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053f2478-4d2d-47df-9af8-522f04e4ea74 | curriculum-sampling-for-dense-retrieval-with | 2212.09114 | null | https://arxiv.org/abs/2212.09114v1 | https://arxiv.org/pdf/2212.09114v1.pdf | Curriculum Sampling for Dense Retrieval with Document Expansion | The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing to fully capture the interactions between the query and document. To alleviate this, recent work expects to get query-informed representation... | ['Nan Duan', 'Siu Ming Yiu', 'Jian Jiao', 'Anlei Dong', 'Hang Zhang', 'A-Long Jin', 'Yeyun Gong', 'Xingwei He'] | 2022-12-18 | null | null | null | null | ['passage-retrieval'] | ['natural-language-processing'] | [-5.23712253e-03 4.12466489e-02 -3.91615272e-01 -2.13855371e-01
-1.14002621e+00 -6.54070199e-01 9.67642307e-01 4.66259532e-02
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3.13585311e-01 1.03443372e+00 2.20959485e-01 -1.83921367... | [11.43510627746582, 7.667717456817627] |
d6769cf3-eddc-4532-bef5-50fd0cf281c2 | pose-estimation-of-specific-rigid-objects | 2112.15075 | null | https://arxiv.org/abs/2112.15075v1 | https://arxiv.org/pdf/2112.15075v1.pdf | Pose Estimation of Specific Rigid Objects | In this thesis, we address the problem of estimating the 6D pose of rigid objects from a single RGB or RGB-D input image, assuming that 3D models of the objects are available. This problem is of great importance to many application fields such as robotic manipulation, augmented reality, and autonomous driving. First, w... | ['Tomas Hodan'] | 2021-12-30 | null | null | null | null | ['6d-pose-estimation'] | ['computer-vision'] | [ 3.60940039e-01 1.13058552e-01 8.26821551e-02 -4.09130484e-01
-6.27113998e-01 -6.11947000e-01 5.03523052e-01 -4.83834267e-01
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1.05943643e-01 1.01341772e+00 4.09690261e-01 -2.43325368... | [7.527201175689697, -2.6418511867523193] |
d4cd935d-3409-4c1d-9ddc-0494492de411 | nonlinear-controllability-and-function | 2212.00896 | null | https://arxiv.org/abs/2212.00896v1 | https://arxiv.org/pdf/2212.00896v1.pdf | Nonlinear controllability and function representation by neural stochastic differential equations | There has been a great deal of recent interest in learning and approximation of functions that can be expressed as expectations of a given nonlinearity with respect to its random internal parameters. Examples of such representations include "infinitely wide" neural nets, where the underlying nonlinearity is given by th... | ['Maxim Raginsky', 'Tanya Veeravalli'] | 2022-12-01 | null | null | null | null | ['motion-planning'] | ['robots'] | [ 4.25845414e-01 5.08876801e-01 -7.34533370e-02 -2.95122355e-01
-9.78427753e-03 -5.10405958e-01 7.62766480e-01 -4.36215401e-01
-7.24077344e-01 7.99029052e-01 1.05759002e-01 -1.30878106e-01
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-1.43846959e-01 4.72152263e-01 1.25096232e-01 -3.07032824... | [6.960142135620117, 3.6128458976745605] |
5fcf1f2b-e877-4ff0-b937-0bdd3e536370 | mask2lesion-mask-constrained-adversarial-skin | 1906.05845 | null | https://arxiv.org/abs/1906.05845v2 | https://arxiv.org/pdf/1906.05845v2.pdf | Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis | Skin lesion segmentation is a vital task in skin cancer diagnosis and further treatment. Although deep learning based approaches have significantly improved the segmentation accuracy, these algorithms are still reliant on having a large enough dataset in order to achieve adequate results. Inspired by the immense succes... | ['Kumar Abhishek', 'Ghassan Hamarneh'] | 2019-06-13 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.58922005e-01 3.27526301e-01 7.40544647e-02 -2.35287383e-01
-8.71586084e-01 -5.85664809e-01 5.96343935e-01 -1.99891943e-02
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5.28303564e-01 3.69986743e-01 1.71453491e-01 -1.42627275... | [15.19552993774414, -2.6967155933380127] |
ea4a41ff-fcf0-40d9-8a73-adcc215082ac | learning-thematic-similarity-metric-from | null | null | https://aclanthology.org/P18-2009 | https://aclanthology.org/P18-2009.pdf | Learning Thematic Similarity Metric from Article Sections Using Triplet Networks | In this paper we suggest to leverage the partition of articles into sections, in order to learn thematic similarity metric between sentences. We assume that a sentence is thematically closer to sentences within its section than to sentences from other sections. Based on this assumption, we use Wikipedia articles to aut... | ['Noam Slonim', 'Yosi Mass', 'Alon Halfon', 'Ilya Shnayderman', 'Ranit Aharonov', 'Elad Venezian', 'Liat Ein Dor'] | 2018-07-01 | null | null | null | acl-2018-7 | ['text-clustering'] | ['natural-language-processing'] | [ 9.71637419e-05 7.53003582e-02 -1.64439768e-01 -6.41063690e-01
-9.69083250e-01 -6.45375967e-01 6.14472330e-01 8.99852812e-01
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1.55604452e-01 3.69894147e-01 7.98300803e-02 -2.35909343... | [11.033638954162598, 8.801326751708984] |
fd22ca6c-2630-4337-b707-7a448eee1382 | does-your-model-classify-entities-reasonably | 2205.12640 | null | https://arxiv.org/abs/2205.12640v2 | https://arxiv.org/pdf/2205.12640v2.pdf | Does Your Model Classify Entities Reasonably? Diagnosing and Mitigating Spurious Correlations in Entity Typing | Entity typing aims at predicting one or more words that describe the type(s) of a specific mention in a sentence. Due to shortcuts from surface patterns to annotated entity labels and biased training, existing entity typing models are subject to the problem of spurious correlations. To comprehensively investigate the f... | ['Muhao Chen', 'Mingtao Dong', 'Bangzheng Li', 'Fei Wang', 'Nan Xu'] | 2022-05-25 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 1.52335703e-01 4.08621073e-01 -6.45460546e-01 -7.97602534e-01
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-4.90557998e-02 5.16865790e-01 -9.07632634e-02 -2.57949471... | [9.78161907196045, 8.503780364990234] |
df0086a6-286b-4a1b-8860-3984ee582025 | automatic-acne-object-detection-and-acne | null | null | https://www.mdpi.com/2075-4418/12/8/1879 | https://www.mdpi.com/2075-4418/12/8/1879 | Automatic Acne Object Detection and Acne Severity Grading Using Smartphone Images and Artificial Intelligence | Skin image analysis using artificial intelligence (AI) has recently attracted significant research interest, particularly for analyzing skin images captured by mobile devices. Acne is one of the most common skin conditions with profound effects in severe cases. In this study, we developed an AI system called AcneDet fo... | ['Hoan Thanh Ngo', 'Trung Xuan Ngo', 'Tsuyoshi Ishii', 'Kazuhiro Tsuji', 'Kazuma Suda', 'Anh Tam Nguyen', 'Nga Thi Vu', 'Hoan Tam Nguyen', 'Mai Thi-Thanh Tran', 'Nhu-Thuy Trinh', 'Lua Thi Ngo', 'Hieu Xuan Le', 'Phuc Hoang Nguyen', 'Quan Thanh Huynh'] | 2022-08-03 | null | null | null | diagnostics-2022-8 | ['medical-object-detection', 'acne-severity-grading'] | ['computer-vision', 'medical'] | [ 3.75667155e-01 -3.16002965e-01 -3.11080813e-01 -6.79224059e-02
-6.66891575e-01 -4.30245608e-01 2.82630831e-01 4.21547405e-02
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4.21179086e-03 2.05830798e-01 -1.34773597e-01 -4.05510329... | [15.677382469177246, -2.9852828979492188] |
b1fb6419-a2ed-4699-ba7e-ebdfa8c07f18 | two-level-graph-network-for-few-shot-class | 2303.13862 | null | https://arxiv.org/abs/2303.13862v1 | https://arxiv.org/pdf/2303.13862v1.pdf | Two-level Graph Network for Few-Shot Class-Incremental Learning | Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge of old classes. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbat... | ['Fenglei Xu', 'Zhenping Xia', 'Fuyuan Hu', 'Fan Lyu', 'Linyan Li', 'Hao Chen'] | 2023-03-24 | null | null | null | null | ['class-incremental-learning', 'few-shot-class-incremental-learning'] | ['computer-vision', 'methodology'] | [ 2.71846056e-01 2.64004290e-01 -5.05793333e-01 -3.89611959e-01
-2.42501706e-01 6.44498616e-02 2.67399520e-01 2.74725586e-01
-9.10103098e-02 8.02338719e-01 -1.02417797e-01 6.71211258e-02
-3.85746658e-01 -1.24346280e+00 -6.79383337e-01 -6.31380439e-01
-1.34633809e-01 4.74653661e-01 6.89633071e-01 -2.11828128... | [9.816107749938965, 3.425081968307495] |
5fb233d7-a0f3-4412-875d-38f6bf1181a8 | it-s-all-in-the-embedding-fake-news-detection | 2304.07781 | null | https://arxiv.org/abs/2304.07781v1 | https://arxiv.org/pdf/2304.07781v1.pdf | It's All in the Embedding! Fake News Detection Using Document Embeddings | With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the u... | ['Elena-Simona Apostol', 'Ciprian-Octavian Truică'] | 2023-04-16 | null | null | null | null | ['misinformation', 'fake-news-detection'] | ['miscellaneous', 'natural-language-processing'] | [-2.19227329e-01 -4.33094576e-02 -4.15703207e-01 -1.70928407e-02
-2.79638737e-01 -5.12174726e-01 1.06869793e+00 7.41520226e-01
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4.57888961e-01 -1.10611093e+00 -7.32698798e-01 -2.98750937e-01
3.38749826e-01 3.02669793e-01 1.69163689e-01 -7.66533136... | [8.20324993133545, 10.271595001220703] |
d5cf9636-9980-4dca-ac64-f8e18f6906b4 | predictive-business-process-monitoring-via | 2003.11268 | null | https://arxiv.org/abs/2003.11268v2 | https://arxiv.org/pdf/2003.11268v2.pdf | Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction | Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the pr... | ['Marcello La Rosa', 'Sarah Erfani', 'Ilya Verenich', 'Zahra Dasht Bozorgi', 'Farbod Taymouri'] | 2020-03-25 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 4.56735104e-01 4.37577397e-01 3.08226734e-01 -1.34545833e-01
-6.55686617e-01 -4.29761440e-01 1.18067563e+00 3.67259443e-01
-2.84959853e-01 8.75013530e-01 1.39710261e-02 -1.98302925e-01
-4.07583982e-01 -1.11192775e+00 -7.05219507e-01 -7.25107372e-01
-2.20552519e-01 6.73163235e-01 3.02370906e-01 -2.81450544... | [8.55825424194336, 5.801504135131836] |
2b56422f-ed39-4635-8664-25f29f270247 | deep-learning-based-ecg-classification-on | 2209.00989 | null | https://arxiv.org/abs/2209.00989v1 | https://arxiv.org/pdf/2209.00989v1.pdf | Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset | The number of IoT devices in healthcare is expected to rise sharply due to increased demand since the COVID-19 pandemic. Deep learning and IoT devices are being employed to monitor body vitals and automate anomaly detection in clinical and non-clinical settings. Most of the current technology requires the transmission ... | ['Rasit Eskicioglu', 'Kushagra Sharma'] | 2022-08-25 | null | null | null | null | ['ecg-classification'] | ['medical'] | [ 1.12658809e-03 -3.69918168e-01 2.57012695e-01 -3.71725827e-01
-2.07353935e-01 -2.24872246e-01 -2.78758258e-01 5.84581614e-01
-5.41725874e-01 6.66187823e-01 4.83195484e-02 -6.20150030e-01
-1.13940701e-01 -5.55532217e-01 -1.90636262e-01 -3.22267920e-01
-3.44761223e-01 6.31498992e-01 1.36552110e-01 1.59752265... | [13.972831726074219, 3.270650625228882] |
ce3067a9-51d2-42b7-bdcf-caef0fee7d93 | stop-a-dataset-for-spoken-task-oriented | 2207.10643 | null | https://arxiv.org/abs/2207.10643v3 | https://arxiv.org/pdf/2207.10643v3.pdf | STOP: A dataset for Spoken Task Oriented Semantic Parsing | End-to-end spoken language understanding (SLU) predicts intent directly from audio using a single model. It promises to improve the performance of assistant systems by leveraging acoustic information lost in the intermediate textual representation and preventing cascading errors from Automatic Speech Recognition (ASR).... | ['Yossi Adi', 'Abdelrahman Mohamed', 'Luke Zettlemoyer', 'Emmanuel Dupoux', 'Tu Ahn Nguyen', 'Robin Algayres', 'Wei-Ning Hsu', 'Jade Copet', 'Ali Elkahky', 'Adithya Sagar', 'Duc Le', 'Daniel Lazar', 'Akshat Shrivastava', 'Po-chun Hsu', 'Paden Tomasello'] | 2022-06-29 | null | null | null | null | ['spoken-language-understanding', 'spoken-language-understanding'] | ['natural-language-processing', 'speech'] | [ 3.25142205e-01 4.98317778e-01 -5.74903004e-02 -9.07572687e-01
-1.60016763e+00 -4.84178364e-01 2.59108782e-01 -3.10372144e-01
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2.34321300e-02 7.49023318e-01 1.67781845e-01 -3.13984305... | [14.061359405517578, 6.982806205749512] |
9fa8c9de-2824-4748-8363-0e372b7df88f | light-source-separation-and-intrinsic-image | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.pdf | Light Source Separation and Intrinsic Image Decomposition Under AC Illumination | Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this pape... | ['Takahiro Okabe', 'Ryo Kawahara', 'Yusaku Yoshida'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 6.44034505e-01 -7.51897097e-01 5.05068541e-01 2.25633066e-02
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5.30576110e-01 -3.61874521e-01 -1.85914293e-01 -8.66991654... | [10.380537986755371, -2.717160224914551] |
ecf9a800-8f67-486a-9a2e-78c8e0dbcd3b | video-specific-query-key-attention-modeling | 2305.04186 | null | https://arxiv.org/abs/2305.04186v1 | https://arxiv.org/pdf/2305.04186v1.pdf | Video-Specific Query-Key Attention Modeling for Weakly-Supervised Temporal Action Localization | Weakly-supervised temporal action localization aims to identify and localize the action instances in the untrimmed videos with only video-level action labels. When humans watch videos, we can adapt our abstract-level knowledge about actions in different video scenarios and detect whether some actions are occurring. In ... | ['Aggelos K. Katsaggelos', 'Xijun Wang'] | 2023-05-07 | null | null | null | null | ['weakly-supervised-temporal-action', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.94399625e-01 -3.61912668e-01 -7.40738153e-01 -2.16137335e-01
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-5.37638843e-01 3.81106257e-01 4.36741352e-01 3.54764134e-01
-5.85066453e-02 -1.98635131e-01 -8.26432884e-01 -6.94557726e-01
-4.51429725e-01 -5.22601791e-02 6.45286500e-01 3.61565679... | [8.522329330444336, 0.5838790535926819] |
167de426-1b10-4afb-8e82-e98b92369a27 | transferring-convnet-features-from-passive-to | 2204.10497 | null | https://arxiv.org/abs/2204.10497v2 | https://arxiv.org/pdf/2204.10497v2.pdf | Active Domain-Invariant Self-Localization Using Ego-Centric and World-Centric Maps | The training of a next-best-view (NBV) planner for visual place recognition (VPR) is a fundamentally important task in autonomous robot navigation, for which a typical approach is the use of visual experiences that are collected in the target domain as training data. However, the collection of a wide variety of visual ... | ['Mitsuki Yoshida', 'Ryogo Yamamoto', 'Kanji Tanaka', 'Kanya Kurauchi'] | 2022-04-22 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [-4.11265269e-02 1.62529483e-01 -2.93702245e-01 -2.02773139e-01
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3.23113054e-01 2.50186920e-01 3.12047362e-01 -3.23080629... | [7.389827728271484, -1.9726543426513672] |
9176cd23-0650-4ef6-8132-fd04db9567ab | selecting-better-samples-from-pre-trained | 2209.11000 | null | https://arxiv.org/abs/2209.11000v1 | https://arxiv.org/pdf/2209.11000v1.pdf | Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation | Large Language Models (LLMs) have in recent years demonstrated impressive prowess in natural language generation. A common practice to improve generation diversity is to sample multiple outputs from the model. However, there lacks a simple and robust way of selecting the best output from these stochastic samples. As a ... | ['Pierre-Yves Oudeyer', 'Hélène Sauzéon', 'Pauline Lucas', 'Rania Abdelghani', 'Emery Fine', 'Yen-Hsiang Wang', 'Tong Wang', 'Xingdi Yuan'] | 2022-09-22 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.58307517e-01 4.82617110e-01 -7.77426139e-02 -1.76893577e-01
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3.59670311e-01 8.57377350e-01 2.36784413e-01 -3.89658570... | [11.713275909423828, 8.388513565063477] |
7b8e465e-82ea-445a-a908-314680fde612 | multi-agent-feedback-enabled-neural-networks | 2205.10750 | null | https://arxiv.org/abs/2205.10750v1 | https://arxiv.org/pdf/2205.10750v1.pdf | Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications | In the intelligent communication field, deep learning (DL) has attracted much attention due to its strong fitting ability and data-driven learning capability. Compared with the typical DL feedforward network structures, an enhancement structure with direct data feedback have been studied and proved to have better perfo... | ['Kai Li', 'Yang Yang', 'Jun Wang', 'Jingchen Hu', 'Ying Wen', 'Yang Li', 'Fanglei Sun'] | 2022-05-22 | null | null | null | null | ['intelligent-communication'] | ['time-series'] | [-3.07069123e-01 4.43049427e-03 -1.90163881e-01 1.23170717e-03
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-5.85789740e-01 -1.33579060e-01 -7.52029717e-02 -7.04113603... | [6.292973041534424, 1.5160455703735352] |
c4098202-c7ac-4315-a02c-2a0f4ec997f6 | local-and-global-context-based-pairwise | 2110.04291 | null | https://arxiv.org/abs/2110.04291v2 | https://arxiv.org/pdf/2110.04291v2.pdf | Local and Global Context-Based Pairwise Models for Sentence Ordering | Sentence Ordering refers to the task of rearranging a set of sentences into the appropriate coherent order. For this task, most previous approaches have explored global context-based end-to-end methods using Sequence Generation techniques. In this paper, we put forward a set of robust local and global context-based pai... | ['Aditya Jyoti Paul', 'Ruskin Raj Manku'] | 2021-10-08 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 4.41387743e-01 1.55326560e-01 -7.90724531e-03 -6.15737975e-01
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-1.74669847e-01 1.01509917e+00 6.91334546e-01 -3.72607261e-01
-1.70346931e-01 -4.64704424e-01 -7.10884571e-01 -4.95086133e-01
-1.07222438e-01 5.31914771e-01 1.99743479e-01 -6.87366545... | [11.680402755737305, 9.247743606567383] |
19ccc995-f156-4051-bd6a-abd9b605ff15 | multi-task-recurrent-convolutional-network | 1907.06099 | null | https://arxiv.org/abs/1907.06099v1 | https://arxiv.org/pdf/1907.06099v1.pdf | Multi-Task Recurrent Convolutional Network with Correlation Loss for Surgical Video Analysis | Surgical tool presence detection and surgical phase recognition are two fundamental yet challenging tasks in surgical video analysis and also very essential components in various applications in modern operating rooms. While these two analysis tasks are highly correlated in clinical practice as the surgical process is ... | ['Pheng-Ann Heng', 'Chi-Wing Fu', 'Qi Dou', 'Hao Chen', 'Huaxia Li', 'Yueming Jin', 'Jing Qin'] | 2019-07-13 | null | null | null | null | ['surgical-tool-detection', 'surgical-phase-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.96551609e-01 -2.31560841e-02 -4.56814647e-01 -1.82072192e-01
-8.64527047e-01 -2.40039691e-01 4.05304879e-01 4.91930917e-02
-6.42314076e-01 1.62194744e-01 1.47790059e-01 -2.54807234e-01
-3.95214766e-01 -2.42769197e-01 -5.84592164e-01 -8.06612730e-01
-3.47468108e-01 -2.24339336e-01 2.34101608e-01 -4.96115461... | [14.166297912597656, -3.2553646564483643] |
d1926d88-d142-45be-8533-ba5d8319f587 | the-effect-of-changing-training-data-on-a | null | null | https://iopscience.iop.org/article/10.1088/1757-899X/568/1/012087 | https://iopscience.iop.org/article/10.1088/1757-899X/568/1/012087/pdf | The effect of changing training data on a fixed deep learning detection model | Within the lack of accurate data, for some computer vision applications, researchers usually use other pictures collected from different sources for the training. To know the effect of these added data, we compare the detection results of a customized dataset of objects, using the same detection model, while changing t... | ['Ammar Alsabbagh1 and Husi Géza', 'Aram Nasser'] | 2019-09-17 | null | null | null | annual-session-of-scientific-papers-imt | ['object-detection-in-indoor-scenes'] | ['computer-vision'] | [ 5.55315316e-02 -3.39942425e-01 -2.45129943e-01 -3.58470261e-01
1.84975415e-01 -4.73879278e-01 2.11677223e-01 -2.16351122e-01
-9.90420640e-01 3.13849449e-01 -4.50605065e-01 2.57924646e-02
2.89707154e-01 -1.03938603e+00 -7.18258142e-01 -9.16577876e-01
1.43038362e-01 2.50565916e-01 8.07572722e-01 2.55278260... | [8.193036079406738, -0.8541222214698792] |
1caa044e-3378-46a0-9a91-bfc39fffb353 | practical-and-ethical-challenges-of-large | 2303.13379 | null | https://arxiv.org/abs/2303.13379v1 | https://arxiv.org/pdf/2303.13379v1.pdf | Practical and Ethical Challenges of Large Language Models in Education: A Systematic Literature Review | Educational technology innovations that have been developed based on large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (e.g., question generation, fee... | ['Dragan Gašević', 'Yueqiao Jin', 'Xinyu Li', 'Guanliang Chen', 'Roberto Martinez-Maldonado', 'Yuheng Li', 'Linxuan Zhao', 'Lele Sha', 'Lixiang Yan'] | 2023-03-17 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [-8.80224332e-02 4.73661155e-01 -2.74921954e-01 2.41527215e-01
-5.48951864e-01 -1.04403913e+00 6.84522152e-01 7.16068625e-01
-4.96417046e-01 3.49439651e-01 7.70197511e-01 -1.03414762e+00
-4.17190373e-01 -3.27395380e-01 -6.36560142e-01 -8.16780031e-02
9.21474040e-01 -3.85321319e-01 5.97712658e-02 1.99448075... | [10.249577522277832, 7.304266452789307] |
9c8507ac-141d-4373-89dc-1782ce8c2d4c | large-scale-entity-alignment-via-knowledge | 2208.11125 | null | https://arxiv.org/abs/2208.11125v1 | https://arxiv.org/pdf/2208.11125v1.pdf | Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding | Entity alignment is a crucial task in knowledge graph fusion. However, most entity alignment approaches have the scalability problem. Recent methods address this issue by dividing large KGs into small blocks for embedding and alignment learning in each. However, such a partitioning and learning process results in an ex... | ['Xiaofang Zhou', 'Jianfeng Qu', 'Wei Hu', 'Wen Hua', 'Zequn Sun', 'Kexuan Xin'] | 2022-08-23 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-5.38553931e-02 3.86526585e-01 -4.15517151e-01 -4.61764127e-01
-5.45171261e-01 -3.49718124e-01 3.13018829e-01 7.96709239e-01
-1.30970240e-01 7.55252600e-01 3.58332813e-01 8.14344957e-02
-3.47583383e-01 -1.30825412e+00 -8.39807153e-01 -5.53789735e-01
-8.37661400e-02 6.33406401e-01 4.54088956e-01 4.94564958... | [8.71433162689209, 8.0066499710083] |
986148ce-7c49-484c-9e9a-9870f4849ecb | a-comprehensive-review-of-modern-object | 2301.07499 | null | https://arxiv.org/abs/2301.07499v1 | https://arxiv.org/pdf/2301.07499v1.pdf | A Comprehensive Review of Modern Object Segmentation Approaches | Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthcare, transportation, robotics, fashion, home improvement, and tourism. Many deep learning-based approaches have been developed for image-lev... | ['Matthew Hagen', 'Hanyan Li', 'Unaiza Ahsan', 'Yuanbo Wang'] | 2023-01-13 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 4.74225283e-01 4.67740893e-02 -2.86651611e-01 -6.07809961e-01
-5.76345026e-01 -4.79273140e-01 4.38327938e-01 3.47659439e-01
-4.60110366e-01 3.28850508e-01 -3.26641947e-01 -8.94112587e-02
-1.68877877e-02 -7.69225419e-01 -6.10275567e-01 -5.77116489e-01
-3.37986685e-02 5.36998391e-01 5.18218756e-01 3.20146203... | [9.586910247802734, 0.34783217310905457] |
e7beb69f-7471-4719-923c-082a469f6145 | caunlp-at-nlp4if-2019-shared-task-context | null | null | https://aclanthology.org/D19-5010 | https://aclanthology.org/D19-5010.pdf | CAUnLP at NLP4IF 2019 Shared Task: Context-Dependent BERT for Sentence-Level Propaganda Detection | The goal of fine-grained propaganda detection is to determine whether a given sentence uses propaganda techniques (sentence-level) or to recognize which techniques are used (fragment-level). This paper presents the sys- tem of our participation in the sentence-level subtask of the propaganda detection shared task. In o... | ['Ying Chen', 'Wenjun Hou'] | 2019-11-01 | null | null | null | ws-2019-11 | ['propaganda-detection'] | ['natural-language-processing'] | [ 4.08834726e-01 -3.31881881e-01 -2.66955048e-01 -6.19229496e-01
-8.13425422e-01 -4.53705221e-01 9.77561355e-01 8.00902426e-01
-3.78973782e-01 5.02347887e-01 7.77272880e-01 -6.86490953e-01
2.02692688e-01 -8.36919308e-01 -2.18778700e-01 -3.77691776e-01
1.82536080e-01 2.52135783e-01 -2.95364093e-02 -4.83552277... | [8.498794555664062, 10.620155334472656] |
6f37ba16-cadd-4220-9fd4-079c1b5aea91 | monocular-3d-human-pose-estimation-in-the | 1611.09813 | null | http://arxiv.org/abs/1611.09813v5 | http://arxiv.org/pdf/1611.09813v5.pdf | Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision | We propose a CNN-based approach for 3D human body pose estimation from single
RGB images that addresses the issue of limited generalizability of models
trained solely on the starkly limited publicly available 3D pose data. Using
only the existing 3D pose data and 2D pose data, we show state-of-the-art
performance on es... | ['Weipeng Xu', 'Pascal Fua', 'Oleksandr Sotnychenko', 'Helge Rhodin', 'Dushyant Mehta', 'Dan Casas', 'Christian Theobalt'] | 2016-11-29 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 4.81337123e-02 5.19349203e-02 -1.98440969e-01 -4.02838856e-01
-8.81760418e-01 -4.19038653e-01 2.75425524e-01 -3.04291606e-01
-5.48598170e-01 6.14568830e-01 5.69183528e-01 3.61271858e-01
1.60953134e-01 -3.78518909e-01 -8.72235954e-01 -2.84159601e-01
-1.74666479e-01 8.52174520e-01 4.12007533e-02 -5.43827415... | [6.9926581382751465, -0.9354548454284668] |
ca409013-f954-4e6a-a4b4-09a48de674aa | condensed-prototype-replay-for-class | 2305.16143 | null | https://arxiv.org/abs/2305.16143v1 | https://arxiv.org/pdf/2305.16143v1.pdf | Condensed Prototype Replay for Class Incremental Learning | Incremental learning (IL) suffers from catastrophic forgetting of old tasks when learning new tasks. This can be addressed by replaying previous tasks' data stored in a memory, which however is usually prone to size limits and privacy leakage. Recent studies store only class centroids as prototypes and augment them wit... | ['Huajie Shao', 'Tianyi Zhou', 'Zhenyu Zong', 'Jiangtao Kong'] | 2023-05-25 | null | null | null | null | ['class-incremental-learning', 'incremental-learning'] | ['computer-vision', 'methodology'] | [ 2.06008807e-01 -6.15767390e-02 -2.56685346e-01 -1.36680514e-01
-4.32509333e-01 -3.51477742e-01 4.98809874e-01 3.36166382e-01
-6.26318097e-01 1.07151008e+00 -3.80525477e-02 9.54409037e-03
-1.80237576e-01 -6.54043853e-01 -9.80983257e-01 -8.90925169e-01
6.63089454e-02 4.88949418e-01 3.21779162e-01 2.77792335... | [9.809309959411621, 3.4126760959625244] |
5a7919f5-bd18-4f42-9e37-0d46cae7c3ce | janus-parallel-tempered-genetic-algorithm | 2106.04011 | null | https://arxiv.org/abs/2106.04011v2 | https://arxiv.org/pdf/2106.04011v2.pdf | JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design | Inverse molecular design, i.e., designing molecules with specific target properties, can be posed as an optimization problem. High-dimensional optimization tasks in the natural sciences are commonly tackled via population-based metaheuristic optimization algorithms such as evolutionary algorithms. However, expensive pr... | ['Alan Aspuru-Guzik', 'Robert Pollice', 'AkshatKumar Nigam'] | 2021-06-07 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 5.24573147e-01 -1.64040610e-01 -2.61862248e-01 1.50164127e-01
-6.74556136e-01 -5.23446858e-01 5.49927473e-01 4.48872566e-01
-5.19940913e-01 1.40997350e+00 -2.66235381e-01 -2.79972970e-01
-3.68503541e-01 -1.14085126e+00 -8.95615637e-01 -1.38951075e+00
1.45113602e-01 8.35446596e-01 -2.89054751e-01 -2.38097668... | [5.056747913360596, 5.414766311645508] |
b5a00351-fb72-47c2-8faf-a9ca11f38603 | eac-net-a-region-based-deep-enhancing-and | 1702.02925 | null | http://arxiv.org/abs/1702.02925v1 | http://arxiv.org/pdf/1702.02925v1.pdf | EAC-Net: A Region-based Deep Enhancing and Cropping Approach for Facial Action Unit Detection | In this paper, we propose a deep learning based approach for facial action
unit detection by enhancing and cropping the regions of interest. The approach
is implemented by adding two novel nets (layers): the enhancing layers and the
cropping layers, to a pretrained CNN model. For the enhancing layers, we
designed an at... | ['Lijun Yin', 'Zhigang Zhu', 'Wei Li', 'Farnaz Abtahi'] | 2017-02-09 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 1.43618420e-01 4.41396534e-01 1.43478751e-01 -5.47427058e-01
-4.28382158e-01 -9.36478078e-02 4.44783986e-01 -4.98499185e-01
-7.28341699e-01 3.45644832e-01 2.77253151e-01 2.14381605e-01
4.18357462e-01 -8.72982800e-01 -8.41041386e-01 -5.95970392e-01
-1.40219852e-01 -2.35020846e-01 2.94755220e-01 -1.17016278... | [13.574636459350586, 1.4641364812850952] |
1fa57bca-f4f0-4b00-8276-b423a8d76933 | empathetic-bert2bert-conversational-model | 2103.04353 | null | https://arxiv.org/abs/2103.04353v1 | https://arxiv.org/pdf/2103.04353v1.pdf | Empathetic BERT2BERT Conversational Model: Learning Arabic Language Generation with Little Data | Enabling empathetic behavior in Arabic dialogue agents is an important aspect of building human-like conversational models. While Arabic Natural Language Processing has seen significant advances in Natural Language Understanding (NLU) with language models such as AraBERT, Natural Language Generation (NLG) remains a cha... | ['Hazem Hajj', 'Reem A. Mahmoud', 'Wissam Antoun', 'Tarek Naous'] | 2021-03-07 | null | https://aclanthology.org/2021.wanlp-1.17 | https://aclanthology.org/2021.wanlp-1.17.pdf | eacl-wanlp-2021-4 | ['empathetic-response-generation'] | ['natural-language-processing'] | [-2.97270775e-01 7.65725434e-01 2.10674465e-01 -4.45220411e-01
-8.54542553e-01 -4.41893578e-01 9.06143010e-01 -2.03144819e-01
-4.63973969e-01 1.17851162e+00 7.47790039e-01 4.37997952e-02
4.01037186e-01 -7.65338898e-01 -2.58129030e-01 -2.52525628e-01
3.62681359e-01 1.10389340e+00 -4.36108947e-01 -1.08547378... | [13.0009765625, 7.827927589416504] |
0e77fed9-5338-4ed3-ab5e-b2e7af4243bf | anomaly-crossing-a-new-method-for-video | 2112.06320 | null | https://arxiv.org/abs/2112.06320v3 | https://arxiv.org/pdf/2112.06320v3.pdf | Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning | Video anomaly detection aims to identify abnormal events that occurred in videos. Since anomalous events are relatively rare, it is not feasible to collect a balanced dataset and train a binary classifier to solve the task. Thus, most previous approaches learn only from normal videos using unsupervised or semi-supervis... | ['Chenliang Xu', 'Jing Shi', 'Lianggong Wen', 'Zhang Liu', 'Guangyu Sun'] | 2021-12-12 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.69104034e-01 -4.15069491e-01 -2.49117404e-01 -3.65807921e-01
-5.14314473e-01 -3.40607196e-01 4.04307693e-01 2.50184596e-01
-2.59342164e-01 3.30570012e-01 2.27063328e-01 4.75244597e-02
1.37626827e-01 -4.89129573e-01 -6.57993078e-01 -6.11524463e-01
-1.35658339e-01 -8.49033818e-02 3.03128570e-01 -1.49171120... | [7.861595153808594, 1.5908324718475342] |
c61d9dcb-e160-4f19-950e-fe049dff4606 | ranking-facts-for-explaining-answers-to | 2110.09036 | null | https://arxiv.org/abs/2110.09036v1 | https://arxiv.org/pdf/2110.09036v1.pdf | Ranking Facts for Explaining Answers to Elementary Science Questions | In multiple-choice exams, students select one answer from among typically four choices and can explain why they made that particular choice. Students are good at understanding natural language questions and based on their domain knowledge can easily infer the question's answer by 'connecting the dots' across various pe... | ['Soeren Auer', "Isaiah Onando Mulang'", "Jennifer D'Souza"] | 2021-10-18 | null | null | null | null | ['science-question-answering'] | ['miscellaneous'] | [ 2.54934818e-01 7.44936883e-01 -4.88619089e-01 -5.96207440e-01
-1.11742759e+00 -9.17974532e-01 6.32264495e-01 8.48247886e-01
-2.17737898e-01 1.09830439e+00 5.42097449e-01 -9.60930824e-01
-8.68961811e-01 -9.35374856e-01 -7.06077874e-01 3.70549671e-02
3.14338923e-01 8.09697032e-01 3.76223177e-01 -6.32107794... | [10.9735746383667, 7.793949127197266] |
2a03b1c7-e9f3-4b3e-be3e-f245ede206e7 | improving-neural-language-processing-with | null | null | https://aclanthology.org/2021.ranlp-main.107 | https://aclanthology.org/2021.ranlp-main.107.pdf | Improving Neural Language Processing with Named Entities | Pretraining-based neural network models have demonstrated state-of-the-art (SOTA) performances on natural language processing (NLP) tasks. The most frequently used sentence representation for neural-based NLP methods is a sequence of subwords that is different from the sentence representation of non-neural methods that... | ['Tomoya Iwakura', 'Takuya Makino', 'Kyoumoto Matsushita'] | null | null | https://aclanthology.org/2021.ranlp-1.107 | https://aclanthology.org/2021.ranlp-1.107.pdf | ranlp-2021-9 | ['headline-generation'] | ['natural-language-processing'] | [ 3.56132895e-01 2.28474662e-01 -1.02103010e-01 -7.35857606e-01
-7.75350630e-01 -5.32849371e-01 2.68927842e-01 1.67772695e-01
-8.73313367e-01 1.23700154e+00 5.24732172e-01 -6.33493662e-01
3.72127295e-01 -9.29262042e-01 -8.71685266e-01 -3.36095989e-01
1.46102637e-01 6.47616506e-01 6.86617121e-02 -3.55708271... | [10.023185729980469, 9.749314308166504] |
0878bf52-78ca-46ee-ae97-2f5302d1e2e6 | survival-kernets-scalable-and-interpretable | 2206.10477 | null | https://arxiv.org/abs/2206.10477v4 | https://arxiv.org/pdf/2206.10477v4.pdf | Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee | Kernel survival analysis models estimate individual survival distributions with the help of a kernel function, which measures the similarity between any two data points. Such a kernel function can be learned using deep kernel survival models. In this paper, we present a new deep kernel survival model called a survival ... | ['George H. Chen'] | 2022-06-21 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [-0.2860886 -0.12880889 -0.40515488 -0.5449456 -1.1241026 -0.43475142
0.24224183 0.81410974 -0.42613348 0.55169255 0.2723748 -0.60261464
-0.6462781 -0.8172001 -0.51580685 -0.95438206 -0.5162084 0.7996524
-0.10707052 0.14708133 -0.16093428 0.43908268 -1.064891 0.19475313
0.7122879 1.0646583 -0.4... | [7.792775630950928, 5.622522830963135] |
917f9e02-e3e7-4228-93fa-ad097f8efc90 | homogeneous-network-embedding-for-massive | 1906.06826 | null | https://arxiv.org/abs/1906.06826v6 | https://arxiv.org/pdf/1906.06826v6.pdf | Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank | Given an input graph G and a node v in G, homogeneous network embedding (HNE) maps the graph structure in the vicinity of v to a compact, fixed-dimensional feature vector. This paper focuses on HNE for massive graphs, e.g., with billions of edges. On this scale, most existing approaches fail, as they incur either prohi... | ['Jieming Shi', 'Xiaokui Xiao', 'Renchi Yang', 'Yin Yang', 'Sourav S. Bhowmick'] | 2019-06-17 | null | null | null | null | ['graph-reconstruction'] | ['graphs'] | [-1.46302879e-01 3.16043496e-01 -4.27032232e-01 1.37709612e-02
-3.00480515e-01 -4.43091959e-01 3.57555777e-01 6.26391411e-01
-3.69572163e-01 2.14787886e-01 1.27992526e-01 -6.66066647e-01
-3.45351666e-01 -1.28596079e+00 -3.65659207e-01 -4.60947514e-01
-6.37069762e-01 4.08931673e-01 3.45121503e-01 -2.80903220... | [7.167760848999023, 6.120738506317139] |
bb74bd8b-b46b-4661-a551-9c14a7db4054 | computing-extremely-accurate-quantiles-using | 1902.04023 | null | http://arxiv.org/abs/1902.04023v1 | http://arxiv.org/pdf/1902.04023v1.pdf | Computing Extremely Accurate Quantiles Using t-Digests | We present on-line algorithms for computing approximations of rank-based
statistics that give high accuracy, particularly near the tails of a
distribution, with very small sketches. Notably, the method allows a quantile
$q$ to be computed with an accuracy relative to $\max(q, 1-q)$ rather than
absolute accuracy as with... | ['Otmar Ertl', 'Ted Dunning'] | 2019-02-11 | null | null | null | null | ['sequential-quantile-estimation'] | ['miscellaneous'] | [-3.20209861e-01 -1.44120738e-01 -4.06826496e-01 -4.59701300e-01
-1.48371887e+00 -1.10602212e+00 5.01716554e-01 5.60330808e-01
-3.37307632e-01 9.82889712e-01 3.37548465e-01 -5.75475514e-01
-6.15545869e-01 -9.48720872e-01 -3.08004946e-01 -2.65684098e-01
-4.48908687e-01 7.07280576e-01 2.62066066e-01 3.41653004... | [7.235615253448486, 4.525822162628174] |
83335df8-1f4c-4bdd-b5cf-8e49173cb7c8 | towards-cnn-map-representation-and | 1709.05972 | null | http://arxiv.org/abs/1709.05972v2 | http://arxiv.org/pdf/1709.05972v2.pdf | Towards CNN map representation and compression for camera relocalisation | This paper presents a study on the use of Convolutional Neural Networks for
camera relocalisation and its application to map compression. We follow state
of the art visual relocalisation results and evaluate the response to different
data inputs. We use a CNN map representation and introduce the notion of map
compressi... | ['Walterio Mayol-Cuevas', 'Luis Contreras'] | 2017-09-15 | null | null | null | null | ['camera-relocalization'] | ['computer-vision'] | [ 2.95533955e-01 -4.21135277e-02 -1.98632792e-01 -2.24335745e-01
-4.55961470e-03 -5.38495541e-01 9.33902740e-01 4.17619556e-01
-1.11420345e+00 3.28121573e-01 3.88605714e-01 -2.60406196e-01
-4.38606232e-01 -8.65404308e-01 -9.29854989e-01 -2.81262726e-01
-2.99581718e-02 2.06184089e-01 6.89660370e-01 -2.35255569... | [7.812493801116943, -1.7015706300735474] |
534f17e4-a447-49c5-914f-302feb5202ee | controllable-abstractive-sentence | null | null | https://aclanthology.org/2020.coling-main.497 | https://aclanthology.org/2020.coling-main.497.pdf | Controllable Abstractive Sentence Summarization with Guiding Entities | Entities are the major proportion and build up the topic of text summaries. Although existing text summarization models can produce promising results of automatic metrics, for example, ROUGE, it is difficult to guarantee that an entity is contained in generated summaries. In this paper, we propose a controllable abstra... | ['Qing Li', 'Guanjie Zhang', 'Yi Cai', 'Changmeng Zheng'] | 2020-12-01 | null | null | null | coling-2020-8 | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 3.60095024e-01 6.60187304e-01 -1.56438410e-01 -2.98990667e-01
-1.02752197e+00 -5.55243969e-01 6.51535571e-01 6.57716155e-01
-2.91808397e-01 1.38146329e+00 1.08854043e+00 1.20781817e-01
4.83077988e-02 -8.96661520e-01 -3.72434795e-01 -2.49905199e-01
2.71101117e-01 4.13448513e-01 2.75405914e-01 -1.21986181... | [12.538616180419922, 9.493229866027832] |
145d5fbc-8439-4600-a748-8a54306d2a60 | mmgcn-multimodal-fusion-via-deep-graph | 2107.06779 | null | https://arxiv.org/abs/2107.06779v1 | https://arxiv.org/pdf/2107.06779v1.pdf | MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation | Emotion recognition in conversation (ERC) is a crucial component in affective dialogue systems, which helps the system understand users' emotions and generate empathetic responses. However, most works focus on modeling speaker and contextual information primarily on the textual modality or simply leveraging multimodal ... | ['Qin Jin', 'Jinming Zhao', 'Yuchen Liu', 'Jingwen Hu'] | 2021-07-14 | null | https://aclanthology.org/2021.acl-long.440 | https://aclanthology.org/2021.acl-long.440.pdf | acl-2021-5 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-2.19931126e-01 -1.00078888e-01 5.33041880e-02 -5.43048024e-01
-7.39086926e-01 -3.87599796e-01 6.50929093e-01 -1.34255111e-01
-3.69213432e-01 4.65158731e-01 7.80620754e-01 -5.33791212e-03
3.97315264e-01 -4.17136848e-01 -2.88120862e-02 -5.39240777e-01
1.86653003e-01 1.81849793e-01 -4.23468888e-01 -8.68404329... | [13.044110298156738, 5.965666770935059] |
da7ee345-c6ff-41ea-8c58-16c3b7416453 | incoder-a-generative-model-for-code-infilling | 2204.05999 | null | https://arxiv.org/abs/2204.05999v3 | https://arxiv.org/pdf/2204.05999v3.pdf | InCoder: A Generative Model for Code Infilling and Synthesis | Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce InCoder, a unified generative model that can perform program synthesis (via left-to-right generation) as well as editing (via infilling). InCoder is trained to generate code files from a large corpus of perm... | ['Mike Lewis', 'Luke Zettlemoyer', 'Wen-tau Yih', 'Ruiqi Zhong', 'Freda Shi', 'Eric Wallace', 'Sida Wang', 'Jessy Lin', 'Armen Aghajanyan', 'Daniel Fried'] | 2022-04-12 | null | null | null | null | ['program-synthesis', 'comment-generation'] | ['computer-code', 'natural-language-processing'] | [ 3.24487001e-01 2.23185301e-01 -3.16648930e-01 -3.43292594e-01
-9.90384519e-01 -9.47063148e-01 8.92341793e-01 2.71657482e-02
2.10012849e-02 6.46542788e-01 3.82612646e-01 -9.24484909e-01
5.74016631e-01 -6.68939292e-01 -9.92811024e-01 -8.13179016e-02
2.60902464e-01 2.13308319e-01 3.87338921e-02 -1.98036879... | [7.730855464935303, 7.8608717918396] |
4877ea0b-21c2-435b-b63e-272456992bb5 | visual-heart-rate-estimation-from-rgb-facial | 2208.04947 | null | https://arxiv.org/abs/2208.04947v1 | https://arxiv.org/pdf/2208.04947v1.pdf | Visual Heart Rate Estimation from RGB Facial Video using Spectral Reflectance | Estimation of the Heart rate from the facial video has a number of applications in the medical and fitness industries. Additionally, it has become useful in the field of gaming as well. Several approaches have been proposed to seamlessly obtain the Heart rate from the facial video, but these approaches have had issues ... | ['Hassan Ali', 'Ruijia Deng', 'Bharath Ramakrishnan'] | 2022-08-09 | null | null | null | null | ['face-detection', 'heart-rate-estimation'] | ['computer-vision', 'medical'] | [-4.20998968e-02 -2.41310045e-01 2.99141079e-01 -1.87450796e-01
-2.14663178e-01 -1.93024471e-01 -4.00113268e-03 -2.64806479e-01
-6.56030715e-01 4.18378711e-01 -3.13146383e-01 -1.84137523e-02
1.41082972e-01 -7.34310389e-01 -3.64183098e-01 -8.74898016e-01
-1.41262650e-01 -4.19683069e-01 1.10774122e-01 -8.70053098... | [13.867256164550781, 2.698054552078247] |
b6d4bb6b-d7f2-495c-bda3-8c543c66bacb | cooperative-multi-agent-path-finding-beyond | 2105.10993 | null | https://arxiv.org/abs/2105.10993v1 | https://arxiv.org/pdf/2105.10993v1.pdf | Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance | We introduce the Cooperative Multi-Agent Path Finding (Co-MAPF) problem, an extension to the classical MAPF problem, where cooperative behavior is incorporated. In this setting, a group of autonomous agents operate in a shared environment and have to complete cooperative tasks while avoiding collisions with the other a... | ['Nahum Shimkin', 'Oren Salzman', 'Ofir Gordon', 'Nir Greshler'] | 2021-05-23 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-9.92631838e-02 5.85706770e-01 -9.64659378e-02 -4.67485674e-02
-4.30306435e-01 -7.50840187e-01 5.60144305e-01 4.70200777e-01
-5.19785583e-01 1.03503764e+00 -1.21780194e-01 -1.43899366e-01
-7.51824200e-01 -7.86133945e-01 -4.23690140e-01 -7.04051733e-01
-9.07588303e-01 1.05896974e+00 8.59302282e-01 -6.74234569... | [4.95358419418335, 1.7350457906723022] |
17253a12-c5ac-4174-9a6e-1e94549d3bcc | projective-view-at-optimization-problem-for | 1912.00197 | null | http://arxiv.org/abs/1912.00197v2 | http://arxiv.org/pdf/1912.00197v2.pdf | Projective view at Optimization Problem for Multiband Filter | The best uniform rational approximation of the \emph{sign} function on two
intervals separated by zero was explicitly solved by E.I. Zolotar\"ev in 1877.
This optimization problem is the initial step in the staircase of the so called
approximation problems for multiband filters which are of great importance for
electri... | [] | 2020-01-21 | null | null | null | null | ['electrical-engineering'] | ['miscellaneous'] | [ 2.20924601e-01 2.70281732e-01 1.26745120e-01 -2.81942278e-01
-5.31293809e-01 -6.88281178e-01 9.73264799e-02 -3.15098137e-01
-4.28880453e-01 1.34880018e+00 -2.15312704e-01 -4.10191834e-01
-6.18800044e-01 -5.16660511e-01 -5.45458794e-01 -9.05622303e-01
2.12184146e-01 2.02638134e-01 -1.80557191e-01 -6.69933796... | [6.301736831665039, 3.338390350341797] |
d0fdd500-33c3-41ca-a44b-2a538dfaf80e | faster-segment-anything-towards-lightweight | 2306.14289 | null | https://arxiv.org/abs/2306.14289v2 | https://arxiv.org/pdf/2306.14289v2.pdf | Faster Segment Anything: Towards Lightweight SAM for Mobile Applications | Segment Anything Model (SAM) has attracted significant attention due to its impressive zero-shot transfer performance and high versatility for numerous vision applications (like image editing with fine-grained control). Many of such applications need to be run on resource-constraint edge devices, like mobile phones. In... | ['Choong Seon Hong', 'Seungkyu Lee', 'Sung-Ho Bae', 'Jung Uk Kim', 'Yu Qiao', 'Dongshen Han', 'Chaoning Zhang'] | 2023-06-25 | null | null | null | null | ['panoptic-segmentation', 'instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.81659687e-01 1.20127663e-01 -1.42710894e-01 6.12707436e-03
-9.06329513e-01 -6.05545521e-01 4.13358301e-01 -3.30438763e-01
-6.43315136e-01 4.75133538e-01 -1.57541960e-01 -6.32361472e-01
3.35526139e-01 -6.80955946e-01 -1.02562833e+00 -7.58152485e-01
3.66633147e-01 4.06796157e-01 3.04133207e-01 9.47160125... | [9.769163131713867, 0.10285787284374237] |
b79c13c9-238f-4825-be8f-2fa2f2b0f7c2 | architecture-agnostic-masked-image-modeling | 2205.13943 | null | https://arxiv.org/abs/2205.13943v4 | https://arxiv.org/pdf/2205.13943v4.pdf | Architecture-Agnostic Masked Image Modeling -- From ViT back to CNN | Masked image modeling, an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers. Its underlying idea is simple: a portion of the input image is masked out and then reconstructed via a pre-text task. However, the working principle behi... | ['Stan. Z. Li', 'Zelin Zang', 'Fang Wu', 'Di wu', 'Siyuan Li'] | 2022-05-27 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 5.01820147e-01 5.60061753e-01 -2.33370647e-01 -3.42780650e-01
-3.75791818e-01 -4.09568042e-01 7.28094220e-01 -5.26081860e-01
-6.73411461e-03 2.40228966e-01 2.70465761e-01 -4.00768787e-01
5.27413338e-02 -6.98135614e-01 -1.16066730e+00 -7.99471796e-01
2.39558756e-01 5.48411235e-02 3.20591420e-01 -4.06963795... | [9.608291625976562, 1.2169467210769653] |
4dca25c0-6567-405d-9320-224354114356 | pi-pe-a-pipeline-for-pulmonary-embolism | 1910.02175 | null | https://arxiv.org/abs/1910.02175v3 | https://arxiv.org/pdf/1910.02175v3.pdf | Pi-PE: A Pipeline for Pulmonary Embolism Detection using Sparsely Annotated 3D CT Images | Pulmonary embolisms (PE) are known to be one of the leading causes for cardiac-related mortality. Due to inherent variabilities in how PE manifests and the cumbersome nature of manual diagnosis, there is growing interest in leveraging AI tools for detecting PE. In this paper, we build a two-stage detection pipeline tha... | ['Ehsan Dehghan', 'David Beymer', 'Shafiqul Abedin', 'Deepta Rajan'] | 2019-10-05 | null | null | null | null | ['pulmonary-embolism-detection'] | ['medical'] | [-1.72749877e-01 -1.67585909e-01 4.89559136e-02 1.83270231e-01
-1.02280891e+00 -8.81893516e-01 1.77211866e-01 5.56826174e-01
-3.41976523e-01 6.00993037e-01 1.02088507e-02 -5.65786481e-01
-2.26223603e-01 -3.84398013e-01 -1.79334000e-01 -3.92335445e-01
-3.87512565e-01 9.24270749e-01 1.00336921e+00 4.45489705... | [15.180645942687988, -2.062096118927002] |
4e3c1618-5e8b-4731-a070-a7feb1e7e5cf | paris-lille-3d-a-large-and-high-quality | 1712.00032 | null | http://arxiv.org/abs/1712.00032v2 | http://arxiv.org/pdf/1712.00032v2.pdf | Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification | This paper introduces a new Urban Point Cloud Dataset for Automatic
Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We
describe how the dataset is obtained from acquisition to post-processing and
labeling. This dataset can be used to learn classification algorithm, however,
given that a great a... | ['François Goulette', 'Jean-Emmanuel Deschaud', 'Xavier Roynard'] | 2017-11-30 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-1.85818709e-02 1.55199245e-01 -1.05545670e-01 -7.03863919e-01
-7.65775740e-01 -2.75163502e-01 6.50948346e-01 2.72980005e-01
-4.91517723e-01 5.82578063e-01 -5.40298343e-01 -4.17209178e-01
5.06934188e-02 -1.24462724e+00 -8.27703655e-01 -5.83397865e-01
-1.42974779e-01 1.17566156e+00 4.59114760e-01 -3.01541984... | [8.437350273132324, -2.453350067138672] |
84d99308-77ee-42b0-ac0d-8f29c41fdef7 | a-transformer-based-framework-for-1 | 2010.02803 | null | https://arxiv.org/abs/2010.02803v3 | https://arxiv.org/pdf/2010.02803v3.pdf | A Transformer-based Framework for Multivariate Time Series Representation Learning | In this work we propose for the first time a transformer-based framework for unsupervised representation learning of multivariate time series. Pre-trained models can be potentially used for downstream tasks such as regression and classification, forecasting and missing value imputation. By evaluating our models on seve... | ['Carsten Eickhoff', 'Anuradha Bhamidipaty', 'Dhaval Patel', 'Srideepika Jayaraman', 'George Zerveas'] | 2020-10-06 | a-transformer-based-framework-for | https://openreview.net/forum?id=lE1AB4stmX | https://openreview.net/pdf?id=lE1AB4stmX | null | ['time-series-regression'] | ['time-series'] | [ 4.80514914e-01 7.86858723e-02 -5.09533405e-01 -2.95259893e-01
-9.48974192e-01 -5.39975464e-01 7.65464842e-01 3.46576929e-01
-2.30737835e-01 7.87729502e-01 1.88587531e-01 -3.92799348e-01
-4.27341431e-01 -6.08950973e-01 -6.12273037e-01 -7.68104136e-01
-4.15906012e-01 6.31091595e-01 -3.15076381e-01 -9.33586583... | [7.1559014320373535, 3.0583484172821045] |
bb1ecc38-4612-4df1-b27e-257d560b6dec | diversity-matters-robustness-of-bias | 2302.14027 | null | https://arxiv.org/abs/2302.14027v1 | https://arxiv.org/pdf/2302.14027v1.pdf | Diversity matters: Robustness of bias measurements in Wikidata | With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measurin... | ['Animesh Mukherjee', 'Soumya Sarkar', 'Bhanu Prakash Reddy Guda', 'Anirban Panda', 'Sai Keerthana Karnam', 'Paramita Das'] | 2023-02-27 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [ 3.00429165e-02 2.81610638e-01 -3.94967973e-01 -2.91977614e-01
-1.67885453e-01 -5.86136341e-01 7.99368203e-01 4.34574783e-01
-6.15696609e-01 4.56145138e-01 9.02877510e-01 -4.35449779e-01
-6.66005731e-01 -9.84933436e-01 -5.37723601e-01 -3.19611698e-01
9.59807411e-02 4.44413245e-01 -8.38009417e-02 -4.63598996... | [9.271663665771484, 10.155893325805664] |
0868139a-dabb-456e-bd86-6b2d70a1ae99 | swift-markov-logic-for-probabilistic | 2210.00283 | null | https://arxiv.org/abs/2210.00283v1 | https://arxiv.org/pdf/2210.00283v1.pdf | Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs | We provide a framework for probabilistic reasoning in Vadalog-based Knowledge Graphs (KGs), satisfying the requirements of ontological reasoning: full recursion, powerful existential quantification, expression of inductive definitions. Vadalog is a Knowledge Representation and Reasoning (KRR) language based on Warded D... | ['Evgeny Sherkhonov', 'Emanuel Sallinger', 'Eleonora Laurenza', 'Luigi Bellomarini'] | 2022-10-01 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-3.09764177e-01 5.51256001e-01 -3.26492697e-01 -3.95220101e-01
-3.40720087e-01 -4.78987366e-01 5.41396916e-01 3.33379768e-02
-5.29772863e-02 1.00699735e+00 -1.84808761e-01 -6.59850121e-01
-9.77293313e-01 -1.64866042e+00 -8.19023132e-01 -6.49024129e-01
-3.42892081e-01 1.31497371e+00 6.73728824e-01 -1.01384513... | [8.593924522399902, 6.742289066314697] |
f16cde17-8b04-49a0-b74c-cf4007b192a7 | motiontrack-learning-robust-short-term-and | 2303.10404 | null | https://arxiv.org/abs/2303.10404v2 | https://arxiv.org/pdf/2303.10404v2.pdf | MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking | The main challenge of Multi-Object Tracking~(MOT) lies in maintaining a continuous trajectory for each target. Existing methods often learn reliable motion patterns to match the same target between adjacent frames and discriminative appearance features to re-identify the lost targets after a long period. However, the r... | ['Wei Tang', 'Gang Hua', 'Jinghai Duan', 'Le Wang', 'Sanping Zhou', 'Zheng Qin'] | 2023-03-18 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Qin_MotionTrack_Learning_Robust_Short-Term_and_Long-Term_Motions_for_Multi-Object_Tracking_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Qin_MotionTrack_Learning_Robust_Short-Term_and_Long-Term_Motions_for_Multi-Object_Tracking_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-prediction'] | ['computer-vision'] | [-3.54670018e-01 -7.17820764e-01 -5.53836748e-02 -8.00252259e-02
-7.24974811e-01 -5.73565185e-01 4.36951250e-01 -1.95765615e-01
-2.83662796e-01 4.53239083e-01 -1.34627447e-01 3.23109001e-01
-5.03729992e-02 -4.04549152e-01 -7.82226682e-01 -9.36898708e-01
-2.01740488e-01 5.23953795e-01 1.19262719e+00 3.31644737... | [6.4028754234313965, -2.096855401992798] |
0ed9abb8-27b5-4c14-9d86-58860a1d9d89 | adaptive-r-peak-detection-on-wearable-ecg | 2112.04369 | null | https://arxiv.org/abs/2112.04369v1 | https://arxiv.org/pdf/2112.04369v1.pdf | Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise | Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sudden physiological changes occur to the signals, compromising the... | ['David Atienza', 'Grégoire P. Millet', 'Tomas Teijeiro', 'Elisabetta De Giovanni'] | 2021-12-08 | null | null | null | null | ['total-energy'] | ['miscellaneous'] | [ 5.59945762e-01 -3.38904500e-01 -3.18900168e-01 -1.65035307e-01
-5.90608895e-01 -3.57640713e-01 -5.39534688e-01 3.92762512e-01
-4.81218040e-01 5.72189033e-01 -1.31936789e-01 -6.24279901e-02
-2.19609104e-02 -5.96656144e-01 -3.84656757e-01 -6.30521774e-01
-3.89356792e-01 -2.10196376e-01 2.78892219e-01 1.20598249... | [13.945591926574707, 3.0895798206329346] |
c43df57e-9d4f-40f7-b8e2-397811fcc278 | towards-document-level-paraphrase-generation | 2109.07095 | null | https://arxiv.org/abs/2109.07095v1 | https://arxiv.org/pdf/2109.07095v1.pdf | Towards Document-Level Paraphrase Generation with Sentence Rewriting and Reordering | Paraphrase generation is an important task in natural language processing. Previous works focus on sentence-level paraphrase generation, while ignoring document-level paraphrase generation, which is a more challenging and valuable task. In this paper, we explore the task of document-level paraphrase generation for the ... | ['Xiaojun Wan', 'Yitao Cai', 'Zhe Lin'] | 2021-09-15 | null | https://aclanthology.org/2021.findings-emnlp.89 | https://aclanthology.org/2021.findings-emnlp.89.pdf | findings-emnlp-2021-11 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 4.28670645e-01 1.09248213e-01 -2.73280293e-01 -3.22427124e-01
-8.03618252e-01 -7.89080262e-01 6.89148664e-01 4.56466824e-01
1.29147619e-01 7.36749530e-01 1.04238224e+00 -4.58361149e-01
6.67066053e-02 -8.19934309e-01 -6.86083436e-01 -1.05602697e-01
5.39685369e-01 3.13893527e-01 3.11806370e-02 -6.60517573... | [11.739031791687012, 9.2855863571167] |
28623a5d-b173-44f0-ae0f-154960584ec4 | unisurf-unifying-neural-implicit-surfaces-and | 2104.10078 | null | https://arxiv.org/abs/2104.10078v2 | https://arxiv.org/pdf/2104.10078v2.pdf | UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction | Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views. Unfortunately, existing methods such as DVR or IDR require accurate per-pixel object masks as supervision. At the same time, neural radiance fields have revolutionized ... | ['Andreas Geiger', 'Songyou Peng', 'Michael Oechsle'] | 2021-04-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Oechsle_UNISURF_Unifying_Neural_Implicit_Surfaces_and_Radiance_Fields_for_Multi-View_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Oechsle_UNISURF_Unifying_Neural_Implicit_Surfaces_and_Radiance_Fields_for_Multi-View_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-object-reconstruction'] | ['computer-vision'] | [ 5.76041639e-01 1.02428310e-01 1.39696568e-01 -2.87164330e-01
-7.13886440e-01 -6.92927778e-01 6.61986947e-01 -2.64228135e-01
2.57692367e-01 7.35490203e-01 8.22114348e-02 -1.73899848e-02
1.44078478e-01 -1.20217168e+00 -9.24139977e-01 -4.94788200e-01
4.72525775e-01 2.84920901e-01 2.03847489e-03 -1.66052476... | [9.218076705932617, -3.149090528488159] |
c9a660c9-6d9c-42fa-a955-54237953a07e | road-segmentation-in-sar-satellite-images | 1802.01445 | null | http://arxiv.org/abs/1802.01445v2 | http://arxiv.org/pdf/1802.01445v2.pdf | Road Segmentation in SAR Satellite Images with Deep Fully-Convolutional Neural Networks | Remote sensing is extensively used in cartography. As transportation networks
grow and change, extracting roads automatically from satellite images is
crucial to keep maps up-to-date. Synthetic Aperture Radar satellites can
provide high resolution topographical maps. However roads are difficult to
identify in these dat... | ['Seyed Majid Azimi', 'Corentin Henry', 'Nina Merkle'] | 2018-02-05 | null | null | null | null | ['road-segementation'] | ['computer-vision'] | [ 4.36642826e-01 2.01838940e-01 -4.18913886e-02 -5.75425923e-01
-3.80419433e-01 -4.97151732e-01 6.48145616e-01 -3.62232864e-01
-6.85838521e-01 8.34649563e-01 -3.30137640e-01 -7.19345987e-01
-1.03103027e-01 -1.41680324e+00 -6.79437160e-01 -5.26363730e-01
-4.26789969e-01 5.90290129e-01 5.88996232e-01 -4.93685186... | [9.219640731811523, -1.5230505466461182] |
e14e0b21-869b-4739-8324-5816d6a44438 | 3d-human-shape-style-transfer | 2109.01587 | null | https://arxiv.org/abs/2109.01587v1 | https://arxiv.org/pdf/2109.01587v1.pdf | 3D Human Shape Style Transfer | We consider the problem of modifying/replacing the shape style of a real moving character with those of an arbitrary static real source character. Traditional solutions follow a pose transfer strategy, from the moving character to the source character shape, that relies on skeletal pose parametrization. In this paper, ... | ['Edmond Boyer', 'Joao Regateiro'] | 2021-09-03 | null | null | null | null | ['pose-transfer'] | ['computer-vision'] | [ 6.85449660e-01 3.81228864e-01 4.44531590e-01 -4.56773728e-01
-5.50011337e-01 -8.43171179e-01 9.07366335e-01 -3.79309833e-01
-5.95793903e-01 4.75010395e-01 -2.25472003e-02 1.59204662e-01
1.96662173e-01 -8.41551304e-01 -9.74730015e-01 -7.41091251e-01
3.79165709e-01 7.49060869e-01 4.13189620e-01 -4.60252017... | [11.65660572052002, -0.5197592973709106] |
75101422-7661-4a6c-b99d-8ffaeacf4b26 | investigating-pretrained-language-models-for | 2007.08426 | null | https://arxiv.org/abs/2007.08426v3 | https://arxiv.org/pdf/2007.08426v3.pdf | Investigating Pretrained Language Models for Graph-to-Text Generation | Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper, we investigate two recently proposed pretrained language models (PLMs) and analyze the impact of different task-adaptive pretraining strategies for PLMs in graph-to-text generation. We present a study across three graph domains... | ['Hinrich Schütze', 'Leonardo F. R. Ribeiro', 'Iryna Gurevych', 'Martin Schmitt'] | 2020-07-16 | null | https://aclanthology.org/2021.nlp4convai-1.20 | https://aclanthology.org/2021.nlp4convai-1.20.pdf | emnlp-nlp4convai-2021-11 | ['kb-to-language-generation', 'kg-to-text'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.79512227e-01 1.08891368e+00 -2.74339437e-01 -9.66407508e-02
-9.28707302e-01 -5.41465819e-01 1.18498850e+00 4.15287882e-01
-2.83390194e-01 1.24298263e+00 5.46139896e-01 -5.83100796e-01
6.11727461e-02 -1.21690094e+00 -9.21265543e-01 -1.82550550e-01
-5.97653091e-02 9.61258173e-01 -9.76254698e-03 -5.37374496... | [10.278118133544922, 8.322210311889648] |
0322dbb0-800a-486d-bb96-ee62e99dcb8f | etop-early-termination-of-pipelines-for | 2304.08597 | null | https://arxiv.org/abs/2304.08597v1 | https://arxiv.org/pdf/2304.08597v1.pdf | eTOP: Early Termination of Pipelines for Faster Training of AutoML Systems | Recent advancements in software and hardware technologies have enabled the use of AI/ML models in everyday applications has significantly improved the quality of service rendered. However, for a given application, finding the right AI/ML model is a complex and costly process, that involves the generation, training, and... | ['Yash Garg', 'Juliana Freire', 'Haoxiang Zhang'] | 2023-04-17 | null | null | null | null | ['feature-engineering', 'automl'] | ['methodology', 'methodology'] | [ 5.75554930e-02 -2.75350779e-01 -2.10056314e-03 -4.26538855e-01
-9.53064024e-01 -7.41360188e-01 4.57160056e-01 4.48564351e-01
-5.24322867e-01 1.23795517e-01 -2.28155449e-01 -5.34181058e-01
-5.92582859e-04 -5.38666904e-01 -6.10988438e-01 -4.23939645e-01
-1.26868919e-01 9.97673213e-01 5.33811927e-01 2.30748981... | [8.526512145996094, 4.105472564697266] |
3d15012d-9748-4660-8016-86787271f62f | user-guided-domain-adaptation-for-rapid | 2009.02455 | null | https://arxiv.org/abs/2009.02455v1 | https://arxiv.org/pdf/2009.02455v1.pdf | User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation | Mask-based annotation of medical images, especially for 3D data, is a bottleneck in developing reliable machine learning models. Using minimal-labor user interactions (UIs) to guide the annotation is promising, but challenges remain on best harmonizing the mask prediction with the UIs. To address this, we propose the u... | ['Chien-Hung Liao', 'Jing Xiao', 'Chi Tung Cheng', 'Le Lu', 'Jinzheng Cai', 'Junzhou Huang', 'Zhanghexuan Ji', 'Ashwin Raju', 'Adam P. Harrison'] | 2020-09-05 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 3.09662521e-01 5.75421035e-01 -5.42513616e-02 -3.06272179e-01
-1.19102156e+00 -7.91722000e-01 3.25496405e-01 9.43974480e-02
-1.23002678e-01 4.66607064e-01 2.79417057e-02 -2.78296620e-01
2.94080168e-01 -2.56638080e-01 -7.84439564e-01 -8.01695108e-01
-8.45231041e-02 6.03462577e-01 2.58095145e-01 1.67937055... | [14.537158966064453, -2.0712192058563232] |
72deac14-1e05-4995-b6d2-2a337172b1dc | block-matching-in-fpga | 2006.14105 | null | https://arxiv.org/abs/2006.14105v1 | https://arxiv.org/pdf/2006.14105v1.pdf | Block-matching in FPGA | Block-matching and 3D filtering (BM3D) is an image denoising algorithm that works in two similar steps. Both of these steps need to perform grouping by block-matching. We implement the block-matching in an FPGA, leveraging its ability to perform parallel computations. Our goal is to enable other researchers to use our ... | ['Michal Pleskowicz', 'Rafael Pizarro Solar'] | 2020-06-24 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 2.10756630e-01 -4.86902714e-01 5.62600076e-01 -3.48499656e-01
-1.69647068e-01 -2.74163127e-01 2.27059051e-01 -3.85161400e-01
-1.96920380e-01 -1.41600996e-01 2.55816668e-01 -7.41777182e-01
1.95931643e-01 -9.24891472e-01 -5.93833923e-01 -3.66743326e-01
-2.51215488e-01 -5.13072431e-01 6.56786025e-01 -3.26618493... | [11.357461929321289, -2.359069585800171] |
42a2862d-88eb-490a-a3e0-0c73716c223e | membership-inference-attacks-from-first | 2112.03570 | null | https://arxiv.org/abs/2112.03570v2 | https://arxiv.org/pdf/2112.03570v2.pdf | Membership Inference Attacks From First Principles | A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training dataset. These attacks are currently evaluated using average-case "accuracy" metrics that fail to characterize whether the attack can confident... | ['Florian Tramer', 'Andreas Terzis', 'Shuang Song', 'Milad Nasr', 'Steve Chien', 'Nicholas Carlini'] | 2021-12-07 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.06540361e-01 8.14812109e-02 -4.57979798e-01 -1.17729537e-01
-1.23204160e+00 -1.29966295e+00 6.24760866e-01 4.72069949e-01
-5.00587583e-01 8.19737136e-01 -7.14077115e-01 -1.03812993e+00
5.84039986e-02 -1.20263529e+00 -8.14866483e-01 -4.47835892e-01
-2.43194550e-01 6.53126419e-01 4.83733445e-01 3.58614296... | [5.905200481414795, 7.344105243682861] |
aca585eb-8566-4389-bcbe-4890a5a68d2a | a-survey-on-measuring-and-mitigating | 2209.01824 | null | https://arxiv.org/abs/2209.01824v1 | https://arxiv.org/pdf/2209.01824v1.pdf | A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension | The issue of shortcut learning is widely known in NLP and has been an important research focus in recent years. Unintended correlations in the data enable models to easily solve tasks that were meant to exhibit advanced language understanding and reasoning capabilities. In this survey paper, we focus on the field of ma... | ['Akiko Aizawa', 'Saku Sugawara', 'Johannes Mario Meissner', 'Xanh Ho'] | 2022-09-05 | null | null | null | null | ['machine-reading-comprehension'] | ['natural-language-processing'] | [ 5.45530021e-01 5.69310725e-01 -3.74406010e-01 -6.96023524e-01
-1.04213572e+00 -9.09473002e-01 6.28973901e-01 6.33453250e-01
-4.52353150e-01 6.55765235e-01 6.27560198e-01 -8.81223679e-01
-5.68988621e-01 -6.86705232e-01 -9.74896729e-01 -8.35663229e-02
9.76352319e-02 4.20216918e-01 -3.25628556e-02 -4.20170844... | [10.829687118530273, 8.147252082824707] |
cf65d83b-9166-408e-9ee8-429a23b8614d | compression-phase-is-not-necessary-for | 2102.07402 | null | https://arxiv.org/abs/2102.07402v2 | https://arxiv.org/pdf/2102.07402v2.pdf | Information flows of diverse autoencoders | The outstanding performance of deep learning in various fields has been a fundamental query, which can be potentially examined using information theory that interprets the learning process as the transmission and compression of information. Information plane analyses of the mutual information between the input-hidden-o... | ['Junghyo Jo', 'Sungyeop Lee'] | 2021-02-15 | null | null | null | null | ['information-plane'] | ['methodology'] | [ 1.99244186e-01 4.28812593e-01 2.42264643e-01 -1.92934170e-01
3.23725194e-02 -3.35218817e-01 6.55016243e-01 6.90386519e-02
-5.73630512e-01 4.98305768e-01 -1.02119200e-01 -4.81693000e-01
-5.67608833e-01 -8.59360695e-01 -7.41376579e-01 -1.00823522e+00
-3.23029369e-01 3.41158926e-01 1.32670328e-01 -9.47466940... | [7.933018207550049, 3.5540614128112793] |
bbaa21e3-438f-4d88-9ee4-abc6060454c6 | discriminative-density-ratio-estimation | 1311.4486 | null | http://arxiv.org/abs/1311.4486v2 | http://arxiv.org/pdf/1311.4486v2.pdf | Discriminative Density-ratio Estimation | The covariate shift is a challenging problem in supervised learning that
results from the discrepancy between the training and test distributions. An
effective approach which recently drew a considerable attention in the research
community is to reweight the training samples to minimize that discrepancy. In
specific, m... | ['Yun-Qian Miao', 'Mohamed S. Kamel', 'Ahmed K. Farahat'] | 2013-11-18 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 4.45597887e-01 -2.74948865e-01 -3.45542550e-01 -4.98075336e-01
-7.44132876e-01 -3.40705849e-02 4.61177409e-01 2.51150697e-01
-4.10986394e-01 8.86808574e-01 -1.07542090e-01 -2.64360663e-02
-4.11939591e-01 -7.78166354e-01 -2.68787801e-01 -9.85077679e-01
3.87048811e-01 3.25322598e-01 2.10570514e-01 2.90597349... | [8.775723457336426, 3.994135856628418] |
7d8321fb-78a3-47ee-bfa7-60a89e63fe34 | contrastive-learning-mri-reconstruction | 2306.00530 | null | https://arxiv.org/abs/2306.00530v1 | https://arxiv.org/pdf/2306.00530v1.pdf | Contrastive Learning MRI Reconstruction | Purpose: We propose a novel contrastive learning latent space representation for MRI datasets with partially acquired scans. We show that this latent space can be utilized for accelerated MR image reconstruction. Theory and Methods: Our novel framework, referred to as COLADA (stands for Contrastive Learning for highly ... | ['Zhaolin Chen', 'Mehrtash Harandi', 'Gary Egan', 'Zhifeng Chen', 'Mevan Ekanayake'] | 2023-06-01 | null | null | null | null | ['image-reconstruction', 'mri-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 4.94443983e-01 1.02058955e-01 -2.25791886e-01 -2.70000428e-01
-1.02757394e+00 -1.66370928e-01 5.94379425e-01 -1.02327831e-01
-4.30090994e-01 5.30772507e-01 6.90577626e-01 -7.16295987e-02
-6.08980298e-01 -5.15639782e-01 -6.43977761e-01 -1.31316221e+00
-4.71610665e-01 5.20187438e-01 -9.58645865e-02 1.94820464... | [13.536686897277832, -2.3677353858947754] |
6144c12d-6fba-4e7d-834c-904664351c3d | feedback-stability-analysis-via-dissipativity | 2209.08322 | null | https://arxiv.org/abs/2209.08322v1 | https://arxiv.org/pdf/2209.08322v1.pdf | Feedback Stability Analysis via Dissipativity with Dynamic Supply Rates | In this paper, we propose a notion of dissipativity with dynamic supply rates for nonlinear differential input-state-output equations via the use of auxiliary systems. This extends the classical dissipativity with static supply rates and miscellaneous dynamic quadratic forms. The main results of this paper concern Lyap... | ['Chao Chen', 'Sei Zhen Khong'] | 2022-09-17 | null | null | null | null | ['miscellaneous'] | ['miscellaneous'] | [-2.04519048e-01 4.50364500e-01 1.42571777e-01 4.25098211e-01
4.05463483e-03 -8.76371861e-01 2.36240417e-01 5.28287366e-02
-3.14552099e-01 1.11358380e+00 -1.74548551e-01 -2.68491954e-01
-5.39001048e-01 -2.69755751e-01 -4.32650805e-01 -1.04172122e+00
1.02272816e-01 -3.89778972e-01 1.86645433e-01 -9.83114898... | [5.453096866607666, 2.665771722793579] |
d0a8fbe2-d5ae-4f20-bf66-b69eba7e5653 | exponential-family-embeddings | 1608.00778 | null | http://arxiv.org/abs/1608.00778v2 | http://arxiv.org/pdf/1608.00778v2.pdf | Exponential Family Embeddings | Word embeddings are a powerful approach for capturing semantic similarity
among terms in a vocabulary. In this paper, we develop exponential family
embeddings, a class of methods that extends the idea of word embeddings to
other types of high-dimensional data. As examples, we studied neural data with
real-valued observ... | ['Maja R. Rudolph', 'Francisco J. R. Ruiz', 'David M. Blei', 'Stephan Mandt'] | 2016-08-02 | exponential-family-embeddings-1 | http://papers.nips.cc/paper/6571-exponential-family-embeddings | http://papers.nips.cc/paper/6571-exponential-family-embeddings.pdf | neurips-2016-12 | ['movie-recommendation'] | ['miscellaneous'] | [-3.89639169e-01 -3.91342163e-01 -3.84052753e-01 -4.58887368e-01
1.37217388e-01 -6.37556136e-01 5.79377413e-01 4.98155147e-01
-9.47191417e-01 3.30425620e-01 5.46950400e-01 -1.22929424e-01
-2.07124397e-01 -8.65481555e-01 -5.74668646e-01 -8.87353063e-01
-1.86737597e-01 4.23057139e-01 2.47529354e-02 -4.88669634... | [10.405820846557617, 8.67973804473877] |
93159f62-aa07-4ccb-b962-ba52f1994567 | solving-the-side-chain-packing-arrangement-of | 2212.03320 | null | https://arxiv.org/abs/2212.03320v1 | https://arxiv.org/pdf/2212.03320v1.pdf | Solving the Side-Chain Packing Arrangement of Proteins from Reinforcement Learned Stochastic Decision Making | Protein structure prediction is a fundamental problem in computational molecular biology. Classical algorithms such as ab-initio or threading as well as many learning methods have been proposed to solve this challenging problem. However, most reinforcement learning methods tend to model the state-action pairs as discre... | ['Minh Nguyen', 'Conrad Li', 'Chandrajit Bajaj'] | 2022-12-06 | null | null | null | null | ['protein-folding'] | ['natural-language-processing'] | [ 4.16218117e-02 3.00132513e-01 -5.38851954e-02 -2.83035815e-01
-6.98306262e-01 -3.88875872e-01 6.36334300e-01 2.17821181e-01
-6.51467204e-01 1.52924478e+00 -6.00294620e-02 -5.33984363e-01
2.48241425e-02 -6.55318677e-01 -1.05526865e+00 -1.14854145e+00
-1.20729946e-01 7.19849825e-01 2.10597426e-01 -5.09371519... | [4.758528232574463, 5.496603012084961] |
38bce51c-a8e9-4b40-8b23-7cfc8064c85c | condition-number-analysis-of-kernel-based | 0912.2800 | null | https://arxiv.org/abs/0912.2800v1 | https://arxiv.org/pdf/0912.2800v1.pdf | Condition Number Analysis of Kernel-based Density Ratio Estimation | The ratio of two probability densities can be used for solving various machine learning tasks such as covariate shift adaptation (importance sampling), outlier detection (likelihood-ratio test), and feature selection (mutual information). Recently, several methods of directly estimating the density ratio have been deve... | ['Taiji Suzuki', 'Masashi Sugiyama', 'Takafumi Kanamori'] | 2009-12-15 | null | null | null | null | ['density-ratio-estimation'] | ['methodology'] | [ 1.25692666e-01 8.32597688e-02 -3.85257483e-01 -3.37724328e-01
-9.21147168e-01 -1.25137389e-01 1.58190891e-01 2.12339833e-01
-5.73958397e-01 9.69481051e-01 -1.77826405e-01 -4.09673840e-01
-5.97702265e-01 -4.18523461e-01 -5.09448886e-01 -8.45190525e-01
-2.51944542e-01 3.49360377e-01 1.06972180e-01 1.15747407... | [7.367610454559326, 4.147401332855225] |
c418f946-6754-4e92-adbf-8c79565401f6 | opfython-a-python-inspired-optimum-path | 2001.10420 | null | https://arxiv.org/abs/2001.10420v3 | https://arxiv.org/pdf/2001.10420v3.pdf | OPFython: A Python-Inspired Optimum-Path Forest Classifier | Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation. Nevertheless, conventional classification algorithms, e.g., Logistic Regression, Decision Trees, and Bayesian clas... | ['Alexandre Xavier Falcão', 'João Paulo Papa', 'Gustavo Henrique de Rosa'] | 2020-01-28 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.79455161e-01 -1.05977394e-01 -5.13780475e-01 -5.16875148e-01
1.30857885e-01 -2.30294243e-01 7.29413629e-01 5.10563016e-01
-3.74969810e-01 8.17689359e-01 -3.27929795e-01 -8.11548531e-01
-3.88727188e-01 -1.10387838e+00 -1.35707259e-01 -4.91665155e-01
-1.10847503e-01 5.76752782e-01 6.22804105e-01 3.72148529... | [8.406439781188965, 4.23612642288208] |
32b36910-caff-41f4-a398-91f2f10940e6 | learning-hierarchical-graph-neural-networks | 2107.01319 | null | https://arxiv.org/abs/2107.01319v2 | https://arxiv.org/pdf/2107.01319v2.pdf | Learning Hierarchical Graph Neural Networks for Image Clustering | We propose a hierarchical graph neural network (GNN) model that learns how to cluster a set of images into an unknown number of identities using a training set of images annotated with labels belonging to a disjoint set of identities. Our hierarchical GNN uses a novel approach to merge connected components predicted at... | ['David Wipf', 'Stefano Soatto', 'Zheng Zhang', 'Wei Xia', 'Yuanjun Xiong', 'Yongxin Wang', 'Tianjun Xiao', 'Tong He', 'Yifan Xing'] | 2021-07-03 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xing_Learning_Hierarchical_Graph_Neural_Networks_for_Image_Clustering_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xing_Learning_Hierarchical_Graph_Neural_Networks_for_Image_Clustering_ICCV_2021_paper.pdf | iccv-2021-1 | ['image-clustering', 'face-clustering'] | ['computer-vision', 'computer-vision'] | [ 2.45163158e-01 4.75742787e-01 -2.07902834e-01 -5.22444487e-01
-4.26868618e-01 -5.74729681e-01 5.42343557e-01 2.97191501e-01
-1.85494602e-01 5.64810574e-01 -1.14202552e-01 -1.59839675e-01
-3.09948176e-01 -9.83053207e-01 -7.27197647e-01 -7.25238144e-01
-2.83264726e-01 1.11021614e+00 -2.82945260e-02 4.61553454... | [9.119307518005371, 3.3062386512756348] |
354f672a-9d33-430f-9692-56b5bdef359d | transforming-bells-inequalities-into-state | 1705.00813 | null | http://arxiv.org/abs/1705.00813v1 | http://arxiv.org/pdf/1705.00813v1.pdf | Transforming Bell's Inequalities into State Classifiers with Machine Learning | Quantum information science has profoundly changed the ways we understand,
store, and process information. A major challenge in this field is to look for
an efficient means for classifying quantum state. For instance, one may want to
determine if a given quantum state is entangled or not. However, the process of
a comp... | ['Man-Hong Yung', 'Yue-Chi Ma'] | 2017-05-02 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 3.02640736e-01 -2.14491025e-01 -7.46859908e-02 -1.96294203e-01
-5.35576344e-01 -8.81385088e-01 3.66632640e-01 4.15120423e-01
-5.96933544e-01 7.90988207e-01 -4.15869504e-01 -7.95719028e-01
-8.49314779e-02 -1.22867954e+00 -2.56244689e-01 -1.01277435e+00
-1.69393539e-01 5.65123856e-01 6.25969991e-02 -2.91569918... | [5.63594388961792, 4.894177436828613] |
d59d0e14-5d4c-4c93-b035-f83a7175ddb4 | exploring-methods-for-generating-feedback | null | null | https://aclanthology.org/2021.emnlp-main.766 | https://aclanthology.org/2021.emnlp-main.766.pdf | Exploring Methods for Generating Feedback Comments for Writing Learning | The task of generating explanatory notes for language learners is known as feedback comment generation. Although various generation techniques are available, little is known about which methods are appropriate for this task. Nagata (2019) demonstrates the effectiveness of neural-retrieval-based methods in generating fe... | ['Kentaro Inui', 'Ryo Nagata', 'Kazuaki Hanawa'] | null | null | null | null | emnlp-2021-11 | ['comment-generation'] | ['natural-language-processing'] | [ 2.10779727e-01 2.07246825e-01 -7.54460469e-02 -1.97458401e-01
-9.50868130e-01 -6.86180592e-01 8.14779341e-01 3.05576593e-01
-3.68076682e-01 9.49295640e-01 6.32726133e-01 -8.06891561e-01
2.31740206e-01 -7.75976300e-01 -5.42374849e-01 -4.05309200e-01
2.43655443e-01 1.84884638e-01 1.59556314e-01 -4.61692512... | [11.753331184387207, 8.969487190246582] |
4e43703b-173a-444c-9e85-bb3a51eddb88 | tfr-texture-defect-detection-with-fourier | 2307.04574 | null | https://arxiv.org/abs/2307.04574v1 | https://arxiv.org/pdf/2307.04574v1.pdf | TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder | Texture is an essential information in image representation, capturing patterns and structures. As a result, texture plays a crucial role in the manufacturing industry and is extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can ... | ['Sungyoung Kim', 'Jongwook Si'] | 2023-07-10 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.73071915e-01 -6.85796976e-01 1.22713335e-01 4.36662957e-02
-1.88044123e-02 1.29916862e-01 1.23291738e-01 7.71390349e-02
3.79868001e-02 2.86861539e-01 -1.02450751e-01 1.91679999e-01
-4.67445552e-01 -1.11402631e+00 -7.01491609e-02 -1.12992549e+00
1.45485550e-01 -6.55606464e-02 2.50458300e-01 -2.22914025... | [7.486916542053223, 1.6883924007415771] |
59eb87d2-3ed2-4d29-9a93-9a011f547323 | cost-aware-asynchronous-multi-agent-active | 2210.02259 | null | https://arxiv.org/abs/2210.02259v1 | https://arxiv.org/pdf/2210.02259v1.pdf | Cost Aware Asynchronous Multi-Agent Active Search | Multi-agent active search requires autonomous agents to choose sensing actions that efficiently locate targets. In a realistic setting, agents also must consider the costs that their decisions incur. Previously proposed active search algorithms simplify the problem by ignoring uncertainty in the agent's environment, us... | ['Jeff Schneider', 'Ramina Ghods', 'Arundhati Banerjee'] | 2022-10-05 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 3.70737553e-01 6.11982644e-01 -7.15307832e-01 -1.01219870e-01
-1.17816842e+00 -8.72944236e-01 5.41854143e-01 2.43962333e-01
-8.08744133e-01 1.30169308e+00 1.01882860e-01 -3.42983037e-01
-6.42988503e-01 -8.15480828e-01 -2.96528220e-01 -8.30081522e-01
-3.67221266e-01 1.09233582e+00 2.86876857e-01 4.16929722... | [4.199602127075195, 2.0815181732177734] |
52c41f47-dc1c-468c-a997-e437928239b6 | the-active-filler-strategy-in-a-move-eager | null | null | https://aclanthology.org/W19-2901 | https://aclanthology.org/W19-2901.pdf | The Active-Filler Strategy in a Move-Eager Left-Corner Minimalist Grammar Parser | Recent psycholinguistic evidence suggests that human parsing of moved elements is {`}active{'}, and perhaps even {`}hyper-active{'}: it seems that a leftward-moved object is related to a verbal position rapidly, perhaps even before the transitivity information associated with the verb is available to the listener. This... | ["Milo{\\v{s}} Stanojevi{\\'c}", 'Tim Hunter', 'Edward Stabler'] | 2019-06-01 | null | null | null | ws-2019-6 | ['human-parsing'] | ['computer-vision'] | [ 3.45745713e-01 6.47829950e-01 8.70812461e-02 -5.18624067e-01
-7.50968337e-01 -9.53935921e-01 2.56979376e-01 6.47666454e-01
-7.29343534e-01 3.72559786e-01 4.25817370e-01 -9.83609200e-01
-2.06364319e-01 -7.82455444e-01 -5.69759548e-01 -3.53922188e-01
-2.40373388e-02 5.79609394e-01 6.50500953e-01 -4.56826627... | [10.32627010345459, 9.347541809082031] |
2ee8419e-4472-4224-8256-cffa0b889502 | cascaded-lstms-based-deep-reinforcement | 1910.14229 | null | https://arxiv.org/abs/1910.14229v1 | https://arxiv.org/pdf/1910.14229v1.pdf | Cascaded LSTMs based Deep Reinforcement Learning for Goal-driven Dialogue | This paper proposes a deep neural network model for joint modeling Natural Language Understanding (NLU) and Dialogue Management (DM) in goal-driven dialogue systems. There are three parts in this model. A Long Short-Term Memory (LSTM) at the bottom of the network encodes utterances in each dialogue turn into a turn emb... | ['Hong Chen', 'Yue Ma', 'Xiaojie Wang', 'Zhenjiang Dong'] | 2019-10-31 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-2.04327956e-01 1.01989758e+00 -5.85183464e-02 -6.73236847e-01
-2.91830242e-01 -1.86081588e-01 8.56630564e-01 7.13944063e-02
-5.92197180e-01 8.14912200e-01 8.76412392e-01 -3.82009387e-01
3.95505846e-01 -9.13242638e-01 -2.87503660e-01 -3.32130283e-01
4.73041162e-02 8.49802732e-01 7.26378784e-02 -9.00710523... | [13.001531600952148, 7.948004245758057] |
81f90aa9-b811-491b-adb7-595ae03c13c5 | learning-the-best-pooling-strategy-for-visual | 2011.04305 | null | https://arxiv.org/abs/2011.04305v5 | https://arxiv.org/pdf/2011.04305v5.pdf | Learning the Best Pooling Strategy for Visual Semantic Embedding | Visual Semantic Embedding (VSE) is a dominant approach for vision-language retrieval, which aims at learning a deep embedding space such that visual data are embedded close to their semantic text labels or descriptions. Recent VSE models use complex methods to better contextualize and aggregate multi-modal features int... | ['Changhu Wang', 'Yuning Jiang', 'Hao Wu', 'Hexiang Hu', 'Jiacheng Chen'] | 2020-11-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Learning_the_Best_Pooling_Strategy_for_Visual_Semantic_Embedding_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Learning_the_Best_Pooling_Strategy_for_Visual_Semantic_Embedding_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-text-retrieval', 'cross-modal-information-retrieval'] | ['computer-vision', 'miscellaneous'] | [-1.95216402e-01 -4.38202381e-01 -2.70376593e-01 -2.38455147e-01
-6.83156312e-01 -6.15139127e-01 8.49731445e-01 2.51617115e-02
-5.91814101e-01 1.30123615e-01 3.25386345e-01 -3.84193435e-02
-3.25304300e-01 -5.48537016e-01 -6.14378870e-01 -5.79141974e-01
-6.13864101e-02 -7.60594532e-02 3.12784493e-01 -1.85783565... | [10.627674102783203, 0.9711435437202454] |
80b9a5de-43f6-4570-b2b6-4998c23e9ddc | balancing-utility-and-fairness-in-submodular | 2211.00980 | null | https://arxiv.org/abs/2211.00980v4 | https://arxiv.org/pdf/2211.00980v4.pdf | Balancing Utility and Fairness in Submodular Maximization (Technical Report) | Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications -- including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the ... | ['Ying Wang', 'Francesco Bonchi', 'Yuchen Li', 'Yanhao Wang'] | 2022-11-02 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.08670309e-01 5.12322068e-01 -6.77378416e-01 -3.19012552e-01
-5.91147602e-01 -6.43030882e-01 -1.19582511e-01 3.41389239e-01
-6.90131858e-02 1.19901109e+00 2.19623789e-01 -1.94844440e-01
-6.61330938e-01 -9.68574762e-01 -5.87258279e-01 -7.59027481e-01
-1.14692137e-01 6.75066054e-01 -2.45885998e-01 -2.02234969... | [6.565415382385254, 4.938504695892334] |
d2cd4ec2-26e1-4984-9320-b11256ee6854 | yolactedge-real-time-instance-segmentation-on | 2012.12259 | null | https://arxiv.org/abs/2012.12259v2 | https://arxiv.org/pdf/2012.12259v2.pdf | YolactEdge: Real-time Instance Segmentation on the Edge | We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti) with a ResNet-101 backbone on 550x550 resolution images. To achieve this, we make two ... | ['Yong Jae Lee', 'Fanyi Xiao', 'Rafael A. Rivera Soto', 'Haotian Liu'] | 2020-12-22 | null | null | null | null | ['real-time-instance-segmentation'] | ['computer-vision'] | [-2.35732824e-01 -2.79024243e-01 -1.77676797e-01 -2.38467723e-01
-5.73431671e-01 -5.75356543e-01 2.70475537e-01 -3.49678993e-01
-5.54860294e-01 2.23930627e-01 -2.24820971e-01 -6.13189578e-01
3.08409154e-01 -5.45662940e-01 -7.19143033e-01 -1.60068318e-01
-3.83347631e-01 1.82567671e-01 6.68678463e-01 1.16551742... | [9.227652549743652, 0.0010232661152258515] |
0785f0b8-d981-4531-beeb-0daf93e9b8b0 | deep-metric-learning-with-soft-orthogonal | 2306.13055 | null | https://arxiv.org/abs/2306.13055v1 | https://arxiv.org/pdf/2306.13055v1.pdf | Deep Metric Learning with Soft Orthogonal Proxies | Deep Metric Learning (DML) models rely on strong representations and similarity-based measures with specific loss functions. Proxy-based losses have shown great performance compared to pair-based losses in terms of convergence speed. However, proxies that are assigned to different classes may end up being closely locat... | ['Mahdi Eftekhari', 'Mahdi Shariatzadeh', 'Mahvash Mohazzebi', 'Dorsa Rahmatian', 'Monireh Moshavash', 'Farid Saberi-Movahed', 'Mohammad K. Ebrahimpour', 'Farshad Saberi-Movahed'] | 2023-06-22 | null | null | null | null | ['metric-learning', 'metric-learning', 'retrieval'] | ['computer-vision', 'methodology', 'methodology'] | [ 1.78224087e-01 -3.59586686e-01 -3.22134167e-01 -5.34610271e-01
-1.13615537e+00 -4.94967729e-01 6.56602681e-01 1.27715468e-01
-5.27646840e-01 4.78090018e-01 2.13322490e-01 7.39744231e-02
-2.95524985e-01 -6.02890491e-01 -6.72155082e-01 -7.41241217e-01
-1.08193874e-01 1.95378304e-01 -1.79229632e-01 1.01631105... | [9.432486534118652, 3.088984727859497] |
dacf78fa-9c26-4d39-8c5c-77ab4c567930 | female-mosquito-detection-by-means-of-ai | 2306.10843 | null | https://arxiv.org/abs/2306.10843v1 | https://arxiv.org/pdf/2306.10843v1.pdf | Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program | The Sterile Insect Technique (SIT) is a biological pest control technique based on the release into the environment of sterile males of the insect species whose population is to be controlled. The entire SIT process involves mass-rearing within a biofactory, sorting of the specimens by sex, sterilization, and subsequen... | ['Pedro Zuccarello', 'David Almenar', 'Jordi Grau-Haro', 'Javier Naranjo-Alcazar'] | 2023-06-19 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [ 1.56915814e-01 -2.71665066e-01 3.19884270e-01 2.44846195e-02
5.40126979e-01 -8.11380208e-01 3.82022202e-01 5.49189210e-01
-6.86418056e-01 7.71685064e-01 -2.17106834e-01 -4.21776026e-01
6.28091842e-02 -8.09245050e-01 -5.24065912e-01 -1.13545096e+00
-4.44078267e-01 4.34929281e-01 8.56969953e-02 5.04539795... | [12.849227905273438, 0.31324324011802673] |
c59b1911-18af-49f9-b6f4-134c141bbfad | visibility-aware-pixelwise-view-selection-for | 2302.07182 | null | https://arxiv.org/abs/2302.07182v1 | https://arxiv.org/pdf/2302.07182v1.pdf | Visibility-Aware Pixelwise View Selection for Multi-View Stereo Matching | The performance of PatchMatch-based multi-view stereo algorithms depends heavily on the source views selected for computing matching costs. Instead of modeling the visibility of different views, most existing approaches handle occlusions in an ad-hoc manner. To address this issue, we propose a novel visibility-guided p... | ['Minglun Gong', 'Yukun Shi', 'Zhentao Huang'] | 2023-02-14 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 4.01522368e-01 -3.24490815e-01 4.00886796e-02 -2.19839424e-01
-4.97905284e-01 -4.58678097e-01 2.28921890e-01 1.44325584e-01
-1.33674026e-01 7.65079618e-01 -3.20481732e-02 9.04752091e-02
-2.74713755e-01 -1.10451555e+00 -4.63696837e-01 -1.05945349e+00
1.97500736e-01 5.35583615e-01 8.78741503e-01 -8.88284892... | [9.309090614318848, -2.4804604053497314] |
39ea6b0b-1d83-4d2e-8029-79b439488ece | convolutional-networks-are-inherently | null | null | https://openreview.net/forum?id=5oF-Z7Uk0tH | https://openreview.net/pdf?id=5oF-Z7Uk0tH | Convolutional Networks are Inherently Foveated | When convolutional layers apply no padding, central pixels have more ways to contribute to the convolution than peripheral pixels. Such discrepancy grows exponentially with the number of layers, leading to implicit foveation of the input pixels. We show that this discrepancy can persist even when padding is applied. In... | ['Orion Reblitz-Richardson', 'David Adkins', 'Narine Kokhlikyan', 'Vivek Miglani', 'Bilal Alsallakh'] | 2021-10-12 | null | null | null | neurips-workshop-svrhm-2021-12 | ['foveation'] | ['computer-vision'] | [ 2.86149681e-01 1.32749185e-01 1.99704021e-01 -4.73685078e-02
5.61117567e-02 -8.12986016e-01 4.88143981e-01 6.15541749e-02
-8.90041709e-01 4.39997673e-01 3.68802637e-01 -6.61986887e-01
1.53857514e-01 -8.15222919e-01 -8.36595833e-01 -5.17499924e-01
-5.97235933e-02 -8.27415168e-01 3.99249345e-01 -1.57783106... | [9.88267993927002, 2.1617813110351562] |
934d2ede-a255-4b66-adff-9b49c20a73da | vakyansh-asr-toolkit-for-low-resource-indic | 2203.16512 | null | https://arxiv.org/abs/2203.16512v2 | https://arxiv.org/pdf/2203.16512v2.pdf | Vakyansh: ASR Toolkit for Low Resource Indic languages | We present Vakyansh, an end to end toolkit for Speech Recognition in Indic languages. India is home to almost 121 languages and around 125 crore speakers. Yet most of the languages are low resource in terms of data and pretrained models. Through Vakyansh, we introduce automatic data pipelines for data creation, model t... | ['Vivek Raghavan', 'Rishabh Gaur', 'Ankur Dhuriya', 'Neeraj Chhimwal', 'Priyanshi Shah', 'Anirudh Gupta', 'Harveen Singh Chadha'] | 2022-03-30 | null | null | null | null | ['punctuation-restoration'] | ['natural-language-processing'] | [-1.30650714e-01 -1.50371520e-02 6.07555360e-02 -7.20431089e-01
-1.05768728e+00 -6.84328377e-01 5.15162349e-01 -4.54874843e-01
-3.57044667e-01 1.85506701e-01 7.81286359e-01 -1.02161050e+00
4.54351246e-01 -3.39951783e-01 -2.45852441e-01 -1.51154563e-01
-6.50709271e-02 8.28425467e-01 -2.54724920e-01 -6.09793007... | [14.311922073364258, 6.805300712585449] |
62b9e64e-3633-4219-8fe0-55e0ee9c6310 | improved-wavelets-for-image-compression-from | 2203.02556 | null | https://arxiv.org/abs/2203.02556v1 | https://arxiv.org/pdf/2203.02556v1.pdf | Improved Wavelets for Image Compression from Unitary Circuits | We benchmark the efficacy of several novel orthogonal, symmetric, dilation-3 wavelets, derived from a unitary circuit based construction, towards image compression. The performance of these wavelets is compared across several photo databases against the CDF-9/7 wavelets in terms of the minimum number of non-zero wavele... | ['Glen Evenbly', 'James C. McCord'] | 2022-03-04 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 6.71371460e-01 -4.39594716e-01 -2.52387494e-01 -3.54215801e-02
-8.51501048e-01 -4.40369248e-02 3.49045873e-01 1.91600174e-01
-4.08581913e-01 3.46074611e-01 5.68276346e-01 -9.70541220e-03
-4.14779752e-01 -8.32166731e-01 -1.75545290e-01 -8.60553801e-01
-5.59890747e-01 -4.55673844e-01 2.46369034e-01 -3.05458993... | [11.59280014038086, -2.0822393894195557] |
0f4ac962-6e2b-4f03-ab6e-134a36338d04 | spiq-data-free-per-channel-static-input | 2203.14642 | null | https://arxiv.org/abs/2203.14642v1 | https://arxiv.org/pdf/2203.14642v1.pdf | SPIQ: Data-Free Per-Channel Static Input Quantization | Computationally expensive neural networks are ubiquitous in computer vision and solutions for efficient inference have drawn a growing attention in the machine learning community. Examples of such solutions comprise quantization, i.e. converting the processing values (weights and inputs) from floating point into intege... | ['Kevin Bailly', 'Matthieu Cord', 'Arnaud Dapogny', 'Edouard Yvinec'] | 2022-03-28 | null | null | null | null | ['data-free-quantization', 'data-free-quantization'] | ['computer-vision', 'methodology'] | [ 4.33324933e-01 -5.23703471e-02 -8.60620141e-02 -5.11675358e-01
-7.86211073e-01 -6.83760464e-01 6.35552347e-01 4.29853052e-01
-1.17852485e+00 7.71740913e-01 -4.16065127e-01 -4.24307257e-01
-5.68539370e-04 -7.88620114e-01 -1.04150295e+00 -8.28848660e-01
9.30076465e-02 2.86990106e-01 3.39142919e-01 -8.03951174... | [8.607053756713867, 3.1164307594299316] |
b77758c0-f739-43ed-8931-5f6561f6d471 | bootstrapped-masked-autoencoders-for-vision | 2207.07116 | null | https://arxiv.org/abs/2207.07116v1 | https://arxiv.org/pdf/2207.07116v1.pdf | Bootstrapped Masked Autoencoders for Vision BERT Pretraining | We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1) momentum encoder that provides online feature as extra BERT prediction targets; 2) target-aware decoder that tries to reduce the pressure o... | ['Nenghai Yu', 'Fang Wen', 'Dong Chen', 'Lu Yuan', 'Weiming Zhang', 'Dongdong Chen', 'Ting Zhang', 'Jianmin Bao', 'Xiaoyi Dong'] | 2022-07-14 | null | null | null | null | ['self-supervised-image-classification'] | ['computer-vision'] | [ 1.82604611e-01 5.01604795e-01 -1.48422867e-01 -4.24269319e-01
-7.05124795e-01 -1.37243062e-01 2.01428935e-01 -3.96922708e-01
-7.56766438e-01 5.26536465e-01 -8.90650749e-02 -2.23327756e-01
4.35161740e-01 -8.97141516e-01 -1.42008758e+00 -7.10654318e-01
2.19023693e-02 3.97523582e-01 4.50223356e-01 -8.42852518... | [9.565206527709961, 0.8753634095191956] |
644fe61e-85de-47f1-8378-d1ab0ffda335 | entire-space-learning-framework-unbias | 2303.00276 | null | https://arxiv.org/abs/2303.00276v1 | https://arxiv.org/pdf/2303.00276v1.pdf | Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System | Recommender system is an essential part of online services, especially for e-commerce platform. Conversion Rate (CVR) prediction in RS plays a significant role in optimizing Gross Merchandise Volume (GMV) goal of e-commerce. However, CVR suffers from well-known Sample Selection Bias (SSB) and Data Sparsity (DS) problem... | ['Junfeng Ge', 'Tao Zhuang', 'Qiwei Chen', 'Shanshan Lyu'] | 2023-03-01 | null | null | null | null | ['selection-bias'] | ['natural-language-processing'] | [-3.15725356e-01 -5.82896352e-01 -5.68337023e-01 -6.48329318e-01
-5.50390065e-01 -5.90541422e-01 2.33917281e-01 -2.93129802e-01
-5.20291887e-02 -3.16528883e-03 1.17794402e-01 -4.64656919e-01
-4.34474885e-01 -9.06759977e-01 -8.17417979e-01 -5.11752844e-01
-2.67866869e-02 5.13770878e-01 -4.96541932e-02 -5.94160318... | [10.126322746276855, 5.577022552490234] |
c322c099-f7c1-47ae-bfd6-ae026a86c68e | automatic-gloss-level-data-augmentation-for | null | null | https://aclanthology.org/2022.lrec-1.734 | https://aclanthology.org/2022.lrec-1.734.pdf | Automatic Gloss-level Data Augmentation for Sign Language Translation | Securing sufficient data to enable automatic sign language translation modeling is challenging. The data insufficiency issue exists in both video and text modalities; however, fewer studies have been performed on text data augmentation compared to video data. In this study, we present three methods of augmenting sign l... | ['Gahgene Gweon', 'Byungcheon Yoon', 'Suna Shin', 'Saim Shin', 'Han-Mu Park', 'Jin Yea Jang'] | null | null | null | null | lrec-2022-6 | ['sign-language-translation'] | ['computer-vision'] | [ 3.13281298e-01 -3.62990320e-01 -4.84664857e-01 -2.36954734e-01
-1.05058324e+00 -4.44075108e-01 4.64099348e-01 -3.74554068e-01
-8.51325154e-01 1.00261784e+00 1.03071988e+00 -2.15943947e-01
2.37471819e-01 -2.83568621e-01 -4.90952581e-01 -3.32667500e-01
4.34911042e-01 1.48893848e-01 -1.15254916e-01 -2.28037626... | [9.17392349243164, -6.496230602264404] |
1a1e4505-9e09-4aa5-b4af-a533dba3a48f | building-low-resource-ner-models-using-non | 2006.09627 | null | https://arxiv.org/abs/2006.09627v2 | https://arxiv.org/pdf/2006.09627v2.pdf | Building Low-Resource NER Models Using Non-Speaker Annotation | In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it. Cross-lingual methods have had notable success in addressing these concerns, but in certain common circumstances, such as insufficient pre-training corpora or lang... | ['Dan Roth', 'Tatiana Tsygankova', 'Stephen Mayhew', 'Francesca Marini'] | 2020-06-17 | null | null | null | null | ['low-resource-named-entity-recognition'] | ['natural-language-processing'] | [ 1.46383733e-01 2.10283458e-01 -2.08902031e-01 -7.97753930e-01
-1.47955120e+00 -8.19836915e-01 4.95538235e-01 3.82570863e-01
-9.17688131e-01 9.03721690e-01 7.27065384e-01 -4.64406997e-01
2.83759445e-01 -1.34580553e-01 -3.47426414e-01 -2.56326169e-01
3.96354586e-01 5.50241292e-01 -1.18128052e-02 -2.35366210... | [10.105134963989258, 9.786418914794922] |
db45b429-f12c-4075-8ec6-e9700744a6a2 | train-on-small-play-the-large-scaling-up | 2107.08387 | null | https://arxiv.org/abs/2107.08387v1 | https://arxiv.org/pdf/2107.08387v1.pdf | Train on Small, Play the Large: Scaling Up Board Games with AlphaZero and GNN | Playing board games is considered a major challenge for both humans and AI researchers. Because some complicated board games are quite hard to learn, humans usually begin with playing on smaller boards and incrementally advance to master larger board strategies. Most neural network frameworks that are currently tasked ... | ['Ran El-Yaniv', 'Shai Ben-Assayag'] | 2021-07-18 | null | null | null | null | ['board-games'] | ['playing-games'] | [-3.57968882e-02 4.30720598e-01 6.94153178e-03 1.07787266e-01
-2.40770906e-01 -7.07036316e-01 -1.95335858e-02 2.01744527e-01
-5.08771896e-01 6.28454089e-01 -5.48206508e-01 -6.56275332e-01
-1.90561399e-01 -1.27030408e+00 -9.13865924e-01 2.06681546e-02
-4.85880017e-01 9.69549656e-01 8.46459270e-01 -8.70856762... | [3.4751126766204834, 1.4457818269729614] |
869f3fbd-11c6-41c3-95df-fb77ed29c91a | automatic-piano-transcription-with | 2307.04305 | null | https://arxiv.org/abs/2307.04305v1 | https://arxiv.org/pdf/2307.04305v1.pdf | Automatic Piano Transcription with Hierarchical Frequency-Time Transformer | Taking long-term spectral and temporal dependencies into account is essential for automatic piano transcription. This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content. In this case, we may rely on the capability of self-attention mechanism in Transformers... | ['Yuki Mitsufuji', 'Wei-Hsiang Liao', 'Yuhta Takida', 'Yukara Ikemiya', 'Taketo Akama', 'Keisuke Toyama'] | 2023-07-10 | null | null | null | null | ['music-transcription'] | ['music'] | [-3.75351869e-02 -5.30978322e-01 -6.24809088e-03 1.23525783e-01
-9.71584916e-01 -7.33424306e-01 1.53168380e-01 -1.94881037e-01
6.37875721e-02 2.88136452e-01 3.93590540e-01 -8.59231204e-02
-7.02907145e-02 -4.72389847e-01 -4.31775600e-01 -7.53353119e-01
1.08010080e-02 2.31505826e-01 2.50666738e-01 -1.36036739... | [15.705209732055664, 5.483864784240723] |
0da2c11a-39ce-4258-8254-1fd0dda3484f | learning-to-classify-images-without-labels | 2005.12320 | null | https://arxiv.org/abs/2005.12320v2 | https://arxiv.org/pdf/2005.12320v2.pdf | SCAN: Learning to Classify Images without Labels | Can we automatically group images into semantically meaningful clusters when ground-truth annotations are absent? The task of unsupervised image classification remains an important, and open challenge in computer vision. Several recent approaches have tried to tackle this problem in an end-to-end fashion. In this paper... | ['Luc van Gool', 'Wouter Van Gansbeke', 'Marc Proesmans', 'Stamatios Georgoulis', 'Simon Vandenhende'] | 2020-05-25 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1057_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550273.pdf | eccv-2020-8 | ['unsupervised-image-classification'] | ['computer-vision'] | [ 9.07967016e-02 -1.38734177e-01 -1.99741960e-01 -4.35305417e-01
-1.10908520e+00 -5.97535729e-01 5.36949754e-01 2.69098729e-01
-7.51794338e-01 4.57407504e-01 -3.45646148e-03 3.02199647e-02
5.02670072e-02 -4.57112074e-01 -6.96794391e-01 -8.38560641e-01
-4.46506999e-02 4.25506115e-01 1.45921901e-01 8.71305019... | [9.456124305725098, 2.5536465644836426] |
95c96ede-eac9-4dbd-a17d-87203babbe47 | hemlets-posh-learning-part-centric-heatmap | 2003.04894 | null | https://arxiv.org/abs/2003.04894v3 | https://arxiv.org/pdf/2003.04894v3.pdf | HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation | Estimating 3D human pose from a single image is a challenging task. This work attempts to address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an intermediate state-Part-Centric Heatmap Triplets (HEMlets), which shortens the gap between the 2D observation and the 3D interpretation. T... | ['Xiaoguang Han', 'Nianjuan Jiang', 'Kun Zhou', 'Kui Jia', 'Jiangbo Lu'] | 2020-03-10 | null | null | null | null | ['3d-human-pose-and-shape-estimation'] | ['computer-vision'] | [-1.42368048e-01 5.36757171e-01 -2.08915085e-01 -1.95867792e-01
-6.59433544e-01 -1.03268228e-01 3.36838543e-01 -2.73375630e-01
-5.60853899e-01 3.46055329e-01 2.06516832e-01 3.62363338e-01
1.93673879e-01 -4.65823710e-01 -9.26555812e-01 -4.33416605e-01
-2.06504822e-01 8.13899159e-01 2.87241906e-01 -3.79400313... | [7.004674911499023, -0.96905118227005] |
b53b34d5-e523-4d41-a006-bbf87539ba4f | clrernet-improving-confidence-of-lane | 2305.08366 | null | https://arxiv.org/abs/2305.08366v1 | https://arxiv.org/pdf/2305.08366v1.pdf | CLRerNet: Improving Confidence of Lane Detection with LaneIoU | Lane marker detection is a crucial component of the autonomous driving and driver assistance systems. Modern deep lane detection methods with row-based lane representation exhibit excellent performance on lane detection benchmarks. Through preliminary oracle experiments, we firstly disentangle the lane representation c... | ['Yusuke Uchida', 'Hiroto Honda'] | 2023-05-15 | null | null | null | null | ['lane-detection'] | ['computer-vision'] | [-4.08983618e-01 1.64641395e-01 -4.39795852e-01 -5.41867614e-01
-1.08965349e+00 -3.91834259e-01 4.89322424e-01 -2.10308373e-01
-5.04434705e-01 7.07704902e-01 -1.41564133e-02 -6.91952825e-01
1.21821046e-01 -4.88702565e-01 -7.75146246e-01 -6.12154245e-01
1.87364314e-02 2.37114504e-01 6.27694666e-01 -4.18500125... | [7.914958953857422, -1.5462111234664917] |
22c9fb68-29c6-4ce2-b42f-dd8b7221491d | document-embedding-for-scientific-articles | 2107.05151 | null | https://arxiv.org/abs/2107.05151v1 | https://arxiv.org/pdf/2107.05151v1.pdf | Document Embedding for Scientific Articles: Efficacy of Word Embeddings vs TFIDF | Over the last few years, neural network derived word embeddings became popular in the natural language processing literature. Studies conducted have mostly focused on the quality and application of word embeddings trained on public available corpuses such as Wikipedia or other news and social media sources. However, th... | ['R. Karimi', 'J. Truong', 'H. J. Meijer'] | 2021-07-11 | null | null | null | null | ['document-embedding'] | ['methodology'] | [-6.14968896e-01 -6.38725236e-02 -5.23775041e-01 6.35739416e-02
-5.25412858e-01 -5.19387424e-01 8.23318243e-01 8.40986550e-01
-9.03042018e-01 4.35513645e-01 6.32491350e-01 -6.13354027e-01
-1.94327682e-01 -9.61232305e-01 -4.00388658e-01 -2.42802471e-01
-2.95291822e-02 1.52225986e-01 -2.47188479e-01 -5.42240404... | [10.185735702514648, 8.524608612060547] |
869e8a76-98b9-4ef5-8899-f3dff4c22576 | a-goal-driven-tree-structured-neural-model | null | null | https://www.ijcai.org/Proceedings/2019/736 | https://www.ijcai.org/Proceedings/2019/0736.pdf | A Goal-Driven Tree-Structured Neural Model for Math Word Problems | Most existing neural models for math word problems exploit Seq2Seq model to generate solution
expressions sequentially from left to right, whose
results are far from satisfactory due to the lack
of goal-driven mechanism commonly seen in human problem solving. This paper proposes a treestructured neural model to gene... | ['Zhipeng Xie and Shichao Sun'] | 2019-08-10 | null | null | null | null | ['math-word-problem-solving', 'math-word-problem-solving', 'math-word-problem-solving'] | ['knowledge-base', 'reasoning', 'time-series'] | [ 3.38315725e-01 4.83717471e-01 1.36560783e-01 -5.86928368e-01
-5.12407303e-01 -5.48596978e-01 1.45178184e-01 1.73603699e-01
-1.86110720e-01 6.46379113e-01 4.60628539e-01 -4.79369462e-01
-7.36706480e-02 -1.35848749e+00 -5.42957783e-01 -5.46363771e-01
-2.95594316e-02 4.75439548e-01 -4.07427326e-02 -4.15666401... | [9.807136535644531, 7.473447799682617] |
21e1fc3a-5b18-4eb4-a95c-fa3dca438932 | vasr-visual-analogies-of-situation | 2212.04542 | null | https://arxiv.org/abs/2212.04542v1 | https://arxiv.org/pdf/2212.04542v1.pdf | VASR: Visual Analogies of Situation Recognition | A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations. We introduce a novel task, Visual Analogies of Situation Recognition, adapting the classical word-analogy task into the visual domain. Given a triplet of images, the task is to s... | ['Gabriel Stanovsky', 'Roy Schwartz', 'Dafna Shahaf', 'Eli Strugo', 'Ron Yosef', 'Yonatan Bitton'] | 2022-12-08 | null | null | null | null | ['visual-reasoning', 'visual-analogies', 'common-sense-reasoning', 'visual-commonsense-reasoning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning', 'reasoning', 'reasoning'] | [ 2.41695672e-01 -5.91035001e-02 4.68162932e-02 -4.87777531e-01
-6.00016594e-01 -9.80572343e-01 9.02563393e-01 3.79455000e-01
-6.89931273e-01 4.83084410e-01 5.09233117e-01 -3.34321320e-01
2.12707669e-01 -2.56142616e-01 -8.67018402e-01 2.20364183e-01
2.96478212e-01 5.31817317e-01 1.52028859e-01 -4.42437649... | [10.719136238098145, 2.0526723861694336] |
dc97eaa8-37c9-44db-8faf-9ab5622a5c5b | denoising-diffusion-models-for-plug-and-play | 2305.08995 | null | https://arxiv.org/abs/2305.08995v1 | https://arxiv.org/pdf/2305.08995v1.pdf | Denoising Diffusion Models for Plug-and-Play Image Restoration | Plug-and-play Image Restoration (IR) has been widely recognized as a flexible and interpretable method for solving various inverse problems by utilizing any off-the-shelf denoiser as the implicit image prior. However, most existing methods focus on discriminative Gaussian denoisers. Although diffusion models have shown... | ['Luc van Gool', 'Radu Timofte', 'Bihan Wen', 'JieZhang Cao', 'Jingyun Liang', 'Kai Zhang', 'Yuanzhi Zhu'] | 2023-05-15 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 1.39564991e-01 -1.79314196e-01 1.74611896e-01 -2.70449907e-01
-9.75266993e-01 -1.85785845e-01 6.78886354e-01 -5.76868355e-01
-1.43631876e-01 5.66388726e-01 1.65852517e-01 -9.27327946e-02
-1.63703352e-01 -6.92660332e-01 -7.05521226e-01 -1.00805700e+00
3.15824181e-01 1.56401515e-01 1.20458961e-01 -2.43570551... | [11.472803115844727, -2.251838445663452] |
4e4264b2-5ee4-4f86-b3fe-08e14b815215 | a-lexicon-based-graph-neural-network-for | null | null | https://aclanthology.org/D19-1096 | https://aclanthology.org/D19-1096.pdf | A Lexicon-Based Graph Neural Network for Chinese NER | Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success. However, the characteristic of chain structure and the lack of global semantics determine that RNN-based models are vulnerable to word ambiguities. In this ... | ['Xuanjing Huang', 'Tao Gui', 'Minlong Peng', 'Yicheng Zou', 'Zhongyu Wei', 'Qi Zhang', 'Jinlan Fu'] | 2019-11-01 | null | null | null | ijcnlp-2019-11 | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-1.32609472e-01 -1.42307356e-01 -2.93639809e-01 -2.03611434e-01
-2.16945603e-01 -6.01714969e-01 1.20070405e-01 5.02198398e-01
-6.43073559e-01 5.97847879e-01 6.65601254e-01 -5.47956944e-01
1.90150976e-01 -1.14773393e+00 -1.95402682e-01 -3.51254106e-01
4.90708910e-02 1.75523490e-01 4.63434964e-01 -6.24049723... | [9.799392700195312, 9.74716854095459] |
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