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
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d6efb8f1-a4e5-4b5d-bf19-e5859e7ef15e | deep-residual-learning-for-image-recognition | 1512.03385 | null | http://arxiv.org/abs/1512.03385v1 | http://arxiv.org/pdf/1512.03385v1.pdf | Deep Residual Learning for Image Recognition | Deeper neural networks are more difficult to train. We present a residual
learning framework to ease the training of networks that are substantially
deeper than those used previously. We explicitly reformulate the layers as
learning residual functions with reference to the layer inputs, instead of
learning unreferenced... | ['Xiangyu Zhang', 'Shaoqing Ren', 'Jian Sun', 'Kaiming He'] | 2015-12-10 | deep-residual-learning-for-image-recognition-1 | http://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf | cvpr-2016-6 | ['retinal-oct-disease-classification'] | ['computer-vision'] | [ 1.59325898e-01 2.49316648e-01 -4.39586341e-02 -3.71373057e-01
-5.43741703e-01 -5.44872046e-01 3.88906121e-01 -3.14377964e-01
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1.91881016e-01 -5.49961567e-01 -1.00516462e+00 -4.65056151e-01
-2.17815340e-01 2.01522186e-01 1.95303887e-01 -2.21798971... | [9.369797706604004, 1.7578859329223633] |
58fd8527-b58b-4662-9dec-631b1135a042 | the-effects-of-skin-lesion-segmentation-on | 2008.12602 | null | https://arxiv.org/abs/2008.12602v1 | https://arxiv.org/pdf/2008.12602v1.pdf | The Effects of Skin Lesion Segmentation on the Performance of Dermatoscopic Image Classification | Malignant melanoma (MM) is one of the deadliest types of skin cancer. Analysing dermatoscopic images plays an important role in the early detection of MM and other pigmented skin lesions. Among different computer-based methods, deep learning-based approaches and in particular convolutional neural networks have shown ex... | ['Isabella Ellinger', 'Georg Langs', 'Rupert Ecker', 'Philipp Tschandl', 'Amirreza Mahbod'] | 2020-08-28 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 6.29349709e-01 1.94471523e-01 8.85242224e-02 5.92462858e-03
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2.14537382e-01 5.84950894e-02 4.20306742e-01 1.64639935... | [15.551468849182129, -3.0700836181640625] |
830ea51c-f5fc-4fc1-a487-ed312d77c216 | gpr1200-a-benchmark-for-general-purpose | 2111.13122 | null | https://arxiv.org/abs/2111.13122v1 | https://arxiv.org/pdf/2111.13122v1.pdf | GPR1200: A Benchmark for General-Purpose Content-Based Image Retrieval | Even though it has extensively been shown that retrieval specific training of deep neural networks is beneficial for nearest neighbor image search quality, most of these models are trained and tested in the domain of landmarks images. However, some applications use images from various other domains and therefore need a... | ['Klaus Jung', 'Nico Hezel', 'Kai Uwe Barthel', 'Konstantin Schall'] | 2021-11-25 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 3.26034911e-02 -4.74955410e-01 -3.65034431e-01 -5.54484665e-01
-1.06770205e+00 -3.45875055e-01 6.97020471e-01 6.78808987e-02
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-5.31221867e-01 -8.38100553e-01 -5.04990339e-01 -5.49143374e-01
-9.16347280e-02 5.78070283e-01 3.19732845e-01 -4.06066269... | [10.655444145202637, 0.6212829947471619] |
4cd0c174-7310-44aa-b185-4ca16eceefc5 | syntax-aware-multi-task-graph-convolutional | null | null | https://aclanthology.org/D19-6204 | https://aclanthology.org/D19-6204.pdf | Syntax-aware Multi-task Graph Convolutional Networks for Biomedical Relation Extraction | In this paper we tackle two unique challenges in biomedical relation extraction. The first challenge is that the contextual information between two entity mentions often involves sophisticated syntactic structures. We propose a novel graph convolutional networks model that incorporates dependency parsing and contextual... | ['Heng Ji', 'Diya Li'] | 2019-11-01 | null | null | null | ws-2019-11 | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 4.17940110e-01 5.08443415e-01 -6.72266185e-01 -5.36846280e-01
-9.65327740e-01 -3.02685529e-01 3.55403483e-01 6.81897283e-01
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-1.32900938e-01 -6.98643506e-01 -8.05117965e-01 -5.28836548e-01
-1.69195458e-01 5.13463676e-01 2.50225812e-02 -1.94855750... | [8.803067207336426, 8.794048309326172] |
08770932-74bf-4631-a9c7-0e06145bc7ff | reconstruction-of-the-external-stimuli-from | 1711.06550 | null | http://arxiv.org/abs/1711.06550v1 | http://arxiv.org/pdf/1711.06550v1.pdf | Reconstruction of the External Stimuli from Brain Signals | Despite the rapid advances in Brain-computer Interfacing (BCI) and continuous
effort to improve the accuracy of brain decoding systems, the urge for the
systems to reconstruct the experiences of the users has been widely
acknowledged. This urge has been investigated by some researchers during the
past years in terms of... | ['Pouya Ghaemmaghami'] | 2017-11-14 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 1.47937521e-01 -4.39139120e-02 6.96066022e-01 -3.22353512e-01
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-2.78916359e-01 -2.73598820e-01 -3.52027901e-02 -1.51299551... | [13.186948776245117, 3.3296055793762207] |
40915926-ad40-40c9-9073-01d03067b4a6 | neural-symbolic-regression-using-control | 2306.04718 | null | https://arxiv.org/abs/2306.04718v1 | https://arxiv.org/pdf/2306.04718v1.pdf | Neural Symbolic Regression using Control Variables | Symbolic regression (SR) is a powerful technique for discovering the analytical mathematical expression from data, finding various applications in natural sciences due to its good interpretability of results. However, existing methods face scalability issues when dealing with complex equations involving multiple variab... | ['Huajie Shao', 'Minghan Chen', 'Hairong Qi', 'Enze Xu', 'Hongjue Zhao', 'Xieting Chu'] | 2023-06-07 | null | null | null | null | ['symbolic-regression'] | ['knowledge-base'] | [ 4.43980843e-01 -2.79724717e-01 -4.38336790e-01 -4.67108488e-01
-8.40422928e-01 -3.70555103e-01 2.73102760e-01 -1.26336366e-01
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-6.40260875e-02 -8.65004957e-01 -8.87247264e-01 -7.31354773e-01
2.55901247e-01 3.15021425e-01 -1.31391212e-01 -2.54679888... | [8.519248962402344, 6.860581874847412] |
46797de5-ac87-4712-b4a3-2401f1d0fc9d | ernie-music-text-to-waveform-music-generation | 2302.04456 | null | https://arxiv.org/abs/2302.04456v1 | https://arxiv.org/pdf/2302.04456v1.pdf | ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models | In recent years, there has been an increased popularity in image and speech generation using diffusion models. However, directly generating music waveforms from free-form text prompts is still under-explored. In this paper, we propose the first text-to-waveform music generation model that can receive arbitrary texts us... | ['Hua Wu', 'Hao Tian', 'Yu Sun', 'Yekun Chai', 'Shuohuan Wang', 'Chao Pang', 'Pengfei Zhu'] | 2023-02-09 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 3.36147636e-01 -2.92744428e-01 -1.32645756e-01 -9.71463174e-02
-1.02515185e+00 -7.12912381e-01 8.56185913e-01 -4.00700361e-01
1.07802665e-02 4.03112233e-01 4.59456414e-01 -2.43845403e-01
-3.40442091e-01 -6.00515425e-01 -5.65654993e-01 -6.64242387e-01
2.48058155e-01 4.34673935e-01 9.26253498e-02 -2.32487693... | [15.539417266845703, 5.74280309677124] |
a259e58f-9f2a-4f57-b343-da36cf17d177 | extracting-dynamical-models-from-data | 2110.06917 | null | https://arxiv.org/abs/2110.06917v5 | https://arxiv.org/pdf/2110.06917v5.pdf | Extracting Dynamical Models from Data | The problem of determining the underlying dynamics of a system when only given data of its state over time has challenged scientists for decades. In this paper, the approach of using machine learning to model the {\em updates} of the phase space variables is introduced; this is done as a function of the phase space var... | ['Michael F. Zimmer'] | 2021-10-13 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.36498371e-01 6.47431910e-02 6.61622435e-02 3.29107434e-01
-4.55909878e-01 -5.92674434e-01 6.54097736e-01 1.96817163e-02
-4.81035709e-01 1.11354256e+00 -6.74700916e-01 -2.49957159e-01
-3.10554177e-01 -6.23423755e-01 -5.88498712e-01 -1.25899959e+00
-2.69428670e-01 3.28857630e-01 -1.38085365e-01 -5.19124031... | [6.469962120056152, 3.469964027404785] |
c6ecae4e-a85d-426c-a984-90838b6eff3f | an-lstm-model-for-twitter-sentiment-analysis | 2212.01791 | null | https://arxiv.org/abs/2212.01791v1 | https://arxiv.org/pdf/2212.01791v1.pdf | An LSTM model for Twitter Sentiment Analysis | Sentiment analysis on social media such as Twitter provides organizations and individuals an effective way to monitor public emotions towards them and their competitors. As a result, sentiment analysis has become an important and challenging task. In this work, we have collected seven publicly available and manually an... | ['Md Parvez Mollah'] | 2022-12-04 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-1.36222467e-01 -3.50695133e-01 -5.37416518e-01 -9.26132381e-01
-3.67239416e-01 -4.01123464e-01 6.75631523e-01 5.33579826e-01
-6.67836905e-01 7.70511746e-01 3.34227771e-01 -9.28688571e-02
7.11497307e-01 -8.64041209e-01 -2.45793253e-01 -2.48153076e-01
3.08053583e-01 8.87494013e-02 -1.71066433e-01 -6.10885382... | [11.179594993591309, 6.943390846252441] |
0961f8a5-09cc-4715-a8df-106216bb7f38 | efficient-multi-order-gated-aggregation | 2211.03295 | null | https://arxiv.org/abs/2211.03295v2 | https://arxiv.org/pdf/2211.03295v2.pdf | Efficient Multi-order Gated Aggregation Network | Since the recent success of Vision Transformers (ViTs), explorations toward ViT-style architectures have triggered the resurgence of ConvNets. In this work, we explore the representation ability of modern ConvNets from a novel view of multi-order game-theoretic interaction, which reflects inter-variable interaction eff... | ['Stan Z. Li', 'Jiangbin Zheng', 'ZhiYuan Chen', 'Di wu', 'Haitao Lin', 'Cheng Tan', 'Zicheng Liu', 'Zedong Wang', 'Siyuan Li'] | 2022-11-07 | null | null | null | null | ['3d-human-pose-estimation', 'video-prediction'] | ['computer-vision', 'computer-vision'] | [-1.47645220e-01 -3.01566198e-02 8.24395642e-02 -2.57202178e-01
-2.48224348e-01 -6.50987625e-01 5.51803172e-01 -3.75440717e-01
-8.75989199e-01 3.87281388e-01 -3.45397145e-01 -3.33508462e-01
-1.28487155e-01 -4.85232323e-01 -7.60231674e-01 -5.17366052e-01
-2.70318627e-01 4.32052195e-01 5.31430602e-01 -4.93844718... | [9.400206565856934, 1.1540828943252563] |
334085a0-d276-4caa-8f5f-ea6f7497223d | coordinating-flexible-ramping-products-with | 2208.00036 | null | https://arxiv.org/abs/2208.00036v1 | https://arxiv.org/pdf/2208.00036v1.pdf | Coordinating Flexible Ramping Products with Dynamics of the Natural Gas Network | In electricity networks with high penetration levels of renewable resources, Flexible Ramping Products (FRPs) are among the utilized measures for dealing with the potential fluctuations in the net demand. This paper investigates the impacts of FRPs on the operation of interdependent electricity and natural gas networks... | ['Saeed D. Manshadi', 'Reza Bayani'] | 2022-07-29 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.25183243e-01 1.04835993e-02 7.46426592e-03 -1.29007012e-01
-1.14213906e-01 -8.69411945e-01 3.95253092e-01 -5.72512411e-02
-5.47422469e-03 1.18470633e+00 -9.45588872e-02 -3.75598490e-01
-6.65686905e-01 -1.21862924e+00 -4.30189192e-01 -1.01043403e+00
-4.08330888e-01 7.97784925e-01 -8.08423102e-01 -2.60192901... | [5.660068035125732, 2.554194211959839] |
1b06585a-ce8f-4d8c-a407-a9f416e95960 | hybrid-decentralized-optimization-first-and | 2210.07703 | null | https://arxiv.org/abs/2210.07703v1 | https://arxiv.org/pdf/2210.07703v1.pdf | Hybrid Decentralized Optimization: First- and Zeroth-Order Optimizers Can Be Jointly Leveraged For Faster Convergence | Distributed optimization has become one of the standard ways of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some computationally-bounded nodes may not be able to implement first-order, gradient-base... | ['Dan Alistarh', 'Giorgi Nadiradze', 'Shayan Talaei'] | 2022-10-14 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [-3.84761244e-01 1.80692181e-01 -9.73784849e-02 -2.12919444e-01
-8.82534802e-01 -6.18184149e-01 1.97729185e-01 3.04177821e-01
-7.62915432e-01 1.15779865e+00 -2.90553957e-01 -3.52744192e-01
-4.07596201e-01 -5.13052940e-01 -9.27944660e-01 -1.13198984e+00
-4.40542579e-01 9.00387108e-01 -5.12416884e-02 -2.13932414... | [6.24462890625, 4.925557613372803] |
1fe73d32-1bb2-4759-9157-a706e2bf5a2b | grasping-the-inconspicuous | 2211.08182 | null | https://arxiv.org/abs/2211.08182v1 | https://arxiv.org/pdf/2211.08182v1.pdf | Grasping the Inconspicuous | Transparent objects are common in day-to-day life and hence find many applications that require robot grasping. Many solutions toward object grasping exist for non-transparent objects. However, due to the unique visual properties of transparent objects, standard 3D sensors produce noisy or distorted measurements. Moder... | ['Markus Vincze', 'Markus Leitner', 'Stefan Thalhammer', 'Hrishikesh Gupta'] | 2022-11-15 | null | null | null | null | ['transparent-objects', '6d-pose-estimation-1'] | ['computer-vision', 'computer-vision'] | [ 1.55846864e-01 2.67160833e-01 2.25498855e-01 -5.44938207e-01
-4.41241384e-01 -6.16626382e-01 6.22051470e-02 -2.91410744e-01
-2.63260275e-01 3.30056846e-01 -2.18766123e-01 3.82572934e-02
-2.16483802e-01 -7.00195849e-01 -8.98094118e-01 -8.97365093e-01
3.55663784e-02 5.86725116e-01 4.49901819e-01 -9.42296609... | [6.023335933685303, -1.1052803993225098] |
6ec9db3c-0e60-4e7c-a021-49ccfeb562ba | instruction-tuning-with-gpt-4 | 2304.03277 | null | https://arxiv.org/abs/2304.03277v1 | https://arxiv.org/pdf/2304.03277v1.pdf | Instruction Tuning with GPT-4 | Prior work has shown that finetuning large language models (LLMs) using machine-generated instruction-following data enables such models to achieve remarkable zero-shot capabilities on new tasks, and no human-written instructions are needed. In this paper, we present the first attempt to use GPT-4 to generate instructi... | ['Jianfeng Gao', 'Michel Galley', 'Pengcheng He', 'Chunyuan Li', 'Baolin Peng'] | 2023-04-06 | null | null | null | null | ['instruction-following'] | ['natural-language-processing'] | [-3.72685790e-02 -1.31786019e-01 -8.01541388e-01 -5.09065449e-01
-1.12286079e+00 -3.24251175e-01 6.26792490e-01 3.73246148e-02
-5.78406334e-01 7.15431154e-01 2.41301611e-01 -1.03750575e+00
4.62690175e-01 -4.84185070e-01 -1.00259352e+00 -3.47748632e-03
-7.14389607e-02 6.94891334e-01 4.10219401e-01 -7.52754688... | [10.57967758178711, 8.44201946258545] |
742f4d5d-3a63-41d4-b0de-09e44c2ba817 | making-table-understanding-work-in-practice | 2109.05173 | null | https://arxiv.org/abs/2109.05173v1 | https://arxiv.org/pdf/2109.05173v1.pdf | Making Table Understanding Work in Practice | Understanding the semantics of tables at scale is crucial for tasks like data integration, preparation, and search. Table understanding methods aim at detecting a table's topic, semantic column types, column relations, or entities. With the rise of deep learning, powerful models have been developed for these tasks with... | ['Çağatay Demiralp', 'Paul Groth', 'Isil Dillig', 'James Gale', 'Sneha Gathani', 'Madelon Hulsebos'] | 2021-09-11 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [-7.62736350e-02 5.50552964e-01 -3.56651813e-01 -6.75299704e-01
-6.87221229e-01 -8.07637930e-01 5.43869317e-01 8.50288391e-01
-7.98594281e-02 5.31212509e-01 3.19122642e-01 -7.17836380e-01
-9.90191177e-02 -1.15783858e+00 -9.89746749e-01 3.48033577e-01
1.33124098e-01 8.50593209e-01 2.48090193e-01 -4.00097787... | [9.5758056640625, 7.878114700317383] |
490fa070-a89c-4177-ad59-c772b795c548 | revisiting-class-incremental-learning-with | 2303.07338 | null | https://arxiv.org/abs/2303.07338v1 | https://arxiv.org/pdf/2303.07338v1.pdf | Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need | Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting old ones. Traditional CIL models are trained from scratch to continually acquire knowledge as data evolves. Recently, pre-training has achieved substantial progress, making vast pre-trained models (PTMs) accessible for CIL. Contra... | ['Ziwei Liu', 'De-Chuan Zhan', 'Han-Jia Ye', 'Da-Wei Zhou'] | 2023-03-13 | null | null | null | null | ['class-incremental-learning'] | ['computer-vision'] | [-1.44498482e-01 -7.20100775e-02 -4.50414628e-01 -3.81813109e-01
-3.36881697e-01 -5.03783524e-01 7.07936168e-01 1.30928019e-02
-4.06337053e-01 6.47820771e-01 -6.50704429e-02 -4.07141335e-02
-1.36210948e-01 -6.82672262e-01 -8.21244657e-01 -4.06407714e-01
-2.77637672e-02 5.22572696e-01 5.44982493e-01 -3.43778104... | [9.821043014526367, 3.360435962677002] |
f7cc63c2-31e8-46b0-9d21-569da5b10efc | machine-learning-for-the-prediction-of-safe | 2302.10952 | null | https://arxiv.org/abs/2302.10952v1 | https://arxiv.org/pdf/2302.10952v1.pdf | Machine learning for the prediction of safe and biologically active organophosphorus molecules | Drug discovery is a complex process with a large molecular space to be considered. By constraining the search space, the fragment-based drug design is an approach that can effectively sample the chemical space of interest. Here we propose a framework of Recurrent Neural Networks (RNN) with an attention model to sample ... | ['Anguang Hu', 'Mohammad Sajjad Ghaemi', 'Hsu Kiang Ooi', 'Hang Hu'] | 2023-02-21 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 7.61263549e-01 -8.17135721e-02 -6.26364350e-01 1.83001179e-02
-5.82142413e-01 -5.29849350e-01 4.25668061e-01 -2.37609278e-02
-3.95139217e-01 1.52206278e+00 3.97992991e-02 -8.00624073e-01
-3.06235343e-01 -6.97994828e-01 -9.24416244e-01 -9.80680227e-01
-9.29606855e-02 3.96636039e-01 -5.74489944e-02 -2.82370020... | [4.922391414642334, 5.712667942047119] |
5fd2f22e-e481-41ec-ab3a-4b8a6dd4efb4 | pyabsa-open-framework-for-aspect-based | 2208.01368 | null | https://arxiv.org/abs/2208.01368v2 | https://arxiv.org/pdf/2208.01368v2.pdf | PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis | The advancement of aspect-based sentiment analysis (ABSA) has urged the lack of a user-friendly framework that can largely lower the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet the demand, we present \our, a modularized framework built on PyTorch for reproducible ABSA.... | ['Ke Li', 'Heng Yang'] | 2022-08-02 | null | null | null | null | ['classification', 'term-extraction', 'aspect-based-sentiment-analysis'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-2.47121722e-01 3.27895321e-02 -1.73789844e-01 -6.68293834e-01
-8.31329048e-01 -7.64706135e-01 5.92372477e-01 2.40980431e-01
-2.16629710e-02 9.56695378e-02 1.76545426e-01 -5.01833856e-01
1.47225305e-01 -9.06017482e-01 -2.87138700e-01 -3.11241239e-01
2.73675472e-01 2.27638692e-01 -1.43277645e-01 -6.17636979... | [11.470094680786133, 6.755216121673584] |
671a1336-410a-43a7-b844-03deb61232a6 | corn-yield-prediction-based-on-remotely | 2211.13286 | null | https://arxiv.org/abs/2211.13286v1 | https://arxiv.org/pdf/2211.13286v1.pdf | Corn Yield Prediction based on Remotely Sensed Variables Using Variational Autoencoder and Multiple Instance Regression | In the U.S., corn is the most produced crop and has been an essential part of the American diet. To meet the demand for supply chain management and regional food security, accurate and timely large-scale corn yield prediction is attracting more attention in precision agriculture. Recently, remote sensing technology and... | ['Zhou Zhang', 'Yuchi Ma', 'Zeyu Cao'] | 2022-11-23 | null | null | null | null | ['crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'miscellaneous'] | [-2.57832348e-01 -3.49265695e-01 -5.05408704e-01 -2.46118858e-01
-3.71519089e-01 -3.28763068e-01 1.03647470e-01 5.84196746e-01
3.26058902e-02 7.37508953e-01 -2.95697749e-01 -4.71536756e-01
1.47065818e-02 -1.69424284e+00 -7.90177584e-01 -8.70005786e-01
-7.14579821e-02 2.17657819e-01 -2.07876619e-02 -4.37230468... | [9.363112449645996, -1.6017647981643677] |
6d101a77-04e6-45a9-b7de-faa79fdf8dbb | joint-entity-and-relation-extraction-for | null | null | https://aclanthology.org/2020.coling-main.137 | https://aclanthology.org/2020.coling-main.137.pdf | Joint Entity and Relation Extraction for Legal Documents with Legal Feature Enhancement | In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments Online. It is in great need of enabling machines to understand the semantic information stored in the documents which ar... | ['Hongfei Lin', 'Zhihao Yang', 'Yuanyuan Sun', 'Yanguang Chen'] | 2020-12-01 | null | null | null | coling-2020-8 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.09192871e-01 -9.85384583e-02 -5.64280450e-01 -5.02889395e-01
-1.12870300e+00 -5.51644325e-01 4.58698303e-01 1.48119867e-01
-5.38435757e-01 1.04577613e+00 3.84350002e-01 -3.84256572e-01
-4.35333848e-01 -7.20820904e-01 -6.49795979e-02 -5.46459794e-01
3.64697576e-01 5.88813663e-01 2.38215122e-02 -2.61819273... | [9.322656631469727, 8.709813117980957] |
bff2b062-c5eb-4995-9f8a-2de1f1ec85c8 | building-a-culture-of-reproducibility-in | 2212.13534 | null | https://arxiv.org/abs/2212.13534v1 | https://arxiv.org/pdf/2212.13534v1.pdf | Building a Culture of Reproducibility in Academic Research | Reproducibility is an ideal that no researcher would dispute "in the abstract", but when aspirations meet the cold hard reality of the academic grind, reproducibility often "loses out". In this essay, I share some personal experiences grappling with how to operationalize reproducibility while balancing its demands agai... | ['Jimmy Lin'] | 2022-12-27 | null | null | null | null | ['culture'] | ['speech'] | [-2.95503587e-01 5.05717006e-03 -2.87957311e-01 -4.12265837e-01
-2.84404039e-01 -5.33385098e-01 3.94130766e-01 3.00582558e-01
-2.27483884e-01 8.25055838e-01 7.43398368e-01 -3.96164447e-01
-4.94704038e-01 -3.97517443e-01 -5.57712495e-01 -3.09406668e-01
5.90367079e-01 -1.55923516e-01 -4.85053629e-01 -3.70644718... | [8.971681594848633, 6.321878433227539] |
b7b2d667-fbcc-4b56-bcc1-e42d2def7a87 | multi-scale-user-behavior-network-for-entire | 2208.01889 | null | https://arxiv.org/abs/2208.01889v2 | https://arxiv.org/pdf/2208.01889v2.pdf | Multi-Scale User Behavior Network for Entire Space Multi-Task Learning | Modelling the user's multiple behaviors is an essential part of modern e-commerce, whose widely adopted application is to jointly optimize click-through rate (CTR) and conversion rate (CVR) predictions. Most of existing methods overlook the effect of two key characteristics of the user's behaviors: for each item list, ... | ['Yong Yu', 'Zhewen Su', 'Zekun Zhu', 'Zaifan Jiang', 'Yuanbo Chen', 'Weinan Zhang', 'Xianyu Chen', 'Jiarui Jin'] | 2022-08-03 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-9.91610065e-02 -7.56880462e-01 -5.28454661e-01 -5.52003741e-01
-3.12928587e-01 -2.96667427e-01 3.16512913e-01 7.44272843e-02
-2.96146929e-01 3.03796768e-01 2.49487296e-01 -2.06540972e-01
-2.59779036e-01 -7.86731243e-01 -5.04864872e-01 -5.52786350e-01
-2.75172383e-01 2.64649272e-01 2.65889674e-01 -4.81694132... | [10.102543830871582, 5.511308670043945] |
6184b7d5-421a-42e8-a06c-969ca5042382 | learning-reward-machines-a-study-in-partially | 2112.09477 | null | https://arxiv.org/abs/2112.09477v1 | https://arxiv.org/pdf/2112.09477v1.pdf | Learning Reward Machines: A Study in Partially Observable Reinforcement Learning | Reinforcement learning (RL) is a central problem in artificial intelligence. This problem consists of defining artificial agents that can learn optimal behaviour by interacting with an environment -- where the optimal behaviour is defined with respect to a reward signal that the agent seeks to maximize. Reward machines... | ['Sheila A. McIlraith', 'Margarita P. Castro', 'Richard Valenzano', 'Toryn Q. Klassen', 'Ethan Waldie', 'Rodrigo Toro Icarte'] | 2021-12-17 | null | null | null | null | ['problem-decomposition'] | ['miscellaneous'] | [ 1.70005828e-01 5.49330950e-01 -4.43707138e-01 1.04302960e-02
-7.72664845e-01 -6.36654496e-01 6.14623308e-01 -2.92276423e-02
-7.60264218e-01 1.12560165e+00 1.01719268e-01 -3.15221757e-01
-3.55802357e-01 -5.34392118e-01 -6.77354276e-01 -8.75442386e-01
-4.58446860e-01 7.58667350e-01 -7.75342062e-02 -2.31485337... | [4.116504669189453, 1.9092142581939697] |
bcf89ec1-8c8a-4eb6-9131-52fb778b0ec9 | autoregressive-stylized-motion-synthesis-with | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Wen_Autoregressive_Stylized_Motion_Synthesis_With_Generative_Flow_CVPR_2021_paper.pdf | Autoregressive Stylized Motion Synthesis With Generative Flow | Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics. Most existing deep learning methods for this problem are supervised and trained by registered motion pairs. In addition, these methods are often limited to yieldi... | ['Yong-Jin Liu', 'Yanan sun', 'Lin Gao', 'Hongbo Fu', 'Zhipeng Yang', 'Yu-Hui Wen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['motion-style-transfer'] | ['computer-code'] | [ 2.18311936e-01 -9.93917417e-03 -2.39266992e-01 -1.47701308e-01
-3.94180119e-01 -5.62313914e-01 9.21816826e-01 -5.74391961e-01
-2.60657340e-01 5.70422888e-01 4.10299331e-01 1.30139723e-01
4.35357094e-01 -9.02572513e-01 -9.54018652e-01 -7.01494515e-01
4.02749866e-01 5.20029604e-01 2.64740467e-01 -1.02261409... | [7.45280122756958, -0.18319538235664368] |
9a2a84c6-53d0-4be1-9886-f574a8bdf8e4 | multiview-detection-with-shadow-transformer | 2108.05888 | null | https://arxiv.org/abs/2108.05888v1 | https://arxiv.org/pdf/2108.05888v1.pdf | Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation) | Multiview detection incorporates multiple camera views to deal with occlusions, and its central problem is multiview aggregation. Given feature map projections from multiple views onto a common ground plane, the state-of-the-art method addresses this problem via convolution, which applies the same calculation regardles... | ['Liang Zheng', 'Yunzhong Hou'] | 2021-08-12 | null | null | null | null | ['multiview-detection'] | ['computer-vision'] | [-1.30146205e-01 -3.42119902e-01 -5.95472902e-02 -5.02902925e-01
-7.61433005e-01 -7.09865868e-01 6.77284598e-01 -1.97446421e-01
-5.63875400e-02 1.29532173e-01 7.79207349e-02 -2.63540745e-02
4.66919333e-01 -5.48675239e-01 -9.02261496e-01 -4.35069263e-01
4.15846556e-01 2.65853018e-01 5.97845912e-01 -1.35742590... | [7.949124336242676, -2.1132357120513916] |
292d172d-86b6-495f-aeb0-a558ce31386f | adversarial-robustness-of-neural-statistical | 2203.07983 | null | https://arxiv.org/abs/2203.07983v1 | https://arxiv.org/pdf/2203.07983v1.pdf | Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers | The detection of computer-generated text is an area of rapidly increasing significance as nascent generative models allow for efficient creation of compelling human-like text, which may be abused for the purposes of spam, disinformation, phishing, or online influence campaigns. Past work has studied detection of curren... | ['Paula Branco', 'Herna Viktor', 'Nathalie Japkowicz', 'Evan Crothers'] | 2022-03-02 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 2.40854710e-01 -2.40773950e-02 1.61180511e-01 -1.56310678e-01
-9.34013605e-01 -9.63285923e-01 1.25419903e+00 4.83156085e-01
-4.66556102e-01 5.00067413e-01 5.72041392e-01 -7.07411647e-01
-3.41158807e-02 -9.71292019e-01 -4.70163673e-01 -3.17074001e-01
1.95126534e-01 2.86007792e-01 -1.15600929e-01 -5.72862983... | [8.213116645812988, 10.043889045715332] |
4bc33b1b-2574-42c6-a39a-b40259691003 | vision-transformers-for-single-image-dehazing | 2204.03883 | null | https://arxiv.org/abs/2204.03883v1 | https://arxiv.org/pdf/2204.03883v1.pdf | Vision Transformers for Single Image Dehazing | Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images. In recent years, convolutional neural network-based methods have dominated image dehazing. However, vision Transformers, which has recently made a breakthrough in high-level vision tasks, has not brought ne... | ['Xin Du', 'Hui Qian', 'Zhuqing He', 'Yuda Song'] | 2022-04-08 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 3.12249839e-01 -3.64903718e-01 2.98148721e-01 -2.26205468e-01
-6.22324228e-01 -4.38824221e-02 4.12498444e-01 -1.70324668e-01
-3.35842371e-01 4.81126219e-01 1.19056456e-01 -2.59915650e-01
5.42209633e-02 -9.00226414e-01 -7.49097764e-01 -1.06366038e+00
-3.19560766e-02 -2.96247959e-01 4.83139366e-01 -4.47373867... | [10.94719409942627, -3.0973522663116455] |
6369fddb-2c59-4a14-9808-fb4f00270876 | freestyle-layout-to-image-synthesis | 2303.14412 | null | https://arxiv.org/abs/2303.14412v1 | https://arxiv.org/pdf/2303.14412v1.pdf | Freestyle Layout-to-Image Synthesis | Typical layout-to-image synthesis (LIS) models generate images for a closed set of semantic classes, e.g., 182 common objects in COCO-Stuff. In this work, we explore the freestyle capability of the model, i.e., how far can it generate unseen semantics (e.g., classes, attributes, and styles) onto a given layout, and cal... | ['Wenjun Zhang', 'Li Song', 'Qianru Sun', 'Zhiwu Huang', 'Han Xue'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xue_Freestyle_Layout-to-Image_Synthesis_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xue_Freestyle_Layout-to-Image_Synthesis_CVPR_2023_paper.pdf | cvpr-2023-1 | ['layout-to-image-generation'] | ['computer-vision'] | [ 3.72167170e-01 3.52513999e-01 -1.16273696e-02 -4.44297671e-01
-3.36768776e-01 -7.33807027e-01 6.91612959e-01 -4.18592006e-01
-8.23293254e-02 4.67606574e-01 -3.70302424e-02 -2.38610938e-01
3.14713657e-01 -1.03703785e+00 -1.06214464e+00 -6.26609206e-01
5.21179020e-01 3.38369370e-01 2.23808497e-01 -2.90925354... | [11.299942016601562, -0.14474837481975555] |
06c1d154-15fc-4383-9c6e-a5cfcaebdbe2 | combinatorial-3d-shape-generation-via | 2004.07414 | null | https://arxiv.org/abs/2004.07414v2 | https://arxiv.org/pdf/2004.07414v2.pdf | Combinatorial 3D Shape Generation via Sequential Assembly | Sequential assembly with geometric primitives has drawn attention in robotics and 3D vision since it yields a practical blueprint to construct a target shape. However, due to its combinatorial property, a greedy method falls short of generating a sequence of volumetric primitives. To alleviate this consequence induced ... | ['Jaesik Park', 'Minsu Cho', 'Jinhwi Lee', 'Hyunsoo Chung', 'Jungtaek Kim'] | 2020-04-16 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [ 2.28763986e-02 1.95868254e-01 2.70709753e-01 1.25049740e-01
-3.24786842e-01 -8.10962856e-01 8.49839389e-01 3.03231716e-01
6.33731112e-02 5.63694835e-01 -1.36534691e-01 -1.26315162e-01
-1.58567578e-01 -9.99927223e-01 -6.87442720e-01 -6.61796927e-01
-9.70693212e-03 1.02228093e+00 1.02600664e-01 -2.44843662... | [8.79296588897705, -3.616032838821411] |
86ec4ecf-c3df-4a3a-8f88-f58a6d9460fb | towards-grand-unification-of-object-tracking | 2207.07078 | null | https://arxiv.org/abs/2207.07078v4 | https://arxiv.org/pdf/2207.07078v4.pdf | Towards Grand Unification of Object Tracking | We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. Due to the fragmented definitions of the object tracking problem itself, most existing trackers are developed to address a single or part of task... | ['Huchuan Lu', 'Ping Luo', 'Zehuan Yuan', 'Dong Wang', 'Peize Sun', 'Yi Jiang', 'Bin Yan'] | 2022-07-14 | null | null | null | null | ['visual-object-tracking', 'multi-object-tracking-and-segmentation'] | ['computer-vision', 'computer-vision'] | [-4.68090773e-01 -3.09032708e-01 -3.47013265e-01 5.80516197e-02
-1.02271877e-01 -9.20267105e-01 5.91871977e-01 -4.55679357e-01
-4.74279851e-01 6.37243807e-01 -1.20723672e-01 -6.75609112e-02
-2.02883363e-01 -2.23728120e-01 -5.57810009e-01 -4.07089859e-01
-1.82638928e-01 4.98225451e-01 7.25336552e-01 -5.32926992... | [6.337717056274414, -2.0606565475463867] |
dee20e28-4dde-4798-9428-be8c24639fe4 | a-multilingual-study-of-compressive-cross | 1810.10639 | null | http://arxiv.org/abs/1810.10639v1 | http://arxiv.org/pdf/1810.10639v1.pdf | A Multilingual Study of Compressive Cross-Language Text Summarization | Cross-Language Text Summarization (CLTS) generates summaries in a language
different from the language of the source documents. Recent methods use
information from both languages to generate summaries with the most informative
sentences. However, these methods have performance that can vary according to
languages, whic... | ['Juan-Manuel Torres-Moreno', 'Stéphane Huet', 'Elvys Linhares Pontes'] | 2018-10-24 | null | null | null | null | ['cross-language-text-summarization'] | ['natural-language-processing'] | [-9.87160206e-03 -5.29008731e-02 -1.82640374e-01 -8.98300186e-02
-1.37012625e+00 -9.60646570e-01 1.00466645e+00 6.35736644e-01
-4.30039167e-01 1.27498329e+00 1.18119168e+00 -1.10955141e-01
2.11378694e-01 -6.08774424e-01 -3.21560472e-01 -7.40978941e-02
3.60446185e-01 4.26445901e-01 2.94105679e-01 -3.50363821... | [12.39795207977295, 9.518388748168945] |
540a5859-90f3-4606-a3e3-bb455dcbdea0 | fine-grained-vr-sketching-dataset-and | 2209.10008 | null | https://arxiv.org/abs/2209.10008v1 | https://arxiv.org/pdf/2209.10008v1.pdf | Fine-Grained VR Sketching: Dataset and Insights | We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the sketch community on fine-grained data analysis, and extends it to an actively developing 3D domain. We argue for the most convenient sketching ... | ['Yi-Zhe Song', 'Tao Xiang', 'Yongxin Yang', 'Yulia Gryaditskaya', 'Ling Luo'] | 2022-09-20 | null | null | null | null | ['3d-shape-reconstruction'] | ['computer-vision'] | [ 5.86910024e-02 -2.05666393e-01 -1.27074137e-01 -2.09576711e-01
-5.52355707e-01 -1.19043946e+00 8.61902535e-01 -9.41680223e-02
4.04166788e-01 3.42439383e-01 3.05516154e-01 -6.26633525e-01
-5.30229986e-01 -1.00823069e+00 -4.39249337e-01 -1.25127643e-01
-4.57861163e-02 8.78679335e-01 1.18734002e-01 -4.54682440... | [8.666247367858887, -3.724386692047119] |
be65c067-71cb-4388-beda-0aff905632fe | computer-assisted-analysis-of-biomedical | 2106.04381 | null | https://arxiv.org/abs/2106.04381v1 | https://arxiv.org/pdf/2106.04381v1.pdf | Computer-Assisted Analysis of Biomedical Images | Nowadays, the amount of heterogeneous biomedical data is increasing more and more thanks to novel sensing techniques and high-throughput technologies. In reference to biomedical image analysis, the advances in image acquisition modalities and high-throughput imaging experiments are creating new challenges. This huge in... | ['Leonardo Rundo'] | 2021-06-04 | null | null | null | null | ['data-integration'] | ['knowledge-base'] | [ 6.22390449e-01 -2.44928673e-01 -2.54311919e-01 -6.60955161e-02
-2.99086332e-01 -2.15320945e-01 1.87973365e-01 8.28117192e-01
-4.53749269e-01 7.85441101e-01 -1.57987624e-01 -1.64299697e-01
-4.55492735e-01 -7.13890076e-01 6.10868409e-02 -1.18945122e+00
-9.85686630e-02 7.47677445e-01 -5.33431657e-02 -7.73911625... | [14.873992919921875, -2.7301135063171387] |
fc93237d-7989-4434-83e3-1a8d3dc5c64f | system-design-for-an-integrated-lifelong | 2212.04603 | null | https://arxiv.org/abs/2212.04603v1 | https://arxiv.org/pdf/2212.04603v1.pdf | System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games | As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastroph... | ['Aswin Raghavan', 'Jesse Hostetler', 'Michael Piacentino', 'Ajay Divakaran', 'Roberto Corizzo', 'Christopher Kanan', 'Zsolt Kira', 'Michael Baron', 'Nathalie Japkowicz', 'Sahana Joshi', 'James Smith', 'Mustafa Burak Gurbuz', 'Cameron E. Taylor', 'Tyler L. Hayes', 'Gianmarco J. Gallardo', 'Kamil Faber', 'Abrar Rahman',... | 2022-12-08 | null | null | null | null | ['real-time-strategy-games', 'starcraft'] | ['playing-games', 'playing-games'] | [-3.84132057e-01 -7.36940478e-04 -1.25767350e-01 2.31230352e-02
-5.23575366e-01 -6.92322254e-01 7.89536834e-01 4.08280529e-02
-6.61579072e-01 9.53823686e-01 -3.21466178e-01 -2.44335592e-01
-4.60944682e-01 -5.77046394e-01 -8.59802842e-01 -4.94021088e-01
-4.81986493e-01 5.53423584e-01 5.84189177e-01 -5.24073899... | [4.130638122558594, 1.4674049615859985] |
754d616d-d0fd-4fb5-b92f-11cb0b4804e3 | single-channel-speech-enhancement-using | 1605.01329 | null | http://arxiv.org/abs/1605.01329v1 | http://arxiv.org/pdf/1605.01329v1.pdf | Single Channel Speech Enhancement Using Outlier Detection | Distortion of the underlying speech is a common problem for single-channel
speech enhancement algorithms, and hinders such methods from being used more
extensively. A dictionary based speech enhancement method that emphasizes
preserving the underlying speech is proposed. Spectral patches of clean speech
are sampled and... | ['Eunjoon Cho', 'Bowon Lee', 'Ronald Schafer', 'Bernard Widrow'] | 2016-05-04 | null | null | null | null | ['noise-estimation'] | ['medical'] | [ 5.18350005e-01 -2.80035436e-01 1.65856734e-01 5.27821295e-02
-8.65682602e-01 -2.07437843e-01 3.22125033e-02 3.63228083e-01
-4.32430685e-01 4.67756659e-01 5.23411572e-01 -2.64498349e-02
-3.77667472e-02 -5.03773510e-01 -3.72827947e-01 -1.36122549e+00
9.44933444e-02 -1.12705976e-01 1.44952446e-01 -2.74183512... | [15.00318431854248, 5.833864212036133] |
4dbc5814-02f8-497d-8029-80a098f22a51 | functions-that-emerge-through-end-to-end | 1703.02239 | null | http://arxiv.org/abs/1703.02239v2 | http://arxiv.org/pdf/1703.02239v2.pdf | Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence - | Recently, triggered by the impressive results in TV-games or game of Go by
Google DeepMind, end-to-end reinforcement learning (RL) is collecting
attentions. Although little is known, the author's group has propounded this
framework for around 20 years and already has shown various functions that
emerge in a neural netw... | ['Katsunari Shibata'] | 2017-03-07 | null | null | null | null | ['color-constancy', 'game-of-go'] | ['computer-vision', 'playing-games'] | [ 7.48312753e-03 3.88511479e-01 -6.84676692e-02 -1.28997892e-01
-2.02177651e-02 -6.03339612e-01 7.47205257e-01 -5.10291278e-01
-4.06876802e-01 8.94941270e-01 1.90437317e-01 -2.40787551e-01
-4.16599661e-01 -5.68446994e-01 -9.03026819e-01 -8.40420544e-01
-1.41518325e-01 8.64199474e-02 1.32337451e-01 -6.99346900... | [4.107966899871826, 1.3666601181030273] |
174791c6-a929-4a75-979d-af965be0b140 | mtr-multi-agent-motion-prediction-with | 2306.17770 | null | https://arxiv.org/abs/2306.17770v1 | https://arxiv.org/pdf/2306.17770v1.pdf | MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying | Motion prediction is crucial for autonomous driving systems to understand complex driving scenarios and make informed decisions. However, this task is challenging due to the diverse behaviors of traffic participants and complex environmental contexts. In this paper, we propose Motion TRansformer (MTR) frameworks to add... | ['Bernt Schiele', 'Dengxin Dai', 'Li Jiang', 'Shaoshuai Shi'] | 2023-06-30 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [ 1.13523759e-01 -2.10851222e-01 -6.27062976e-01 -1.70046493e-01
-9.04252827e-01 -2.78775930e-01 8.63764822e-01 -1.35884270e-01
-5.11337757e-01 3.21462333e-01 5.75317264e-01 -3.21819156e-01
1.80424735e-01 -7.17309475e-01 -6.06436670e-01 -4.88715678e-01
-1.59392267e-01 3.91253233e-01 7.86719799e-01 -3.64977866... | [5.904587745666504, 0.7840268015861511] |
54790de6-d473-4702-b13b-f9ba4d656477 | extraction-of-sleep-information-from-clinical | 2204.09601 | null | https://arxiv.org/abs/2204.09601v1 | https://arxiv.org/pdf/2204.09601v1.pdf | Extraction of Sleep Information from Clinical Notes of Alzheimer's Disease Patients Using Natural Language Processing | Alzheimer's Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. . However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for ... | ['Yanshan Wang', 'Shyam Visweswaran', 'David Oniani', 'Samual Viggiano', 'Sonish Sivarajkumar', 'Haneef Ahamed Mohammad'] | 2022-03-08 | null | null | null | null | ['sleep-quality-prediction'] | ['medical'] | [-3.06737959e-01 -3.79156560e-01 -2.26398528e-01 -5.75916827e-01
-5.15700459e-01 -2.33489931e-01 -1.64444432e-01 5.57478964e-01
-6.35611296e-01 1.24390185e+00 5.94479740e-01 -5.60233533e-01
-4.27872807e-01 -5.64188004e-01 2.31467664e-01 -3.45118761e-01
-2.25270629e-01 6.65907145e-01 1.49506480e-01 1.72395572... | [13.600671768188477, 3.400681734085083] |
3a7d5b26-7f41-4f6b-a55b-abc48d9027db | evaluating-and-modeling-attribution-for-cross | 2305.14332 | null | https://arxiv.org/abs/2305.14332v1 | https://arxiv.org/pdf/2305.14332v1.pdf | Evaluating and Modeling Attribution for Cross-Lingual Question Answering | Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems, yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much ... | ['Xinyi Wang', 'Jonathan Herzig', 'Roee Aharoni', 'Livio Baldini Soares', 'Sebastian Ruder', 'Tom Kwiatkowski', 'Jonathan H. Clark', 'John Wieting', 'Benjamin Muller'] | 2023-05-23 | null | null | null | null | ['cross-lingual-question-answering'] | ['natural-language-processing'] | [-3.43263894e-01 2.86028773e-01 -1.62060916e-01 -3.50116402e-01
-1.96621108e+00 -9.07083690e-01 7.55018651e-01 2.14030683e-01
-2.19250619e-01 8.34020019e-01 2.67551363e-01 -4.91555065e-01
1.22878011e-02 -7.15339601e-01 -7.24661231e-01 -1.38062403e-01
7.54386246e-01 1.08508694e+00 1.36791036e-01 -7.56183445... | [11.292728424072266, 8.192617416381836] |
f9dbb25a-8bc7-4062-9ca0-8728052d1827 | safeamc-adversarial-training-for-robust | 2105.13746 | null | https://arxiv.org/abs/2105.13746v1 | https://arxiv.org/pdf/2105.13746v1.pdf | SafeAMC: Adversarial training for robust modulation recognition models | In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. However, these models have been shown to be susceptible to adversarial perturbations, namely imperceptible additive noise crafted to induce misclassification. This raises questions about the se... | ['Pascal Frossard', 'Gérôme Bovet', 'Javier Maroto'] | 2021-05-28 | null | null | null | null | ['automatic-modulation-recognition'] | ['time-series'] | [ 8.85576010e-01 4.28162873e-01 2.33877480e-01 -3.20276231e-01
-7.85750270e-01 -9.33996081e-01 8.96415114e-01 -5.29229164e-01
-6.66900277e-02 8.49449098e-01 1.10491402e-01 -8.03146064e-01
-2.71385312e-01 -7.43236899e-01 -9.37690496e-01 -8.72345328e-01
-5.54178894e-01 -7.56832436e-02 -2.06260070e-01 -3.80405396... | [5.63763952255249, 7.7013936042785645] |
e07d7fdd-df14-4235-b6e9-13a020f32113 | mapa-project-ready-to-go-open-source-datasets | null | null | https://aclanthology.org/2022.legal-1.12 | https://aclanthology.org/2022.legal-1.12.pdf | MAPA Project: Ready-to-Go Open-Source Datasets and Deep Learning Technology to Remove Identifying Information from Text Documents | This paper presents the outcomes of the MAPA project, a set of annotated corpora for 24 languages of the European Union and an open-source customisable toolkit able to detect and substitute sensitive information in text documents from any domain, using state-of-the art, deep learning-based named entity recognition tech... | ['Pierre Zweigenbaum', 'Patrick Paroubek', 'Manuel Herranz', 'Cyril Grouin', 'Lucie Gianola', 'Aitor García Pablos', 'Montse Cuadros', 'Khalid Choukri', 'Victoria Arranz'] | null | null | null | null | legal-lrec-2022-6 | ['de-identification'] | ['natural-language-processing'] | [-2.62933791e-01 3.91797423e-01 -7.84061328e-02 -6.74564362e-01
-1.08257723e+00 -6.24965370e-01 9.63945210e-01 5.52284718e-01
-8.25066626e-01 7.92863190e-01 4.01979685e-01 -8.72131735e-02
-1.99125394e-01 -6.03521824e-01 -2.15787172e-01 -1.94331035e-01
1.83085520e-02 9.50309932e-01 3.96457553e-01 -2.16716975... | [9.675333023071289, 9.620084762573242] |
a62df25d-ebaa-44bd-bfc2-2edb40d22d12 | esports-pro-players-behavior-during-the-game | 1908.06402 | null | https://arxiv.org/abs/1908.06402v1 | https://arxiv.org/pdf/1908.06402v1.pdf | eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair | Today's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team. There are two main approaches to such an estimation: obtaining features and metrics directly from the in-game data or collecting detailed information abo... | ['Andrey Somov', 'Evgeny Burnaev', 'Anton Smerdov'] | 2019-08-18 | null | null | null | null | ['sensor-modeling', 'skills-evaluation', 'skills-assessment', 'fps-games'] | ['computer-vision', 'computer-vision', 'computer-vision', 'playing-games'] | [-2.95391798e-01 -2.68012792e-01 -3.83041948e-01 -3.55800271e-01
-3.30402315e-01 -1.79207906e-01 3.45464535e-02 4.45569605e-01
-7.79932857e-01 3.89521599e-01 3.05510104e-01 1.67159364e-01
-4.85100061e-01 -1.03928316e+00 -1.09568775e-01 -4.84464735e-01
1.25753060e-01 4.94119108e-01 4.23821539e-01 -6.78193092... | [6.886430740356445, 0.37750786542892456] |
c0283a3a-9a75-4caf-b72a-5152b35156f5 | a-penalized-poisson-likelihood-approach-to | 2306.06756 | null | https://arxiv.org/abs/2306.06756v1 | https://arxiv.org/pdf/2306.06756v1.pdf | A Penalized Poisson Likelihood Approach to High-Dimensional Semi-Parametric Inference for Doubly-Stochastic Point Processes | Doubly-stochastic point processes model the occurrence of events over a spatial domain as an inhomogeneous Poisson process conditioned on the realization of a random intensity function. They are flexible tools for capturing spatial heterogeneity and dependence. However, implementations of doubly-stochastic spatial mode... | ['Ali Shojaie', 'Jon Wakefield', 'Si Cheng'] | 2023-06-11 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 2.56336898e-01 -5.12603283e-01 -3.01261783e-01 -2.79814005e-01
-6.47303820e-01 -3.53107095e-01 5.63656926e-01 4.04520601e-01
-4.69216824e-01 1.10523486e+00 1.91441521e-01 -4.96537268e-01
-5.73518753e-01 -1.01179206e+00 -6.43335760e-01 -7.79842675e-01
-1.16672151e-01 4.76695508e-01 1.54685050e-01 3.12301397... | [7.066011428833008, 4.216282844543457] |
4b57baac-f573-40ba-98d7-76b07ae06902 | connectivity-estimation-of-high-dimensional | 2005.07083 | null | https://arxiv.org/abs/2005.07083v1 | https://arxiv.org/pdf/2005.07083v1.pdf | Connectivity estimation of high dimensional data recorded from neuronal cells | The main result of this thesis is the development of a novel connectivity estimation method, called Total Spiking Probability Edges (TSPE). Based on cross-correlation and edge filtering at different time scales this method is proposed and the theoretical framework is outlined in this work. TSPE enables the classificati... | ['Stefano De Blasi'] | 2020-05-01 | null | null | null | null | ['connectivity-estimation'] | ['graphs'] | [ 4.37619947e-02 2.86615919e-02 4.76830900e-01 1.40220374e-01
5.10513246e-01 -4.40549403e-01 4.29009825e-01 2.44367495e-01
-7.40355730e-01 1.28998172e+00 -4.94672626e-01 4.20594960e-02
-5.43701351e-01 -8.28130960e-01 -6.22673035e-01 -7.75303304e-01
-7.40009427e-01 4.49131668e-01 6.15930915e-01 -1.29655764... | [8.078728675842285, 2.8183159828186035] |
74b04219-3a78-4e63-83bf-367b0bc6e67d | d-2-im-net-learning-detail-disentangled | 2012.06650 | null | https://arxiv.org/abs/2012.06650v2 | https://arxiv.org/pdf/2012.06650v2.pdf | D$^2$IM-Net: Learning Detail Disentangled Implicit Fields from Single Images | We present the first single-view 3D reconstruction network aimed at recovering geometric details from an input image which encompass both topological shape structures and surface features. Our key idea is to train the network to learn a detail disentangled reconstruction consisting of two functions, one implicit field ... | ['Hao Zhang', 'Manyi Li'] | 2020-12-11 | null | null | null | null | ['single-view-3d-reconstruction'] | ['computer-vision'] | [ 3.38621497e-01 4.45852101e-01 2.15766251e-01 -4.02772933e-01
-9.47453499e-01 -5.67733586e-01 5.91507554e-01 -8.95824432e-02
-6.13171756e-02 4.81524259e-01 3.47160518e-01 2.42223069e-01
-2.91905291e-02 -1.06891739e+00 -1.06024003e+00 -9.75694239e-01
1.47429287e-01 7.20773757e-01 -7.58807063e-02 7.61420727... | [8.852066993713379, -3.486140251159668] |
eb2199c8-5794-49bc-88ab-d45e56dfa6b7 | multi-frequency-image-reconstruction-for | 1703.03608 | null | http://arxiv.org/abs/1703.03608v1 | http://arxiv.org/pdf/1703.03608v1.pdf | Multi-frequency image reconstruction for radio-interferometry with self-tuned regularization parameters | As the world's largest radio telescope, the Square Kilometer Array (SKA) will
provide radio interferometric data with unprecedented detail. Image
reconstruction algorithms for radio interferometry are challenged to scale well
with TeraByte image sizes never seen before. In this work, we investigate one
such 3D image re... | ['Rémi Flamary', 'André Ferrari', 'Chiara Ferrari', 'Rita Ammanouil', 'David Mary'] | 2017-03-10 | null | null | null | null | ['radio-interferometry'] | ['miscellaneous'] | [ 1.54089063e-01 -1.38275757e-01 4.02827054e-01 1.79311067e-01
-1.15466964e+00 -3.07131797e-01 2.62933046e-01 -6.75870717e-01
-6.22749925e-01 9.30728853e-01 1.38098402e-02 -4.51603800e-01
-4.44975138e-01 -4.74230975e-01 -4.66392905e-01 -8.74795794e-01
-1.65865585e-01 5.97721756e-01 -1.11092143e-01 1.72655806... | [10.744527816772461, -2.3272340297698975] |
962da5fc-0aed-47bf-8860-4a7f2dddf5f8 | cross-attention-of-disentangled-modalities | 2207.13820 | null | https://arxiv.org/abs/2207.13820v1 | https://arxiv.org/pdf/2207.13820v1.pdf | Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers | Transformer encoder architectures have recently achieved state-of-the-art results on monocular 3D human mesh reconstruction, but they require a substantial number of parameters and expensive computations. Due to the large memory overhead and slow inference speed, it is difficult to deploy such models for practical use.... | ['Tae-Hyun Oh', 'Kim Youwang', 'Junhyeong Cho'] | 2022-07-27 | null | null | null | null | ['3d-hand-pose-estimation', '3d-hand-pose-estimation'] | ['computer-vision', 'graphs'] | [ 7.79590458e-02 8.10449645e-02 -1.43302411e-01 -1.10887833e-01
-7.24804640e-01 -1.30033419e-01 3.12764645e-01 -2.36344591e-01
-3.40596199e-01 3.87507558e-01 2.66032487e-01 -1.52506977e-01
2.54533857e-01 -8.64593506e-01 -1.02837634e+00 -2.76543111e-01
1.74783871e-01 6.86961293e-01 3.11456591e-01 -3.54735889... | [7.126330852508545, -1.0513135194778442] |
803b88ac-1ec2-4e5f-a220-6530383b60ec | gloss-attention-for-gloss-free-sign-language | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yin_Gloss_Attention_for_Gloss-Free_Sign_Language_Translation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yin_Gloss_Attention_for_Gloss-Free_Sign_Language_Translation_CVPR_2023_paper.pdf | Gloss Attention for Gloss-Free Sign Language Translation | Most sign language translation (SLT) methods to date require the use of gloss annotations to provide additional supervision information, however, the acquisition of gloss is not easy. To solve this problem, we first perform an analysis of existing models to confirm how gloss annotations make SLT easier. We find tha... | ['Zhou Zhao', 'Tao Jin', 'Weike Jin', 'Li Tang', 'Tianyun Zhong', 'Aoxiong Yin'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['sign-language-translation'] | ['computer-vision'] | [ 1.81309357e-01 -1.65617868e-01 -5.74539185e-01 -5.16099274e-01
-6.49020493e-01 -5.31988263e-01 5.65734766e-02 -6.43012166e-01
-4.50747102e-01 4.44235533e-01 7.99072623e-01 -1.47201970e-01
2.69039005e-01 -1.87328175e-01 -6.95273459e-01 -4.43835020e-01
3.21609229e-01 2.73556918e-01 5.10762036e-01 -9.36487243... | [9.226713180541992, -6.529380798339844] |
5a2314a9-5a1b-4889-8743-75f5f879e10b | neural-graph-reasoning-complex-logical-query | 2303.14617 | null | https://arxiv.org/abs/2303.14617v1 | https://arxiv.org/pdf/2303.14617v1.pdf | Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases | Complex logical query answering (CLQA) is a recently emerged task of graph machine learning that goes beyond simple one-hop link prediction and solves a far more complex task of multi-hop logical reasoning over massive, potentially incomplete graphs in a latent space. The task received a significant traction in the com... | ['Jure Leskovec', 'Zhaocheng Zhu', 'Michael Cochez', 'Mikhail Galkin', 'Hongyu Ren'] | 2023-03-26 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 2.70610917e-02 5.20834267e-01 -5.60489595e-01 -2.81266481e-01
-5.92931271e-01 -5.40420592e-01 4.71545935e-01 5.95330238e-01
1.01710871e-01 3.36611658e-01 1.23381652e-01 -3.70055646e-01
-4.45290089e-01 -1.57136691e+00 -8.44813108e-01 -2.33547345e-01
-2.62494981e-01 9.15496051e-01 5.67469895e-01 -4.06061232... | [9.124780654907227, 7.731012344360352] |
00121970-7864-4a82-921b-4b531a392e38 | explain-adapt-and-retrain-how-to-improve-the | 2303.14939 | null | https://arxiv.org/abs/2303.14939v1 | https://arxiv.org/pdf/2303.14939v1.pdf | Explain, Adapt and Retrain: How to improve the accuracy of a PPM classifier through different explanation styles | Recent papers have introduced a novel approach to explain why a Predictive Process Monitoring (PPM) model for outcome-oriented predictions provides wrong predictions. Moreover, they have shown how to exploit the explanations, obtained using state-of-the art post-hoc explainers, to identify the most common features that... | ['Fabrizio Maria Maggi', 'Chiara Ghidini', 'Chiara Di Francescomarino', 'Williams Rizzi'] | 2023-03-27 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 5.67419052e-01 7.53593326e-01 1.01700783e-01 -3.06296021e-01
-1.95492059e-01 -1.60736114e-01 8.39586318e-01 9.75349784e-01
1.99121758e-01 3.91712010e-01 1.32340327e-01 -5.32999277e-01
-6.92808211e-01 -8.55237544e-01 -6.93333328e-01 -1.04578950e-01
-2.12360770e-01 7.58717835e-01 4.13569987e-01 3.66734684... | [8.663803100585938, 5.962955474853516] |
b0f317bd-3c05-43c1-913d-c6d4ce972920 | learned-sorted-table-search-and-static | 2107.09480 | null | https://arxiv.org/abs/2107.09480v6 | https://arxiv.org/pdf/2107.09480v6.pdf | Learned Sorted Table Search and Static Indexes in Small Model Space | Machine Learning Techniques, properly combined with Data Structures, have resulted in Learned Static Indexes, innovative and powerful tools that speed-up Binary Search, with the use of additional space with respect to the table being searched into. Such space is devoted to the Machine Learning Model. Although in their ... | ['Raffaele Giancarlo', 'Giosuè Lo Bosco', 'Domenico Amato'] | 2021-07-19 | null | null | null | null | ['table-search'] | ['natural-language-processing'] | [ 4.86086085e-02 -1.56360477e-01 -4.65705901e-01 5.14782779e-02
-5.57001054e-01 -7.53768027e-01 7.17914343e-01 6.94266796e-01
-6.97688639e-01 6.42682910e-01 -2.02842250e-01 -6.34555042e-01
-8.21397305e-01 -1.07109141e+00 -6.43948138e-01 -6.03464901e-01
-1.05909787e-01 7.70491123e-01 6.98219419e-01 -4.52932149... | [8.361262321472168, 4.243413925170898] |
2581c8b3-c4ef-4be5-b4fa-4c90b56c754c | ga-drl-graph-neural-network-augmented-deep | 2307.00777 | null | https://arxiv.org/abs/2307.00777v1 | https://arxiv.org/pdf/2307.00777v1.pdf | GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds | Vehicular clouds (VCs) are modern platforms for processing of computation-intensive tasks over vehicles. Such tasks are often represented as directed acyclic graphs (DAGs) consisting of interdependent vertices/subtasks and directed edges. In this paper, we propose a graph neural network-augmented deep reinforcement lea... | ['Huaiyu Dai', 'Seyyedali Hosseinalipour', 'Manman Luo', 'Zhibin Gao', 'Lianfen Huang', 'Zhang Liu'] | 2023-07-03 | null | null | null | null | ['graph-attention'] | ['graphs'] | [-2.67853022e-01 -8.82402137e-02 -2.95396358e-01 -2.31842488e-01
-2.56321549e-01 -4.33876425e-01 4.33632344e-01 -5.16783856e-02
-2.07149580e-01 7.40694046e-01 6.57360628e-02 -7.66389489e-01
-2.03061193e-01 -7.61392772e-01 -9.33764100e-01 -6.22820377e-01
-4.79777783e-01 7.28972614e-01 6.49351895e-01 -3.17944854... | [5.932770252227783, 1.0224945545196533] |
3f28cbd1-12de-4799-99bc-38cf3d6df855 | unsupervised-3d-shape-reconstruction-by-part | 2303.01999 | null | https://arxiv.org/abs/2303.01999v1 | https://arxiv.org/pdf/2303.01999v1.pdf | Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly | Representing a 3D shape with a set of primitives can aid perception of structure, improve robotic object manipulation, and enable editing, stylization, and compression of 3D shapes. Existing methods either use simple parametric primitives or learn a generative shape space of parts. Both have limitations: parametric pri... | ['Daniel Ritchie', 'Siddhartha Chaudhuri', 'Matthew Fisher', 'Paul Guerrero', 'Xianghao Xu'] | 2023-03-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Xu_Unsupervised_3D_Shape_Reconstruction_by_Part_Retrieval_and_Assembly_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_Unsupervised_3D_Shape_Reconstruction_by_Part_Retrieval_and_Assembly_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-shape-reconstruction'] | ['computer-vision'] | [-1.66032419e-01 1.87220648e-01 -2.12016866e-01 -1.08575456e-01
-3.67950082e-01 -1.11016512e+00 7.48434663e-01 1.86878473e-01
1.50716200e-01 1.50911212e-01 2.07625523e-01 5.82781807e-02
-2.06325024e-01 -9.55240607e-01 -7.21835077e-01 -4.72544104e-01
3.12871486e-01 1.25797582e+00 4.15207863e-01 -1.99460208... | [8.768264770507812, -3.6005115509033203] |
1540d753-af6d-49b7-9de9-6b6cf62119b5 | t360rrd-a-dataset-for-360-degree-rotated | 2303.01894 | null | https://arxiv.org/abs/2303.01894v3 | https://arxiv.org/pdf/2303.01894v3.pdf | TRR360D: A dataset for 360 degree rotated rectangular box table detection | To address the problem of scarcity and high annotation costs of rotated image table detection datasets, this paper proposes a method for building a rotated image table detection dataset. Based on the ICDAR2019MTD modern table detection dataset, we refer to the annotation format of the DOTA dataset to create the TRR360D... | ['Minglei Tong', 'Wenxing Hu'] | 2023-03-03 | null | null | null | null | ['2d-object-detection', 'table-detection'] | ['computer-vision', 'miscellaneous'] | [ 2.04743624e-01 -2.63302810e-02 -4.43137020e-01 -1.54089242e-01
-9.43251729e-01 -8.46216142e-01 3.43034565e-01 -6.64023608e-02
-1.20722599e-01 2.60283649e-01 1.87192619e-01 -1.20024957e-01
-7.75380954e-02 -8.66257250e-01 -5.22960901e-01 -3.81779134e-01
1.64903075e-01 6.47020578e-01 9.45054814e-02 -5.79473823... | [11.697653770446777, 2.985502004623413] |
a2a35da1-0332-4e91-8c23-dbfe860a28fb | recognising-known-configurations-of-garments | 2205.00225 | null | https://arxiv.org/abs/2205.00225v2 | https://arxiv.org/pdf/2205.00225v2.pdf | Recognising Known Configurations of Garments For Dual-Arm Robotic Flattening | Robotic deformable-object manipulation is a challenge in the robotic industry because deformable objects have complicated and various object states. Predicting those object states and updating manipulation planning is time-consuming and computationally expensive. In this paper, we propose learning known configurations ... | ['Gerardo Argon-Camarasa', 'Li Duan'] | 2022-04-30 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-5.72642609e-02 3.06956265e-02 -4.03461903e-01 -3.58481616e-01
-8.03826079e-02 -8.32264364e-01 9.36328024e-02 -5.55427074e-01
-2.61737686e-02 6.99930847e-01 -2.45422021e-01 2.46423900e-01
-5.13119459e-01 -7.49512792e-01 -8.05302739e-01 -6.18124902e-01
-3.53304476e-01 1.24670315e+00 5.15819073e-01 -4.64019567... | [5.021374225616455, 0.18460458517074585] |
acc4f64b-850a-4995-a979-6da51ef049e5 | unsupervised-learning-of-landmarks-by | 1908.06427 | null | https://arxiv.org/abs/1908.06427v1 | https://arxiv.org/pdf/1908.06427v1.pdf | Unsupervised Learning of Landmarks by Descriptor Vector Exchange | Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision. However, this method does not explicitly guarantee that the learned landmarks are consistent with changes between different instances of the sa... | ['Hakan Bilen', 'Andrea Vedaldi', 'Samuel Albanie', 'James Thewlis'] | 2019-08-18 | unsupervised-learning-of-landmarks-by-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Thewlis_Unsupervised_Learning_of_Landmarks_by_Descriptor_Vector_Exchange_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Thewlis_Unsupervised_Learning_of_Landmarks_by_Descriptor_Vector_Exchange_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 5.44876941e-02 7.80264363e-02 -1.22037388e-01 -4.94380385e-01
-3.98620993e-01 -8.17006171e-01 8.30165327e-01 -7.31218457e-02
-5.06616414e-01 4.12701488e-01 -1.24147478e-02 3.54063928e-01
-3.13639849e-01 -6.12116456e-01 -9.15293932e-01 -7.67179489e-01
1.54329106e-01 4.90177602e-01 1.60960168e-01 -2.43761614... | [8.115537643432617, -1.9960315227508545] |
5049ad36-f2a5-485f-be40-b8ea788651b5 | hulmona-the-universal-language-model-in | null | null | https://aclanthology.org/W19-4608 | https://aclanthology.org/W19-4608.pdf | hULMonA: The Universal Language Model in Arabic | Arabic is a complex language with limited resources which makes it challenging to produce accurate text classification tasks such as sentiment analysis. The utilization of transfer learning (TL) has recently shown promising results for advancing accuracy of text classification in English. TL models are pre-trained on l... | ['Wassim El-Hajj', 'Obeida ElJundi', 'Nour El Droubi', 'Hazem Hajj', 'Wissam Antoun', 'Khaled Shaban'] | 2019-08-01 | null | null | null | ws-2019-8 | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [-3.50407451e-01 -2.32690394e-01 -1.67746976e-01 -5.37202954e-01
-8.74456644e-01 -5.92921376e-01 7.54678547e-01 3.57322007e-01
-5.88340580e-01 8.08577478e-01 -7.17196465e-02 -4.00651276e-01
1.65994972e-01 -7.34006941e-01 -4.04516816e-01 -4.15196478e-01
-1.04067750e-01 9.16836560e-01 1.34862691e-01 -1.14190185... | [11.12940502166748, 7.066923141479492] |
deacced6-1ec6-487d-8c80-e7531238942b | 3d-line-mapping-revisited | 2303.17504 | null | https://arxiv.org/abs/2303.17504v1 | https://arxiv.org/pdf/2303.17504v1.pdf | 3D Line Mapping Revisited | In contrast to sparse keypoints, a handful of line segments can concisely encode the high-level scene layout, as they often delineate the main structural elements. In addition to offering strong geometric cues, they are also omnipresent in urban landscapes and indoor scenes. Despite their apparent advantages, current l... | ['Viktor Larsson', 'Marc Pollefeys', 'Rémi Pautrat', 'Yifan Yu', 'Shaohui Liu'] | 2023-03-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Liu_3D_Line_Mapping_Revisited_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Liu_3D_Line_Mapping_Revisited_CVPR_2023_paper.pdf | cvpr-2023-1 | ['visual-localization'] | ['computer-vision'] | [-6.98015317e-02 -3.41997057e-01 -2.76801497e-01 -9.89271849e-02
-7.33937383e-01 -9.16605175e-01 7.59290516e-01 2.61667579e-01
1.10113889e-01 3.54957134e-01 4.35539484e-02 -4.37823921e-01
-1.56250730e-01 -9.96846855e-01 -7.98631430e-01 -1.24583244e-01
-5.73836416e-02 3.94715548e-01 4.35843050e-01 -3.07192713... | [7.965060234069824, -2.276822328567505] |
2483abd4-c3f7-426f-915d-4059a71ff2c9 | a-parallel-algorithm-for-exact-bayesian | 1408.1664 | null | http://arxiv.org/abs/1408.1664v3 | http://arxiv.org/pdf/1408.1664v3.pdf | A Parallel Algorithm for Exact Bayesian Structure Discovery in Bayesian Networks | Exact Bayesian structure discovery in Bayesian networks requires exponential
time and space. Using dynamic programming (DP), the fastest known sequential
algorithm computes the exact posterior probabilities of structural features in
$O(2(d+1)n2^n)$ time and space, if the number of nodes (variables) in the
Bayesian netw... | ['Yetian Chen', 'Olga Nikolova', 'Jin Tian', 'Srinivas Aluru'] | 2014-08-07 | null | null | null | null | ['2048'] | ['playing-games'] | [ 2.57329136e-01 3.73552628e-02 7.06037804e-02 -2.60632634e-01
-4.92158890e-01 -6.27276480e-01 2.62466520e-02 3.45307469e-01
-8.08710337e-01 8.17674994e-01 -6.70340776e-01 -5.40104747e-01
-6.12574339e-01 -1.13015175e+00 -7.12701261e-01 -1.00977468e+00
-1.03865850e+00 1.03839231e+00 5.14169157e-01 2.49964416... | [6.728865623474121, 4.8850274085998535] |
ccac7b0f-18e2-4ef7-a0fa-5b47fe80aa6e | attention-based-cnn-lstm-and-xgboost-hybrid | 2204.02623 | null | https://arxiv.org/abs/2204.02623v2 | https://arxiv.org/pdf/2204.02623v2.pdf | Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction | Stock market plays an important role in the economic development. Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors. The traditional time series model ARIMA can not describe the nonlinearity, and can not achieve satisfac... | ['Jian Wu', 'Guangliang Mo', 'Yang Hu', 'Zhuangwei Shi'] | 2022-04-06 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-9.30188775e-01 -6.51288450e-01 -2.08344147e-01 -1.75737143e-01
6.35884553e-02 -2.17507064e-01 3.51624548e-01 -6.95784986e-01
-3.92259240e-01 5.54260969e-01 1.60948426e-01 -6.20753706e-01
-7.20548257e-02 -1.21972156e+00 -5.49920917e-01 -6.49879754e-01
-2.16044828e-01 -6.38163707e-04 2.58592218e-01 -4.02363986... | [4.455032825469971, 4.234303951263428] |
0e94297b-0d66-4ed1-ac49-5ee20c4151d4 | a-benchmark-for-generalizable-and | 2201.05793 | null | https://arxiv.org/abs/2201.05793v1 | https://arxiv.org/pdf/2201.05793v1.pdf | A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases | Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily on generic multi-hop reasoning over explicit facts, largely ignoring other reasoning types such as temporal, spatial, and taxonomic reasonin... | ['L Venkata Subramaniam', 'Francois Luus', 'Ryan Riegel', 'Guilherme Lima', 'Alexander Gray', 'Salim Roukos', 'Rosario Uceda-Sosa', 'Maria Chang', 'Sairam Gurajada', 'Srinivas Ravishankar', 'Dinesh Khandelwal', 'G P Shrivatsa Bhargav', 'Achille Fokoue', 'Dinesh Garg', 'Saswati Dana', 'Cezar Pendus', 'Santosh Srivastava... | 2022-01-15 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-5.51776350e-01 2.79888064e-01 -4.43152666e-01 -4.15750831e-01
-9.11042333e-01 -9.07773614e-01 4.96004164e-01 4.17902559e-01
-2.30679929e-01 1.01973176e+00 5.44476211e-01 -4.03972715e-01
-5.44192016e-01 -1.26494646e+00 -6.74714029e-01 -1.60433531e-01
1.60302855e-02 9.82446492e-01 1.04435825e+00 -7.62192607... | [10.216981887817383, 7.967022895812988] |
ea056d51-ca20-4970-a1ba-d1140f7a5356 | you-only-evaluate-once-a-simple-baseline | 2110.02304 | null | https://arxiv.org/abs/2110.02304v1 | https://arxiv.org/pdf/2110.02304v1.pdf | You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL | The goal of offline reinforcement learning (RL) is to find an optimal policy given prerecorded trajectories. Many current approaches customize existing off-policy RL algorithms, especially actor-critic algorithms in which policy evaluation and improvement are iterated. However, the convergence of such approaches is not... | ['Scott Niekum', 'Wonjoon Goo'] | 2021-10-05 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.63733736e-01 1.70101151e-01 -5.96945524e-01 -8.90836269e-02
-7.98043489e-01 -7.86274612e-01 8.50888908e-01 2.01841280e-01
-7.39486098e-01 1.03456986e+00 1.04974605e-01 -6.68713868e-01
-1.52690783e-01 -3.07891697e-01 -6.21766627e-01 -7.16296554e-01
-3.45924944e-01 3.88513863e-01 9.99684930e-02 -4.35347676... | [4.110957622528076, 2.1513941287994385] |
1ea0bdcc-671e-4619-b2f5-2611b7a815d6 | revisiting-graph-neural-networks-for-link-1 | 2010.16103 | null | https://arxiv.org/abs/2010.16103v5 | https://arxiv.org/pdf/2010.16103v5.pdf | Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning | In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a representation for a set of more than one node, such as link). We know that GNN is designed to learn single-node representations. When we want to learn a node set repres... | ['Long Jin', 'Kai Wang', 'Yinglong Xia', 'Pan Li', 'Muhan Zhang'] | 2020-10-30 | revisiting-graph-neural-networks-for-link | http://proceedings.neurips.cc/paper/2021/hash/4be49c79f233b4f4070794825c323733-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/4be49c79f233b4f4070794825c323733-Paper.pdf | neurips-2021-12 | ['link-property-prediction'] | ['graphs'] | [ 3.20870221e-01 7.47406065e-01 -5.30584395e-01 -2.72524357e-01
-3.55225623e-01 -6.16817296e-01 4.35324490e-01 4.94106710e-01
2.58945171e-02 5.47603846e-01 -1.17276445e-01 -6.17336571e-01
-4.77315307e-01 -1.43678880e+00 -7.14821994e-01 -6.90930426e-01
-4.49548632e-01 7.78327107e-01 4.14903834e-02 -5.08357465... | [7.042252063751221, 6.24869441986084] |
94f9a74a-e22e-4008-a6c1-066a392538fa | icn-interactive-convolutional-network-for | 2306.13897 | null | https://arxiv.org/abs/2306.13897v1 | https://arxiv.org/pdf/2306.13897v1.pdf | ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility | Accurate shared micromobility demand predictions are essential for transportation planning and management. Although deep learning models provide powerful tools to deal with demand prediction problems, studies on forecasting highly-accurate spatiotemporal shared micromobility demand are still lacking. This paper propose... | ['Xilei Zhao', 'Xiaojian Zhang', 'Qian Ke', 'Yiming Xu'] | 2023-06-24 | null | null | null | null | ['management'] | ['miscellaneous'] | [-5.84339082e-01 -6.14769876e-01 -5.64347148e-01 -7.42204368e-01
-3.88480484e-01 -2.71871686e-01 4.53918934e-01 -1.53941587e-01
-2.19890088e-01 4.95497346e-01 4.62421507e-01 -8.36724997e-01
-3.85549188e-01 -1.24920893e+00 -4.16175812e-01 -5.40093780e-01
-4.02450651e-01 3.93182427e-01 1.79628238e-01 -5.86051106... | [6.435644149780273, 2.0472424030303955] |
d9749c36-a06b-458c-9ce3-1046f2062cfc | egfi-drug-drug-interaction-extraction-and | 2101.09914 | null | https://arxiv.org/abs/2101.09914v1 | https://arxiv.org/pdf/2101.09914v1.pdf | EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence Information | The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we pr... | ['Ka-Chun Wong', 'Linqi Song', 'Xiangtao Li', 'Jiecong Lin', 'Lei Huang'] | 2021-01-25 | null | null | null | null | ['drug-drug-interaction-extraction'] | ['natural-language-processing'] | [ 3.27766001e-01 9.42270160e-02 -3.11801791e-01 -2.42693216e-01
-1.06999433e+00 -2.84598261e-01 3.73691916e-01 4.74433959e-01
-4.00214851e-01 1.42111099e+00 3.71441841e-01 -4.87104356e-01
-3.82232696e-01 -6.80421472e-01 -8.90825391e-01 -6.07461452e-01
2.04548240e-01 5.15670419e-01 -7.20955655e-02 -3.08378842... | [8.46512508392334, 8.691789627075195] |
7908e0bc-81c7-46b0-b799-b899d0fba035 | human-art-a-versatile-human-centric-dataset | 2303.02760 | null | https://arxiv.org/abs/2303.02760v2 | https://arxiv.org/pdf/2303.02760v2.pdf | Human-Art: A Versatile Human-Centric Dataset Bridging Natural and Artificial Scenes | Humans have long been recorded in a variety of forms since antiquity. For example, sculptures and paintings were the primary media for depicting human beings before the invention of cameras. However, most current human-centric computer vision tasks like human pose estimation and human image generation focus exclusively... | ['Lei Zhang', 'Qiang Xu', 'Jianan Wang', 'Ailing Zeng', 'Xuan Ju'] | 2023-03-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ju_Human-Art_A_Versatile_Human-Centric_Dataset_Bridging_Natural_and_Artificial_Scenes_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ju_Human-Art_A_Versatile_Human-Centric_Dataset_Bridging_Natural_and_Artificial_Scenes_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-human-pose-estimation', 'human-detection'] | ['computer-vision', 'computer-vision'] | [ 8.58318731e-02 -2.27691203e-01 3.31593864e-02 -1.52240500e-01
-1.20801784e-01 -5.41621089e-01 1.04391348e+00 -3.74173701e-01
-4.37430322e-01 6.45753086e-01 3.97414178e-01 2.85398006e-01
4.11079079e-01 -6.21668458e-01 -5.54507017e-01 -3.93554568e-01
1.06262647e-01 7.92616308e-01 3.31897438e-01 -5.32386720... | [7.234841346740723, -0.8071698546409607] |
166df0f1-041f-4774-b2c7-1bdcab30cef1 | camb-at-cwi-shared-task-2018-complex-word | null | null | https://aclanthology.org/W18-0520 | https://aclanthology.org/W18-0520.pdf | CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting | This paper presents the winning systems we submitted to the Complex Word Identification Shared Task 2018. We describe our best performing systems{'} implementations and discuss our key findings from this research. Our best-performing systems achieve an F1 score of 0.8792 on the NEWS, 0.8430 on the WIKINEWS and 0.8115 o... | ['Sian Gooding', 'Ekaterina Kochmar'] | 2018-06-01 | null | null | null | ws-2018-6 | ['complex-word-identification'] | ['natural-language-processing'] | [-0.46704742 0.10509726 -0.31144825 -0.14912735 -0.9989537 -0.82055056
0.78671753 0.2623794 -1.2306752 1.0890563 0.13030738 -0.4339785
-0.30762047 -0.41061085 -0.41613773 -0.38474357 -0.24123041 0.75750357
0.17366843 -0.47076482 0.10019413 -0.20689127 -1.1906964 0.11315796
1.0172603 0.94888055 0.1... | [10.492707252502441, 10.480818748474121] |
2a32d207-e13c-4fe8-b870-4b5b936d947e | multiview-deep-learning-for-predicting | 1712.08091 | null | http://arxiv.org/abs/1712.08091v1 | http://arxiv.org/pdf/1712.08091v1.pdf | Multiview Deep Learning for Predicting Twitter Users' Location | The problem of predicting the location of users on large social networks like
Twitter has emerged from real-life applications such as social unrest detection
and online marketing. Twitter user geolocation is a difficult and active
research topic with a vast literature. Most of the proposed methods follow
either a conte... | ['Bruno Cornelis', 'Tien Huu Do', 'Nikos Deligiannis', 'Duc Minh Nguyen', 'Evaggelia Tsiligianni'] | 2017-12-21 | null | null | null | null | ['multiview-learning'] | ['computer-vision'] | [-1.87193736e-01 -5.81659302e-02 -5.00494242e-01 -2.14577526e-01
-4.24286067e-01 -3.63998801e-01 9.83624101e-01 6.25784814e-01
-6.47955954e-01 6.31339967e-01 5.20471215e-01 -9.95110795e-02
-1.22972906e-01 -1.06471205e+00 -5.90780914e-01 -5.43594718e-01
2.05578953e-02 3.33202183e-01 1.17544793e-01 -3.30088258... | [9.982928276062012, 6.805452823638916] |
2c955ae0-572b-43f3-b16b-60f51e1262e3 | example-guided-learning-of-stochastic-human | null | null | https://link.springer.com/article/10.1007/s00521-022-07947-2 | https://link.springer.com/article/10.1007/s00521-022-07947-2 | Example-guided learning of stochastic human driving policies using deep reinforcement learning | Deep reinforcement learning has been successfully applied to the generation of goal-directed behavior in artificial agents. However, existing algorithms are often not designed to reproduce human-like behavior, which may be desired in many environments, such as human–robot collaborations, social robotics and autonomous ... | ['Armin Biess', 'Avinoam Borowsky', 'Rotem Duffney', 'Ran Emuna'] | 2022-12-23 | null | null | null | neural-computing-and-applications-2022-12 | ['unity'] | ['computer-vision'] | [-7.24584563e-03 3.81681263e-01 3.09676290e-01 -3.61009389e-01
-4.01656896e-01 -2.36732751e-01 8.71571302e-01 5.51746376e-02
-7.13737071e-01 1.05626607e+00 -2.55275697e-01 -5.10335624e-01
-1.85579821e-01 -9.61332560e-01 -9.13830340e-01 -6.95593297e-01
-2.16260329e-01 9.60180283e-01 2.18535751e-01 -6.62136972... | [4.403140068054199, 1.538069486618042] |
6d5fe98c-e354-4037-9573-5ab688b4872f | reference-based-variational-autoencoders | null | null | https://openreview.net/forum?id=H1e1XeXlP4 | https://openreview.net/pdf?id=H1e1XeXlP4 | Reference-based Variational Autoencoders | Learning disentangled representations from visual data, where different high-level generative factors are independently encoded, is of importance for many computer vision tasks. Solving this problem, however, typically requires to explicitly label all the factors of interest in training images. To alleviate the annota... | ['Jakob Verbeek', 'Xavier Binefa', 'Oriol Martinez', 'Adrià Ruiz'] | 2019-03-08 | null | null | null | iclr-workshop-lld-2019 | ['conditional-image-generation'] | ['computer-vision'] | [ 3.90864104e-01 5.00483632e-01 -1.81475252e-01 -4.08645481e-01
-7.73231328e-01 -5.76246679e-01 9.58690107e-01 -4.03184533e-01
-3.06853801e-01 8.39604497e-01 1.67546615e-01 1.72630876e-01
-6.34700246e-03 -5.99676609e-01 -9.49439645e-01 -1.18536425e+00
2.80981779e-01 4.83056217e-01 -3.60112667e-01 2.07300168... | [10.960392951965332, 0.5820977091789246] |
8eda43de-30bf-422c-8fad-7a2e410f96a0 | differences-between-human-and-machine | 2011.14036 | null | https://arxiv.org/abs/2011.14036v1 | https://arxiv.org/pdf/2011.14036v1.pdf | Differences between human and machine perception in medical diagnosis | Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which human perception remains invariant. If we can better understand the differences between human and machine perception, we can potentially char... | ['Krzysztof J. Geras', 'Kyunghyun Cho', 'Linda Moy', 'Laura Heacock', 'Daniel K. Sodickson', 'James Park', 'Alice Kim', 'Linda Du', 'Divya Awal', 'Hildegard Toth', 'Beatriu Reig', 'Kristine Pysarenko', 'Jiyon Lee', 'Eric Kim', 'Chloe Chhor', 'Naziya Samreen', 'Robin Ehrenpreis', 'Celin Chacko', 'Witold Oleszkiewicz', '... | 2020-11-28 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 8.42648327e-01 6.79438293e-01 -1.33971483e-01 -1.91044107e-01
-8.59211564e-01 -7.71788239e-01 5.76786697e-01 5.23218870e-01
-5.49173772e-01 2.83229649e-01 5.80420494e-01 -5.73230207e-01
-4.75742757e-01 -7.13484406e-01 -6.56936467e-01 -8.68944407e-01
-4.99003194e-02 1.38212457e-01 1.49394006e-01 1.66800637... | [15.069869041442871, -2.318751811981201] |
a0111632-dee5-449d-9d27-f0839a819427 | conditional-generative-modeling-is-all-you | 2305.12569 | null | https://arxiv.org/abs/2305.12569v1 | https://arxiv.org/pdf/2305.12569v1.pdf | Conditional Generative Modeling is All You Need for Marked Temporal Point Processes | Recent advancements in generative modeling have made it possible to generate high-quality content from context information, but a key question remains: how to teach models to know when to generate content? To answer this question, this study proposes a novel event generative model that draws its statistical intuition f... | ['Shixiang Zhu', 'Zekai Fan', 'Zheng Dong'] | 2023-05-21 | null | null | null | null | ['point-processes'] | ['methodology'] | [ 1.71066135e-01 -1.55262128e-01 -4.38069403e-02 -2.24025875e-01
-9.59200561e-01 -5.20794630e-01 1.02965879e+00 2.09912926e-01
-5.22209480e-02 9.08284485e-01 2.70644993e-01 -2.26210207e-01
-1.90803155e-01 -1.15284586e+00 -8.05111587e-01 -7.65865564e-01
-1.36302337e-01 7.04205334e-01 1.70844153e-01 1.10503733... | [7.089450836181641, 3.54091477394104] |
c0be3a62-9eb1-44be-99d8-83e90ab5322e | 3d-bevis-birds-eye-view-instance-segmentation | 1904.02199 | null | https://arxiv.org/abs/1904.02199v3 | https://arxiv.org/pdf/1904.02199v3.pdf | 3D-BEVIS: Bird's-Eye-View Instance Segmentation | Recent deep learning models achieve impressive results on 3D scene analysis tasks by operating directly on unstructured point clouds. A lot of progress was made in the field of object classification and semantic segmentation. However, the task of instance segmentation is less explored. In this work, we present 3D-BEVIS... | ['Francis Engelmann', 'Cathrin Elich', 'Theodora Kontogianni', 'Bastian Leibe'] | 2019-04-03 | null | null | null | null | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [-1.08701818e-01 2.03508362e-02 3.67051549e-02 -7.65601814e-01
-4.74779516e-01 -6.49391055e-01 5.45235336e-01 4.66343850e-01
-2.08795384e-01 -1.45358071e-01 -4.23306853e-01 -1.42813949e-02
-1.07690450e-02 -1.11841750e+00 -8.98387015e-01 -3.69076639e-01
3.04798763e-02 9.73587930e-01 4.82617378e-01 -3.43360640... | [8.061105728149414, -3.20847225189209] |
54e58487-f66c-43e2-b671-0d1d7a1707f1 | discriminative-unsupervised-feature-learning | 1406.6909 | null | http://arxiv.org/abs/1406.6909v2 | http://arxiv.org/pdf/1406.6909v2.pdf | Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks | Deep convolutional networks have proven to be very successful in learning
task specific features that allow for unprecedented performance on various
computer vision tasks. Training of such networks follows mostly the supervised
learning paradigm, where sufficiently many input-output pairs are required for
training. Acq... | ['Thomas Brox', 'Martin Riedmiller', 'Alexey Dosovitskiy', 'Philipp Fischer', 'Jost Tobias Springenberg'] | 2014-06-26 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [ 4.17051047e-01 -1.85547128e-01 -2.73938388e-01 -6.28613174e-01
-6.44552410e-01 -5.11118233e-01 1.05097890e+00 3.49631280e-01
-8.02569687e-01 5.99397659e-01 -2.99888819e-01 4.61337231e-02
-4.44676071e-01 -7.87185490e-01 -7.46483147e-01 -7.80576229e-01
-1.32728651e-01 5.90282738e-01 2.34422132e-01 -2.67959356... | [9.503396987915039, 2.2626307010650635] |
b259d9ab-43fa-4e00-af8b-3768ed37bd0a | lamd-latent-motion-diffusion-for-video | 2304.11603 | null | https://arxiv.org/abs/2304.11603v1 | https://arxiv.org/pdf/2304.11603v1.pdf | LaMD: Latent Motion Diffusion for Video Generation | Generating coherent and natural movement is the key challenge in video generation. This research proposes to condense video generation into a problem of motion generation, to improve the expressiveness of motion and make video generation more manageable. This can be achieved by breaking down the video generation proces... | ['Chong Luo', 'Zhenzhong Chen', 'Yaosi Hu'] | 2023-04-23 | null | null | null | null | ['video-generation', 'video-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 1.43319413e-01 -2.16966689e-01 -1.95648432e-01 2.23919779e-01
-5.53579152e-01 -4.68420208e-01 8.36341381e-01 -8.63252938e-01
1.23030499e-01 6.78536534e-01 6.73614085e-01 -6.46883175e-02
1.16282448e-01 -1.04702532e+00 -9.11778390e-01 -1.02524698e+00
-2.19756756e-02 6.38640746e-02 1.49575800e-01 -1.61663339... | [10.837860107421875, -0.5500283241271973] |
be5aa763-1142-4a52-80af-299e1d80f2f0 | bargainnet-background-guided-domain | 2009.09169 | null | https://arxiv.org/abs/2009.09169v2 | https://arxiv.org/pdf/2009.09169v2.pdf | BargainNet: Background-Guided Domain Translation for Image Harmonization | Image composition is a fundamental operation in image editing field. However, unharmonious foreground and background downgrade the quality of composite image. Image harmonization, which adjusts the foreground to improve the consistency, is an essential yet challenging task. Previous deep learning based methods mainly f... | ['Wenyan Cong', 'Li Niu', 'Jing Liang', 'Jianfu Zhang', 'Liqing Zhang'] | 2020-09-19 | null | null | null | null | ['image-harmonization'] | ['computer-vision'] | [ 4.34860200e-01 -2.04779446e-01 -8.34643170e-02 -3.07120472e-01
-5.02919495e-01 -4.78659570e-01 4.69262570e-01 -2.93641120e-01
-2.04204947e-01 6.31326199e-01 2.84993947e-01 -1.29702881e-01
1.27660543e-01 -6.85329199e-01 -8.38059664e-01 -1.01390183e+00
8.99135828e-01 2.11765748e-02 1.80089310e-01 -1.93534583... | [11.20981216430664, -1.2640913724899292] |
b93d6eb1-9dc0-4aff-8fca-cda121fdea1d | d3rlpy-an-offline-deep-reinforcement-learning | 2111.03788 | null | https://arxiv.org/abs/2111.03788v2 | https://arxiv.org/pdf/2111.03788v2.pdf | d3rlpy: An Offline Deep Reinforcement Learning Library | In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL) library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy online algorithms via a fully documented plug-and-play API. To address a reproducibility issue, we conduct a large-scale benchmark wi... | ['Michita Imai', 'Takuma Seno'] | 2021-11-06 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.05313432e+00 -1.62519097e-01 -5.49651146e-01 -2.47168019e-01
-9.69584048e-01 -8.97379994e-01 4.64147687e-01 -1.71826571e-01
-4.57632512e-01 1.04837573e+00 1.49599031e-01 -7.12892652e-01
2.12633729e-01 -7.48138964e-01 -7.75693774e-01 -5.17739415e-01
-2.62397975e-01 3.82537037e-01 2.05667987e-01 -1.36241332... | [4.0651984214782715, 1.5559855699539185] |
4205edc5-b27e-47ec-97e5-75e3f4ee4906 | matched-sample-selection-with-gans-for | 2103.13455 | null | https://arxiv.org/abs/2103.13455v1 | https://arxiv.org/pdf/2103.13455v1.pdf | Matched sample selection with GANs for mitigating attribute confounding | Measuring biases of vision systems with respect to protected attributes like gender and age is critical as these systems gain widespread use in society. However, significant correlations between attributes in benchmark datasets make it difficult to separate algorithmic bias from dataset bias. To mitigate such attribute... | ['Pietro Perona', 'Guha Balakrishnan', 'Chandan Singh'] | 2021-03-24 | null | null | null | null | ['gender-bias-detection', 'gender-bias-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 5.29827774e-01 1.63966432e-01 -2.86596477e-01 -1.02350593e+00
-6.78250730e-01 -6.77485764e-01 7.42490351e-01 4.81251851e-02
-4.89567935e-01 4.56819743e-01 5.53663075e-01 4.36846819e-03
-2.45547183e-02 -8.79541457e-01 -7.41682112e-01 -6.47616565e-01
2.94581652e-01 2.43335992e-01 -4.91770148e-01 2.26827249... | [13.048284530639648, 1.1712429523468018] |
8793aaaf-a706-4bb3-bf40-55cb445a3303 | a-generative-modeling-approach-to-limited | 1802.06458 | null | http://arxiv.org/abs/1802.06458v3 | http://arxiv.org/pdf/1802.06458v3.pdf | A Generative Modeling Approach to Limited Channel ECG Classification | Processing temporal sequences is central to a variety of applications in
health care, and in particular multi-channel Electrocardiogram (ECG) is a
highly prevalent diagnostic modality that relies on robust sequence modeling.
While Recurrent Neural Networks (RNNs) have led to significant advances in
automated diagnosis ... | ['Jayaraman J. Thiagarajan', 'Deepta Rajan'] | 2018-02-18 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 7.97717929e-01 -1.13448367e-01 2.88388748e-02 -2.88491666e-01
-8.73697281e-01 -4.10552710e-01 3.17834616e-01 9.68566760e-02
-2.85063386e-01 8.29183638e-01 3.59819233e-01 -3.97175997e-01
-3.11780870e-01 -4.11207318e-01 -2.88289309e-01 -8.47874463e-01
-1.09879814e-01 4.56926495e-01 -1.46250367e-01 -1.54516876... | [14.248808860778809, 3.3317036628723145] |
38d78163-b6fb-4617-b787-69ec5b35ab83 | uav-images-dataset-for-moving-object | 2103.11460 | null | https://arxiv.org/abs/2103.11460v2 | https://arxiv.org/pdf/2103.11460v2.pdf | UAV Images Dataset for Moving Object Detection from Moving Cameras | This paper presents a new high resolution aerial images dataset in which moving objects are labelled manually. It aims to contribute to the evaluation of the moving object detection methods for moving cameras. The problem of recognizing moving objects from aerial images is one of the important issues in computer vision... | ['Ibrahim Delibasoglu'] | 2021-03-21 | null | null | null | null | ['motion-detection', 'moving-object-detection'] | ['computer-vision', 'computer-vision'] | [ 5.24553657e-01 -6.86944246e-01 1.82011843e-01 6.52113408e-02
2.71120165e-02 -9.15342927e-01 5.42838395e-01 -3.17134380e-01
-6.00848377e-01 7.50790358e-01 -2.39018083e-01 1.22119218e-01
-3.84909451e-01 -5.79941988e-01 -1.08029887e-01 -1.04896092e+00
-1.13105893e-01 -7.65654370e-02 9.91568446e-01 -1.18356667... | [8.770750999450684, -0.9296712279319763] |
d810a6e1-1da3-410f-88c3-b8f63b63ab75 | few-shot-action-recognition-with-implicit | 2010.06215 | null | https://arxiv.org/abs/2010.06215v1 | https://arxiv.org/pdf/2010.06215v1.pdf | Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization | Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application. Although there has been a lot of work in this area recently, most of the existing work is based on image classification tasks. Video-based few-shot action recognition has not bee... | ['Yanning Zhang', 'Peng Wang', 'Qinyi Lv', 'Yajuan Li', 'Congqi Cao'] | 2020-10-13 | null | null | null | null | ['few-shot-action-recognition'] | ['computer-vision'] | [ 5.51633358e-01 -5.26497960e-01 -5.41665554e-01 -4.32153314e-01
-7.24755406e-01 1.25744820e-01 6.41341031e-01 -3.56369317e-01
-4.49949801e-01 4.97083724e-01 1.11064389e-01 2.79025704e-01
-2.33670890e-01 -4.15933460e-01 -6.57884240e-01 -7.91434288e-01
-1.04865897e-02 3.65239978e-02 5.70013046e-01 -8.51786211... | [8.469121932983398, 0.779491662979126] |
5827ef00-2c48-4cc2-9995-47eb7fd82857 | tail-batch-sampling-approximating-global | 2210.12874 | null | https://arxiv.org/abs/2210.12874v4 | https://arxiv.org/pdf/2210.12874v4.pdf | Global Contrastive Batch Sampling via Optimization on Sample Permutations | Contrastive Learning has recently achieved state-of-the-art performance in a wide range of tasks. Many contrastive learning approaches use mined hard negatives to make batches more informative during training but these approaches are inefficient as they increase epoch length proportional to the number of mined negative... | ['Chenguang Zhu', 'ZiYi Yang', 'Vin Sachidananda'] | 2022-10-23 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [-3.77056897e-02 1.09928705e-01 -5.56014359e-01 -6.52551770e-01
-1.14266050e+00 -3.96877795e-01 4.13151801e-01 6.29192650e-01
-9.94461954e-01 7.85588443e-01 -4.32884514e-01 -7.21031964e-01
-2.13525921e-01 -6.80532694e-01 -7.69171119e-01 -3.70569199e-01
-3.73876154e-01 5.74218452e-01 5.00259638e-01 -2.68265992... | [8.700562477111816, 3.514206647872925] |
1f374b51-d18a-4a78-8f41-94e7c14e6a83 | intrinsic-normalization-and-extrinsic | 1609.05104 | null | http://arxiv.org/abs/1609.05104v2 | http://arxiv.org/pdf/1609.05104v2.pdf | Intrinsic normalization and extrinsic denormalization of formant data of vowels | Using a known speaker-intrinsic normalization procedure, formant data are
scaled by the reciprocal of the geometric mean of the first three formant
frequencies. This reduces the influence of the talker but results in a
distorted vowel space. The proposed speaker-extrinsic procedure re-scales the
normalized values by th... | ['A. G. Ramakrishnan', 'T. V. Ananthapadmanabha'] | 2016-09-16 | null | null | null | null | ['vowel-classification'] | ['audio'] | [ 6.25394434e-02 -9.95359793e-02 5.22410274e-02 -3.02427799e-01
-7.00880826e-01 -6.71609282e-01 5.39110720e-01 4.14114505e-01
-7.86625385e-01 4.58687484e-01 6.74546778e-01 -9.31793824e-02
-5.22324294e-02 -5.42584062e-01 -3.16280097e-01 -8.69238377e-01
2.77177423e-01 4.26125266e-02 1.20908238e-01 -2.74462789... | [14.973129272460938, 5.938873767852783] |
bf4eaf69-5461-4446-abca-52bc9db71712 | does-deep-machine-vision-have-just-noticeable | 2102.08168 | null | https://arxiv.org/abs/2102.08168v2 | https://arxiv.org/pdf/2102.08168v2.pdf | Just Noticeable Difference for Deep Machine Vision | As an important perceptual characteristic of the Human Visual System (HVS), the Just Noticeable Difference (JND) has been studied for decades with image and video processing (e.g., perceptual visual signal compression). However, there is little exploration on the existence of JND for the Deep Machine Vision (DMV), alth... | ['Yao Zhao', 'Jian Lou', 'Weisi Lin', 'huan zhang', 'Xin Fu', 'Xingxing Zhang', 'Jian Jin'] | 2021-02-16 | null | null | null | null | ['neural-network-security'] | ['miscellaneous'] | [ 3.84606749e-01 -1.95916086e-01 -2.17118502e-01 -1.41791198e-02
3.02530080e-02 -4.04299190e-03 4.44616348e-01 -3.82862752e-03
-2.29542926e-01 4.16352391e-01 3.08635712e-01 -3.94777596e-01
-1.50709271e-01 -5.61143875e-01 -5.36489069e-01 -8.20594370e-01
-1.00455202e-01 -5.87800324e-01 3.48102897e-01 -2.42956877... | [11.425293922424316, -1.7933350801467896] |
5a679198-6bfa-43f8-ac26-61f93bcf1399 | faasta-a-fast-solver-for-total-variation | 1512.06999 | null | http://arxiv.org/abs/1512.06999v1 | http://arxiv.org/pdf/1512.06999v1.pdf | FAASTA: A fast solver for total-variation regularization of ill-conditioned problems with application to brain imaging | The total variation (TV) penalty, as many other analysis-sparsity problems,
does not lead to separable factors or a proximal operatorwith a closed-form
expression, such as soft thresholding for the $\ell\_1$ penalty. As a result,
in a variational formulation of an inverse problem or statisticallearning
estimation, it l... | ['Michael Eickenberg', 'Gaël Varoquaux', 'Elvis Dohmatob', 'Bertand Thirion'] | 2015-12-22 | null | null | null | null | ['brain-decoding', 'brain-decoding'] | ['medical', 'miscellaneous'] | [ 9.19180140e-02 8.82688239e-02 1.37607992e-01 -4.51909065e-01
-9.93113637e-01 -2.56097198e-01 3.49400938e-01 -9.36873183e-02
-4.76220787e-01 9.00094390e-01 -1.73139438e-01 -2.97304660e-01
-2.01391548e-01 -3.16261768e-01 -8.33820939e-01 -1.12179005e+00
8.76725912e-02 4.16003346e-01 9.42457560e-03 -5.60617894... | [6.969590663909912, 4.153221607208252] |
368525d8-f066-4783-a431-ef19d0090deb | gpatcher-a-simple-and-adaptive-mlp-model-for | 2306.14340 | null | https://arxiv.org/abs/2306.14340v1 | https://arxiv.org/pdf/2306.14340v1.pdf | GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily | While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect the prediction performance of graph neural networks (GNNs)? In this paper, we aim to demystify the impact of graph heterophily on GNN spectr... | ['Dawei Zhou', 'Si Zhang', 'Haohui Wang', 'Shuaicheng Zhang'] | 2023-06-25 | null | null | null | null | ['node-classification'] | ['graphs'] | [-1.02783866e-01 1.89781889e-01 -1.49901032e-01 -1.47407666e-01
3.08335394e-01 -3.34159642e-01 5.27711630e-01 1.48634598e-01
1.07838370e-01 2.94794858e-01 6.53401166e-02 -3.65793020e-01
-3.74190301e-01 -1.36922741e+00 -9.09962893e-01 -7.65321672e-01
-3.82790715e-01 3.65647405e-01 4.14578110e-01 -4.36205238... | [6.9641900062561035, 6.131599426269531] |
3937a5ce-07a2-4ff2-aeef-c0aed54a21e1 | dreamtime-an-improved-optimization-strategy | 2306.12422 | null | https://arxiv.org/abs/2306.12422v1 | https://arxiv.org/pdf/2306.12422v1.pdf | DreamTime: An Improved Optimization Strategy for Text-to-3D Content Creation | Text-to-image diffusion models pre-trained on billions of image-text pairs have recently enabled text-to-3D content creation by optimizing a randomly initialized Neural Radiance Fields (NeRF) with score distillation. However, the resultant 3D models exhibit two limitations: (a) quality concerns such as saturated color ... | ['Lei Zhang', 'Zheng-Jun Zha', 'Xianbiao Qi', 'Yukai Shi', 'Jianan Wang', 'Yukun Huang'] | 2023-06-21 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 4.55756247e-01 -5.27012460e-02 9.97799560e-02 -2.63875306e-01
-7.92236507e-01 -5.42308271e-01 1.04416752e+00 -2.47739494e-01
-2.44790986e-01 7.02355683e-01 6.67391300e-01 -3.05826098e-01
-3.14586200e-02 -8.13838124e-01 -5.59525371e-01 -7.16644108e-01
1.68896690e-01 2.11191684e-01 1.46236330e-01 -2.99285799... | [11.323905944824219, -0.35568946599960327] |
bd207645-22c8-445e-b4d9-c2a6db3b59c4 | polarized-reflection-removal-with-perfect | 2003.12789 | null | https://arxiv.org/abs/2003.12789v1 | https://arxiv.org/pdf/2003.12789v1.pdf | Polarized Reflection Removal with Perfect Alignment in the Wild | We present a novel formulation to removing reflection from polarized images in the wild. We first identify the misalignment issues of existing reflection removal datasets where the collected reflection-free images are not perfectly aligned with input mixed images due to glass refraction. Then we build a new dataset wit... | ['Xuhua Huang', 'Wenxiu Sun', 'Qifeng Chen', 'Mengdi Zhang', 'Qiong Yan', 'Chenyang Lei'] | 2020-03-28 | polarized-reflection-removal-with-perfect-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Lei_Polarized_Reflection_Removal_With_Perfect_Alignment_in_the_Wild_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Lei_Polarized_Reflection_Removal_With_Perfect_Alignment_in_the_Wild_CVPR_2020_paper.pdf | cvpr-2020-6 | ['reflection-removal'] | ['computer-vision'] | [ 8.71944010e-01 -6.68767467e-02 4.93957222e-01 -1.50128648e-01
-6.01520777e-01 -2.43200749e-01 3.30769718e-01 -1.03750587e+00
3.07372641e-02 3.14214885e-01 4.41378623e-01 -1.00534640e-01
9.72369537e-02 -7.82851577e-01 -7.53461897e-01 -1.18084884e+00
5.39459586e-01 -1.58832833e-01 1.40146613e-01 -3.79553437... | [10.482540130615234, -2.7876832485198975] |
629cfc83-d391-4250-bb9e-00d66fb2226a | stroke-extraction-of-chinese-character-based | 2307.04341 | null | https://arxiv.org/abs/2307.04341v1 | https://arxiv.org/pdf/2307.04341v1.pdf | Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration | Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stro... | ['Jian Wang', 'Guanghao Ren', 'Yi Yang', 'Yahan Yu', 'Meng Li'] | 2023-07-10 | null | null | null | null | ['image-registration'] | ['computer-vision'] | [ 3.44979227e-01 -6.48589194e-01 -1.87311649e-01 -3.08029950e-01
-3.78992170e-01 -8.24316859e-01 7.39774823e-01 -3.82772058e-01
-5.79686761e-01 3.05293024e-01 2.94962585e-01 -1.06929056e-01
3.97473089e-02 -1.06133199e+00 -2.95944393e-01 -6.72196090e-01
8.51270378e-01 4.57469016e-01 8.84473801e-01 -1.27297014... | [12.017559051513672, 2.2170488834381104] |
b12e81ff-621e-4998-a973-2a42f4569a35 | 3d-affordancenet-a-benchmark-for-visual | 2103.16397 | null | https://arxiv.org/abs/2103.16397v2 | https://arxiv.org/pdf/2103.16397v2.pdf | 3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding | The ability to understand the ways to interact with objects from visual cues, a.k.a. visual affordance, is essential to vision-guided robotic research. This involves categorizing, segmenting and reasoning of visual affordance. Relevant studies in 2D and 2.5D image domains have been made previously, however, a truly fun... | ['Kui Jia', 'Ke Chen', 'Chaozheng Wu', 'Xun Xu', 'Shengheng Deng'] | 2021-03-30 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Deng_3D_AffordanceNet_A_Benchmark_for_Visual_Object_Affordance_Understanding_CVPR_2021_paper.pdf | cvpr-2021-1 | ['affordance-detection'] | ['computer-vision'] | [-1.38090402e-01 -4.61488105e-02 -3.22614461e-01 -4.59813207e-01
7.23935023e-04 -7.66312599e-01 8.10737491e-01 1.56055138e-01
-1.48110911e-01 5.78002408e-02 2.42716089e-01 -3.02326709e-01
-2.02353612e-01 -3.90244901e-01 -9.29939687e-01 -3.41357619e-01
-2.36884192e-01 7.60863245e-01 2.90988415e-01 -3.49814475... | [5.178782939910889, -0.13537679612636566] |
408a56bd-563f-4f49-8287-7aeabd9c484b | sentiment-perception-adversarial-attacks-on | 2305.01437 | null | https://arxiv.org/abs/2305.01437v2 | https://arxiv.org/pdf/2305.01437v2.pdf | Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems | With the advent of deep learning methods, Neural Machine Translation (NMT) systems have become increasingly powerful. However, deep learning based systems are susceptible to adversarial attacks, where imperceptible changes to the input can cause undesirable changes at the output of the system. To date there has been li... | ['Mark Gales', 'Vyas Raina'] | 2023-05-02 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 8.46222878e-01 2.34323427e-01 3.47686023e-01 -1.81287691e-01
-6.80791795e-01 -1.29351532e+00 6.43022120e-01 -1.08663231e-01
-2.74884820e-01 4.14042026e-01 -1.09324031e-01 -8.49937916e-01
9.07157302e-01 -6.65460110e-01 -1.17091990e+00 -6.21272027e-01
2.64440656e-01 1.20456882e-01 -1.80741027e-01 -5.82840264... | [6.027517795562744, 8.15935230255127] |
1c4a886d-8e09-4905-9d36-10195535ccd8 | despite-super-human-performance-current-llms | 2212.06295 | null | https://arxiv.org/abs/2212.06295v1 | https://arxiv.org/pdf/2212.06295v1.pdf | Despite "super-human" performance, current LLMs are unsuited for decisions about ethics and safety | Large language models (LLMs) have exploded in popularity in the past few years and have achieved undeniably impressive results on benchmarks as varied as question answering and text summarization. We provide a simple new prompting strategy that leads to yet another supposedly "super-human" result, this time outperformi... | ['Abraham J. Fetterman', 'Ellie Kitanidis', 'Joshua Albrecht'] | 2022-12-13 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 3.80766660e-01 8.30642998e-01 -1.45245492e-02 -3.49287897e-01
-8.44956100e-01 -7.19565213e-01 1.01575661e+00 2.96813428e-01
-5.03765523e-01 9.36663628e-01 5.02230108e-01 -6.66324615e-01
1.00356698e-01 -4.54389423e-01 -6.93157017e-01 -2.77510434e-01
4.47866321e-01 4.25552368e-01 -1.83875605e-01 -3.65378886... | [10.372310638427734, 7.607524394989014] |
7ca2142e-d6c8-48cd-9e27-2af2fa841c2f | strategize-before-teaching-a-conversational | 2302.13496 | null | https://arxiv.org/abs/2302.13496v1 | https://arxiv.org/pdf/2302.13496v1.pdf | Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation | Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog. CTSs have become a key pillar in educational data mining research. A key challenge in CTSs is to engage the student in the conversation while exposing them to a diverse set ... | ['Kam-Fai Wong', 'Xingshan Zeng', 'Mrinmaya Sachan', 'Lingzhi Wang'] | 2023-02-27 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [ 4.42405820e-01 4.95322406e-01 -2.32980818e-01 -5.57563424e-01
-1.95736617e-01 -8.13102782e-01 8.52771521e-01 3.42228711e-01
-2.21437916e-01 6.73577249e-01 2.80655831e-01 -6.82464004e-01
-3.73990946e-02 -1.05950880e+00 -2.04433948e-01 -4.02256787e-01
7.72406161e-01 6.32462561e-01 6.10318303e-01 -8.64095628... | [12.228671073913574, 8.074029922485352] |
c1fff60c-bece-4426-b854-0653ccbf97b8 | linear-span-network-for-object-skeleton | 1807.09601 | null | http://arxiv.org/abs/1807.09601v1 | http://arxiv.org/pdf/1807.09601v1.pdf | Linear Span Network for Object Skeleton Detection | Robust object skeleton detection requires to explore rich representative
visual features and effective feature fusion strategies. In this paper, we
first re-visit the implementation of HED, the essential principle of which can
be ideally described with a linear reconstruction model. Hinted by this, we
formalize a Linea... | ['Chang Liu', 'Qixiang Ye', 'Fei Qin', 'Wei Ke'] | 2018-07-25 | linear-span-network-for-object-skeleton-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Chang_Liu_Linear_Span_Network_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Chang_Liu_Linear_Span_Network_ECCV_2018_paper.pdf | eccv-2018-9 | ['object-skeleton-detection'] | ['computer-vision'] | [ 5.22710271e-02 -3.28435972e-02 -1.64535865e-02 -1.11285314e-01
-2.69332290e-01 -3.16627771e-02 3.30348551e-01 -6.12230599e-01
-2.55099446e-01 4.57888633e-01 1.59737557e-01 1.42180845e-01
-2.88354546e-01 -7.11967170e-01 -8.06896329e-01 -7.56582439e-01
2.59618253e-01 -3.14799726e-01 4.85942185e-01 -9.77956131... | [9.468149185180664, -0.6149161458015442] |
3f4f1fda-c592-4751-96e0-11926c712127 | definition-extraction-from-mathematical-texts | null | null | https://aclanthology.org/2021.konvens-1.9 | https://aclanthology.org/2021.konvens-1.9.pdf | Definition Extraction from Mathematical Texts on Graph Theory in German and English | null | ['Fritz Kliche', 'Theresa Kruse'] | null | null | null | null | konvens-ws-2021-9 | ['definition-extraction'] | ['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... | [-7.485416889190674, 3.7858314514160156] |
c8317dd6-31ca-415c-900b-f74fae6eab78 | deep-parametric-3d-filters-for-joint-video | 2207.01797 | null | https://arxiv.org/abs/2207.01797v2 | https://arxiv.org/pdf/2207.01797v2.pdf | Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution | Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the im... | ['Jiaya Jia', 'Chi-Wing Fu', 'RuiXing Wang', 'Xiaogang Xu'] | 2022-07-05 | null | null | null | null | ['video-super-resolution', 'video-denoising'] | ['computer-vision', 'computer-vision'] | [ 2.82256633e-01 -5.45014858e-01 3.00467084e-03 -2.57193595e-01
-9.39272463e-01 -1.98373049e-01 1.59976333e-01 -7.10021377e-01
-4.63800207e-02 6.79804862e-01 6.46218836e-01 2.01897636e-01
9.86595526e-02 -3.79906207e-01 -8.53674054e-01 -6.31679773e-01
8.27741399e-02 -4.20734614e-01 4.51428920e-01 -2.45402798... | [11.111259460449219, -2.0631000995635986] |
1d19c046-7162-4063-9287-1c6c6d9d9a55 | learning-personalized-high-quality-volumetric | 2304.01436 | null | https://arxiv.org/abs/2304.01436v1 | https://arxiv.org/pdf/2304.01436v1.pdf | Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos | We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a ... | ['yinda zhang', 'Sean Fanello', 'Thabo Beeler', 'Ping Tan', 'Rohit Pandey', 'Sergio Orts-Escolano', 'Mingsong Dou', 'Ruofei Du', 'Abhimitra Meka', 'Di Qiu', 'Danhang Tang', 'Kripasindhu Sarkar', 'Zeng Huang', 'Feitong Tan', 'Ziqian Bai'] | 2023-04-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Bai_Learning_Personalized_High_Quality_Volumetric_Head_Avatars_From_Monocular_RGB_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Bai_Learning_Personalized_High_Quality_Volumetric_Head_Avatars_From_Monocular_RGB_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-model'] | ['computer-vision'] | [-1.30927349e-02 2.56769359e-01 3.29868555e-01 -7.15139568e-01
-7.87041664e-01 -5.77774763e-01 6.86262727e-01 -5.11064351e-01
4.14042697e-05 4.51638013e-01 2.59224623e-01 4.94549811e-01
3.64296287e-01 -6.55117512e-01 -8.37561488e-01 -5.45507908e-01
-1.74767226e-02 5.20440996e-01 -8.55101645e-02 -3.30461472... | [12.767753601074219, -0.37280645966529846] |
69261a8a-e162-4ea0-b141-beccceac4d15 | camouflaged-chinese-spam-content-detection | null | null | https://aclanthology.org/2020.acl-main.279 | https://aclanthology.org/2020.acl-main.279.pdf | Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning | We propose a Semi-supervIsed GeNerative Active Learning (SIGNAL) model to address the imbalance, efficiency, and text camouflage problems of Chinese text spam detection task. A {``}self-diversity{''} criterion is proposed for measuring the {``}worthiness{''} of a candidate for annotation. A semi-supervised variational ... | ['Zhuoren Jiang', 'Zhe Gao', 'Yu Duan', 'Yangyang Kang', 'Xiaozhong Liu', 'Qiong Zhang', 'Changlong Sun'] | 2020-07-01 | null | null | null | acl-2020-6 | ['spam-detection'] | ['natural-language-processing'] | [ 3.01487535e-01 -9.25027765e-03 -1.15586765e-01 -3.95066351e-01
-1.02478743e+00 -3.20666105e-01 7.24802911e-01 1.06345780e-01
-4.12043720e-01 6.31843984e-01 1.81332082e-01 -4.99207526e-01
8.57800469e-02 -5.84020495e-01 -3.57261807e-01 -9.84990239e-01
2.81137615e-01 6.56266034e-01 3.53867412e-01 -2.70762652... | [7.881422519683838, 9.956777572631836] |
e9274e45-37bb-4aee-98d1-3f00ece0b57e | efficient-fine-grained-road-segmentation | 2207.02844 | null | https://arxiv.org/abs/2207.02844v1 | https://arxiv.org/pdf/2207.02844v1.pdf | Efficient fine-grained road segmentation using superpixel-based CNN and CRF models | Towards a safe and comfortable driving, road scene segmentation is a rudimentary problem in camera-based advance driver assistance systems (ADAS). Despite of the great achievement of Convolutional Neural Networks (CNN) for semantic segmentation task, the high computational efforts of CNN based methods is still a challe... | ['Josef Pauli', 'Mirko Meuter', 'Jan Siegemund', 'Farnoush Zohourian'] | 2022-06-22 | null | null | null | null | ['scene-segmentation', 'road-segementation'] | ['computer-vision', 'computer-vision'] | [ 3.74224156e-01 4.27406192e-01 3.48819122e-02 -5.51815391e-01
-3.74171466e-01 -9.42811444e-02 7.28485763e-01 -9.16922912e-02
-9.75842774e-01 7.30884075e-01 -5.12237966e-01 -5.63010275e-01
-1.99262220e-02 -1.14117599e+00 -5.05614996e-01 -6.09885812e-01
5.09825468e-01 5.43346465e-01 8.77672315e-01 -2.38749623... | [8.8145170211792, -1.4199206829071045] |
a13a6f71-6443-44f6-b952-a889b715f2cc | online-sequence-clustering-algorithm-for | 2305.08418 | null | https://arxiv.org/abs/2305.08418v1 | https://arxiv.org/pdf/2305.08418v1.pdf | Online Sequence Clustering Algorithm for Video Trajectory Analysis | Target tracking and trajectory modeling have important applications in surveillance video analysis and have received great attention in the fields of road safety and community security. In this work, we propose a lightweight real-time video analysis scheme that uses a model learned from motion patterns to monitor the b... | ['Zhitang Song', 'Shunfeng Li', 'Longfei Liang', 'Xingyu Qian', 'Xiaogang Chen', 'Aximu Yuemaier'] | 2023-05-15 | null | null | null | null | ['online-clustering', 'incremental-learning', 'trajectory-modeling'] | ['computer-vision', 'methodology', 'time-series'] | [ 2.17502832e-01 -4.36225981e-01 -3.50248486e-01 -3.85638714e-01
-1.05200969e-01 -4.50711340e-01 3.96462142e-01 1.73213199e-01
-3.52799058e-01 2.59238817e-02 -3.96669298e-01 -4.34438348e-01
-3.83392662e-01 -6.42075598e-01 -5.90216875e-01 -9.42578018e-01
-5.60569823e-01 1.32444846e-02 7.74253607e-01 2.36303002... | [8.357586860656738, -0.7898454666137695] |
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