.07871079e-01 9.79480073e-02
-2.53550142e-01 4.59516436e-01 -5.98733611e-02 -3.75941023e-03
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1.70783594e-01 -1.29611611e-01 5.24092674e-01 -4.54619169... | [5.63074254989624, 7.8706793785095215] |
854ea9f2-3685-4e1e-8e88-967d90be8f35 | two-sample-testing-in-reinforcement-learning | 2201.08078 | null | https://arxiv.org/abs/2201.08078v2 | https://arxiv.org/pdf/2201.08078v2.pdf | Two-Sample Testing in Reinforcement Learning | Value-based reinforcement-learning algorithms have shown strong performances in games, robotics, and other real-world applications. The most popular sample-based method is $Q$-Learning. It subsequently performs updates by adjusting the current $Q$-estimate towards the observed reward and the maximum of the $Q$-estimate... | ['Ostap Okhrin', 'Martin Waltz'] | 2022-01-20 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [-2.47477144e-01 2.75541663e-01 -2.65415668e-01 -3.63370389e-01
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-5.20876348e-01 4.77058828e-01 3.63587767e-01 -2.34443501... | [4.239161968231201, 2.3050150871276855] |
f97a4687-5fd8-4f8c-9037-30ce0a982579 | rics-a-2d-self-occlusion-map-for-harmonizing | 2205.06975 | null | https://arxiv.org/abs/2205.06975v1 | https://arxiv.org/pdf/2205.06975v1.pdf | RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects | There have been remarkable successes in computer vision with deep learning. While such breakthroughs show robust performance, there have still been many challenges in learning in-depth knowledge, like occlusion or predicting physical interactions. Although some recent works show the potential of 3D data in serving such... | ['Honglak Lee', 'Xin Sun', 'Duygu Ceylan', 'Jimei Yang', 'Ruben Villegas', 'Yunseok Jang'] | 2022-05-14 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 1.51556745e-01 1.39090244e-03 2.81764060e-01 -3.46681237e-01
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-2.70367824e-02 4.80494738e-01 4.86325502e-01 -1.09449357... | [9.3689603805542, -3.010524034500122] |
69e3e8b7-ec20-47f9-9d70-cd3b89a5dc3c | matching-distributions-between-model-and-data | null | null | https://aclanthology.org/2021.acl-long.421 | https://aclanthology.org/2021.acl-long.421.pdf | Matching Distributions between Model and Data: Cross-domain Knowledge Distillation for Unsupervised Domain Adaptation | Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge of source domain to the unlabeled target domain. Existing methods typically require to learn to adapt the target model by exploiting the source data and sharing the network architecture across domains. However, this pipeline makes the source data risky... | ['Zhoujun Li', 'Lei Cheng', 'Yun Liu', 'XiaoMing Zhang', 'Bo Zhang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['cross-domain-text-classification'] | ['natural-language-processing'] | [ 9.70490053e-02 1.36454195e-01 -3.25456530e-01 -6.33874059e-01
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1.98440418e-01 7.56244898e-01 3.00998509e-01 -1.96029112... | [10.413710594177246, 3.1192824840545654] |
40aad670-ad52-45eb-9619-9e9555015c79 | ntu-rgbd-a-large-scale-dataset-for-3d-human | 1604.02808 | null | http://arxiv.org/abs/1604.02808v1 | http://arxiv.org/pdf/1604.02808v1.pdf | NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis | Recent approaches in depth-based human activity analysis achieved outstanding
performance and proved the effectiveness of 3D representation for
classification of action classes. Currently available depth-based and
RGB+D-based action recognition benchmarks have a number of limitations,
including the lack of training sam... | ['Tian-Tsong Ng', 'Gang Wang', 'Amir Shahroudy', 'Jun Liu'] | 2016-04-11 | ntu-rgbd-a-large-scale-dataset-for-3d-human-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/Shahroudy_NTU_RGBD_A_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Shahroudy_NTU_RGBD_A_CVPR_2016_paper.pdf | cvpr-2016-6 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.30408028e-01 -5.09581387e-01 -4.99610245e-01 -4.39205557e-01
-6.26502156e-01 -1.29964843e-01 4.11229759e-01 -2.33458459e-01
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-4.00990069e-01 6.73898235e-02 3.64692390e-01 -2.42715571... | [7.850456237792969, 0.41867539286613464] |
dcc50ec1-c223-4139-aac7-f3b58edb1a4b | context-aware-attention-for-understanding | 1809.08726 | null | https://arxiv.org/abs/1809.08726v2 | https://arxiv.org/pdf/1809.08726v2.pdf | Context-Aware Attention for Understanding Twitter Abuse | The original goal of any social media platform is to facilitate users to indulge in healthy and meaningful conversations. But more often than not, it has been found that it becomes an avenue for wanton attacks. We want to alleviate this issue and hence we try to provide a detailed analysis of how abusive behavior can b... | ['Kilol Gupta', 'Tuhin Chakrabarty'] | 2018-09-24 | null | null | null | null | ['abuse-detection'] | ['natural-language-processing'] | [-2.06409708e-01 -3.15466784e-02 -7.37963676e-01 -4.37563986e-01
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1.50799036e-01 -6.67414606e-01 -1.75123841e-01 -7.30900317e-02
-3.17631811e-01 8.88994262e-02 -7.39376694e-02 -6.69439852... | [8.711833953857422, 10.490084648132324] |
8e98f969-ba2c-468a-989f-ea7f71ea2f50 | i-see-you-a-vehicle-pedestrian-interaction | 2211.09342 | null | https://arxiv.org/abs/2211.09342v1 | https://arxiv.org/pdf/2211.09342v1.pdf | I see you: A Vehicle-Pedestrian Interaction Dataset from Traffic Surveillance Cameras | The development of autonomous vehicles arises new challenges in urban traffic scenarios where vehicle-pedestrian interactions are frequent e.g. vehicle yields to pedestrians, pedestrian slows down due approaching to the vehicle. Over the last years, several datasets have been developed to model these interactions. Howe... | ['Harley Vera', 'Edwin Alvarez', 'Patricia Condori', 'Jorshinno Sumire', 'Hanan Quispe'] | 2022-11-17 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-8.08235228e-01 -2.31672496e-01 -4.09386009e-01 -4.22501564e-01
-5.84757388e-01 -5.18410683e-01 8.15275013e-01 -5.19090295e-02
-5.05671680e-01 8.43285859e-01 2.89816577e-02 -6.43928051e-01
1.40528038e-01 -8.86198223e-01 -8.60791862e-01 -5.68363428e-01
-1.17493533e-02 4.87334758e-01 6.72698617e-01 -4.96786445... | [6.103032112121582, 0.86481773853302] |
524fe954-2336-4bf2-a008-149439574cfd | rdfnet-regional-dynamic-fista-net-for | 2302.02519 | null | https://arxiv.org/abs/2302.02519v1 | https://arxiv.org/pdf/2302.02519v1.pdf | RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imaging | Deep convolutional neural networks have recently shown promising results in compressive spectral reconstruction. Previous methods, however, usually adopt a single mapping function for sparse representation. Considering that different regions have distinct characteristics, it is desirable to apply various mapping functi... | ['Jianan Li', 'Shaocong Dong', 'Tingfa Xu', 'Shiyun Zhou'] | 2023-02-06 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 4.14154530e-01 -3.64422083e-01 -2.69758612e-01 -4.14117724e-01
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-6.94384053e-02 -5.45874313e-02 2.98224062e-01 -3.17223877... | [11.117714881896973, -2.004115104675293] |
b089badb-02d0-4edb-a154-bf3e6f7ba953 | automated-ischemic-stroke-lesion-segmentation | 2209.09546 | null | https://arxiv.org/abs/2209.09546v2 | https://arxiv.org/pdf/2209.09546v2.pdf | Automated ischemic stroke lesion segmentation from 3D MRI | Ischemic Stroke Lesion Segmentation challenge (ISLES 2022) offers a platform for researchers to compare their solutions to 3D segmentation of ischemic stroke regions from 3D MRIs. In this work, we describe our solution to ISLES 2022 segmentation task. We re-sample all images to a common resolution, use two input MRI mo... | ['Andriy Myronenko', 'Daguang Xu', 'Yufan He', 'Dong Yang', 'Md Mahfuzur Rahman Siddique'] | 2022-09-20 | null | null | null | null | ['ischemic-stroke-lesion-segmentation'] | ['medical'] | [ 1.53806999e-01 2.18999326e-01 -2.92473465e-01 -5.33975303e-01
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-3.77724916e-01 8.55751753e-01 7.44179428e-01 1.33684233... | [14.282951354980469, -2.1158077716827393] |
b43ddd2d-b0cf-48fe-9d42-7db246849226 | differential-privacy-may-have-a-potential | 2306.17370 | null | https://arxiv.org/abs/2306.17370v1 | https://arxiv.org/pdf/2306.17370v1.pdf | Differential Privacy May Have a Potential Optimization Effect on Some Swarm Intelligence Algorithms besides Privacy-preserving | Differential privacy (DP), as a promising privacy-preserving model, has attracted great interest from researchers in recent years. Currently, the study on combination of machine learning and DP is vibrant. In contrast, another widely used artificial intelligence technique, the swarm intelligence (SI) algorithm, has rec... | ['Meiyi Xie', 'Hong Zhu', 'Zhiqiang Zhang'] | 2023-06-30 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 9.07507837e-02 -1.73250288e-01 -2.41437882e-01 1.09388418e-01
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-3.47678512e-01 -9.90969598e-01 -2.06355721e-01 -1.32566464e+00
1.14264395e-02 -7.12136179e-02 -3.22913527e-02 -2.36108437... | [5.864260196685791, 6.533927917480469] |
9b62a645-0923-4602-86e5-9658d235782b | unified-functional-hashing-in-automatic | 2302.05433 | null | https://arxiv.org/abs/2302.05433v1 | https://arxiv.org/pdf/2302.05433v1.pdf | Unified Functional Hashing in Automatic Machine Learning | The field of Automatic Machine Learning (AutoML) has recently attained impressive results, including the discovery of state-of-the-art machine learning solutions, such as neural image classifiers. This is often done by applying an evolutionary search method, which samples multiple candidate solutions from a large space... | ['Esteban Real', 'Quoc V. Le', 'David R. So', 'Chen Liang', 'Jonathan Dungay', 'Connal de Souza', 'Michael Munn', 'Yingjie Miao', 'Stephen Jonany', 'Ryan Gillard'] | 2023-02-10 | null | null | null | null | ['automl'] | ['methodology'] | [-4.60124202e-02 -2.34557912e-01 -2.33471453e-01 -1.73404545e-01
-8.92153740e-01 -6.82119071e-01 3.33754063e-01 6.11004114e-01
-6.84675574e-01 5.37886977e-01 -2.32583001e-01 -3.88206899e-01
1.36576191e-01 -7.45366514e-01 -1.05548310e+00 -7.39682257e-01
-2.20644310e-01 5.99161685e-01 3.92983526e-01 5.27995788... | [8.552741050720215, 3.3786227703094482] |
ba9dbb95-25f6-4051-9ef6-592a0f93eab3 | intrusion-detection-with-segmented-federated | null | null | https://ieeexplore.ieee.org/document/9207094 | https://ieeexplore.ieee.org/document/9207094 | Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs | Traditional approaches to cybersecurity issues usually protect users from attacks after the occurrence of specific types of attacks. Besides, patterns of recent cyberattacks tend to be changeable, which add up to unpredictability of them. On the other hand, machine learning, as a new method used to detect intrusion, is... | ['Hiroshi Esaki', 'Hideya Ochiai', 'Yuwei Sun'] | 2020-09-28 | null | null | null | international-joint-conference-on-neural | ['network-intrusion-detection'] | ['miscellaneous'] | [-4.36586171e-01 -4.28627670e-01 -2.88389444e-01 -3.91111821e-01
-2.20353380e-01 -6.25232756e-01 2.47183055e-01 3.93434346e-01
-5.20514011e-01 6.30965889e-01 -3.75408620e-01 -5.31104624e-01
-3.40167493e-01 -1.19563997e+00 -4.45656657e-01 -5.78589678e-01
-2.35596791e-01 2.80247837e-01 6.43748522e-01 -1.81179881... | [5.288843154907227, 7.178753852844238] |
db885865-37a1-44e2-9fca-2aaab2330a25 | deformable-kernel-networks-for-joint-image | 1910.08373 | null | https://arxiv.org/abs/1910.08373v3 | https://arxiv.org/pdf/1910.08373v3.pdf | Deformable Kernel Networks for Joint Image Filtering | Joint image filters are used to transfer structural details from a guidance picture used as a prior to a target image, in tasks such as enhancing spatial resolution and suppressing noise. Previous methods based on convolutional neural networks (CNNs) combine nonlinear activations of spatially-invariant kernels to estim... | ['Bumsub Ham', 'Jean Ponce', 'Beomjun Kim'] | 2019-10-17 | null | null | null | null | ['depth-map-super-resolution'] | ['computer-vision'] | [ 4.98344809e-01 -1.05012648e-01 -1.67526212e-02 -5.49505293e-01
-7.70792544e-01 -5.38364127e-02 4.27462310e-01 -2.15418458e-01
-6.70135856e-01 6.55498207e-01 6.31394684e-01 1.68172151e-01
-6.95939362e-02 -7.43592262e-01 -1.01419008e+00 -6.23016775e-01
1.81012526e-01 -2.63057679e-01 7.17464328e-01 4.49653938... | [10.895106315612793, -1.4665940999984741] |
ee45676d-0416-4e8b-a00d-555e14f5a57e | a-continuum-of-generation-tasks-for | 2210.10817 | null | https://arxiv.org/abs/2210.10817v1 | https://arxiv.org/pdf/2210.10817v1.pdf | A Continuum of Generation Tasks for Investigating Length Bias and Degenerate Repetition | Language models suffer from various degenerate behaviors. These differ between tasks: machine translation (MT) exhibits length bias, while tasks like story generation exhibit excessive repetition. Recent work has attributed the difference to task constrainedness, but evidence for this claim has always involved many con... | ['David Chiang', 'Darcey Riley'] | 2022-10-19 | null | null | null | null | ['story-generation'] | ['natural-language-processing'] | [ 2.41415009e-01 2.07540423e-01 -3.65291268e-01 -1.36816740e-01
-7.27841198e-01 -8.59887660e-01 1.00775361e+00 -9.36884061e-03
-4.79590058e-01 9.16814089e-01 7.16149628e-01 -4.63912785e-01
5.68786077e-02 -3.47679138e-01 -7.12460637e-01 -6.94583118e-01
4.76717144e-01 4.15055782e-01 2.46763721e-01 -1.02445558... | [11.4000244140625, 9.438535690307617] |
43db8815-8d98-47f0-82f5-f37c39b8d578 | survey-of-hallucination-in-natural-language | 2202.03629 | null | https://arxiv.org/abs/2202.03629v5 | https://arxiv.org/pdf/2202.03629v5.pdf | Survey of Hallucination in Natural Language Generation | Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstr... | ['Pascale Fung', 'Andrea Madotto', 'Wenliang Dai', 'Yejin Bang', 'Etsuko Ishii', 'Yan Xu', 'Dan Su', 'Tiezheng Yu', 'Rita Frieske', 'Nayeon Lee', 'Ziwei Ji'] | 2022-02-08 | null | null | null | null | ['generative-question-answering', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.45625365e-01 6.28689587e-01 2.06507832e-01 -6.11987114e-02
-1.16261303e+00 -5.15961051e-01 9.36835229e-01 1.56065404e-01
7.57559463e-02 1.09457278e+00 1.21141434e+00 -2.58871578e-02
3.92803669e-01 -6.18739426e-01 -1.99255928e-01 -3.52129668e-01
2.53092945e-01 6.47762418e-01 -4.22024608e-01 -6.71798468... | [12.072305679321289, 9.127997398376465] |
726b0eba-7832-4241-80c6-88f3aa807bfe | deep-set-conditioned-latent-representations-1 | 2212.11030 | null | https://arxiv.org/abs/2212.11030v1 | https://arxiv.org/pdf/2212.11030v1.pdf | Deep set conditioned latent representations for action recognition | In recent years multi-label, multi-class video action recognition has gained significant popularity. While reasoning over temporally connected atomic actions is mundane for intelligent species, standard artificial neural networks (ANN) still struggle to classify them. In the real world, atomic actions often temporally ... | ['Steven Latre', 'Jose Oramas', 'Peter Hellinckx', 'Kevin Mets', 'Tom De Schepper', 'Akash Singh'] | 2022-12-21 | deep-set-conditioned-latent-representations | https://www.scitepress.org/Link.aspx?doi=10.5220/0010838400003124 | https://orbi.uliege.be/bitstream/2268/290532/1/SinghAl-deepSetConditionedRepresentations_VISAPP_2022.pdf | international-joint-conference-on-computer-1 | ['composite-action-recognition', 'atomic-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 7.51717091e-01 1.33080613e-02 -4.77636576e-01 -4.07380998e-01
-4.38898265e-01 -5.32141268e-01 1.02838755e+00 -2.86605638e-02
-1.33306086e-01 3.87802124e-01 5.34203827e-01 -2.26361817e-03
-2.17979133e-01 -5.50488710e-01 -7.15475857e-01 -7.46228874e-01
-2.96571761e-01 6.23941720e-01 2.10191533e-01 2.16499716... | [8.534077644348145, 0.7215256690979004] |
874f68f7-088d-4870-be99-581f493ee14b | deep-transfer-reinforcement-learning-for-text | 1810.06667 | null | http://arxiv.org/abs/1810.06667v2 | http://arxiv.org/pdf/1810.06667v2.pdf | Deep Transfer Reinforcement Learning for Text Summarization | Deep neural networks are data hungry models and thus face difficulties when
attempting to train on small text datasets. Transfer learning is a potential
solution but their effectiveness in the text domain is not as explored as in
areas such as image analysis. In this paper, we study the problem of transfer
learning for... | ['Naren Ramakrishnan', 'Chandan K. Reddy', 'Yaser Keneshloo'] | 2018-10-15 | null | null | null | null | ['transfer-reinforcement-learning'] | ['methodology'] | [ 2.80511737e-01 1.93704665e-01 -2.30251908e-01 -4.03822243e-01
-9.68269408e-01 -2.44691238e-01 5.23880363e-01 2.73967505e-01
-5.37422597e-01 1.00635302e+00 3.95967185e-01 -1.97681174e-01
6.74367100e-02 -5.85570574e-01 -8.11441958e-01 -3.73697758e-01
1.41155422e-01 7.24875689e-01 2.52027065e-01 -5.34921646... | [12.297266006469727, 9.383249282836914] |
120e23a2-261a-4699-ab2c-0a717b6a3e33 | weighted-model-estimation-for-offline-model | null | null | http://proceedings.neurips.cc/paper/2021/hash/949694a5059302e7283073b502f094d7-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/949694a5059302e7283073b502f094d7-Paper.pdf | Weighted model estimation for offline model-based reinforcement learning | This paper discusses model estimation in offline model-based reinforcement learning (MBRL), which is important for subsequent policy improvement using an estimated model. From the viewpoint of covariate shift, a natural idea is model estimation weighted by the ratio of the state-action distributions of offline data and... | ['Kei Senda', 'Toru Hishinuma'] | 2021-12-01 | null | https://openreview.net/forum?id=zdC5eXljMPy | https://openreview.net/pdf?id=zdC5eXljMPy | neurips-2021-12 | ['density-ratio-estimation'] | ['methodology'] | [-1.11102872e-01 2.15899125e-01 -7.01671660e-01 -1.02924883e-01
-6.77784860e-01 -1.26075596e-01 3.99436831e-01 4.39593226e-01
-9.45647418e-01 1.22435403e+00 -1.33691907e-01 -5.81531882e-01
-4.86286491e-01 -6.13872647e-01 -8.05599928e-01 -7.88404167e-01
-1.75137743e-01 4.59416240e-01 -1.40410298e-02 -2.82018837... | [4.246837615966797, 2.471400737762451] |
09f8b157-c1c2-4853-9ed8-183363f7bda5 | next-step-conditioned-deep-convolutional | 1702.03865 | null | http://arxiv.org/abs/1702.03865v1 | http://arxiv.org/pdf/1702.03865v1.pdf | Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction | Recently developed deep learning techniques have significantly improved the
accuracy of various speech and image recognition systems. In this paper we show
how to adapt some of these techniques to create a novel chained convolutional
architecture with next-step conditioning for improving performance on protein
sequence... | ['Navdeep Jaitly', 'Akosua Busia'] | 2017-02-13 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 4.99853492e-01 -3.74091044e-02 -7.82189593e-02 -5.18128157e-01
-1.05511355e+00 -5.27875602e-01 4.43032414e-01 -5.46502694e-02
-7.97352374e-01 7.78918803e-01 1.67076383e-02 -8.42535377e-01
3.13523173e-01 -1.64570242e-01 -9.41376448e-01 -8.41533244e-01
-1.34002626e-01 3.94343734e-01 2.00240374e-01 -3.36951911... | [4.710888862609863, 5.659704685211182] |
a3ad7018-06c4-4dd1-a50e-2ff7ee541307 | navigating-explanatory-multiverse-through | 2306.02786 | null | https://arxiv.org/abs/2306.02786v1 | https://arxiv.org/pdf/2306.02786v1.pdf | Navigating Explanatory Multiverse Through Counterfactual Path Geometry | Counterfactual explanations are the de facto standard when tasked with interpreting decisions of (opaque) predictive models. Their generation is often subject to algorithmic and domain-specific constraints -- such as density-based feasibility for the former and attribute (im)mutability or directionality of change for t... | ['Yueqing Xuan', 'Edward Small', 'Kacper Sokol'] | 2023-06-05 | null | null | null | null | ['navigate'] | ['reasoning'] | [ 2.15921924e-01 4.76880938e-01 -4.64538515e-01 -2.68522710e-01
-2.19733685e-01 -8.04073513e-01 1.06194592e+00 3.07943225e-01
-3.36013407e-01 1.08260429e+00 3.76445204e-01 -1.16858888e+00
-9.27164435e-01 -8.54295433e-01 -3.93603176e-01 -5.49483955e-01
-4.42233056e-01 6.30711317e-01 -1.59267947e-01 -1.42281473... | [8.644810676574707, 5.584219932556152] |
518b4a28-74fc-49f7-9da3-b38bd95f6968 | mcl-iitk-at-semeval-2021-task-2-multilingual | 2104.01567 | null | https://arxiv.org/abs/2104.01567v1 | https://arxiv.org/pdf/2104.01567v1.pdf | MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers | In this work, we present our approach for solving the SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). The task is a sentence pair classification problem where the goal is to detect whether a given word common to both the sentences evokes the same meaning. We submit systems ... | ['Ashutosh Modi', 'Deepak Mahajan', 'Jay Mundra', 'Rohan Gupta'] | 2021-04-04 | null | https://aclanthology.org/2021.semeval-1.62 | https://aclanthology.org/2021.semeval-1.62.pdf | semeval-2021 | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 1.62156954e-01 -1.10100217e-01 1.57954305e-01 -3.63231868e-01
-1.29087043e+00 -8.71270716e-01 8.86429131e-01 1.96254551e-01
-8.48219156e-01 9.78666127e-01 2.21124485e-01 -8.32309067e-01
8.20695758e-02 -3.80647451e-01 -6.53030872e-01 -3.09176058e-01
4.23191972e-02 6.40943527e-01 9.41045210e-02 -6.90310359... | [10.927149772644043, 9.983525276184082] |
86dbdf5b-f3c9-4f4b-b677-edcb8fc902bf | ems-net-efficient-multi-temporal-self | 2303.13753 | null | https://arxiv.org/abs/2303.13753v1 | https://arxiv.org/pdf/2303.13753v1.pdf | EMS-Net: Efficient Multi-Temporal Self-Attention For Hyperspectral Change Detection | Hyperspectral change detection plays an essential role of monitoring the dynamic urban development and detecting precise fine object evolution and alteration. In this paper, we have proposed an original Efficient Multi-temporal Self-attention Network (EMS-Net) for hyperspectral change detection. The designed EMS module... | ['Bo Du', 'Chen Wu', 'Meiqi Hu'] | 2023-03-24 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 3.25087845e-01 -6.13697886e-01 2.39662960e-01 -1.10235766e-01
9.05922279e-02 -3.59687716e-01 3.92763406e-01 -8.80832747e-02
-1.87899217e-01 7.20520198e-01 1.30366623e-01 2.06112508e-02
-8.24649155e-01 -1.02384973e+00 -2.24219248e-01 -1.00245440e+00
-1.29873425e-01 -7.99027011e-02 2.02651158e-01 -4.17734057... | [9.822577476501465, -1.3733774423599243] |
420e76d2-cca4-4cc0-8bba-2548770dfe15 | dagrid-directed-accumulator-grid | 2306.02589 | null | https://arxiv.org/abs/2306.02589v1 | https://arxiv.org/pdf/2306.02589v1.pdf | DAGrid: Directed Accumulator Grid | Recent research highlights that the Directed Accumulator (DA), through its parametrization of geometric priors into neural networks, has notably improved the performance of medical image recognition, particularly with small and imbalanced datasets. However, DA's potential in pixel-wise dense predictions is unexplored. ... | ['Jiahao Li', 'Jinwei Zhang', 'Rongguang Wang', 'Xiang Chen', 'Renjiu Hu', 'Hang Zhang'] | 2023-06-05 | null | null | null | null | ['image-registration', 'skin-lesion-segmentation', 'lesion-segmentation'] | ['computer-vision', 'medical', 'medical'] | [ 2.93036193e-01 2.26503313e-01 -1.06100127e-01 -2.49875367e-01
-5.18268466e-01 -9.81822237e-02 1.82428688e-01 1.97626814e-01
-5.29742599e-01 6.09273672e-01 -1.80702597e-01 -1.96735650e-01
9.40160230e-02 -9.08391535e-01 -4.69995677e-01 -1.06055009e+00
-7.25577176e-02 2.11292133e-01 1.58180222e-01 2.34307304... | [14.522920608520508, -2.4902915954589844] |
2a42082e-904f-46c2-b7e6-83b545e4c338 | mlp-singer-towards-rapid-parallel-singing | null | null | https://arxiv.org/abs/2106.07886 | https://arxiv.org/pdf/2106.07886.pdf | MLP Singer: Towards Rapid Parallel Singing Voice Synthesis | Recent developments in deep learning have significantly improved the quality of synthesized singing voice audio. However, prominent neural singing voice synthesis systems suffer from slow inference speed due to their autoregressive design. Inspired by MLP-Mixer, a novel architecture introduced in the vision literature ... | ['Younggun Lee', 'Hyeongju Kim', 'Jaesung Tae'] | 2021-06-15 | null | null | null | arxiv-2021-6 | ['singing-voice-synthesis'] | ['speech'] | [-5.98624572e-02 3.88929769e-02 2.34426603e-01 2.04501271e-01
-1.02930319e+00 -3.62622291e-01 4.48320955e-01 -5.26316285e-01
-1.76844131e-02 5.26423395e-01 3.57147455e-01 -2.02499762e-01
3.72326493e-01 -4.66313571e-01 -7.39069343e-01 -7.70260692e-01
3.00906122e-01 3.42148304e-01 7.09394109e-04 -8.52947496... | [15.466376304626465, 6.138849258422852] |
e897c417-e987-4ffc-b864-33ccb3b66a77 | federated-variational-inference-towards | 2305.13672 | null | https://arxiv.org/abs/2305.13672v2 | https://arxiv.org/pdf/2305.13672v2.pdf | Federated Variational Inference: Towards Improved Personalization and Generalization | Conventional federated learning algorithms train a single global model by leveraging all participating clients' data. However, due to heterogeneity in client generative distributions and predictive models, these approaches may not appropriately approximate the predictive process, converge to an optimal state, or genera... | ['Warren Richard Morningstar', 'Arash Afkanpour', 'Karan Singhal', 'Philip Andrew Mansfield', 'Joshua V. Dillon', 'Elahe Vedadi'] | 2023-05-23 | null | null | null | null | ['bayesian-inference', 'generalization-bounds'] | ['methodology', 'methodology'] | [-3.62430394e-01 5.84322810e-02 -4.79141057e-01 -7.02930093e-01
-1.33512616e+00 -6.66655898e-01 7.79703915e-01 -5.92960775e-01
-9.19091702e-02 8.06889713e-01 1.28638208e-01 -2.90970981e-01
-3.47671896e-01 -3.68506372e-01 -1.03231633e+00 -7.85290718e-01
-7.94417039e-02 1.19527447e+00 -1.20490417e-01 7.21525490... | [5.838552474975586, 6.279998302459717] |
e27ffd3e-d2ef-4d59-9829-b2ed0efc90fb | image-augmentation-improves-few-shot | 2208.12613 | null | https://arxiv.org/abs/2208.12613v1 | https://arxiv.org/pdf/2208.12613v1.pdf | Image augmentation improves few-shot classification performance in plant disease recognition | With the world population projected to near 10 billion by 2050, minimizing crop damage and guaranteeing food security has never been more important. Machine learning has been proposed as a solution to quickly and efficiently identify diseases in crops. Convolutional Neural Networks typically require large datasets of a... | ['Frank Xiao'] | 2022-08-25 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [ 4.53342468e-01 5.86658120e-02 -2.52009183e-01 -9.36019495e-02
-2.76148766e-01 -9.43046212e-01 4.71290261e-01 6.00609541e-01
-3.49468708e-01 5.44020176e-01 -1.30591229e-01 -6.01712644e-01
2.68798351e-01 -9.40901756e-01 -6.77616298e-01 -4.12717223e-01
7.81482384e-02 2.08176166e-01 7.77589753e-02 -3.78496975... | [9.130762100219727, -1.549551010131836] |
29c912a5-342b-451c-889a-7840ec1f85af | numglue-a-suite-of-fundamental-yet | 2204.05660 | null | https://arxiv.org/abs/2204.05660v1 | https://arxiv.org/pdf/2204.05660v1.pdf | NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks | Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been developed to this end, state-of-the-art AI systems are brittle; failing to perform the underlying mathematical reasoning when they appear in... | ['Ashwin Kalyan', 'Chitta Baral', 'Peter Clark', 'Bhavdeep Sachdeva', 'Neeraj Varshney', 'Arindam Mitra', 'Swaroop Mishra'] | 2022-04-12 | null | https://aclanthology.org/2022.acl-long.246 | https://aclanthology.org/2022.acl-long.246.pdf | acl-2022-5 | ['mathematical-reasoning', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [-5.30892909e-02 1.63725287e-01 1.97807342e-01 -3.81854683e-01
-4.20032293e-01 -5.48282206e-01 8.72705877e-01 4.27276373e-01
-5.66107273e-01 4.34727579e-01 2.90734887e-01 -5.75112760e-01
-1.94156244e-01 -1.00995040e+00 -7.25171506e-01 -2.58728601e-02
-3.66674960e-02 8.31183851e-01 -2.93300506e-02 -7.90810347... | [9.54235553741455, 7.323666572570801] |
65086b76-36e4-48c5-85fb-dd88f2370536 | iotmalware-android-iot-malware-detection | 2102.13376 | null | https://arxiv.org/abs/2102.13376v2 | https://arxiv.org/pdf/2102.13376v2.pdf | Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain | The widespread significance of Android IoT devices is due to its flexibility and hardware support features which revolutionized the digital world by introducing exciting applications almost in all walks of daily life, such as healthcare, smart cities, smart environments, safety, remote sensing, and many more. Such vers... | ['Waqar Ali', 'Ting Yang', 'Zakria', 'Jay Kumar', 'Wenyong Wang', 'Rajesh Kumar'] | 2021-02-26 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [-4.72819284e-02 -3.37712735e-01 -6.52065039e-01 6.21227324e-02
-2.65266865e-01 -7.14670420e-01 1.02059209e+00 -2.22401381e-01
-1.50548369e-01 5.81383407e-01 -6.61922321e-02 -8.56441498e-01
3.15402895e-02 -7.73454726e-01 -6.61003649e-01 -8.25271845e-01
-7.70275146e-02 4.20471251e-01 4.63470131e-01 -1.06393613... | [14.423099517822266, 9.681137084960938] |
661cfcf7-075e-439d-a3e7-d35946698c2a | superyolo-super-resolution-assisted-object | 2209.13351 | null | https://arxiv.org/abs/2209.13351v2 | https://arxiv.org/pdf/2209.13351v2.pdf | SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery | Accurately and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remains challenging. Most of the existing solutions primarily design complex deep neural networks to learn strong feature representations for objects separated from the background, which often results i... | ['Qian Du', 'Yunsong Li', 'Zhenman Fang', 'Weiying Xie', 'Jie Lei', 'Jiaqing Zhang'] | 2022-09-27 | null | null | null | null | ['real-time-object-detection', 'small-object-detection'] | ['computer-vision', 'computer-vision'] | [ 3.07693183e-01 -3.85644168e-01 1.23917192e-01 -7.86288157e-02
-7.63666332e-01 -1.50676131e-01 1.27857149e-01 -2.89842904e-01
-3.69930476e-01 6.62814319e-01 -4.17377830e-01 -1.40579790e-01
-2.34100148e-01 -1.15008223e+00 -5.84045053e-01 -1.13391066e+00
1.27306744e-01 -4.66313101e-02 4.67399001e-01 -8.82152244... | [9.18464183807373, -0.9795426726341248] |
c63e16a0-ab24-44e8-94e0-65e362b63597 | autonomous-capability-assessment-of-black-box | 2306.04806 | null | https://arxiv.org/abs/2306.04806v1 | https://arxiv.org/pdf/2306.04806v1.pdf | Autonomous Capability Assessment of Black-Box Sequential Decision-Making Systems | It is essential for users to understand what their AI systems can and can't do in order to use them safely. However, the problem of enabling users to assess AI systems with evolving sequential decision making (SDM) capabilities is relatively understudied. This paper presents a new approach for modeling the capabilities... | ['Siddharth Srivastava', 'Rushang Karia', 'Pulkit Verma'] | 2023-06-07 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 8.17400739e-02 3.60509306e-01 -1.77679896e-01 -3.38658571e-01
-3.94661695e-01 -6.77765727e-01 9.32216167e-01 2.08924294e-01
-3.81239176e-01 6.16584182e-01 -1.01688623e-01 -4.61974144e-01
-5.56068599e-01 -6.04833961e-01 -2.55587786e-01 -7.21336722e-01
-6.42972827e-01 1.28296351e+00 2.66594797e-01 -1.80929080... | [4.3485026359558105, 2.158092737197876] |
da606d92-1a1f-4d67-8e93-9d2489d8bf6e | unsupervised-multi-object-segmentation-by | 2210.12148 | null | https://arxiv.org/abs/2210.12148v1 | https://arxiv.org/pdf/2210.12148v1.pdf | Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns | We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision. While prior works have considered motion for segmentation, a key insight is that, while motion can be used to identify objects, not all obje... | ['Andrea Vedaldi', 'Christian Rupprecht', 'Iro Laina', 'Subhabrata Choudhury', 'Laurynas Karazija'] | 2022-10-21 | null | null | null | null | ['unsupervised-object-segmentation'] | ['computer-vision'] | [ 3.82829607e-01 4.76363488e-02 -3.96452606e-01 -3.24894220e-01
-5.58086336e-01 -9.40068364e-01 4.37519461e-01 -3.51081461e-01
-6.27108455e-01 5.47386646e-01 -1.50731236e-01 -3.11135978e-01
-5.08755594e-02 -6.07180297e-01 -1.11503255e+00 -9.68197465e-01
-7.44770393e-02 7.80606806e-01 7.23392427e-01 1.19972184... | [9.00213623046875, -0.4278172552585602] |
f1015411-d0d3-4f2c-895f-f7cee3728321 | vicunaner-zero-few-shot-named-entity | 2305.03253 | null | https://arxiv.org/abs/2305.03253v1 | https://arxiv.org/pdf/2305.03253v1.pdf | VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna | Large Language Models (LLMs, e.g., ChatGPT) have shown impressive zero- and few-shot capabilities in Named Entity Recognition (NER). However, these models can only be accessed via online APIs, which may cause data leak and non-reproducible problems. In this paper, we propose VicunaNER, a zero/few-shot NER framework bas... | ['Bin Ji'] | 2023-05-05 | null | null | null | null | ['few-shot-ner', 'named-entity-recognition-ner'] | ['natural-language-processing', 'natural-language-processing'] | [-3.91940773e-01 2.05442861e-01 1.45851495e-02 -2.06021249e-01
-1.18451095e+00 -7.54223824e-01 6.26174867e-01 -1.66964196e-02
-7.94985533e-01 6.43161535e-01 3.54875296e-01 -2.10559145e-01
1.90969422e-01 -6.86220288e-01 -4.26446497e-01 -2.18591988e-01
2.71862775e-01 6.44525528e-01 5.15784979e-01 -3.36243689... | [9.664621353149414, 9.376605987548828] |
9e16d8a8-1a9c-45ca-a9f6-8a306a25a093 | relation-graph-network-for-3d-object | 1912.00202 | null | https://arxiv.org/abs/1912.00202v1 | https://arxiv.org/pdf/1912.00202v1.pdf | Relation Graph Network for 3D Object Detection in Point Clouds | Convolutional Neural Networks (CNNs) have emerged as a powerful strategy for most object detection tasks on 2D images. However, their power has not been fully realised for detecting 3D objects in point clouds directly without converting them to regular grids. Existing state-of-art 3D object detection methods aim to rec... | ['Ajmal Mian', 'Syed Zulqarnain Gilani', 'Mingtao Feng', 'Yaonan Wang', 'Liang Zhang'] | 2019-11-30 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [-1.37676641e-01 1.06950186e-01 -1.07328542e-01 -4.75418687e-01
-3.32791239e-01 -3.18652272e-01 8.49401891e-01 2.06896365e-01
-2.75549620e-01 -2.57478595e-01 -3.91668707e-01 -3.22027534e-01
5.25620580e-02 -7.90901899e-01 -1.04152060e+00 -3.72172982e-01
-3.08077931e-01 8.44981968e-01 7.44109929e-01 -1.01527229... | [7.735595226287842, -2.8771121501922607] |
027d4605-3a74-44ee-aff4-5efd154bf89f | earthnet2021-a-novel-large-scale-dataset-and | 2012.06246 | null | https://arxiv.org/abs/2012.06246v1 | https://arxiv.org/pdf/2012.06246v1.pdf | EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts | Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation, modelling impacts to < 100 m is needed. Yet, the relationship between driving variable... | ['Markus Reichstein', 'Jakob Runge', 'Joachim Denzler', 'Vitus Benson', 'Christian Requena-Mesa'] | 2020-12-11 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction', 'earth-surface-forecasting'] | ['computer-vision', 'miscellaneous', 'time-series'] | [ 2.60898113e-01 -3.18323761e-01 -1.19748555e-01 -2.44859144e-01
-2.83001155e-01 -8.84478211e-01 8.84035766e-01 2.53685564e-01
-2.51718640e-01 8.01232576e-01 2.50444412e-01 -7.74223268e-01
2.12794971e-02 -1.33782125e+00 -7.45887220e-01 -6.58923984e-01
-7.12253034e-01 9.22685582e-03 1.15948394e-01 -6.49131536... | [9.523691177368164, -1.5625641345977783] |
7cf82dfb-2789-4036-86b4-aaba8f399d3d | all-information-is-necessary-integrating | 2304.13439 | null | https://arxiv.org/abs/2304.13439v1 | https://arxiv.org/pdf/2304.13439v1.pdf | All Information is Necessary: Integrating Speech Positive and Negative Information by Contrastive Learning for Speech Enhancement | Monaural speech enhancement (SE) is an ill-posed problem due to the irreversible degradation process. Recent methods to achieve SE tasks rely solely on positive information, e.g., ground-truth speech and speech-relevant features. Different from the above, we observe that the negative information, such as original speec... | ['Yuhong Yang', 'Chang Han', 'Weiping tu', 'Xinmeng Xu'] | 2023-04-26 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.83144236e-01 -9.68073756e-02 2.78901666e-01 -3.47531855e-01
-9.94860530e-01 1.87194981e-02 3.71747702e-01 -3.78607124e-01
-4.14827436e-01 5.57009101e-01 6.03636622e-01 2.15087086e-02
-1.76612943e-01 -3.48597437e-01 -7.36414731e-01 -9.24228311e-01
1.12188168e-01 -4.09790397e-01 2.83295274e-01 -5.53659499... | [14.810855865478516, 5.935832977294922] |
08e5c278-6f83-4718-8c91-174643f24d54 | location-free-spectrum-cartography | 1812.11539 | null | https://arxiv.org/abs/1812.11539v2 | https://arxiv.org/pdf/1812.11539v2.pdf | Location-free Spectrum Cartography | Spectrum cartography constructs maps of metrics such as channel gain or received signal power across a geographic area of interest using spatially distributed sensor measurements. Applications of these maps include network planning, interference coordination, power control, localization, and cognitive radios to name a ... | ['Baltasar Beferull-Lozano', 'Luis Miguel Lopez Ramos', 'Daniel Romero', 'Yves Teganya'] | 2018-12-30 | null | null | null | null | ['spectrum-cartography'] | ['computer-vision'] | [ 5.28831542e-01 -5.47663420e-02 -1.42436504e-01 -8.32730308e-02
-6.32696807e-01 -5.78434706e-01 3.90648276e-01 1.82588294e-01
-3.09092760e-01 1.18896699e+00 2.51925200e-01 -4.92736876e-01
-8.21668506e-01 -9.53766763e-01 -1.47545680e-01 -7.31256843e-01
-6.09537065e-01 -2.85866950e-02 -9.10626724e-02 -2.47773398... | [6.309925556182861, 1.158268928527832] |
735324cf-1001-4982-9136-98695ca2aabb | dynamic-graph-modules-for-modeling-higher | 1812.05637 | null | https://arxiv.org/abs/1812.05637v3 | https://arxiv.org/pdf/1812.05637v3.pdf | Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition | Video action recognition, a critical problem in video understanding, has been gaining increasing attention. To identify actions induced by complex object-object interactions, we need to consider not only spatial relations among objects in a single frame, but also temporal relations among different or the same objects a... | ['Wei zhang', 'Chenliang Xu', 'Luowei Zhou', 'Hao Huang', 'Jason J. Corso'] | 2018-12-13 | null | null | null | null | ['3d-human-action-recognition'] | ['computer-vision'] | [ 2.59858966e-01 -3.10070395e-01 -3.14891458e-01 -1.30391568e-01
2.69425251e-02 -4.53940660e-01 6.99020565e-01 3.68713528e-01
-1.52897209e-01 3.98053318e-01 4.02842194e-01 1.60987288e-01
-3.63468647e-01 -5.85040390e-01 -7.95280099e-01 -7.48841763e-01
-4.98956352e-01 8.74839649e-02 8.70254755e-01 2.78926790... | [8.488816261291504, 0.6490561366081238] |
9cbec039-dc54-46cd-aef9-ef3056086806 | pseudo-label-correction-and-learning-for-semi | 2303.02998 | null | https://arxiv.org/abs/2303.02998v1 | https://arxiv.org/pdf/2303.02998v1.pdf | Pseudo-label Correction and Learning For Semi-Supervised Object Detection | Pseudo-Labeling has emerged as a simple yet effective technique for semi-supervised object detection (SSOD). However, the inevitable noise problem in pseudo-labels significantly degrades the performance of SSOD methods. Recent advances effectively alleviate the classification noise in SSOD, while the localization noise... | ['Yulan Guo', 'Zhengfa Liang', 'Yusong Tan', 'Ke Liang', 'Wei Chen', 'Yulin He'] | 2023-03-06 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 1.45277530e-01 -1.06592335e-01 1.96612123e-02 -4.59372103e-01
-1.17161191e+00 -4.80899483e-01 4.70563203e-01 3.14813405e-01
-8.32278788e-01 8.67875278e-01 -2.57149011e-01 8.04372281e-02
1.64480671e-01 -4.59457040e-01 -8.43388438e-01 -9.89092350e-01
6.52672470e-01 3.31353486e-01 7.76843786e-01 2.13750958... | [9.153697967529297, 1.2654545307159424] |
3a694aa4-56a4-40fe-94c3-6db4ab428292 | expr-at-semeval-2018-task-9-a-combined | null | null | https://aclanthology.org/S18-1150 | https://aclanthology.org/S18-1150.pdf | EXPR at SemEval-2018 Task 9: A Combined Approach for Hypernym Discovery | In this paper, we present our proposed system (EXPR) to participate in the hypernym discovery task of SemEval 2018. The task addresses the challenge of discovering hypernym relations from a text corpus. Our proposal is a combined approach of path-based technique and distributional technique. We use dependency parser on... | ["Nicolas B{\\'e}chet", 'Ahmad Issa Alaa Aldine', 'Mounira Harzallah', 'Giuseppe Berio', 'Ahmad Faour'] | 2018-06-01 | null | null | null | semeval-2018-6 | ['hypernym-discovery'] | ['natural-language-processing'] | [ 2.85162460e-02 5.63315809e-01 -3.09276432e-01 -2.71696359e-01
-2.18612626e-02 -5.86050928e-01 8.65962565e-01 6.85019016e-01
-8.27508450e-01 8.57585371e-01 3.16370368e-01 -4.59591269e-01
-4.49506015e-01 -1.25795603e+00 -2.54654318e-01 -5.55994101e-02
-1.64520800e-01 1.02707314e+00 2.82447547e-01 -6.79781497... | [9.816080093383789, 8.711825370788574] |
b5632080-a274-4476-85d1-faf03942995e | reducing-audio-membership-inference-attack | 1911.01888 | null | https://arxiv.org/abs/1911.01888v1 | https://arxiv.org/pdf/1911.01888v1.pdf | Reducing audio membership inference attack accuracy to chance: 4 defenses | It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope. In membership inference attacks, adversaries can determine whether a particular set of data was used in training, putting the privacy of the data at risk. Existing work has mostl... | ['Zigfried Hampel-Arias', 'Nina Lopatina', 'Felipe A. Mejia', 'Paul Gamble', 'Maria Alejandra Barrios', 'Michael Lomnitz', 'Lucas Tindall'] | 2019-10-31 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 6.49898112e-01 2.68402398e-01 4.94255796e-02 -1.41690969e-01
-8.80335510e-01 -1.44208157e+00 5.20205677e-01 -4.57046255e-02
-3.45515132e-01 4.77652192e-01 -1.42792553e-01 -8.12483370e-01
5.88405058e-02 -7.06244826e-01 -8.46209228e-01 -6.26494169e-01
-3.46066922e-01 -1.20643955e-02 -1.50814414e-01 2.43401974... | [5.804698944091797, 7.5753254890441895] |
c7e589c0-6409-4db1-89e8-4b6d148492c7 | interference-cancellation-based-channel | 2006.14508 | null | https://arxiv.org/abs/2006.14508v2 | https://arxiv.org/pdf/2006.14508v2.pdf | Interference Cancellation Based Channel Estimation for Massive MIMO Systems with Time Shifted Pilots | In massive multiple-input multiple-output (MIMO) systems with time shifted pilot (TSP) schemes, the inter-group interference caused by the pilot contamination can be eliminated when the number of base station (BS) antennas M approaches infinity. However, M is finite in practice and the effectiveness of the TSP is limit... | ['Jinglin Shi', 'Jinhong Yuan', 'Yiqing Zhou', 'Bule Sun'] | 2020-06-25 | null | null | null | null | ['2048'] | ['playing-games'] | [ 4.72655833e-01 5.57641387e-01 1.37186065e-01 7.10074306e-01
-4.87677604e-01 -2.28314519e-01 -8.39077011e-02 8.28066245e-02
-3.05977315e-01 1.24969578e+00 -1.77382380e-01 -8.50397885e-01
-3.03757399e-01 -4.93698448e-01 -4.82770085e-01 -1.39372492e+00
-6.58928216e-01 -3.81842822e-01 1.86133817e-01 -3.99496645... | [6.1847381591796875, 1.4240915775299072] |
8ea83600-a7ca-4fea-a2d4-5a3107bea16d | vibration-based-damage-detection-in-wind | 1804.00558 | null | http://arxiv.org/abs/1804.00558v1 | http://arxiv.org/pdf/1804.00558v1.pdf | Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification | Vibration-based Structural Health Monitoring (SHM) techniques are among the
most common approaches for structural damage identification. The presence of
damage in structures may be identified by monitoring the changes in dynamic
behavior subject to external loading, and is typically performed by using
experimental moda... | ['Christopher Niezrecki', 'Zhu Mao', 'Peyman Poozesh', 'Aral Sarrafi'] | 2018-03-30 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 1.68895945e-01 -6.09945297e-01 4.72795188e-01 5.22090256e-01
-5.97195148e-01 -6.27353370e-01 -1.40473396e-01 1.58834770e-01
-1.75571311e-02 3.43505442e-01 -7.97965750e-02 -7.80265480e-02
-4.39833492e-01 -7.23542511e-01 -2.18954027e-01 -1.04843211e+00
-3.70235771e-01 -8.54335502e-02 5.81938624e-01 -2.01432273... | [6.545657157897949, 2.4896340370178223] |
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