paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5547b82e-a576-461d-9dcc-f9f8c5eb1d1c | methodology-for-capacity-credit-evaluation-of | 2303.09560 | null | https://arxiv.org/abs/2303.09560v1 | https://arxiv.org/pdf/2303.09560v1.pdf | Methodology for Capacity Credit Evaluation of Physical and Virtual Energy Storage in Decarbonized Power System | Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization. Although ES and VES have been proven to deliver various types of grid services, little work has so far provided a systematical framework for quantifying their adequacy contribution and credible capacity va... | ['Weiwei Yang', 'Wenrui Huang', 'Ziyi Zhang', 'Lin Cheng', 'Peng Li', 'Ning Qi'] | 2023-03-16 | null | null | null | null | ['energy-management'] | ['time-series'] | [-8.15476954e-01 -1.12294219e-01 2.82910429e-02 1.01867929e-01
-3.15600112e-02 -5.15568554e-01 6.37921333e-01 2.17959240e-01
1.13390379e-01 1.12705624e+00 1.54106408e-01 -4.96547580e-01
-3.20946723e-01 -9.45713639e-01 -1.02773398e-01 -9.10098314e-01
-2.68314004e-01 1.71207026e-01 -9.20530781e-02 -2.44958550... | [5.669347763061523, 2.5211541652679443] |
716443d0-083f-44ac-a19e-940401dbf019 | bira-net-bilinear-attention-net-for-diabetic | 1905.06312 | null | https://arxiv.org/abs/1905.06312v2 | https://arxiv.org/pdf/1905.06312v2.pdf | BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading | Diabetic retinopathy (DR) is a common retinal disease that leads to blindness. For diagnosis purposes, DR image grading aims to provide automatic DR grade classification, which is not addressed in conventional research methods of binary DR image classification. Small objects in the eye images, like lesions and microane... | ['Matthew Chin Heng Chua', 'Kerui Zhang', 'Ziyuan Zhao', 'Xuejie Hao', 'Li Chen', 'Xin Xu', 'Jing Tian'] | 2019-05-15 | null | null | null | null | ['diabetic-retinopathy-grading'] | ['medical'] | [-3.17288004e-02 -2.32268602e-01 -3.04368045e-02 -6.24616265e-01
-4.11634266e-01 -1.75745636e-02 2.64345318e-01 -2.74057984e-01
-1.52715325e-01 7.75871813e-01 4.45814043e-01 -2.76696682e-01
-1.79021195e-01 -7.83421814e-01 -6.75670058e-02 -8.57160091e-01
3.30782324e-01 -6.04158528e-02 2.42530316e-01 7.70374984... | [15.811872482299805, -3.981214761734009] |
db249a63-ec4d-4879-b0fd-71e1bab09126 | deep-learning-for-end-to-end-atrial | 1810.00475 | null | http://arxiv.org/abs/1810.00475v1 | http://arxiv.org/pdf/1810.00475v1.pdf | Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation | Left atrium shape has been shown to be an independent predictor of recurrence
after atrial fibrillation (AF) ablation. Shape-based representation is
imperative to such an estimation process, where correspondence-based
representation offers the most flexibility and ease-of-computation for
population-level shape statisti... | ['Shireen Elhabian', 'Riddhish Bhalodia', 'Evgueni Kholmovski', 'Anupama Goparaju', 'Nassir Marrouche', 'Alan Morris', 'Ross Whitaker', 'Tim Sodergren', 'Joshua Cates'] | 2018-09-30 | null | null | null | null | ['atrial-fibrillation-recurrence-estimation'] | ['medical'] | [ 7.88179114e-02 8.99592862e-02 -4.27880883e-03 -5.49414575e-01
-1.10954010e+00 -6.15656972e-01 3.49570811e-01 5.70020139e-01
-3.22739154e-01 6.15686715e-01 1.44615278e-01 -7.59930432e-01
-2.20201880e-01 -7.65679955e-01 -3.80198151e-01 -4.93239611e-01
-3.26871514e-01 9.29277837e-01 -5.59555173e-01 1.96683347... | [14.217845916748047, -2.4569380283355713] |
c436ee24-93c8-4e2f-98d8-3c62f91df129 | electrocardiography-separation-of-mother-and | 1411.1446 | null | http://arxiv.org/abs/1411.1446v1 | http://arxiv.org/pdf/1411.1446v1.pdf | Electrocardiography Separation of Mother and Baby | Extraction of Electrocardiography (ECG or EKG) signals of mother and baby is
a challenging task, because one single device is used and it receives a mixture
of multiple heart beats. In this paper, we would like to design a filter to
separate the signals from each other. | ['Wei Wang'] | 2014-11-05 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 3.38899940e-01 -1.79831535e-01 3.06025565e-01 -5.30503631e-01
-5.45425825e-02 -4.52052861e-01 -3.58599275e-01 1.76375881e-01
-9.49501246e-02 6.47469640e-01 -1.33665621e-01 -4.00205821e-01
1.17216878e-01 -4.91413385e-01 -6.48634881e-02 -6.67998016e-01
-6.16395026e-02 -2.73881555e-01 -1.88549489e-01 1.96616456... | [14.171606063842773, 3.171779155731201] |
3102fced-683c-40a6-b7e2-b646f104476f | icassp-2022-acoustic-echo-cancellation | 2202.13290 | null | https://arxiv.org/abs/2202.13290v1 | https://arxiv.org/pdf/2202.13290v1.pdf | ICASSP 2022 Acoustic Echo Cancellation Challenge | The ICASSP 2022 Acoustic Echo Cancellation Challenge is intended to stimulate research in acoustic echo cancellation (AEC), which is an important area of speech enhancement and still a top issue in audio communication. This is the third AEC challenge and it is enhanced by including mobile scenarios, adding speech recog... | ['Robert Aichner', 'Karsten Sørensen', 'Sebastian Braun', 'Hannes Gamper', 'Marju Purin', 'Tanel Parnamaa', 'Ando Saabas', 'Ross Cutler'] | 2022-02-27 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.42663226e-01 -2.92279065e-01 5.90974689e-01 -2.99945951e-01
-1.46760917e+00 -5.47424138e-01 3.80751520e-01 -2.81585395e-01
-5.70817113e-01 2.43024051e-01 6.97951674e-01 -3.65835726e-01
1.23602502e-01 6.73803389e-02 -3.99003863e-01 -4.53086764e-01
-3.84829253e-01 -2.16657650e-02 2.86469340e-01 -3.48557562... | [14.976506233215332, 5.934491157531738] |
6f460abc-0242-441f-840c-a14bfa38dfe1 | object-contour-and-edge-detection-with | 1904.13353 | null | https://arxiv.org/abs/1904.13353v2 | https://arxiv.org/pdf/1904.13353v2.pdf | Object Contour and Edge Detection with RefineContourNet | A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse the high, mid and low-level features ... | ['Vijesh Soorya Rao', 'Udo Zoelzer', 'Andre Peter Kelm'] | 2019-04-30 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [-4.00228538e-02 5.18959016e-02 7.30434358e-02 6.85751811e-02
-5.08431375e-01 -2.98168153e-01 4.80527014e-01 2.90972203e-01
-7.59934664e-01 2.81753540e-01 -1.01643831e-01 -8.63073394e-02
1.07610881e-01 -9.57415938e-01 -5.38715541e-01 -3.50665718e-01
-3.10470790e-01 2.19548285e-01 1.11836231e+00 -5.59057117... | [9.461695671081543, 0.1936718225479126] |
fdc8b43a-25d3-4910-a9db-515530c891d9 | a-clustering-framework-for-lexical | 2004.00088 | null | https://arxiv.org/abs/2004.00088v1 | https://arxiv.org/pdf/2004.00088v1.pdf | A Clustering Framework for Lexical Normalization of Roman Urdu | Roman Urdu is an informal form of the Urdu language written in Roman script, which is widely used in South Asia for online textual content. It lacks standard spelling and hence poses several normalization challenges during automatic language processing. In this article, we present a feature-based clustering framework f... | ['Jia Xu', 'Hassan Sajjad', 'Faisal Kamiran', 'Asim Karim', 'Abdul Rafae Khan'] | 2020-03-31 | null | null | null | null | ['lexical-normalization'] | ['natural-language-processing'] | [ 3.06425303e-01 -7.34753847e-01 -2.27052048e-01 -4.16485250e-01
-9.70167398e-01 -1.19378507e+00 5.64551234e-01 1.08435929e-01
-3.59550059e-01 5.98717153e-01 4.96517211e-01 -3.17771643e-01
4.16167974e-01 -9.67460811e-01 -1.36739492e-01 -5.13817370e-01
7.02113032e-01 5.33990741e-01 -1.80534050e-01 -2.06932157... | [10.757638931274414, 10.524426460266113] |
b3024531-2abd-48cb-aec3-e4702a8adb4e | investigating-sampling-bias-in-abusive | null | null | https://aclanthology.org/2020.alw-1.9 | https://aclanthology.org/2020.alw-1.9.pdf | Investigating Sampling Bias in Abusive Language Detection | Abusive language detection is becoming increasingly important, but we still understand little about the biases in our datasets for abusive language detection, and how these biases affect the quality of abusive language detection. In the work reported here, we reproduce the investigation of Wiegand et al. (2019) to dete... | ['Sandra Kübler', 'Dante Razo'] | null | null | null | null | emnlp-alw-2020-11 | ['abusive-language'] | ['natural-language-processing'] | [ 8.08107257e-02 -1.36950463e-01 -6.37169123e-01 -1.15246356e-01
-7.06536353e-01 -7.71962225e-01 9.85300601e-01 4.07206804e-01
-8.11513007e-01 9.28090632e-01 8.25090826e-01 -6.06528878e-01
2.34727487e-01 -6.85669422e-01 -2.68395156e-01 -3.43056053e-01
3.76323014e-01 2.62869805e-01 1.49818853e-01 -6.78506792... | [8.68535327911377, 10.380178451538086] |
f65c325b-fb0f-4263-9913-ba9ff1732563 | diffmix-diffusion-model-based-data-synthesis | 2306.14132 | null | https://arxiv.org/abs/2306.14132v1 | https://arxiv.org/pdf/2306.14132v1.pdf | DiffMix: Diffusion Model-based Data Synthesis for Nuclei Segmentation and Classification in Imbalanced Pathology Image Datasets | Nuclei segmentation and classification is a significant process in pathology image analysis. Deep learning-based approaches have greatly contributed to the higher accuracy of this task. However, those approaches suffer from the imbalanced nuclei data composition, which shows lower classification performance on the rare... | ['Won-Ki Jeong', 'Hyun-Jic Oh'] | 2023-06-25 | null | null | null | null | ['nuclei-classification', 'classification-1'] | ['medical', 'methodology'] | [ 1.24689907e-01 1.67124882e-01 -1.48293644e-01 -2.55843848e-01
-6.98904395e-01 -1.57227516e-01 2.14847967e-01 2.04839379e-01
-5.43103158e-01 6.51367486e-01 -3.27066868e-03 1.26757145e-01
1.80802912e-01 -1.05721176e+00 -3.95411164e-01 -1.34298480e+00
3.92974645e-01 6.73407197e-01 4.94821489e-01 -2.50117946... | [14.880566596984863, -3.038268566131592] |
26172129-f57e-4b8e-8f46-4c70bd9ad114 | analysing-mathematical-reasoning-abilities-of-1 | 1904.01557 | null | http://arxiv.org/abs/1904.01557v1 | http://arxiv.org/pdf/1904.01557v1.pdf | Analysing Mathematical Reasoning Abilities of Neural Models | Mathematical reasoning---a core ability within human intelligence---presents
some unique challenges as a domain: we do not come to understand and solve
mathematical problems primarily on the back of experience and evidence, but on
the basis of inferring, learning, and exploiting laws, axioms, and symbol
manipulation ru... | ['Felix Hill', 'Edward Grefenstette', 'David Saxton', 'Pushmeet Kohli'] | 2019-04-02 | analysing-mathematical-reasoning-abilities-of | https://openreview.net/forum?id=H1gR5iR5FX | https://openreview.net/pdf?id=H1gR5iR5FX | iclr-2019-5 | ['math-word-problem-solving', 'mathematical-question-answering', 'mathematical-reasoning', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing', 'reasoning', 'time-series'] | [ 2.33204156e-01 3.17524038e-02 3.23566109e-01 -5.07164657e-01
-3.02494764e-01 -9.13273573e-01 8.08813751e-01 2.99237847e-01
-2.28494585e-01 6.61735356e-01 6.62645325e-02 -9.57126796e-01
-7.98451245e-01 -1.00178480e+00 -6.34385765e-01 -8.71149674e-02
-2.28281841e-01 8.63335609e-01 -3.74912210e-02 -5.45109808... | [9.44970417022705, 7.216930866241455] |
4264ccd9-ba6c-4963-9d0b-53c932be431b | budgeted-multi-armed-bandits-with-asymmetric | 2306.07071 | null | https://arxiv.org/abs/2306.07071v1 | https://arxiv.org/pdf/2306.07071v1.pdf | Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals | We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from $K$ arms with unknown expected rewards and costs. The goal is to maximize the total reward under a budget constraint. A player thus seeks to choose the arm with the highest reward-cost ratio as often as possible. Current stat... | ['Klemens Böhm', 'Edouard Fouché', 'Vadim Arzamasov', 'Marco Heyden'] | 2023-06-12 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [-1.67681605e-01 2.70337705e-02 -9.56821144e-01 -3.01467985e-01
-1.34459674e+00 -8.09312165e-01 -3.60241123e-02 9.37651023e-02
-7.23974824e-01 1.48077989e+00 -1.50990263e-01 -6.58454716e-01
-7.14445412e-01 -7.00443685e-01 -8.17538977e-01 -7.25584567e-01
-1.38053507e-01 9.41029847e-01 -1.10796236e-01 9.83515531... | [4.500638484954834, 3.2593393325805664] |
52689a52-0dd2-4e48-a154-39615b6a6d0a | automatic-classification-of-variable-stars-in | 1310.7868 | null | http://arxiv.org/abs/1310.7868v1 | http://arxiv.org/pdf/1310.7868v1.pdf | Automatic Classification of Variable Stars in Catalogs with missing data | We present an automatic classification method for astronomical catalogs with
missing data. We use Bayesian networks, a probabilistic graphical model, that
allows us to perform inference to pre- dict missing values given observed data
and dependency relationships between variables. To learn a Bayesian network
from incom... | ['Karim Pichara', 'Pavlos Protopapas'] | 2013-10-29 | null | null | null | null | ['classification-of-variable-stars'] | ['miscellaneous'] | [-1.34643570e-01 -1.65697888e-01 -1.89368993e-01 -7.54118383e-01
-5.85316598e-01 -6.61541700e-01 7.48401105e-01 -1.92743987e-01
-2.40090087e-01 1.37152827e+00 -3.68435904e-02 -5.32669902e-01
-6.83653116e-01 -7.74786770e-01 -3.24177772e-01 -6.58716261e-01
-1.55138016e-01 1.18280494e+00 3.42291564e-01 2.70994186... | [7.305171489715576, 3.9191501140594482] |
add8eac0-e91f-4144-ac92-74f82c17bd91 | cia-net-robust-nuclei-instance-segmentation | 1903.05358 | null | http://arxiv.org/abs/1903.05358v1 | http://arxiv.org/pdf/1903.05358v1.pdf | CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation | Accurate segmenting nuclei instances is a crucial step in computer-aided
image analysis to extract rich features for cellular estimation and following
diagnosis as well as treatment. While it still remains challenging because the
wide existence of nuclei clusters, along with the large morphological variances
among diff... | ['Pheng-Ann Heng', 'Efstratios Tsougenis', 'Yanning Zhou', 'Omer Fahri Onder', 'Qi Dou', 'Hao Chen'] | 2019-03-13 | null | null | null | null | ['multi-tissue-nucleus-segmentation'] | ['medical'] | [ 2.16703445e-01 2.76141584e-01 -1.71805188e-01 -3.99179667e-01
-1.12378490e+00 -5.56341529e-01 1.50799349e-01 4.14814174e-01
-5.89049578e-01 7.50379384e-01 -4.16808203e-03 4.42332551e-02
6.27074465e-02 -4.40544367e-01 -7.56227672e-01 -1.21892226e+00
2.19024494e-01 5.17035365e-01 2.98495531e-01 2.64487773... | [14.926093101501465, -3.00956392288208] |
4d848584-b86d-417b-9e84-7df18c2cea98 | extraction-of-cropland-field-parcels-with | null | null | https://www.tandfonline.com/doi/full/10.1080/22797254.2023.2181874?src= | https://www.tandfonline.com/doi/epdf/10.1080/22797254.2023.2181874?needAccess=true&role=button | Extraction of cropland field parcels with high resolution remote sensing using multi-task learning | Parcel-level farmland information contains rich spatial distribution and boundary details, which is crucial for digital agriculture and agricultural resource surveys. However, the spatial complexity and heterogeneity of features resulting from high resolution makes it difficult to obtain parcel-level information quickl... | ['Shiran Song &Yongxing Wu', 'Jia Xu', 'Fei Peng', 'Juanjuan Yu', 'Peng Yang', 'Leilei Xu'] | 2023-02-14 | null | null | null | european-journal-of-remote-sensing-2023-2 | ['edge-detection'] | ['computer-vision'] | [ 1.13511369e-01 -2.82722831e-01 -1.71717048e-01 -2.55457640e-01
-2.08954707e-01 -6.96880102e-01 2.32925624e-01 5.04955828e-01
-1.38450325e-01 8.20778847e-01 -3.40800472e-02 -6.04783297e-01
-4.29466635e-01 -1.48962402e+00 -5.16088188e-01 -6.78661048e-01
-5.30663848e-01 -8.26160014e-02 2.11301133e-01 -4.89486933... | [9.35145092010498, -1.586998462677002] |
9e075a31-8944-47fd-b7ec-65c13c81e785 | visual-storytelling-via-predicting-anchor | 2001.04541 | null | https://arxiv.org/abs/2001.04541v1 | https://arxiv.org/pdf/2001.04541v1.pdf | Visual Storytelling via Predicting Anchor Word Embeddings in the Stories | We propose a learning model for the task of visual storytelling. The main idea is to predict anchor word embeddings from the images and use the embeddings and the image features jointly to generate narrative sentences. We use the embeddings of randomly sampled nouns from the groundtruth stories as the target anchor wor... | ['Bowen Zhang', 'Hexiang Hu', 'Fei Sha'] | 2020-01-13 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.45991340e-01 2.22089067e-01 -2.53828943e-01 -4.25005436e-01
-8.46194923e-01 -4.73307192e-01 1.01584268e+00 -1.64533511e-01
-6.80389822e-01 7.83831179e-01 8.83713603e-01 2.42225513e-01
5.37666857e-01 -6.95127487e-01 -9.38206136e-01 -4.88871992e-01
2.16998711e-01 3.63691539e-01 9.88323390e-02 -2.76753485... | [11.173843383789062, 0.7338366508483887] |
5c707b77-9111-4470-b791-b86918312352 | hasp-a-high-performance-adaptive-mobile | 1809.01697 | null | http://arxiv.org/abs/1809.01697v1 | http://arxiv.org/pdf/1809.01697v1.pdf | HASP: A High-Performance Adaptive Mobile Security Enhancement Against Malicious Speech Recognition | Nowadays, machine learning based Automatic Speech Recognition (ASR) technique
has widely spread in smartphones, home devices, and public facilities. As
convenient as this technology can be, a considerable security issue also raises
-- the users' speech content might be exposed to malicious ASR monitoring and
cause seve... | ['ChenChen Liu', 'Zirui Xu', 'Xiang Chen', 'Fuxun Yu'] | 2018-09-04 | null | null | null | null | ['mobile-security'] | ['miscellaneous'] | [ 1.55294403e-01 -1.02498755e-01 1.99212343e-01 -1.06598800e-02
-9.61690485e-01 -6.69618666e-01 2.11714432e-01 -3.57722938e-01
-3.22754681e-01 3.71400982e-01 2.06482679e-01 -7.56777525e-01
3.79945606e-01 -4.80242848e-01 -5.18687487e-01 -6.63319230e-01
1.11870140e-01 -3.39045435e-01 3.32382739e-01 -5.07250309... | [13.99654483795166, 5.829981803894043] |
eb1d089a-b206-4ac7-81e6-0c5a9b26fc4d | easy-and-efficient-transformer-scalable | 2104.12470 | null | https://arxiv.org/abs/2104.12470v5 | https://arxiv.org/pdf/2104.12470v5.pdf | Easy and Efficient Transformer : Scalable Inference Solution For large NLP model | Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Tran... | ['Gongzheng li', 'Zeng Zhao', 'Xiaoxi Mao', 'Changjie Fan', 'Bai Liu', 'Duan Wang', 'Jingzhen Ding', 'Yadong Xi'] | 2021-04-26 | null | null | null | null | ['inference-optimization'] | ['audio'] | [-1.20348506e-01 -4.26287912e-02 -8.28724280e-02 -3.52103561e-01
-9.54196036e-01 -4.63437915e-01 2.12344870e-01 -3.80161822e-01
-7.73654506e-02 5.43897867e-01 -4.18376364e-02 -8.41015041e-01
3.14412504e-01 -1.12081754e+00 -9.20953274e-01 -4.73359317e-01
5.70090473e-01 6.21501923e-01 4.69089627e-01 -4.83194254... | [8.709635734558105, 3.608332395553589] |
9c3c771e-c5c9-4acd-a3c2-a5c339b4c008 | estimating-the-frame-potential-of-large-scale | 2205.09900 | null | https://arxiv.org/abs/2205.09900v3 | https://arxiv.org/pdf/2205.09900v3.pdf | Estimating the randomness of quantum circuit ensembles up to 50 qubits | Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information. The ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and train... | ['Liang Jiang', 'Yuri Alexeev', 'Junyu Liu', 'Minzhao Liu'] | 2022-05-19 | null | null | null | null | ['tensor-networks'] | ['methodology'] | [ 2.98744231e-01 1.88562900e-01 -7.49425516e-02 -9.66127142e-02
-5.01583040e-01 -9.82873380e-01 5.07989466e-01 -1.06115816e-02
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-7.91626051e-02 -1.13450134e+00 -7.22608805e-01 -1.23668694e+00
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c7ae22c8-9727-436e-b7d6-275f03fc1726 | deepkey-an-eeg-and-gait-based-dual | 1706.01606 | null | https://arxiv.org/abs/1706.01606v2 | https://arxiv.org/pdf/1706.01606v2.pdf | DeepKey: An EEG and Gait Based Dual-Authentication System | Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are facing an increasing risk of being ... | ['Chaoran Huang', 'Lina Yao', 'Tao Gu', 'Yunhao Liu', 'Zheng Yang', 'Xiang Zhang'] | 2017-06-06 | null | null | null | null | ['gait-identification'] | ['computer-vision'] | [-3.95145155e-02 -3.62522274e-01 1.37157485e-01 -1.19487002e-01
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-8.85359645e-02 -4.01775002e-01 -2.07826197e-01 3.06744158... | [13.466826438903809, 2.031203031539917] |
b0313cec-6184-4cca-a78d-192861e0e10c | mggr-multimodal-guided-gaze-redirection-with | 2004.03064 | null | https://arxiv.org/abs/2004.03064v4 | https://arxiv.org/pdf/2004.03064v4.pdf | Coarse-to-Fine Gaze Redirection with Numerical and Pictorial Guidance | Gaze redirection aims at manipulating the gaze of a given face image with respect to a desired direction (i.e., a reference angle) and it can be applied to many real life scenarios, such as video-conferencing or taking group photos. However, previous work on this topic mainly suffers of two limitations: (1) Low-quality... | ['Jiayuan Fan', 'Enver Sangineto', 'Tao Chen', 'Nicu Sebe', 'Jingjing Chen', 'Jichao Zhang'] | 2020-04-07 | null | null | null | null | ['gaze-redirection'] | ['computer-vision'] | [ 5.43887138e-01 -1.13486730e-01 -2.92292535e-02 -4.27551955e-01
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5.83439887e-01 -1.19302817e-01 1.64474919e-01 -3.72991949... | [13.96638298034668, -0.013678831979632378] |
38397679-64be-48c4-ae0f-70c592a7c5ef | towards-arbitrary-view-face-alignment-by | 1511.06627 | null | http://arxiv.org/abs/1511.06627v1 | http://arxiv.org/pdf/1511.06627v1.pdf | Towards Arbitrary-View Face Alignment by Recommendation Trees | Learning to simultaneously handle face alignment of arbitrary views, e.g.
frontal and profile views, appears to be more challenging than we thought. The
difficulties lay in i) accommodating the complex appearance-shape relations
exhibited in different views, and ii) encompassing the varying landmark point
sets due to s... | ['Cheng Li', 'Chen Change Loy', 'Xiaoou Tang', 'Shizhan Zhu'] | 2015-11-20 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-3.19437236e-02 -6.59259483e-02 -1.65527165e-01 -6.31257117e-01
-7.46814489e-01 -5.12668192e-01 5.56884706e-01 -5.01202524e-01
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-3.11874300e-01 -2.98189729e-01 -4.20168519e-01 -6.88543141e-01
9.11489204e-02 9.26715851e-01 -3.06954775e-02 -1.21337697... | [13.404942512512207, 0.3428318500518799] |
e0aa01ae-c29e-4f7a-8ffa-dc549db45627 | semantic-clustering-and-convolutional-neural | null | null | https://aclanthology.org/P15-2058 | https://aclanthology.org/P15-2058.pdf | Semantic Clustering and Convolutional Neural Network for Short Text Categorization | null | ['Hong-Wei Hao', 'Cheng-Lin Liu', 'Heng Zhang', 'Bo Xu', 'Peng Wang', 'Jiaming Xu', 'Fangyuan Wang'] | 2015-07-01 | semantic-clustering-and-convolutional-neural-1 | https://aclanthology.org/P15-2058 | https://aclanthology.org/P15-2058.pdf | ijcnlp-2015-7 | ['learning-word-embeddings'] | ['methodology'] | [-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... | [-7.4483771324157715, 3.7581849098205566] |
7d0fab62-665f-4bcb-847b-e66c8e428b9f | sentence-level-subjectivity-detection-using | null | null | https://aclanthology.org/W13-1615 | https://aclanthology.org/W13-1615.pdf | Sentence-Level Subjectivity Detection Using Neuro-Fuzzy Models | null | ['Mark Clements', 'Samir Rustamov', 'Elshan Mustafayev'] | 2013-06-01 | null | null | null | ws-2013-6 | ['subjectivity-analysis'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.278043270111084, 3.736532211303711] |
4b897728-7b34-44ef-89a4-b5947ce4bea8 | fully-convolutional-geometric-features | null | null | https://github.com/chrischoy/FCGF | https://node1.chrischoy.org/data/publications/fcgf/fcgf.pdf | Fully Convolutional Geometric Features | Extracting geometric features from 3D scans or point clouds is the first step in applications such as registration, reconstruction, and tracking. State-of-the-art methods require computing low-level features as input or extracting patch-based features with limited receptive field. In this work, we present fully-convolu... | ['Christopher Choy', 'Vladlen Koltun', 'Jaesik Park'] | 2019-10-27 | fully-convolutional-geometric-features-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Choy_Fully_Convolutional_Geometric_Features_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Choy_Fully_Convolutional_Geometric_Features_ICCV_2019_paper.pdf | international-conference-on-computer-vision | ['3d-feature-matching', '3d-point-cloud-matching', '3d-shape-representation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.68822229e-02 -2.47819215e-01 1.04454271e-01 -6.26934469e-01
-1.04809260e+00 -4.25335646e-01 6.96581066e-01 4.42481607e-01
-6.99852109e-01 2.37194419e-01 2.01037712e-02 -2.57522482e-02
-3.58180106e-01 -1.02021372e+00 -1.18121386e+00 -2.95848604e-02
-4.24633086e-01 6.14882410e-01 3.95232826e-01 8.86922255... | [7.748584270477295, -3.2763779163360596] |
d35e7008-6b9b-418b-9838-ea9bb74a3f3d | url-a-representation-learning-benchmark-for | 2307.03810 | null | https://arxiv.org/abs/2307.03810v1 | https://arxiv.org/pdf/2307.03810v1.pdf | URL: A Representation Learning Benchmark for Transferable Uncertainty Estimates | Representation learning has significantly driven the field to develop pretrained models that can act as a valuable starting point when transferring to new datasets. With the rising demand for reliable machine learning and uncertainty quantification, there is a need for pretrained models that not only provide embeddings... | ['Enkelejda Kasneci', 'Seong Joon Oh', 'Bálint Mucsányi', 'Michael Kirchhof'] | 2023-07-07 | null | null | null | null | ['representation-learning'] | ['methodology'] | [-7.60203972e-02 4.12785172e-01 -2.31624335e-01 -6.81860626e-01
-1.15928864e+00 -6.73151016e-01 9.14240658e-01 5.61198950e-01
-4.35195208e-01 6.74314797e-01 6.76991940e-01 -3.87212485e-01
-3.34403157e-01 -9.58017707e-01 -8.27045739e-01 -2.05739528e-01
-3.06846388e-02 5.57918966e-01 -1.27346933e-01 -2.09645219... | [9.446083068847656, 3.3209407329559326] |
004ac58a-848a-4292-8a4e-8d290b58f72f | on-the-role-of-seed-lexicons-in-learning | null | null | https://aclanthology.org/P16-1024 | https://aclanthology.org/P16-1024.pdf | On the Role of Seed Lexicons in Learning Bilingual Word Embeddings | null | ["Ivan Vuli{\\'c}", 'Anna Korhonen'] | 2016-08-01 | null | null | null | acl-2016-8 | ['cross-lingual-entity-linking'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
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-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.245156764984131, 3.7968530654907227] |
ef1f3c1c-8543-4812-a533-ed17e03f4057 | boosting-weakly-supervised-object-detection-2 | 2303.10937 | null | https://arxiv.org/abs/2303.10937v1 | https://arxiv.org/pdf/2303.10937v1.pdf | Boosting Weakly Supervised Object Detection using Fusion and Priors from Hallucinated Depth | Despite recent attention and exploration of depth for various tasks, it is still an unexplored modality for weakly-supervised object detection (WSOD). We propose an amplifier method for enhancing the performance of WSOD by integrating depth information. Our approach can be applied to any WSOD method based on multiple-i... | ['Adriana Kovashka', 'Cagri Gungor'] | 2023-03-20 | null | null | null | null | ['weakly-supervised-object-detection', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 1.04612619e-01 1.20167963e-01 -4.71828543e-02 -3.45556676e-01
-9.76738632e-01 -4.07585651e-01 6.18127108e-01 2.56370991e-01
-7.44061947e-01 6.52853966e-01 7.49657080e-02 1.80626720e-01
3.05718660e-01 -6.69628441e-01 -8.02489460e-01 -6.60723567e-01
3.01002264e-01 4.96839643e-01 9.52634394e-01 2.00019032... | [9.2296142578125, 0.9354334473609924] |
5356fb62-e93e-44fe-8e8d-d36b496f9e1b | one-stage-video-instance-segmentation-from | 2203.06421 | null | https://arxiv.org/abs/2203.06421v1 | https://arxiv.org/pdf/2203.06421v1.pdf | One-stage Video Instance Segmentation: From Frame-in Frame-out to Clip-in Clip-out | Many video instance segmentation (VIS) methods partition a video sequence into individual frames to detect and segment objects frame by frame. However, such a frame-in frame-out (FiFo) pipeline is ineffective to exploit the temporal information. Based on the fact that adjacent frames in a short clip are highly coherent... | ['Lei Zhang', 'Minghan Li'] | 2022-03-12 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 7.94221833e-02 -1.09042246e-02 -2.55197525e-01 -1.99549466e-01
-4.14402485e-01 -4.11044240e-01 2.13772416e-01 -2.70361215e-01
-3.10576022e-01 6.23807132e-01 -2.69343644e-01 -2.30815068e-01
1.00599989e-01 -8.14131081e-01 -8.60169530e-01 -4.89853680e-01
-2.65648961e-01 -4.14929166e-02 9.70713615e-01 6.87485784... | [9.185556411743164, -0.10735717415809631] |
dd8b78c2-5700-4223-b190-514ebcebfe96 | stacked-adversarial-network-for-zero-shot | 2001.06657 | null | https://arxiv.org/abs/2001.06657v1 | https://arxiv.org/pdf/2001.06657v1.pdf | Stacked Adversarial Network for Zero-Shot Sketch based Image Retrieval | Conventional approaches to Sketch-Based Image Retrieval (SBIR) assume that the data of all the classes are available during training. The assumption may not always be practical since the data of a few classes may be unavailable, or the classes may not appear at the time of training. Zero-Shot Sketch-Based Image Retriev... | ['Vinay Kumar Verma', 'Ashish Mishra', 'Anurag Mittal', 'Anubha Pandey', 'Hema A. Murthy'] | 2020-01-18 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 3.15149099e-01 -2.81999737e-01 -7.05095977e-02 -2.30090335e-01
-1.35776782e+00 -6.86375380e-01 8.10949862e-01 -1.57846332e-01
-3.00588697e-01 8.02157879e-01 -2.16228247e-01 -4.50812653e-02
-5.33706307e-01 -1.07276344e+00 -7.24195123e-01 -8.72390628e-01
3.07546347e-01 6.90600455e-01 3.38296890e-01 -3.91090780... | [11.52896785736084, 0.7251691818237305] |
83373af1-e96f-4328-81a5-203e33899232 | exploiting-selection-bias-on-underspecified | 2210.00131 | null | https://arxiv.org/abs/2210.00131v2 | https://arxiv.org/pdf/2210.00131v2.pdf | Selection Induced Collider Bias: A Gender Pronoun Uncertainty Case Study | In this paper, we cast the problem of task underspecification in causal terms, and develop a method for empirical measurement of spurious associations between gender and gender-neutral entities for unmodified large language models, detecting previously unreported spurious correlations. We then describe a lightweight me... | ['Emily McMilin'] | 2022-09-30 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-2.78534405e-02 8.86376858e-01 -8.76493871e-01 -7.34316945e-01
-8.47192109e-01 -3.52512777e-01 1.01365054e+00 4.55437481e-01
-3.25166166e-01 1.48766840e+00 4.22830999e-01 -2.81213492e-01
-3.21867704e-01 -6.75144970e-01 -7.44600058e-01 -5.20915203e-02
-5.12618721e-01 8.30220819e-01 -7.23445639e-02 4.89884205... | [9.770108222961426, 8.126242637634277] |
5f820029-139d-4475-9c29-631ad25f5859 | image-manipulation-via-multi-hop-instructions | 2305.14410 | null | https://arxiv.org/abs/2305.14410v1 | https://arxiv.org/pdf/2305.14410v1.pdf | Image Manipulation via Multi-Hop Instructions -- A New Dataset and Weakly-Supervised Neuro-Symbolic Approach | We are interested in image manipulation via natural language text -- a task that is useful for multiple AI applications but requires complex reasoning over multi-modal spaces. We extend recently proposed Neuro Symbolic Concept Learning (NSCL), which has been quite effective for the task of Visual Question Answering (VQ... | ['Dinesh Garg', 'Parag Singla', 'Dinesh Khandelwal', 'Arnab Kumar Mondal', 'Kevin Shah', 'Mohit Gupta', 'Poorva Garg', 'Harman Singh'] | 2023-05-23 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 3.47642481e-01 2.07413867e-01 -2.27051571e-01 -3.49224240e-01
-4.73435730e-01 -5.67186058e-01 7.53572762e-01 2.73419231e-01
-3.91685009e-01 2.76381195e-01 -8.36756229e-02 -6.48796499e-01
2.75280233e-02 -7.95222998e-01 -1.20390975e+00 -2.25304753e-01
6.02727793e-02 7.50573993e-01 5.24269164e-01 -5.38871408... | [10.649928092956543, 2.07368803024292] |
fdb7ae71-1e88-4699-89e2-6c4a9fbef6b7 | cmath-can-your-language-model-pass-chinese | 2306.16636 | null | https://arxiv.org/abs/2306.16636v1 | https://arxiv.org/pdf/2306.16636v1.pdf | CMATH: Can Your Language Model Pass Chinese Elementary School Math Test? | We present the Chinese Elementary School Math Word Problems (CMATH) dataset, comprising 1.7k elementary school-level math word problems with detailed annotations, source from actual Chinese workbooks and exams. This dataset aims to provide a benchmark tool for assessing the following question: to what grade level of el... | ['Bin Wang', 'Shuang Dong', 'Wei Liu', 'Jian Luan', 'Tianwen Wei'] | 2023-06-29 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [-3.70265007e-01 1.40796751e-01 -8.54202583e-02 1.02286704e-03
-8.71854365e-01 -9.06237662e-01 2.89438307e-01 7.89695203e-01
-5.04714012e-01 5.74641228e-01 3.43726665e-01 -7.58312106e-01
-6.02170050e-01 -1.14130640e+00 -6.08832240e-01 -1.11809082e-01
1.50992155e-01 4.66172993e-01 2.28040159e-01 -5.78335583... | [9.781982421875, 7.413180351257324] |
42a0e197-9cb1-4674-9d1e-fdde36229612 | segnbdt-visual-decision-rules-for | 2006.06868 | null | https://arxiv.org/abs/2006.06868v1 | https://arxiv.org/pdf/2006.06868v1.pdf | SegNBDT: Visual Decision Rules for Segmentation | The black-box nature of neural networks limits model decision interpretability, in particular for high-dimensional inputs in computer vision and for dense pixel prediction tasks like segmentation. To address this, prior work combines neural networks with decision trees. However, such models (1) perform poorly when comp... | ['Joseph E. Gonzalez', 'Henk Tillman', 'Sarah Adel Bargal', 'Alvin Wan', 'Younjin Song', 'Daniel Ho'] | 2020-06-11 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [ 4.64684993e-01 7.73780763e-01 -4.49058771e-01 -6.29223645e-01
-4.42771524e-01 -3.18356603e-01 2.07980245e-01 6.37922287e-02
-5.12882844e-02 3.92594993e-01 6.52140155e-02 -8.82655680e-01
2.59026140e-01 -6.92734778e-01 -7.82489836e-01 -1.84188932e-01
3.39111656e-01 5.12293279e-01 4.04051632e-01 -1.83648914... | [9.612991333007812, 0.5520899891853333] |
4524addf-4e85-4af0-b1ff-2a27445cc0c6 | survode-extrapolating-gene-expression | 2111.15080 | null | https://arxiv.org/abs/2111.15080v1 | https://arxiv.org/pdf/2111.15080v1.pdf | SurvODE: Extrapolating Gene Expression Distribution for Early Cancer Identification | With the increasingly available large-scale cancer genomics datasets, machine learning approaches have played an important role in revealing novel insights into cancer development. Existing methods have shown encouraging performance in identifying genes that are predictive for cancer survival, but are still limited in ... | ['Sheng Wang', 'Tong Chen'] | 2021-11-30 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [-5.38242757e-02 -1.85742795e-01 -3.64005923e-01 -1.18094668e-01
-7.69084692e-01 -3.68786037e-01 3.24802786e-01 4.38351899e-01
-9.57922935e-02 8.63269269e-01 -7.45907018e-04 -7.28796899e-01
-2.63194054e-01 -8.21183562e-01 -3.13654631e-01 -1.12848425e+00
-5.95867991e-01 3.85370255e-01 -2.06672221e-01 -1.62582859... | [6.058891296386719, 5.539056777954102] |
878687d3-f91d-497f-b591-82d5b2926684 | hide-and-tell-learning-to-bridge-photo | 2002.00774 | null | https://arxiv.org/abs/2002.00774v1 | https://arxiv.org/pdf/2002.00774v1.pdf | Hide-and-Tell: Learning to Bridge Photo Streams for Visual Storytelling | Visual storytelling is a task of creating a short story based on photo streams. Unlike existing visual captioning, storytelling aims to contain not only factual descriptions, but also human-like narration and semantics. However, the VIST dataset consists only of a small, fixed number of photos per story. Therefore, the... | ['Kyung-Su Kim', 'Sanghyun Woo', 'Dahun Kim', 'Yunjae Jung', 'Sungjin Kim', 'In So Kweon'] | 2020-02-03 | null | null | null | null | ['visual-storytelling'] | ['natural-language-processing'] | [ 2.39617124e-01 5.87921619e-01 -2.32151449e-02 -3.58867854e-01
-6.36430323e-01 -6.00396633e-01 1.07047641e+00 -6.98924437e-02
2.89110005e-01 8.40038657e-01 7.92341053e-01 -1.18249550e-01
2.02522278e-01 -8.54396522e-01 -1.21589887e+00 -1.72440901e-01
2.32129365e-01 4.85881686e-01 3.83284427e-02 -1.97771192... | [11.203455924987793, 0.7780712246894836] |
6ea5d9c0-c13b-4ce0-b201-96705dbf1caa | hact-net-a-hierarchical-cell-to-tissue-graph | 2007.00584 | null | https://arxiv.org/abs/2007.00584v1 | https://arxiv.org/pdf/2007.00584v1.pdf | HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification | Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization i... | ['Maria Gabrani', 'Maria Frucci', 'Jean-Philippe Thiran', 'Orcun Goksel', 'Gerardo Botti', 'Giuseppe De Pietro', 'Nadia Brancati', 'Giosue Scognamiglio', 'Lauren Alisha Fernandes', 'Pushpak Pati', 'Guillaume Jaume', 'Florinda Feroce', 'Antonio Foncubierta', 'Anna Maria Anniciello', 'Maurizio Do Bonito', 'Daniel Riccio'... | 2020-07-01 | null | null | null | null | ['histopathological-image-classification'] | ['medical'] | [ 1.70999840e-01 5.40054619e-01 -3.53020698e-01 -1.34889096e-01
-1.74556389e-01 -4.56860721e-01 6.93682313e-01 1.05012476e+00
-1.07386962e-01 4.54968870e-01 2.35439166e-01 -5.11392951e-01
-4.56851512e-01 -1.05765855e+00 -3.94090801e-01 -9.94951904e-01
-5.29349148e-01 7.31219113e-01 2.71264553e-01 -1.73694164... | [15.023271560668945, -2.9427850246429443] |
fb724a59-a6f1-4197-acc3-c34de0ce42da | diverse-controllable-and-keyphrase-aware-a | 2004.03875 | null | https://arxiv.org/abs/2004.03875v2 | https://arxiv.org/pdf/2004.03875v2.pdf | Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation | News headline generation aims to produce a short sentence to attract readers to read the news. One news article often contains multiple keyphrases that are of interest to different users, which can naturally have multiple reasonable headlines. However, most existing methods focus on the single headline generation. In t... | ['Jiancheng Lv', 'Yeyun Gong', 'Dayiheng Liu', 'Yu Yan', 'Wei Liu', 'Nan Duan', 'Jie Fu', 'Daxin Jiang', 'Bo Shao'] | 2020-04-08 | null | https://aclanthology.org/2020.emnlp-main.505 | https://aclanthology.org/2020.emnlp-main.505.pdf | emnlp-2020-11 | ['headline-generation'] | ['natural-language-processing'] | [ 0.07652437 -0.1340456 -0.2583487 -0.2274738 -1.5146 -0.55916405
0.68611366 0.42064855 -0.5707927 1.1037326 1.1682484 -0.079721
0.0445856 -0.8567742 -1.1006771 -0.41366673 0.36502376 0.3529141
0.5177987 -0.8575714 0.6834857 -0.2298248 -1.4086692 0.6992798
1.0017363 0.90508884 0.7840... | [12.296685218811035, 9.00045108795166] |
bdd5b8f6-91ec-455b-ab86-7534effc4093 | what-do-end-to-end-speech-models-learn-about | 2107.00439 | null | https://arxiv.org/abs/2107.00439v3 | https://arxiv.org/pdf/2107.00439v3.pdf | What do End-to-End Speech Models Learn about Speaker, Language and Channel Information? A Layer-wise and Neuron-level Analysis | Deep neural networks are inherently opaque and challenging to interpret. Unlike hand-crafted feature-based models, we struggle to comprehend the concepts learned and how they interact within these models. This understanding is crucial not only for debugging purposes but also for ensuring fairness in ethical decision-ma... | ['Ahmed Ali', 'Nadir Durrani', 'Shammur Absar Chowdhury'] | 2021-07-01 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [ 4.35197592e-01 4.85409439e-01 2.45808158e-02 -5.50733030e-01
-3.45344901e-01 -8.87506723e-01 6.52675986e-01 3.08850288e-01
-3.27139407e-01 3.91849041e-01 6.80159330e-01 -5.66173851e-01
-2.87266999e-01 -3.24265093e-01 -9.21814919e-01 -5.97157717e-01
-2.77638376e-01 2.14661524e-01 -1.37455598e-01 -2.96451718... | [10.503838539123535, 8.53777027130127] |
2ccf6d4e-90e4-4cd9-90b0-6485620360dd | sampling-from-gaussian-process-posteriors | 2306.11589 | null | https://arxiv.org/abs/2306.11589v1 | https://arxiv.org/pdf/2306.11589v1.pdf | Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent | Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems. In general, this has a cubic cost in dataset size and is sensitive to conditioning. We explore stochastic gradient algorithms as a computationally effi... | ['Alexander Terenin', 'José Miguel Hernández-Lobato', 'David Janz', 'Shreyas Padhy', 'Javier Antorán', 'Jihao Andreas Lin'] | 2023-06-20 | null | null | null | null | ['gaussian-processes', 'bayesian-optimization'] | ['methodology', 'methodology'] | [-1.78892631e-02 -1.43383965e-01 -1.78193182e-01 -7.21607447e-01
-1.54082727e+00 -4.65164870e-01 4.92489874e-01 7.33010843e-02
-6.18549049e-01 1.02304161e+00 2.06587747e-01 -4.15951371e-01
-1.61820408e-02 -4.18581635e-01 -7.93659270e-01 -7.81493843e-01
-6.06622882e-02 1.00904596e+00 -1.96176656e-02 1.86018944... | [6.819884777069092, 3.9244351387023926] |
312d4e4a-9e4f-40a4-8768-9be5282f28a4 | quantifying-facial-age-by-posterior-of-age | 1708.09687 | null | http://arxiv.org/abs/1708.09687v2 | http://arxiv.org/pdf/1708.09687v2.pdf | Quantifying Facial Age by Posterior of Age Comparisons | We introduce a novel approach for annotating large quantity of in-the-wild
facial images with high-quality posterior age distribution as labels. Each
posterior provides a probability distribution of estimated ages for a face. Our
approach is motivated by observations that it is easier to distinguish who is
the older of... | ['Chen Change Loy', 'Li Liu', 'Yunxuan Zhang', 'Cheng Li'] | 2017-08-31 | null | null | null | null | ['age-and-gender-classification'] | ['computer-vision'] | [-2.13939399e-01 2.01854035e-01 -2.22778931e-01 -9.78283823e-01
-8.78459573e-01 1.31136579e-02 2.14157775e-01 -2.20306039e-01
-6.62258387e-01 8.01698744e-01 -1.43401064e-02 1.48078442e-01
3.20760190e-01 -6.90144062e-01 -4.97231543e-01 -8.18947315e-01
-1.62950948e-01 8.20322275e-01 -3.76641214e-01 3.70844126... | [13.556483268737793, 0.8640176653862] |
71b742a6-28ef-4875-8016-96c80c16a78f | information-redundancy-and-biases-in-public | 2304.14936 | null | https://arxiv.org/abs/2304.14936v1 | https://arxiv.org/pdf/2304.14936v1.pdf | Information Redundancy and Biases in Public Document Information Extraction Benchmarks | Advances in the Visually-rich Document Understanding (VrDU) field and particularly the Key-Information Extraction (KIE) task are marked with the emergence of efficient Transformer-based approaches such as the LayoutLM models. Despite the good performance of KIE models when fine-tuned on public benchmarks, they still st... | ['Fabien Caspani', 'William Vanhuffel', 'Laurent Lam', 'Joel Tang', 'Pirashanth Ratnamogan', 'Seif Laatiri'] | 2023-04-28 | null | null | null | null | ['key-information-extraction'] | ['natural-language-processing'] | [ 1.69789866e-01 1.73645481e-01 -9.40841883e-02 -7.10177049e-02
-1.06025577e+00 -1.09425247e+00 1.02436507e+00 3.97228271e-01
-1.81675568e-01 5.75745642e-01 2.69129246e-01 -5.33379257e-01
-4.98785973e-01 -6.53617084e-01 -8.02601099e-01 -2.52338380e-01
-1.05280839e-01 5.56509495e-01 3.51420909e-01 -2.03075662... | [11.604430198669434, 2.607855796813965] |
b33ac5be-4d62-4888-ab9c-f6f8280ac5cf | joint-weakly-supervised-at-and-aed-using-deep | 2103.12388 | null | https://arxiv.org/abs/2103.12388v2 | https://arxiv.org/pdf/2103.12388v2.pdf | Joint framework with deep feature distillation and adaptive focal loss for weakly supervised audio tagging and acoustic event detection | A good joint training framework is very helpful to improve the performances of weakly supervised audio tagging (AT) and acoustic event detection (AED) simultaneously. In this study, we propose three methods to improve the best teacher-student framework in the IEEE AASP Challenge on Detection and Classification of Acous... | ['Yuping Wang', 'Jiaen Liang', 'Yijie Li', 'Yanhua Long', 'Yunhao Liang'] | 2021-03-23 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.05807611e-01 -1.89284056e-01 1.20548874e-01 -5.00016510e-01
-1.68487549e+00 -4.57680285e-01 5.35428345e-01 4.20974255e-01
-6.21342659e-01 2.41979674e-01 3.34274679e-01 1.50579512e-01
6.83010519e-02 -3.46131980e-01 -7.39826858e-01 -6.08245194e-01
-2.68125087e-01 5.16184978e-02 5.66523850e-01 1.87568754... | [15.203570365905762, 5.1490278244018555] |
dbcf8274-039d-4cc1-a94f-f85510fe4f29 | chan-vese-attention-u-net-an-attention | 2306.16098 | null | https://arxiv.org/abs/2306.16098v1 | https://arxiv.org/pdf/2306.16098v1.pdf | Chan-Vese Attention U-Net: An attention mechanism for robust segmentation | When studying the results of a segmentation algorithm using convolutional neural networks, one wonders about the reliability and consistency of the results. This leads to questioning the possibility of using such an algorithm in applications where there is little room for doubt. We propose in this paper a new attention... | ['Laurent D. Cohen', 'Nicolas Makaroff'] | 2023-06-28 | null | null | null | null | ['medical-image-segmentation'] | ['medical'] | [ 4.28425461e-01 4.63350624e-01 1.62190303e-01 -3.19882065e-01
-2.95922663e-02 -3.07219148e-01 5.38930953e-01 3.48554850e-01
-9.25462961e-01 8.14359903e-01 -3.87653440e-01 -3.00000191e-01
-2.71497548e-01 -8.78674865e-01 -6.44058049e-01 -8.90452027e-01
-3.36434413e-03 4.00582880e-01 4.85468984e-01 -3.04336488... | [14.416716575622559, -2.574636220932007] |
46323bb7-b298-4f03-9183-42bb25b83839 | the-starcraft-multi-agent-challenge | 1902.04043 | null | https://arxiv.org/abs/1902.04043v5 | https://arxiv.org/pdf/1902.04043v5.pdf | The StarCraft Multi-Agent Challenge | In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only... | ['Chia-Man Hung', 'Tim G. J. Rudner', 'Gregory Farquhar', 'Shimon Whiteson', 'Nantas Nardelli', 'Jakob Foerster', 'Tabish Rashid', 'Philip H. S. Torr', 'Mikayel Samvelyan', 'Christian Schroeder de Witt'] | 2019-02-11 | null | null | null | null | ['smac-1', 'real-time-strategy-games', 'smac'] | ['playing-games', 'playing-games', 'playing-games'] | [-4.97886240e-01 -1.17511123e-01 -2.27309048e-01 4.57483195e-02
-9.07070875e-01 -6.54845297e-01 9.72789824e-01 1.56298652e-01
-8.70118737e-01 1.29281962e+00 -1.44964522e-02 -1.72030181e-01
-3.14357460e-01 -6.47413194e-01 -6.84097946e-01 -9.32540298e-01
-7.39623904e-01 1.12708092e+00 2.99457639e-01 -7.59315312... | [3.7609143257141113, 1.888580560684204] |
96aea135-97f5-43df-8aaf-57c09bdb3bfd | ddgk-learning-graph-representations-for-deep | 1904.09671 | null | http://arxiv.org/abs/1904.09671v1 | http://arxiv.org/pdf/1904.09671v1.pdf | DDGK: Learning Graph Representations for Deep Divergence Graph Kernels | Can neural networks learn to compare graphs without feature engineering? In
this paper, we show that it is possible to learn representations for graph
similarity with neither domain knowledge nor supervision (i.e.\ feature
engineering or labeled graphs). We propose Deep Divergence Graph Kernels, an
unsupervised method ... | ['Rami Al-Rfou', 'Bryan Perozzi', 'Dustin Zelle'] | 2019-04-21 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 1.36181101e-01 6.63042307e-01 -3.14644665e-01 -5.30198812e-01
-3.72777551e-01 -6.22442484e-01 5.94611287e-01 5.78658164e-01
1.24706894e-01 3.03410023e-01 2.93223202e-01 -3.51065129e-01
-1.79375872e-01 -1.10819256e+00 -8.87135148e-01 -4.26933140e-01
-3.45889211e-01 4.32193488e-01 3.95850837e-02 -1.97487772... | [7.043728828430176, 6.293851852416992] |
fe03e5ed-a5fe-4c9b-8c75-163f69197c14 | gaitvibe-enhancing-structural-vibration-based | 2212.03377 | null | https://arxiv.org/abs/2212.03377v1 | https://arxiv.org/pdf/2212.03377v1.pdf | GaitVibe+: Enhancing Structural Vibration-based Footstep Localization Using Temporary Cameras for In-home Gait Analysis | In-home gait analysis is important for providing early diagnosis and adaptive treatments for individuals with gait disorders. Existing systems include wearables and pressure mats, but they have limited scalability. Recent studies have developed vision-based systems to enable scalable, accurate in-home gait analysis, bu... | ['Hae Young Noh', 'Jingxiao Liu', 'Yiwen Dong'] | 2022-12-07 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [-6.24170080e-02 -5.60976505e-01 1.08975112e-01 6.01433404e-02
-8.56832266e-01 -2.20214456e-01 -4.76616800e-01 9.06111896e-02
-5.13280988e-01 6.34041190e-01 2.49880970e-01 2.52693355e-01
1.91667363e-01 -8.14278424e-01 -4.13741946e-01 -6.85435891e-01
-2.81382620e-01 -1.22916982e-01 4.37077135e-01 -1.92021169... | [6.893762588500977, 0.47452932596206665] |
c244d46e-2c2f-46b7-9403-7ed31e2c2bdf | fully-convolutional-variational-autoencoder | null | null | https://www.researchgate.net/publication/340049776_Fully_Convolutional_Variational_Autoencoder_For_Feature_Extraction_Of_Fire_Detection_System | https://www.researchgate.net/profile/Herminarto-Nugroho/publication/340049776_Fully_Convolutional_Variational_Autoencoder_For_Feature_Extraction_Of_Fire_Detection_System/links/5e7437ad92851c3587599b32/Fully-Convolutional-Variational-Autoencoder-For-Feature-Extraction-Of-Fire-Detection-System.pdf | Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System | This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common is... | ['Ariana Yunita', 'Muhammad Koyimatu', 'Ade Irawan', 'Meredita Susanty', 'Herminarto Nugroho'] | 2020-03-01 | null | null | null | jurnal-ilmu-komputer-dan-informasi-2020-3 | ['fire-detection'] | ['time-series'] | [-3.25809896e-01 -3.71426314e-01 2.26285696e-01 4.70026582e-02
-1.47996128e-01 -2.86173254e-01 7.44548142e-01 -4.79644001e-01
-3.65326673e-01 6.43904388e-01 2.09271058e-01 8.83565173e-02
-4.37651873e-01 -1.45798445e+00 -5.28216779e-01 -1.15233970e+00
1.26395643e-01 3.10794979e-01 2.45776385e-01 -3.91336530... | [9.117972373962402, 2.932912588119507] |
ea3561ea-5030-4dd8-8b58-a753808172ce | very-large-language-model-as-a-unified | 2212.09271 | null | https://arxiv.org/abs/2212.09271v2 | https://arxiv.org/pdf/2212.09271v2.pdf | Very Large Language Model as a Unified Methodology of Text Mining | Text data mining is the process of deriving essential information from language text. Typical text mining tasks include text categorization, text clustering, topic modeling, information extraction, and text summarization. Various data sets are collected and various algorithms are designed for the different types of tas... | ['Meng Jiang'] | 2022-12-19 | null | null | null | null | ['text-clustering', 'text-categorization'] | ['natural-language-processing', 'natural-language-processing'] | [-1.9613930e-03 3.3787642e-02 -5.7306117e-01 -4.7384465e-01
-5.5840224e-01 -2.9538092e-01 8.3661985e-01 8.6085701e-01
-4.4187078e-01 7.1046758e-01 7.3811972e-01 -5.2809346e-01
-1.8076986e-01 -7.2211313e-01 2.9246667e-01 -3.2988578e-01
-1.2440496e-03 7.6977420e-01 -6.7850649e-02 -1.8271147e-01
9.0577352e-01... | [10.521321296691895, 8.312297821044922] |
1bc30d4b-998a-466a-a3d1-58433ed5a969 | discriminative-feature-encoding-for-intrinsic | 2209.12155 | null | https://arxiv.org/abs/2209.12155v1 | https://arxiv.org/pdf/2209.12155v1.pdf | Discriminative feature encoding for intrinsic image decomposition | Intrinsic image decomposition is an important and long-standing computer vision problem. Given an input image, recovering the physical scene properties is ill-posed. Several physically motivated priors have been used to restrict the solution space of the optimization problem for intrinsic image decomposition. This work... | ['Feng Lu', 'Yunfei Liu', 'Zongji Wang'] | 2022-09-25 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 1.42256260e-01 -1.09470867e-01 -1.04737073e-01 -2.33773485e-01
-4.93173480e-01 -1.31841049e-01 4.09935415e-01 -5.96827865e-01
-4.16984707e-01 4.31007862e-01 2.30436936e-01 3.74606073e-01
-5.16296625e-01 -5.02845466e-01 -6.90464616e-01 -1.23561478e+00
2.19036832e-01 8.56325701e-02 -1.01269707e-01 1.31915621... | [9.394131660461426, -2.5944108963012695] |
e5eb5bef-6994-4aef-9357-36c7e76d7716 | ceil-a-general-classification-enhanced | 2304.11061 | null | https://arxiv.org/abs/2304.11061v1 | https://arxiv.org/pdf/2304.11061v1.pdf | CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering | Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, deep clustering has achieved significant advantages over traditional clustering methods. Despite the... | ['Haijiang Wu', 'Di Niu', 'Yinglong Ma', 'Mengzhen Wang', 'Mingjun Zhao'] | 2023-04-20 | null | null | null | null | ['classification', 'deep-clustering', 'text-clustering', 'deep-clustering', 'short-text-clustering'] | ['methodology', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-8.27938393e-02 -4.10155892e-01 -1.28885165e-01 -3.72985691e-01
-7.88981855e-01 -4.05772507e-01 7.37841964e-01 4.50448513e-01
-4.08012360e-01 1.54240668e-01 3.63663316e-01 2.64176279e-02
-3.08171034e-01 -4.82334614e-01 -2.71531135e-01 -1.06802011e+00
3.02433282e-01 8.38794708e-01 -2.38422915e-01 2.67745018... | [10.414748191833496, 6.712684154510498] |
902e5aec-246b-419b-a031-2fd4195b96ef | partial-domain-adaptation-without-domain | 2108.12867 | null | https://arxiv.org/abs/2108.12867v2 | https://arxiv.org/pdf/2108.12867v2.pdf | Partial Domain Adaptation without Domain Alignment | Unsupervised domain adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to a different but related unlabeled target domain with identical label space. Currently, the main workhorse for solving UDA is domain alignment, which has proven successful. However, it is often difficult to find an appro... | ['Songcan Chen', 'Weikai Li'] | 2021-08-29 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [ 5.23346484e-01 2.21380293e-01 -3.22185934e-01 -2.27164268e-01
-8.45020890e-01 -7.75625706e-01 3.84962648e-01 1.16272300e-01
-2.33147651e-01 8.28822792e-01 -1.58903748e-01 -2.84500420e-01
-2.38431886e-01 -6.04038835e-01 -6.50294542e-01 -8.71112108e-01
2.46521756e-01 6.05682313e-01 2.85546452e-01 -2.86296517... | [10.326617240905762, 3.1414012908935547] |
d8f4bd1a-9efb-4111-9dbf-75cb48a1648f | transfer-language-selection-for-zero-shot | 2206.00962 | null | https://arxiv.org/abs/2206.00962v1 | https://arxiv.org/pdf/2206.00962v1.pdf | Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection | We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot abusive language detection. This way we can use existing data from higher-resource languages to buil... | ['Michal Wroczynski', 'Gniewosz Leliwa', 'Masaki Arata', 'Fumito Masui', 'Michal Ptaszynski', 'Juuso Eronen'] | 2022-06-02 | null | null | null | null | ['abusive-language'] | ['natural-language-processing'] | [-4.98681754e-01 -4.69241828e-01 -8.06519568e-01 -3.14654887e-01
-9.73594964e-01 -8.28818262e-01 5.68251431e-01 1.75049454e-01
-6.69593692e-01 6.61364138e-01 3.41490746e-01 -2.21404538e-01
3.38627160e-01 -6.49035692e-01 -1.44266486e-01 -2.59917766e-01
-7.89518431e-02 5.79591870e-01 3.76528829e-01 -6.75354540... | [8.806977272033691, 10.560308456420898] |
28b8fad1-e618-4c59-b807-1c6ec346c36b | rmdl-random-multimodel-deep-learning-for | 1805.01890 | null | http://arxiv.org/abs/1805.01890v2 | http://arxiv.org/pdf/1805.01890v2.pdf | RMDL: Random Multimodel Deep Learning for Classification | The continually increasing number of complex datasets each year necessitates
ever improving machine learning methods for robust and accurate categorization
of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a
new ensemble, deep learning approach for classification. Deep learning models
have ac... | ['Laura E. Barnes', 'Kiana Jafari Meimandi', 'Donald E. Brown', 'Mojtaba Heidarysafa', 'Kamran Kowsari'] | 2018-05-03 | null | null | null | null | ['hierarchical-text-classification-of-blurbs'] | ['natural-language-processing'] | [-4.09516037e-01 -5.30736387e-01 -2.23348677e-01 -4.71941531e-01
-7.91327357e-01 -4.36664134e-01 9.05855060e-01 9.02559087e-02
-5.30632675e-01 6.96844757e-01 1.45742401e-01 -3.50118428e-01
-1.55549824e-01 -5.61176658e-01 -4.18758601e-01 -3.94367099e-01
-9.95540395e-02 6.61522388e-01 -1.73825964e-01 -8.42240453... | [9.6412935256958, 2.7974863052368164] |
39d9552c-2d75-4153-a9df-c2392ea04283 | improving-and-benchmarking-offline | 2306.00972 | null | https://arxiv.org/abs/2306.00972v1 | https://arxiv.org/pdf/2306.00972v1.pdf | Improving and Benchmarking Offline Reinforcement Learning Algorithms | Recently, Offline Reinforcement Learning (RL) has achieved remarkable progress with the emergence of various algorithms and datasets. However, these methods usually focus on algorithmic advancements, ignoring that many low-level implementation choices considerably influence or even drive the final performance. As a res... | ['Shuicheng Yan', 'Yang Yue', 'Yirui Wang', 'Xiao Ma', 'Bingyi Kang'] | 2023-06-01 | null | null | null | null | ['offline-rl', 'd4rl'] | ['playing-games', 'robots'] | [-4.19878095e-01 -4.89272714e-01 -6.47769809e-01 -1.07406594e-01
-6.59403145e-01 -9.00713146e-01 5.18068492e-01 1.16585955e-01
-5.73622465e-01 7.89261758e-01 7.01738149e-02 -5.53635240e-01
-1.19686402e-01 -6.58396542e-01 -7.23950207e-01 -7.24077284e-01
5.87876840e-03 3.13799322e-01 8.24862123e-02 -3.74124587... | [3.984215021133423, 1.7580773830413818] |
03b4a9a5-229a-4af9-9fb4-ca6214c4b2c0 | random-copolymer-inverse-design-system | 2212.00023 | null | https://arxiv.org/abs/2212.00023v2 | https://arxiv.org/pdf/2212.00023v2.pdf | Random Copolymer inverse design system orienting on Accurate discovering of Antimicrobial peptide-mimetic copolymers | Antimicrobial resistance is one of the biggest health problem, especially in the current period of COVID-19 pandemic. Due to the unique membrane-destruction bactericidal mechanism, antimicrobial peptide-mimetic copolymers are paid more attention and it is urgent to find more potential candidates with broad-spectrum ant... | ['Yang Tang', 'Tianyu Wu'] | 2022-11-30 | null | null | null | null | ['activity-prediction', 'activity-prediction'] | ['computer-vision', 'time-series'] | [ 6.17589116e-01 -4.91765022e-01 -5.43978333e-01 2.75826544e-01
-5.48059046e-01 -5.85366189e-01 3.00663739e-01 3.82048368e-01
-1.43615872e-01 1.38126957e+00 1.73798099e-01 -3.00998420e-01
-1.07745498e-01 -9.95421946e-01 -8.27787220e-01 -1.06164467e+00
5.08240536e-02 5.38311779e-01 -7.94001594e-02 -3.24858904... | [4.979572296142578, 5.719258785247803] |
8584178e-dbeb-42bb-942a-2979a746dadc | behavior-from-the-void-unsupervised-active | 2103.04551 | null | https://arxiv.org/abs/2103.04551v4 | https://arxiv.org/pdf/2103.04551v4.pdf | Behavior From the Void: Unsupervised Active Pre-Training | We introduce a new unsupervised pre-training method for reinforcement learning called APT, which stands for Active Pre-Training. APT learns behaviors and representations by actively searching for novel states in reward-free environments. The key novel idea is to explore the environment by maximizing a non-parametric en... | ['Pieter Abbeel', 'Hao liu'] | 2021-03-08 | null | http://proceedings.neurips.cc/paper/2021/hash/99bf3d153d4bf67d640051a1af322505-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/99bf3d153d4bf67d640051a1af322505-Paper.pdf | neurips-2021-12 | ['unsupervised-pre-training'] | ['methodology'] | [-2.11455151e-02 1.36053503e-01 -4.55646694e-01 -1.02912858e-01
-9.26969707e-01 -5.27625144e-01 8.87923837e-01 -6.32969141e-02
-9.87872839e-01 1.01534164e+00 1.71744809e-01 -1.26674160e-01
-5.03621437e-02 -6.49951160e-01 -7.98243761e-01 -7.46711731e-01
-5.09786308e-01 8.99564028e-01 4.64728698e-02 -2.54857630... | [4.007068634033203, 1.5225600004196167] |
c6dcf410-7cb6-463b-8c5c-2bbd4c79ce65 | what-is-learned-in-knowledge-graph-embeddings | 2110.09978 | null | https://arxiv.org/abs/2110.09978v1 | https://arxiv.org/pdf/2110.09978v1.pdf | What is Learned in Knowledge Graph Embeddings? | A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with edge types. KGs are an important primitive in modern machine learning and artificial intelligence. Embedding-based models, such as the seminal TransE [Bordes et al., 2013] and the recent... | ['Andrew Wood', 'Trung V. Dang', 'Peter Chin', 'Tianqi Wu', 'Omri Ben-Eliezer', 'Michael Simkin', 'Michael R. Douglas'] | 2021-10-19 | null | null | null | null | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-1.47680286e-03 6.55215740e-01 -5.37649989e-01 -7.08376169e-02
9.57794338e-02 -5.53620815e-01 7.42884874e-01 4.95683044e-01
5.32731675e-02 6.27000272e-01 2.11229309e-01 -5.57375550e-01
-6.30980968e-01 -1.29659557e+00 -1.05827248e+00 -2.57707238e-01
-9.45620239e-01 6.35748923e-01 3.34010303e-01 -2.67591000... | [8.695473670959473, 7.587416172027588] |
576e8823-fa72-46ef-ba50-bcd89a16c463 | sneakyprompt-evaluating-robustness-of-text-to | 2305.12082 | null | https://arxiv.org/abs/2305.12082v2 | https://arxiv.org/pdf/2305.12082v2.pdf | SneakyPrompt: Evaluating Robustness of Text-to-image Generative Models' Safety Filters | Text-to-image generative models such as Stable Diffusion and DALL$\cdot$E 2 have attracted much attention since their publication due to their wide application in the real world. One challenging problem of text-to-image generative models is the generation of Not-Safe-for-Work (NSFW) content, e.g., those related to viol... | ['Yinzhi Cao', 'Neil Gong', 'Haolin Yuan', 'Bo Hui', 'Yuchen Yang'] | 2023-05-20 | null | null | null | null | ['semantic-textual-similarity', 'semantic-similarity'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.68405786e-01 8.08651745e-02 -6.23065233e-02 -3.15387212e-02
-8.50368977e-01 -9.59044099e-01 9.37330723e-01 -1.34011552e-01
-3.93420994e-01 3.80417526e-01 -6.54559955e-02 -4.92975652e-01
-4.75759655e-02 -1.12602496e+00 -8.24322999e-01 -4.19498384e-01
7.92673901e-02 4.11331713e-01 5.18110037e-01 -3.85736674... | [5.701298236846924, 7.821197032928467] |
3abc30a1-ab3f-435f-9f3f-2fe0399e3d77 | rpn-a-word-vector-level-data-augmentation | 2212.05961 | null | https://arxiv.org/abs/2212.05961v3 | https://arxiv.org/pdf/2212.05961v3.pdf | RPN: A Word Vector Level Data Augmentation Algorithm in Deep Learning for Language Understanding | Data augmentation is a widely used technique in machine learning to improve model performance. However, existing data augmentation techniques in natural language understanding (NLU) may not fully capture the complexity of natural language variations, and they can be challenging to apply to large datasets. This paper pr... | ['Huiwen Xue', 'Xuecong Hou', 'Yue Wang', 'Xiaolong Zhang', 'Yongming Liu', 'Zhuanzhe Zhao', 'Zhengqing Yuan'] | 2022-12-12 | null | null | null | null | ['text-augmentation'] | ['natural-language-processing'] | [ 4.17344153e-01 -2.33311534e-01 -6.57599747e-01 -1.07285753e-01
-3.85568082e-01 -4.53417867e-01 7.13551641e-01 5.75990975e-01
-6.23985708e-01 5.08696854e-01 4.97049809e-01 -4.59337234e-01
2.69101322e-01 -9.75148201e-01 -7.40507662e-01 -2.97429711e-01
1.82962060e-01 4.80270833e-01 -2.84643948e-01 -4.07576889... | [10.663989067077637, 8.309526443481445] |
3402e557-e29d-46ce-82b7-be616f69120c | self-meta-pseudo-labels-meta-pseudo-labels | 2212.13420 | null | https://arxiv.org/abs/2212.13420v1 | https://arxiv.org/pdf/2212.13420v1.pdf | Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher | We present Self Meta Pseudo Labels, a novel semi-supervised learning method similar to Meta Pseudo Labels but without the teacher model. We introduce a novel way to use a single model for both generating pseudo labels and classification, allowing us to store only one model in memory instead of two. Our method attains s... | ['Qingchen Wang', 'Kei-Sing Ng'] | 2022-12-27 | null | null | null | null | ['semi-supervised-image-classification'] | ['computer-vision'] | [ 7.49038577e-01 8.42513323e-01 -4.39237684e-01 -8.33223343e-01
-9.84746218e-01 -6.77557468e-01 1.07285845e+00 4.33835268e-01
-4.83984083e-01 1.12586021e+00 -1.05967157e-01 -2.31058225e-01
3.20669889e-01 -8.12925041e-01 -6.79601192e-01 -5.18816710e-01
4.35680985e-01 9.46778357e-01 2.07314998e-01 2.92797804... | [9.61387825012207, 3.9717977046966553] |
9d5aa2c0-0bdd-431c-8f3a-9dd062e03b16 | motility-at-the-origin-of-life-its | 1311.2531 | null | http://arxiv.org/abs/1311.2531v1 | http://arxiv.org/pdf/1311.2531v1.pdf | Motility at the origin of life: Its characterization and a model | Due to recent advances in synthetic biology and artificial life, the origin
of life is currently a hot topic of research. We review the literature and
argue that the two traditionally competing "replicator-first" and
"metabolism-first" approaches are merging into one integrated theory of
individuation and evolution. We... | ['Nathaniel Virgo', 'Tom Froese', 'Takashi Ikegami'] | 2013-11-11 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [ 2.39274681e-01 1.71650574e-01 2.27298677e-01 2.51714498e-01
6.39413178e-01 -6.62353635e-01 1.01229191e+00 4.41319287e-01
-4.27782178e-01 9.17964935e-01 -1.01550438e-01 -3.70592713e-01
-2.52589643e-01 -7.60751963e-01 -4.43387270e-01 -1.08844543e+00
-1.48888811e-01 3.70599568e-01 2.96638340e-01 -6.60896242... | [5.5889201164245605, 4.1747307777404785] |
c8bd4860-1f88-4655-8a30-9306bd87595d | a-more-fine-grained-aspect-sentiment-opinion | 2103.15255 | null | https://arxiv.org/abs/2103.15255v5 | https://arxiv.org/pdf/2103.15255v5.pdf | A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task | Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA). However, some triplets extracted by ASTE are confusing, since the sentiment in a triplet extracted by ASTE is th... | ['Yancheng He', 'Cunxiang Yin', 'Fang Wang', 'Yuncong Li', 'Sheng-hua Zhong', 'Wenjun Zhang'] | 2021-03-29 | null | null | null | null | ['aspect-sentiment-opinion-triplet-extraction'] | ['natural-language-processing'] | [ 2.03186944e-01 -1.11516751e-01 -6.68343157e-02 -7.67435133e-01
-8.05223048e-01 -6.70845032e-01 6.29524708e-01 4.09894317e-01
1.13914767e-02 2.00823605e-01 6.31732941e-01 -1.54676706e-01
4.82144654e-02 -9.48705614e-01 -3.13744009e-01 -6.35236025e-01
3.38334024e-01 2.51957059e-01 -1.35423228e-01 -8.16909492... | [11.463924407958984, 6.652829647064209] |
a7972026-fafe-4940-842c-4536d29aa4c6 | boundary-aware-self-supervised-learning-for | 2201.05277 | null | https://arxiv.org/abs/2201.05277v1 | https://arxiv.org/pdf/2201.05277v1.pdf | Boundary-aware Self-supervised Learning for Video Scene Segmentation | Self-supervised learning has drawn attention through its effectiveness in learning in-domain representations with no ground-truth annotations; in particular, it is shown that properly designed pretext tasks (e.g., contrastive prediction task) bring significant performance gains for downstream tasks (e.g., classificatio... | ['Eun-Sol Kim', 'Joonseok Lee', 'Seongsu Ha', 'Sangho Lee', 'Gunsoo Han', 'Minchul Shin', 'Jonghwan Mun'] | 2022-01-14 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 6.13922358e-01 -1.24017419e-02 -6.46014214e-01 -3.55295509e-01
-8.09760928e-01 -5.10474443e-01 5.91591537e-01 1.48628339e-01
-2.24806398e-01 1.26286656e-01 3.56368184e-01 -1.67910293e-01
-1.57727879e-02 -4.70608681e-01 -9.20094132e-01 -5.63858330e-01
-3.52385151e-03 6.18979409e-02 5.78526497e-01 -9.33110062... | [9.255255699157715, 0.5724532008171082] |
c54532bb-13fd-482b-a93a-fb481b38b402 | hlt-fbk-a-complete-temporal-processing-system | null | null | https://aclanthology.org/S15-2135 | https://aclanthology.org/S15-2135.pdf | HLT-FBK: a Complete Temporal Processing System for QA TempEval | null | ['Anne-Lyse Minard', 'Paramita Mirza'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['temporal-information-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... | [-7.234249591827393, 3.82053279876709] |
847a50bd-9586-4c56-bfd0-a867d475c20e | deep-graph-learning-for-spatially-varying | 2202.06300 | null | https://arxiv.org/abs/2202.06300v1 | https://arxiv.org/pdf/2202.06300v1.pdf | Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction | Lighting prediction from a single image is becoming increasingly important in many vision and augmented reality (AR) applications in which shading and shadow consistency between virtual and real objects should be guaranteed. However, this is a notoriously ill-posed problem, especially for indoor scenarios, because of t... | ['Yanwen Guo', 'Yan Zhang', 'Piaopiao Yu', 'Shan Yang', 'Zhen He', 'Zhenyu Chen', 'Chenchen Wan', 'Jie Guo', 'Jiayang Bai'] | 2022-02-13 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 2.40644619e-01 -9.97194350e-02 4.64762568e-01 -5.01705706e-01
2.34287102e-02 -4.65576589e-01 4.19164449e-01 -4.57395732e-01
1.57036364e-01 7.08753228e-01 4.10072058e-02 -4.20575023e-01
3.35212201e-01 -1.00222993e+00 -7.91272223e-01 -8.84215355e-01
2.87718654e-01 1.00629412e-01 2.15347320e-01 8.10215324... | [9.790523529052734, -2.9688525199890137] |
14374aba-1952-4097-8ef3-5b30a2f4216d | high-dimensional-mr-reconstruction | 2306.08630 | null | https://arxiv.org/abs/2306.08630v2 | https://arxiv.org/pdf/2306.08630v2.pdf | High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models | We present a novel method that integrates subspace modeling with an adaptive generative image prior for high-dimensional MR image reconstruction. The subspace model imposes an explicit low-dimensional representation of the high-dimensional images, while the generative image prior serves as a spatial constraint on the "... | ['Fan Lam', 'Mark A. Anastasio', 'Varun A. Kelkar', 'Xi Peng', 'Ruiyang Zhao'] | 2023-06-14 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 4.94135559e-01 -3.00186984e-02 8.37088749e-02 -5.42473733e-01
-9.85077322e-01 -2.11251587e-01 5.17332017e-01 -5.75878084e-01
-5.49761593e-01 6.60843015e-01 4.96137142e-01 2.40069535e-02
-4.46520954e-01 -2.10328013e-01 -4.83977139e-01 -1.12165701e+00
-9.78881046e-02 5.59996367e-01 6.98855985e-03 6.19830340... | [13.50711441040039, -2.388105630874634] |
c49b3094-3dd7-4b77-9da0-39f0a1b6224c | estimating-mutual-information-for-discrete | 1709.06212 | null | http://arxiv.org/abs/1709.06212v3 | http://arxiv.org/pdf/1709.06212v3.pdf | Estimating Mutual Information for Discrete-Continuous Mixtures | Estimating mutual information from observed samples is a basic primitive,
useful in several machine learning tasks including correlation mining,
information bottleneck clustering, learning a Chow-Liu tree, and conditional
independence testing in (causal) graphical models. While mutual information is
a well-defined quan... | ['Sreeram Kannan', 'Sewoong Oh', 'Pramod Viswanath', 'Weihao Gao'] | 2017-09-19 | estimating-mutual-information-for-discrete-1 | http://papers.nips.cc/paper/7180-estimating-mutual-information-for-discrete-continuous-mixtures | http://papers.nips.cc/paper/7180-estimating-mutual-information-for-discrete-continuous-mixtures.pdf | neurips-2017-12 | ['mutual-information-estimation'] | ['methodology'] | [ 1.33348271e-01 -8.67889747e-02 -3.59577328e-01 -3.13615829e-01
-6.53646469e-01 -3.27988416e-01 5.43087900e-01 3.22748482e-01
-2.65531898e-01 1.10219204e+00 -2.02837989e-01 -4.10085827e-01
-7.13345528e-01 -7.77625382e-01 -4.20400977e-01 -9.94658351e-01
-5.74579298e-01 8.03163767e-01 7.88013265e-02 3.75614077... | [7.356607437133789, 4.3609418869018555] |
081be829-6170-40ce-8ede-1ee0f2035359 | neuro-symbolic-spatio-temporal-reasoning | 2211.15566 | null | https://arxiv.org/abs/2211.15566v2 | https://arxiv.org/pdf/2211.15566v2.pdf | Neuro-Symbolic Spatio-Temporal Reasoning | Knowledge about space and time is necessary to solve problems in the physical world: An AI agent situated in the physical world and interacting with objects often needs to reason about positions of and relations between objects; and as soon as the agent plans its actions to solve a task, it needs to consider the tempor... | ['Stefan Wermter', 'Matthias Kerzel', 'Marjan Alirezaie', 'Kyra Ahrens', 'Michael Sioutis', 'Jae Hee Lee'] | 2022-11-28 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.18253298e-01 7.37271309e-02 -1.79711014e-01 -2.04347342e-01
1.78180575e-01 -7.66822696e-01 7.51850188e-01 4.78362590e-01
-3.70492518e-01 4.96421963e-01 -1.81194156e-01 -4.94295150e-01
-6.45496547e-01 -1.21342874e+00 -4.08604771e-01 -5.54334462e-01
-1.51325122e-01 5.62324762e-01 5.35806119e-01 -3.31038028... | [10.386296272277832, 1.944320559501648] |
dc2ff790-6aa9-4946-9f2d-a195c8d1174f | semi-supervised-3d-hand-object-pose | 2107.07676 | null | https://arxiv.org/abs/2107.07676v1 | https://arxiv.org/pdf/2107.07676v1.pdf | Semi-supervised 3D Hand-Object Pose Estimation via Pose Dictionary Learning | 3D hand-object pose estimation is an important issue to understand the interaction between human and environment. Current hand-object pose estimation methods require detailed 3D labels, which are expensive and labor-intensive. To tackle the problem of data collection, we propose a semi-supervised 3D hand-object pose es... | ['Ya zhang', 'Siheng Chen', 'Zida Cheng'] | 2021-07-16 | null | null | null | null | ['hand-object-pose'] | ['computer-vision'] | [-2.87584871e-01 -3.63921016e-01 -2.88549721e-01 -2.38276839e-01
-5.48244059e-01 -5.87099612e-01 2.21257970e-01 -5.04812479e-01
-3.69715452e-01 4.59244668e-01 2.55155742e-01 1.86471298e-01
1.06097415e-01 -2.87631541e-01 -4.52261895e-01 -5.09480834e-01
8.59386697e-02 1.02409720e+00 9.66897383e-02 -3.32992077... | [6.672255992889404, -0.753339409828186] |
06df7148-e7d1-4039-99ea-8be535a1802d | exploring-temporal-context-and-human-movement | 2106.13967 | null | https://arxiv.org/abs/2106.13967v1 | https://arxiv.org/pdf/2106.13967v1.pdf | Exploring Temporal Context and Human Movement Dynamics for Online Action Detection in Videos | Nowadays, the interaction between humans and robots is constantly expanding, requiring more and more human motion recognition applications to operate in real time. However, most works on temporal action detection and recognition perform these tasks in offline manner, i.e. temporally segmented videos are classified as a... | ['Petros Maragos', 'Nikolaos Kardaris', 'Vasiliki I. Vasileiou'] | 2021-06-26 | null | null | null | null | ['online-action-detection'] | ['computer-vision'] | [ 4.16938692e-01 -3.36894274e-01 -5.70287466e-01 2.12647066e-01
-4.83150452e-01 -3.23940843e-01 7.79056370e-01 -3.33899975e-01
-7.55493283e-01 4.81932223e-01 3.36409688e-01 2.33554542e-01
1.57551289e-01 -3.00974727e-01 -3.59517097e-01 -7.79384375e-01
-4.34973955e-01 1.22720204e-01 7.55558491e-01 -1.00270519... | [8.209405899047852, 0.5016278028488159] |
e539a276-b8e7-4ce3-8c2c-712aa68a8d71 | iiit-dwd-lt-edi-eacl2021-hope-speech | null | null | https://aclanthology.org/2021.ltedi-1.14 | https://aclanthology.org/2021.ltedi-1.14.pdf | IIIT_DWD@LT-EDI-EACL2021: Hope Speech Detection in YouTube multilingual comments | Language as a significant part of communication should be inclusive of equality and diversity. The internet user’s language has a huge influence on peer users all over the world. People express their views through language on virtual platforms like Facebook, Twitter, YouTube etc. People admire the success of others, pr... | ['Ankit Kumar Mishra', 'Sunil Saumya'] | null | null | null | null | eacl-ltedi-2021-4 | ['hope-speech-detection'] | ['natural-language-processing'] | [-5.91521680e-01 1.44311085e-01 -7.56266952e-01 -2.31454208e-01
-3.17407250e-01 -2.69703306e-02 8.86226356e-01 4.36950177e-01
-3.42046589e-01 8.97508800e-01 1.28598034e+00 -4.08655256e-01
8.76889899e-02 -6.64415121e-01 3.00558537e-01 -1.06213003e-01
3.62967551e-01 1.73817173e-01 -3.23263168e-01 -9.85606432... | [8.932292938232422, 10.702166557312012] |
e5731280-f904-4f8a-97cb-52ef29f01d72 | analysis-of-climate-campaigns-on-social-media | 2305.06174 | null | https://arxiv.org/abs/2305.06174v2 | https://arxiv.org/pdf/2305.06174v2.pdf | Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging | Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns... | ['Dan Goldwasser', 'Ruqi Zhang', 'Tunazzina Islam'] | 2023-05-06 | null | null | null | null | ['opinion-mining'] | ['natural-language-processing'] | [ 5.46494603e-01 4.50603217e-01 -5.45479953e-01 -2.18801051e-01
-5.09749055e-01 -7.85558701e-01 9.33148921e-01 9.75392938e-01
-2.75106072e-01 6.18384659e-01 1.07366550e+00 -8.40684652e-01
2.18165442e-01 -1.14126480e+00 -6.28023624e-01 -5.20574510e-01
3.39286804e-01 -3.23778361e-01 -5.73644228e-02 -4.36694235... | [8.768917083740234, 9.871844291687012] |
d63f5ac4-df86-4b69-85b5-4e557918759a | fast-convergence-in-learning-two-layer-neural | 2305.13471 | null | https://arxiv.org/abs/2305.13471v2 | https://arxiv.org/pdf/2305.13471v2.pdf | Fast Convergence in Learning Two-Layer Neural Networks with Separable Data | Normalized gradient descent has shown substantial success in speeding up the convergence of exponentially-tailed loss functions (which includes exponential and logistic losses) on linear classifiers with separable data. In this paper, we go beyond linear models by studying normalized GD on two-layer neural nets. We pro... | ['Christos Thrampoulidis', 'Hossein Taheri'] | 2023-05-22 | null | null | null | null | ['generalization-bounds'] | ['methodology'] | [-2.45751232e-01 2.07182035e-01 -2.20909223e-01 -8.56542051e-01
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-5.11304975e-01 3.46949458e-01 1.00943185e-01 2.78405305... | [7.78222131729126, 3.9349617958068848] |
fbe8ca7e-6f7e-4db6-804d-a3b8a58fd094 | cscd-ime-correcting-spelling-errors-generated | 2211.08788 | null | https://arxiv.org/abs/2211.08788v2 | https://arxiv.org/pdf/2211.08788v2.pdf | CSCD-IME: Correcting Spelling Errors Generated by Pinyin IME | Chinese Spelling Correction (CSC) is a task to detect and correct spelling mistakes in texts. In fact, most of Chinese input is based on pinyin input method, so the study of spelling errors in this process is more practical and valuable. However, there is still no research dedicated to this essential scenario. In this ... | ['Jie zhou', 'Fandong Meng', 'Yong Hu'] | 2022-11-16 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 9.25575010e-03 -6.12311780e-01 3.84720474e-01 -2.06885591e-01
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6.90519094e-01 5.01600683e-01 4.20617670e-01 -5.23504794... | [10.982450485229492, 10.759657859802246] |
aa010301-e174-4087-ac6a-5b4984273065 | universal-learned-image-compression-with-low | 2206.11599 | null | https://arxiv.org/abs/2206.11599v1 | https://arxiv.org/pdf/2206.11599v1.pdf | Universal Learned Image Compression With Low Computational Cost | Recently, learned image compression methods have developed rapidly and exhibited excellent rate-distortion performance when compared to traditional standards, such as JPEG, JPEG2000 and BPG. However, the learning-based methods suffer from high computational costs, which is not beneficial for deployment on devices with ... | ['Wen Tan', 'Yongsheng Liang', 'Fanyang Meng', 'Youneng Bao', 'Yao Xin', 'Bowen Li'] | 2022-06-23 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 1.70638874e-01 -1.06367081e-01 -2.57015198e-01 -3.80277574e-01
-7.05669940e-01 2.81033590e-02 3.67591172e-01 1.37280494e-01
-4.24342811e-01 4.86227572e-01 6.86472952e-02 -2.65488297e-01
-4.91718799e-02 -7.41260052e-01 -7.47073293e-01 -7.77242005e-01
-1.94483206e-01 -2.71879762e-01 2.00906932e-01 4.56577092... | [11.31792163848877, -1.637199878692627] |
2db6b8dc-22d1-48bb-98b4-cf42a78ae5ac | an-empirical-and-comparative-analysis-of-data | null | null | https://openreview.net/forum?id=SygBIxSFDS | https://openreview.net/pdf?id=SygBIxSFDS | An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms | This paper focuses on valuating training data for supervised learning tasks and studies the Shapley value, a data value notion originated in cooperative game theory. The Shapley value defines a unique value distribution scheme that satisfies a set of appealing properties desired by a data value notion. However, the Sha... | ['Dawn Song', 'Bo Li', 'Ce Zhang', 'Jiacen Xu', 'Xuehui Sun', 'Ruoxi Jia'] | 2019-09-25 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 3.15405369e-01 3.08112085e-01 -7.32141018e-01 -1.34351656e-01
-9.95632350e-01 -6.90361857e-01 5.42766750e-02 6.52162910e-01
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-6.25841260e-01 -8.29181612e-01 -5.33540547e-01 -8.97297025e-01
-3.28339726e-01 3.08634698e-01 1.89519495e-01 -1.61709428... | [8.70521068572998, 5.025423049926758] |
93c957ee-a4fa-42ff-bf47-10e34f582ce3 | track-anything-segment-anything-meets-videos | 2304.11968 | null | https://arxiv.org/abs/2304.11968v2 | https://arxiv.org/pdf/2304.11968v2.pdf | Track Anything: Segment Anything Meets Videos | Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images. Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos. Therefore, in this... | ['Feng Zheng', 'Fangjing Wang', 'Shang Gao', 'Zhe Li', 'Mingqi Gao', 'Jinyu Yang'] | 2023-04-24 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-1.71029940e-01 -9.21162069e-02 -7.04436243e-01 -4.01698709e-01
-7.66422987e-01 -6.85486257e-01 2.32396856e-01 -2.83823878e-01
-3.65126222e-01 4.46827531e-01 -1.96320280e-01 -4.61367965e-01
2.68693298e-01 -3.60035181e-01 -9.42093849e-01 -3.52710754e-01
1.31287575e-01 2.55091578e-01 7.22740889e-01 1.36161089... | [9.215188026428223, -0.14706513285636902] |
03736594-eb84-43b6-b40f-08f96d61e5e1 | benchmarking-bonus-based-exploration-methods | 1908.02388 | null | https://arxiv.org/abs/1908.02388v3 | https://arxiv.org/pdf/1908.02388v3.pdf | Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment | This paper provides an empirical evaluation of recently developed exploration algorithms within the Arcade Learning Environment (ALE). We study the use of different reward bonuses that incentives exploration in reinforcement learning. We do so by fixing the learning algorithm used and focusing only on the impact of the... | ['Adrien Ali Taïga', 'William Fedus', 'Marc G. Bellemare', 'Marlos C. Machado', 'Aaron Courville'] | 2019-08-06 | null | null | null | null | ['montezumas-revenge'] | ['playing-games'] | [-4.78968203e-01 1.52153254e-01 -1.26217127e-01 2.57333547e-01
-6.43543661e-01 -7.52732992e-01 5.74502468e-01 2.22237557e-01
-1.11356819e+00 1.07499623e+00 2.09679425e-01 -5.52483976e-01
-6.46567643e-01 -7.78669238e-01 -6.09977722e-01 -7.17968762e-01
-8.35249186e-01 5.69577157e-01 1.20055161e-01 -7.47768104... | [3.7964119911193848, 1.7081049680709839] |
d804532c-c620-441d-b902-96ca8050c00b | scene-labeling-using-sparse-precision-matrix | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Souly_Scene_Labeling_Using_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Souly_Scene_Labeling_Using_CVPR_2016_paper.pdf | Scene Labeling Using Sparse Precision Matrix | Scene labeling task is to segment the image into meaningful regions and categorize them into classes of objects which comprised the image. Commonly used methods typically find the local features for each segment and label them using classifiers. Afterwards, labeling is smoothed in order to make sure that neighboring r... | ['Nasim Souly', 'Mubarak Shah'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['scene-labeling'] | ['computer-vision'] | [ 3.66091490e-01 9.31054726e-02 -3.90030146e-01 -5.98204434e-01
-4.29682791e-01 -6.82457983e-01 5.33858359e-01 5.84591448e-01
-2.08766580e-01 3.50178361e-01 3.13215703e-01 2.45485768e-01
-3.59827280e-01 -7.78481483e-01 -7.39953101e-01 -8.27603936e-01
-1.39626354e-01 1.97411016e-01 1.90039352e-02 3.72095942... | [7.7956366539001465, 4.53635311126709] |
990576c7-8e95-4c40-90d6-41e6c86f04ff | tree-constrained-graph-neural-networks-for | 2110.00124 | null | https://arxiv.org/abs/2110.00124v1 | https://arxiv.org/pdf/2110.00124v1.pdf | Tree-Constrained Graph Neural Networks For Argument Mining | We propose a novel architecture for Graph Neural Networks that is inspired by the idea behind Tree Kernels of measuring similarity between trees by taking into account their common substructures, named fragments. By imposing a series of regularization constraints to the learning problem, we exploit a pooling mechanism ... | ['Paolo Torroni', 'Marco Lippi', 'Federico Ruggeri'] | 2021-09-02 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 4.67718452e-01 5.84959567e-01 -3.71346414e-01 -5.14008760e-01
-2.04379350e-01 -2.91342437e-01 7.10292518e-01 9.88442361e-01
-7.02177286e-01 3.61128688e-01 2.95998961e-01 -7.12187767e-01
-3.37174088e-01 -1.05797064e+00 -7.71566570e-01 -5.03678381e-01
-4.61576998e-01 2.03246608e-01 3.63710821e-01 -1.77107483... | [10.205313682556152, 9.101496696472168] |
0f8992d3-5ae9-4d26-a1f7-949f65fd1ad0 | deff-gan-diverse-attribute-transfer-for-few | 2302.14533 | null | https://arxiv.org/abs/2302.14533v1 | https://arxiv.org/pdf/2302.14533v1.pdf | DEff-GAN: Diverse Attribute Transfer for Few-Shot Image Synthesis | Requirements of large amounts of data is a difficulty in training many GANs. Data efficient GANs involve fitting a generators continuous target distribution with a limited discrete set of data samples, which is a difficult task. Single image methods have focused on modeling the internal distribution of a single image a... | ['G. Sivakumar', 'Rajiv Kumar'] | 2023-02-28 | null | null | null | null | ['single-class-few-shot-image-synthesis', 'multi-class-one-shot-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 6.53167605e-01 1.16439655e-01 -4.03878301e-01 -4.64460611e-01
-8.42505574e-01 -6.64018333e-01 8.36759508e-01 -5.18022954e-01
4.94450442e-02 8.99999440e-01 -1.47465408e-01 2.39215732e-01
3.18417579e-01 -1.03283834e+00 -6.90851092e-01 -8.54298234e-01
7.36286819e-01 1.05239570e+00 -1.22507714e-01 1.88216269... | [11.598494529724121, -0.332459032535553] |
f99ad71b-ec9a-47f0-ac7d-e233008fe688 | a-generative-model-to-synthesize-eeg-data-for | 2012.00430 | null | https://arxiv.org/abs/2012.00430v1 | https://arxiv.org/pdf/2012.00430v1.pdf | A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction | Prediction of seizure before they occur is vital for bringing normalcy to the lives of patients. Researchers employed machine learning methods using hand-crafted features for seizure prediction. However, ML methods are too complicated to select the best ML model or best features. Deep Learning methods are beneficial in... | ['Adeel Razi', 'Levin Kuhlmann', "Terence J. O'Brien", 'Junaid Qadir', 'Khansa Rasheed'] | 2020-12-01 | null | null | null | null | ['seizure-prediction'] | ['medical'] | [-4.88007665e-02 2.46615842e-01 3.30814958e-01 -2.55830139e-01
-9.30524468e-01 -3.61567557e-01 5.21888196e-01 -1.75683483e-01
-2.28967398e-01 1.28249574e+00 -1.59518681e-02 -2.49228105e-01
-1.42032743e-01 -7.24575579e-01 -6.40848339e-01 -6.51845634e-01
-5.15957117e-01 1.68975562e-01 -1.05720110e-01 -3.70260149... | [13.240281105041504, 3.533935308456421] |
9598c349-e7e3-4bf3-bd45-3a13e99aa357 | nas-fm-neural-architecture-search-for-tunable | 2305.12868 | null | https://arxiv.org/abs/2305.12868v1 | https://arxiv.org/pdf/2305.12868v1.pdf | NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis based on Frequency Modulation | Developing digital sound synthesizers is crucial to the music industry as it provides a low-cost way to produce high-quality sounds with rich timbres. Existing traditional synthesizers often require substantial expertise to determine the overall framework of a synthesizer and the parameters of submodules. Since expert ... | ['Yike Guo', 'Qifeng Liu', 'Xu Tan', 'Wei Xue', 'Zhen Ye'] | 2023-05-22 | null | null | null | null | ['architecture-search'] | ['methodology'] | [ 2.26250634e-01 -4.72465068e-01 -5.69458753e-02 8.02788511e-03
-6.95972204e-01 -9.04681742e-01 -1.17197379e-01 -5.32478929e-01
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-3.11247706e-01 -6.70101583e-01 -4.49036032e-01 -6.53201342e-01
1.74964935e-01 1.54004902e-01 2.42550410e-02 -5.11236548... | [15.686223983764648, 5.91954231262207] |
48658b0f-052d-4649-9b7e-ab333e547bde | community-detection-in-the-stochastic-block | 2101.12336 | null | https://arxiv.org/abs/2101.12336v2 | https://arxiv.org/pdf/2101.12336v2.pdf | Community Detection in the Stochastic Block Model by Mixed Integer Programming | The Degree-Corrected Stochastic Block Model (DCSBM) is a popular model to generate random graphs with community structure given an expected degree sequence. The standard approach of community detection based on the DCSBM is to search for the model parameters that are the most likely to have produced the observed networ... | ['Thibaut Vidal', 'Breno Serrano'] | 2021-01-26 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 2.75164425e-01 2.91156173e-01 -1.07205182e-01 -1.99059546e-02
-4.41193193e-01 -6.72711909e-01 5.86673021e-01 2.42331013e-01
-1.20111831e-01 1.03260946e+00 -2.95235753e-01 -4.79808748e-01
-5.92134655e-01 -1.10757709e+00 -5.61937034e-01 -7.46038139e-01
-7.52813876e-01 1.25080144e+00 3.30393761e-01 6.21572062... | [6.962234973907471, 5.282273292541504] |
61011d67-a2ce-4e85-8dd0-5da1493f7274 | generating-natural-language-attacks-in-a-hard | 2012.14956 | null | https://arxiv.org/abs/2012.14956v2 | https://arxiv.org/pdf/2012.14956v2.pdf | Generating Natural Language Attacks in a Hard Label Black Box Setting | We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks. Our proposed attack strategy leverages population-based opt... | ['Vikram Pudi', 'Saket Maheshwary', 'Rishabh Maheshwary'] | 2020-12-29 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 8.05983007e-01 5.21116614e-01 8.10344368e-02 -2.40324557e-01
-1.05029178e+00 -1.03382754e+00 8.51601362e-01 3.90422702e-01
-6.81198061e-01 6.73082829e-01 1.09444566e-01 -4.22273934e-01
1.77480921e-01 -6.88508749e-01 -8.47032130e-01 -4.02260005e-01
2.65302807e-01 5.49205959e-01 2.76918877e-02 -4.35657382... | [6.0221147537231445, 8.108580589294434] |
56a04e2c-8348-49aa-8dd3-adb446c449fa | leaping-into-memories-space-time-deep-feature | 2303.09941 | null | https://arxiv.org/abs/2303.09941v3 | https://arxiv.org/pdf/2303.09941v3.pdf | Leaping Into Memories: Space-Time Deep Feature Synthesis | The success of deep learning models has led to their adaptation and adoption by prominent video understanding methods. The majority of these approaches encode features in a joint space-time modality for which the inner workings and learned representations are difficult to visually interpret. We propose LEArned Preconsc... | ['Nikos Deligiannis', 'Alexandros Stergiou'] | 2023-03-17 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [ 1.80816412e-01 -3.95451374e-02 -1.67822354e-02 -3.92492056e-01
-3.29217911e-01 -8.59992921e-01 9.61264670e-01 -3.39812189e-01
-1.69630930e-01 5.34852922e-01 6.44000411e-01 -2.96501704e-02
3.30031663e-02 -3.81478310e-01 -1.12005830e+00 -5.57840586e-01
-8.22681189e-02 -4.42085229e-02 1.05746932e-01 -8.09979811... | [8.699512481689453, 0.3707480728626251] |
7a491eb4-becf-4fd0-a43e-a9ed1d7d2860 | conditional-diffusion-models-for-weakly | 2306.03878 | null | https://arxiv.org/abs/2306.03878v1 | https://arxiv.org/pdf/2306.03878v1.pdf | Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation | Recent advances in denoising diffusion probabilistic models have shown great success in image synthesis tasks. While there are already works exploring the potential of this powerful tool in image semantic segmentation, its application in weakly supervised semantic segmentation (WSSS) remains relatively under-explored. ... | ['Yiyu Shi', 'Tsung-Yi Ho', 'Yu-Jen Chen', 'Xinrong Hu'] | 2023-06-06 | null | null | null | null | ['weakly-supervised-semantic-segmentation'] | ['computer-vision'] | [ 6.25470817e-01 4.76945609e-01 -1.94179937e-01 -3.42366070e-01
-8.18580449e-01 -2.80064881e-01 6.07813835e-01 2.74657793e-02
-4.81899768e-01 3.02293569e-01 5.83084933e-02 -1.40914202e-01
1.23308368e-01 -9.22883570e-01 -5.77111721e-01 -1.10165298e+00
4.12952363e-01 6.99306905e-01 9.12214935e-01 9.69625264... | [14.38696575164795, -2.0432510375976562] |
8e1e8779-ad16-44a7-9606-7995c5e09261 | explaining-prediction-uncertainty-of-pre | 2201.03742 | null | https://arxiv.org/abs/2201.03742v2 | https://arxiv.org/pdf/2201.03742v2.pdf | Explaining Predictive Uncertainty by Looking Back at Model Explanations | Predictive uncertainty estimation of pre-trained language models is an important measure of how likely people can trust their predictions. However, little is known about what makes a model prediction uncertain. Explaining predictive uncertainty is an important complement to explaining prediction labels in helping users... | ['Yangfeng Ji', 'Wanyu Du', 'Hanjie Chen'] | 2022-01-11 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 3.43267340e-03 1.02661836e+00 -9.16753113e-01 -1.06247675e+00
-3.43433887e-01 -4.85724092e-01 5.96786380e-01 4.50863421e-01
-1.63056515e-02 8.62731040e-01 5.48347533e-01 -7.37967849e-01
2.20848516e-01 -5.25109828e-01 -7.12194264e-01 2.91602463e-01
2.30481729e-01 7.37833261e-01 -5.69708720e-02 -1.05730994... | [9.561667442321777, 6.810164928436279] |
ef0d4cf2-c6ba-475e-bcf5-34500f10e67d | enhancing-cross-lingual-natural-language | null | null | https://aclanthology.org/2022.acl-long.134 | https://aclanthology.org/2022.acl-long.134.pdf | Enhancing Cross-lingual Natural Language Inference by Prompt-learning from Cross-lingual Templates | Cross-lingual natural language inference (XNLI) is a fundamental task in cross-lingual natural language understanding. Recently this task is commonly addressed by pre-trained cross-lingual language models. Existing methods usually enhance pre-trained language models with additional data, such as annotated parallel corp... | ['Haolan Chen', 'Jianfeng Du', 'Hai Wan', 'Kunxun Qi'] | null | null | null | null | acl-2022-5 | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 2.09222347e-01 -3.48300524e-02 -3.70114803e-01 -6.79580390e-01
-1.54044056e+00 -5.56437671e-01 7.12808013e-01 -3.62793654e-02
-8.05048287e-01 9.05823767e-01 2.38445565e-01 -2.91180909e-01
9.24232826e-02 -8.03586960e-01 -9.65560019e-01 -3.64493072e-01
6.82947576e-01 6.14148021e-01 3.32605034e-01 -2.08853230... | [10.961204528808594, 9.436274528503418] |
9870feae-7940-4677-b224-7bcfae569069 | group-anomaly-detection-using-deep-generative | 1804.04876 | null | http://arxiv.org/abs/1804.04876v1 | http://arxiv.org/pdf/1804.04876v1.pdf | Group Anomaly Detection using Deep Generative Models | Unlike conventional anomaly detection research that focuses on point
anomalies, our goal is to detect anomalous collections of individual data
points. In particular, we perform group anomaly detection (GAD) with an
emphasis on irregular group distributions (e.g. irregular mixtures of image
pixels). GAD is an important ... | ['Raghavendra Chalapathy', 'Edward Toth', 'Sanjay Chawla'] | 2018-04-13 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [-5.43870069e-02 -7.43600652e-02 7.04574883e-01 -1.95232153e-01
-4.75218862e-01 -1.39961213e-01 8.82481456e-01 5.34896612e-01
-6.22743974e-05 3.76239240e-01 -9.52304229e-02 -3.24437797e-01
5.37545495e-02 -1.05510783e+00 -8.29437852e-01 -8.50943029e-01
-3.40066433e-01 5.68189561e-01 1.36685491e-01 -2.07400262... | [7.617884159088135, 2.3114383220672607] |
0a9782ff-59ba-4610-9691-061d512f0564 | two-level-temporal-relation-model-for-online | 2210.16795 | null | https://arxiv.org/abs/2210.16795v1 | https://arxiv.org/pdf/2210.16795v1.pdf | Two-Level Temporal Relation Model for Online Video Instance Segmentation | In Video Instance Segmentation (VIS), current approaches either focus on the quality of the results, by taking the whole video as input and processing it offline; or on speed, by handling it frame by frame at the cost of competitive performance. In this work, we propose an online method that is on par with the performa... | ['Fatma Güney', 'Jordi Pont-Tuset', 'Oğuzhan Keskin', 'Çağan Selim Çoban'] | 2022-10-30 | null | null | null | null | ['video-instance-segmentation', 'video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.68317515e-03 -8.05746615e-02 -1.54304922e-01 -3.65379959e-01
-7.49146938e-01 -5.46993911e-01 2.50926226e-01 6.32767975e-02
-4.45536762e-01 2.47476056e-01 -1.40951527e-02 -2.42644757e-01
1.13021418e-01 -6.25250876e-01 -1.08823144e+00 -1.91036478e-01
-2.93098986e-01 1.32346228e-01 5.27253926e-01 8.50783810... | [9.143362045288086, -0.018099702894687653] |
5f793a38-3590-4080-b6de-62a9fdcc3a41 | analysis-of-nuanced-stances-and-sentiment | null | null | https://aclanthology.org/2021.socialnlp-1.1 | https://aclanthology.org/2021.socialnlp-1.1.pdf | Analysis of Nuanced Stances and Sentiment Towards Entities of US Politicians through the Lens of Moral Foundation Theory | The Moral Foundation Theory suggests five moral foundations that can capture the view of a user on a particular issue. It is widely used to identify sentence-level sentiment. In this paper, we study the Moral Foundation Theory in tweets by US politicians on two politically divisive issues - Gun Control and Immigration.... | ['Dan Goldwasser', 'Shamik Roy'] | null | null | null | null | naacl-socialnlp-2021-6 | ['relational-reasoning'] | ['natural-language-processing'] | [-6.36907220e-01 4.25332665e-01 -7.12768793e-01 -4.96735930e-01
-4.08502698e-01 -5.20498455e-01 9.63150263e-01 6.56813800e-01
-3.84797066e-01 3.55107307e-01 1.22106266e+00 -3.58135223e-01
-8.68889019e-02 -1.11978519e+00 -3.19216341e-01 -4.50078964e-01
3.59760910e-01 3.71806234e-01 -1.66021451e-01 -8.06292951... | [8.950910568237305, 10.021453857421875] |
afab0552-cd68-4ac1-8562-5ee4b74db053 | multi-modal-hypergraph-diffusion-network-with | 2204.02399 | null | https://arxiv.org/abs/2204.02399v3 | https://arxiv.org/pdf/2204.02399v3.pdf | Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification | The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider either... | ['Carola-Bibiane Schönlieb', 'Zoe Kourtzi', 'Nicolas Papadakis', 'Christina Runkel', 'Angelica I. Aviles-Rivero'] | 2022-04-04 | null | null | null | null | ['multi-modal-classification'] | ['miscellaneous'] | [ 3.45925152e-01 4.24010873e-01 -1.32445619e-01 -4.22462493e-01
-7.45204389e-01 -3.26412737e-01 6.56798601e-01 4.00759250e-01
-3.33808631e-01 5.63153803e-01 3.56085598e-01 -3.30646262e-02
-7.87004948e-01 -7.82532632e-01 -2.04105645e-01 -8.10809553e-01
-5.12859643e-01 8.66839468e-01 5.31644046e-01 -1.67372122... | [12.377704620361328, 3.364475727081299] |
001352a2-f610-4642-8000-7396ad76b264 | a2j-transformer-anchor-to-joint-transformer | 2304.03635 | null | https://arxiv.org/abs/2304.03635v1 | https://arxiv.org/pdf/2304.03635v1.pdf | A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image | 3D interacting hand pose estimation from a single RGB image is a challenging task, due to serious self-occlusion and inter-occlusion towards hands, confusing similar appearance patterns between 2 hands, ill-posed joint position mapping from 2D to 3D, etc.. To address these, we propose to extend A2J-the state-of-the-art... | ['Joey Tianyi Zhou', 'Zhiguo Cao', 'Jinghong Zheng', 'Mingyang Zhang', 'Cunlin Wu', 'Yang Xiao', 'Changlong Jiang'] | 2023-04-07 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jiang_A2J-Transformer_Anchor-to-Joint_Transformer_Network_for_3D_Interacting_Hand_Pose_Estimation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jiang_A2J-Transformer_Anchor-to-Joint_Transformer_Network_for_3D_Interacting_Hand_Pose_Estimation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pose-prediction', 'hand-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-3.43638450e-01 -6.88573942e-02 -9.75464061e-02 -6.18260503e-02
-7.82836497e-01 -4.59997654e-01 -7.47365952e-02 -7.38202691e-01
-3.89161818e-02 3.11704785e-01 3.81391108e-01 3.60719651e-01
-2.42585003e-01 -3.34363699e-01 -6.12636626e-01 -6.12077773e-01
-6.20961525e-02 8.76355588e-01 4.57010716e-01 -3.02310437... | [6.671361446380615, -0.836001455783844] |
30a40b1a-30f3-4fac-a0ed-f2a90ee1bf4b | resource-lean-modeling-of-coherence-in | null | null | https://aclanthology.org/W17-0910 | https://aclanthology.org/W17-0910.pdf | Resource-Lean Modeling of Coherence in Commonsense Stories | We present a resource-lean neural recognizer for modeling coherence in commonsense stories. Our lightweight system is inspired by successful attempts to modeling discourse relations and stands out due to its simplicity and easy optimization compared to prior approaches to narrative script learning. We evaluate our appr... | ['Niko Schenk', 'Christian Chiarcos'] | 2017-04-01 | null | null | null | ws-2017-4 | ['cloze-test'] | ['natural-language-processing'] | [ 3.29183906e-01 3.50751370e-01 -3.89941812e-01 -4.89023566e-01
-7.39687860e-01 -4.08987314e-01 1.15030932e+00 1.73090190e-01
-4.39926088e-01 8.02924037e-01 1.13487756e+00 -2.80054569e-01
-1.40754223e-01 -5.61529994e-01 -5.06371021e-01 -9.63776745e-03
-1.75556362e-01 6.92112565e-01 1.68551251e-01 -5.68551958... | [11.189542770385742, 8.872467041015625] |
3c2cbae1-f2eb-4fc7-975a-e7f0f29536e6 | w-mae-pre-trained-weather-model-with-masked | 2304.08754 | null | https://arxiv.org/abs/2304.08754v1 | https://arxiv.org/pdf/2304.08754v1.pdf | W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting | Weather forecasting is a long-standing computational challenge with direct societal and economic impacts. This task involves a large amount of continuous data collection and exhibits rich spatiotemporal dependencies over long periods, making it highly suitable for deep learning models. In this paper, we apply pre-train... | ['Jie Shao', 'Changyu Li', 'Chenghong Zhang', 'Xin Man'] | 2023-04-18 | null | null | null | null | ['weather-forecasting'] | ['miscellaneous'] | [-3.82356882e-01 -3.39885443e-01 -8.41869798e-04 -5.91325581e-01
-1.27694845e-01 -3.64573598e-01 7.44534850e-01 -1.73209667e-01
-2.44406298e-01 9.61346209e-01 4.95032459e-01 -7.04551697e-01
-1.40964955e-01 -1.20832586e+00 -5.58668911e-01 -8.63929987e-01
-7.23550558e-01 6.43427223e-02 -1.19806185e-01 -6.76201463... | [6.5988945960998535, 2.907141923904419] |
84c924c5-acc7-4f5c-a2ed-cc9e1f457c0e | w-posenet-dense-correspondence-regularized | 1912.11888 | null | https://arxiv.org/abs/1912.11888v2 | https://arxiv.org/pdf/1912.11888v2.pdf | W-PoseNet: Dense Correspondence Regularized Pixel Pair Pose Regression | Solving 6D pose estimation is non-trivial to cope with intrinsic appearance and shape variation and severe inter-object occlusion, and is made more challenging in light of extrinsic large illumination changes and low quality of the acquired data under an uncontrolled environment. This paper introduces a novel pose esti... | ['Ke Chen', 'Kui Jia', 'Zelin Xu'] | 2019-12-26 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [ 1.08542576e-01 -2.43616179e-01 -1.89762354e-01 -6.98074818e-01
-9.28497493e-01 -2.48397946e-01 1.75120905e-01 -4.15139824e-01
-2.53138214e-01 6.16123617e-01 -3.76397111e-02 3.63112271e-01
-1.37714788e-01 -4.09894735e-01 -1.01316595e+00 -6.69462621e-01
1.11640267e-01 6.76418126e-01 9.79955718e-02 7.76653886... | [7.524645805358887, -2.639711856842041] |
035feac2-c4b0-4570-810f-afaf9f2fa534 | vital-node-identification-in-complex-networks | 2202.06229 | null | https://arxiv.org/abs/2202.06229v1 | https://arxiv.org/pdf/2202.06229v1.pdf | Vital Node Identification in Complex Networks Using a Machine Learning-Based Approach | Vital node identification is the problem of finding nodes of highest importance in complex networks. This problem has crucial applications in various contexts such as viral marketing or controlling the propagation of virus or rumours in real-world networks. Existing approaches for vital node identification mainly focus... | ['Hamid Khayyam', 'Mahdi Jalili', 'Justin Munoz', 'Ahmad Asgharian Rezaei'] | 2022-02-13 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-1.57365073e-02 2.49680176e-01 -5.96765459e-01 1.57306597e-01
4.68336940e-01 -4.92914617e-01 1.08205044e+00 6.14316940e-01
-2.40710735e-01 7.11511254e-01 -1.87341392e-01 -4.02962506e-01
-6.46457076e-01 -1.22576976e+00 -1.94360867e-01 -9.78228986e-01
-6.05381250e-01 9.77479696e-01 3.62085134e-01 -6.79360390... | [6.957242488861084, 5.661983489990234] |
f4283269-044b-4c4b-9e89-d468ffa2d433 | locally-non-linear-embeddings-for-extreme | 1507.02743 | null | http://arxiv.org/abs/1507.02743v1 | http://arxiv.org/pdf/1507.02743v1.pdf | Locally Non-linear Embeddings for Extreme Multi-label Learning | The objective in extreme multi-label learning is to train a classifier that
can automatically tag a novel data point with the most relevant subset of
labels from an extremely large label set. Embedding based approaches make
training and prediction tractable by assuming that the training label matrix is
low-rank and hen... | ['Manik Varma', 'Kush Bhatia', 'Purushottam Kar', 'Prateek Jain', 'Himanshu Jain'] | 2015-07-09 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.40626651e-01 1.27612635e-01 -4.07734245e-01 -4.62530941e-01
-1.20180166e+00 -8.29559505e-01 5.71650386e-01 4.91276532e-01
-3.12919647e-01 4.49007511e-01 7.89534971e-02 -2.06788793e-01
-4.51242119e-01 -6.66587472e-01 -3.12260747e-01 -8.95047545e-01
-1.60137385e-01 8.41014683e-01 -5.67262853e-03 1.88083351... | [9.496755599975586, 4.349610328674316] |
74658466-796e-483c-9163-c91eba0dfe2f | planar-structure-matching-under-projective | null | null | https://www.cs.umd.edu/sites/default/files/scholarly_papers/AngLi.pdf | https://www.cs.umd.edu/sites/default/files/scholarly_papers/AngLi.pdf | Planar Structure Matching Under Projective Uncertainty for Geolocation | Image based geolocation aims to answer the question: where
was this ground photograph taken? We present an approach to geolocalating a single image based on matching human delineated line segments
in the ground image to automatically detected line segments in ortho
images. Our approach is based on distance transform... | ['Larry S. Davis', 'Vlad I. Morariu', 'Ang Li'] | 2014-01-01 | null | null | null | eccv-2014-1 | ['geometric-matching'] | ['computer-vision'] | [ 1.89747155e-01 4.33100045e-01 2.14645132e-01 -4.23609644e-01
-1.01224220e+00 -8.49498391e-01 6.77663803e-01 2.32234314e-01
-4.76001352e-01 4.60931510e-01 -9.84207019e-02 -2.09138557e-01
3.01254471e-03 -9.15838301e-01 -6.67770386e-01 -2.33292580e-01
-2.00444162e-01 4.95349884e-01 4.68395531e-01 -1.77338779... | [7.820370197296143, -2.2888660430908203] |
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