paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ee41f843-eac7-4cb1-82bf-79eb60df3c0b | cma-clip-cross-modality-attention-clip-for | 2112.03562 | null | https://arxiv.org/abs/2112.03562v2 | https://arxiv.org/pdf/2112.03562v2.pdf | CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification | Modern Web systems such as social media and e-commerce contain rich contents expressed in images and text. Leveraging information from multi-modalities can improve the performance of machine learning tasks such as classification and recommendation. In this paper, we propose the Cross-Modality Attention Contrastive Lang... | ['Yi Sun', 'Bryan Wang', 'Chien-Chih Wang', 'Ning Xie', 'Yang Liu', 'Jinmiao Fu', 'Shaoyuan Xu', 'Huidong Liu'] | 2021-12-07 | null | null | null | null | ['multimodal-text-and-image-classification', 'image-text-classification'] | ['methodology', 'miscellaneous'] | [ 4.25802708e-01 -4.85083073e-01 -2.57390022e-01 -4.60600793e-01
-1.13412392e+00 -5.26302636e-01 6.98123097e-01 1.70900881e-01
-5.13890088e-01 2.72551686e-01 3.45399499e-01 -1.52980253e-01
7.14032724e-02 -5.78713894e-01 -1.11138761e+00 -6.27373695e-01
3.09352368e-01 -8.12126882e-03 -9.31295380e-02 -2.12037936... | [10.686193466186523, 1.4989436864852905] |
d4bb1cd3-0a43-45af-8625-0799a4467eac | facefusion-exploiting-full-spectrum-of | 2305.14601 | null | https://arxiv.org/abs/2305.14601v1 | https://arxiv.org/pdf/2305.14601v1.pdf | FaceFusion: Exploiting Full Spectrum of Multiple Datasets | The size of training dataset is known to be among the most dominating aspects of training high-performance face recognition embedding model. Building a large dataset from scratch could be cumbersome and time-intensive, while combining multiple already-built datasets poses the risk of introducing large amount of label n... | ['Dongjae Lee', 'Chiyoung Song'] | 2023-05-24 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 1.78065330e-01 3.54493968e-02 -4.44437489e-02 -4.36369896e-01
-5.69340646e-01 -6.49979234e-01 6.57484531e-01 -3.17841709e-01
-3.28659981e-01 6.46873057e-01 6.04464747e-02 1.48077104e-02
-1.28329664e-01 -6.99345231e-01 -4.87792522e-01 -7.97134042e-01
1.14487462e-01 4.47925121e-01 -2.07156494e-01 6.80444464... | [13.139633178710938, 0.699047327041626] |
7c108976-be1f-4e72-9b69-8bc1b1343ad3 | salprop-salient-object-proposals-via | 1706.04472 | null | http://arxiv.org/abs/1706.04472v1 | http://arxiv.org/pdf/1706.04472v1.pdf | SalProp: Salient object proposals via aggregated edge cues | In this paper, we propose a novel object proposal generation scheme by
formulating a graph-based salient edge classification framework that utilizes
the edge context. In the proposed method, we construct a Bayesian probabilistic
edge map to assign a saliency value to the edgelets by exploiting low level
edge features. ... | ['Brejesh lall', 'Prerana Mukherjee', 'Sarvaswa Tandon'] | 2017-06-14 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 1.10247605e-01 1.20358564e-01 -4.40553427e-01 -4.17337000e-01
-5.15268564e-01 -1.08147949e-01 4.96554732e-01 3.56915593e-01
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-2.28195731e-02 -8.71452034e-01 -5.79656422e-01 -4.93769586e-01
-9.82603207e-02 -9.71796736e-02 1.09254444e+00 2.29712576... | [9.420331954956055, 0.5657960176467896] |
fc7ba5b6-88f7-4183-bb76-c69f1f43db42 | point-to-the-expression-solving-algebraic | null | null | https://aclanthology.org/2020.emnlp-main.308 | https://aclanthology.org/2020.emnlp-main.308.pdf | Point to the Expression: Solving Algebraic Word Problems using the Expression-Pointer Transformer Model | Solving algebraic word problems has recently emerged as an important natural language processing task. To solve algebraic word problems, recent studies suggested neural models that generate solution equations by using {`}Op (operator/operand){'} tokens as a unit of input/output. However, such a neural model suffered tw... | ['Gahgene Gweon', 'Donggeon Lee', 'Kyung Seo Ki', 'Bugeun Kim'] | null | null | null | null | emnlp-2020-11 | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 1.44872189e-01 -1.26554072e-01 -1.89350024e-01 -1.78322718e-01
-5.22490799e-01 -5.55572271e-01 4.18281615e-01 1.41663879e-01
-6.76009119e-01 6.50224686e-01 -2.62162000e-01 -7.76214123e-01
-1.42176196e-01 -1.26795244e+00 -5.42003572e-01 -3.05392772e-01
5.88422315e-03 8.84104744e-02 7.81394690e-02 -3.93450677... | [9.767332077026367, 7.461395740509033] |
2fafb0e4-f938-4f5d-9f86-9336f62345a4 | aphmm-accelerating-profile-hidden-markov | 2207.09765 | null | https://arxiv.org/abs/2207.09765v1 | https://arxiv.org/pdf/2207.09765v1.pdf | ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis | Profile hidden Markov models (pHMMs) are widely used in many bioinformatics applications to accurately identify similarities between biological sequences (e.g., DNA or protein sequences). PHMMs use a commonly-adopted and highly-accurate method, called the Baum-Welch algorithm, to calculate these similarities. However, ... | ['Onur Mutlu', 'Sreenivas Subramoney', 'Juan Gómez Luna', 'Mohammed Alser', 'Joel Lindegger', 'Meryem Banu Cavlak', 'Taha Shahroodi', 'Jeremie Kim', 'Damla Senol Cali', 'Bharathwaj Suresh', 'Gurpreet S. Kalsi', 'Kamlesh Pillai', 'Can Firtina'] | 2022-07-20 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 1.18412092e-01 -6.26095235e-01 -1.22802034e-01 -8.79127234e-02
-8.61328840e-01 -2.59420693e-01 1.46003798e-01 4.22193408e-01
-4.01103228e-01 4.63610888e-01 -7.55342692e-02 -7.94968665e-01
3.43538165e-01 -6.04117990e-01 -6.39635921e-01 -9.41905677e-01
1.13791950e-01 4.25771385e-01 3.94583166e-01 9.23165381... | [8.354012489318848, 3.254936695098877] |
3be7b102-dc25-4b60-994d-86bf6de79c07 | learning-transferable-features-for-speech | 1912.11547 | null | https://arxiv.org/abs/1912.11547v1 | https://arxiv.org/pdf/1912.11547v1.pdf | Learning Transferable Features for Speech Emotion Recognition | Emotion recognition from speech is one of the key steps towards emotional intelligence in advanced human-machine interaction. Identifying emotions in human speech requires learning features that are robust and discriminative across diverse domains that differ in terms of language, spontaneity of speech, recording condi... | ['Nívio Ziviani', 'Alison Marczewski', 'Adriano Veloso'] | 2019-12-23 | null | null | null | null | ['emotional-intelligence'] | ['natural-language-processing'] | [ 1.76492602e-01 -8.68400410e-02 -4.75800000e-02 -9.54987824e-01
-8.16575825e-01 -7.83781111e-01 4.90641296e-01 -2.17457250e-01
-3.20488751e-01 6.30348980e-01 3.92390460e-01 1.34308398e-01
1.83728337e-01 -2.05760226e-01 -3.30934405e-01 -2.69868493e-01
-9.94054079e-02 2.83833951e-01 -4.36269492e-01 -4.40827250... | [13.58491325378418, 5.847331523895264] |
f4992cd5-e2ba-4932-8976-a7eed423adb8 | diachronic-word-embeddings-and-semantic | 1806.03537 | null | http://arxiv.org/abs/1806.03537v2 | http://arxiv.org/pdf/1806.03537v2.pdf | Diachronic word embeddings and semantic shifts: a survey | Recent years have witnessed a surge of publications aimed at tracing temporal
changes in lexical semantics using distributional methods, particularly
prediction-based word embedding models. However, this vein of research lacks
the cohesion, common terminology and shared practices of more established areas
of natural la... | ['Lilja Øvrelid', 'Andrey Kutuzov', 'Terrence Szymanski', 'Erik Velldal'] | 2018-06-09 | diachronic-word-embeddings-and-semantic-1 | https://aclanthology.org/C18-1117 | https://aclanthology.org/C18-1117.pdf | coling-2018-8 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [ 3.19450093e-03 -7.45170265e-02 -6.39882088e-01 -1.71676025e-01
-2.62528956e-01 -7.78421998e-01 9.11194384e-01 8.90890539e-01
-8.66019726e-01 3.62415642e-01 8.24483454e-01 -4.52332616e-01
-3.64744067e-01 -7.89872110e-01 1.18045703e-01 -2.64881015e-01
-2.26648167e-01 3.13045919e-01 1.39482528e-01 -6.22321129... | [10.244091033935547, 8.891861915588379] |
e82746cd-e465-4f30-8d1a-d68d3a2cdbff | towards-enriched-controllability-for | 2306.14917 | null | https://arxiv.org/abs/2306.14917v1 | https://arxiv.org/pdf/2306.14917v1.pdf | Towards Enriched Controllability for Educational Question Generation | Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of co... | ['Henrique Lopes Cardoso', 'Bernardo Leite'] | 2023-06-21 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.21885532e-01 9.76444364e-01 1.35227274e-02 -2.78671503e-01
-8.99276733e-01 -8.98885250e-01 9.35316741e-01 6.36882663e-01
-2.22240146e-02 9.45632875e-01 1.14710128e+00 -4.79300320e-01
-4.13011491e-01 -1.27565348e+00 -6.26389086e-01 3.34847271e-02
4.12456185e-01 4.70127225e-01 1.84848577e-01 -4.86924142... | [11.542010307312012, 8.119457244873047] |
48d01c93-ea8c-45e7-9d06-e65a52ad04ed | bilingual-low-resource-neural-machine | null | null | https://aclanthology.org/R19-1003 | https://aclanthology.org/R19-1003.pdf | Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish | The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually high-resource scenarios but are inadequate for low-resource conditions. This paper describes a round-... | ['Bonnie Dorr', 'Benyamin Ahmadnia'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [-2.29125172e-01 -2.57032871e-01 -7.44150400e-01 -3.08650821e-01
-1.44732869e+00 -8.17809582e-01 7.83947408e-01 -4.26431477e-01
-8.17957580e-01 1.37511420e+00 5.13209939e-01 -8.60938609e-01
4.23299134e-01 -4.40710902e-01 -9.23587799e-01 -1.48640066e-01
5.44822752e-01 9.39611673e-01 -6.06933713e-01 -7.06400394... | [11.578033447265625, 10.325313568115234] |
cd02087e-042a-4914-933c-2d98d21a6dd8 | styletts-2-towards-human-level-text-to-speech | 2306.07691 | null | https://arxiv.org/abs/2306.07691v1 | https://arxiv.org/pdf/2306.07691v1.pdf | StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models | In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis. StyleTTS 2 differs from its predecessor by modeling styles as a latent random variable through diffusion models to gen... | ['Nima Mesgarani', 'Gavin Mischler', 'Vinay S. Raghavan', 'Cong Han', 'Yinghao Aaron Li'] | 2023-06-13 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.27011940e-01 2.49452099e-01 -1.39186904e-01 -3.73860598e-01
-1.28272402e+00 -9.65476751e-01 6.07653916e-01 -6.26089990e-01
-2.81887829e-01 4.07935083e-01 5.85815191e-01 -6.26974702e-01
5.05068004e-01 -1.78608626e-01 -4.69193280e-01 -4.91748810e-01
4.49827433e-01 5.33996522e-01 5.44124562e-03 -3.64232928... | [14.938545227050781, 6.54503059387207] |
aeb9b158-b6d0-4ece-8933-8cbb6ebdf4d9 | live-detection-of-face-using-machine-learning | null | null | https://link.springer.com/article/10.1007/s11277-018-5913-0 | https://doi.org/10.1007/s11277-018-5913-0 | Live Detection of Face Using Machine Learning with Multi-feature Method | Facial expression detection (FED) and extraction show the most important role in face recognition. This research proposed a new algorithm for automatic live FED using radial basis function; Haar discrete wavelet transform and Gray-level difference method is used for feature extraction and classification. Detect edges o... | ['Jagdish Kumar', 'Sukhwinder Singh', 'Sandeep Kumar'] | 2018-07-27 | null | null | null | null | ['face-image-quality'] | ['computer-vision'] | [ 1.75290376e-01 -4.02721554e-01 -9.03494284e-02 -5.92380524e-01
-4.44995493e-01 -1.18262038e-01 1.18896820e-01 -6.11193299e-01
-7.16806591e-01 8.08350325e-01 1.62592605e-02 2.05690116e-01
-2.22582355e-01 -7.97615290e-01 -1.18233198e-02 -1.03235865e+00
-1.28819361e-01 1.94374904e-01 -1.30553037e-01 -2.80190438... | [13.25735092163086, 0.9300004243850708] |
33c71481-b7d1-4538-96df-28af52c3f816 | multi-agent-reinforcement-learning-for-14 | 2211.16385 | null | https://arxiv.org/abs/2211.16385v1 | https://arxiv.org/pdf/2211.16385v1.pdf | Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration | Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fine-tuning architectural parameters across multiple sub-systems (e.g., datapath, memory blocks in different hierarchies, interconnects, compi... | ['Aleksandra Faust', 'Vijay Janapa Reddi', 'Izzeddin Gur', 'Dan Zhang', 'Shayegan Omidshafiei', 'Natasha Jaques', 'Srivatsan Krishnan'] | 2022-11-29 | null | null | null | null | ['compiler-optimization'] | ['computer-code'] | [-3.78337204e-01 -2.28761405e-01 -7.04017460e-01 -2.35590152e-02
-8.99758160e-01 -6.23766780e-01 3.21370959e-01 1.64115623e-01
-3.00703675e-01 9.91284728e-01 1.47910163e-01 -5.58296561e-01
-1.23824224e-01 -7.37876892e-01 -6.05584443e-01 -8.51663530e-01
2.26436798e-02 6.34276390e-01 1.73656851e-01 -3.33183944... | [5.603866100311279, 3.1329166889190674] |
0038a3be-8928-467b-8b01-181b82bacc0e | deep-flow-guided-video-inpainting | 1905.02884 | null | https://arxiv.org/abs/1905.02884v1 | https://arxiv.org/pdf/1905.02884v1.pdf | Deep Flow-Guided Video Inpainting | Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video contents. In this work we propose a novel flow-guided video inpainting approach. Rather than filling in the RGB pixels of each frame directly... | ['Chen Change Loy', 'Rui Xu', 'Xiaoxiao Li', 'Bolei Zhou'] | 2019-05-08 | deep-flow-guided-video-inpainting-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Xu_Deep_Flow-Guided_Video_Inpainting_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Deep_Flow-Guided_Video_Inpainting_CVPR_2019_paper.pdf | cvpr-2019-6 | ['one-shot-visual-object-segmentation', 'video-inpainting'] | ['computer-vision', 'computer-vision'] | [ 1.87534511e-01 -1.53869212e-01 -1.48824856e-01 -1.14249490e-01
-4.79590386e-01 -5.10050774e-01 8.69354159e-02 -2.17399895e-01
-2.96282530e-01 1.02623153e+00 3.01204175e-01 2.46271808e-02
2.03840345e-01 -6.97755516e-01 -9.08025563e-01 -4.52222943e-01
4.88014407e-02 -3.02253179e-02 2.32385963e-01 1.33003637... | [10.756552696228027, -1.3934400081634521] |
7f6bea25-9291-4b5e-8012-de213da0407f | rethinking-semi-supervised-learning-with | 2305.13002 | null | https://arxiv.org/abs/2305.13002v1 | https://arxiv.org/pdf/2305.13002v1.pdf | Rethinking Semi-supervised Learning with Language Models | Semi-supervised learning (SSL) is a popular setting aiming to effectively utilize unlabelled data to improve model performance in downstream natural language processing (NLP) tasks. Currently, there are two popular approaches to make use of unlabelled data: Self-training (ST) and Task-adaptive pre-training (TAPT). ST u... | ['Yunlong Jiao', 'Gabriella Kazai', 'Emine Yilmaz', 'Nikolaos Aletras', 'Francesco Tonolini', 'Zhengxiang Shi'] | 2023-05-22 | null | null | null | null | ['unsupervised-pre-training', 'pseudo-label', 'semi-supervised-text-classification-1'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [ 5.53041875e-01 4.83733177e-01 -5.52860081e-01 -5.85773647e-01
-1.06521547e+00 -7.34522104e-01 8.44443083e-01 4.23595935e-01
-8.62436593e-01 8.68115067e-01 2.46248141e-01 -5.35326838e-01
-1.20215967e-01 -3.20753664e-01 -6.28021657e-01 -4.51317936e-01
2.10966885e-01 8.18290114e-01 2.22781733e-01 -7.24161267... | [10.715947151184082, 8.15636920928955] |
d0e3f2a6-b2d7-4f63-a293-008e351b84c1 | training-frankensteins-creature-to-stack | 1810.11714 | null | http://arxiv.org/abs/1810.11714v2 | http://arxiv.org/pdf/1810.11714v2.pdf | The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints | A robot can now grasp an object more effectively than ever before, but once
it has the object what happens next? We show that a mild relaxation of the task
and workspace constraints implicit in existing object grasping datasets can
cause neural network based grasping algorithms to fail on even a simple block
stacking t... | ['Gregory D. Hager', 'Chia-Hung Lin', 'Chris Paxton', 'Andrew Hundt', 'Varun Jain'] | 2018-10-27 | null | null | null | null | ['6d-pose-estimation-using-rgbd', 'industrial-robots', 'robot-task-planning'] | ['computer-vision', 'robots', 'robots'] | [ 2.32461050e-01 -1.32868411e-02 -1.09812669e-01 -3.79776806e-01
-4.70579177e-01 -6.88849628e-01 1.96517855e-01 -3.44173163e-01
-1.47176415e-01 4.88141805e-01 5.79245389e-02 -3.43554050e-01
-4.94319916e-01 -4.19007391e-01 -1.32808971e+00 -6.45636976e-01
-5.24538696e-01 7.45639205e-01 1.76226124e-01 -3.72969478... | [5.686373233795166, -0.7590504288673401] |
8581c516-c998-4ee7-b9d5-8ef41cfb6f8a | sequential-pattern-mining-in-educational-data | 2302.01932 | null | https://arxiv.org/abs/2302.01932v1 | https://arxiv.org/pdf/2302.01932v1.pdf | Sequential pattern mining in educational data: The application context, potential, strengths, and limitations | Increasingly, researchers have suggested the benefits of temporal analysis to improve our understanding of the learning process. Sequential pattern mining (SPM), as a pattern recognition technique, has the potential to reveal the temporal aspects of learning and can be a valuable tool in educational data science. Howev... | ['Luc Paquette', 'Yingbin Zhang'] | 2023-02-03 | null | null | null | null | ['sequential-pattern-mining'] | ['natural-language-processing'] | [ 9.54240784e-02 -4.14649397e-01 -8.06128502e-01 -3.14567953e-01
1.07287459e-01 -5.99226415e-01 3.35780144e-01 8.87531817e-01
-3.26143112e-03 2.76985496e-01 5.22322714e-01 -6.49492919e-01
-7.81009793e-01 -8.38140666e-01 -2.89686024e-01 -5.59331894e-01
-1.06280416e-01 -9.30435136e-02 3.54093611e-01 -1.33456783... | [10.113402366638184, 7.194495677947998] |
01fe9311-1298-4831-aab2-4f2c14c8c057 | hierarchical-and-decentralised-federated | 2304.14982 | null | https://arxiv.org/abs/2304.14982v1 | https://arxiv.org/pdf/2304.14982v1.pdf | Hierarchical and Decentralised Federated Learning | Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, the rise of complex cyber-physical systems, such as the Internet-of-Things, presents new challenges that are not met with traditional FL metho... | ['Aftab Khan', 'Ian Foster', 'Kyle Chard', 'Matt Baughman', 'Nathaniel Hudson', 'Theodoros Spyridopoulos', 'Omer Rana'] | 2023-04-28 | null | null | null | null | ['energy-management'] | ['time-series'] | [-4.92738634e-02 -2.66244709e-01 -1.92547753e-01 -3.84936482e-01
-2.81255633e-01 -9.38958704e-01 3.33132356e-01 5.73076606e-01
-2.73495764e-02 4.66434270e-01 9.45221484e-02 -5.39330542e-01
-7.56398976e-01 -1.07488751e+00 -5.19840479e-01 -6.35527909e-01
-5.12754023e-01 5.11031687e-01 6.83216751e-02 -2.54652146... | [5.931283473968506, 6.5209808349609375] |
f5416041-ebfb-48fe-9fb0-c7260e28da1c | nystrom-method-for-accurate-and-scalable | 2302.09726 | null | https://arxiv.org/abs/2302.09726v1 | https://arxiv.org/pdf/2302.09726v1.pdf | Nystrom Method for Accurate and Scalable Implicit Differentiation | The essential difficulty of gradient-based bilevel optimization using implicit differentiation is to estimate the inverse Hessian vector product with respect to neural network parameters. This paper proposes to tackle this problem by the Nystrom method and the Woodbury matrix identity, exploiting the low-rankness of th... | ['Makoto Yamada', 'Ryuichiro Hataya'] | 2023-02-20 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-4.57592934e-01 -2.24021778e-01 -2.89977044e-01 -1.34522811e-01
-7.49257863e-01 -3.70593399e-01 4.01778251e-01 -2.21675619e-01
-8.12257349e-01 9.87055242e-01 -1.60496324e-01 -3.86589140e-01
-3.88228834e-01 -1.59030646e-01 -6.18149698e-01 -9.68430400e-01
-1.35389036e-02 5.08541763e-01 -2.62174457e-01 -3.04204255... | [6.940527439117432, 4.21420955657959] |
efd807d2-c6eb-4d6d-b53e-dae62bad7a94 | self-learning-with-rectification-strategy-for | 2004.08055 | null | https://arxiv.org/abs/2004.08055v1 | https://arxiv.org/pdf/2004.08055v1.pdf | Self-Learning with Rectification Strategy for Human Parsing | In this paper, we solve the sample shortage problem in the human parsing task. We begin with the self-learning strategy, which generates pseudo-labels for unlabeled data to retrain the model. However, directly using noisy pseudo-labels will cause error amplification and accumulation. Considering the topology structure ... | ['Zhiyuan Liang', 'Jianbing Shen', 'Jiahao Gong', 'Tao Li', 'Sanyuan Zhao'] | 2020-04-17 | self-learning-with-rectification-strategy-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Self-Learning_With_Rectification_Strategy_for_Human_Parsing_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Self-Learning_With_Rectification_Strategy_for_Human_Parsing_CVPR_2020_paper.pdf | cvpr-2020-6 | ['human-parsing'] | ['computer-vision'] | [ 0.37861693 0.8524986 -0.3140355 -0.74566716 -0.5229971 -0.2433031
-0.24782343 -0.17021653 -0.00898938 0.5671691 0.23714522 0.19766113
0.09009293 -0.9431665 -0.8351458 -0.6391111 0.21616374 0.39422834
0.4028415 -0.03954855 -0.0188586 0.1615017 -1.5149848 0.35294348
1.1315161 0.9659557 0.1... | [9.073946952819824, 0.4223184585571289] |
532bae58-9480-4a2f-9ae3-0070cad54663 | bert-memorisation-and-pitfalls-in-low | 2105.00828 | null | https://arxiv.org/abs/2105.00828v2 | https://arxiv.org/pdf/2105.00828v2.pdf | Memorisation versus Generalisation in Pre-trained Language Models | State-of-the-art pre-trained language models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we study their generalisation and memorisation capabilities in noisy and low-resource scenarios. We find that the training of t... | ['Marek Rei', 'Sebastian Ruder', 'Michael Tänzer'] | 2021-04-16 | null | https://aclanthology.org/2022.acl-long.521 | https://aclanthology.org/2022.acl-long.521.pdf | acl-2022-5 | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 2.40512509e-02 1.11788370e-01 -1.99837133e-01 -4.06552941e-01
-5.92529416e-01 -2.64560789e-01 8.89017522e-01 3.72953534e-01
-1.03235948e+00 1.03914952e+00 4.97621685e-01 -2.29706511e-01
-9.14768055e-02 -1.04973972e+00 -6.34072304e-01 -2.08440095e-01
-2.01693207e-01 5.13457417e-01 2.98709571e-01 -2.65334249... | [9.717352867126465, 9.319334030151367] |
196703d9-afe3-4b9e-bcd6-8c34faf5f5b0 | clubmark-a-parallel-isolation-framework-for | null | null | https://arxiv.org/abs/1902.00475 | https://arxiv.org/pdf/1902.00475 | Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures | There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking framework, which is publicly available and suitable for the parallel benchmarking ... | ['Philippe Cudré-Mauroux', 'Mourad Khayati', 'Artem Lutov'] | 2018-11-17 | null | null | null | 2018-ieee-international-conference-on-data | ['clustering-algorithms-evaluation'] | ['methodology'] | [-2.33456209e-01 -7.00171828e-01 6.63490966e-02 -2.81195194e-01
-3.27591628e-01 -7.74731100e-01 6.73428297e-01 6.28690541e-01
-4.76280838e-01 5.58529437e-01 -1.27646312e-01 -2.98853964e-01
-7.20179677e-01 -9.75406170e-01 6.70271590e-02 -1.01576638e+00
-5.37274718e-01 1.16262639e+00 7.11503327e-01 -2.36815494... | [7.570792198181152, 4.528024673461914] |
d6b50c7d-da8c-4ea1-a437-e0fcd39b8d72 | unveiling-the-political-agenda-of-the | 1505.07302 | null | http://arxiv.org/abs/1505.07302v4 | http://arxiv.org/pdf/1505.07302v4.pdf | Unveiling the Political Agenda of the European Parliament Plenary: A Topical Analysis | This study analyzes political interactions in the European Parliament (EP) by
considering how the political agenda of the plenary sessions has evolved over
time and the manner in which Members of the European Parliament (MEPs) have
reacted to external and internal stimuli when making Parliamentary speeches. It
does so ... | ['James P. Cross', 'Derek Greene'] | 2015-05-27 | null | null | null | null | ['dynamic-topic-modeling'] | ['natural-language-processing'] | [-6.21477105e-02 4.85836774e-01 -4.54064049e-02 -3.14430833e-01
-5.65809786e-01 -1.01923120e+00 1.15408576e+00 6.52384758e-01
-8.57746601e-01 5.46308339e-01 1.48328006e+00 -8.51806879e-01
-1.61358178e-01 -8.10942829e-01 -4.76561189e-01 -6.35726869e-01
5.48802555e-01 2.21991062e-01 -2.46422008e-01 -4.36824769... | [8.943558692932129, 9.848655700683594] |
48bf0fdf-9a0e-40ec-a987-6c4a0f029b51 | t-wavenet-tree-structured-wavelet-neural | 2012.05456 | null | https://arxiv.org/abs/2012.05456v1 | https://arxiv.org/pdf/2012.05456v1.pdf | T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis | Sensor-based time series analysis is an essential task for applications such as activity recognition and brain-computer interface. Recently, features extracted with deep neural networks (DNNs) are shown to be more effective than conventional hand-crafted ones. However, most of these solutions rely solely on the network... | ['Qiang Xu', 'Qiuxia Lai', 'Ailing Zeng', 'Minhao Liu'] | 2020-12-10 | null | null | null | null | ['muscular-movement-recognition'] | ['medical'] | [ 5.06456673e-01 -3.09636593e-01 -3.06024998e-01 -2.36497670e-01
-3.55689377e-01 -2.88028326e-02 3.23909186e-02 -1.13929644e-01
-5.65382898e-01 4.85012293e-01 3.48242819e-01 -1.07789606e-01
-3.47175509e-01 -8.54890406e-01 -4.11658227e-01 -1.01105011e+00
-3.54149610e-01 -4.14106727e-01 9.73085389e-02 -2.26941079... | [13.926629066467285, 3.2617621421813965] |
61bc0879-d5f5-47e5-a83a-51fb10026211 | mtp-multi-hypothesis-tracking-and-prediction | 2110.09481 | null | https://arxiv.org/abs/2110.09481v1 | https://arxiv.org/pdf/2110.09481v1.pdf | MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error Propagation | Recently, there has been tremendous progress in developing each individual module of the standard perception-planning robot autonomy pipeline, including detection, tracking, prediction of other agents' trajectories, and ego-agent trajectory planning. Nevertheless, there has been less attention given to the principled i... | ['Marco Pavone', 'Boris Ivanovic', 'Xinshuo Weng'] | 2021-10-18 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 1.26467377e-01 1.70928642e-01 -1.75177157e-01 -6.44384474e-02
-6.77211761e-01 -8.00507307e-01 1.00389254e+00 2.89468646e-01
-4.65863734e-01 5.92356324e-01 1.55793235e-01 -2.68717080e-01
-9.56233442e-02 -7.93489993e-01 -8.99875641e-01 -4.63259578e-01
-4.42859977e-01 7.65683651e-01 8.67675662e-01 -2.97971368... | [5.77230978012085, 0.8203360438346863] |
01d65e3c-f55d-4715-a7a7-89ad5db6e81b | question-type-driven-question-generation | 1909.00140 | null | https://arxiv.org/abs/1909.00140v1 | https://arxiv.org/pdf/1909.00140v1.pdf | Question-type Driven Question Generation | Question generation is a challenging task which aims to ask a question based on an answer and relevant context. The existing works suffer from the mismatching between question type and answer, i.e. generating a question with type $how$ while the answer is a personal name. We propose to automatically predict the questio... | ['Minghua Zhang', 'Yunfang Wu', 'Wenjie Zhou'] | 2019-08-31 | question-type-driven-question-generation-1 | https://aclanthology.org/D19-1622 | https://aclanthology.org/D19-1622.pdf | ijcnlp-2019-11 | ['type-prediction'] | ['computer-code'] | [ 4.40752208e-01 1.22251272e-01 1.43176332e-01 -5.66486716e-01
-1.04988766e+00 -8.96892667e-01 5.01228333e-01 1.96304917e-01
-3.44513357e-01 6.76550210e-01 3.17011625e-01 -4.16505367e-01
1.78980559e-01 -9.22387064e-01 -5.69505692e-01 9.05906409e-02
8.05054307e-01 3.62155825e-01 3.13724667e-01 -5.54043114... | [11.546599388122559, 8.18320369720459] |
3bd65df9-48ae-4cba-9269-db5c73b71af6 | few-shot-learning-with-retrieval-augmented | 2208.03299 | null | https://arxiv.org/abs/2208.03299v3 | https://arxiv.org/pdf/2208.03299v3.pdf | Atlas: Few-shot Learning with Retrieval Augmented Language Models | Large language models have shown impressive few-shot results on a wide range of tasks. However, when knowledge is key for such results, as is the case for tasks such as question answering and fact checking, massive parameter counts to store knowledge seem to be needed. Retrieval augmented models are known to excel at k... | ['Jane Dwivedi-Yu', 'Edouard Grave', 'Sebastian Riedel', 'Armand Joulin', 'Timo Schick', 'Fabio Petroni', 'Lucas Hosseini', 'Maria Lomeli', 'Patrick Lewis', 'Gautier Izacard'] | 2022-08-05 | null | null | null | null | ['multi-task-language-understanding', 'natural-questions'] | ['methodology', 'miscellaneous'] | [ 1.04817124e-02 1.39277384e-01 -2.90961742e-01 5.52378111e-02
-1.14699984e+00 -6.75949812e-01 9.03376520e-01 5.30576229e-01
-9.99239683e-01 8.86489570e-01 1.73526600e-01 -5.18676579e-01
-4.48214471e-01 -6.03311241e-01 -7.98022151e-01 -2.78797179e-01
3.02313529e-02 7.16542542e-01 6.27514541e-01 -6.34827733... | [11.282938003540039, 7.8761420249938965] |
7d8fca9e-37b8-4c18-8dac-c6a9e28bdcf0 | two-level-classification-for-dialogue-act | null | null | https://aclanthology.org/2020.coling-main.431 | https://aclanthology.org/2020.coling-main.431.pdf | Two-level classification for dialogue act recognition in task-oriented dialogues | Dialogue act classification becomes a complex task when dealing with fine-grain labels. Many applications require such level of labelling, typically automatic dialogue systems. We present in this paper a 2-level classification technique, distinguishing between generic and specific dialogue acts (DA). This approach make... | ['Houda Oufaida', 'Magalie Ochs', "St{\\'e}phane Rauzy", 'Massina Abderrahmane', 'Philippe Blache'] | 2020-12-01 | null | null | null | coling-2020-8 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 4.14432921e-02 5.42440832e-01 7.33174980e-02 -5.49836755e-01
-4.13785487e-01 -6.57312810e-01 1.38607991e+00 3.27563167e-01
-6.45044446e-01 1.09784949e+00 5.36433280e-01 -2.06047162e-01
-1.89451754e-01 -7.61687219e-01 5.33283949e-01 -7.43624270e-01
1.04248591e-01 8.57235730e-01 3.89316469e-01 -8.36095572... | [12.898841857910156, 7.947561740875244] |
a5b9d4a1-0fc9-4a8d-bb09-2fdacaad4ab9 | review-of-data-analysis-in-vision-inspection | 2003.09802 | null | https://arxiv.org/abs/2003.09802v1 | https://arxiv.org/pdf/2003.09802v1.pdf | Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology | The widespread popularity of unmanned aerial vehicles enables an immense amount of power lines inspection data to be collected. How to employ massive inspection data especially the visible images to maintain the reliability, safety, and sustainability of power transmission is a pressing issue. To date, substantial work... | ['Xiren Miao', 'Hao Jiang', 'Jing Chen', 'Xinyu Liu'] | 2020-03-22 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [-2.67754734e-01 -3.55133921e-01 2.05150887e-01 -3.19494575e-01
-3.83486897e-01 -3.34359795e-01 -3.73876035e-01 1.41015038e-01
3.85390699e-01 3.93468052e-01 -5.29343307e-01 -3.08608532e-01
-3.98003370e-01 -8.20240736e-01 -6.68360814e-02 -1.19810832e+00
-4.35892195e-01 -9.32929888e-02 5.17575592e-02 -3.01339954... | [7.475847244262695, 1.7500332593917847] |
5a5f55db-1dfb-464b-89ac-d14f902f7a8c | a-study-of-left-before-treatment-complete | 2212.11879 | null | https://arxiv.org/abs/2212.11879v1 | https://arxiv.org/pdf/2212.11879v1.pdf | A Study of Left Before Treatment Complete Emergency Department Patients: An Optimized Explanatory Machine Learning Framework | The issue of left before treatment complete (LBTC) patients is common in emergency departments (EDs). This issue represents a medico-legal risk and may cause a revenue loss. Thus, understanding the factors that cause patients to leave before treatment is complete is vital to mitigate and potentially eliminate these adv... | ['Salih Tutun', 'Khalid Y. Aram', 'Abdulaziz Ahmed'] | 2022-12-22 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [-4.98033166e-02 -1.34197459e-01 -2.69415736e-01 -3.89435858e-01
-2.78904736e-01 -1.80736423e-01 1.63389482e-02 6.33857131e-01
-3.22682112e-01 9.62448359e-01 2.26180404e-01 -6.48599923e-01
-9.25432622e-01 -5.85789144e-01 -2.70043075e-01 -9.72435236e-01
-7.06031173e-02 7.07189560e-01 -5.01905918e-01 -2.19738171... | [8.495078086853027, 4.87343692779541] |
705a63de-89dc-43b7-a3f8-ea68c390afe6 | multi-label-class-balancing-algorithm-for | 2002.03238 | null | https://arxiv.org/abs/2002.03238v1 | https://arxiv.org/pdf/2002.03238v1.pdf | Multi-Label Class Balancing Algorithm for Action Unit Detection | Isolated facial movements, so-called Action Units, can describe combined emotions or physical states such as pain. As datasets are limited and mostly imbalanced, we present an approach incorporating a multi-label class balancing algorithm. This submission is subject to the Action Unit detection task of the Affective Be... | ['Jaspar Pahl', 'Dominik Seuss', 'Ines Rieger'] | 2020-02-08 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 2.85640597e-01 1.59121007e-01 -7.81025648e-01 -9.60848808e-01
-5.19600093e-01 -4.79082912e-01 3.67104024e-01 -4.04529899e-01
-1.95379078e-01 7.34515131e-01 2.39007294e-01 6.01000786e-01
1.20815217e-01 -4.38756756e-02 -1.44305145e-02 -7.33499408e-01
-1.20613329e-01 8.38998333e-02 -6.91251516e-01 8.10590163... | [13.594712257385254, 1.9109392166137695] |
9b83ec71-9959-4b44-8013-2c897e9b31ee | question-answering-classification-for-amharic | null | null | https://aclanthology.org/2022.sigul-1.18 | https://aclanthology.org/2022.sigul-1.18.pdf | Question Answering Classification for Amharic Social Media Community Based Questions | In this work, we build a Question Answering (QA) classification dataset from a social media platform, namely the Telegram public channel called @AskAnythingEthiopia. The channel has more than 78k subscribers and has existed since May 31, 2019. The platform allows asking questions that belong to various domains, like po... | ['Chris Biemann', 'Abinew Ayele', 'Seid Muhie Yimam', 'Tadesse Destaw'] | null | null | null | null | sigul-lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [-0.41255668 0.13016453 0.15460913 -0.53807133 -1.1137083 -0.9089743
0.46576712 0.42547458 -0.48009124 0.52881604 0.3977735 -0.897504
-0.17606667 -1.0535517 -0.49040014 0.13276875 0.44017655 0.65695727
0.39656153 -0.87754536 0.18063092 -0.01071811 -1.1194259 0.7478117
1.4482065 1.0781021 -0.2373... | [11.421598434448242, 8.061500549316406] |
6a468f87-c475-4732-ae7f-3856e940a757 | synthetic-point-cloud-generation-for-class | 2205.03738 | null | https://arxiv.org/abs/2205.03738v1 | https://arxiv.org/pdf/2205.03738v1.pdf | Synthetic Point Cloud Generation for Class Segmentation Applications | Maintenance of industrial facilities is a growing hazard due to the cumbersome process needed to identify infrastructure degradation. Digital Twins have the potential to improve maintenance by monitoring the continuous digital representation of infrastructure. However, the time needed to map the existing geometry makes... | ['Dr. Eva Agapaki', 'Sandeep Kamal Jalui', 'Avi Rajesh Jain', 'Maria Gonzalez Stefanelli'] | 2022-05-07 | null | null | null | null | ['point-cloud-generation'] | ['computer-vision'] | [ 2.74108112e-01 8.94919634e-02 3.26944232e-01 -1.89273074e-01
-5.60730577e-01 -5.76448500e-01 4.73621666e-01 4.59605426e-01
2.11638838e-01 6.35868609e-01 -7.19613314e-01 -6.19690895e-01
-5.41276038e-02 -1.25382936e+00 -4.91856605e-01 -3.86655122e-01
-2.31804345e-02 1.11161625e+00 5.34887731e-01 -1.37778565... | [8.376724243164062, -2.5839271545410156] |
3dcd3a75-ea8f-4210-903b-d97824cc9013 | spin-structure-preserving-inner-offset | 2005.13117 | null | https://arxiv.org/abs/2005.13117v4 | https://arxiv.org/pdf/2005.13117v4.pdf | SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition | Arbitrary text appearance poses a great challenge in scene text recognition tasks. Existing works mostly handle with the problem in consideration of the shape distortion, including perspective distortions, line curvature or other style variations. Therefore, methods based on spatial transformers are extensively studied... | ['ShiLiang Pu', 'Zhanzhan Cheng', 'Yunlu Xu', 'Yi Niu', 'Fei Wu', 'Futai Zou', 'Chengwei Zhang'] | 2020-05-27 | null | null | null | null | ['color-manipulation'] | ['computer-vision'] | [ 4.46816951e-01 -2.22798377e-01 4.19022422e-03 -4.71484005e-01
-2.11783707e-01 -6.58669949e-01 8.95830989e-01 -2.81007141e-01
-4.74334955e-01 3.24979663e-01 -3.56096216e-02 -1.74281597e-01
1.62993819e-01 -5.20943522e-01 -6.71620667e-01 -9.27891374e-01
6.83181584e-01 2.14450821e-01 2.85210758e-01 -3.02828044... | [11.553667068481445, -0.4230239987373352] |
8300dd9d-d4bc-4059-a603-f83aa0eec2be | learning-to-infer-3d-object-models-from | 2006.06130 | null | https://arxiv.org/abs/2006.06130v3 | https://arxiv.org/pdf/2006.06130v3.pdf | ROOTS: Object-Centric Representation and Rendering of 3D Scenes | A crucial ability of human intelligence is to build up models of individual 3D objects from partial scene observations. Recent works achieve object-centric generation but without the ability to infer the representation, or achieve 3D scene representation learning but without object-centric compositionality. Therefore, ... | ['Sungjin Ahn', 'Fei Deng', 'Chang Chen'] | 2020-06-11 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 2.82165051e-01 2.50294328e-01 2.15904698e-01 -5.17725766e-01
-5.62444150e-01 -6.42733574e-01 9.01810467e-01 -9.23150778e-02
2.97273159e-01 3.12693983e-01 7.06046373e-02 9.25172865e-02
-1.19691327e-01 -8.22621226e-01 -1.03466594e+00 -4.94811952e-01
9.74394977e-02 1.02803004e+00 2.23022193e-01 1.90593496... | [8.715457916259766, -3.0560364723205566] |
059e808b-f1c0-41c0-ac77-42887bce87b8 | video-event-restoration-based-on-keyframes | 2304.05112 | null | https://arxiv.org/abs/2304.05112v1 | https://arxiv.org/pdf/2304.05112v1.pdf | Video Event Restoration Based on Keyframes for Video Anomaly Detection | Video anomaly detection (VAD) is a significant computer vision problem. Existing deep neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or frame prediction. However, the lack of mining and learning of higher-level visual features and temporal context relationships in videos limits t... | ['Xiaotao Liu', 'Peng Wu', 'Zhaoyang Wu', 'Jing Liu', 'Zhiwei Yang'] | 2023-04-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Video_Event_Restoration_Based_on_Keyframes_for_Video_Anomaly_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Video_Event_Restoration_Based_on_Keyframes_for_Video_Anomaly_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection'] | ['computer-vision'] | [ 1.83677763e-01 -4.43148822e-01 -2.85073698e-01 -1.96233451e-01
-4.02687453e-02 -8.02939609e-02 5.25718212e-01 -1.74410984e-01
-2.60059029e-01 5.32155633e-01 4.33576733e-01 -3.68929118e-01
2.03423332e-02 -4.51311797e-01 -1.02817035e+00 -5.78400612e-01
-1.81568578e-01 -5.00646830e-01 5.24653912e-01 -7.75907785... | [8.17750072479248, 1.1925944089889526] |
5378be86-eea7-452b-9f42-6a480e083a78 | learning-to-learn-generative-programs-with | 2007.03132 | null | https://arxiv.org/abs/2007.03132v2 | https://arxiv.org/pdf/2007.03132v2.pdf | Learning to learn generative programs with Memoised Wake-Sleep | We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs. As generative models, these programs capture compositional structures in a naturally explainable form. To tackle the challenge of performing program induction ... | ['Tuan Anh Le', 'Luke B. Hewitt', 'Joshua B. Tenenbaum'] | 2020-07-06 | null | null | null | null | ['program-induction', 'explainable-models'] | ['computer-code', 'computer-vision'] | [ 6.25387311e-01 3.21745634e-01 -3.77013564e-01 -4.74050999e-01
-3.23828578e-01 -4.34757680e-01 7.65585363e-01 -7.74747180e-03
2.11509522e-02 6.67310059e-01 1.79941133e-01 -5.86291671e-01
5.80680557e-02 -1.18704283e+00 -1.33181775e+00 -4.22964334e-01
-4.88058776e-02 9.80950952e-01 3.14432949e-01 -1.26487300... | [8.472187042236328, 7.29198694229126] |
bf6b7fa3-ba61-456d-a68f-0ba8ea3eff78 | towards-social-engaging-peer-learning | 2007.11346 | null | https://arxiv.org/abs/2007.11346v1 | https://arxiv.org/pdf/2007.11346v1.pdf | Towards Social & Engaging Peer Learning: Predicting Backchanneling and Disengagement in Children | Social robots and interactive computer applications have the potential to foster early language development in young children by acting as peer learning companions. However, studies have found that children only trust robots which behave in a natural and interpersonal manner. To help robots come across as engaging and ... | ['Mononito Goswami', 'Maitree Leekha', 'Minkush Manuja'] | 2020-07-22 | null | null | null | null | ['pupil-dilation'] | ['computer-vision'] | [-1.66391134e-01 7.68565357e-01 1.20089747e-01 -5.09572446e-01
1.29096434e-01 -3.31578761e-01 4.95642394e-01 4.72572535e-01
-4.22264397e-01 4.39343721e-01 3.09264839e-01 6.22328185e-02
-1.55054450e-01 -5.17923653e-01 -6.72317982e-01 -5.24493217e-01
-4.63485897e-01 -3.78783257e-03 -5.88177331e-02 -2.03904465... | [10.456947326660156, 8.541485786437988] |
5e360a07-0481-4c89-82c0-8cf0f5ec1962 | efficient-direct-density-ratio-estimation-for | null | null | http://papers.nips.cc/paper/3387-efficient-direct-density-ratio-estimation-for-non-stationarity-adaptation-and-outlier-detection | http://papers.nips.cc/paper/3387-efficient-direct-density-ratio-estimation-for-non-stationarity-adaptation-and-outlier-detection.pdf | Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection | We address the problem of estimating the ratio of two probability density functions (a.k.a.~the importance). The importance values can be used for various succeeding tasks such as non-stationarity adaptation or outlier detection. In this paper, we propose a new importance estimation method that has a closed-form soluti... | ['Masashi Sugiyama', 'Takafumi Kanamori', 'Shohei Hido'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['density-ratio-estimation'] | ['methodology'] | [-3.01569998e-01 -4.09772635e-01 -1.64428741e-01 -3.25720757e-01
-1.24977434e+00 -3.86157602e-01 1.00853130e-01 3.03741872e-01
-5.25068939e-01 1.26953578e+00 -3.72347176e-01 -2.63531148e-01
-3.65591347e-01 -3.20847064e-01 -5.76548576e-01 -9.17729437e-01
-2.19970480e-01 4.26507890e-01 3.01549494e-01 1.76065966... | [7.323700904846191, 4.062187194824219] |
c7e0ede6-0fe9-47f9-8da6-dcad7ea082a3 | low-light-video-enhancement-by-learning-on | 2210.04290 | null | https://arxiv.org/abs/2210.04290v1 | https://arxiv.org/pdf/2210.04290v1.pdf | Low Light Video Enhancement by Learning on Static Videos with Cross-Frame Attention | The design of deep learning methods for low light video enhancement remains a challenging problem owing to the difficulty in capturing low light and ground truth video pairs. This is particularly hard in the context of dynamic scenes or moving cameras where a long exposure ground truth cannot be captured. We approach t... | ['Rajiv Soundararajan', 'Sameer Malik', 'Shivam Chhirolya'] | 2022-10-09 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 3.91114444e-01 -4.12646621e-01 1.62728131e-01 -2.20384926e-01
-5.34411907e-01 -4.52305466e-01 4.12321866e-01 -3.83588552e-01
-6.93015218e-01 8.10798347e-01 1.41642436e-01 1.02710925e-01
-7.32122585e-02 -6.39432788e-01 -1.06524813e+00 -7.76667535e-01
-1.06580667e-01 -2.40370214e-01 4.77385491e-01 -1.97769299... | [10.79822063446045, -1.57267427444458] |
c3e01462-903f-4522-b886-a0bdac2ff5d7 | a-multi-head-relevance-weighting-framework | 2107.14793 | null | https://arxiv.org/abs/2107.14793v1 | https://arxiv.org/pdf/2107.14793v1.pdf | A Multi-Head Relevance Weighting Framework For Learning Raw Waveform Audio Representations | In this work, we propose a multi-head relevance weighting framework to learn audio representations from raw waveforms. The audio waveform, split into windows of short duration, are processed with a 1-D convolutional layer of cosine modulated Gaussian filters acting as a learnable filterbank. The key novelty of the prop... | ['Sriram Ganapathy', 'Purvi Agrawal', 'Debottam Dutta'] | 2021-07-30 | null | null | null | null | ['sound-classification'] | ['audio'] | [ 5.08327067e-01 1.72218665e-01 3.08751941e-01 -3.52398932e-01
-1.39449298e+00 -2.99774319e-01 4.36992437e-01 2.17464358e-01
-3.55395526e-01 3.81815970e-01 5.57924628e-01 8.63306373e-02
-2.18282327e-01 -3.76214653e-01 -4.99969691e-01 -6.13465250e-01
-6.16946518e-01 -2.85698563e-01 2.03546733e-01 -2.01783657... | [15.193710327148438, 5.345523357391357] |
b06993d8-71ec-46f7-8407-a21212de9922 | predicting-foreign-language-usage-from | null | null | https://aclanthology.org/N18-2096 | https://aclanthology.org/N18-2096.pdf | Predicting Foreign Language Usage from English-Only Social Media Posts | Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced b... | ['Lawrence Phillips', 'Svitlana Volkova', 'Stephen Ranshous'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['native-language-identification'] | ['natural-language-processing'] | [-4.98361528e-01 -2.27372900e-01 -6.98650360e-01 -4.04163480e-01
-1.02481401e+00 -7.29907632e-01 8.64024818e-01 2.55690724e-01
-7.96167195e-01 6.51035190e-01 7.16094196e-01 -7.12501466e-01
4.36855316e-01 -5.58411360e-01 -5.95055997e-01 -1.32521885e-02
1.69685587e-01 4.51409161e-01 -3.46666634e-01 -6.25799179... | [10.16694450378418, 10.278953552246094] |
0b486f05-a500-4fed-b362-d3ff158ca3be | a-complete-recipe-for-stochastic-gradient | 1506.04696 | null | http://arxiv.org/abs/1506.04696v2 | http://arxiv.org/pdf/1506.04696v2.pdf | A Complete Recipe for Stochastic Gradient MCMC | Many recent Markov chain Monte Carlo (MCMC) samplers leverage continuous
dynamics to define a transition kernel that efficiently explores a target
distribution. In tandem, a focus has been on devising scalable variants that
subsample the data and use stochastic gradients in place of full-data gradients
in the dynamic s... | ['Tianqi Chen', 'Yi-An Ma', 'Emily B. Fox'] | 2015-06-15 | a-complete-recipe-for-stochastic-gradient-1 | http://papers.nips.cc/paper/5891-a-complete-recipe-for-stochastic-gradient-mcmc | http://papers.nips.cc/paper/5891-a-complete-recipe-for-stochastic-gradient-mcmc.pdf | neurips-2015-12 | ['physical-intuition'] | ['reasoning'] | [-5.75921834e-02 -2.00190559e-01 1.16861589e-01 4.77465391e-02
-8.96025836e-01 -5.92575848e-01 9.23757255e-01 -2.42046878e-01
-4.37334388e-01 9.48789358e-01 1.25581160e-01 -7.62996197e-01
-8.68074223e-02 -9.26418364e-01 -7.66283691e-01 -1.08643115e+00
-3.46982688e-01 7.56834209e-01 2.98088491e-01 -1.04939058... | [6.90904426574707, 3.9341232776641846] |
458d3dd1-a8fd-449c-91f7-24659887f01c | pose2pose-3d-positional-pose-guided-3d | 2011.11534 | null | https://arxiv.org/abs/2011.11534v4 | https://arxiv.org/pdf/2011.11534v4.pdf | Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation | Whole-body 3D human mesh estimation aims to reconstruct the 3D human body, hands, and face simultaneously. Although several methods have been proposed, accurate prediction of 3D hands, which consist of 3D wrist and fingers, still remains challenging due to two reasons. First, the human kinematic chain has not been care... | ['Kyoung Mu Lee', 'Hongsuk Choi', 'Gyeongsik Moon'] | 2020-11-23 | null | null | null | null | ['3d-human-reconstruction'] | ['computer-vision'] | [-6.37589335e-01 1.85115233e-01 -4.30791646e-01 7.85250738e-02
-3.81543845e-01 -3.20091039e-01 2.01514348e-01 -5.30506968e-01
-1.02895148e-01 4.46816623e-01 3.77165377e-01 1.44518152e-01
4.43712287e-02 -6.54792190e-01 -5.24178624e-01 -7.73472711e-02
8.28820746e-03 9.18066680e-01 4.03104663e-01 -2.69035429... | [7.020819187164307, -1.0933732986450195] |
cbc0bf03-60bb-4a96-81d8-72ba30d61597 | crowd-powered-face-manipulation-detection | 2201.13084 | null | https://arxiv.org/abs/2201.13084v1 | https://arxiv.org/pdf/2201.13084v1.pdf | Crowd-powered Face Manipulation Detection: Fusing Human Examiner Decisions | We investigate the potential of fusing human examiner decisions for the task of digital face manipulation detection. To this end, various decision fusion methods are proposed incorporating the examiners' decision confidence, experience level, and their time to take a decision. Conducted experiments are based on a psych... | ['Christoph Busch', 'Pawel Drozdowski', 'Mathias Ibsen', 'Robert Nichols', 'Christian Rathgeb'] | 2022-01-31 | null | null | null | null | ['image-manipulation-detection'] | ['computer-vision'] | [ 2.29268357e-01 5.84334172e-02 4.40081358e-01 -3.40727478e-01
-5.00300944e-01 -5.87031722e-01 4.09431607e-01 2.70113889e-02
-5.77481270e-01 3.19787383e-01 -1.79568335e-01 -2.02697292e-01
-9.40414742e-02 -2.16076970e-01 -1.66989431e-01 -5.11073291e-01
3.61937642e-01 -8.44942704e-02 2.87595421e-01 -9.82019603... | [13.088458061218262, 1.0404903888702393] |
ce470f40-320b-4554-a285-55e6cdf31326 | domain-adaptation-for-structured-output-via | 1901.05427 | null | https://arxiv.org/abs/1901.05427v4 | https://arxiv.org/pdf/1901.05427v4.pdf | Domain Adaptation for Structured Output via Discriminative Patch Representations | Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks. However, models trained on one data domain may not generalize well to other domains without annotations for model finetuning. To avoid the labor-intensive ... | ['Yi-Hsuan Tsai', 'Kihyuk Sohn', 'Samuel Schulter', 'Manmohan Chandraker'] | 2019-01-16 | domain-adaptation-for-structured-output-via-2 | http://openaccess.thecvf.com/content_ICCV_2019/html/Tsai_Domain_Adaptation_for_Structured_Output_via_Discriminative_Patch_Representations_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Tsai_Domain_Adaptation_for_Structured_Output_via_Discriminative_Patch_Representations_ICCV_2019_paper.pdf | iccv-2019-10 | ['synthetic-to-real-translation'] | ['computer-vision'] | [ 6.42253816e-01 3.72581571e-01 -1.91149399e-01 -7.18977094e-01
-9.60856736e-01 -9.69241261e-01 4.39800411e-01 -2.05889285e-01
-1.11564152e-01 7.50271022e-01 -8.50382969e-02 -6.44533783e-02
2.03856379e-01 -8.28280985e-01 -1.11083746e+00 -7.40341663e-01
2.27932662e-01 6.54058456e-01 4.32545245e-01 1.28699029... | [9.783434867858887, 1.3582803010940552] |
e389de76-b06b-421b-ba8c-5ae73ddcabe2 | safe-reinforcement-learning-via-shielding-for | 2204.00755 | null | https://arxiv.org/abs/2204.00755v2 | https://arxiv.org/pdf/2204.00755v2.pdf | Safe Reinforcement Learning via Shielding under Partial Observability | Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment. A family of approaches to this problem assume domain knowledge in the form of a (partial) model of this environment to decide upon the safety of an action. ... | ['Ufuk Topcu', 'Sebastian Junges', 'Nils Jansen', 'Steven Carr'] | 2022-04-02 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 4.64006215e-02 4.38606709e-01 -2.73191959e-01 -9.16478038e-02
-8.62939835e-01 -8.64968777e-01 5.88694453e-01 -1.22984173e-03
-8.83344412e-01 1.15775752e+00 -9.46326554e-03 -4.51169103e-01
-1.22373633e-01 -7.84739971e-01 -1.04357588e+00 -8.88558865e-01
-5.67462385e-01 4.92896855e-01 3.30074996e-01 -4.89080489... | [4.439940929412842, 2.1311991214752197] |
219d53b6-684c-4b70-a384-14e0d04eb031 | defending-black-box-classifiers-by-bayesian | 2306.16979 | null | https://arxiv.org/abs/2306.16979v1 | https://arxiv.org/pdf/2306.16979v1.pdf | Defending Black-box Classifiers by Bayesian Boundary Correction | Classifiers based on deep neural networks have been recently challenged by Adversarial Attack, where the widely existing vulnerability has invoked the research in defending them from potential threats. Given a vulnerable classifier, existing defense methods are mostly white-box and often require re-training the victim ... | ['Yunfeng Diao', 'He Wang'] | 2023-06-29 | null | null | null | null | ['adversarial-attack', 'activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['adversarial', 'computer-vision', 'computer-vision', 'time-series'] | [ 4.41747606e-01 6.72118785e-03 -8.95674434e-03 -1.10202909e-01
-5.78446090e-01 -7.27601886e-01 6.71785951e-01 -1.51025787e-01
-5.87634563e-01 7.22448170e-01 -7.90223777e-02 -2.61810839e-01
-2.22943395e-01 -8.24105740e-01 -6.62614644e-01 -1.04467773e+00
-1.76575825e-01 2.53722906e-01 4.90597546e-01 -2.69385964... | [5.557491779327393, 7.782773971557617] |
dbbeaeac-85cf-44c0-8f65-66ddce4fbb2d | efficient-multi-scale-attention-module-with | 2305.13563 | null | https://arxiv.org/abs/2305.13563v2 | https://arxiv.org/pdf/2305.13563v2.pdf | Efficient Multi-Scale Attention Module with Cross-Spatial Learning | Remarkable effectiveness of the channel or spatial attention mechanisms for producing more discernible feature representation are illustrated in various computer vision tasks. However, modeling the cross-channel relationships with channel dimensionality reduction may bring side effect in extracting deep visual represen... | ['Jian Zhan', 'Guozhong Zhang', 'Mingzhu Luo', 'Zhijie Huang', 'Huaiyong Guo', 'Su He', 'Daliang Ouyang'] | 2023-05-23 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 1.75819024e-02 -3.40776294e-01 1.72067091e-01 -5.20469785e-01
-6.57761395e-01 -3.59939069e-01 3.92230690e-01 -1.21072315e-01
-5.52755713e-01 5.21738887e-01 2.74833918e-01 -1.20972291e-01
-1.34416565e-01 -5.96087158e-01 -8.59277725e-01 -8.85884345e-01
-2.42089644e-01 -3.93061668e-01 1.14273071e-01 7.30576888... | [9.56281852722168, 2.0169005393981934] |
2b73bc69-bdb3-4f94-9d1b-645425317f16 | reliability-adaptive-consistency | 2303.05164 | null | https://arxiv.org/abs/2303.05164v1 | https://arxiv.org/pdf/2303.05164v1.pdf | Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation | Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores to apply the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with m... | ['Jianfei Cai', 'Guosheng Lin', 'Yicheng Wu', 'Zhonghua Wu'] | 2023-03-09 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-1.54010832e-01 2.18779385e-01 -4.33019340e-01 -6.78279161e-01
-1.04771304e+00 -4.83902574e-01 1.81281373e-01 1.80811331e-01
-2.71054953e-01 5.25740027e-01 -5.07372797e-01 -1.98113233e-01
-1.59293205e-01 -3.54901910e-01 -8.79054606e-01 -6.95463598e-01
8.27624742e-03 1.20172691e+00 4.22949165e-01 2.45463967... | [7.995454788208008, -3.218722105026245] |
6e010a06-b2ba-463c-bee8-6da144acd03d | innovators-smm4h22-an-ensembles-approach-for-1 | null | null | https://aclanthology.org/2022.smm4h-1.35 | https://aclanthology.org/2022.smm4h-1.35.pdf | Innovators@SMM4H’22: An Ensembles Approach for Stance and Premise Classification of COVID-19 Health Mandates Tweets | This paper presents our submission for the Shared Task-2 of classification of stance and premise in tweets about health mandates related to COVID-19 at the Social Media Mining for Health 2022. There have been a plethora of tweets about people expressing their opinions on the COVID-19 epidemic since it first emerged. Th... | ['Muskaan Singh', 'Nidhir Bhavsar', 'Aakash Bhatnagar', 'Vatsal Savaliya'] | null | null | null | null | smm4h-coling-2022-10 | ['stance-detection'] | ['natural-language-processing'] | [ 1.56682104e-01 7.09270239e-01 -3.29810560e-01 -5.15220404e-01
-9.62782443e-01 -5.07718623e-01 9.16005731e-01 7.46981323e-01
-3.28327328e-01 5.96786141e-01 8.11022460e-01 -8.89286220e-01
6.14138320e-02 -7.59685338e-01 -6.56201959e-01 -5.19859850e-01
6.52573407e-02 6.09875023e-01 -1.73930481e-01 -5.28731048... | [8.566055297851562, 9.520858764648438] |
25ded87e-7a9a-48e5-ad51-17a42394a08e | universal-language-modelling-agent | 2306.06521 | null | https://arxiv.org/abs/2306.06521v1 | https://arxiv.org/pdf/2306.06521v1.pdf | Universal Language Modelling agent | Large Language Models are designed to understand complex Human Language. Yet, Understanding of animal language has long intrigued researchers striving to bridge the communication gap between humans and other species. This research paper introduces a novel approach that draws inspiration from the linguistic concepts fou... | ['Anees Aslam'] | 2023-06-10 | null | null | null | null | ['word-translation'] | ['natural-language-processing'] | [ 2.80422926e-01 2.04214886e-01 3.24710347e-02 1.12260096e-02
-1.38237655e-01 -6.40108585e-01 6.40304863e-01 2.26196229e-01
-1.76981077e-01 3.16233158e-01 8.91924381e-01 -5.21377623e-01
2.10000034e-02 -6.24618590e-01 -2.96106994e-01 -5.59544921e-01
-3.67407113e-01 -1.23410285e-01 -3.34745795e-01 -6.84791744... | [10.671309471130371, 9.28238582611084] |
5f0be6ff-81a3-47bb-8dd9-7618b050bf4e | mbw-multi-view-bootstrapping-in-the-wild | 2210.01721 | null | https://arxiv.org/abs/2210.01721v1 | https://arxiv.org/pdf/2210.01721v1.pdf | MBW: Multi-view Bootstrapping in the Wild | Labeling articulated objects in unconstrained settings have a wide variety of applications including entertainment, neuroscience, psychology, ethology, and many fields of medicine. Large offline labeled datasets do not exist for all but the most common articulated object categories (e.g., humans). Hand labeling these l... | ['Simon Lucey', 'Ian R. Fasel', 'Laszlo Attila Jeni', 'Tim Clifford', 'Chaoyang Wang', 'Mosam Dabhi'] | 2022-10-04 | null | null | null | null | ['unsupervised-landmark-detection', 'semi-supervised-2d-and-3d-landmark-labeling'] | ['computer-vision', 'computer-vision'] | [-1.54413581e-02 -5.63994087e-02 7.39610791e-02 -2.69714624e-01
-7.01741278e-01 -8.08996141e-01 2.88509369e-01 -2.22023293e-01
-7.73893178e-01 7.33850479e-01 -1.46287590e-01 1.64100736e-01
2.65777618e-01 -2.35569254e-01 -7.94706702e-01 -4.53487605e-01
-2.74452180e-01 8.98273706e-01 5.38888991e-01 9.73774418... | [7.538674354553223, -0.9517372250556946] |
8ee2f3a3-5eba-4c79-901e-31bb456000be | deep-generative-models-on-3d-representations | 2210.15663 | null | https://arxiv.org/abs/2210.15663v2 | https://arxiv.org/pdf/2210.15663v2.pdf | Deep Generative Models on 3D Representations: A Survey | Generative models, as an important family of statistical modeling, target learning the observed data distribution via generating new instances. Along with the rise of neural networks, deep generative models, such as variational autoencoders (VAEs) and generative adversarial network (GANs), have made tremendous progress... | ['Yujun Shen', 'Yiyi Liao', 'Yinghao Xu', 'Sida Peng', 'Zifan Shi'] | 2022-10-27 | null | null | null | null | ['3d-shape-generation', '3d-aware-image-synthesis'] | ['computer-vision', 'computer-vision'] | [ 1.71800196e-01 2.29685888e-01 2.34655868e-02 -2.47564651e-02
-4.00336921e-01 -4.61992532e-01 8.83912861e-01 -3.51882398e-01
2.89646834e-01 7.44195759e-01 8.12512860e-02 -1.34771839e-01
1.75910920e-01 -1.44448566e+00 -8.12007725e-01 -8.93174291e-01
2.39536926e-01 5.82276165e-01 -1.18092522e-01 -2.67298430... | [9.01052188873291, -3.546456813812256] |
28e6296f-46eb-4ce9-b32a-8ed5d7940b37 | an-empirical-evaluation-of-generic | 1803.01271 | null | http://arxiv.org/abs/1803.01271v2 | http://arxiv.org/pdf/1803.01271v2.pdf | An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling | For most deep learning practitioners, sequence modeling is synonymous with
recurrent networks. Yet recent results indicate that convolutional
architectures can outperform recurrent networks on tasks such as audio
synthesis and machine translation. Given a new sequence modeling task or
dataset, which architecture should... | ['Shaojie Bai', 'Vladlen Koltun', 'J. Zico Kolter'] | 2018-03-04 | null | null | null | null | ['sequential-image-classification', 'music-modeling'] | ['computer-vision', 'music'] | [ 4.45867002e-01 -7.10597932e-02 -3.34969580e-01 -2.21075475e-01
-5.45019746e-01 -5.35275519e-01 6.00234628e-01 -3.16423118e-01
-4.26037997e-01 5.12218297e-01 4.53041971e-01 -7.90707111e-01
4.05877143e-01 -3.12125534e-01 -7.21512258e-01 -3.35721344e-01
3.65671590e-02 1.01444989e-01 -7.87797272e-02 -2.77687490... | [10.84047794342041, 6.832894802093506] |
d68d3532-8a7b-4048-8ae1-829fc35551a7 | survey-of-matrix-completion-algorithms | 2204.01532 | null | https://arxiv.org/abs/2204.01532v2 | https://arxiv.org/pdf/2204.01532v2.pdf | Survey of Matrix Completion Algorithms | Matrix completion problem has been investigated under many different conditions since Netflix announced the Netflix Prize problem. Many research work has been done in the field once it has been discovered that many real life dataset could be estimated with a low-rank matrix. Since then compressed sensing, adaptive sign... | ['Jafar Jafarov'] | 2022-04-01 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 1.02647424e+00 -2.76654363e-02 -2.85130814e-02 -9.03680399e-02
-7.50146627e-01 -5.25114536e-01 3.03196669e-01 -5.57724126e-02
-4.90052968e-01 5.65331399e-01 4.93362933e-01 -6.89842552e-02
-5.94882607e-01 -3.31166267e-01 -4.41424072e-01 -8.73522401e-01
-5.29242337e-01 1.37274474e-01 -2.94818848e-01 -3.19610387... | [6.988105773925781, 4.6638689041137695] |
e81610e6-8c91-4aec-bb99-0ca47fe72b47 | causal-models-in-string-diagrams | 2304.07638 | null | https://arxiv.org/abs/2304.07638v1 | https://arxiv.org/pdf/2304.07638v1.pdf | Causal models in string diagrams | The framework of causal models provides a principled approach to causal reasoning, applied today across many scientific domains. Here we present this framework in the language of string diagrams, interpreted formally using category theory. A class of string diagrams, called network diagrams, are in 1-to-1 correspondenc... | ['Sean Tull', 'Robin Lorenz'] | 2023-04-15 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 5.06287873e-01 7.64668465e-01 -4.08605009e-01 -2.33747691e-01
1.54620484e-01 -7.34713554e-01 1.45206988e+00 4.65476662e-02
9.72031578e-02 6.92545176e-01 8.16406608e-01 -1.04528821e+00
-1.04956174e+00 -1.16777384e+00 -8.63906741e-01 -6.41022444e-01
-6.82803750e-01 2.55082399e-01 9.60843340e-02 -1.14266485... | [8.125129699707031, 5.754879474639893] |
ce5d8db2-b8ac-4eb7-bb88-c8cb5ef824ec | distributed-adversarial-training-to-robustify-1 | 2206.06257 | null | https://arxiv.org/abs/2206.06257v2 | https://arxiv.org/pdf/2206.06257v2.pdf | Distributed Adversarial Training to Robustify Deep Neural Networks at Scale | Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective and popular approach, known as adversarial training (AT), has been shown to mitigate the negative impact of adver... | ['Sijia Liu', 'Mingyi Hong', 'Lior Horesh', 'Lee Martie', 'Quanfu Fan', 'Pin-Yu Chen', 'Xiangyi Chen', 'Yihua Zhang', 'Songtao Lu', 'Gaoyuan Zhang'] | 2022-06-13 | distributed-adversarial-training-to-robustify | https://openreview.net/forum?id=kmBFHJ5pr0o | https://openreview.net/pdf?id=kmBFHJ5pr0o | null | ['distributed-optimization'] | ['methodology'] | [ 8.14477727e-02 -4.49745264e-03 1.11712657e-01 -3.76883209e-01
-1.01896250e+00 -1.02082109e+00 5.21327019e-01 -1.90084912e-02
-7.28832960e-01 6.98033929e-01 -2.61223644e-01 -6.22986615e-01
4.00198903e-03 -8.12390268e-01 -1.27565706e+00 -9.33506727e-01
-3.44255924e-01 3.36699247e-01 2.58459784e-02 -2.19425902... | [5.672677993774414, 7.798150062561035] |
745df7b0-6c00-4875-8514-c1cf81ed2bc2 | the-provable-benefits-of-unsupervised-data | 2302.13493 | null | https://arxiv.org/abs/2302.13493v1 | https://arxiv.org/pdf/2302.13493v1.pdf | The Provable Benefits of Unsupervised Data Sharing for Offline Reinforcement Learning | Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations. However, the question of how to conduct self-supervised offline reinforcement learning (RL) in a principled way remains unclear. In this paper, we address this issue by investi... | ['Chongjie Zhang', 'Qianchuan Zhao', 'Yiqin Yang', 'Hao Hu'] | 2023-02-27 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 2.53687426e-02 4.57357913e-01 -7.75010645e-01 -6.53212905e-01
-1.19299138e+00 -6.24903738e-01 3.17766756e-01 8.88766870e-02
-7.70656407e-01 1.16599095e+00 8.29015598e-02 -3.56897980e-01
1.20445386e-01 -3.80743444e-01 -8.69035065e-01 -7.06710279e-01
-3.47143084e-01 3.89498502e-01 -6.46317005e-02 2.06176698... | [4.080907344818115, 2.1831188201904297] |
0a31368c-ee4c-4d67-a2a7-28ae4a2bbece | light-weighted-cnn-for-text-classification | 2004.07922 | null | https://arxiv.org/abs/2004.07922v1 | https://arxiv.org/pdf/2004.07922v1.pdf | Light-Weighted CNN for Text Classification | For management, documents are categorized into a specific category, and to do these, most of the organizations use manual labor. In today's automation era, manual efforts on such a task are not justified, and to avoid this, we have so many software out there in the market. However, efficiency and minimal resource consu... | ['Ritu Yadav'] | 2020-04-16 | null | null | null | null | ['document-image-classification'] | ['computer-vision'] | [-0.04988106 -0.18714315 0.10613034 -0.59252 0.2420434 -0.45964152
0.59907776 0.17518361 -0.72071356 0.36096328 -0.2898486 -0.50386333
-0.11041854 -1.1624225 -0.19068234 -0.41870746 0.45290148 0.3257912
0.24727085 -0.27794665 0.6544092 0.42256984 -1.6222981 0.54902935
0.79350823 1.207264 0.6... | [11.354191780090332, 2.7221827507019043] |
97fabfe8-8e15-469c-a09b-db1da18611ef | transfer-learning-with-joint-fine-tuning-for | 2210.05790 | null | https://arxiv.org/abs/2210.05790v1 | https://arxiv.org/pdf/2210.05790v1.pdf | Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment Analysis | Most existing methods focus on sentiment analysis of textual data. However, recently there has been a massive use of images and videos on social platforms, motivating sentiment analysis from other modalities. Current studies show that exploring other modalities (e.g., images) increases sentiment analysis performance. S... | ['Ricardo Marcondes Marcacini', 'Guilherme Lourenço de Toledo'] | 2022-10-11 | null | null | null | null | ['multimodal-sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing'] | [ 6.79294243e-02 -1.95163548e-01 -2.16144443e-01 -4.88439023e-01
-9.34840560e-01 -8.99872661e-01 9.21597004e-01 8.44494551e-02
-7.97619581e-01 4.34490025e-01 3.55980277e-01 -7.62599707e-02
3.56783688e-01 -5.36810637e-01 -7.27819204e-01 -6.79968238e-01
3.06345284e-01 3.66830856e-01 3.74521166e-02 -4.60478276... | [13.083312034606934, 5.079772472381592] |
1438ff8b-8ae2-4b9e-a56a-e31cc681d0fa | a-threefold-review-on-deep-semantic | 2303.04315 | null | https://arxiv.org/abs/2303.04315v1 | https://arxiv.org/pdf/2303.04315v1.pdf | A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design | Semantic image and video segmentation stand among the most important tasks in computer vision nowadays, since they provide a complete and meaningful representation of the environment by means of a dense classification of the pixels in a given scene. Recently, Deep Learning, and more precisely Convolutional Neural Netwo... | ['Fernando Santos Osório', 'Felipe Manfio Barbosa'] | 2023-03-08 | null | null | null | null | ['video-semantic-segmentation', 'data-integration'] | ['computer-vision', 'knowledge-base'] | [ 4.12238210e-01 -1.66617081e-01 -1.82921529e-01 -4.78526533e-01
-2.21929684e-01 -4.47072566e-01 4.33133930e-01 7.79635981e-02
-5.80536544e-01 4.25861835e-01 -4.81276244e-01 -1.23178571e-01
-3.35689992e-01 -9.11731839e-01 -4.84083235e-01 -8.56071472e-01
2.49637682e-02 4.06340182e-01 5.08814275e-01 -2.09708601... | [8.521341323852539, -1.9626970291137695] |
ffe9c755-7e38-4468-9e1b-f0ba90bedaf2 | temporal-word-meaning-disambiguation-using | 2210.08207 | null | https://arxiv.org/abs/2210.08207v2 | https://arxiv.org/pdf/2210.08207v2.pdf | Temporal Word Meaning Disambiguation using TimeLMs | Meaning of words constantly changes given the events in modern civilization. Large Language Models use word embeddings, which are often static and thus cannot cope with this semantic change. Thus,it is important to resolve ambiguity in word meanings. This paper is an effort in this direction, where we explore methods f... | ['Aditya Kane', 'Parth Dandavate', 'Mihir Godbole'] | 2022-10-15 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-5.22032343e-02 -1.33744121e-01 -3.77133965e-01 -4.09091920e-01
-4.67410058e-01 -8.07814717e-01 7.26750135e-01 4.99063462e-01
-9.42995071e-01 6.40052319e-01 6.85050905e-01 -5.22139311e-01
-1.87901467e-01 -8.64643037e-01 4.46636900e-02 -3.68268043e-01
-2.23775402e-01 4.10124481e-01 1.78422183e-01 -6.67097747... | [10.27392292022705, 8.951506614685059] |
c3ad5947-9c79-4bca-a3b8-87adbcaaa267 | real-time-online-skeleton-extraction-and | 2206.11376 | null | https://arxiv.org/abs/2206.11376v1 | https://arxiv.org/pdf/2206.11376v1.pdf | Real-Time Online Skeleton Extraction and Gesture Recognition on Pepper | We present a multi-stage pipeline for simple gesture recognition. The novelty of our approach is the association of different technologies, resulting in the first real-time system as of now to conjointly extract skeletons and recognise gesture on a Pepper robot. For this task, Pepper has been augmented with an embedded... | ['Jean-Marc Montanier', 'Axel Lefrant'] | 2022-06-22 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 6.67006299e-02 8.36847797e-02 4.25869167e-01 -1.30806088e-01
-2.79942930e-01 -4.34003621e-01 5.69778681e-01 -4.13462132e-01
-8.41869473e-01 1.46395728e-01 -1.53760731e-01 5.45773916e-02
1.61528543e-01 -3.72299939e-01 -4.98614371e-01 -4.96297598e-01
-1.87846884e-01 6.27882659e-01 8.44549417e-01 -3.80216002... | [6.619039058685303, -0.171511709690094] |
e98dfc82-1f80-4ee1-ad23-1b8740ae7def | dasnet-dual-attentive-fully-convolutional | 2003.03608 | null | https://arxiv.org/abs/2003.03608v2 | https://arxiv.org/pdf/2003.03608v2.pdf | DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images | Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impr... | ['Yu Liu', 'Haifeng Li', 'Jie Chen', 'Li Chen', 'Haozhe Huang', 'Ziyang Yuan', 'Jiawei Zhu', 'Jian Peng'] | 2020-03-07 | null | null | null | null | ['change-detection-for-remote-sensing-images'] | ['miscellaneous'] | [ 2.58538604e-01 -6.18592978e-01 2.08507732e-01 -4.71227974e-01
-4.75217849e-01 -1.84482232e-01 4.66139525e-01 -1.55140534e-01
-4.81375158e-01 6.24956250e-01 1.55994758e-01 8.74813199e-02
-2.01745152e-01 -9.47859764e-01 -6.00985408e-01 -9.76851463e-01
7.83548057e-02 -2.03124493e-01 2.02797413e-01 -4.36194599... | [9.770905494689941, -1.2613641023635864] |
489c51de-0c0d-4ce5-b7a2-04accd6e4e58 | a-resource-efficient-embedded-iris | 1909.03385 | null | https://arxiv.org/abs/1909.03385v1 | https://arxiv.org/pdf/1909.03385v1.pdf | A Resource-Efficient Embedded Iris Recognition System Using Fully Convolutional Networks | Applications of Fully Convolutional Networks (FCN) in iris segmentation have shown promising advances. For mobile and embedded systems, a significant challenge is that the proposed FCN architectures are extremely computationally demanding. In this article, we propose a resource-efficient, end-to-end iris recognition fl... | ['Hokchhay Tann', 'Sherief Reda', 'Heng Zhao'] | 2019-09-08 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 3.75372291e-01 -2.93762296e-01 -3.47208142e-01 -3.57283622e-01
-2.38354191e-01 -4.08995986e-01 1.34095341e-01 3.05912327e-02
-7.05894887e-01 1.55332938e-01 -8.60532820e-02 -7.80796409e-01
-2.51068711e-01 -5.62919319e-01 -5.04624367e-01 -3.50143671e-01
1.14754401e-01 3.34848836e-02 3.72637138e-02 -1.72100868... | [8.597228050231934, 2.824005126953125] |
941ee1c7-4eba-46dc-9ca7-14ee4446a225 | diachronic-word-embeddings-reveal-statistical | 1605.09096 | null | http://arxiv.org/abs/1605.09096v6 | http://arxiv.org/pdf/1605.09096v6.pdf | Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change | Understanding how words change their meanings over time is key to models of
language and cultural evolution, but historical data on meaning is scarce,
making theories hard to develop and test. Word embeddings show promise as a
diachronic tool, but have not been carefully evaluated. We develop a robust
methodology for q... | ['Dan Jurafsky', 'William L. Hamilton', 'Jure Leskovec'] | 2016-05-30 | diachronic-word-embeddings-reveal-statistical-1 | https://aclanthology.org/P16-1141 | https://aclanthology.org/P16-1141.pdf | acl-2016-8 | ['diachronic-word-embeddings'] | ['natural-language-processing'] | [-1.02440529e-01 -4.11008537e-01 -3.01082462e-01 -1.93159029e-01
6.82402216e-03 -8.44293296e-01 1.20973074e+00 5.52919209e-01
-9.23751712e-01 6.02201283e-01 9.25535142e-01 -6.04660153e-01
-4.06437889e-02 -9.54302788e-01 -4.37327534e-01 -4.22502011e-01
-6.82781562e-02 1.85286999e-01 2.04389617e-01 -6.22919798... | [10.148149490356445, 8.865030288696289] |
799dc09c-75e2-4e21-bace-1d073776789b | auco-resnet-an-end-to-end-network-for-covid | null | null | https://www.sciencedirect.com/science/article/pii/S0031320322001376 | https://www.researchgate.net/publication/359245461_AUCO_ResNet_an_end-to-end_network_for_Covid-19_pre-screening_from_cough_and_breath | AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breath | This study presents the Auditory Cortex ResNet (AUCO ResNet), it is a biologically inspired deep neural network especially designed for sound classification and more specifically for Covid-19 recognition from audio tracks of coughs and breaths. Differently from other approaches, it can be trained end-to-end thus optimi... | ['Giuseppe Pirlo', 'Luigi Moretti', 'Donato Impedovo', 'Paolo Giglio', 'Vincenzo Dentamaro'] | 2022-03-15 | null | null | null | pattern-recognition-2022-3 | ['environmental-sound-classification', 'sound-classification', 'covid-19-detection'] | ['audio', 'audio', 'medical'] | [-1.20358326e-01 5.83958928e-04 4.18881208e-01 -6.63664797e-03
-3.71365875e-01 -2.34246567e-01 3.40081722e-01 3.01929832e-01
-8.83437812e-01 4.53342468e-01 2.12376580e-01 1.80922840e-02
-3.82811397e-01 -6.38527751e-01 -4.33702677e-01 -6.91147208e-01
-3.58728528e-01 4.26672429e-01 3.01335305e-01 -3.68391484... | [15.203173637390137, 5.308226108551025] |
c5b19a57-bcb0-423e-8b85-39be92f363c2 | variational-latent-discrete-representation | 2306.15282 | null | https://arxiv.org/abs/2306.15282v2 | https://arxiv.org/pdf/2306.15282v2.pdf | Variational Latent Discrete Representation for Time Series Modelling | Discrete latent space models have recently achieved performance on par with their continuous counterparts in deep variational inference. While they still face various implementation challenges, these models offer the opportunity for a better interpretation of latent spaces, as well as a more direct representation of na... | ['Sylvain Le Corff', 'Maurice Charbit', 'Max Cohen'] | 2023-06-27 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 5.22316769e-02 1.45488933e-01 -7.97931552e-02 -4.45867717e-01
-9.24246669e-01 -4.87461656e-01 1.30996776e+00 -1.84619635e-01
9.03508142e-02 6.04205489e-01 3.49223256e-01 -2.57767141e-01
-2.40577415e-01 -9.07905161e-01 -5.32000542e-01 -9.26056027e-01
1.89426571e-01 1.02527475e+00 -7.12683573e-02 1.30752161... | [6.973645210266113, 3.8490617275238037] |
5652de72-b0e3-4ed0-ba5e-42ead70e66c6 | school-based-malaria-chemoprevention-as-a | 2303.10684 | null | https://arxiv.org/abs/2303.10684v1 | https://arxiv.org/pdf/2303.10684v1.pdf | School-based malaria chemoprevention as a cost-effective approach to improve cognitive and educational outcomes: a meta-analysis | There is limited evidence of health interventions impact on cognitive function and educational outcomes. We build on two prior systematic reviews to conduct a meta-analysis, exploring the effects of one of the most consequential health interventions, malaria chemoprevention, on education outcomes. We pool data from nin... | ['Lauren M. Cohee', 'Donald Bundy', 'Charles Opondo', 'R. Matthew Chico', 'Sian Clarke', 'Matthew C. H. Jukes', 'Noam Angrist'] | 2023-03-19 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 4.33585010e-02 2.06327170e-01 -4.87583965e-01 -8.12349934e-03
-3.17467153e-01 -3.67669374e-01 4.86668587e-01 8.48744452e-01
-7.18067586e-01 6.57455206e-01 5.95902264e-01 -9.28987205e-01
-4.22601342e-01 -9.62546051e-01 -1.08584082e+00 -3.50594491e-01
-8.05291981e-02 -1.24170333e-01 1.11311655e-02 2.37745985... | [7.9983391761779785, 5.408670902252197] |
0c73d43d-849c-45d3-812a-e988029b3a86 | the-benefits-of-close-domain-fine-tuning-for | 1912.05846 | null | https://arxiv.org/abs/1912.05846v1 | https://arxiv.org/pdf/1912.05846v1.pdf | The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images | A correct localisation of tables in a document is instrumental for determining their structure and extracting their contents; therefore, table detection is a key step in table understanding. Nowadays, the most successful methods for table detection in document images employ deep learning algorithms; and, particularly, ... | ['Jónathan Heras', 'César Domínguez', 'Ángela Casado-García', 'Vico Pascual', 'Eloy Mata'] | 2019-12-12 | null | null | null | null | ['table-detection'] | ['miscellaneous'] | [ 1.43345788e-01 9.13986936e-02 2.04580091e-03 -2.34472919e-02
-8.58578324e-01 -9.92156088e-01 8.19556952e-01 5.47014713e-01
-4.49149340e-01 4.59925950e-01 1.20516434e-01 -7.16745527e-03
-3.62453945e-02 -9.48228955e-01 -9.57023025e-01 -4.94927913e-01
3.06512117e-01 8.86053741e-01 5.15295029e-01 -3.07007283... | [11.685132026672363, 2.9879727363586426] |
8efb0d84-1c74-4a5f-8fd4-871d3d56254f | sparsity-exploitation-via-discovering | 2306.17090 | null | https://arxiv.org/abs/2306.17090v1 | https://arxiv.org/pdf/2306.17090v1.pdf | Sparsity exploitation via discovering graphical models in multi-variate time-series forecasting | Graph neural networks (GNNs) have been widely applied in multi-variate time-series forecasting (MTSF) tasks because of their capability in capturing the correlations among different time-series. These graph-based learning approaches improve the forecasting performance by discovering and understanding the underlying gra... | ['Duy Khuong Nguyen', 'Truong Son Hy', 'Ngoc-Dung Do'] | 2023-06-29 | null | null | null | null | ['time-series-forecasting'] | ['time-series'] | [-6.82821274e-02 5.73644154e-02 -2.32052878e-01 -5.20646989e-01
-1.28146455e-01 -4.96724218e-01 4.73493010e-01 4.53207530e-02
6.47902310e-01 4.50566858e-01 2.59766132e-01 -6.96081340e-01
-1.00549489e-01 -8.60294819e-01 -8.11671019e-01 -5.74120998e-01
-4.96008813e-01 4.14251745e-01 -1.28112167e-01 -3.53534907... | [6.794571876525879, 2.878671407699585] |
9e549c8b-578d-4334-bddc-5112d27ab7df | self-organising-maps-in-computer-security | 1608.01668 | null | http://arxiv.org/abs/1608.01668v1 | http://arxiv.org/pdf/1608.01668v1.pdf | Self-Organising Maps in Computer Security | Some argue that biologically inspired algorithms are the future of solving
difficult problems in computer science. Others strongly believe that the future
lies in the exploration of mathematical foundations of problems at hand. The
field of computer security tends to accept the latter view as a more
appropriate approac... | ['Jan Feyereisl', 'Uwe Aickelin'] | 2016-08-05 | null | null | null | null | ['computer-security'] | ['miscellaneous'] | [ 3.07733029e-01 5.23561127e-02 2.23146543e-01 -7.96851050e-03
7.17903316e-01 -4.17018056e-01 8.76510382e-01 3.95356864e-01
-7.46474743e-01 6.17227614e-01 -5.49895577e-02 -5.06271243e-01
-5.44078112e-01 -8.67014647e-01 -2.61962600e-02 -9.73055005e-01
-1.77805752e-01 1.86120108e-01 5.12283623e-01 -7.94941247... | [5.715864181518555, 4.107307434082031] |
f8f9fe18-7634-4e48-9f87-2a756adbe0ff | smm4h-2022-task-2-dataset-for-stance-and | null | null | https://aclanthology.org/2022.smm4h-1.53 | https://aclanthology.org/2022.smm4h-1.53.pdf | SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19 | This paper is an organizers’ report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise cl... | ['Elena Tutubalina', 'Vera Davydova'] | null | null | null | null | smm4h-coling-2022-10 | ['stance-detection', 'argument-mining'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.59080738e-01 9.07622993e-01 -7.60632336e-01 -4.28664625e-01
-7.45590448e-01 -3.79302979e-01 9.01287436e-01 1.30128455e+00
-6.61716521e-01 1.05356002e+00 9.75665331e-01 -7.58637488e-01
-1.28623322e-01 -6.21297538e-01 -7.83732235e-01 -1.91513389e-01
-7.42948875e-02 5.56599259e-01 1.09277256e-01 -5.10324538... | [8.562263488769531, 9.50046157836914] |
5d1fec2d-b44d-40ca-a804-589853675502 | differentially-private-distributed-convex-1 | 2302.14514 | null | https://arxiv.org/abs/2302.14514v1 | https://arxiv.org/pdf/2302.14514v1.pdf | Differentially Private Distributed Convex Optimization | This paper considers distributed optimization (DO) where multiple agents cooperate to minimize a global objective function, expressed as a sum of local objectives, subject to some constraints. In DO, each agent iteratively solves a local optimization model constructed by its own data and communicates some information (... | ['Kibaek Kim', 'Minseok Ryu'] | 2023-02-28 | null | null | null | null | ['distributed-optimization'] | ['methodology'] | [ 5.31916842e-02 3.29651237e-02 -3.03697646e-01 -1.85801640e-01
-8.44766557e-01 -8.72784555e-01 1.00801624e-02 5.25250077e-01
-6.54679358e-01 9.84877527e-01 1.16580948e-01 -8.48503634e-02
-4.07654852e-01 -9.02403831e-01 -5.99108934e-01 -1.48108566e+00
-2.18036950e-01 1.55953437e-01 -5.33564270e-01 1.31137341... | [5.947376251220703, 5.959488391876221] |
fb8cdd95-d4da-43da-abab-3dbf0ab4a4a5 | multiple-fusion-adaptation-a-strong-framework | 2112.00295 | null | https://arxiv.org/abs/2112.00295v1 | https://arxiv.org/pdf/2112.00295v1.pdf | Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation | This paper challenges the cross-domain semantic segmentation task, aiming to improve the segmentation accuracy on the unlabeled target domain without incurring additional annotation. Using the pseudo-label-based unsupervised domain adaptation (UDA) pipeline, we propose a novel and effective Multiple Fusion Adaptation (... | ['Xiaohui Hu', 'Haichang Li', 'Rui Wang', 'Yifan Sun', 'Kai Zhang'] | 2021-12-01 | null | null | null | null | ['unsupervised-semantic-segmentation', 'synthetic-to-real-translation'] | ['computer-vision', 'computer-vision'] | [ 2.21366793e-01 2.11103827e-01 -1.87300384e-01 -5.28623581e-01
-1.19240332e+00 -5.72642922e-01 4.18466091e-01 6.50511086e-02
-6.10309601e-01 6.12295389e-01 -2.21393824e-01 -1.64279193e-01
2.19416142e-01 -6.16093993e-01 -5.86631119e-01 -7.24901974e-01
4.80811536e-01 6.53451681e-01 7.98327804e-01 -7.66100511... | [9.583600044250488, 1.3464170694351196] |
362c9902-c19f-4792-a15f-c66a90fc3324 | a-mixed-reality-dataset-for-category-level-6d | 2211.10470 | null | https://arxiv.org/abs/2211.10470v1 | https://arxiv.org/pdf/2211.10470v1.pdf | A mixed-reality dataset for category-level 6D pose and size estimation of hand-occluded containers | Estimating the 6D pose and size of household containers is challenging due to large intra-class variations in the object properties, such as shape, size, appearance, and transparency. The task is made more difficult when these objects are held and manipulated by a person due to varying degrees of hand occlusions caused... | ['Andrea Cavallaro', 'Alessio Xompero', 'Xavier Weber'] | 2022-11-18 | null | null | null | null | ['mixed-reality', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-8.04860443e-02 -3.25654089e-01 2.28689656e-01 -3.00755471e-01
-2.34445557e-01 -9.56824303e-01 2.52231628e-01 4.63413484e-02
-1.51143089e-01 1.15458921e-01 2.43574500e-01 1.72574461e-01
2.04742700e-01 -3.16870779e-01 -1.02384830e+00 -4.39010292e-01
-2.72562951e-01 9.14963543e-01 2.45722264e-01 2.60229737... | [6.5428996086120605, -1.1248953342437744] |
08304d9a-65d0-4cd3-a85a-7e0c3151d36b | temporal-event-knowledge-acquisition-via | 1805.10956 | null | http://arxiv.org/abs/1805.10956v1 | http://arxiv.org/pdf/1805.10956v1.pdf | Temporal Event Knowledge Acquisition via Identifying Narratives | Inspired by the double temporality characteristic of narrative texts, we
propose a novel approach for acquiring rich temporal "before/after" event
knowledge across sentences in narrative stories. The double temporality states
that a narrative story often describes a sequence of events following the
chronological order ... | ['Wenlin Yao', 'Ruihong Huang'] | 2018-05-28 | temporal-event-knowledge-acquisition-via-1 | https://aclanthology.org/P18-1050 | https://aclanthology.org/P18-1050.pdf | acl-2018-7 | ['temporal-relation-classification'] | ['natural-language-processing'] | [ 6.90268949e-02 -1.06692992e-01 -9.11599100e-01 -3.06336403e-01
-6.13362670e-01 -9.26717818e-01 1.50077188e+00 5.46313524e-01
-4.03417826e-01 9.83562708e-01 1.37918925e+00 -1.73823014e-01
-4.56101716e-01 -8.66401434e-01 -6.97716951e-01 -1.73858345e-01
-3.77652138e-01 2.42078304e-01 2.91965842e-01 -3.68689358... | [10.851222038269043, 8.91960334777832] |
45470468-6a28-4427-a791-076c112a4996 | self-supervised-shadow-removal | 2010.11619 | null | https://arxiv.org/abs/2010.11619v1 | https://arxiv.org/pdf/2010.11619v1.pdf | Self-Supervised Shadow Removal | Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photo-realistic restoration of the image contents. Decades of re-search produced a multitude of hand-crafted restoration techniques and, more recently, learned solu... | ['Radu Timofte', 'Luc van Gool', 'Andres Romero', 'Florin-Alexandru Vasluianu'] | 2020-10-22 | null | null | null | null | ['shadow-removal', 'image-shadow-removal'] | ['computer-vision', 'computer-vision'] | [ 9.67347383e-01 7.28518888e-02 3.99736434e-01 -3.51149350e-01
-4.38829720e-01 -2.81728894e-01 6.34768665e-01 -4.68777418e-01
-4.45642442e-01 1.00425637e+00 1.72609147e-02 -2.64144927e-01
1.49372727e-01 -2.02458784e-01 -7.47134209e-01 -9.05562758e-01
3.73359293e-01 3.96279663e-01 5.74286878e-01 -1.19696788... | [10.830410957336426, -4.089817047119141] |
71704da5-b1ec-46ce-b1a9-943cd31f98fe | allophant-cross-lingual-phoneme-recognition | 2306.04306 | null | https://arxiv.org/abs/2306.04306v1 | https://arxiv.org/pdf/2306.04306v1.pdf | Allophant: Cross-lingual Phoneme Recognition with Articulatory Attributes | This paper proposes Allophant, a multilingual phoneme recognizer. It requires only a phoneme inventory for cross-lingual transfer to a target language, allowing for low-resource recognition. The architecture combines a compositional phone embedding approach with individually supervised phonetic attribute classifiers in... | ['Munir Georges', 'Aaricia Herygers', 'Kevin Glocker'] | 2023-06-07 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.94135606e-01 5.12148738e-02 -2.57034123e-01 -4.51602310e-01
-1.76404905e+00 -9.34505343e-01 6.05694532e-01 -1.14997253e-01
-6.95639372e-01 6.48768306e-01 3.16207498e-01 -4.78220463e-01
5.10899484e-01 -4.64630097e-01 -1.01094544e+00 -4.13999945e-01
3.16093475e-01 1.09462428e+00 2.25234535e-02 -7.48737082... | [14.254584312438965, 7.007551193237305] |
5dc798b7-067b-46c8-b429-4a6906848d77 | adaptive-human-matting-for-dynamic-videos | 2304.06018 | null | https://arxiv.org/abs/2304.06018v1 | https://arxiv.org/pdf/2304.06018v1.pdf | Adaptive Human Matting for Dynamic Videos | The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with high... | ['Zicheng Liu', 'Lijuan Wang', 'Linjie Li', 'Kevin Lin', 'Kun Luo', 'Jiang Wang', 'Chung-Ching Lin'] | 2023-04-12 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [ 3.89994979e-01 -1.61723256e-01 -4.61571291e-03 -2.80503631e-01
-5.10918081e-01 -4.14761633e-01 4.71263021e-01 -5.48399746e-01
-8.01744163e-02 4.74290997e-01 4.51702476e-01 -1.43106878e-01
2.67045170e-01 -5.09584427e-01 -9.41020250e-01 -6.86248362e-01
-8.42675865e-02 2.72830725e-01 6.40119255e-01 5.44209033... | [10.648574829101562, -0.8793579339981079] |
9cc224c2-4bf2-471f-9a7a-d3f65966215e | event-transition-planning-for-open-ended-text | 2204.09453 | null | https://arxiv.org/abs/2204.09453v1 | https://arxiv.org/pdf/2204.09453v1.pdf | Event Transition Planning for Open-ended Text Generation | Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context. The open-ended nature of these tasks brings new challenges to the neural auto-regressive text generators nowadays. Despite these neural models are good ... | ['Lingpeng Kong', 'Yuxuan Lai', 'Zhaochun Ren', 'Wei Bi', 'Piji Li', 'Qintong Li'] | 2022-04-20 | null | https://aclanthology.org/2022.findings-acl.269 | https://aclanthology.org/2022.findings-acl.269.pdf | findings-acl-2022-5 | ['story-completion'] | ['natural-language-processing'] | [ 2.77900934e-01 6.68938577e-01 -6.86142519e-02 -2.26368636e-01
-6.79512262e-01 -5.61511278e-01 1.11794174e+00 1.04158446e-01
4.99719866e-02 1.10699475e+00 8.99723530e-01 -2.31078893e-01
2.86052767e-02 -9.49033618e-01 -6.33868277e-01 -3.22648138e-01
2.50260562e-01 6.37852073e-01 -1.52345508e-01 -5.17444670... | [11.72531795501709, 8.928329467773438] |
5cc4c94f-6a11-4817-8de1-0e35ca8cae8c | sensenet-neural-keyphrase-generation-with | 2012.06754 | null | https://arxiv.org/abs/2012.06754v1 | https://arxiv.org/pdf/2012.06754v1.pdf | SenSeNet: Neural Keyphrase Generation with Document Structure | Keyphrase Generation (KG) is the task of generating central topics from a given document or literary work, which captures the crucial information necessary to understand the content. Documents such as scientific literature contain rich meta-sentence information, which represents the logical-semantic structure of the do... | ['Xuanjing Huang', 'Qi Zhang', 'Xiaoyu Xing', 'Bingning Wang', 'Zhengyan Li', 'Yichao Luo'] | 2020-12-12 | null | null | null | null | ['keyphrase-generation'] | ['natural-language-processing'] | [ 4.67514366e-01 4.28602785e-01 -2.59050906e-01 -3.85525703e-01
-1.03618622e+00 -4.89544094e-01 6.71092093e-01 2.12276220e-01
-4.53340262e-01 1.08276558e+00 1.05989277e+00 -2.00015351e-01
2.30981022e-01 -8.83011758e-01 -8.89814794e-01 -4.15513784e-01
4.45888042e-01 7.80381933e-02 3.67176645e-02 -4.09882575... | [12.242025375366211, 9.068913459777832] |
cfcd2be5-93e7-4ae5-a809-cce413ff2044 | decodingtrust-a-comprehensive-assessment-of | 2306.11698 | null | https://arxiv.org/abs/2306.11698v1 | https://arxiv.org/pdf/2306.11698v1.pdf | DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models | Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applicati... | ['Bo Li', 'Dawn Song', 'Sanmi Koyejo', 'Yu Cheng', 'Zinan Lin', 'Dan Hendrycks', 'Mantas Mazeika', 'Simran Arora', 'Sang T. Truong', 'Rylan Schaeffer', 'Ritik Dutta', 'Zidi Xiong', 'Chejian Xu', 'Chenhui Zhang', 'Mintong Kang', 'Chulin Xie', 'Hengzhi Pei', 'Weixin Chen', 'Boxin Wang'] | 2023-06-20 | null | null | null | null | ['adversarial-robustness', 'ethics'] | ['adversarial', 'miscellaneous'] | [-1.23950854e-01 5.02372205e-01 -6.44508079e-02 -3.66201282e-01
-9.95347738e-01 -1.08279455e+00 7.17866242e-01 4.67331707e-02
1.07914330e-02 8.18495810e-01 2.02263042e-01 -8.12479258e-01
2.20960066e-01 -5.05748570e-01 -9.42700028e-01 -5.02580881e-01
-7.59161934e-02 2.67564297e-01 -3.53861302e-01 -3.01277667... | [6.1702985763549805, 7.975152015686035] |
dfdb67be-35c0-4eaf-b4f2-510092700a2e | sequential-point-clouds-a-survey | 2204.09337 | null | https://arxiv.org/abs/2204.09337v2 | https://arxiv.org/pdf/2204.09337v2.pdf | Sequential Point Clouds: A Survey | Point cloud has drawn more and more research attention as well as real-world applications. However, many of these applications (e.g. autonomous driving and robotic manipulation) are actually based on sequential point clouds (i.e. four dimensions) because the information of the static point cloud data could provide is s... | ['YingLi Tian', 'HaiYan Wang'] | 2022-04-20 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [-4.42254692e-01 -6.96575701e-01 -3.20567787e-01 -2.39044651e-01
7.02748299e-02 -6.62344635e-01 5.35449147e-01 1.84443429e-01
-2.54179418e-01 2.89613515e-01 -6.91958070e-01 -5.95531523e-01
8.02410617e-02 -8.74411941e-01 -7.22008526e-01 -5.92888653e-01
-2.90486634e-01 7.66785204e-01 5.32553136e-01 -2.03036204... | [8.055304527282715, -3.0829920768737793] |
de360762-7ed4-46e5-a3ac-46c25f936ef2 | protagonists-tagger-in-literary-domain-new | 2110.01349 | null | https://arxiv.org/abs/2110.01349v1 | https://arxiv.org/pdf/2110.01349v1.pdf | Protagonists' Tagger in Literary Domain -- New Datasets and a Method for Person Entity Linkage | Semantic annotation of long texts, such as novels, remains an open challenge in Natural Language Processing (NLP). This research investigates the problem of detecting person entities and assigning them unique identities, i.e., recognizing people (especially main characters) in novels. We prepared a method for person en... | ['Anna Wróblewska', 'Weronika Łajewska'] | 2021-10-04 | null | null | null | null | ['entity-disambiguation'] | ['natural-language-processing'] | [-4.57401387e-02 1.63279489e-01 1.40642017e-01 -3.88870060e-01
-8.02287519e-01 -8.42690349e-01 1.01631129e+00 5.19949019e-01
-1.04955661e+00 1.28973055e+00 5.66835880e-01 2.85834104e-01
3.77387479e-02 -1.06652844e+00 -5.84019125e-01 -2.87157893e-01
3.32367718e-01 1.20921075e+00 3.17235380e-01 -1.80030331... | [9.571850776672363, 9.320758819580078] |
3d46e332-1bb1-4a73-aa61-42cb50e05f32 | deep-dual-stream-residual-network-with | 2207.12004 | null | https://arxiv.org/abs/2207.12004v1 | https://arxiv.org/pdf/2207.12004v1.pdf | Deep dual stream residual network with contextual attention for pansharpening of remote sensing images | Pansharpening enhances spatial details of high spectral resolution multispectral images using features of high spatial resolution panchromatic image. There are a number of traditional pansharpening approaches but producing an image exhibiting high spectral and spatial fidelity is still an open problem. Recently, deep l... | ['Muhammad Shahzad', 'Anis Ur Rahman', 'Syeda Roshana Ali'] | 2022-07-25 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 8.65052760e-01 -5.80323398e-01 -5.15767485e-02 -2.67060786e-01
-9.13985848e-01 -3.49225134e-01 4.82096016e-01 -2.59031981e-01
-4.68331009e-01 6.67102695e-01 9.99401063e-02 -3.34665366e-02
-6.22971237e-01 -1.15256691e+00 -4.69648749e-01 -1.07174599e+00
4.88675497e-02 -1.63357690e-01 2.27568537e-01 -5.47652245... | [10.138443946838379, -1.9282076358795166] |
129247ac-a2bf-4fd9-a6d8-53403fb8fec6 | radiff-controllable-diffusion-models-for | 2307.02392 | null | https://arxiv.org/abs/2307.02392v1 | https://arxiv.org/pdf/2307.02392v1.pdf | RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation | Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from the vast amount of data it will allow acquiring. Automated source finding is a particularly important task in this context, as it enables t... | ['Concetto Spampinato', 'Filomena Bufano', 'Cristobal Bordiu', 'Adriano Ingallinera', 'Eva Sciacca', 'Simone Riggi', 'Daniel Magro', 'Andrew M. Hopkins', 'Andrea Pilzer', 'Giuseppe Fiameni', 'Andrea DeMarco', 'Thomas Cecconello', 'Renato Sortino'] | 2023-07-05 | null | null | null | null | ['object-detection', 'astronomy'] | ['computer-vision', 'miscellaneous'] | [ 5.35714447e-01 2.91250497e-01 3.20869952e-01 -2.72709668e-01
-9.96538579e-01 -6.71354175e-01 7.19855309e-01 -3.81163247e-02
-5.74372172e-01 7.78393269e-01 -1.76084042e-01 -2.50910312e-01
-1.32698938e-01 -9.42158580e-01 -8.92560720e-01 -5.45010746e-01
1.35636941e-01 9.90546107e-01 3.31150830e-01 -4.33596484... | [9.7783784866333, 0.8905373215675354] |
c3f4ad1c-7b71-45f0-91a5-c3213cda1d8b | graph-construction-using-principal-axis-trees | 2302.12000 | null | https://arxiv.org/abs/2302.12000v2 | https://arxiv.org/pdf/2302.12000v2.pdf | Graph Construction using Principal Axis Trees for Simple Graph Convolution | Graph Neural Networks (GNNs) are increasingly becoming the favorite method for graph learning. They exploit the semi-supervised nature of deep learning, and they bypass computational bottlenecks associated with traditional graph learning methods. In addition to the feature matrix $X$, GNNs need an adjacency matrix $A$ ... | ['Masahiro Takatsuka', 'Adel F. Ahmed', 'John Stavrakakis', 'Mashaan Alshammari'] | 2023-02-22 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-1.39366016e-01 4.16855484e-01 2.32237905e-01 -4.70189691e-01
1.59425437e-01 -4.33780611e-01 4.32673305e-01 4.88242298e-01
-4.22274083e-01 4.19459671e-01 -1.40231445e-01 -4.35664177e-01
-2.63267338e-01 -1.35979354e+00 -8.17703724e-01 -7.79687405e-01
-7.46565402e-01 2.41843998e-01 3.59809577e-01 -7.08331838... | [7.024289608001709, 6.043178081512451] |
5e514ee3-8640-4d2b-91e0-e2c1800ed91f | a-neural-network-trained-to-predict-future | 1805.10734 | null | http://arxiv.org/abs/1805.10734v2 | http://arxiv.org/pdf/1805.10734v2.pdf | A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception | While deep neural networks take loose inspiration from neuroscience, it is an
open question how seriously to take the analogies between artificial deep
networks and biological neuronal systems. Interestingly, recent work has shown
that deep convolutional neural networks (CNNs) trained on large-scale image
recognition t... | ['William Lotter', 'David Cox', 'Gabriel Kreiman'] | 2018-05-28 | null | null | null | null | ['predict-future-video-frames'] | ['computer-vision'] | [ 4.13207054e-01 9.92854238e-02 1.85201824e-01 -2.69775480e-01
1.63768977e-01 -6.63235903e-01 8.26894760e-01 -1.96271315e-01
-1.36723965e-01 5.65226734e-01 2.13688284e-01 -1.83619305e-01
-8.61213543e-03 -7.95155346e-01 -8.20733428e-01 -9.85709548e-01
-5.98061793e-02 1.29482418e-01 2.86108315e-01 -4.04337406... | [9.504203796386719, 2.534649610519409] |
db890700-9224-4292-8abf-ac0c4b6b78ca | identity-deception-detection | null | null | https://aclanthology.org/I17-1089 | https://aclanthology.org/I17-1089.pdf | Identity Deception Detection | This paper addresses the task of detecting identity deception in language. Using a novel identity deception dataset, consisting of real and portrayed identities from 600 individuals, we show that we can build accurate identity detectors targeting both age and gender, with accuracies of up to 88. We also perform an anal... | ['Quincy Davenport', "Ver{\\'o}nica P{\\'e}rez-Rosas", 'Anna Mengdan Dai', 'Mohamed Abouelenien', 'Rada Mihalcea'] | 2017-11-01 | identity-deception-detection-1 | https://aclanthology.org/I17-1089 | https://aclanthology.org/I17-1089.pdf | ijcnlp-2017-11 | ['deception-detection'] | ['miscellaneous'] | [ 2.92227864e-02 3.44462171e-02 -8.25948343e-02 -7.48921692e-01
-4.61741596e-01 -9.55305219e-01 1.30508399e+00 5.42083308e-02
-2.17404827e-01 7.71093607e-01 4.43172961e-01 -8.43572170e-02
5.99470794e-01 -6.57303393e-01 -4.49180514e-01 -2.24898309e-01
-2.04694510e-01 4.13263053e-01 -4.48235035e-01 -4.80323732... | [8.325241088867188, 10.42712688446045] |
5f2326f8-8ff2-4ac1-8e6b-22488cf33150 | balanced-energy-regularization-loss-for-out-1 | 2306.10485 | null | https://arxiv.org/abs/2306.10485v1 | https://arxiv.org/pdf/2306.10485v1.pdf | Balanced Energy Regularization Loss for Out-of-distribution Detection | In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data as OOD data has shown promising performance. However, the method provides an equal loss to all auxiliary data to differentiate them from inliers. However, based on our observation, in various tasks, there is a general imbalan... | ['Jin Young Choi', 'Hawook Jeong', 'Hyunjun Choi'] | 2023-06-18 | balanced-energy-regularization-loss-for-out | http://openaccess.thecvf.com//content/CVPR2023/html/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Choi_Balanced_Energy_Regularization_Loss_for_Out-of-Distribution_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['out-of-distribution-detection'] | ['computer-vision'] | [ 3.34143117e-02 1.65460929e-01 -4.10574526e-01 -4.30113941e-01
-1.18346786e+00 -2.53895283e-01 2.58214563e-01 2.04291835e-01
-2.20344499e-01 3.81303221e-01 -1.50106931e-02 9.00306851e-02
3.62857789e-01 -4.73917633e-01 -6.27792120e-01 -9.44499433e-01
4.98472989e-01 5.32831550e-01 3.42149228e-01 2.16728792... | [9.63196086883545, 1.3219611644744873] |
77dc500f-0b3e-4946-9af8-a385ab7f8452 | serialized-interacting-mixed-membership | 2209.07813 | null | https://arxiv.org/abs/2209.07813v1 | https://arxiv.org/pdf/2209.07813v1.pdf | Serialized Interacting Mixed Membership Stochastic Block Model | Last years have seen a regain of interest for the use of stochastic block modeling (SBM) in recommender systems. These models are seen as a flexible alternative to tensor decomposition techniques that are able to handle labeled data. Recent works proposed to tackle discrete recommendation problems via SBMs by consideri... | ['Sabine Loudcher', 'Julien Velcin', 'Gaël Poux-Médard'] | 2022-09-16 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [-8.97913277e-02 -2.26897776e-01 -4.58979070e-01 -3.97181183e-01
-4.20054585e-01 -6.50295198e-01 1.08149981e+00 3.42767267e-03
-6.73575625e-02 4.17971164e-01 7.09205270e-01 -7.01275766e-01
-5.30228257e-01 -5.76699436e-01 -7.44738221e-01 -8.13557267e-01
-3.67718518e-01 7.07600474e-01 2.00214818e-01 -6.23152018... | [9.611677169799805, 5.499703884124756] |
9c75a92f-d936-417a-a849-d1472992fb00 | clip-art-contrastive-pre-training-for-fine-1 | 2204.14244 | null | https://arxiv.org/abs/2204.14244v1 | https://arxiv.org/pdf/2204.14244v1.pdf | CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification | Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. To the best of our knowledge, we are one of the first methods to use CLIP (Contrastive Language-Image Pre-Training) to train a neural network on ... | ['Kerem Turgutlu', 'Marcos V. Conde'] | 2022-04-29 | clip-art-contrastive-pre-training-for-fine | https://openaccess.thecvf.com/content/CVPR2021W/CVFAD/html/Conde_CLIP-Art_Contrastive_Pre-Training_for_Fine-Grained_Art_Classification_CVPRW_2021_paper.html | https://openaccess.thecvf.com/content/CVPR2021W/CVFAD/papers/Conde_CLIP-Art_Contrastive_Pre-Training_for_Fine-Grained_Art_Classification_CVPRW_2021_paper.pdf | proceedings-of-the-ieee-cvf-conference-on | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 3.87619525e-01 -2.46269792e-01 -3.27925950e-01 -4.81320024e-01
-1.00210977e+00 -8.39837670e-01 1.01192760e+00 -1.08232170e-01
-2.59476125e-01 5.54232419e-01 9.62060317e-02 3.05261761e-01
-3.50919813e-01 -5.91061592e-01 -9.12240446e-01 -2.71915197e-01
4.43727702e-01 1.35477388e+00 -1.12856358e-01 7.95277059... | [11.033929824829102, 0.9021198153495789] |
965c0834-459a-4fe5-91ee-82df1489a944 | learning-deformable-kernels-for-image-and | 1904.06903 | null | http://arxiv.org/abs/1904.06903v1 | http://arxiv.org/pdf/1904.06903v1.pdf | Learning Deformable Kernels for Image and Video Denoising | Most of the classical denoising methods restore clear results by selecting
and averaging pixels in the noisy input. Instead of relying on hand-crafted
selecting and averaging strategies, we propose to explicitly learn this process
with deep neural networks. Specifically, we propose deformable 2D kernels for
image denoi... | ['Muchen Li', 'Xiangyu Xu', 'Wenxiu Sun'] | 2019-04-15 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 2.77687043e-01 -5.40511072e-01 3.98082197e-01 -3.93631697e-01
-9.68565047e-01 -4.43115324e-01 5.54381490e-01 -3.22209656e-01
-7.02123761e-01 5.50362527e-01 1.34146705e-01 1.80605203e-01
-1.18001238e-01 -6.65762663e-01 -8.40974927e-01 -1.12581909e+00
-3.03655453e-02 -2.06839547e-01 2.20186129e-01 -8.42895657... | [11.473234176635742, -2.231491804122925] |
d116d8f1-58a2-4dca-803e-d1e4674da97c | automatic-grammatical-error-correction-for | null | null | https://aclanthology.org/P19-1609 | https://aclanthology.org/P19-1609.pdf | Automatic Grammatical Error Correction for Sequence-to-sequence Text Generation: An Empirical Study | Sequence-to-sequence (seq2seq) models have achieved tremendous success in text generation tasks. However, there is no guarantee that they can always generate sentences without grammatical errors. In this paper, we present a preliminary empirical study on whether and how much automatic grammatical error correction can h... | ['Furu Wei', 'Tao Ge', 'Ming Zhou', 'Xingxing Zhang'] | 2019-07-01 | null | null | null | acl-2019-7 | ['formality-style-transfer', 'sentence-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 7.06516743e-01 6.09223843e-01 1.96233034e-01 -6.47700310e-01
-1.04049933e+00 -4.83903706e-01 5.24081767e-01 1.08474284e-01
-2.49391466e-01 1.52047932e+00 6.00589991e-01 -5.69233775e-01
3.88158619e-01 -8.64300370e-01 -8.84948730e-01 4.72453516e-03
4.05078590e-01 7.57036030e-01 -2.45532319e-01 -8.78850758... | [11.756550788879395, 9.265534400939941] |
ee4c7e56-8260-4dbd-8195-721f56e0b222 | radiology-text-analysis-system-radtext | 2204.09599 | null | https://arxiv.org/abs/2204.09599v1 | https://arxiv.org/pdf/2204.09599v1.pdf | Radiology Text Analysis System (RadText): Architecture and Evaluation | Analyzing radiology reports is a time-consuming and error-prone task, which raises the need for an efficient automated radiology report analysis system to alleviate the workloads of radiologists and encourage precise diagnosis. In this work, we present RadText, an open-source radiology text analysis system developed by... | ['Yifan Peng', 'Zhiyong Lu', 'George Shih', 'Ying Ding', 'Mingquan Lin', 'Song Wang'] | 2022-03-19 | null | null | null | null | ['negation-detection'] | ['natural-language-processing'] | [ 1.00133084e-01 1.45527527e-01 -4.41931993e-01 -5.04147649e-01
-1.39962530e+00 -5.95336854e-01 4.08122353e-02 1.21520174e+00
-6.77428544e-01 4.81207639e-01 7.31468678e-01 -8.87442112e-01
-3.63551974e-01 -5.04381359e-01 -2.15367779e-01 -4.97874826e-01
1.84756026e-01 4.84464347e-01 -2.28931289e-02 3.94698858... | [8.402182579040527, 8.654760360717773] |
39d90ed2-b02f-4606-9224-3349d661e626 | the-steep-road-to-happily-ever-after-an | 1904.03366 | null | http://arxiv.org/abs/1904.03366v1 | http://arxiv.org/pdf/1904.03366v1.pdf | The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models | Visual storytelling is an intriguing and complex task that only recently
entered the research arena. In this work, we survey relevant work to date, and
conduct a thorough error analysis of three very recent approaches to visual
storytelling. We categorize and provide examples of common types of errors, and
identify key... | ['Natalie Parde', 'Yatri Modi'] | 2019-04-06 | the-steep-road-to-happily-ever-after-an-1 | https://aclanthology.org/W19-1805 | https://aclanthology.org/W19-1805.pdf | ws-2019-6 | ['visual-storytelling'] | ['natural-language-processing'] | [ 1.22381926e-01 2.36078352e-01 -2.99349949e-02 -8.37228298e-02
-2.75893092e-01 -7.97333777e-01 6.66265965e-01 2.89859921e-01
9.01777819e-02 5.76101065e-01 7.15985835e-01 -6.75838768e-01
-1.19779864e-02 -3.34048092e-01 -5.31620741e-01 5.83915645e-03
-1.63334593e-01 -1.78804733e-02 3.85704309e-01 -2.79897511... | [11.195836067199707, 0.8555691838264465] |
eb1414b0-ee0e-4bdc-ac7e-e968f7a0a966 | perceptual-generative-adversarial-networks | 1706.05274 | null | http://arxiv.org/abs/1706.05274v2 | http://arxiv.org/pdf/1706.05274v2.pdf | Perceptual Generative Adversarial Networks for Small Object Detection | Detecting small objects is notoriously challenging due to their low
resolution and noisy representation. Existing object detection pipelines
usually detect small objects through learning representations of all the
objects at multiple scales. However, the performance gain of such ad hoc
architectures is usually limited ... | ['Xiaodan Liang', 'Yunchao Wei', 'Jiashi Feng', 'Tingfa Xu', 'Shuicheng Yan', 'Jianan Li'] | 2017-06-16 | perceptual-generative-adversarial-networks-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Perceptual_Generative_Adversarial_CVPR_2017_paper.pdf | cvpr-2017-7 | ['small-object-detection'] | ['computer-vision'] | [ 3.47217888e-01 1.65639430e-01 9.75429565e-02 -1.57910854e-01
-1.00206435e+00 -4.43428934e-01 4.60597098e-01 -7.68960565e-02
-2.39345372e-01 5.44185340e-01 -2.29554743e-01 1.67169925e-02
4.69944894e-01 -9.93321836e-01 -9.02572989e-01 -9.03883576e-01
-6.40242696e-02 5.36618173e-01 7.96895981e-01 -2.44903713... | [9.522294998168945, 1.6255810260772705] |
2fee74cf-6ead-4e40-9ee3-f506ee427d74 | ihs_rd-lexical-normalization-for-english | null | null | https://aclanthology.org/W15-4311 | https://aclanthology.org/W15-4311.pdf | IHS\_RD: Lexical Normalization for English Tweets | null | ['Viachaslau Patsepnia', 'Dmitry Supranovich'] | 2015-07-01 | null | null | null | ws-2015-7 | ['lexical-normalization'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.3938212394714355, 3.7356112003326416] |
074b9416-ea4d-4fe4-b5d7-d4e09c255487 | on-the-use-of-different-feature-extraction | 1406.7314 | null | http://arxiv.org/abs/1406.7314v1 | http://arxiv.org/pdf/1406.7314v1.pdf | On the Use of Different Feature Extraction Methods for Linear and Non Linear kernels | The speech feature extraction has been a key focus in robust speech
recognition research; it significantly affects the recognition performance. In
this paper, we first study a set of different features extraction methods such
as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC)
and perceptual li... | ['Imen Trabelsi', 'Dorra Ben Ayed'] | 2014-06-27 | null | null | null | null | ['robust-speech-recognition'] | ['speech'] | [ 1.06248178e-01 -4.47244406e-01 4.44777645e-02 -3.14487547e-01
-5.83157778e-01 -4.66718018e-01 9.62333500e-01 2.77549654e-01
-4.26404208e-01 6.21277750e-01 4.38402206e-01 -3.94649982e-01
-1.94763750e-01 -1.93699613e-01 1.62019148e-01 -9.44181561e-01
-2.00062633e-01 -2.03543261e-01 2.67179042e-01 -1.37864873... | [14.601326942443848, 5.972989082336426] |
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