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f48edbbc-662d-42ee-81fb-8df5793f99f9 | maximum-likelihood-based-gridless-doa | 2210.03266 | null | https://arxiv.org/abs/2210.03266v1 | https://arxiv.org/pdf/2210.03266v1.pdf | Maximum Likelihood-based Gridless DoA Estimation Using Structured Covariance Matrix Recovery and SBL with Grid Refinement | We consider the parametric data model employed in applications such as line spectral estimation and direction-of-arrival estimation. We focus on the stochastic maximum likelihood estimation (MLE) framework and offer approaches to estimate the parameter of interest in a gridless manner, overcoming the model complexities... | ['Bhaskar D. Rao', 'Rohan R. Pote'] | 2022-10-07 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 3.80860597e-01 -2.87652373e-01 7.39472434e-02 2.03071252e-01
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-9.12378013e-01 -5.08759439e-01 -6.43289387e-01 -1.13197184e+00
-3.40776712e-01 3.86192530e-01 -2.85542637e-01 1.05489194... | [6.502893924713135, 1.4092731475830078] |
d435b5cf-30f7-4ce9-bbcb-bff9c6f35e07 | skin-lesion-segmentation-and-classification-3 | 2112.10307 | null | https://arxiv.org/abs/2112.10307v1 | https://arxiv.org/pdf/2112.10307v1.pdf | Skin lesion segmentation and classification using deep learning and handcrafted features | Accurate diagnostics of a skin lesion is a critical task in classification dermoscopic images. In this research, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single method features. This study involves a new technique where we inject the handcrafted featur... | ['Hussin K. Ragb', 'Redha Ali'] | 2021-12-20 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 3.69118094e-01 2.35359699e-01 -4.26040560e-01 -4.40789640e-01
-1.80881888e-01 -3.28462392e-01 5.23087680e-01 -2.27583311e-02
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-9.61302593e-02 -7.95855999e-01 -5.94007909e-01 -7.02201068e-01
3.20974767e-01 -2.29919985e-01 3.50481659e-01 1.49071559... | [15.66838264465332, -2.998950958251953] |
f30ce8f2-35f4-44c2-990f-f462fc1cb1ce | learning-one-class-hyperspectral-classifier | 2210.15457 | null | https://arxiv.org/abs/2210.15457v1 | https://arxiv.org/pdf/2210.15457v1.pdf | Learning One-Class Hyperspectral Classifier from Positive and Unlabeled Data for Low Proportion Target | Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only positive labels, which can significantly reduce the requirements for annotation. However, HSI one-class classification is far more challenging than HSI multi-class classification, due the lack o... | ['Hong Shu', 'Xinyu Wang', 'Xin He', 'Yanfei Zhong', 'Hengwei Zhao'] | 2022-10-27 | null | null | null | null | ['one-class-classifier', 'one-class-classification'] | ['methodology', 'miscellaneous'] | [ 8.52266014e-01 6.12785220e-02 -3.86662874e-03 -4.59342092e-01
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5.46668433e-02 -5.95010333e-02 9.24328119e-02 5.30517846... | [9.922492980957031, -1.566275715827942] |
8ebf145e-9a9e-401d-97b5-523e55ed559e | quantification-of-damage-using-indirect | 2301.09791 | null | https://arxiv.org/abs/2301.09791v1 | https://arxiv.org/pdf/2301.09791v1.pdf | Quantification of Damage Using Indirect Structural Health Monitoring | Structural health monitoring is important to make sure bridges do not fail. Since direct monitoring can be complicated and expensive, indirect methods have been a focus on research. Indirect monitoring can be much cheaper and easier to conduct, however there are challenges with getting accurate results. This work focus... | ['Achyuth Madabhushi'] | 2023-01-24 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [-4.54652682e-02 -2.67114252e-01 8.97504166e-02 -9.19112489e-02
-5.32026887e-01 -8.95311031e-03 1.25123560e-01 2.73666084e-01
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-2.85201997e-01 2.99514949e-01 4.92824703e-01 -2.74816215... | [6.508732795715332, 2.654818534851074] |
9ee96350-c9d0-4f4a-8a7d-75eaa16a0666 | discriminative-co-saliency-and-background | 2305.00514 | null | https://arxiv.org/abs/2305.00514v2 | https://arxiv.org/pdf/2305.00514v2.pdf | Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection | Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignoring explicit exploration of background regions. In this paper, we propose a Discriminative co-saliency and background Mining Transformer framework (DMT) based on sever... | ['Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Hisham Cholakkal', 'Salman Khan', 'Nian Liu', 'Ni Zhang', 'Junwei Han', 'Long Li'] | 2023-04-30 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Discriminative_Co-Saliency_and_Background_Mining_Transformer_for_Co-Salient_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Discriminative_Co-Saliency_and_Background_Mining_Transformer_for_Co-Salient_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['co-saliency-detection', 'salient-object-detection-1'] | ['computer-vision', 'computer-vision'] | [ 4.43807364e-01 -2.55931735e-01 -3.97656471e-01 -3.44780535e-01
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-5.76827629e-03 -1.53811336e-01 8.24113607e-01 8.87447745... | [9.793310165405273, -0.3010389804840088] |
ef7b7ec5-a0b6-4140-b350-588be81970ef | vehicle-speed-estimation-using-computer | null | null | https://openreview.net/forum?id=Pl7uHR-Oe6l | https://openreview.net/pdf?id=Pl7uHR-Oe6l | Vehicle Speed Estimation Using Computer Vision And Evolutionary Camera Calibration | Currently, the standard for vehicle speed estimation is radar or lidar speed signs which can be costly to buy and maintain. However, most major cities already implement networks of traffic surveillance cameras that can be utilized for vehicle speed estimation using computer vision. This work implements such a system us... | ['Rigoberto Fonseca', 'Israel Pineda', 'Ezequiel López-Rubio', 'Esteban Palomo', 'Hector Mejia'] | 2021-10-16 | null | null | null | neurips-workshop-latinx-in-ai-2021-12 | ['vehicle-speed-estimation', 'homography-estimation'] | ['computer-vision', 'computer-vision'] | [-3.35821480e-01 -3.68565291e-01 -2.49150321e-01 -3.33136916e-01
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2.78500974e-01 1.24798191e+00 5.01192391e-01 2.60115545... | [8.00910758972168, -1.2183858156204224] |
b3ac1cb4-6b48-4b06-bfea-2dbb4c79839f | multi-label-topic-classification-for-covid-19 | 2204.06758 | null | https://arxiv.org/abs/2204.06758v1 | https://arxiv.org/pdf/2204.06758v1.pdf | Multi-label topic classification for COVID-19 literature with Bioformer | We describe Bioformer team's participation in the multi-label topic classification task for COVID-19 literature (track 5 of BioCreative VII). Topic classification is performed using different BERT models (BioBERT, PubMedBERT, and Bioformer). We formulate the topic classification task as a sentence pair classification p... | ['Kai Wang', 'Li Fang'] | 2022-04-14 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 6.50638267e-02 2.90730298e-01 -5.99293113e-01 -1.29975051e-01
-1.11481047e+00 -6.52011156e-01 6.15167022e-01 7.69561112e-01
-3.52570087e-01 1.02222455e+00 1.87001243e-01 -1.05024837e-02
-9.00899619e-03 -2.23593473e-01 -8.59232485e-01 -4.24846828e-01
1.98151544e-01 4.69755471e-01 -8.04294050e-02 1.85532004... | [8.503544807434082, 8.724860191345215] |
02acdbc4-4db6-4d92-a7d7-0d04a25c680f | switch-based-active-deep-dyna-q-efficient | 1811.07550 | null | http://arxiv.org/abs/1811.07550v1 | http://arxiv.org/pdf/1811.07550v1.pdf | Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning | Training task-completion dialogue agents with reinforcement learning usually
requires a large number of real user experiences. The Dyna-Q algorithm extends
Q-learning by integrating a world model, and thus can effectively boost
training efficiency using simulated experiences generated by the world model.
The effectiven... | ['Jingjing Liu', 'Jianfeng Gao', 'Yuexin Wu', 'Yiming Yang', 'Xiujun Li'] | 2018-11-19 | null | null | null | null | ['task-completion-dialogue-policy-learning'] | ['natural-language-processing'] | [-2.57461578e-01 1.91304579e-01 -1.17925204e-01 -1.05630659e-01
-9.11320984e-01 -6.95139468e-01 8.06353271e-01 2.16233537e-01
-9.72625792e-01 1.07417417e+00 1.50099501e-01 -3.22223365e-01
-4.95371874e-03 -1.10894704e+00 -6.26747251e-01 -5.73261678e-01
-2.76515305e-01 8.53511691e-01 2.72895604e-01 -5.18753886... | [3.9903290271759033, 1.74907648563385] |
284a28d6-eeee-4d5b-b68e-5faf9fa59481 | convergence-analysis-of-map-based-blur-kernel | 1611.07752 | null | http://arxiv.org/abs/1611.07752v2 | http://arxiv.org/pdf/1611.07752v2.pdf | Convergence Analysis of MAP based Blur Kernel Estimation | One popular approach for blind deconvolution is to formulate a maximum a
posteriori (MAP) problem with sparsity priors on the gradients of the latent
image, and then alternatingly estimate the blur kernel and the latent image.
While several successful MAP based methods have been proposed, there has been
much controvers... | ['Seungyong Lee', 'Sunghyun Cho'] | 2016-11-23 | convergence-analysis-of-map-based-blur-kernel-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Cho_Convergence_Analysis_of_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Cho_Convergence_Analysis_of_ICCV_2017_paper.pdf | iccv-2017-10 | ['defocus-estimation'] | ['computer-vision'] | [ 1.63095921e-01 -3.26001972e-01 4.52180684e-01 -3.48728657e-01
-3.71564001e-01 -6.01054966e-01 5.46277225e-01 -5.94738424e-01
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-4.49149072e-01 -3.49670723e-02 -3.08181703e-01 -1.06343949e+00
9.84385386e-02 4.04855907e-02 1.97229430e-01 2.83873707... | [11.640213966369629, -2.7440996170043945] |
fed69c25-ba1e-4ee0-aa3b-2cb99fdeafcc | anchors-based-method-for-fingertips-position | 2005.01351 | null | https://arxiv.org/abs/2005.01351v2 | https://arxiv.org/pdf/2005.01351v2.pdf | Anchors Based Method for Fingertips Position Estimation from a Monocular RGB Image using Deep Neural Network | In Virtual, augmented, and mixed reality, the use of hand gestures is increasingly becoming popular to reduce the difference between the virtual and real world. The precise location of the fingertip is essential/crucial for a seamless experience. Much of the research work is based on using depth information for the est... | ['Purnendu Mishra', 'Kishor Sarawadekar'] | 2020-05-04 | null | null | null | null | ['hand-detection'] | ['computer-vision'] | [ 1.78966597e-01 -4.74057496e-01 1.15468457e-01 -5.88490665e-02
-3.46372336e-01 -6.50601804e-01 1.40645370e-01 -1.99457332e-01
-8.31444085e-01 4.67267483e-01 -2.82017648e-01 -2.16989107e-02
-6.51549101e-02 -5.91131985e-01 -5.46593606e-01 -5.56100965e-01
2.69963205e-01 9.37130861e-03 4.41892862e-01 7.41095245... | [6.485979080200195, -0.3856714963912964] |
a841897b-7f71-45af-a595-84c2d953280e | text-is-no-more-enough-a-benchmark-for | 2112.11953 | null | https://arxiv.org/abs/2112.11953v3 | https://arxiv.org/pdf/2112.11953v3.pdf | Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding | Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.g., intent and slots). Unfortunately, such a simple setting may fail to work in complex real-world scenari... | ['Wanxiang Che', 'Linlin Li', 'Guoxing Wu', 'Kaiji Chen', 'Libo Qin', 'Xiao Xu'] | 2021-12-22 | null | null | null | null | ['slot-filling'] | ['natural-language-processing'] | [ 4.84656096e-01 2.62280434e-01 -2.21722081e-01 -7.70199716e-01
-9.37285185e-01 -4.84719634e-01 3.55136752e-01 4.07513753e-02
-2.07445875e-01 6.74123466e-01 6.64058566e-01 -4.54505444e-01
3.10129404e-01 -5.84652483e-01 -5.56781769e-01 -3.17781448e-01
3.55551004e-01 6.12939179e-01 4.37096566e-01 -5.56861699... | [12.63809585571289, 7.411095142364502] |
8180fc7f-4553-421d-8177-f41731b34e8a | learning-to-encode-position-for-transformer | 2003.09229 | null | https://arxiv.org/abs/2003.09229v1 | https://arxiv.org/pdf/2003.09229v1.pdf | Learning to Encode Position for Transformer with Continuous Dynamical Model | We introduce a new way of learning to encode position information for non-recurrent models, such as Transformer models. Unlike RNN and LSTM, which contain inductive bias by loading the input tokens sequentially, non-recurrent models are less sensitive to position. The main reason is that position information among inpu... | ['Cho-Jui Hsieh', 'Hsiang-Fu Yu', 'Xuanqing Liu', 'Inderjit Dhillon'] | 2020-03-13 | null | https://proceedings.icml.cc/static/paper_files/icml/2020/955-Paper.pdf | https://proceedings.icml.cc/static/paper_files/icml/2020/955-Paper.pdf | icml-2020-1 | ['linguistic-acceptability'] | ['natural-language-processing'] | [ 2.37715468e-01 1.36810467e-01 -2.58632869e-01 -1.83358014e-01
2.65505034e-02 -6.98538005e-01 8.08687508e-01 -2.97150850e-01
-4.53989446e-01 7.62799680e-01 1.70954794e-01 -4.96422619e-01
-6.16212822e-02 -1.02814150e+00 -9.28986430e-01 -8.35956275e-01
1.72818273e-01 4.17970359e-01 3.34574699e-01 -6.80961311... | [10.783337593078613, 6.570048809051514] |
078b4438-8107-4ce7-84b5-4e450fd442aa | inferring-point-clouds-from-single-monocular | 1812.01402 | null | https://arxiv.org/abs/1812.01402v3 | https://arxiv.org/pdf/1812.01402v3.pdf | Inferring Point Clouds from Single Monocular Images by Depth Intermediation | In this paper, we propose a pipeline to generate 3D point cloud of an object from a single-view RGB image. Most previous work predict the 3D point coordinates from single RGB images directly. We decompose this problem into depth estimation from single images and point cloud completion from partial point clouds. Our met... | ['Theo Gevers', 'Sezer Karaoglu', 'Wei Zeng'] | 2018-12-04 | null | null | null | null | ['point-cloud-completion', '3d-object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 7.58018643e-02 1.90095469e-01 1.94961041e-01 -5.04151344e-01
-6.70283496e-01 -6.53403163e-01 5.86008608e-01 -2.94687212e-01
-2.26150960e-01 2.39285594e-03 -3.53977114e-01 -1.34475632e-02
4.12324429e-01 -7.66107500e-01 -9.73371089e-01 -2.79768556e-01
5.38362741e-01 1.10269272e+00 6.46955192e-01 1.91749468... | [8.462869644165039, -2.9656128883361816] |
3223631d-d967-457a-bea2-754f47e3481e | analyzing-and-reducing-the-performance-gap-in | 2305.11449 | null | https://arxiv.org/abs/2305.11449v1 | https://arxiv.org/pdf/2305.11449v1.pdf | Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast | Existing research has shown that a multilingual pre-trained language model fine-tuned with one (source) language also performs well on downstream tasks for non-source languages, even though no fine-tuning is done on these languages. However, there is a clear gap between the performance of the source language and that o... | ['Duan Nan', 'Bing Liu', 'Dongyan Zhao', 'Yaobo Liang', 'Yiduo Guo'] | 2023-05-19 | null | null | null | null | ['cross-lingual-transfer'] | ['natural-language-processing'] | [-4.36397016e-01 1.06610045e-01 -5.60713053e-01 -4.77090180e-01
-6.91796005e-01 -6.41734540e-01 6.72470212e-01 -2.09078535e-01
-6.95620596e-01 1.08295012e+00 7.39067197e-01 -7.53784060e-01
1.93035051e-01 -5.44734657e-01 -6.28672898e-01 -3.47020507e-01
1.82291567e-01 4.42209810e-01 2.56489873e-01 -6.32154763... | [10.944826126098633, 9.961233139038086] |
28633c11-c8ed-4d1b-921c-0e43a7d2d7f2 | whole-slide-images-based-cancer-survival | 2009.11169 | null | https://arxiv.org/abs/2009.11169v1 | https://arxiv.org/pdf/2009.11169v1.pdf | Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks | Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are available in classification task... | ['Xinliang Zhu', 'Nicholas Hawkins', 'Junzhou Huang', 'Jitendra Jonnagaddala', 'Jiawen Yao'] | 2020-09-23 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.49038970e-01 1.19395718e-01 -4.20104921e-01 -4.95971113e-01
-1.55172157e+00 -1.88487768e-01 2.53472447e-01 6.41956925e-01
-4.23664749e-01 7.67279685e-01 4.14188802e-01 -2.49260798e-01
-4.82247621e-01 -5.87696731e-01 -4.72375751e-01 -1.27322710e+00
-2.16387168e-01 5.59885144e-01 1.00408219e-01 -1.18083050... | [15.114165306091309, -2.8623342514038086] |
892c9220-9118-4dba-9930-c1039a5603f9 | on-learning-word-embeddings-from | null | null | https://aclanthology.org/W19-0508 | https://aclanthology.org/W19-0508.pdf | On Learning Word Embeddings From Linguistically Augmented Text Corpora | Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such a... | ['Amila Silva', 'Chathurika Amarathunga'] | 2019-05-01 | null | null | null | ws-2019-5 | ['learning-word-embeddings'] | ['methodology'] | [-1.94166079e-01 -7.20274597e-02 -4.17453855e-01 -2.02093959e-01
-5.36539257e-01 -3.92952353e-01 8.43618214e-01 7.48532593e-01
-8.86633515e-01 4.32531148e-01 7.42556512e-01 -4.36840028e-01
3.36831547e-02 -8.84007990e-01 1.01374984e-01 -3.97012770e-01
5.67916781e-02 3.24937403e-01 1.99165002e-01 -4.57780570... | [10.491153717041016, 8.669203758239746] |
3e83ef54-902f-4180-8a6c-bb192d83e6da | birdsnap-large-scale-fine-grained-visual | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Berg_Birdsnap_Large-scale_Fine-grained_2014_CVPR_paper.pdf | Birdsnap: Large-scale Fine-grained Visual Categorization of Birds | We address the problem of large-scale fine-grained visual categorization, describing new methods we have used to produce an online field guide to 500 North American bird species. We focus on the challenges raised when such a system is asked to distinguish between highly similar species of birds. First, we introduce ... | ['Thomas Berg', 'Seung Woo Lee', 'David W. Jacobs', 'Peter N. Belhumeur', 'Jiongxin Liu', 'Michelle L. Alexander'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 2.38205064e-02 -7.74212837e-01 -2.01537535e-01 -6.33745432e-01
-5.17449558e-01 -9.66077864e-01 7.60303319e-01 1.14444621e-01
-7.93507397e-01 5.69893301e-01 1.14799298e-01 5.34237828e-03
-1.01665705e-01 -3.42325330e-01 -6.61673009e-01 -3.60168189e-01
-5.94995856e-01 4.18447286e-01 2.52466202e-01 -2.70911623... | [9.883163452148438, 2.315584659576416] |
b51b1008-b1bb-4251-840e-9e6042e11ec2 | self-supervised-spatio-temporal-2 | 2003.02692 | null | https://arxiv.org/abs/2003.02692v2 | https://arxiv.org/pdf/2003.02692v2.pdf | Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video | We propose a self-supervised visual learning method by predicting the variable playback speeds of a video. Without semantic labels, we learn the spatio-temporal visual representation of the video by leveraging the variations in the visual appearance according to different playback speeds under the assumption of tempora... | ['Tae-hoon Kim', 'Hyung Jin Chang', 'Wonjun Hwang', 'Hyeon Cho'] | 2020-03-05 | null | null | null | null | ['self-supervised-action-recognition'] | ['computer-vision'] | [-2.06502825e-01 -7.37450898e-01 -5.42065740e-01 -6.50261641e-01
-3.71866405e-01 -6.83908105e-01 6.17102027e-01 -2.59572417e-01
-3.57419223e-01 1.73385605e-01 2.68369734e-01 1.02556460e-01
-2.39412859e-01 -3.87784868e-01 -9.69145179e-01 -7.18452394e-01
-5.44769526e-01 -3.36333141e-02 4.32557583e-01 -3.04647703... | [8.667234420776367, 0.699465811252594] |
0c24190f-d476-4c1c-9380-3541d908eb1c | counterfactual-explanations-for-arbitrary | 2106.15212 | null | https://arxiv.org/abs/2106.15212v1 | https://arxiv.org/pdf/2106.15212v1.pdf | Counterfactual Explanations for Arbitrary Regression Models | We present a new method for counterfactual explanations (CFEs) based on Bayesian optimisation that applies to both classification and regression models. Our method is a globally convergent search algorithm with support for arbitrary regression models and constraints like feature sparsity and actionable recourse, and fu... | ['Daniele Magazzeni', 'Jiahao Chen', 'Jon Shepard', 'Jason Long', 'Danial Dervovic', 'Thomas Spooner'] | 2021-06-29 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.57945347e-01 4.76705194e-01 -6.31811678e-01 -3.66338998e-01
-1.33234274e+00 -5.24788320e-01 5.42266130e-01 1.53379776e-02
-2.44726732e-01 1.28182769e+00 -3.02904006e-02 -7.92510271e-01
-8.73028517e-01 -6.40121162e-01 -1.06054199e+00 -6.30426049e-01
-3.61138701e-01 7.19162405e-01 -1.76592134e-02 4.58378792... | [8.594921112060547, 5.465735912322998] |
0afe8f44-c460-486d-a0a8-b83d6ef723bf | stance-prediction-and-analysis-of-twitter | 2306.14203 | null | https://arxiv.org/abs/2306.14203v2 | https://arxiv.org/pdf/2306.14203v2.pdf | Stance Prediction and Analysis of Twitter data : A case study of Ghana 2020 Presidential Elections | On December 7, 2020, Ghanaians participated in the polls to determine their president for the next four years. To gain insights from this presidential election, we conducted stance analysis (which is not always equivalent to sentiment analysis) to understand how Twitter, a popular social media platform, reflected the o... | ['Rose-Mary Owusuaa Mensah Gyening', 'Shester Gueuwou'] | 2023-06-25 | null | null | null | null | ['sentiment-analysis', 'stance-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-2.77596802e-01 8.68923441e-02 -7.00309217e-01 -4.78755295e-01
-9.94743228e-01 -8.76366198e-01 9.52055454e-01 7.49347150e-01
-6.09761357e-01 9.41670120e-01 6.57643139e-01 -9.78859186e-01
3.79885495e-01 -9.73822474e-01 -1.99453399e-01 -3.71432215e-01
2.51480103e-01 4.38065082e-01 -1.59715280e-01 -4.46422577... | [8.79794979095459, 9.912731170654297] |
961d7577-2c42-441f-9769-cf170381ba42 | learning-neural-implicit-functions-as-object | null | null | https://openreview.net/forum?id=I-nQMZfQz7F | https://openreview.net/pdf?id=I-nQMZfQz7F | Learning Neural Implicit Functions as Object Representations for Robotic Manipulation | Robotic manipulation planning is the problem of finding a sequence of robot configurations that involves interactions with objects in the scene, e.g., grasp, placement, tool-use, etc. To achieve such interactions, traditional approaches require hand-designed features and object representations, and it still remains an ... | ['Marc Toussaint', 'Danny Driess', 'Jung-Su Ha'] | 2021-09-29 | null | null | null | null | ['robot-manipulation'] | ['robots'] | [ 2.69828349e-01 2.99568415e-01 1.27576545e-01 -3.98225993e-01
3.97863872e-02 -7.59285390e-01 7.98148453e-01 3.73042971e-01
-2.37125710e-01 2.74111807e-01 -1.07726254e-01 1.54617205e-01
-5.56709945e-01 -8.70299876e-01 -1.10901058e+00 -3.48772883e-01
1.50120561e-03 1.01934135e+00 1.93458185e-01 -2.41251111... | [5.753650188446045, -0.7499637007713318] |
83d171b9-67f9-491b-b61a-7fad08fdcde4 | an-overview-of-challenges-in-egocentric-text | 2306.04345 | null | https://arxiv.org/abs/2306.04345v1 | https://arxiv.org/pdf/2306.04345v1.pdf | An Overview of Challenges in Egocentric Text-Video Retrieval | Text-video retrieval contains various challenges, including biases coming from diverse sources. We highlight some of them supported by illustrations to open a discussion. Besides, we address one of the biases, frame length bias, with a simple method which brings a very incremental but promising increase. We conclude wi... | ['Joo Hwee Lim', 'Hanwang Zhang', 'Hongyuan Zhu', 'Burak Satar'] | 2023-06-07 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [ 2.56426632e-01 -3.60574037e-01 -5.82534432e-01 -1.11567356e-01
-1.16244555e+00 -7.79353917e-01 7.17354476e-01 4.78821062e-02
-5.34395278e-01 8.43033195e-01 4.99205321e-01 3.06259785e-02
-2.10740298e-01 -2.16814548e-01 -5.36752403e-01 -6.09308660e-01
-1.32408977e-01 -5.58114760e-02 4.66329366e-01 -3.33053350... | [10.352919578552246, 0.7670363187789917] |
e5534e0f-9868-42ea-8675-b1c4e4d2b0d3 | torsion-graph-neural-networks | 2306.13541 | null | https://arxiv.org/abs/2306.13541v1 | https://arxiv.org/pdf/2306.13541v1.pdf | Torsion Graph Neural Networks | Geometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological information of non-Euclidian data into the end-to-end deep learning architectures. Motivated by the recent success of discrete Ricci curvature ... | ['Kelin Xia', 'Jiawei Luo', 'Xiang Liu', 'Cong Shen'] | 2023-06-23 | null | null | null | null | ['node-classification', 'link-prediction'] | ['graphs', 'graphs'] | [-3.97298127e-01 2.36849517e-01 -9.21363384e-02 -1.28045872e-01
7.38176182e-02 -3.91592950e-01 6.67879403e-01 1.69001207e-01
-1.15172267e-01 3.79952252e-01 -9.73366126e-02 -7.45462179e-01
-3.72084320e-01 -1.40245950e+00 -7.21785963e-01 -7.28838086e-01
-8.59061658e-01 6.61732018e-01 1.90748468e-01 -5.51962197... | [6.962043762207031, 6.154064655303955] |
51197adb-88b2-4597-a466-a4219a44cefd | cs60075-team2-at-semeval-2021-task-1-lexical-1 | null | null | https://aclanthology.org/2021.semeval-1.87 | https://aclanthology.org/2021.semeval-1.87.pdf | cs60075\_team2 at SemEval-2021 Task 1 : Lexical Complexity Prediction using Transformer-based Language Models pre-trained on various text corpora | The main contribution of this paper is to fine-tune transformer-based language models pre-trained on several text corpora, some being general (E.g., Wikipedia, BooksCorpus), some being the corpora from which the CompLex Dataset was extracted, and others being from other specific domains such as Finance, Law, etc. We pe... | ['Sai Mahesh Pokala', 'Tanurima Halder', 'Sayantan Adak', 'Abhilash Nandy'] | 2021-08-01 | null | null | null | semeval-2021 | ['lexical-complexity-prediction'] | ['natural-language-processing'] | [-1.92187905e-01 1.92082658e-01 -1.27657697e-01 -1.75266638e-01
-9.85202968e-01 -8.57522964e-01 9.16771531e-01 2.82790422e-01
-6.54478669e-01 9.19596791e-01 3.78162622e-01 -5.71501851e-01
-1.36207342e-01 -7.04401851e-01 -3.03199351e-01 -1.98857322e-01
7.43653206e-03 5.27525187e-01 3.85465592e-01 -5.09238005... | [10.537109375, 9.396242141723633] |
9d5a219c-aab5-4abe-8ab2-b32cc9096aaa | multi-label-zero-shot-learning-with-transfer | 1808.02474 | null | http://arxiv.org/abs/1808.02474v1 | http://arxiv.org/pdf/1808.02474v1.pdf | Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection | Zero-shot learning transfers knowledge from seen classes to novel unseen
classes to reduce human labor of labelling data for building new classifiers.
Much effort on zero-shot learning however has focused on the standard
multi-class setting, the more challenging multi-label zero-shot problem has
received limited attent... | ['Yuhong Guo', 'Meng Ye'] | 2018-08-07 | null | null | null | null | ['multi-label-zero-shot-learning', 'multi-label-image-classification'] | ['computer-vision', 'computer-vision'] | [ 6.12112343e-01 2.65853852e-01 -5.60533643e-01 -6.86563849e-01
-8.89937758e-01 -2.95127690e-01 4.37276721e-01 8.60416591e-02
-3.10669750e-01 5.47767699e-01 1.43142149e-01 1.48568735e-01
-1.42762229e-01 -7.45839477e-01 -2.91840822e-01 -9.67813909e-01
5.44943988e-01 2.54649132e-01 1.31235152e-01 2.71165788... | [10.07826042175293, 2.541215419769287] |
d97e5877-685d-4b5f-818d-eba8cfa46a58 | salsa-attacking-lattice-cryptography-with | 2207.04785 | null | https://arxiv.org/abs/2207.04785v2 | https://arxiv.org/pdf/2207.04785v2.pdf | SALSA: Attacking Lattice Cryptography with Transformers | Currently deployed public-key cryptosystems will be vulnerable to attacks by full-scale quantum computers. Consequently, "quantum resistant" cryptosystems are in high demand, and lattice-based cryptosystems, based on a hard problem known as Learning With Errors (LWE), have emerged as strong contenders for standardizati... | ['Kristin Lauter', 'François Charton', 'Mingjie Chen', 'Emily Wenger'] | 2022-07-11 | null | null | null | null | ['cryptanalysis'] | ['miscellaneous'] | [ 2.12896496e-01 -1.14313349e-01 -1.16814360e-01 -7.08311722e-02
-1.21473062e+00 -6.87128067e-01 4.08728868e-01 2.96336979e-01
-4.38887149e-01 6.88086450e-01 -3.31568688e-01 -1.22965181e+00
1.25407889e-01 -1.42491174e+00 -6.88462973e-01 -8.53560686e-01
-5.72432876e-01 6.16142392e-01 1.33540690e-01 -9.43763971... | [5.574436187744141, 5.037612438201904] |
21ef82e2-25bf-4b3f-b8f8-f0ebbffa6b45 | can-current-explainability-help-provide | 2210.15882 | null | https://arxiv.org/abs/2210.15882v1 | https://arxiv.org/pdf/2210.15882v1.pdf | Can Current Explainability Help Provide References in Clinical Notes to Support Humans Annotate Medical Codes? | The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods. However, most previous SOTA works based on deep learning are still in ea... | ['Varun Ganapathi', 'Philip S. Yu', 'Zhongfen Deng', 'Byung-Hak Kim'] | 2022-10-28 | null | null | null | null | ['medical-code-prediction'] | ['medical'] | [-5.42146042e-02 7.48922944e-01 -2.25099504e-01 -5.21249652e-01
-1.04501581e+00 -2.49191031e-01 1.22857615e-02 6.22195482e-01
2.99684703e-01 4.23373640e-01 5.57562053e-01 -8.16044152e-01
-8.08351457e-01 -4.41958189e-01 -5.78441679e-01 -9.44592729e-02
3.50103751e-02 1.04150498e+00 -5.12034059e-01 -2.72427768... | [8.107114791870117, 6.602052211761475] |
68523289-ead3-4c0c-8345-63aeef0a1a87 | robust-anomaly-map-assisted-multiple-defect | 2212.09352 | null | https://arxiv.org/abs/2212.09352v1 | https://arxiv.org/pdf/2212.09352v1.pdf | Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques | Industry 4.0 aims to optimize the manufacturing environment by leveraging new technological advances, such as new sensing capabilities and artificial intelligence. The DRAEM technique has shown state-of-the-art performance for unsupervised classification. The ability to create anomaly maps highlighting areas where defe... | ['Dunja Mladenić', 'Blaž Fortuna', 'Erik Koehorst', 'Spyros Theodoropoulos', 'Patrik Zajec', 'Jože M. Rožanec'] | 2022-12-19 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.16963434e-01 7.57014528e-02 -1.02450170e-01 -5.29593289e-01
-2.28547066e-01 -1.37029454e-01 4.65553373e-01 5.00580966e-01
2.09868833e-01 1.96899429e-01 -3.20952713e-01 -2.80155092e-01
-3.16279918e-01 -8.56134117e-01 -4.92571622e-01 -6.84527874e-01
-1.72507688e-01 3.70520800e-01 2.01060161e-01 -1.09666236... | [7.339083194732666, 2.0104117393493652] |
755bd3ab-6aec-4c79-9bf2-833dd57f9339 | parallel-residual-bi-fusion-feature-pyramid | 2012.01724 | null | https://arxiv.org/abs/2012.01724v5 | https://arxiv.org/pdf/2012.01724v5.pdf | Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object Detection | This paper proposes the Parallel Residual Bi-Fusion Feature Pyramid Network (PRB-FPN) for fast and accurate single-shot object detection. Feature Pyramid (FP) is widely used in recent visual detection, however the top-down pathway of FP cannot preserve accurate localization due to pooling shifting. The advantage of FP ... | ['Yong-Sheng Chen', 'Jun-Wei Hsieh', 'Ming-Ching Chang', 'Ping-Yang Chen'] | 2020-12-03 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [-1.37377530e-01 -4.35766071e-01 7.69797862e-02 -1.42093316e-01
-5.16608238e-01 -3.82427841e-01 1.26310050e-01 -5.42408526e-02
-3.72728586e-01 3.82681012e-01 -1.71483066e-02 1.76033497e-01
9.47968960e-02 -8.89818728e-01 -8.82813275e-01 -6.79884732e-01
-1.46545216e-01 -3.39881063e-01 1.13324893e+00 -2.68989891... | [8.96838665008545, -0.5086450576782227] |
9a1ac1b7-0a01-4249-9ac0-258918dacd4e | sociocultural-knowledge-is-needed-for | 2304.01890 | null | https://arxiv.org/abs/2304.01890v4 | https://arxiv.org/pdf/2304.01890v4.pdf | Sociocultural knowledge is needed for selection of shots in hate speech detection tasks | We introduce HATELEXICON, a lexicon of slurs and targets of hate speech for the countries of Brazil, Germany, India and Kenya, to aid training and interpretability of models. We demonstrate how our lexicon can be used to interpret model predictions, showing that models developed to classify extreme speech rely heavily ... | ['Hinrich Schütze', 'Abdullatif Köksal', 'Antonis Maronikolakis'] | 2023-04-04 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [ 4.30579595e-02 1.41349435e-01 -1.76547110e-01 -1.90251797e-01
-8.04344177e-01 -3.82243693e-01 6.69561446e-01 7.90406764e-02
-4.78697687e-01 6.67847574e-01 5.99598289e-01 -3.79624009e-01
6.88319579e-02 -5.89841366e-01 -4.24769074e-01 -3.06837529e-01
1.20497383e-01 6.00504637e-01 1.52781680e-01 -5.52368045... | [8.875016212463379, 10.456783294677734] |
2226f56a-a73e-4d60-b00a-22e454534de1 | zqm-at-semeval-2019-task9-a-single-layer-cnn | null | null | https://aclanthology.org/S19-2226 | https://aclanthology.org/S19-2226.pdf | ZQM at SemEval-2019 Task9: A Single Layer CNN Based on Pre-trained Model for Suggestion Mining | This paper describes our system that competed at SemEval 2019 Task 9 - SubTask A: {''}Sug- gestion Mining from Online Reviews and Forums{''}. Our system fuses the convolutional neural network and the latest BERT model to conduct suggestion mining. In our system, the input of convolutional neural network is the embeddin... | ['Zhengxin Zhang', 'Linmao Wang', 'Qimin Zhou', 'Hao Wu'] | 2019-06-01 | null | null | null | semeval-2019-6 | ['suggestion-mining'] | ['natural-language-processing'] | [-3.59104782e-01 2.55439818e-01 -2.33210012e-01 -4.64924991e-01
-3.76730919e-01 -2.12384969e-01 6.20109200e-01 2.35422641e-01
-9.28395450e-01 7.34250605e-01 2.42495313e-01 -7.19639301e-01
-1.88772321e-01 -7.68179953e-01 -7.24775076e-01 -2.11341619e-01
2.34204647e-03 1.94053307e-01 1.79975271e-01 -8.41338396... | [10.925023078918457, 7.515463829040527] |
7d62d234-803a-4e43-8dba-5032034cf7db | moe-fusion-instance-embedded-mixture-of | 2302.01392 | null | https://arxiv.org/abs/2302.01392v2 | https://arxiv.org/pdf/2302.01392v2.pdf | Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion | Infrared and visible image fusion aims to integrate comprehensive information from multiple sources to achieve superior performances on various practical tasks, such as detection, over that of a single modality. However, most existing methods directly combined the texture details and object contrast of different modali... | ['QinGhua Hu', 'Pengfei Zhu', 'Bing Cao', 'Yiming Sun'] | 2023-02-02 | null | null | null | null | ['infrared-and-visible-image-fusion'] | ['computer-vision'] | [ 1.78780407e-01 -5.53818345e-01 -7.01640174e-02 -1.66189611e-01
-1.20600736e+00 -3.08022648e-01 3.55139315e-01 -2.27963045e-01
-2.83134788e-01 3.55082363e-01 5.57425395e-02 1.50339499e-01
-3.29630263e-02 -7.17033446e-01 -6.09873116e-01 -1.26933670e+00
4.36134160e-01 -2.16459468e-01 4.23359126e-01 -2.58798033... | [10.514341354370117, -1.8770943880081177] |
f6caf367-9af5-4305-8cb7-1369b235b985 | discourse-aware-prompt-design-for-text-1 | null | null | https://openreview.net/forum?id=cTgq8D-LFj0 | https://openreview.net/pdf?id=cTgq8D-LFj0 | Discourse-Aware Prompt Design for Text Generation | Current efficient fine-tuning methods (e.g., adapters, prefix-tuning, etc.) have optimized conditional text generation via training a small set of extra parameters of the neural language model, while freezing the rest for efficiency. While showing strong performance on some generation tasks, they don't generalize acros... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['conditional-text-generation'] | ['natural-language-processing'] | [ 3.69531751e-01 8.83159280e-01 -3.38207573e-01 -3.21150482e-01
-6.96043849e-01 -5.51133931e-01 9.73721921e-01 -1.87283695e-01
-2.26098508e-01 9.12241697e-01 1.02482212e+00 -1.70272946e-01
1.89047217e-01 -9.38168526e-01 -8.94489765e-01 -3.98117900e-01
2.99113631e-01 5.41848660e-01 1.60270631e-01 -5.51875472... | [11.646098136901855, 9.014582633972168] |
1f4e71c0-3be5-4402-8124-041d8af13bff | tweet-normalization-with-syllables | null | null | https://aclanthology.org/P15-1089 | https://aclanthology.org/P15-1089.pdf | Tweet Normalization with Syllables | null | ['Chin-Hui Lee', 'Yunqing Xia', 'Ke Xu'] | 2015-07-01 | tweet-normalization-with-syllables-1 | https://aclanthology.org/P15-1089 | https://aclanthology.org/P15-1089.pdf | ijcnlp-2015-7 | ['lexical-normalization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.243384838104248, 3.744414806365967] |
d7e77a9c-c9d6-4077-abdd-e88b88035052 | causal-discovery-using-bayesian-model | 2306.02931 | null | https://arxiv.org/abs/2306.02931v1 | https://arxiv.org/pdf/2306.02931v1.pdf | Causal Discovery using Bayesian Model Selection | With only observational data on two variables, and without other assumptions, it is not possible to infer which one causes the other. Much of the causal literature has focused on guaranteeing identifiability of causal direction in statistical models for datasets where strong assumptions hold, such as additive noise or ... | ['Mark van der Wilk', 'Anish Dhir'] | 2023-06-05 | null | null | null | null | ['causal-discovery'] | ['knowledge-base'] | [ 6.10898077e-01 3.04013550e-01 -4.58804816e-01 -4.04058635e-01
-5.58554828e-01 -7.06510186e-01 8.95641983e-01 7.52503872e-02
-1.88828722e-01 9.41791654e-01 3.19291353e-01 -8.19144845e-01
-8.61828506e-01 -8.35502148e-01 -7.62828946e-01 -5.81999958e-01
-4.83785063e-01 6.95058107e-01 4.04673994e-01 3.38530809... | [7.844910621643066, 5.243464946746826] |
9240d97d-210b-464a-983d-e2e659903bfa | curriculum-graph-co-teaching-for-multi-target | 2104.00808 | null | https://arxiv.org/abs/2104.00808v1 | https://arxiv.org/pdf/2104.00808v1.pdf | Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation | In this paper we address multi-target domain adaptation (MTDA), where given one labeled source dataset and multiple unlabeled target datasets that differ in data distributions, the task is to learn a robust predictor for all the target domains. We identify two key aspects that can help to alleviate multiple domain-shif... | ['Elisa Ricci', 'Nicu Sebe', 'Zhun Zhong', 'Evgeny Krivosheev', 'Subhankar Roy'] | 2021-04-01 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Roy_Curriculum_Graph_Co-Teaching_for_Multi-Target_Domain_Adaptation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['blended-target-domain-adaptation', 'multi-target-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 0.34355512 0.17667098 -0.1389821 -0.5522069 -0.8443007 -0.7337771
0.5261361 0.1993784 -0.322678 0.82073873 -0.1686136 -0.18907194
-0.1562189 -0.7372398 -0.81984335 -0.8397837 0.12026338 0.72988224
0.4874419 -0.14002061 -0.0745066 0.16194138 -1.2108498 0.274315
1.3032098 1.0973033 0.295... | [10.294510841369629, 2.975114107131958] |
6b9c1d0f-1535-4084-966e-8a84a547094c | sg-gan-fine-stereoscopic-aware-generation-for | 2305.12646 | null | https://arxiv.org/abs/2305.12646v1 | https://arxiv.org/pdf/2305.12646v1.pdf | SG-GAN: Fine Stereoscopic-Aware Generation for 3D Brain Point Cloud Up-sampling from a Single Image | In minimally-invasive brain surgeries with indirect and narrow operating environments, 3D brain reconstruction is crucial. However, as requirements of accuracy for some new minimally-invasive surgeries (such as brain-computer interface surgery) are higher and higher, the outputs of conventional 3D reconstruction, such ... | ['Shuqiang Wang', 'Baiying Lei', 'Bowen Hu'] | 2023-05-22 | null | null | null | null | ['3d-reconstruction'] | ['computer-vision'] | [ 1.32817179e-01 4.60523516e-01 2.47365281e-01 -9.60928798e-02
-7.77813613e-01 -8.87433067e-02 4.18805301e-01 -2.21199110e-01
-2.62357146e-01 7.02159524e-01 2.93977298e-02 -2.06220485e-02
3.48289981e-02 -9.34990525e-01 -8.36537540e-01 -8.67822647e-01
3.75354677e-01 6.57485962e-01 1.21220231e-01 -8.29733014... | [13.917082786560059, -2.3379032611846924] |
c3f0207a-c015-47d0-864d-a321da5301fb | discourse-connectors-for-latent-subjectivity | null | null | https://aclanthology.org/N13-1100 | https://aclanthology.org/N13-1100.pdf | Discourse Connectors for Latent Subjectivity in Sentiment Analysis | null | ['Rakshit Trivedi', 'Jacob Eisenstein'] | 2013-06-01 | null | null | null | naacl-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... | [-7.209657669067383, 3.594505548477173] |
68279a47-de01-4c76-b391-e35590d7ae8a | semantic-aware-chinese-zero-pronoun | null | null | https://aclanthology.org/2020.ccl-1.77 | https://aclanthology.org/2020.ccl-1.77.pdf | Semantic-aware Chinese Zero Pronoun Resolution with Pre-trained Semantic Dependency Parser | Deep learning-based Chinese zero pronoun resolution model has achieved better performance than traditional machine learning-based model. However, the existing work related to Chinese zero pronoun resolution has not yet well integrated linguistic information into the deep learningbased Chinese zero pronoun resolution mo... | ['Yanqiu Shao', 'Zizhuo Shen', 'Lanqiu Zhang'] | null | null | null | null | ccl-2020-10 | ['chinese-zero-pronoun-resolution'] | ['natural-language-processing'] | [-3.76237661e-01 3.79053205e-01 -2.62311190e-01 -2.05064908e-01
-1.26398706e+00 -2.77922392e-01 4.27492201e-01 -2.17607737e-01
-7.37808228e-01 8.77095819e-01 8.68080616e-01 -1.29611358e-01
2.24756449e-01 -8.88175249e-01 -4.19405878e-01 -3.70613009e-01
3.79072398e-01 7.51047969e-01 4.21448976e-01 -4.96303290... | [10.240226745605469, 9.312986373901367] |
1043a9b4-d433-4cb1-a460-0f7c3d451b0d | mdcspell-a-multi-task-detector-corrector | null | null | https://aclanthology.org/2022.findings-acl.98 | https://aclanthology.org/2022.findings-acl.98.pdf | MDCSpell: A Multi-task Detector-Corrector Framework for Chinese Spelling Correction | Chinese Spelling Correction (CSC) is a task to detect and correct misspelled characters in Chinese texts. CSC is challenging since many Chinese characters are visually or phonologically similar but with quite different semantic meanings. Many recent works use BERT-based language models to directly correct each characte... | ['Feng Mao', 'Boyu Zhang', 'Ziqiang Ying', 'Chenxi Zhu'] | null | null | null | null | findings-acl-2022-5 | ['spelling-correction'] | ['natural-language-processing'] | [ 4.81718600e-01 -4.77494717e-01 3.24297339e-01 -1.22866392e-01
-8.07482839e-01 -4.12547857e-01 4.98647630e-01 6.23242319e-01
-5.63410163e-01 6.95784807e-01 2.03153983e-01 -1.63829833e-01
5.71388304e-01 -5.17391682e-01 -6.70883596e-01 -7.66119123e-01
7.87985444e-01 8.23516473e-02 7.84998834e-01 -1.26891956... | [10.948201179504395, 10.838134765625] |
5cc985dd-d11f-436d-a041-3c155c2142ac | graph-neural-networks-with-learnable-1 | 2110.07875 | null | https://arxiv.org/abs/2110.07875v2 | https://arxiv.org/pdf/2110.07875v2.pdf | Graph Neural Networks with Learnable Structural and Positional Representations | Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional informat... | ['Xavier Bresson', 'Yoshua Bengio', 'Thomas Laurent', 'Anh Tuan Luu', 'Vijay Prakash Dwivedi'] | 2021-10-15 | graph-neural-networks-with-learnable | https://openreview.net/forum?id=wTTjnvGphYj | https://openreview.net/pdf?id=wTTjnvGphYj | iclr-2022-4 | ['graph-regression'] | ['graphs'] | [ 4.32465196e-01 3.83688867e-01 -2.52551824e-01 -4.89028431e-02
1.22345798e-01 -7.60357141e-01 5.69578886e-01 4.86590117e-01
-1.53813273e-01 7.55640984e-01 -2.87462603e-02 -5.53374827e-01
-4.59544659e-01 -1.22513127e+00 -8.58966708e-01 -8.84529412e-01
-3.97190392e-01 4.40337539e-01 2.67587662e-01 -3.03421021... | [6.818356990814209, 6.185519218444824] |
77b0ba0c-712c-4685-a833-d275bc6f0135 | end-to-end-deep-residual-learning-with | 1909.12923 | null | https://arxiv.org/abs/1909.12923v1 | https://arxiv.org/pdf/1909.12923v1.pdf | End-to-End Deep Residual Learning with Dilated Convolutions for Myocardial Infarction Detection and Localization | In this report, I investigate the use of end-to-end deep residual learning with dilated convolutions for myocardial infarction (MI) detection and localization from electrocardiogram (ECG) signals. Although deep residual learning has already been applied to MI detection and localization, I propose a more accurate system... | ['Iván López-Espejo'] | 2019-09-15 | null | null | null | null | ['myocardial-infarction-detection'] | ['medical'] | [ 3.63501400e-01 -2.42960677e-01 2.39556462e-01 -4.69516784e-01
-1.12670231e+00 -3.18247288e-01 -8.76814872e-02 1.01515457e-01
-5.98523080e-01 3.85698736e-01 7.32724369e-02 -6.01543665e-01
-3.86212409e-01 -4.26447511e-01 -3.18149716e-01 -6.98170125e-01
-5.55948973e-01 7.43195713e-02 -5.14373839e-01 2.26632923... | [14.292505264282227, 3.275240659713745] |
6ce35740-fea6-4232-8a3b-fe4bdb7755e8 | better-smatch-better-parser-amr-evaluation-is | 2210.06461 | null | https://arxiv.org/abs/2210.06461v1 | https://arxiv.org/pdf/2210.06461v1.pdf | Better Smatch = Better Parser? AMR evaluation is not so simple anymore | Recently, astonishing advances have been observed in AMR parsing, as measured by the structural Smatch metric. In fact, today's systems achieve performance levels that seem to surpass estimates of human inter annotator agreement (IAA). Therefore, it is unclear how well Smatch (still) relates to human estimates of parse... | ['Anette Frank', 'Juri Opitz'] | 2022-10-12 | null | null | null | null | ['amr-parsing'] | ['natural-language-processing'] | [ 1.92804039e-01 4.93048072e-01 3.64846140e-01 -6.92654312e-01
-1.35314989e+00 -1.04948485e+00 2.47176155e-01 8.21016192e-01
-5.68808138e-01 4.51867759e-01 5.47833681e-01 -7.29176998e-01
7.04439878e-02 -5.68943322e-01 -4.32478130e-01 -1.37580529e-01
5.32331705e-01 3.68977338e-01 9.38642621e-02 -3.22897524... | [10.674198150634766, 9.64814567565918] |
5b8ad32e-609a-4bc5-9fb9-7e393a5b65d7 | disentangled-variational-autoencoder-for | 2305.14071 | null | https://arxiv.org/abs/2305.14071v1 | https://arxiv.org/pdf/2305.14071v1.pdf | Disentangled Variational Autoencoder for Emotion Recognition in Conversations | In Emotion Recognition in Conversations (ERC), the emotions of target utterances are closely dependent on their context. Therefore, existing works train the model to generate the response of the target utterance, which aims to recognise emotions leveraging contextual information. However, adjacent response generation i... | ['Sophia Ananiadou', 'Tianlin Zhang', 'Kailai Yang'] | 2023-05-23 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-1.64682209e-01 1.15138784e-01 -1.69846192e-02 -7.11083114e-01
-5.56273818e-01 -4.12709147e-01 6.50246084e-01 -2.71327645e-01
-5.40848672e-02 6.21681690e-01 5.86118400e-01 1.90708444e-01
1.53123245e-01 -5.98618388e-01 -2.64687896e-01 -8.70788932e-01
3.42191100e-01 3.53873819e-01 -7.07978964e-01 -4.57758963... | [13.092716217041016, 6.032811641693115] |
0a62bd39-02d1-42bf-ac25-2e7b1ac32c15 | deep-multi-view-learning-using-neuron-wise | 1904.11151 | null | http://arxiv.org/abs/1904.11151v1 | http://arxiv.org/pdf/1904.11151v1.pdf | Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers | Many machine learning problems concern with discovering or associating common
patterns in data of multiple views or modalities. Multi-view learning is of the
methods to achieve such goals. Recent methods propose deep multi-view networks
via adaptation of generic Deep Neural Networks (DNNs), which concatenate
features o... | ['DaCheng Tao', 'Kui Jia', 'Mingkui Tan', 'Jiehong Lin'] | 2019-04-25 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-1.63689512e-03 -3.17207724e-01 -3.92062925e-02 -5.82647681e-01
-6.61389649e-01 -3.77105087e-01 5.79067707e-01 -3.10018986e-01
-2.34200045e-01 2.02501863e-01 1.42804086e-01 1.36365876e-01
-3.92644167e-01 -6.75592780e-01 -9.50029016e-01 -8.74104619e-01
8.87798294e-02 2.41469741e-01 -1.19165611e-02 2.83667129... | [8.162250518798828, -3.6250386238098145] |
85df3761-0e45-4955-96c7-d04f71a57a9f | integratedpifu-integrated-pixel-aligned | 2211.07955 | null | https://arxiv.org/abs/2211.07955v1 | https://arxiv.org/pdf/2211.07955v1.pdf | IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-view Human Reconstruction | We propose IntegratedPIFu, a new pixel aligned implicit model that builds on the foundation set by PIFuHD. IntegratedPIFu shows how depth and human parsing information can be predicted and capitalised upon in a pixel-aligned implicit model. In addition, IntegratedPIFu introduces depth oriented sampling, a novel trainin... | ['Weisi Lin', 'Haiyu Zhao', 'Guosheng Lin', 'Kennard Yanting Chan'] | 2022-11-15 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 3.14878494e-01 8.50162506e-01 -1.44956172e-01 -1.77750424e-01
-5.72836280e-01 -1.24898054e-01 4.12259072e-01 -2.37730116e-01
1.43579394e-01 8.12657595e-01 3.58050227e-01 1.67310938e-01
5.05339429e-02 -1.15077364e+00 -1.15772545e+00 -1.83368959e-02
5.10117784e-02 7.15990424e-01 5.83322883e-01 -1.12743273... | [8.718070030212402, -3.1532838344573975] |
f4c1a29f-5a29-4b4b-bf2e-1ed886273406 | sa-net-a-deep-spectral-analysis-network-for | 2009.07026 | null | https://arxiv.org/abs/2009.07026v1 | https://arxiv.org/pdf/2009.07026v1.pdf | SA-Net: A deep spectral analysis network for image clustering | Although supervised deep representation learning has attracted enormous attentions across areas of pattern recognition and computer vision, little progress has been made towards unsupervised deep representation learning for image clustering. In this paper, we propose a deep spectral analysis network for unsupervised re... | ['Jinghua Wang', 'Jianmin Jiang'] | 2020-09-11 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [ 2.06966177e-01 -3.42513829e-01 -2.57196911e-02 -2.54677206e-01
-5.87544620e-01 -2.96499163e-01 2.62508273e-01 1.57893732e-01
-1.67180881e-01 -3.73830497e-02 -6.58202097e-02 -4.23318846e-03
-5.34765482e-01 -7.11641967e-01 -4.98837471e-01 -1.06568229e+00
-1.52346149e-01 4.08239037e-01 2.10367426e-01 6.27580732... | [9.01819133758545, 3.2889184951782227] |
843df6b0-f10d-4279-8d75-852b0c877479 | task-splitting-for-dnn-based-acoustic-echo | 2205.06931 | null | https://arxiv.org/abs/2205.06931v2 | https://arxiv.org/pdf/2205.06931v2.pdf | Task splitting for DNN-based acoustic echo and noise removal | Neural networks have led to tremendous performance gains for single-task speech enhancement, such as noise suppression and acoustic echo cancellation (AEC). In this work, we evaluate whether it is more useful to use a single joint or separate modules to tackle these problems. We describe different possible implementati... | ['Maria Luis Valero', 'Sebastian Braun'] | 2022-05-13 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 2.58641183e-01 -3.59891690e-02 6.66675270e-01 -3.86071801e-01
-9.06678438e-01 -3.46233845e-01 4.72697318e-01 -1.84321597e-01
-6.70520723e-01 3.72830421e-01 5.79494298e-01 -5.52056372e-01
9.12047997e-02 1.28258415e-03 -3.60385060e-01 -6.00993037e-01
-1.69226333e-01 -4.72049773e-01 4.05259192e-01 -3.43824625... | [14.996086120605469, 5.990204334259033] |
391bec05-9f62-46cb-964c-a09238e590f8 | sync-draw-automatic-video-generation-using | 1611.10314 | null | http://arxiv.org/abs/1611.10314v4 | http://arxiv.org/pdf/1611.10314v4.pdf | Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures | This paper introduces a novel approach for generating videos called
Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also
perform text-to-video generation which, to the best of our knowledge, makes it
the first approach of its kind. It combines a Variational Autoencoder~(VAE)
with a Recurrent Att... | ['Gaurav Mittal', 'Tanya Marwah', 'Vineeth N. Balasubramanian'] | 2016-11-30 | null | null | null | null | ['text-to-video-generation'] | ['natural-language-processing'] | [-6.02958258e-03 2.20143870e-01 -2.29379237e-02 1.23409308e-01
-5.66249371e-01 -4.25515890e-01 9.08252776e-01 -6.95138276e-01
-6.03327788e-02 8.40907216e-01 6.36808693e-01 1.73126198e-02
2.78249621e-01 -7.05008447e-01 -1.28562784e+00 -7.39256203e-01
9.63298662e-04 3.06756586e-01 2.03837410e-01 -2.22633600... | [10.797235488891602, -0.29522526264190674] |
905b5973-4574-4ba3-8d77-f69226f3378c | ir-gan-image-manipulation-with-linguistic | 2204.00792 | null | https://arxiv.org/abs/2204.00792v1 | https://arxiv.org/pdf/2204.00792v1.pdf | IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning | Conditional image generation is an active research topic including text2image and image translation. Recently image manipulation with linguistic instruction brings new challenges of multimodal conditional generation. However, traditional conditional image generation models mainly focus on generating high-quality and vi... | ['Qingming Huang', 'Shuhui Wang', 'Qianqian Xu', 'Shaofei Cai', 'Liang Li', 'Jincan Deng', 'Zhenhuan Liu'] | 2022-04-02 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 6.76548958e-01 3.19699377e-01 -1.81420892e-01 -3.04597706e-01
-6.18860006e-01 -3.42426032e-01 8.39845002e-01 -5.12242615e-01
-7.36219659e-02 7.66830027e-01 3.26087952e-01 -2.77652562e-01
5.54769635e-01 -9.40715909e-01 -1.16340804e+00 -6.74103856e-01
5.72489977e-01 6.23782054e-02 3.99524234e-02 -1.32943094... | [11.258056640625, 0.2968175709247589] |
fd59de6f-f3ea-45c7-a7ec-ee7e13b51cb5 | the-power-of-character-n-grams-in-native | null | null | https://aclanthology.org/W17-5043 | https://aclanthology.org/W17-5043.pdf | The Power of Character N-grams in Native Language Identification | In this paper, we explore the performance of a linear SVM trained on language independent character features for the NLI Shared Task 2017. Our basic system (GRONINGEN) achieves the best performance (87.56 F1-score) on the evaluation set using only 1-9 character n-grams as features. We compare this against several ensem... | ['Gertjan Van Noord', 'Artur Kulmizev', 'Martijn Wieling', 'Johannes Bjerva', 'Malvina Nissim', 'Bo Blankers', 'Barbara Plank'] | 2017-09-01 | null | null | null | ws-2017-9 | ['native-language-identification'] | ['natural-language-processing'] | [-6.39842302e-02 1.05593331e-01 -5.69455445e-01 -5.21685898e-01
-8.95957530e-01 -6.85728133e-01 9.19607878e-01 5.48050284e-01
-9.82888043e-01 1.03834653e+00 5.54483473e-01 -6.56186163e-01
-1.10234879e-01 -4.01811481e-01 -1.66693911e-01 -4.79004562e-01
2.27545857e-01 5.53376913e-01 -1.23490006e-01 -2.69074678... | [10.501398086547852, 10.316629409790039] |
e3644184-64ae-4467-950e-25fc6c25047a | model-based-gym-environments-for-limit-order | 2209.07823 | null | https://arxiv.org/abs/2209.07823v1 | https://arxiv.org/pdf/2209.07823v1.pdf | Model-based gym environments for limit order book trading | Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems -- such as market-making and optimal execution -- in limit order books. This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcemen... | ['Martin Herdegen', 'Rahul Savani', 'Leandro Sanchez-Betancourt', 'Joseph Jerome'] | 2022-09-16 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-8.18612933e-01 -2.03850791e-01 -3.46040964e-01 -2.36754343e-01
-4.10177052e-01 -8.54359448e-01 5.94573379e-01 -1.55221686e-01
-6.13607526e-01 7.28189647e-01 -2.14033321e-01 -6.59829855e-01
-3.28607231e-01 -1.00943267e+00 -7.05752432e-01 -3.54901731e-01
-4.67968792e-01 1.05055571e+00 3.09140943e-02 -7.50805855... | [4.357748031616211, 3.8465678691864014] |
4c4ee918-4686-4e47-8a9a-578b50c81046 | predictive-modelling-of-training-loads-and | 1706.04336 | null | http://arxiv.org/abs/1706.04336v1 | http://arxiv.org/pdf/1706.04336v1.pdf | Predictive modelling of training loads and injury in Australian football | To investigate whether training load monitoring data could be used to predict
injuries in elite Australian football players, data were collected from elite
athletes over 3 seasons at an Australian football club. Loads were quantified
using GPS devices, accelerometers and player perceived exertion ratings.
Absolute and ... | ['Kok-Leong Ong', 'Rod Whiteley', 'Meg E. Morris', 'Kay M. Crossley', 'Justin Crow', 'David L. Carey'] | 2017-06-14 | null | null | null | null | ['injury-prediction'] | ['playing-games'] | [ 7.55648464e-02 -2.03025132e-01 -5.04063189e-01 7.14448839e-02
-5.37550628e-01 -2.78382838e-01 -1.97413892e-01 6.98025584e-01
-8.97373319e-01 6.99672401e-01 5.09660482e-01 -5.28721392e-01
-6.33790612e-01 -8.38139892e-01 -4.09942836e-01 -1.46299645e-01
-4.68117207e-01 4.93603915e-01 7.44525015e-01 -2.36922160... | [6.899536609649658, 0.4002732038497925] |
1bc3333f-fb5f-41d7-9f41-4bbbd5a321e6 | chatgpt-is-not-all-you-need-a-state-of-the | 2301.04655 | null | https://arxiv.org/abs/2301.04655v1 | https://arxiv.org/pdf/2301.04655v1.pdf | ChatGPT is not all you need. A State of the Art Review of large Generative AI models | During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to perform tasks such as being a general question and answering system or automatically creating artistic images that are revolutionizing several... | ['Eduardo C. Garrido-Merchan', 'Roberto Gozalo-Brizuela'] | 2023-01-11 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 1.03407405e-01 4.24576074e-01 4.12322015e-01 -1.71890676e-01
-4.13852304e-01 -7.31245279e-01 1.45706630e+00 -6.97457075e-01
6.89578205e-02 6.10350311e-01 6.58224225e-02 -4.30115551e-01
-3.17409337e-01 -8.53300393e-01 -5.86451352e-01 -8.43289316e-01
1.97945505e-01 1.01336551e+00 1.04781002e-01 -4.72565860... | [11.498686790466309, -0.10167334973812103] |
dd1c3fc2-306f-4063-b63b-8a2474e21fba | exploring-chain-of-thought-style-prompting | 2305.14215 | null | https://arxiv.org/abs/2305.14215v1 | https://arxiv.org/pdf/2305.14215v1.pdf | Exploring Chain-of-Thought Style Prompting for Text-to-SQL | Conventional supervised approaches for text-to-SQL parsing often require large amounts of annotated data, which is costly to obtain in practice. Recently, in-context learning with large language models (LLMs) has caught increasing attention due to its superior few-shot performance in a wide range of tasks. However, mos... | ['Huan Sun', 'Xiang Deng', 'Tianshu Zhang', 'Ziru Chen', 'Chang-You Tai'] | 2023-05-23 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 1.77471995e-01 3.33780795e-01 -2.11576238e-01 -8.18351030e-01
-1.39437878e+00 -5.75137854e-01 3.16585064e-01 6.79949820e-01
-3.54259968e-01 3.42459053e-01 8.67087767e-02 -9.80812311e-01
5.36456611e-03 -9.03581560e-01 -8.79459262e-01 1.06513798e-01
1.18366562e-01 6.06560349e-01 5.08129537e-01 -2.22084448... | [9.96765422821045, 7.896803379058838] |
aca7dc2b-c69a-4570-ab55-d488a3538a7c | u-sleep-resilient-to-aasm-guidelines | 2209.11173 | null | https://arxiv.org/abs/2209.11173v3 | https://arxiv.org/pdf/2209.11173v3.pdf | U-Sleep's resilience to AASM guidelines | AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications,e.g., recommended EEG derivations, to detailed sleep scoring rules according... | ['Jan D. Warncke', 'Francesca D. Faraci', 'Paolo Favaro', 'Athina Tzovara', 'Claudio L. A. Bassetti', 'Markus H. Schmidt', 'Marco Pesce', 'Julia van der Meer', 'Giuliana Monachino', 'Luigi Fiorillo'] | 2022-09-19 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [-1.84211344e-01 -1.83758080e-01 -2.16603458e-01 -3.97354007e-01
-5.19440055e-01 -2.41052106e-01 9.51801986e-02 1.97786137e-01
-7.45750487e-01 1.05799019e+00 4.34209481e-02 -4.14951414e-01
-3.64004046e-01 -2.97813058e-01 -1.18576288e-01 -6.62212849e-01
-1.12071700e-01 6.78987145e-01 1.01881944e-01 -6.36068210... | [13.505010604858398, 3.5050504207611084] |
80215ffe-c16c-4f2d-a7a4-a59f88a1c6d2 | a-simple-but-powerful-graph-encoder-for-1 | 2112.07791 | null | https://arxiv.org/abs/2112.07791v2 | https://arxiv.org/pdf/2112.07791v2.pdf | A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion | Knowledge graphs contain rich knowledge about various entities and the relational information among them, while temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest. Recent TKGC met... | ['Volker Tresp', 'Bailan He', 'Yunpu Ma', 'Zifeng Ding'] | 2021-12-14 | null | null | null | null | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-2.67769843e-01 1.23095445e-01 -5.64225912e-01 -1.35083675e-01
-3.90405536e-01 -3.52477580e-01 6.01459086e-01 2.44253308e-01
-4.18349475e-01 5.52043021e-01 3.67557079e-01 -2.13480324e-01
-2.39031121e-01 -1.00033057e+00 -7.99020350e-01 -5.92871487e-01
-3.07409316e-01 2.39248395e-01 3.55563134e-01 -1.91575080... | [8.609091758728027, 7.887713432312012] |
446893de-f13a-4f9c-9c44-96386ee6325d | multimodal-argument-mining-a-case-study-in | null | null | https://aclanthology.org/2022.argmining-1.15 | https://aclanthology.org/2022.argmining-1.15.pdf | Multimodal Argument Mining: A Case Study in Political Debates | We propose a study on multimodal argument mining in the domain of political debates. We collate and extend existing corpora and provide an initial empirical study on multimodal architectures, with a special emphasis on input encoding methods. Our results provide interesting indications about future directions in this i... | ['Paolo Torroni', 'Andrea Galassi', 'Federico Ruggeri', 'Eleonora Mancini'] | null | null | null | null | argmining-acl-2022-10 | ['argument-mining'] | ['natural-language-processing'] | [ 3.52261215e-01 8.37014318e-01 -5.42129159e-01 -6.98524833e-01
-1.12089264e+00 -9.84268367e-01 1.11201358e+00 6.47871554e-01
-6.26192689e-01 1.00986278e+00 1.17168021e+00 -9.20863926e-01
-1.75731421e-01 -5.83550870e-01 -4.99994218e-01 -8.14236030e-02
-1.53698057e-01 1.08583939e+00 -1.44275680e-01 -1.03025544... | [10.49975872039795, 9.648178100585938] |
6f56fa60-0361-4c69-886e-8c35c6af9a28 | finer-grained-correlations-location-priors | 2211.16290 | null | https://arxiv.org/abs/2211.16290v2 | https://arxiv.org/pdf/2211.16290v2.pdf | LocPoseNet: Robust Location Prior for Unseen Object Pose Estimation | Object location priors have been shown to be critical for the standard 6D object pose estimation setting, where the training and testing objects are the same. Specifically, they can be used to initialize the 3D object translation and facilitate 3D object rotation estimation. Unfortunately, the object detectors that are... | ['Mathieu Salzmann', 'Yinlin Hu', 'Chen Zhao'] | 2022-11-29 | null | null | null | null | ['template-matching', '6d-pose-estimation-1', '6d-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.55106202e-01 -3.41860861e-01 -1.68274909e-01 -3.10572386e-01
-8.08624744e-01 -9.83179033e-01 5.89431703e-01 -1.26565993e-01
-3.65208566e-01 2.74115264e-01 -1.42220020e-01 7.20396871e-03
3.03791650e-02 -4.41508442e-01 -9.38129723e-01 -7.86110103e-01
1.88724279e-01 7.54200339e-01 5.79970658e-01 2.12276757... | [7.572151184082031, -2.672485589981079] |
f431e499-f75d-49d1-bbd0-e8e25a2cd0ab | unsupervised-part-segmentation-through | 2105.12405 | null | https://arxiv.org/abs/2105.12405v1 | https://arxiv.org/pdf/2105.12405v1.pdf | Unsupervised Part Segmentation through Disentangling Appearance and Shape | We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which a... | ['Jun Zhu', 'Hang Su', 'Xiao Yang', 'Lei Zhang', 'Shilong Liu'] | 2021-05-26 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Liu_Unsupervised_Part_Segmentation_Through_Disentangling_Appearance_and_Shape_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Liu_Unsupervised_Part_Segmentation_Through_Disentangling_Appearance_and_Shape_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-facial-landmark-detection'] | ['computer-vision'] | [ 3.78888190e-01 3.19578618e-01 -1.76040605e-01 -5.52167833e-01
-2.80559778e-01 -4.53370094e-01 2.06130594e-01 1.54615581e-01
-1.85454473e-01 5.26013494e-01 7.58411887e-04 2.18764305e-01
-1.04360372e-01 -6.38671994e-01 -8.40055406e-01 -8.09538126e-01
4.59326267e-01 3.86283457e-01 4.15475368e-01 1.33010954... | [9.548202514648438, 0.7477856874465942] |
4b6471b7-dafa-4532-a35c-6cd0d465b730 | progressive-refinement-network-for-occluded | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4211_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680035.pdf | Progressive Refinement Network for Occluded Pedestrian Detection | We present Progressive Refinement Network (PRNet), a novel single-stage detector that tackles occluded pedestrian detection. Motivated by human's progressive process on annotating occluded pedestrians, PRNet achieves sequential refinement by three phases: Finding high-confident anchors of visible parts, calibrating suc... | ['Xiaolin Song Kaili Zhao Wen-Sheng Chu Honggang Zhang Jun Guo'] | null | null | null | null | eccv-2020-8 | ['body-detection'] | ['computer-vision'] | [-5.45125455e-02 1.62090749e-01 -1.27457559e-01 -5.22599220e-01
-6.61349952e-01 -1.90991074e-01 2.96089292e-01 1.51224092e-01
-8.13735664e-01 6.07900083e-01 1.76634178e-01 1.50194511e-01
6.24391198e-01 -5.47580481e-01 -6.70727015e-01 -4.15927678e-01
-2.41020426e-01 3.99051577e-01 1.12111783e+00 -4.11984436... | [8.064990043640137, -0.5004330277442932] |
79e0ad45-46fd-4cda-9fa2-8a9e3bae9b45 | unsupervised-visual-defect-detection-with | 2211.16092 | null | https://arxiv.org/abs/2211.16092v1 | https://arxiv.org/pdf/2211.16092v1.pdf | Unsupervised Visual Defect Detection with Score-Based Generative Model | Anomaly Detection (AD), as a critical problem, has been widely discussed. In this paper, we specialize in one specific problem, Visual Defect Detection (VDD), in many industrial applications. And in practice, defect image samples are very rare and difficult to collect. Thus, we focus on the unsupervised visual defect d... | ['Siyu Xia', 'Ming Shao', 'Fuzhen Cai', 'Haoyang Li', 'Yapeng Teng'] | 2022-11-29 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 3.53354067e-01 -3.35097432e-01 2.29096085e-01 -2.87682004e-02
-3.21874559e-01 -7.64441490e-02 2.49092564e-01 1.31257817e-01
5.57311811e-03 3.58968735e-01 -3.19679052e-01 -5.69830462e-02
1.19203642e-01 -9.56033468e-01 -6.52138770e-01 -9.87836480e-01
3.50605190e-01 -1.01331053e-02 2.05958024e-01 -2.47474033... | [7.573638439178467, 2.032390594482422] |
a9e964fe-305f-4801-b0d8-4c708cf0a53c | three-dimensional-lip-motion-network-for-text | 2010.06363 | null | https://arxiv.org/abs/2010.06363v1 | https://arxiv.org/pdf/2010.06363v1.pdf | Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition | Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two-dimensional (2D) lip images to recognize speaker in a textdependent context. However, 2D lip easily suffers from various face orientations. To this... | ['Li Liu', 'Ju Zhang', 'Qiang Fang', 'Mei Yu', 'Shanyu Wang', 'Tong Wu', 'Jianrong Wang'] | 2020-10-13 | null | null | null | null | ['text-independent-speaker-recognition'] | ['speech'] | [-3.77622932e-01 -5.47927976e-01 -5.63555241e-01 -4.43868309e-01
-9.15438414e-01 -2.62116790e-01 3.62115175e-01 -8.10540497e-01
-2.99707353e-01 1.39823452e-01 5.90360582e-01 -2.47877166e-01
4.88585353e-01 -1.13915421e-01 -3.85885298e-01 -1.00153685e+00
3.97830129e-01 -1.24466233e-01 4.06550691e-02 1.39354646... | [14.306367874145508, 4.973727703094482] |
40fa055c-30d0-4821-bb8f-f71db0c118b9 | reasoning-for-complex-data-through-ensemble | 2202.03126 | null | https://arxiv.org/abs/2202.03126v4 | https://arxiv.org/pdf/2202.03126v4.pdf | Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution | Learning from fully-unlabeled data is challenging in Multimedia Forensics problems, such as Person Re-Identification and Text Authorship Attribution. Recent self-supervised learning methods have shown to be effective when dealing with fully-unlabeled data in cases where the underlying classes have significant semantic ... | ['Antônio Theophilo', 'Anderson Rocha', 'Fernanda Andaló', 'Gabriel Bertocco'] | 2022-02-07 | null | null | null | null | ['unsupervised-person-re-identification', 'authorship-verification'] | ['computer-vision', 'natural-language-processing'] | [ 2.78141111e-01 -1.20398305e-01 1.09817460e-03 -4.52795744e-01
-5.74777842e-01 -7.60029733e-01 6.30576015e-01 5.02033532e-01
-7.17642546e-01 6.29976392e-01 6.84138834e-02 1.16434440e-01
-2.38624334e-01 -6.52849257e-01 -3.90932769e-01 -8.22452307e-01
2.02281445e-01 8.22425246e-01 2.05724865e-01 3.19686800... | [14.59992790222168, 1.1483991146087646] |
4d38cb2b-b36c-4c26-b4d7-6182f5b16bd3 | improving-inference-performance-of-machine | 2301.05099 | null | https://arxiv.org/abs/2301.05099v2 | https://arxiv.org/pdf/2301.05099v2.pdf | Improving Inference Performance of Machine Learning with the Divide-and-Conquer Principle | Many popular machine learning models scale poorly when deployed on CPUs. In this paper we explore the reasons why and propose a simple, yet effective approach based on the well-known Divide-and-Conquer Principle to tackle this problem of great practical importance. Given an inference job, instead of using all available... | ['Alex Kogan'] | 2023-01-12 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [-1.15581967e-01 -1.28548115e-01 -2.90260781e-02 -2.77374685e-01
-5.32977462e-01 -4.57240701e-01 4.36019033e-01 1.23344138e-02
-8.00404191e-01 6.35737240e-01 -4.47541684e-01 -7.29450226e-01
-8.69224295e-02 -9.69251633e-01 -5.49354672e-01 -7.65436471e-01
1.31775379e-01 9.59510207e-01 5.09392560e-01 2.59304762... | [8.487333297729492, 3.7595770359039307] |
75a66e91-ebff-4d0d-abbd-7412435c5731 | a-multi-task-deep-learning-model-for-the | 1812.00422 | null | http://arxiv.org/abs/1812.00422v1 | http://arxiv.org/pdf/1812.00422v1.pdf | A multi-task deep learning model for the classification of Age-related Macular Degeneration | Age-related Macular Degeneration (AMD) is a leading cause of blindness.
Although the Age-Related Eye Disease Study group previously developed a 9-step
AMD severity scale for manual classification of AMD severity from color fundus
images, manual grading of images is time-consuming and expensive. Built on our
previous wo... | ['Elvira Agron', 'Tiarnan Keenan', 'Wai T. Wong', 'Shazia Dharssi', 'Qingyu Chen', 'Emily Y. Chew', 'Yifan Peng', 'Zhiyong Lu'] | 2018-12-02 | null | null | null | null | ['classification-of-age-related-macular'] | ['medical'] | [ 7.00173751e-02 -9.50463265e-02 7.66669512e-02 -3.59220356e-01
-7.87867129e-01 -3.82931054e-01 1.81958914e-01 1.84609309e-01
-6.35594189e-01 6.55315101e-01 2.79167682e-01 -6.29546046e-01
-1.27920598e-01 -6.96722209e-01 -1.64814547e-01 -2.92205334e-01
-8.61493126e-02 2.75088191e-01 4.65651393e-01 3.24933499... | [15.822015762329102, -3.9960744380950928] |
3a8bc2c0-1317-4cb6-af3a-f6410cb788b9 | mcts-geb-monte-carlo-tree-search-is-a-good-e | 2303.04651 | null | https://arxiv.org/abs/2303.04651v3 | https://arxiv.org/pdf/2303.04651v3.pdf | MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder | Rewrite systems [6, 10, 12] have been widely employing equality saturation [9], which is an optimisation methodology that uses a saturated e-graph to represent all possible sequences of rewrite simultaneously, and then extracts the optimal one. As such, optimal results can be achieved by avoiding the phase-ordering pro... | ['Eiko Yoneki', 'Zak Singh', 'Guoliang He'] | 2023-03-08 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 2.55390927e-02 4.60197896e-01 -4.83300745e-01 1.51107579e-01
-8.22777748e-01 -6.08097970e-01 5.05419254e-01 -2.15228334e-01
-6.92022890e-02 8.29008877e-01 -6.85451552e-02 -1.02382839e+00
2.16305163e-02 -9.59636450e-01 -6.49903417e-01 -3.07268798e-01
1.47798821e-01 5.45563459e-01 6.43248200e-01 -6.57633305... | [8.457974433898926, 7.20605993270874] |
943e1638-4f51-4ef6-91bd-c6f16f7cf3d2 | meds-net-self-distilled-multi-encoders | 2211.00003 | null | https://arxiv.org/abs/2211.00003v2 | https://arxiv.org/pdf/2211.00003v2.pdf | MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection | In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed... | ['Yeong Gil Shin', 'Byung il Lee', 'Sung Hyun Kim', 'Byoung Dai Lee', 'Shi Sub Byon', 'Siddique Latif', 'Abdullah Shahid', 'Azka Rehman', 'Muhammad Usman'] | 2022-10-30 | null | null | null | null | ['lung-nodule-detection'] | ['medical'] | [ 3.02445740e-01 4.29715365e-01 -1.85857370e-01 -5.91590255e-02
-6.92852676e-01 -2.88837224e-01 2.71859735e-01 -1.66679308e-01
-4.07493830e-01 4.60559368e-01 2.46413305e-01 -5.77304006e-01
-6.66215569e-02 -9.19016957e-01 -6.96081936e-01 -7.35533893e-01
-1.26928743e-02 2.32764333e-01 7.25282252e-01 4.08763111... | [15.345739364624023, -2.11576509475708] |
b168be21-0a5a-45db-baac-29dec49b8398 | diversification-quotient-measuring | 2206.13679 | null | https://arxiv.org/abs/2206.13679v4 | https://arxiv.org/pdf/2206.13679v4.pdf | Diversification quotients: Quantifying diversification via risk measures | We establish the first axiomatic theory for diversification indices using six intuitive axioms -- non-negativity, location invariance, scale invariance, rationality, normalization, and continuity -- together with risk measures. The unique class of indices satisfying these axioms, called the diversification quotients (D... | ['Ruodu Wang', 'Liyuan Lin', 'Xia Han'] | 2022-06-28 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-5.68217516e-01 -2.49286890e-01 -4.54372764e-01 -2.49107450e-01
1.39535949e-01 -8.39921832e-01 2.80339062e-01 -1.48741961e-01
-2.22842172e-01 6.52250230e-01 2.12513968e-01 -5.26794910e-01
-9.06009138e-01 -1.09793198e+00 4.59106117e-01 -6.26654863e-01
-3.39498580e-01 4.87073570e-01 2.96407789e-01 -5.72753668... | [4.965508460998535, 3.9753313064575195] |
abcafc4c-84b9-4b4d-9620-7bc75f6de9cb | a-problem-reduction-approach-for-visual | 1809.09828 | null | http://arxiv.org/abs/1809.09828v1 | http://arxiv.org/pdf/1809.09828v1.pdf | A Problem Reduction Approach for Visual Relationships Detection | Identifying different objects (man and cup) is an important problem on its
own, but identifying the relationship between them (holding) is critical for
many real world use cases. This paper describes an approach to reduce a visual
relationship detection problem to object detection problems. The method was
applied to Go... | ['Toshiyuki Fukuzawa'] | 2018-09-26 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 1.06021119e-02 1.30215352e-02 -4.47837859e-02 -3.29479247e-01
-2.08896816e-01 -5.37914753e-01 7.89333165e-01 5.64334691e-01
-1.21436298e-01 2.00096250e-01 -8.56608972e-02 -4.16060202e-02
-1.81814089e-01 -4.99859393e-01 -6.31312668e-01 -1.46317765e-01
-2.57019103e-01 7.72280693e-01 6.10917866e-01 -3.87257576... | [10.244418144226074, 1.6171537637710571] |
b33bfa55-4d8a-422a-ac88-318292a66555 | ddx7-differentiable-fm-synthesis-of-musical | 2208.06169 | null | https://arxiv.org/abs/2208.06169v1 | https://arxiv.org/pdf/2208.06169v1.pdf | DDX7: Differentiable FM Synthesis of Musical Instrument Sounds | FM Synthesis is a well-known algorithm used to generate complex timbre from a compact set of design primitives. Typically featuring a MIDI interface, it is usually impractical to control it from an audio source. On the other hand, Differentiable Digital Signal Processing (DDSP) has enabled nuanced audio rendering by De... | ['Mark Sandler', 'Andrew McPherson', 'Franco Caspe'] | 2022-08-12 | null | null | null | null | ['spectral-reconstruction'] | ['computer-vision'] | [ 6.01608098e-01 1.02488779e-01 1.65754661e-01 -9.28694978e-02
-1.13485861e+00 -6.12176478e-01 4.23262328e-01 -4.27424729e-01
-8.01212117e-02 6.01146519e-01 5.93790971e-02 -1.82647169e-01
-1.96278811e-01 -4.78406399e-01 -1.01274383e+00 -5.36387742e-01
3.22396345e-02 2.04649180e-01 -4.00568843e-01 -4.66023654... | [15.634758949279785, 5.928028106689453] |
31f20be1-a93e-42a4-9992-8aa0b443db36 | umutextstats-a-linguistic-feature-extraction | null | null | https://aclanthology.org/2022.lrec-1.649 | https://aclanthology.org/2022.lrec-1.649.pdf | UMUTextStats: A linguistic feature extraction tool for Spanish | Feature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of the art concerning automatic document classification tasks relies on word and sentence embeddings built upon de... | ['Rafael Valencia-García', 'Ángela Almela', 'Pedro José Vivancos-Vicente', 'José Antonio García-Díaz'] | null | null | null | null | lrec-2022-6 | ['sentence-embeddings', 'sentence-embeddings', 'document-classification', 'authorship-verification'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-4.31576997e-01 -7.33657256e-02 -5.83955869e-02 -1.33835733e-01
-6.05958700e-02 -4.19674635e-01 1.04529607e+00 7.60172784e-01
-6.27256870e-01 6.11480951e-01 2.53910214e-01 -2.11986482e-01
-6.97112828e-02 -8.15203667e-01 -2.64050178e-02 -5.45243382e-01
1.95796549e-01 3.20041209e-01 3.00853830e-02 -4.20516312... | [9.784337997436523, 10.050958633422852] |
c273a3d3-a4df-4dbe-aa00-e4ae40b108b3 | deeper-task-specificity-improves-joint-entity | 2002.06424 | null | https://arxiv.org/abs/2002.06424v1 | https://arxiv.org/pdf/2002.06424v1.pdf | Deeper Task-Specificity Improves Joint Entity and Relation Extraction | Multi-task learning (MTL) is an effective method for learning related tasks, but designing MTL models necessitates deciding which and how many parameters should be task-specific, as opposed to shared between tasks. We investigate this issue for the problem of jointly learning named entity recognition (NER) and relation... | ['Phil Crone'] | 2020-02-15 | null | null | null | null | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.39233936e-02 1.29089534e-01 -6.48305267e-02 -5.27416050e-01
-1.14304626e+00 -7.49112666e-01 8.69539440e-01 -1.61980093e-01
-1.08381748e+00 7.80194819e-01 3.44690591e-01 -4.41727936e-01
-2.71144181e-01 -3.36134762e-01 -5.72052777e-01 -2.73156017e-01
1.36013210e-01 7.30728745e-01 2.17899173e-01 -2.17086449... | [10.052563667297363, 9.453882217407227] |
b66bb078-69e3-40d9-9dde-1fb9e45973e3 | physical-energy-cost-serves-as-the-invisible | 2306.02328 | null | https://arxiv.org/abs/2306.02328v1 | https://arxiv.org/pdf/2306.02328v1.pdf | Physical energy cost serves as the ''invisible hand'' governing economic valuation: Direct evidence from biogeochemical data and the U.S. metal market | Energy supply is mandatory for the production of economic value. Nevertheless, tradition dictates that an enigmatic 'invisible hand' governs economic valuation. Physical scientists have long proposed alternative but testable energy cost theories of economic valuation, and have shown the gross correlation between energy... | ['Zhicen Liu'] | 2023-06-04 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [-1.45179421e-01 -1.45184919e-01 -5.83460450e-01 2.20858991e-01
-7.72924442e-03 -5.61682045e-01 8.16160440e-01 2.21455231e-01
-5.75218379e-01 8.88035953e-01 9.43005905e-02 -6.12932682e-01
-3.42006922e-01 -1.18724608e+00 -3.57761055e-01 -8.80750477e-01
1.20282367e-01 2.31829762e-01 -6.23456612e-02 -4.36412334... | [5.602479934692383, 3.9051625728607178] |
0a0e1249-ad4c-4f9c-9b89-4810cf987ee0 | high-resolution-talking-face-generation-via | 1812.06589 | null | https://arxiv.org/abs/1812.06589v2 | https://arxiv.org/pdf/1812.06589v2.pdf | Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning | Talking face generation aims to synthesize a face video with precise lip synchronization as well as a smooth transition of facial motion over the entire video via the given speech clip and facial image. Most existing methods mainly focus on either disentangling the information in a single image or learning temporal inf... | ['Ran He', 'Aihua Zheng', 'Huaibo Huang', 'Yi Li', 'Hao Zhu'] | 2018-12-17 | null | null | null | null | ['talking-face-generation'] | ['computer-vision'] | [ 3.11879724e-01 -4.28783596e-02 -2.73249120e-01 -4.18704748e-01
-1.06524789e+00 -2.70625502e-01 7.60317981e-01 -7.79860079e-01
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-7.20771030e-02 -1.58566594e-01 -6.64778411e-01 -9.10775483e-01
2.57545024e-01 -1.01787470e-01 -2.32125834e-01 1.03212148... | [13.276350021362305, -0.3165101706981659] |
ce7e0a26-547e-4896-83fa-e418fbbd94d9 | adaptive-probabilistic-forecasting-of | 2301.10090 | null | https://arxiv.org/abs/2301.10090v2 | https://arxiv.org/pdf/2301.10090v2.pdf | Adaptive Probabilistic Forecasting of Electricity (Net-)Load | Electricity load forecasting is a necessary capability for power system operators and electricity market participants. The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modellin... | ['Olivier Wintenberger', 'Yannig Goude', 'Matteo Fasiolo', 'Jethro Browell', 'Joseph de Vilmarest'] | 2023-01-24 | null | null | null | null | ['load-forecasting'] | ['miscellaneous'] | [-2.31207833e-01 -5.67050017e-02 -1.74325295e-02 -3.50602776e-01
-7.97070324e-01 -7.83254504e-01 7.89617717e-01 1.39062926e-01
-1.65052190e-01 9.89246190e-01 3.71715635e-01 -5.91284513e-01
-4.74156350e-01 -1.05977595e+00 -2.90203899e-01 -8.00045669e-01
-2.03897566e-01 7.20228553e-01 -2.29188293e-01 -1.26910418... | [6.1141133308410645, 2.9525198936462402] |
59455a38-d516-40d2-b49a-09f058bec5f5 | a-study-on-extracting-named-entities-from | 2212.03749 | null | https://arxiv.org/abs/2212.03749v1 | https://arxiv.org/pdf/2212.03749v1.pdf | A Study on Extracting Named Entities from Fine-tuned vs. Differentially Private Fine-tuned BERT Models | Privacy preserving deep learning is an emerging field in machine learning that aims to mitigate the privacy risks in the use of deep neural networks. One such risk is training data extraction from language models that have been trained on datasets , which contain personal and privacy sensitive information. In our study... | ['Ansgar Scherp', 'Aygul Garifullina', 'Nicolas Lell', 'Andor Diera'] | 2022-12-07 | null | null | null | null | ['privacy-preserving-deep-learning', 'memorization', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.29332960e-01 3.22236717e-01 3.05020697e-02 -5.89532495e-01
-7.25741327e-01 -1.15919733e+00 7.01744616e-01 3.20288002e-01
-7.47726023e-01 8.13943744e-01 7.88872391e-02 -5.99139094e-01
1.44779682e-01 -1.09554744e+00 -1.03527689e+00 -6.02388322e-01
-4.31114919e-02 3.30523461e-01 -8.03771801e-03 -4.91301976... | [5.988544464111328, 7.06138801574707] |
153b1264-468d-4072-b3ec-7a5b28b05644 | autodime-automatic-design-of-interesting | 2203.02481 | null | https://arxiv.org/abs/2203.02481v1 | https://arxiv.org/pdf/2203.02481v1.pdf | AutoDIME: Automatic Design of Interesting Multi-Agent Environments | Designing a distribution of environments in which RL agents can learn interesting and useful skills is a challenging and poorly understood task, for multi-agent environments the difficulties are only exacerbated. One approach is to train a second RL agent, called a teacher, who samples environments that are conducive f... | ['Harri Edwards', 'Ingmar Kanitscheider'] | 2022-03-04 | null | null | null | null | ['value-prediction'] | ['computer-code'] | [-1.84743315e-01 2.72455007e-01 3.78201120e-02 -1.40063614e-01
-9.30058360e-01 -8.13403606e-01 6.09294116e-01 2.41572544e-01
-8.18776190e-01 1.30241811e+00 -4.14399877e-02 -1.76653504e-01
-7.14489877e-01 -4.71568882e-01 -5.48224449e-01 -9.28594708e-01
-1.32174194e-01 1.05460250e+00 2.06964374e-01 -4.44710135... | [3.950336217880249, 1.678794503211975] |
2ee5c142-e03e-47af-a636-146cbd7a313b | cross-corpus-data-augmentation-for-acoustic | null | null | https://aclanthology.org/W19-5933 | https://aclanthology.org/W19-5933.pdf | Cross-Corpus Data Augmentation for Acoustic Addressee Detection | Acoustic addressee detection (AD) is a modern paralinguistic and dialogue challenge that especially arises in voice assistants. In the present study, we distinguish addressees in two settings (a conversation between several people and a spoken dialogue system, and a conversation between several adults and a child) and ... | ['Ingo Siegert', 'Wolfgang Minker', 'Oleg Akhtiamov', 'Alexey Karpov'] | 2019-09-01 | null | null | null | ws-2019-9 | ['cross-corpus'] | ['computer-vision'] | [ 4.98585105e-01 4.03959155e-01 4.02101785e-01 -4.65025544e-01
-1.11974752e+00 -5.31189620e-01 8.41222644e-01 3.12538683e-01
-7.22873688e-01 3.97752225e-01 5.14832199e-01 -1.71537533e-01
2.71879602e-03 -4.02837157e-01 -1.51091993e-01 -6.44536138e-01
-8.33648592e-02 7.38343120e-01 1.88145965e-01 -3.14586639... | [14.429047584533691, 6.4262166023254395] |
f830d852-2663-41fd-8b8e-85ceaafd0ee4 | code-synonyms-do-matter-multiple-synonyms | null | null | https://openreview.net/forum?id=kXo7lEh7OaX | https://openreview.net/pdf?id=kXo7lEh7OaX | Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding | Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs).
Existing methods apply label attention with code representations to match related text snippets for coding.
Unlike these works that model the label with the code hierarchy or description, we argue that the code synonyms can... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['code-classification'] | ['computer-code'] | [ 1.69970691e-01 2.07587749e-01 -9.70798016e-01 -5.31729817e-01
-6.41611636e-01 -5.53015709e-01 7.29565024e-02 9.45715129e-01
1.27859995e-01 2.38713324e-01 8.40733111e-01 -3.33569497e-01
-4.50078994e-01 -5.78379691e-01 -8.87842700e-02 -4.16533928e-03
6.78960606e-02 4.41675931e-01 -2.75743902e-01 -1.00508677... | [8.001913070678711, 6.870965480804443] |
171fe629-029e-4267-9207-f907f981ebd6 | fc4-fully-convolutional-color-constancy-with | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Hu_FC4_Fully_Convolutional_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Hu_FC4_Fully_Convolutional_CVPR_2017_paper.pdf | FC4: Fully Convolutional Color Constancy With Confidence-Weighted Pooling | Improvements in color constancy have arisen from the use of convolutional neural networks (CNNs). However, the patch-based CNNs that exist for this problem are faced with the issue of estimation ambiguity, where a patch may contain insufficient information to establish a unique or even a limited possible range of illum... | ['Stephen Lin', 'Baoyuan Wang', 'Yuanming Hu'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['color-constancy'] | ['computer-vision'] | [ 2.38625765e-01 -3.05213183e-01 2.87474953e-02 -5.76536596e-01
-5.67325592e-01 -4.24799770e-01 1.89066112e-01 2.59399880e-02
-5.05726278e-01 8.03747833e-01 -2.57090300e-01 5.29359430e-02
5.41016087e-02 -7.98527896e-01 -7.98134565e-01 -9.17080045e-01
1.97674215e-01 -2.20707566e-01 3.55032116e-01 1.41859561... | [10.535200119018555, -2.49868106842041] |
6550ad54-6974-49e7-a5af-00a6c8f2fd67 | justifying-and-improving-meta-agent-conflict | 1410.6519 | null | http://arxiv.org/abs/1410.6519v1 | http://arxiv.org/pdf/1410.6519v1.pdf | Justifying and Improving Meta-Agent Conflict-Based Search | The Meta-Agent Conflict-Based Search~(MA-CBS) is a recently proposed
algorithm for the multi-agent path finding problem. The algorithm is an
extension of Conflict-Based Search~(CBS), which automatically merges
conflicting agents into meta-agents if the number of conflicts exceeds a
certain threshold. However, the decis... | ['David Tolpin'] | 2014-10-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 1.57230169e-01 5.90433665e-02 -2.85032421e-01 -1.47077352e-01
-5.83080314e-02 -5.91143489e-01 8.02450538e-01 5.27313173e-01
-6.60959184e-01 1.11671221e+00 -6.74623400e-02 -3.47757667e-01
-6.02668941e-01 -7.91902125e-01 3.60782184e-02 -6.34956062e-01
-3.37935030e-01 1.01282954e+00 9.76542056e-01 -6.44859552... | [4.982121467590332, 1.8119441270828247] |
397d629e-722d-433f-9968-752c379beec0 | interactive-shadow-removal-and-ground-truth | null | null | https://www.osapublishing.org/abstract.cfm?uri=josaa-33-9-1798 | https://arxiv.org/pdf/1608.00762 | Interactive Shadow Removal and Ground Truth for Difficult Shadow Scenes | A user-centric method for fast, interactive, robust and high-quality shadow removal is presented. Our algorithm can perform detection and removal in a range of difficult cases: such as highly textured and colored shadows. To perform detection an on-the-fly learning approach is adopted guided by two rough user inputs fo... | ['Han Gong; Darren Cosker'] | 2016-09-01 | null | null | null | josa-a-2016-9 | ['shadow-removal'] | ['computer-vision'] | [ 8.59075189e-01 -2.26960167e-01 4.33357120e-01 -3.92069280e-01
-4.45509881e-01 -5.79111040e-01 5.17217875e-01 -8.91917348e-02
-2.38362849e-01 8.83284628e-01 2.81475447e-02 -3.86218816e-01
2.44914427e-01 -3.76037657e-01 -2.87836730e-01 -9.34368849e-01
-8.96410272e-02 5.06206632e-01 8.67603123e-01 -3.00287336... | [10.816987991333008, -4.067660808563232] |
f8f9397b-f0c7-4a00-9da8-5d1ddaf8150f | multi-view-neural-surface-reconstruction-with | 2211.11971 | null | https://arxiv.org/abs/2211.11971v1 | https://arxiv.org/pdf/2211.11971v1.pdf | Multi-View Neural Surface Reconstruction with Structured Light | Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. DR-based methods minimize the difference between the rendered and target images by optimizing both the shape and appearance and realizing a high visual reproductivity. However, most approa... | ['Hiroharu Kato', 'Eiichi Matsumoto', 'Taisuke Hashimoto', 'Chunyu Li'] | 2022-11-22 | null | null | null | null | ['3d-object-reconstruction', 'object-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 6.36716664e-01 -1.24526024e-01 2.58865923e-01 -2.51548320e-01
-6.43598139e-01 -5.48606157e-01 2.75953561e-01 -4.80972797e-01
-6.98041320e-02 3.38840097e-01 -1.61098346e-01 1.67247787e-01
9.81916264e-02 -6.49741769e-01 -6.25066161e-01 -9.79210675e-01
7.44173527e-01 3.62465918e-01 2.46366426e-01 1.23161869... | [9.529609680175781, -2.9156582355499268] |
49161d03-8fed-4b12-a94b-827ed78dada8 | nibbling-at-the-hard-core-of-word-sense | null | null | https://aclanthology.org/2022.acl-long.324 | https://aclanthology.org/2022.acl-long.324.pdf | Nibbling at the Hard Core of Word Sense Disambiguation | With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models. And yet, i... | ['Roberto Navigli', 'Michele Bevilacqua', 'Simone Conia', 'Marco Maru'] | null | null | null | null | acl-2022-5 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 2.67151505e-01 2.25915864e-01 1.54232398e-01 -2.78333157e-01
-7.60826647e-01 -6.75947726e-01 8.63590896e-01 5.36735892e-01
-8.47553790e-01 6.93349004e-01 1.74353436e-01 -6.10863805e-01
-2.08039582e-01 -5.61105609e-01 -2.42649779e-01 -2.80913889e-01
-1.77883711e-02 7.45042801e-01 3.87949109e-01 -9.02060330... | [10.197488784790039, 9.020938873291016] |
09b05c26-b49f-4eaa-a630-2340d58fc0e2 | detection-and-segmentation-of-lesion-areas-in | null | null | https://www.medrxiv.org/content/10.1101/2020.10.23.20218461v1 | https://www.medrxiv.org/content/10.1101/2020.10.23.20218461v1.full.pdf | Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19 | In this paper we compare the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans. These lesion areas are often associated both with common pneumonia and COVID-19. We train a Mask R-CNN model to segment these areas with high accuracy using three approaches: merging masks... | ['Aram Ter-Sarkisov'] | 2020-10-26 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 2.27865234e-01 5.16336337e-02 -1.31842971e-01 -2.72092372e-01
-8.95440102e-01 -4.85599250e-01 1.55991167e-01 2.23327577e-01
-5.56869209e-01 4.44347918e-01 -3.02202292e-02 -5.94841063e-01
-2.39114091e-01 -6.35728359e-01 -6.68432355e-01 -7.74034142e-01
-1.65041648e-02 1.05352664e+00 5.89239895e-01 7.16388285... | [15.384324073791504, -1.88983952999115] |
e9b8f57f-d7a7-4f2c-aadc-d1b2076bfb54 | data-augmentation-for-conflict-and-duplicate | 2305.09608 | null | https://arxiv.org/abs/2305.09608v1 | https://arxiv.org/pdf/2305.09608v1.pdf | Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs | This paper explores the use of text data augmentation techniques to enhance conflict and duplicate detection in software engineering tasks through sentence pair classification. The study adapts generic augmentation techniques such as shuffling, back translation, and paraphrasing and proposes new data augmentation techn... | ['Ayşe Başar', 'Mucahit Cevik', 'Garima Malik'] | 2023-05-16 | null | null | null | null | ['sentence-pair-classification'] | ['natural-language-processing'] | [ 5.80504298e-01 -1.01445643e-02 -7.76860416e-02 -5.97991526e-01
-4.20569599e-01 -2.33998597e-01 3.80532622e-01 6.30003691e-01
-1.76069915e-01 5.03973722e-01 2.19376162e-01 -5.73513448e-01
7.28719234e-02 -4.19044286e-01 -3.32702279e-01 -3.55378632e-03
2.92997152e-01 8.38271752e-02 -5.46321720e-02 -7.84597933... | [7.800215721130371, 7.913950443267822] |
dfc70f8a-5343-477c-8333-d83b4d8f064b | integrating-pre-trained-model-into-rule-based | 2102.08553 | null | https://arxiv.org/abs/2102.08553v1 | https://arxiv.org/pdf/2102.08553v1.pdf | Integrating Pre-trained Model into Rule-based Dialogue Management | Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to maintain the dialogue logic when the scenarios get more and more complex. On the other hand, data-driven dialogue systems, usually with end-t... | ['Daxin Jiang', 'Yang Yang', 'Yongzhi Li', 'Ruiling Deng', 'Jun Tian', 'Fangxin Ouyang', 'Yuchen Dong', 'Yiming Liu', 'Deyi Xiong', 'Qiang Gan', 'Meng Yang', 'Jun Quan'] | 2021-02-17 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-1.98720574e-01 5.17510831e-01 4.24655601e-02 -6.22692943e-01
-2.66929299e-01 -6.77593052e-01 7.80921280e-01 -2.01174140e-01
2.80072838e-02 7.82992601e-01 2.82029420e-01 -5.16404390e-01
4.44951560e-03 -6.83542371e-01 8.30747485e-02 1.16588315e-03
3.37013423e-01 5.36441684e-01 4.58085060e-01 -9.57921386... | [12.880621910095215, 7.982189178466797] |
961b072f-97d3-492a-8fc9-7eea32e337f0 | co-driven-recognition-of-semantic-consistency | 2302.10570 | null | https://arxiv.org/abs/2302.10570v1 | https://arxiv.org/pdf/2302.10570v1.pdf | Co-Driven Recognition of Semantic Consistency via the Fusion of Transformer and HowNet Sememes Knowledge | Semantic consistency recognition aims to detect and judge whether the semantics of two text sentences are consistent with each other. However, the existing methods usually encounter the challenges of synonyms, polysemy and difficulty to understand long text. To solve the above problems, this paper proposes a co-driven ... | ['Ruixian He', 'Jinxuan Zhu', 'Kang Luo', 'Xinfang Zhang', 'Yan Huang', 'Fan Chen'] | 2023-02-21 | null | null | null | null | ['text-matching', 'paraphrase-identification'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.47616370e-02 -1.79451048e-01 1.38331994e-01 -4.87521350e-01
-4.35109347e-01 -2.21114233e-01 5.89675725e-01 3.90443802e-01
-5.57691872e-01 3.16461176e-01 2.23526925e-01 -2.75816411e-01
-1.84725776e-01 -7.93308735e-01 -7.26289570e-01 -2.05988705e-01
6.36519849e-01 5.23419738e-01 1.05694691e-02 -5.00260949... | [11.00013256072998, 8.3488130569458] |
6cb016fd-3f82-48c7-b8c7-107975f9fbe6 | h2tne-temporal-heterogeneous-information | 2304.06970 | null | https://arxiv.org/abs/2304.06970v2 | https://arxiv.org/pdf/2304.06970v2.pdf | H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces | Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN... | ['Xiaojie Yuan', 'Lin Zhang', 'Changli Nie', 'Haiwei Zhang', 'JiaWen Guo', 'Qijie Bai'] | 2023-04-14 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-3.38874251e-01 -2.62207165e-02 -3.29716265e-01 -1.59761578e-01
-3.47947255e-02 -4.65872169e-01 5.82381070e-01 2.09201828e-01
-1.61790162e-01 4.23115700e-01 4.01322752e-01 -3.37971777e-01
-9.18280005e-01 -1.13351750e+00 6.83257952e-02 -9.75315988e-01
-6.96493983e-01 2.65071422e-01 6.45575702e-01 -2.86198884... | [7.223080158233643, 6.123502254486084] |
1b23e7ec-d054-4106-a3de-5b418d14e5a3 | detection-of-epilepsy-seizure-using-different | 2302.12012 | null | https://arxiv.org/abs/2302.12012v1 | https://arxiv.org/pdf/2302.12012v1.pdf | Detection of Epilepsy Seizure using Different Dimensionality Reduction Techniques and Machine Learning on Transform Domain | An Electroencephalogram (EEG) is a non-invasive exam that records the electrical activity of the brain. This exam is used to help diagnose conditions such as different brain problems. EEG signals are taken for the purpose of epilepsy detection and with Discrete Wavelet Transform (DWT) and machine learning classifier, t... | ['Suparna Biswas', 'Nanda Dulal Jana', 'Rabel Guharoy'] | 2023-02-17 | null | null | null | null | ['seizure-detection'] | ['medical'] | [-1.65887773e-01 -6.09168410e-01 2.60955065e-01 -2.31360689e-01
-1.77886799e-01 -3.94888401e-01 2.60647655e-01 1.97641850e-01
-2.95295864e-01 1.07213867e+00 2.99220651e-01 -4.28920030e-04
-7.15739608e-01 -5.38580000e-01 1.49239421e-01 -9.48590040e-01
-5.42519450e-01 3.42794955e-01 -5.12713976e-02 1.30044430... | [13.374039649963379, 3.368251323699951] |
d1dc32e9-7041-4f7e-93be-710a72735970 | a-high-efficiency-framework-for-constructing | 1905.04830 | null | https://arxiv.org/abs/1905.04830v1 | https://arxiv.org/pdf/1905.04830v1.pdf | A High-Efficiency Framework for Constructing Large-Scale Face Parsing Benchmark | Face parsing, which is to assign a semantic label to each pixel in face images, has recently attracted increasing interest due to its huge application potentials. Although many face related fields (e.g., face recognition and face detection) have been well studied for many years, the existing datasets for face parsing a... | ['Hailin Shi', 'Yue Si', 'Yinglu Liu', 'Xiaobo Wang', 'Tao Mei', 'Hao Shen'] | 2019-05-13 | null | null | null | null | ['face-parsing'] | ['computer-vision'] | [ 2.21413642e-01 9.84180793e-02 -1.16318397e-01 -8.40056598e-01
-7.93218493e-01 -3.47357005e-01 2.09160671e-01 -4.53773528e-01
-2.43946359e-01 4.87216651e-01 -1.62880063e-01 1.01943865e-01
2.06355602e-01 -7.33352423e-01 -5.79974771e-01 -7.26005971e-01
3.26114088e-01 4.65019822e-01 1.75145969e-01 8.98532793... | [13.440773010253906, 0.6544033288955688] |
64db2180-8301-4f69-b90f-f50cf0925c50 | demonstration-of-the-emotewizard-of-oz | null | null | https://aclanthology.org/W13-4058 | https://aclanthology.org/W13-4058.pdf | Demonstration of the EmoteWizard of Oz Interface for Empathic Robotic Tutors | null | ['Ruth Aylett', 'Srinivasan Janarthanam', 'Ginevra Castellano', 'Helen Hastie', 'Amol Deshmukh', 'Shweta Bhargava', 'Lee Corrigan'] | 2013-08-01 | null | null | null | ws-2013-8 | ['gesture-generation'] | ['robots'] | [-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... | [-7.215083599090576, 3.814117908477783] |
43cf9eb9-7395-4b9f-a581-fdd6042b65b9 | energy-bounded-learning-for-robust-models-of | 2112.11226 | null | https://arxiv.org/abs/2112.11226v2 | https://arxiv.org/pdf/2112.11226v2.pdf | Energy-bounded Learning for Robust Models of Code | In programming, learning code representations has a variety of applications, including code classification, code search, comment generation, bug prediction, and so on. Various representations of code in terms of tokens, syntax trees, dependency graphs, code navigation paths, or a combination of their variants have been... | ['Yijun Yu', 'Nghi D. Q. Bui'] | 2021-12-20 | null | null | null | null | ['code-classification', 'code-search', 'code-search', 'comment-generation'] | ['computer-code', 'computer-code', 'computer-vision', 'natural-language-processing'] | [ 1.00557052e-03 1.38718009e-01 -4.68154132e-01 -2.71832705e-01
-6.75742924e-01 -6.76919401e-01 3.22497934e-01 4.57923591e-01
2.01453775e-01 3.39037269e-01 -5.54073304e-02 -5.09366930e-01
1.33738860e-01 -8.24177921e-01 -7.55228221e-01 -3.74529958e-01
-2.08705455e-01 -2.04784542e-01 3.93845081e-01 1.93181783... | [7.194119930267334, 7.807507514953613] |
39e57f5b-4c9b-4386-a540-f5501e3b48e8 | learnable-differencing-center-for-nighttime | 2306.14538 | null | https://arxiv.org/abs/2306.14538v2 | https://arxiv.org/pdf/2306.14538v2.pdf | Learnable Differencing Center for Nighttime Depth Perception | Depth completion is the task of recovering dense depth maps from sparse ones, usually with the help of color images. Existing image-guided methods perform well on daytime depth perception self-driving benchmarks, but struggle in nighttime scenarios with poor visibility and complex illumination. To address these challen... | ['Jian Yang', 'Jun Li', 'Shuo Chen', 'Zhenyu Zhang', 'Xiang Li', 'Kun Wang', 'Yupeng Zheng', 'Zhiqiang Yan'] | 2023-06-26 | null | null | null | null | ['depth-estimation', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 2.19187587e-01 -2.13866815e-01 3.53824347e-01 -5.64626753e-01
-3.51209998e-01 -4.13614899e-01 5.71178138e-01 -3.66932929e-01
-5.71866810e-01 6.47521675e-01 3.77849877e-01 1.01973169e-01
2.47980475e-01 -6.91624761e-01 -6.44396186e-01 -1.06158245e+00
3.27065349e-01 -3.96987110e-01 2.29921058e-01 -1.76558629... | [9.399065971374512, -2.61946439743042] |
001503fc-3bde-4528-affd-bf1c7f19409b | tet-gan-text-effects-transfer-via-stylization | 1812.06384 | null | http://arxiv.org/abs/1812.06384v2 | http://arxiv.org/pdf/1812.06384v2.pdf | TET-GAN: Text Effects Transfer via Stylization and Destylization | Text effects transfer technology automatically makes the text dramatically
more impressive. However, previous style transfer methods either study the
model for general style, which cannot handle the highly-structured text effects
along the glyph, or require manual design of subtle matching criteria for text
effects. In... | ['Zongming Guo', 'Jiaying Liu', 'Shuai Yang', 'Wenjing Wang'] | 2018-12-16 | null | null | null | null | ['text-effects-transfer'] | ['natural-language-processing'] | [ 5.44625878e-01 -1.07358202e-01 4.86361086e-02 -2.92666286e-01
-3.27836037e-01 -6.48364723e-01 6.41150713e-01 -6.68706954e-01
3.82305495e-02 8.04479301e-01 4.17721093e-01 -5.13638742e-02
7.75017366e-02 -8.70746017e-01 -8.15542758e-01 -7.36045420e-01
6.53880119e-01 2.44929507e-01 -8.29281807e-02 -5.63996017... | [11.584521293640137, -0.46069133281707764] |
2e4a11a6-0029-449f-a534-4eeb37fe6301 | towards-applying-powerful-large-ai-models-in | 2305.03433 | null | https://arxiv.org/abs/2305.03433v2 | https://arxiv.org/pdf/2305.03433v2.pdf | Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospects | This perspective paper proposes a series of interactive scenarios that utilize Artificial Intelligence (AI) to enhance classroom teaching, such as dialogue auto-completion, knowledge and style transfer, and assessment of AI-generated content. By leveraging recent developments in Large Language Models (LLMs), we explore... | ['Song Yu', 'Chenyou Fan', 'Tianqi Pang', 'Kehui Tan'] | 2023-05-05 | null | null | null | null | ['style-transfer'] | ['computer-vision'] | [ 2.88733274e-01 7.91488230e-01 -7.99050853e-02 -5.20738006e-01
-8.14156055e-01 -1.00882900e+00 6.03058994e-01 3.59747320e-01
-1.51864424e-01 7.81383812e-01 4.63977605e-01 -7.67582238e-01
-9.44611281e-02 -7.83644617e-01 -4.45669174e-01 -2.48419628e-01
4.04048890e-01 6.54120207e-01 1.72321826e-01 -6.50691271... | [12.155712127685547, 8.104996681213379] |
261c3a49-eb70-419d-aae5-e8c9c4bb1f83 | variable-selection-with-copula-entropy | 1910.12389 | null | https://arxiv.org/abs/1910.12389v2 | https://arxiv.org/pdf/1910.12389v2.pdf | Variable Selection with Copula Entropy | Variable selection is of significant importance for classification and regression tasks in machine learning and statistical applications where both predictability and explainability are needed. In this paper, a Copula Entropy (CE) based method for variable selection which use CE based ranks to select variables is propo... | ['Jian Ma'] | 2019-10-28 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-2.49050371e-02 -3.27263474e-02 -4.82666612e-01 -5.68021834e-01
-5.92056751e-01 -2.59279132e-01 6.56768084e-02 2.58948863e-01
-1.46886900e-01 1.49824548e+00 7.02134296e-02 -4.58928764e-01
-6.95007443e-01 -5.86475194e-01 2.66629960e-02 -7.46114135e-01
-5.63106120e-01 6.61704421e-01 -4.68559086e-01 -3.13663259... | [7.835700035095215, 4.7720746994018555] |
99f3ee5f-c428-408a-89ee-722e58114226 | weakly-supervised-segmentation-using | 2206.05148 | null | https://arxiv.org/abs/2206.05148v1 | https://arxiv.org/pdf/2206.05148v1.pdf | Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification | Deep learning models have shown their potential for several applications. However, most of the models are opaque and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Co... | ['Oliver Speck', 'Andreas Nürnberger', 'Florian Dubost', 'Hadya Yassin', 'Soumick Chatterjee'] | 2022-06-10 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 3.49963605e-01 1.18884397e+00 -2.16093987e-01 -7.98077226e-01
-6.54707372e-01 -3.38609874e-01 4.74507183e-01 3.10641766e-01
-4.83399212e-01 6.73684835e-01 -1.43133894e-01 -6.57476664e-01
-1.64924756e-01 -6.46161318e-01 -7.23735571e-01 -1.06595683e+00
8.73873830e-02 7.37436831e-01 7.87868798e-02 2.21264109... | [14.73071002960205, -2.513576030731201] |
0b03ddaf-675b-4aeb-9e31-9b7929bc8e95 | scene-aware-prompt-for-multi-modal-dialogue | 2207.01823 | null | https://arxiv.org/abs/2207.01823v1 | https://arxiv.org/pdf/2207.01823v1.pdf | Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation | This paper introduces the schemes of Team LingJing's experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided into two phases: multi-modal context understanding and response generation. To fully leverage the visual information for both scene understa... | ['Shutao Li', 'Bin Sun', 'Ziyu Ma', 'Yixuan Weng', 'Bin Li'] | 2022-07-05 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 5.59307411e-02 3.25888276e-01 1.48888916e-01 -4.98512894e-01
-1.48944199e+00 -5.83836675e-01 1.22162032e+00 -4.05080378e-01
-1.71881750e-01 7.77395427e-01 9.25420821e-01 -3.78566116e-01
5.41466475e-01 -3.18286747e-01 -2.94025958e-01 -6.82066143e-01
3.54785889e-01 7.86730766e-01 2.67184317e-01 -4.29329813... | [11.005735397338867, 1.3695557117462158] |
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