paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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34408229-60a2-4b63-9658-eaf4c6628b0d | new-designs-on-mvdr-robust-adaptive | 1810.11360 | null | http://arxiv.org/abs/1810.11360v1 | http://arxiv.org/pdf/1810.11360v1.pdf | New Designs on MVDR Robust Adaptive Beamforming Based on Optimal Steering Vector Estimation | The robust adaptive beamforming design problem based on estimation of the
signal of interest steering vector is considered in the paper. In this case,
the optimal beamformer is obtained by computing the sample matrix inverse and
an optimal estimate of the signal of interest steering vector. The common
criteria to find ... | [] | 2018-10-26 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 2.60763168e-01 1.08328521e-01 3.12574774e-01 7.55736604e-02
-5.51719189e-01 -7.89746284e-01 -2.29871318e-01 -4.22943741e-01
-1.59317940e-01 7.35038579e-01 4.26251531e-01 -1.63190439e-01
-6.89160824e-01 -5.49032032e-01 -4.90781724e-01 -1.39680827e+00
-2.00612515e-01 -2.12412700e-01 -3.78072321e-01 -2.38241464... | [6.420071125030518, 1.3550186157226562] |
deb392c9-a682-4204-a0c5-68f5936c45a6 | full-transformer-framework-for-robust-point | 2112.09385 | null | https://arxiv.org/abs/2112.09385v1 | https://arxiv.org/pdf/2112.09385v1.pdf | Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction | Recent Transformer-based methods have achieved advanced performance in point cloud registration by utilizing advantages of the Transformer in order-invariance and modeling dependency to aggregate information. However, they still suffer from indistinct feature extraction, sensitivity to noise, and outliers. The reasons ... | ['Li Yuan', 'Qingxiang Zhang', 'Yufeng Yue', 'Meiling Wang', 'Guangyan Chen'] | 2021-12-17 | null | null | null | null | ['geometric-matching'] | ['computer-vision'] | [-3.44515264e-01 -1.81607947e-01 1.29681513e-01 -2.42288873e-01
-9.70781863e-01 -2.61127174e-01 5.18177271e-01 1.62292242e-01
-3.69896591e-02 1.60328358e-01 -6.76606670e-02 5.03518991e-02
-3.96319956e-01 -9.66791272e-01 -1.00611806e+00 -5.09909451e-01
-2.03919351e-01 7.51211345e-01 2.82948494e-01 -3.84125084... | [7.688384532928467, -3.0790059566497803] |
c64bd691-d81c-4336-a422-46abd03ddd66 | effective-matching-of-patients-to-clinical | 2307.00381 | null | https://arxiv.org/abs/2307.00381v1 | https://arxiv.org/pdf/2307.00381v1.pdf | Effective Matching of Patients to Clinical Trials using Entity Extraction and Neural Re-ranking | Clinical trials (CTs) often fail due to inadequate patient recruitment. This paper tackles the challenges of CT retrieval by presenting an approach that addresses the patient-to-trials paradigm. Our approach involves two key components in a pipeline-based model: (i) a data enrichment technique for enhancing both querie... | ['Allan Hanbury', 'Gabriella Pasi', 'Petr Knoth', 'Óscar E. Mendoza', 'Wojciech Kusa'] | 2023-07-01 | null | null | null | null | ['retrieval', 'negation-detection'] | ['methodology', 'natural-language-processing'] | [ 2.15892196e-01 1.19371735e-01 -6.95593059e-01 -2.23808035e-01
-1.13295960e+00 -3.90432507e-01 5.54323852e-01 9.50871825e-01
-1.00602186e+00 7.18308628e-01 6.77248180e-01 -4.18304890e-01
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1.43028542e-01 7.85151660e-01 3.09616238e-01 -1.84533730... | [8.710406303405762, 8.643304824829102] |
2a4726e2-4116-47f2-b518-18df189bd18e | person-retrieval-in-surveillance-video-using-1 | 1810.05080 | null | https://arxiv.org/abs/1810.05080v1 | https://arxiv.org/pdf/1810.05080v1.pdf | Person Retrieval in Surveillance Video using Height, Color and Gender | A person is commonly described by attributes like height, build, cloth color, cloth type, and gender. Such attributes are known as soft biometrics. They bridge the semantic gap between human description and person retrieval in surveillance video. The paper proposes a deep learning-based linear filtering approach for pe... | ['Mehul S. Raval', 'Vandit Gajjar', 'Kenil Shah', 'Hiren Galiyawala'] | 2018-09-24 | person-retrieval-in-surveillance-video-using | https://ieeexplore.ieee.org/document/8639145 | https://ieeexplore.ieee.org/document/8639145 | 2018-15th-ieee-international-conference-on | ['person-retrieval'] | ['computer-vision'] | [-2.90446937e-01 -5.01201928e-01 4.93347682e-02 -6.69010103e-01
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1.19202308e-01 -1.02534175e+00 -2.51558691e-01 -5.64130962e-01
2.63453811e-01 5.93766391e-01 -4.75935042e-02 -6.00130670... | [14.58775520324707, 0.882739782333374] |
e7f1c82b-43be-47d7-aa2b-9d775edfd62e | causalr-causal-reasoning-over-natural | null | null | https://openreview.net/forum?id=vuAX_4bv8A | https://openreview.net/pdf?id=vuAX_4bv8A | CausalR: Causal Reasoning over Natural Language Rulebases | Transformers have been shown to perform deductive reasoning on a logical rulebase containing rules and statements written in natural language. Recent works show that such models can also produce the reasoning steps (i.e., the proof graph) that emulate the model’s logical reasoning process. But these models behave as a ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['fact-selection'] | ['natural-language-processing'] | [ 2.94606864e-01 1.03716707e+00 -1.49196684e-01 -1.55518770e-01
-5.86912706e-02 -7.40644217e-01 1.32654488e+00 1.38944983e-01
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-3.13485712e-02 6.36805236e-01 6.92548752e-01 -2.49299169... | [9.207515716552734, 7.145236015319824] |
b31fd768-8264-49c7-b9dd-7a7e912ae04a | msr-net-multi-scale-relighting-network-for | 2107.06125 | null | https://arxiv.org/abs/2107.06125v1 | https://arxiv.org/pdf/2107.06125v1.pdf | MSR-Net: Multi-Scale Relighting Network for One-to-One Relighting | Deep image relighting allows photo enhancement by illumination-specific retouching without human effort and so it is getting much interest lately. Most of the existing popular methods available for relighting are run-time intensive and memory inefficient. Keeping these issues in mind, we propose the use of Stacked Deep... | ['Saikat Dutta', 'Nisarg A. Shah', 'Sourya Dipta Das'] | 2021-07-13 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 3.38623077e-01 -2.59655654e-01 1.90453678e-01 -1.98946044e-01
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3.04642111e-01 -1.87538460e-01 4.67347413e-01 -3.94949257... | [10.867100715637207, -2.178257942199707] |
1f8f697f-16bc-445f-b61d-80096dcc2c83 | few-shot-learning-for-video-object-detection | 2103.14724 | null | https://arxiv.org/abs/2103.14724v3 | https://arxiv.org/pdf/2103.14724v3.pdf | When Few-Shot Learning Meets Video Object Detection | Different from static images, videos contain additional temporal and spatial information for better object detection. However, it is costly to obtain a large number of videos with bounding box annotations that are required for supervised deep learning. Although humans can easily learn to recognize new objects by watchi... | ['Jiebo Luo', 'Sebastian Raschka', 'Lin Chen', 'Gaoang Wang', 'Zhongjie Yu'] | 2021-03-26 | null | null | null | null | ['few-shot-video-object-detection'] | ['computer-vision'] | [ 1.16081543e-01 -4.54163194e-01 -1.99338064e-01 -3.77859473e-01
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-2.42740437e-02 -5.16427040e-01 -1.18196809e+00 -6.16242051e-01
-2.86859035e-01 2.64557358e-02 9.10598814e-01 -8.25127494... | [8.730405807495117, 0.8950499296188354] |
a4dba4f4-61d0-499f-88a1-6fb18d94f38a | unsupervised-text-style-transfer-using | 1805.11749 | null | http://arxiv.org/abs/1805.11749v3 | http://arxiv.org/pdf/1805.11749v3.pdf | Unsupervised Text Style Transfer using Language Models as Discriminators | Binary classifiers are often employed as discriminators in GAN-based
unsupervised style transfer systems to ensure that transferred sentences are
similar to sentences in the target domain. One difficulty with this approach is
that the error signal provided by the discriminator can be unstable and is
sometimes insuffici... | ['Taylor Berg-Kirkpatrick', 'Zhiting Hu', 'Chris Dyer', 'Zichao Yang', 'Eric P. Xing'] | 2018-05-30 | unsupervised-text-style-transfer-using-1 | http://papers.nips.cc/paper/7959-unsupervised-text-style-transfer-using-language-models-as-discriminators | http://papers.nips.cc/paper/7959-unsupervised-text-style-transfer-using-language-models-as-discriminators.pdf | neurips-2018-12 | ['decipherment'] | ['natural-language-processing'] | [ 6.48176193e-01 4.66112286e-01 -3.69906314e-02 -3.69546592e-01
-7.91447997e-01 -8.68640721e-01 7.06363559e-01 -1.11245766e-01
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5.17907798e-01 4.04079676e-01 1.47861661e-02 -4.83128756... | [11.82553482055664, 9.525010108947754] |
f091e59b-0bdf-4467-80ce-40eefcc12913 | psd-principled-synthetic-to-real-dehazing | null | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_PSD_Principled_Synthetic-to-Real_Dehazing_Guided_by_Physical_Priors_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_PSD_Principled_Synthetic-to-Real_Dehazing_Guided_by_Physical_Priors_CVPR_2021_paper.pdf | PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors | Deep learning-based methods have achieved remarkable performance for image dehazing. However, previous studies are mostly focused on training models with synthetic hazy images, which incurs performance drop when the models are used for real-world hazy images. We propose a Principled Synthetic-to-real Dehazing (PSD)... | ['Dong Liu', 'Yang Yang', 'Yangchao Wang', 'Zeyuan Chen'] | 2021-06-19 | null | null | null | cvpr-2021-1 | ['image-dehazing'] | ['computer-vision'] | [ 1.90359682e-01 -1.38454869e-01 4.22825009e-01 -2.02808127e-01
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9.00446251e-02 -1.72029376e-01 6.09833658e-01 -4.94446218... | [10.94159984588623, -3.137730598449707] |
730386b7-d2fe-4001-bfa1-0d97eb1e04e5 | mesa-offline-meta-rl-for-safe-adaptation-and | 2112.03575 | null | https://arxiv.org/abs/2112.03575v1 | https://arxiv.org/pdf/2112.03575v1.pdf | MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance | Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments. Recent work learns risk measures which measure the probability of violating constraints, which can then be used to enable safety. However, learning such risk measures requires significant interaction with the environment,... | ['Ken Goldberg', 'Ion Stoica', 'Chelsea Finn', 'Jie Tan', 'Julian Ibarz', 'Suraj Nair', 'Brijen Thananjeyan', 'Ashwin Balakrishna', 'Michael Luo'] | 2021-12-07 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 1.58320963e-02 7.36477003e-02 -1.31096020e-01 -3.15367758e-01
-1.02731764e+00 -7.46796966e-01 3.14789981e-01 3.97035599e-01
-7.52638936e-01 9.66152549e-01 1.43270746e-01 -3.68428469e-01
-3.47994834e-01 -5.25335073e-01 -9.42297101e-01 -4.32019114e-01
-7.28353620e-01 -3.35602090e-02 1.64826602e-01 -3.00590813... | [4.457327842712402, 2.096083879470825] |
1cc8c7e9-997f-4741-8942-22a47e36e15e | describing-videos-by-exploiting-temporal | 1502.08029 | null | http://arxiv.org/abs/1502.08029v5 | http://arxiv.org/pdf/1502.08029v5.pdf | Describing Videos by Exploiting Temporal Structure | Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural... | ['Hugo Larochelle', 'Kyunghyun Cho', 'Christopher Pal', 'Nicolas Ballas', 'Li Yao', 'Atousa Torabi', 'Aaron Courville'] | 2015-02-27 | describing-videos-by-exploiting-temporal-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Yao_Describing_Videos_by_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Yao_Describing_Videos_by_ICCV_2015_paper.pdf | iccv-2015-12 | ['video-description'] | ['computer-vision'] | [ 1.00864798e-01 -2.91667700e-01 -6.74722731e-01 -4.07484114e-01
-6.31507874e-01 -4.39906955e-01 1.07499051e+00 -1.33979887e-01
-3.80698174e-01 3.03711772e-01 8.60115170e-01 1.18843809e-01
1.20950714e-01 -3.50823134e-01 -6.64694071e-01 -3.76667589e-01
-2.88333923e-01 2.61247814e-01 2.07240403e-01 -7.54140541... | [10.441507339477539, 0.6188522577285767] |
cbe4d7a1-e85e-40cb-8d3f-7aaddf169758 | pathgan-visual-scanpath-prediction-with | 1809.00567 | null | http://arxiv.org/abs/1809.00567v1 | http://arxiv.org/pdf/1809.00567v1.pdf | PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks | We introduce PathGAN, a deep neural network for visual scanpath prediction
trained on adversarial examples. A visual scanpath is defined as the sequence
of fixation points over an image defined by a human observer with its gaze.
PathGAN is composed of two parts, the generator and the discriminator. Both
parts extract f... | ["Noel E. O'Connor", 'Kevin McGuinness', 'Xavier Giro-i-Nieto', 'Marc Assens'] | 2018-09-03 | null | null | null | null | ['scanpath-prediction'] | ['computer-vision'] | [ 4.02583688e-01 3.49140316e-01 -4.10929829e-01 -3.32159698e-01
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5.06441593e-01 -8.04257095e-01 -1.14395237e+00 -5.24133325e-01
2.05009639e-01 3.29274386e-01 1.83062911e-01 -1.34992540... | [11.588163375854492, -0.19343160092830658] |
7f0e3498-39f4-47ca-ba08-924482257d77 | share-price-prediction-of-aerospace-relevant | 2008.11788 | null | https://arxiv.org/abs/2008.11788v1 | https://arxiv.org/pdf/2008.11788v1.pdf | Share Price Prediction of Aerospace Relevant Companies with Recurrent Neural Networks based on PCA | The capital market plays a vital role in marketing operations for aerospace industry. However, due to the uncertainty and complexity of the stock market and many cyclical factors, the stock prices of listed aerospace companies fluctuate significantly. This makes the share price prediction challengeable. To improve the ... | ['Linyu Zheng', 'Hongmei He'] | 2020-08-26 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-4.45394903e-01 -3.02654058e-01 -1.47751585e-01 1.02046505e-01
1.34157300e-01 -6.11884713e-01 4.63196546e-01 -1.90583989e-01
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-5.63268006e-01 -9.72015202e-01 -1.95775628e-01 -8.18499148e-01
-6.40924722e-02 4.60778236e-01 1.20056607e-02 -5.18558741... | [4.563955783843994, 4.172496318817139] |
4b138b99-0fd8-4b55-8215-52860e6df258 | exploring-feature-representation-learning-for | 2111.10989 | null | https://arxiv.org/abs/2111.10989v1 | https://arxiv.org/pdf/2111.10989v1.pdf | Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation | This paper presents a simple yet effective two-stage framework for semi-supervised medical image segmentation. Our key insight is to explore the feature representation learning with labeled and unlabeled (i.e., pseudo labeled) images to enhance the segmentation performance. In the first stage, we present an aleatoric u... | ['Kwang-Ting Cheng', 'Xiaomeng Li', 'Huimin Wu'] | 2021-11-22 | null | null | null | null | ['semi-supervised-medical-image-segmentation'] | ['computer-vision'] | [ 5.35404921e-01 4.34388608e-01 -4.27973032e-01 -7.32411623e-01
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2.59505302e-01 5.33640981e-01 2.28220478e-01 4.41923469... | [14.654770851135254, -2.0935847759246826] |
c739c719-a541-41e0-9fdb-354e640477eb | modeling-semantic-compositionality-with | 1907.04744 | null | https://arxiv.org/abs/1907.04744v1 | https://arxiv.org/pdf/1907.04744v1.pdf | Modeling Semantic Compositionality with Sememe Knowledge | Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify ... | ['Jun-Jie Huang', 'Chenghao Yang', 'Qun Liu', 'Fanchao Qi', 'Maosong Sun', 'Zhiyuan Liu', 'Xiao Chen'] | 2019-07-10 | modeling-semantic-compositionality-with-1 | https://aclanthology.org/P19-1571 | https://aclanthology.org/P19-1571.pdf | acl-2019-7 | ['multi-word-expression-embedding', 'multi-word-expression-sememe-prediction'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.00992623e-02 1.20924719e-01 -3.74442458e-01 -4.28613931e-01
-3.13589126e-01 -5.40342212e-01 7.69028604e-01 -4.84792516e-02
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4.31340456e-01 2.02349499e-01 2.45580822e-01 -4.53590304... | [10.537849426269531, 9.090995788574219] |
5d2b1139-7564-4de6-9426-5415e5bdbb3c | stillfast-an-end-to-end-approach-for-short | 2304.03959 | null | https://arxiv.org/abs/2304.03959v1 | https://arxiv.org/pdf/2304.03959v1.pdf | StillFast: An End-to-End Approach for Short-Term Object Interaction Anticipation | Anticipation problem has been studied considering different aspects such as predicting humans' locations, predicting hands and objects trajectories, and forecasting actions and human-object interactions. In this paper, we studied the short-term object interaction anticipation problem from the egocentric point of view, ... | ['Antonino Furnari', 'Giovanni Maria Farinella', 'Francesco Ragusa'] | 2023-04-08 | null | null | null | null | ['short-term-object-interaction-anticipation', 'human-object-interaction-detection'] | ['computer-vision', 'computer-vision'] | [-2.98154622e-01 3.62203158e-02 -1.18186690e-01 -5.24967790e-01
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-4.05482888e-01 2.18021855e-01 8.62151444e-01 3.34790260e-01
-2.16126710e-01 -2.20676914e-01 -6.20394707e-01 -3.70202988e-01
-4.56783175e-01 7.99015939e-01 1.41910836e-01 1.35735825... | [8.146052360534668, 0.48572850227355957] |
be5e172e-5aca-41ab-bc8f-1e22d99c658a | multi-body-se-3-equivariance-for-unsupervised | 2306.05584 | null | https://arxiv.org/abs/2306.05584v1 | https://arxiv.org/pdf/2306.05584v1.pdf | Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation | A truly generalizable approach to rigid segmentation and motion estimation is fundamental to 3D understanding of articulated objects and moving scenes. In view of the tightly coupled relationship between segmentation and motion estimates, we present an SE(3) equivariant architecture and a training strategy to tackle th... | ['Niki Trigoni', 'Andrew Markham', 'Kaichen Zhou', 'Kai Lu', 'Yuhang He', 'Ta-Ying Cheng', 'Jia-Xing Zhong'] | 2023-06-08 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [ 7.19882771e-02 -1.19566303e-02 -5.46694286e-02 -3.71142805e-01
-7.03535318e-01 -8.16173434e-01 6.90881491e-01 -3.44315618e-01
-4.38176155e-01 2.42071211e-01 -9.79898125e-02 -1.70532987e-01
-6.59397021e-02 -4.05872762e-01 -8.62079680e-01 -4.88409430e-01
-6.10668026e-02 9.61024344e-01 7.23643184e-01 -1.23855494... | [8.427116394042969, -2.0043795108795166] |
17879dab-85db-4dc0-af4f-dd1cc63b4b99 | quality-matters-embracing-quality-clues-for | 2208.10976 | null | https://arxiv.org/abs/2208.10976v1 | https://arxiv.org/pdf/2208.10976v1.pdf | Quality Matters: Embracing Quality Clues for Robust 3D Multi-Object Tracking | 3D Multi-Object Tracking (MOT) has achieved tremendous achievement thanks to the rapid development of 3D object detection and 2D MOT. Recent advanced works generally employ a series of object attributes, e.g., position, size, velocity, and appearance, to provide the clues for the association in 3D MOT. However, these c... | ['Wenbing Tao', 'Xiaoping Li', 'Zeming Li', 'En Yu', 'Jinrong Yang'] | 2022-08-23 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [-2.44550049e-01 -7.82136083e-01 -5.45360565e-01 1.00662090e-01
-6.08953118e-01 -6.53695047e-01 3.71357769e-01 -1.37481792e-02
-2.82194048e-01 4.48336840e-01 -1.72649160e-01 9.93766170e-03
-2.72062838e-01 -5.24692178e-01 -5.56411982e-01 -8.14388156e-01
1.31827770e-02 2.78838336e-01 6.99674189e-01 1.31629556... | [6.43826961517334, -2.184223175048828] |
948c51ee-b7f5-4b92-869f-7826baf4756e | neural-program-synthesis-with-query-1 | 2205.07857 | null | https://arxiv.org/abs/2205.07857v1 | https://arxiv.org/pdf/2205.07857v1.pdf | Neural Program Synthesis with Query | Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples.... | ['Yunji Chen', 'Qi Guo', 'Zidong Du', 'Nan Li', 'Pengwei Jin', 'Xishan Zhang', 'Xing Hu', 'Rui Zhang', 'Di Huang'] | 2022-05-08 | neural-program-synthesis-with-query | https://openreview.net/forum?id=NyJ2KIN8P17 | https://openreview.net/pdf?id=NyJ2KIN8P17 | iclr-2022-4 | ['program-synthesis'] | ['computer-code'] | [ 3.50317508e-01 -7.67515078e-02 -3.92297179e-01 -6.33643985e-01
-8.09683383e-01 -5.85846305e-01 3.41479748e-01 4.10008430e-01
-2.84220070e-01 3.22726399e-01 -1.44898906e-01 -3.42885137e-01
-2.58592844e-01 -1.07603276e+00 -1.02356231e+00 -2.99330533e-01
1.36399895e-01 4.73470002e-01 1.52478144e-01 -1.43429533... | [7.907402038574219, 7.702089786529541] |
25f96eeb-4353-4121-bfcb-ec0917b0219d | learning-object-language-alignments-for-open | 2211.14843 | null | https://arxiv.org/abs/2211.14843v1 | https://arxiv.org/pdf/2211.14843v1.pdf | Learning Object-Language Alignments for Open-Vocabulary Object Detection | Existing object detection methods are bounded in a fixed-set vocabulary by costly labeled data. When dealing with novel categories, the model has to be retrained with more bounding box annotations. Natural language supervision is an attractive alternative for its annotation-free attributes and broader object concepts. ... | ['Jianfei Cai', 'Zehuan Yuan', 'Gholamreza Haffari', 'Lizhen Qu', 'Ping Luo', 'Yi Jiang', 'Peize Sun', 'Chuang Lin'] | 2022-11-27 | null | null | null | null | ['set-matching', 'open-vocabulary-object-detection'] | ['computer-vision', 'computer-vision'] | [ 1.23588070e-01 3.14167365e-02 -3.59643430e-01 -6.19212329e-01
-1.04874706e+00 -6.02388203e-01 6.38768494e-01 3.36458892e-01
-6.37800336e-01 2.89975435e-01 -2.12958649e-01 -4.39289063e-02
3.51839572e-01 -4.61313456e-01 -7.62750030e-01 -4.97007102e-01
2.27137700e-01 7.27556527e-01 5.42935371e-01 9.99843515... | [9.653886795043945, 1.4865347146987915] |
5d77bd09-3bf5-4e57-9a1d-2dc339503d66 | scalable-gaussian-process-regression-enables | 2302.03294 | null | https://arxiv.org/abs/2302.03294v2 | https://arxiv.org/pdf/2302.03294v2.pdf | Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability | Gaussian process (GP) is a Bayesian model which provides several advantages for regression tasks in machine learning such as reliable quantitation of uncertainty and improved interpretability. Their adoption has been precluded by their excessive computational cost and by the difficulty in adapting them for analyzing se... | ['Wei Wang', 'Jonathan Parkinson'] | 2023-02-07 | null | null | null | null | ['protein-function-prediction', 'formation-energy'] | ['medical', 'miscellaneous'] | [-2.77024046e-05 -8.67805332e-02 7.20168557e-03 -3.01619053e-01
-6.83172047e-01 -6.72816217e-01 4.45847780e-01 6.98812187e-01
-3.56499553e-01 9.94738519e-01 -1.20012403e-01 -6.43773139e-01
-1.62077069e-01 -7.97743201e-01 -1.04648304e+00 -9.97361660e-01
-3.22527856e-01 8.09206009e-01 2.21062452e-01 2.11759150... | [5.054610252380371, 5.607425212860107] |
f31adb28-b73e-4422-b635-8219bacca040 | conv-nilm-net-a-causal-and-multi-appliance | 2208.02173 | null | https://arxiv.org/abs/2208.02173v2 | https://arxiv.org/pdf/2208.02173v2.pdf | Conv-NILM-Net, a causal and multi-appliance model for energy source separation | Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attempting to solve NILM problems. However most used models are used for Load Identification rather than online Source Se... | ['Simo Alami C.', 'Jesse Read', 'Rim Kaddah', 'Jérémie Decock'] | 2022-08-03 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'speech-separation', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'speech', 'time-series'] | [ 2.58776903e-01 5.63242398e-02 -6.13791645e-01 -3.58652472e-01
-8.60924184e-01 -2.15752244e-01 5.21641076e-01 3.69839519e-02
1.90349475e-01 6.58107877e-01 3.99036556e-01 -3.16608436e-02
-1.22213811e-01 -5.24994791e-01 -5.81486523e-01 -6.65109694e-01
5.30686742e-03 5.43218791e-01 -2.46771157e-01 2.14935914... | [16.07076644897461, 7.582086086273193] |
748731c1-fa88-4eb7-8ace-6936bc35f452 | spectral-goodness-of-fit-tests-for-complete | 2106.09702 | null | https://arxiv.org/abs/2106.09702v1 | https://arxiv.org/pdf/2106.09702v1.pdf | Spectral goodness-of-fit tests for complete and partial network data | Networks describe the, often complex, relationships between individual actors. In this work, we address the question of how to determine whether a parametric model, such as a stochastic block model or latent space model, fits a dataset well and will extrapolate to similar data. We use recent results in random matrix th... | ['Tyler H. McCormick', 'Bolun Liu', 'Shane Lubold'] | 2021-06-17 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.51748091e-01 -4.26213443e-02 -1.37464345e-01 -2.60590583e-01
-2.61879086e-01 -7.02174485e-01 6.72052145e-01 2.30841309e-01
-9.81957316e-02 5.87763667e-01 1.21065147e-01 -6.25720561e-01
-8.34376872e-01 -1.03386283e+00 -5.22546232e-01 -6.43105567e-01
-5.80102921e-01 7.87367344e-01 4.22204882e-01 6.00745492... | [6.966357231140137, 5.259241580963135] |
554a6faf-74d1-416a-a7e1-ef7511578202 | autonomous-extraction-of-gleason-patterns-for | 2011.00527 | null | https://arxiv.org/abs/2011.00527v5 | https://arxiv.org/pdf/2011.00527v5.pdf | A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images | Prostate cancer (PCa) is the second deadliest form of cancer in males, and it can be clinically graded by examining the structural representations of Gleason tissues. This paper proposes \RV{a new method} for segmenting the Gleason tissues \RV{(patch-wise) in order to grade PCa from the whole slide images (WSI).} Also,... | ['Ayman El-Baz', 'Bilal Hassan', 'Naoufel Werghi', 'Taimur Hassan'] | 2020-11-01 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 5.82928717e-01 4.96960551e-01 -3.01168770e-01 -5.59316397e-01
-1.45500588e+00 -7.19278216e-01 4.37876612e-01 2.13658199e-01
-1.28634170e-01 3.75436634e-01 6.42005429e-02 5.33190668e-02
-6.35819554e-01 -8.10228229e-01 -2.36628398e-01 -1.31636894e+00
-3.43995720e-01 8.93204749e-01 2.22134382e-01 4.09778655... | [14.921553611755371, -2.8670806884765625] |
f0d020b7-ca0e-4070-af55-842ad73b085e | hokem-human-and-object-keypoint-based | 2306.14260 | null | https://arxiv.org/abs/2306.14260v1 | https://arxiv.org/pdf/2306.14260v1.pdf | HOKEM: Human and Object Keypoint-based Extension Module for Human-Object Interaction Detection | Human-object interaction (HOI) detection for capturing relationships between humans and objects is an important task in the semantic understanding of images. When processing human and object keypoints extracted from an image using a graph convolutional network (GCN) to detect HOI, it is crucial to extract appropriate o... | ['Yoshiki Ito'] | 2023-06-25 | null | null | null | null | ['human-object-interaction-detection'] | ['computer-vision'] | [-3.40665244e-02 7.88253266e-03 2.17341363e-01 -9.01814401e-02
-5.69724478e-03 -1.56745687e-01 5.37532568e-01 3.29759389e-01
-3.12836468e-01 4.80692126e-02 -3.01426083e-01 4.35783379e-02
-2.61507124e-01 -5.92607081e-01 -7.31471241e-01 -2.87519217e-01
-5.67206601e-03 4.37374741e-01 7.05544531e-01 -1.23010173... | [9.589496612548828, 1.3682410717010498] |
cdeeb020-23c1-4011-abf9-117f143567e6 | personapkt-building-personalized-dialogue | 2306.08126 | null | https://arxiv.org/abs/2306.08126v1 | https://arxiv.org/pdf/2306.08126v1.pdf | PersonaPKT: Building Personalized Dialogue Agents via Parameter-efficient Knowledge Transfer | Personalized dialogue agents (DAs) powered by large pre-trained language models (PLMs) often rely on explicit persona descriptions to maintain personality consistency. However, such descriptions may not always be available or may pose privacy concerns. To tackle this bottleneck, we introduce PersonaPKT, a lightweight t... | ['Chenlei Guo', 'Xiaohu Liu', 'Yu Zhang', 'Benjamin Yao', 'Yoon Jung', 'Bin Guo', 'Xu Han'] | 2023-06-13 | null | null | null | null | ['response-generation'] | ['natural-language-processing'] | [-6.16217554e-01 5.19716203e-01 -1.75521806e-01 -7.49996960e-01
-8.39964628e-01 -7.11788952e-01 8.99306476e-01 2.56232083e-01
-5.99940062e-01 1.11551404e+00 4.57827657e-01 2.58993983e-01
2.45754614e-01 -6.96193337e-01 -3.69348466e-01 -3.62138242e-01
1.92470834e-01 9.65713739e-01 -4.28857833e-01 -1.84880003... | [12.70572280883789, 8.184429168701172] |
b5e18b51-ff3b-4846-95c9-aa9d7946bd7f | sefnet-bridging-tabular-datasets-with | 2306.11636 | null | https://arxiv.org/abs/2306.11636v1 | https://arxiv.org/pdf/2306.11636v1.pdf | SeFNet: Bridging Tabular Datasets with Semantic Feature Nets | Machine learning applications cover a wide range of predictive tasks in which tabular datasets play a significant role. However, although they often address similar problems, tabular datasets are typically treated as standalone tasks. The possibilities of using previously solved problems are limited due to the lack of ... | ['Przemysław Biecek', 'Piotr Wilczyński', 'Katarzyna Woźnica'] | 2023-06-20 | null | null | null | null | ['meta-learning', 'semantic-textual-similarity', 'semantic-similarity'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 2.64593929e-01 2.18311876e-01 -5.91191709e-01 -4.50469345e-01
-5.37529647e-01 -2.95969009e-01 6.85832679e-01 1.01716936e+00
-7.68241733e-02 8.22947860e-01 4.99085397e-01 1.14884719e-01
-1.21318257e+00 -1.04884255e+00 -3.82222384e-01 -5.45946479e-01
-6.00076132e-02 5.35801172e-01 1.72629029e-01 -4.57948774... | [9.023551940917969, 7.924521446228027] |
8f5651d1-54ac-455e-9060-bc4e050562c6 | taskmix-data-augmentation-for-meta-learning | 2210.06341 | null | https://arxiv.org/abs/2210.06341v1 | https://arxiv.org/pdf/2210.06341v1.pdf | TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding | Meta-Learning has emerged as a research direction to better transfer knowledge from related tasks to unseen but related tasks. However, Meta-Learning requires many training tasks to learn representations that transfer well to unseen tasks; otherwise, it leads to overfitting, and the performance degenerates to worse tha... | ['Surya Kant Sahu'] | 2022-09-26 | null | null | null | null | ['intent-classification'] | ['natural-language-processing'] | [ 3.75008821e-01 8.64349753e-02 -2.73632407e-01 -5.20745516e-01
-1.35858393e+00 -6.72755301e-01 6.91621482e-01 -3.36892694e-01
-5.69711685e-01 9.23634827e-01 3.63990396e-01 -3.09338927e-01
3.23611140e-01 9.93715483e-04 -7.26144016e-01 -4.44712669e-01
5.28579235e-01 8.60916793e-01 -1.35336360e-02 -2.97528833... | [10.877861022949219, 8.249903678894043] |
02401c3d-b1d3-49c4-a0e7-c5836075ed8a | sheffield-s-submission-to-the-americasnlp | 2306.09830 | null | https://arxiv.org/abs/2306.09830v1 | https://arxiv.org/pdf/2306.09830v1.pdf | Sheffield's Submission to the AmericasNLP Shared Task on Machine Translation into Indigenous Languages | In this paper we describe the University of Sheffield's submission to the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages which comprises the translation from Spanish to eleven indigenous languages. Our approach consists of extending, training, and ensembling different variations of NLLB-2... | ['Danae Sánchez Villegas', 'Edward Gow-Smith'] | 2023-06-16 | null | null | null | null | ['machine-translation'] | ['natural-language-processing'] | [-1.97792143e-01 9.49636474e-02 -5.94878793e-01 -1.92609802e-01
-1.47343135e+00 -1.12662971e+00 1.00221276e+00 7.75633263e-04
-6.26079440e-01 1.42525935e+00 5.66838384e-01 -8.37604225e-01
3.65780443e-01 -3.87412935e-01 -9.49836850e-01 -1.42577171e-01
3.85523021e-01 1.00034809e+00 -1.87651902e-01 -7.12605894... | [11.529510498046875, 10.305675506591797] |
fb9c6f6d-318a-40da-b2e6-9deae8f807e5 | style-interleaved-learning-for-generalizable | 2207.03132 | null | https://arxiv.org/abs/2207.03132v3 | https://arxiv.org/pdf/2207.03132v3.pdf | Style Interleaved Learning for Generalizable Person Re-identification | Domain generalization (DG) for person re-identification (ReID) is a challenging problem, as access to target domain data is not permitted during the training process. Most existing DG ReID methods update the feature extractor and classifier parameters based on the same features. This common practice causes the model to... | ['Changxing Ding', 'Kui Jia', 'Mingming Gong', 'Pengfei Wang', 'Wentao Tan'] | 2022-07-07 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [ 7.61943832e-02 -3.04730147e-01 -1.71421707e-01 -5.03384590e-01
-3.14111590e-01 -6.25238419e-01 6.32766187e-01 2.97565684e-02
-5.10075808e-01 7.96941042e-01 5.32817990e-02 1.07980691e-01
-1.51094124e-01 -6.97829485e-01 -5.38028479e-01 -5.88936567e-01
1.10262036e-01 5.76596797e-01 1.80094838e-01 -1.61765561... | [14.726839065551758, 1.100634217262268] |
13a11af7-e026-4327-bfe0-ab0661362b03 | evaluating-language-models-for-knowledge-base | 2303.11082 | null | https://arxiv.org/abs/2303.11082v1 | https://arxiv.org/pdf/2303.11082v1.pdf | Evaluating Language Models for Knowledge Base Completion | Structured knowledge bases (KBs) are a foundation of many intelligent applications, yet are notoriously incomplete. Language models (LMs) have recently been proposed for unsupervised knowledge base completion (KBC), yet, despite encouraging initial results, questions regarding their suitability remain open. Existing ev... | ['Gerhard Weikum', 'Simon Razniewski', 'Sneha Singhania', 'Blerta Veseli'] | 2023-03-20 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion'] | ['graphs', 'knowledge-base'] | [-1.83672607e-01 9.17944670e-01 -3.85038406e-01 -2.10640311e-01
-1.20767462e+00 -6.46609187e-01 7.73257434e-01 3.22362989e-01
-6.31106436e-01 1.41770482e+00 3.45745951e-01 -4.06770080e-01
-4.43346888e-01 -9.06068742e-01 -9.80010808e-01 -2.01434940e-01
-7.26606473e-02 1.02136528e+00 4.04155523e-01 -5.38728952... | [9.508665084838867, 8.399956703186035] |
c6fb00a2-eabd-4df5-8457-63bf415298e4 | hynet-local-descriptor-with-hybrid-similarity | 2006.10202 | null | https://arxiv.org/abs/2006.10202v3 | https://arxiv.org/pdf/2006.10202v3.pdf | HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss | Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature. In this paper, we investigate how L2 normalisation affects the back-propagated descriptor gradients during training. Based on our observations, we propose HyN... | ['Axel Barroso-Laguna', 'Yurun Tian', 'Krystian Mikolajczyk', 'Vassileios Balntas', 'Tony Ng'] | 2020-06-17 | null | http://proceedings.neurips.cc/paper/2020/hash/52d2752b150f9c35ccb6869cbf074e48-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/52d2752b150f9c35ccb6869cbf074e48-Paper.pdf | neurips-2020-12 | ['patch-matching'] | ['computer-vision'] | [ 2.07617983e-01 -1.94829985e-01 -2.78581619e-01 -5.46704650e-01
-9.68105614e-01 -2.90395021e-01 9.05393779e-01 3.06474745e-01
-4.36583519e-01 1.01441227e-01 3.31842840e-01 1.77073389e-01
-3.68927956e-01 -5.96510291e-01 -7.12786555e-01 -6.30080283e-01
-2.18515620e-01 1.30763367e-01 2.20056847e-01 -9.53220427... | [8.264126777648926, -1.8211110830307007] |
9ed2570b-52c7-48cb-8909-f006f07b1a8e | on-the-computation-communication-trade-off | 2306.07159 | null | https://arxiv.org/abs/2306.07159v1 | https://arxiv.org/pdf/2306.07159v1.pdf | On the Computation-Communication Trade-Off with A Flexible Gradient Tracking Approach | We propose a flexible gradient tracking approach with adjustable computation and communication steps for solving distributed stochastic optimization problem over networks. The proposed method allows each node to perform multiple local gradient updates and multiple inter-node communications in each round, aiming to stri... | ['Jinming Xu', 'Yan Huang'] | 2023-06-12 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-3.59003365e-01 -9.08422023e-02 -3.89021903e-01 -1.73507109e-01
-6.25792682e-01 -6.99603438e-01 3.07580233e-02 3.13556850e-01
-5.08616447e-01 9.72347319e-01 -3.34120125e-01 -3.03759634e-01
-6.53089643e-01 -6.62777483e-01 -4.35782343e-01 -8.06542933e-01
-7.96263695e-01 1.81589097e-01 3.15873511e-02 -1.43654734... | [6.243598937988281, 4.928659915924072] |
ff342912-8db0-47e4-8083-97c5a3f39cb6 | all-about-knowledge-graphs-for-actions | 2008.12432 | null | https://arxiv.org/abs/2008.12432v1 | https://arxiv.org/pdf/2008.12432v1.pdf | All About Knowledge Graphs for Actions | Current action recognition systems require large amounts of training data for recognizing an action. Recent works have explored the paradigm of zero-shot and few-shot learning to learn classifiers for unseen categories or categories with few labels. Following similar paradigms in object recognition, these approaches ut... | ['Abhinav Shrivastava', 'Nirat Saini', 'Larry S. Davis', 'Pallabi Ghosh'] | 2020-08-28 | null | null | null | null | ['zero-shot-action-recognition', 'few-shot-action-recognition'] | ['computer-vision', 'computer-vision'] | [ 1.62173554e-01 7.17053469e-03 -5.19192576e-01 -3.85876417e-01
-3.56287032e-01 -2.56166577e-01 7.97724903e-01 1.47733063e-01
-4.43360865e-01 6.85701251e-01 3.53684425e-01 6.60714656e-02
-1.84513792e-01 -9.44213748e-01 -5.77256799e-01 -5.75302064e-01
-1.22323044e-01 1.95427492e-01 6.59377038e-01 1.85762420... | [8.640262603759766, 1.0306315422058105] |
809f2032-80a9-48da-8cae-24e688638a15 | describe-me-if-you-can-characterized-instance | 2201.09594 | null | https://arxiv.org/abs/2201.09594v1 | https://arxiv.org/pdf/2201.09594v1.pdf | Describe me if you can! Characterized Instance-level Human Parsing | Several computer vision applications such as person search or online fashion rely on human description. The use of instance-level human parsing (HP) is therefore relevant since it localizes semantic attributes and body parts within a person. But how to characterize these attributes? To our knowledge, only some single-H... | ['Romaric Audigier', 'Angelique Loesch'] | 2022-01-24 | null | null | null | null | ['human-parsing', 'person-search'] | ['computer-vision', 'computer-vision'] | [-9.21226442e-02 1.04115754e-01 -3.06548804e-01 -6.72257602e-01
-9.07181501e-01 -5.72585285e-01 6.10312104e-01 4.22615319e-01
-4.88994390e-01 7.15709925e-01 1.97226733e-01 5.26497900e-01
-3.44266184e-02 -5.62416255e-01 -6.43267870e-01 -3.81253332e-01
2.12189272e-01 1.32852709e+00 4.22381818e-01 -1.63418688... | [8.180706977844238, -0.23299065232276917] |
422d10d5-68c1-432b-af37-9ff2e6ede317 | grayscale-data-construction-and-multi-level | 2004.02421 | null | https://arxiv.org/abs/2004.02421v4 | https://arxiv.org/pdf/2004.02421v4.pdf | The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection | Response selection plays a vital role in building retrieval-based conversation systems. Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zer... | ['Hai-Tao Zheng', 'Zibo Lin', 'Xiaojiang Liu', 'Shuming Shi', 'Deng Cai', 'Yan Wang'] | 2020-04-06 | null | https://aclanthology.org/2020.emnlp-main.741 | https://aclanthology.org/2020.emnlp-main.741.pdf | emnlp-2020-11 | ['conversational-response-selection'] | ['natural-language-processing'] | [ 4.79685485e-01 -2.46780410e-01 -3.31041992e-01 -7.74678111e-01
-1.45259953e+00 -4.55650866e-01 6.12127066e-01 2.49315873e-01
-2.11777046e-01 7.32000053e-01 1.83222651e-01 -3.30778450e-01
-2.11172178e-01 -8.85553837e-01 -1.42085955e-01 -5.85896254e-01
4.01536614e-01 7.03769088e-01 3.27271432e-01 -5.39308548... | [12.379620552062988, 7.933199882507324] |
33777c0d-4ad6-4493-bed7-362473d9017f | bkt-lstm-efficient-student-modeling-for | 2012.12218 | null | https://arxiv.org/abs/2012.12218v3 | https://arxiv.org/pdf/2012.12218v3.pdf | BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance prediction | Recently, we have seen a rapid rise in usage of online educational platforms. The personalized education became crucially important in future learning environments. Knowledge tracing (KT) refers to the detection of students' knowledge states and predict future performance given their past outcomes for providing adaptiv... | ['Sein Minn'] | 2020-12-22 | null | null | null | null | ['skill-mastery'] | ['robots'] | [-4.58569527e-02 2.24631980e-01 -2.11005062e-01 -3.47450912e-01
-1.86574072e-01 -5.25937080e-01 2.11986437e-01 5.77967167e-01
-3.52135390e-01 7.95612574e-01 3.41429450e-02 -6.18828356e-01
-1.13881683e+00 -9.93791461e-01 -6.50880337e-01 -4.11499113e-01
-4.28507701e-02 5.73951006e-01 5.21704733e-01 -4.06222045... | [10.131118774414062, 7.173466682434082] |
6c277f33-aa0b-4341-8349-c4238ad0c7d1 | negation-handling-in-machine-learning-based | 2107.11597 | null | https://arxiv.org/abs/2107.11597v1 | https://arxiv.org/pdf/2107.11597v1.pdf | Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic | One crucial aspect of sentiment analysis is negation handling, where the occurrence of negation can flip the sentiment of a sentence and negatively affects the machine learning-based sentiment classification. The role of negation in Arabic sentiment analysis has been explored only to a limited extent, especially for co... | ['Omar Al-Harbi'] | 2021-07-24 | null | null | null | null | ['arabic-sentiment-analysis'] | ['natural-language-processing'] | [ 1.52400015e-02 -1.14715798e-02 -2.44185445e-03 -6.19559348e-01
-2.90444251e-02 -7.93317497e-01 6.57888055e-01 6.48156106e-01
-6.16593421e-01 9.40483510e-01 4.84752171e-02 -2.69405127e-01
1.28064275e-01 -8.42165649e-01 -2.71145910e-01 -6.17126465e-01
2.11947367e-01 3.73236835e-03 4.87169214e-02 -1.03534794... | [11.051799774169922, 6.91135835647583] |
96858f20-b829-4a36-94c8-da8b53b7e2e2 | a-computational-model-of-infant-learning-and | 2106.16059 | null | https://arxiv.org/abs/2106.16059v1 | https://arxiv.org/pdf/2106.16059v1.pdf | A Computational Model of Infant Learning and Reasoning with Probabilities | Recent experiments reveal that 6- to 12-month-old infants can learn probabilities and reason with them. In this work, we present a novel computational system called Neural Probability Learner and Sampler (NPLS) that learns and reasons with probabilities, providing a computationally sufficient mechanism to explain infan... | ['Ardavan S Nobandegani', 'Thomas R Shultz'] | 2021-06-30 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [-3.81596549e-03 5.63914418e-01 -1.29142299e-01 -5.75409770e-01
3.75262275e-02 -2.29478702e-01 5.70336521e-01 3.79128635e-01
-3.91864896e-01 5.23404837e-01 4.03709292e-01 -5.65300107e-01
-5.44606864e-01 -9.34482992e-01 -1.08716726e+00 -4.54178810e-01
-3.35128248e-01 3.75289381e-01 1.90527365e-01 3.00455898... | [8.81006908416748, 6.858198165893555] |
86eba407-ae32-4936-a4e6-7434684548b4 | nemto-neural-environment-matting-for-novel | 2303.11963 | null | https://arxiv.org/abs/2303.11963v1 | https://arxiv.org/pdf/2303.11963v1.pdf | NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects | We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction. Commonly used appearance modeling such as the Disney BSDF model cannot accurately address this challenging problem due to the complex light paths bending through refr... | ['Sabine Süsstrunk', 'Tong Zhang', 'Dongqing Wang'] | 2023-03-21 | null | null | null | null | ['transparent-objects', 'image-matting'] | ['computer-vision', 'computer-vision'] | [ 3.60649586e-01 7.23490119e-02 7.80161440e-01 -4.54114228e-01
-2.94472009e-01 -5.83201230e-01 6.78376377e-01 -4.76843417e-01
2.21265763e-01 4.71582323e-01 -1.59274098e-02 -3.40176404e-01
3.35727066e-01 -9.13943470e-01 -9.13050830e-01 -2.84224302e-01
-9.10311639e-02 5.39171934e-01 5.20634234e-01 -1.30615816... | [9.615654945373535, -3.1530494689941406] |
547602d2-f831-4024-88be-45e560aea83f | training-ensembles-with-inliers-and-outliers | 2307.03741 | null | https://arxiv.org/abs/2307.03741v1 | https://arxiv.org/pdf/2307.03741v1.pdf | Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning | Deep active learning in the presence of outlier examples poses a realistic yet challenging scenario. Acquiring unlabeled data for annotation requires a delicate balance between avoiding outliers to conserve the annotation budget and prioritizing useful inlier examples for effective training. In this work, we present an... | ['Giorgos Tolias', 'Zakaria Laskar', 'Vladan Stojnić'] | 2023-07-07 | null | null | null | null | ['active-learning', 'outlier-detection', 'active-learning'] | ['methodology', 'methodology', 'natural-language-processing'] | [-4.12409678e-02 1.28852919e-01 -1.14914954e-01 -4.05707508e-01
-1.49126124e+00 -7.40912735e-01 4.49506372e-01 4.49504048e-01
-4.96618956e-01 6.12976074e-01 1.33437544e-01 -2.37395540e-02
-5.53395599e-02 -1.60470486e-01 -8.96507382e-01 -6.05809629e-01
1.90617517e-01 4.00708765e-01 -1.76872276e-02 1.24361791... | [9.105600357055664, 1.6285475492477417] |
e49dc172-142c-425f-b726-5171e405c753 | knowledge-graph-embedding-with-electronic | 2305.19997 | null | https://arxiv.org/abs/2305.19997v1 | https://arxiv.org/pdf/2305.19997v1.pdf | Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model | Due to the increasing adoption of electronic health records (EHR), large scale EHRs have become another rich data source for translational clinical research. Despite its potential, deriving generalizable knowledge from EHR data remains challenging. First, EHR data are generated as part of clinical care with data elemen... | ['Tianxi Cai', 'Jin Yin', 'Junwei Lu'] | 2023-05-31 | null | null | null | null | ['graph-embedding', 'knowledge-graph-embedding'] | ['graphs', 'graphs'] | [ 4.44416776e-02 3.17050487e-01 -2.38996774e-01 -4.17199552e-01
-5.03802657e-01 -2.14635506e-01 4.94517460e-02 7.70729721e-01
-8.66738260e-02 6.02638543e-01 5.55400491e-01 -3.70154232e-01
-6.39418602e-01 -7.21552372e-01 -4.74086136e-01 -6.01364970e-01
-4.71108556e-01 1.78440183e-01 -3.00652474e-01 2.07121179... | [7.461201190948486, 6.2496657371521] |
7c694f3a-1a42-4353-96d7-4f2bbdbbea16 | evconv-fast-cnn-inference-on-event-camera | 2303.04670 | null | https://arxiv.org/abs/2303.04670v1 | https://arxiv.org/pdf/2303.04670v1.pdf | EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception | Event cameras capture visual information with a high temporal resolution and a wide dynamic range. This enables capturing visual information at fine time granularities (e.g., microseconds) in rapidly changing environments. This makes event cameras highly useful for high-speed robotics tasks involving rapid motion, such... | ['Nandita Vijaykumar', 'Yushi Guan', 'Sankeerth Durvasula'] | 2023-03-08 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [-1.97691638e-02 -4.76330817e-01 -1.25690803e-01 -8.89897570e-02
-2.86710203e-01 -5.39183140e-01 4.95763808e-01 1.99590653e-01
-8.31296206e-01 3.76395434e-01 -8.47733170e-02 -2.57737935e-01
1.21491998e-01 -7.84378290e-01 -9.87788796e-01 -4.19041395e-01
-2.07734495e-01 3.91748808e-02 6.35729373e-01 3.06270838... | [8.621220588684082, -0.940251350402832] |
32b3206c-32f8-4bfb-81d9-d5c302535234 | towards-efficient-and-accurate-approximation | 2211.14828 | null | https://arxiv.org/abs/2211.14828v1 | https://arxiv.org/pdf/2211.14828v1.pdf | Towards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration | Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools to meet this need, but most existing methods perform poorly in the presence of noise interference. Inspired by the remarkable performance o... | ['Qibin Zhao', 'Guoxu Zhou', 'Weijun Sun', 'Yichun Qiu'] | 2022-11-27 | null | null | null | null | ['data-compression'] | ['time-series'] | [-2.03619376e-01 -6.01223290e-01 1.25898898e-01 2.25780517e-01
-8.54294121e-01 -2.22758040e-01 5.91200218e-02 6.47363812e-02
-1.55817062e-01 4.86372083e-01 6.90618515e-01 -1.63668767e-01
-6.20150805e-01 -6.48959100e-01 -4.92517442e-01 -1.15299606e+00
-3.38222772e-01 1.41187375e-02 -9.77224782e-02 -2.25232348... | [7.397461891174316, 4.4796271324157715] |
95a3e563-002b-45ca-9568-6f7a2f05ffef | remasc-realistic-replay-attack-corpus-for | 1904.03365 | null | https://arxiv.org/abs/1904.03365v2 | https://arxiv.org/pdf/1904.03365v2.pdf | ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems | This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VC... | ['Christian Poellabauer', 'Jian Yang', 'Yuan Gong', 'Jacob Huber', 'Mitchell MacKnight'] | 2019-04-06 | null | null | null | null | ['voice-anti-spoofing'] | ['audio'] | [ 8.94492120e-02 -4.10884678e-01 2.06820428e-01 9.03936774e-02
-6.37877047e-01 -9.95325565e-01 4.31549430e-01 -8.96092430e-02
2.65144277e-03 4.32927728e-01 1.65159583e-01 -8.85724187e-01
1.81052536e-02 -1.42637268e-01 -2.62350887e-01 -5.79963028e-01
-1.18531108e-01 -2.04313412e-01 4.89484221e-01 -2.11216122... | [14.07616138458252, 5.871841907501221] |
eb932b03-f124-41c8-9bba-2f7b72b90c4e | small-footprint-keyword-spotting-using-deep | 1709.03665 | null | http://arxiv.org/abs/1709.03665v1 | http://arxiv.org/pdf/1709.03665v1.pdf | Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier | Mainly for the sake of solving the lack of keyword-specific data, we propose
one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and
Connectionist Temporal Classifier (CTC) on power-constrained small-footprint
mobile devices, taking full advantage of general corpus from continuous speech
recognition which... | ['Jun Zhou', 'Zhiming Wang', 'Xiaolong Li'] | 2017-09-12 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 2.30083227e-01 -1.46286100e-01 -4.51516360e-01 -1.46425933e-01
-9.10715580e-01 -3.66769791e-01 4.58622247e-01 -4.49368745e-01
-6.72127843e-01 8.03422689e-01 3.45862031e-01 -8.16033602e-01
-1.59106866e-01 -3.18921804e-01 -3.86408031e-01 -5.98606288e-01
2.61227548e-01 3.29387069e-01 3.40355098e-01 1.10919811... | [14.362648963928223, 6.3947224617004395] |
c5891514-9b34-4914-85d4-52e0a21b5c61 | towards-generating-adversarial-examples-on | 2210.09405 | null | https://arxiv.org/abs/2210.09405v1 | https://arxiv.org/pdf/2210.09405v1.pdf | Towards Generating Adversarial Examples on Mixed-type Data | The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as financial forecasting, fraudulent detection, and anomaly detection, the data samples are usually mixed-type, which contain plenty of numeri... | ['Hao Yang', 'Mahashweta Das', 'Xiaoting Li', 'Huiyuan Chen', 'Zhimeng Jiang', 'Menghai Pan', 'Han Xu'] | 2022-10-17 | null | null | null | null | ['type'] | ['speech'] | [ 1.82446763e-01 1.36042222e-01 3.89840342e-02 -1.00308463e-01
-5.20874858e-01 -9.36380923e-01 5.78133881e-01 2.09237963e-01
-9.96678174e-02 7.72778213e-01 -3.64246398e-01 -5.25747836e-01
1.11096300e-01 -1.21625781e+00 -7.98423350e-01 -8.45850289e-01
-1.77511007e-01 1.10327184e-01 -1.86295718e-01 -2.91166604... | [5.789521217346191, 7.772523880004883] |
1f7c82d7-54fd-4fac-8dc9-430c3d149d25 | federated-multi-agent-deep-reinforcement | 2301.00641 | null | https://arxiv.org/abs/2301.00641v1 | https://arxiv.org/pdf/2301.00641v1.pdf | Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management | The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely us... | ['Zhigang Zeng', 'Yang Shi', 'Yang Li', 'Shangyang He', 'Yuanzheng Li'] | 2022-12-29 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.08207321e+00 -1.35584071e-01 -3.69135197e-03 -1.13485239e-01
-3.84676546e-01 -5.27891278e-01 2.58889496e-01 2.18234405e-01
-4.51514363e-01 1.19140208e+00 -2.28688359e-01 -5.67946620e-02
-2.45075375e-02 -1.27172804e+00 -5.28672993e-01 -1.56377125e+00
-3.81179780e-01 2.58754939e-01 -2.10707173e-01 7.34405518... | [5.592895030975342, 2.568253755569458] |
717bb6b9-b8bd-4a83-9f6d-3eb0a067844c | adversarial-attacks-and-defenses-on-3d-point | 2307.00309 | null | https://arxiv.org/abs/2307.00309v1 | https://arxiv.org/pdf/2307.00309v1.pdf | Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey | Deep learning has successfully solved a wide range of tasks in 2D vision as a dominant AI technique. Recently, deep learning on 3D point clouds is becoming increasingly popular for addressing various tasks in this field. Despite remarkable achievements, deep learning algorithms are vulnerable to adversarial attacks. Th... | ['Ivan V. Bajić', 'Hanieh Naderi'] | 2023-07-01 | null | null | null | null | ['adversarial-attack', '3d-point-cloud-classification', 'point-cloud-classification'] | ['adversarial', 'computer-vision', 'computer-vision'] | [-9.31088999e-03 -1.52447507e-01 2.29238793e-01 -1.62997440e-01
-4.23361301e-01 -1.00696802e+00 7.60801613e-01 -2.85125166e-01
-2.61542290e-01 2.47957841e-01 -5.78856945e-01 -6.39564037e-01
1.72729075e-01 -8.14905882e-01 -8.61447394e-01 -7.48533249e-01
-1.09731458e-01 4.16129082e-01 1.13981001e-01 -2.12583005... | [7.698335647583008, -4.480578899383545] |
86bf39a5-5df0-4fbb-8004-85375d8fa799 | kernel-learning-for-visual-perception | null | null | https://dr.ntu.edu.sg/handle/10220/47835 | https://dr.ntu.edu.sg/bitstream/10356/105527/1/thesis.pdf | Kernel learning for visual perception | The visual perceptual system in animals allows them to assimilate information from their surroundings. In artificial intelligence, the objective of visual perception is to enable the capability of a computer system to interpret the surrounding environment using data acquired from cameras and other aided sensors. Since ... | ['Chen Wang'] | 2019-12-06 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.24918327e-01 -8.56459498e-01 -1.43421069e-01 1.06819153e-01
3.88722122e-01 -4.00108337e-01 2.84057945e-01 -7.11184368e-02
-6.60346389e-01 4.63555634e-01 -4.05853242e-01 -8.42871610e-03
-2.65659630e-01 -4.37823653e-01 -3.28088135e-01 -8.67696166e-01
-1.74418211e-01 -4.67419565e-01 5.43455899e-01 1.42505974... | [8.271773338317871, -1.7379542589187622] |
c94b96ec-7cad-4ddc-bfd3-095cfb13d9c3 | towards-robust-semantic-segmentation-of | 2203.10395 | null | https://arxiv.org/abs/2203.10395v1 | https://arxiv.org/pdf/2203.10395v1.pdf | Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning | Autonomous vehicles utilize urban scene segmentation to understand the real world like a human and react accordingly. Semantic segmentation of normal scenes has experienced a remarkable rise in accuracy on conventional benchmarks. However, a significant portion of real-life accidents features abnormal scenes, such as t... | ['Rainer Stiefelhagen', 'Kunyu Peng', 'Alina Roitberg', 'Kailun Yang', 'Jiaming Zhang', 'Xinyu Luo'] | 2022-03-19 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 2.80899107e-01 2.57593781e-01 -1.39993176e-01 -5.97779036e-01
-9.51259017e-01 -3.68037999e-01 5.09846866e-01 -2.97429800e-01
-4.78083163e-01 4.97811377e-01 -2.94473600e-02 -3.01090509e-01
2.83054948e-01 -8.93417120e-01 -1.10484588e+00 -7.03643084e-01
5.54112554e-01 4.04485315e-01 7.91430533e-01 -4.15823430... | [8.48824691772461, -1.021630048751831] |
a33763ad-2db9-4a0f-ade2-de1de2e4f457 | mgh-metadata-guided-hypergraph-modeling-for | 2110.05886 | null | https://arxiv.org/abs/2110.05886v1 | https://arxiv.org/pdf/2110.05886v1.pdf | MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification | As a challenging task, unsupervised person ReID aims to match the same identity with query images which does not require any labeled information. In general, most existing approaches focus on the visual cues only, leaving potentially valuable auxiliary metadata information (e.g., spatio-temporal context) unexplored. In... | ['Jian Tian', 'Xi Li', 'Xintian Wu', 'Yiming Wu'] | 2021-10-12 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.31449960e-02 -4.86076325e-02 -1.03491008e-01 -5.64559162e-01
-5.82343280e-01 -5.14648318e-01 5.16366661e-01 5.33593237e-01
-5.51780701e-01 5.68665147e-01 1.62725151e-01 1.67919993e-01
-4.57534581e-01 -7.77003169e-01 -7.69208848e-01 -9.04958904e-01
1.21977657e-01 4.49438125e-01 1.48864806e-01 2.04006225... | [14.838753700256348, 1.0297423601150513] |
f3765c95-7d1e-4810-ba3c-d68c706b5e9f | towards-good-practices-for-video-object | 1909.13583 | null | https://arxiv.org/abs/1909.13583v1 | https://arxiv.org/pdf/1909.13583v1.pdf | Towards Good Practices for Video Object Segmentation | Semi-supervised video object segmentation is an interesting yet challenging task in machine learning. In this work, we conduct a series of refinements with the propagation-based video object segmentation method and empirically evaluate their impact on the final model performance through ablation study. By taking all th... | ['Dongdong Yu', 'Jian Wang', 'Minghui Dong', 'Jie Shao', 'Kaihui Zhou', 'Hengkai Guo', 'Changhu Wang', 'Yuanyuan Huang', 'Kai Su'] | 2019-09-30 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.84264839e-01 -4.48485278e-02 -4.41170990e-01 -4.53495115e-01
-7.40536690e-01 -4.38671082e-01 2.68841088e-01 -3.20968390e-01
-9.79903281e-01 5.60709000e-01 -1.68678612e-01 -4.15483296e-01
3.58803004e-01 -2.54495829e-01 -9.70880747e-01 -3.62440079e-01
-2.09313944e-01 2.47741029e-01 7.61138856e-01 2.24573001... | [9.24412727355957, -0.0812799409031868] |
8397195a-520a-47aa-b252-779158d94355 | morphology-is-not-just-a-naive-bayes-unimelb | null | null | https://aclanthology.org/2022.sigmorphon-1.25 | https://aclanthology.org/2022.sigmorphon-1.25.pdf | Morphology is not just a naive Bayes – UniMelb Submission to SIGMORPHON 2022 ST on Morphological Inflection | The paper describes the Flexica team’s submission to the SIGMORPHON 2022 Shared Task 1 Part 1: Typologically Diverse Morphological Inflection. Our team submitted a nonneural system that extracted transformation patterns from alignments between a lemma and inflected forms. For each inflection category, we chose a patter... | ['Ekaterina Vylomova', 'Andreas Sherbakov'] | null | null | null | null | naacl-sigmorphon-2022-7 | ['morphological-inflection'] | ['natural-language-processing'] | [ 1.36452824e-01 2.34935462e-01 -2.04325289e-01 -3.99249434e-01
-4.77892756e-01 -1.12477732e+00 5.89622617e-01 2.46398866e-01
-6.75171018e-01 7.96880424e-01 4.10970032e-01 -5.34296870e-01
-8.50379393e-02 -6.84543848e-01 -6.53250992e-01 -2.29685068e-01
6.78765634e-03 7.66111135e-01 1.00230068e-01 -4.51588452... | [10.718247413635254, 9.728987693786621] |
b46b5859-6833-4a5b-9779-0752a9598f60 | magnificent-minified-models | 2306.10177 | null | https://arxiv.org/abs/2306.10177v1 | https://arxiv.org/pdf/2306.10177v1.pdf | Magnificent Minified Models | This paper concerns itself with the task of taking a large trained neural network and 'compressing' it to be smaller by deleting parameters or entire neurons, with minimal decreases in the resulting model accuracy. We compare various methods of parameter and neuron selection: dropout-based neuron damage estimation, neu... | ['Hillary Sanders', 'Rich Harang'] | 2023-06-16 | null | null | null | null | ['quantization'] | ['methodology'] | [ 4.68752414e-01 3.16722125e-01 1.05695911e-01 -2.70731270e-01
-4.88103658e-01 -2.95768470e-01 3.13381702e-01 2.95663506e-01
-1.20323801e+00 9.87515628e-01 1.58822879e-01 -1.56736165e-01
-3.21039140e-01 -5.67950010e-01 -7.01971114e-01 -9.82138753e-01
-8.83970112e-02 4.92283553e-01 7.11470425e-01 -7.02984110... | [8.570712089538574, 3.2968051433563232] |
5f3539e5-b663-4f4b-bb3a-d11aa3c010cd | hybrid-bayesian-neural-networks-with | 2107.07014 | null | https://arxiv.org/abs/2107.07014v1 | https://arxiv.org/pdf/2107.07014v1.pdf | Hybrid Bayesian Neural Networks with Functional Probabilistic Layers | Bayesian neural networks provide a direct and natural way to extend standard deep neural networks to support probabilistic deep learning through the use of probabilistic layers that, traditionally, encode weight (and bias) uncertainty. In particular, hybrid Bayesian neural networks utilize standard deterministic layers... | ['Daniel T. Chang'] | 2021-07-14 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-3.16581726e-01 3.06865811e-01 9.01745632e-02 -8.42098534e-01
-3.55674595e-01 -6.09263897e-01 9.89903152e-01 -2.39768431e-01
-2.82427907e-01 8.06943715e-01 1.05433889e-01 -4.29851949e-01
-3.74081582e-01 -1.30764115e+00 -9.64924276e-01 -7.84235775e-01
2.41734814e-02 7.99207091e-01 1.95677668e-01 1.43979058... | [7.2930006980896, 3.848222494125366] |
b9758dc9-db35-44a8-a39c-9c93c1dc7862 | birdsoundsdenoising-deep-visual-audio | 2210.10196 | null | https://arxiv.org/abs/2210.10196v1 | https://arxiv.org/pdf/2210.10196v1.pdf | BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds | Audio denoising has been explored for decades using both traditional and deep learning-based methods. However, these methods are still limited to either manually added artificial noise or lower denoised audio quality. To overcome these challenges, we collect a large-scale natural noise bird sound dataset. We are the fi... | ['Jialu Li', 'Youshan Zhang'] | 2022-10-18 | null | null | null | null | ['audio-denoising', 'noise-estimation', 'speech-denoising'] | ['audio', 'medical', 'speech'] | [ 4.56636399e-01 -3.80869001e-01 4.45967376e-01 -2.51899332e-01
-1.24815643e+00 -4.92350698e-01 3.36474866e-01 -8.23151544e-02
-4.57843840e-01 2.26663843e-01 5.98008372e-02 1.43252075e-01
1.18317492e-01 -5.61571300e-01 -5.79816401e-01 -6.12281442e-01
-1.18328311e-01 -1.04830571e-01 3.43949407e-01 -2.07904175... | [15.194742202758789, 5.559787750244141] |
3384ba0e-8a3a-4e8d-b57c-17d76e279eb8 | disk-learning-local-features-with-policy | 2006.13566 | null | https://arxiv.org/abs/2006.13566v2 | https://arxiv.org/pdf/2006.13566v2.pdf | DISK: Learning local features with policy gradient | Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints. We introduce DISK (DIScrete Keypoints), a novel method that overcomes these obstacles by leveraging principles from Reinforcement Learning (RL), optimizing end-to... | ['Michał J. Tyszkiewicz', 'Pascal Fua', 'Eduard Trulls'] | 2020-06-24 | null | http://proceedings.neurips.cc/paper/2020/hash/a42a596fc71e17828440030074d15e74-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a42a596fc71e17828440030074d15e74-Paper.pdf | neurips-2020-12 | ['image-matching'] | ['computer-vision'] | [-4.50576097e-01 -1.79212764e-01 -4.91565883e-01 -1.41142741e-01
-1.48524606e+00 -6.94646657e-01 9.28849161e-01 2.63587952e-01
-4.58285451e-01 6.55719757e-01 2.75960773e-01 1.66277692e-01
-4.91076887e-01 -5.14643431e-01 -9.26044226e-01 -6.04448855e-01
-4.85553920e-01 4.03773040e-01 1.94767416e-01 -2.37713289... | [8.149181365966797, -1.8818085193634033] |
8f0467ab-43e1-442f-8e35-37983291de51 | aaai-fss-19-human-centered-ai-trustworthiness | 2001.05375 | null | https://arxiv.org/abs/2001.05375v1 | https://arxiv.org/pdf/2001.05375v1.pdf | AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings | To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems. Not only in safety-critical applications such as autonomous driving or medicine, but also in dynamic open world systems in industry and go... | ['Ulli Waltinger', 'John Piorkowski', 'Ian McCulloh', 'Florian Buettner'] | 2020-01-15 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-8.81447196e-02 6.31582916e-01 -2.21155331e-01 -7.81688154e-01
-3.96656811e-01 -5.46379685e-01 3.29091698e-01 2.35624209e-01
-2.40582287e-01 8.16218913e-01 -3.27999778e-02 -6.56999707e-01
-3.01061124e-01 -5.39819002e-01 -5.09124815e-01 1.41085973e-02
1.14604771e-01 5.72270811e-01 -2.50642926e-01 -1.61827832... | [8.938403129577637, 6.203271389007568] |
7d02a42e-ff90-4026-b99d-851b0c00aeb7 | mitodet-simple-and-robust-mitosis-detection | 2109.01485 | null | https://arxiv.org/abs/2109.01485v2 | https://arxiv.org/pdf/2109.01485v2.pdf | MitoDet: Simple and robust mitosis detection | Mitotic figure detection is a challenging task in digital pathology that has a direct impact on therapeutic decisions. While automated methods often achieve acceptable results under laboratory conditions, they frequently fail in the clinical deployment phase. This problem can be mainly attributed to a phenomenon called... | ['Thomas Wittenberg', 'Petr Kuritcyn', 'Volker Bruns', 'Michaela Benz', 'Jakob Dexl'] | 2021-09-02 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 5.07298589e-01 -4.83636335e-02 -2.81446636e-01 -1.51155740e-01
-6.34411395e-01 -6.13367140e-01 5.36265075e-01 5.01899838e-01
-6.48491502e-01 9.54664588e-01 -4.78028432e-02 -1.97518125e-01
3.93338889e-01 -3.82907301e-01 -5.39998889e-01 -9.09424782e-01
3.67720813e-01 4.99934971e-01 4.65033352e-01 -5.75626045... | [15.115635871887207, -3.146883726119995] |
15f6e321-e824-4485-8e48-2df3b8178e30 | robust-and-explainable-identification-of | 2212.07425 | null | https://arxiv.org/abs/2212.07425v2 | https://arxiv.org/pdf/2212.07425v2.pdf | Robust and Explainable Identification of Logical Fallacies in Natural Language Arguments | The spread of misinformation, propaganda, and flawed argumentation has been amplified in the Internet era. Given the volume of data and the subtlety of identifying violations of argumentation norms, supporting information analytics tasks, like content moderation, with trustworthy methods that can identify logical falla... | ['Alain Mermoud', 'Hông-Ân Sandlin', 'Filip Ilievski', 'Himanshu Rawlani', 'Darshan Deshpande', 'Vishnu Priya Prasanna Venkatesh', 'Zhivar Sourati'] | 2022-12-12 | null | null | null | null | ['logical-fallacies', 'propaganda-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 3.55468750e-01 5.20657539e-01 -8.77504289e-01 -2.52030849e-01
-7.75260866e-01 -6.98334932e-01 1.01494873e+00 7.04392374e-01
1.36126637e-01 5.98682582e-01 7.63085723e-01 -8.85493517e-01
-5.02046943e-01 -6.28366411e-01 -7.96623826e-01 1.32421359e-01
1.87603682e-01 3.22570294e-01 2.20498294e-02 -3.94699991... | [9.74504280090332, 8.136551856994629] |
dfe5ab80-6c1c-4bad-b704-fe4a3c0a350d | unitts-residual-learning-of-unified-embedding | 2106.11171 | null | https://arxiv.org/abs/2106.11171v3 | https://arxiv.org/pdf/2106.11171v3.pdf | UniTTS: Residual Learning of Unified Embedding Space for Speech Style Control | We propose a novel high-fidelity expressive speech synthesis model, UniTTS, that learns and controls overlapping style attributes avoiding interference. UniTTS represents multiple style attributes in a single unified embedding space by the residuals between the phoneme embeddings before and after applying the attribute... | ['Injung Kim', 'Sungjae Kim', 'Minsu Kang'] | 2021-06-21 | null | null | null | null | ['expressive-speech-synthesis'] | ['speech'] | [-2.46258482e-01 4.36395109e-02 1.16114490e-01 -3.45189333e-01
-7.13382602e-01 -6.69360042e-01 4.01467115e-01 -6.92947730e-02
9.91066173e-02 5.90530336e-01 6.50418878e-01 1.48242623e-01
-6.35574162e-02 -5.64590693e-01 -3.73252571e-01 -8.13351691e-01
-1.82917833e-01 1.51433483e-01 -4.34815437e-01 -2.34428197... | [15.006948471069336, 6.44118070602417] |
22bf033f-5c41-4640-8fc5-f688b07c7aa5 | short-utterance-compensation-in-speaker | 1810.10884 | null | http://arxiv.org/abs/1810.10884v2 | http://arxiv.org/pdf/1810.10884v2.pdf | Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings | The short duration of an input utterance is one of the most critical threats
that degrade the performance of speaker verification systems. This study aimed
to develop an integrated text-independent speaker verification system that
inputs utterances with short duration of 2 seconds or less. We propose an
approach using ... | ['Ha-Jin Yu', 'Hye-jin Shim', 'Hee-Soo Heo', 'Jee-weon Jung'] | 2018-10-25 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 3.29921812e-01 3.88960928e-01 8.45976770e-02 -6.57114089e-01
-9.84304667e-01 -3.61355335e-01 2.95553476e-01 1.23523679e-02
-6.36979163e-01 2.93769866e-01 4.37806517e-01 -4.45112884e-01
2.22465210e-02 -1.13539599e-01 -4.45642918e-01 -8.17403257e-01
1.46845773e-01 -1.35199025e-01 -1.85569823e-02 -2.12387443... | [14.378564834594727, 6.061183452606201] |
c7661106-c6ce-48b3-b361-2308d28332a0 | towards-adversarial-denoising-of-radar-micro | 1811.04678 | null | https://arxiv.org/abs/1811.04678v3 | https://arxiv.org/pdf/1811.04678v3.pdf | Towards Adversarial Denoising of Radar Micro-Doppler Signatures | Generative Adversarial Networks (GANs) are considered the state-of-the-art in the field of image generation. They learn the joint distribution of the training data and attempt to generate new data samples in high dimensional space following the same distribution as the input. Recent improvements in GANs opened the fiel... | ['Fady Aziz', 'Urs Schneider', 'Sherif Abdulatif', 'Karim Armanious', 'Bin Yang'] | 2018-11-12 | null | null | null | null | ['denoising-of-radar-micro-doppler-signatures'] | ['miscellaneous'] | [ 6.64547384e-01 1.29273552e-02 5.39657891e-01 -1.87633634e-01
-7.06653833e-01 -1.10168867e-01 3.66988569e-01 -3.98770928e-01
-4.60275233e-01 1.05743623e+00 7.93506857e-03 2.69402325e-01
-2.84632295e-01 -1.12854111e+00 -6.30402029e-01 -1.17518473e+00
-2.44975030e-01 3.11502665e-01 -1.52453274e-01 -1.96864203... | [14.080498695373535, 1.6488419771194458] |
5f61d115-9bc2-4e1c-89ab-ea958cf8360c | actively-supervised-clustering-for-open | 2306.04968 | null | https://arxiv.org/abs/2306.04968v1 | https://arxiv.org/pdf/2306.04968v1.pdf | Actively Supervised Clustering for Open Relation Extraction | Current clustering-based Open Relation Extraction (OpenRE) methods usually adopt a two-stage pipeline. The first stage simultaneously learns relation representations and assignments. The second stage manually labels several instances and thus names the relation for each cluster. However, unsupervised objectives struggl... | ['Mingming Sun', 'Minlong Peng', 'Zhongyu Wei', 'Tao Gui', 'Qi Zhang', 'Yongxin Zhang', 'Jun Zhao'] | 2023-06-08 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 2.80220598e-01 6.63036764e-01 -4.32905257e-01 -3.44191611e-01
-7.54183292e-01 -7.68356740e-01 3.35524857e-01 5.22769272e-01
-1.82503805e-01 7.19805062e-01 -2.59305626e-01 -1.69392034e-01
-4.45219845e-01 -8.42131257e-01 -3.55139285e-01 -8.22350502e-01
-1.43015072e-01 1.11777568e+00 2.50043988e-01 2.44631290... | [9.274344444274902, 8.521838188171387] |
30e10b55-2c8f-4e4b-aec2-27e677134211 | deep-impression-audiovisual-deep-residual | 1609.05119 | null | http://arxiv.org/abs/1609.05119v1 | http://arxiv.org/pdf/1609.05119v1.pdf | Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition | Here, we develop an audiovisual deep residual network for multimodal apparent
personality trait recognition. The network is trained end-to-end for predicting
the Big Five personality traits of people from their videos. That is, the
network does not require any feature engineering or visual analysis such as
face detecti... | ['Rob Van Lier', 'Umut Güçlü', 'Yağmur Güçlütürk', 'Marcel A. J. van Gerven'] | 2016-09-16 | null | null | null | null | ['personality-trait-recognition'] | ['computer-vision'] | [-4.71572399e-01 1.69674203e-01 2.16743246e-01 -9.08887923e-01
-3.12768549e-01 -1.80551603e-01 3.46926630e-01 -5.07296324e-01
-4.95460838e-01 3.09508443e-01 1.02607869e-01 4.96479303e-01
5.24631813e-02 -4.67042923e-02 -1.33928701e-01 -4.77972746e-01
-2.76334316e-01 2.83116758e-01 -7.40906060e-01 3.55435126... | [13.490335464477539, 1.7255851030349731] |
fa7f3c94-7770-408b-9c0e-1562e601a4fe | style-guided-domain-adaptation-for-face | 2203.14565 | null | https://arxiv.org/abs/2203.14565v2 | https://arxiv.org/pdf/2203.14565v2.pdf | Style-Guided Domain Adaptation for Face Presentation Attack Detection | Domain adaptation (DA) or domain generalization (DG) for face presentation attack detection (PAD) has attracted attention recently with its robustness against unseen attack scenarios. Existing DA/DG-based PAD methods, however, have not yet fully explored the domain-specific style information that can provide knowledge ... | ['Seong-Whan Lee', 'Kyungseo Min', 'Woo-Jeoung Nam', 'Young-Eun Kim'] | 2022-03-28 | null | null | null | null | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.07429826e-01 -7.14187801e-01 -2.82986283e-01 -2.95073420e-01
-6.94691062e-01 -9.32074070e-01 5.98286569e-01 -2.71472126e-01
-4.85032275e-02 5.30911863e-01 6.54455321e-03 -4.24798280e-01
-2.72459667e-02 -5.81867754e-01 -3.31839085e-01 -8.36050928e-01
2.64349908e-01 1.48341924e-01 2.45132044e-01 -4.32833195... | [13.152069091796875, 1.2140318155288696] |
c1512730-32fb-4bf7-bae9-ef71b335dd02 | pengi-an-audio-language-model-for-audio-tasks | 2305.11834 | null | https://arxiv.org/abs/2305.11834v1 | https://arxiv.org/pdf/2305.11834v1.pdf | Pengi: An Audio Language Model for Audio Tasks | In the domain of audio processing, Transfer Learning has facilitated the rise of Self-Supervised Learning and Zero-Shot Learning techniques. These approaches have led to the development of versatile models capable of tackling a wide array of tasks, while delivering state-of-the-art performance. However, current models ... | ['Huaming Wang', 'Rita Singh', 'Benjamin Elizalde', 'Soham Deshmukh'] | 2023-05-19 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.91301262e-01 2.62347609e-01 -1.26759127e-01 -3.63382578e-01
-1.50869608e+00 -5.78396559e-01 7.01105893e-01 1.15596019e-01
-2.55245596e-01 4.77603346e-01 6.18325770e-01 -3.10006827e-01
2.72848904e-01 -6.13381505e-01 -8.11072409e-01 -2.13941112e-01
-2.22950354e-01 3.59464377e-01 3.17395717e-01 -2.11826682... | [15.25653076171875, 5.036684513092041] |
2098391f-c7da-4729-9d8f-0aac7f40e1cf | large-scale-fast-and-accurate-shot-boundary | 1705.03281 | null | http://arxiv.org/abs/1705.03281v2 | http://arxiv.org/pdf/1705.03281v2.pdf | Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks | Shot boundary detection (SBD) is an important pre-processing step for video
manipulation. Here, each segment of frames is classified as either sharp,
gradual or no transition. Current SBD techniques analyze hand-crafted features
and attempt to optimize both detection accuracy and processing speed. However,
the heavy co... | ['Sung-Ho Bae', 'Mohamed Hefeeda', 'Ahmed Selim', 'Ahmed Hassanien', 'Wojciech Matusik', 'Mohamed Elgharib'] | 2017-05-09 | null | null | null | null | ['camera-shot-boundary-detection'] | ['computer-vision'] | [ 0.20726568 -0.41458553 -0.04468744 0.1425563 -0.3434368 -0.387747
0.61516553 0.02001347 -0.5070615 0.4498191 -0.09967913 -0.3070643
0.44013706 -0.77441365 -0.8100338 -0.21078812 -0.3795757 0.11544651
1.029617 -0.28334644 0.28930447 0.5851329 -1.5575094 0.43817058
0.5824959 1.2088269 0.199... | [8.50622844696045, 0.10755906999111176] |
e06fce1a-da5c-40f6-b9fd-724771be1adf | on-momentum-based-gradient-methods-for | 2303.03944 | null | https://arxiv.org/abs/2303.03944v1 | https://arxiv.org/pdf/2303.03944v1.pdf | On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level | Bilevel optimization is a popular two-level hierarchical optimization, which has been widely applied to many machine learning tasks such as hyperparameter learning, meta learning and continual learning. Although many bilevel optimization methods recently have been developed, the bilevel methods are not well studied whe... | ['Feihu Huang'] | 2023-03-07 | null | null | null | null | ['bilevel-optimization'] | ['methodology'] | [-3.83920252e-01 -8.83814469e-02 -8.35532025e-02 1.23618104e-01
-1.00010383e+00 -2.51806945e-01 -3.03364843e-01 8.20020437e-02
-6.67292774e-01 1.17312288e+00 -4.90633279e-01 -4.70538735e-01
-7.92486191e-01 -7.42394328e-01 -9.99491751e-01 -1.20358288e+00
-2.54306883e-01 3.73524904e-01 1.88540630e-02 -3.26082230... | [6.499183177947998, 4.570314407348633] |
ab022ffd-affd-4757-a5d7-d291e667ea5d | 3d-aided-data-augmentation-for-robust-face | 2010.01246 | null | https://arxiv.org/abs/2010.01246v2 | https://arxiv.org/pdf/2010.01246v2.pdf | 3D-Aided Data Augmentation for Robust Face Understanding | Data augmentation has been highly effective in narrowing the data gap and reducing the cost for human annotation, especially for tasks where ground truth labels are difficult and expensive to acquire. In face recognition, large pose and illumination variation of face images has been a key factor for performance degrada... | ['Wei Xia', 'Yuanjun Xiong', 'Yifan Xing'] | 2020-10-03 | null | null | null | null | ['3d-face-modeling'] | ['computer-vision'] | [ 1.19710341e-01 -2.20817253e-02 -8.37757438e-02 -7.82014012e-01
-5.10292172e-01 -4.34433788e-01 4.62241650e-01 -3.27874184e-01
-4.14044783e-02 4.14279014e-01 -1.40527919e-01 -1.43701918e-02
1.30383298e-01 -3.54528993e-01 -5.69811761e-01 -7.55706787e-01
2.67403454e-01 7.06804872e-01 -3.92112374e-01 -2.11929884... | [13.336557388305664, 0.3359694182872772] |
5741c89b-dbc5-45e5-98e6-b389c7e17d39 | considerations-on-the-evaluation-of-biometric | 2303.13294 | null | https://arxiv.org/abs/2303.13294v3 | https://arxiv.org/pdf/2303.13294v3.pdf | Considerations on the Evaluation of Biometric Quality Assessment Algorithms | Quality assessment algorithms can be used to estimate the utility of a biometric sample for the purpose of biometric recognition. "Error versus Discard Characteristic" (EDC) plots, and "partial Area Under Curve" (pAUC) values of curves therein, are generally used by researchers to evaluate the predictive performance of... | ['Christoph Busch', 'Juan Tapia', 'Christian Rathgeb', 'Torsten Schlett'] | 2023-03-23 | null | null | null | null | ['face-image-quality', 'image-quality-assessment', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 4.99094695e-01 -3.74102652e-01 -9.66172516e-02 -6.03433132e-01
-8.52826953e-01 -6.19897902e-01 4.74430591e-01 7.42696047e-01
-6.22240603e-01 5.72604418e-01 -1.69752352e-02 -2.84223109e-01
-6.55250728e-01 -6.24718606e-01 -6.06553406e-02 -6.51016295e-01
-1.25433654e-01 4.53098148e-01 1.26304463e-01 1.82697833... | [12.981123924255371, 1.087037444114685] |
29156a0e-edc5-43f9-9588-ae172dba24b7 | m-nca-texture-generation-with-ultra-compact | 2111.13545 | null | https://arxiv.org/abs/2111.13545v1 | https://arxiv.org/pdf/2111.13545v1.pdf | $μ$NCA: Texture Generation with Ultra-Compact Neural Cellular Automata | We study the problem of example-based procedural texture synthesis using highly compact models. Given a sample image, we use differentiable programming to train a generative process, parameterised by a recurrent Neural Cellular Automata (NCA) rule. Contrary to the common belief that neural networks should be significan... | ['Eyvind Niklasson', 'Alexander Mordvintsev'] | 2021-11-26 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 6.79910719e-01 7.34182119e-01 2.19019964e-01 -1.97067574e-01
-5.82277656e-01 -4.99726802e-01 8.97281289e-01 -1.52167350e-01
-2.01468334e-01 9.20164943e-01 -2.00082570e-01 -6.27637625e-01
1.03575237e-01 -1.38023329e+00 -9.40125763e-01 -7.56886363e-01
-1.10912696e-01 5.46692729e-01 1.30494878e-01 -2.55321622... | [11.49544906616211, -0.46926456689834595] |
8eca2497-f588-4cb3-b897-3ac2ec2dc328 | analysis-of-function-approximation-and | 2206.05997 | null | https://arxiv.org/abs/2206.05997v1 | https://arxiv.org/pdf/2206.05997v1.pdf | Analysis of function approximation and stability of general DNNs in directed acyclic graphs using un-rectifying analysis | A general lack of understanding pertaining to deep feedforward neural networks (DNNs) can be attributed partly to a lack of tools with which to analyze the composition of non-linear functions, and partly to a lack of mathematical models applicable to the diversity of DNN architectures. In this paper, we made a number o... | ['Shih-Shuo Tung', 'Wen-Liang Hwang'] | 2022-06-13 | null | null | null | null | ['mathematical-induction'] | ['reasoning'] | [ 3.54331583e-01 3.93633783e-01 2.86295973e-02 -2.84926564e-01
5.90681672e-01 -7.23689795e-01 5.44840455e-01 1.26830682e-01
-4.78887595e-02 7.57855117e-01 -1.21560141e-01 -7.17648745e-01
-6.81321800e-01 -9.32477236e-01 -8.19295406e-01 -7.13424027e-01
-4.71462190e-01 5.59750460e-02 2.20740825e-01 -5.12648821... | [7.986122131347656, 3.552229404449463] |
aafadb15-d6a3-488b-b804-95821a5f34d7 | learning-progressive-modality-shared | 2212.00226 | null | https://arxiv.org/abs/2212.00226v1 | https://arxiv.org/pdf/2212.00226v1.pdf | Learning Progressive Modality-shared Transformers for Effective Visible-Infrared Person Re-identification | Visible-Infrared Person Re-Identification (VI-ReID) is a challenging retrieval task under complex modality changes. Existing methods usually focus on extracting discriminative visual features while ignoring the reliability and commonality of visual features between different modalities. In this paper, we propose a nove... | ['Pingping Zhang', 'Xuezhang Zou', 'Hu Lu'] | 2022-12-01 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [ 5.01970686e-02 -7.84875154e-01 -1.78415347e-02 -4.59162235e-01
-8.47300410e-01 -4.98730332e-01 4.99360234e-01 -1.23308778e-01
-5.11136472e-01 5.39059997e-01 2.77233541e-01 1.03757210e-01
-8.71162787e-02 -4.22641397e-01 -5.04040062e-01 -7.08473146e-01
4.25749093e-01 -1.16922677e-01 -2.75478289e-02 -6.05283491... | [14.695296287536621, 0.9333258867263794] |
91db46da-6a03-4f74-a6f8-ca3ba6e686b5 | clustering-images-by-unmasking-a-new-baseline | 1905.00773 | null | https://arxiv.org/abs/1905.00773v1 | https://arxiv.org/pdf/1905.00773v1.pdf | Clustering Images by Unmasking - A New Baseline | We propose a novel agglomerative clustering method based on unmasking, a technique that was previously used for authorship verification of text documents and for abnormal event detection in videos. In order to join two clusters, we alternate between (i) training a binary classifier to distinguish between the samples fr... | ['Mariana-Iuliana Georgescu', 'Radu Tudor Ionescu'] | 2019-05-02 | null | null | null | null | ['texture-classification', 'handwritten-digit-recognition', 'authorship-verification'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 1.46422565e-01 -2.61673421e-01 1.39686853e-01 -2.88535058e-01
-2.28235111e-01 -5.06216586e-01 8.69190574e-01 6.70049250e-01
-5.18265247e-01 2.49292478e-01 -1.99025899e-01 -1.96786612e-01
-2.36847878e-01 -6.45680189e-01 -7.58140981e-02 -1.04026222e+00
-1.36297643e-01 7.83970118e-01 4.18662280e-01 3.49306732... | [8.13994026184082, 1.7829595804214478] |
4517a41f-c929-4f54-a00d-cc9c94540cc8 | learning-recommendations-from-user-actions-in | 2211.15360 | null | https://arxiv.org/abs/2211.15360v1 | https://arxiv.org/pdf/2211.15360v1.pdf | Learning Recommendations from User Actions in the Item-poor Insurance Domain | While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem. The insurance domain is notoriously data-sparse because the number of ... | ['Christina Lioma', 'Maria Maistro', 'Simone Borg Bruun'] | 2022-11-28 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [ 2.93065608e-01 1.49922878e-01 -7.99533129e-01 -6.64805770e-01
-5.59736013e-01 -5.81442297e-01 3.45117569e-01 -3.00652231e-03
-2.89098769e-01 6.56873405e-01 9.79888201e-01 -6.20721996e-01
-3.47190410e-01 -8.18313777e-01 -9.05236185e-01 -3.38894457e-01
-3.48784715e-01 4.68448460e-01 -1.16703704e-01 -5.02466977... | [10.073685646057129, 5.662058353424072] |
d4f31009-e514-4baf-a96b-2a4131267a93 | locec-local-community-based-edge | 2002.04180 | null | https://arxiv.org/abs/2002.04180v2 | https://arxiv.org/pdf/2002.04180v2.pdf | LoCEC: Local Community-based Edge Classification in Large Online Social Networks | Relationships in online social networks often imply social connections in the real world. An accurate understanding of relationship types benefits many applications, e.g. social advertising and recommendation. Some recent attempts have been proposed to classify user relationships into predefined types with the help of ... | ['Hongzhao Chen', 'Zongyi Zhang', 'Qian Lin', 'Jun Liao', 'Guohui Ling', 'Chuan Chen', 'Chonggang Song'] | 2020-02-11 | null | null | null | null | ['local-community-detection'] | ['graphs'] | [-5.15255965e-02 -1.43340394e-01 -5.14437258e-01 -4.76284683e-01
2.59648293e-01 -5.29370487e-01 4.11849976e-01 5.01774311e-01
-9.41340327e-02 6.15835369e-01 -6.93277866e-02 -3.33979636e-01
-4.93599772e-01 -1.31073856e+00 -5.62239438e-02 -1.96587101e-01
-5.78412652e-01 5.09303987e-01 3.44393939e-01 -1.25902146... | [7.390580654144287, 6.242337703704834] |
ab5e6a59-ecd7-4409-a42e-93f2abdbad0d | on-the-safety-of-interpretable-machine-1 | 2211.01498 | null | https://arxiv.org/abs/2211.01498v1 | https://arxiv.org/pdf/2211.01498v1.pdf | On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach | Interpretable and explainable machine learning has seen a recent surge of interest. We focus on safety as a key motivation behind the surge and make the relationship between interpretability and safety more quantitative. Toward assessing safety, we introduce the concept of maximum deviation via an optimization problem ... | ['Moninder Singh', 'Elizabeth M. Daly', 'Kush R. Varshney', 'Amit Dhurandhar', 'Rahul Nair', 'Dennis Wei'] | 2022-11-02 | on-the-safety-of-interpretable-machine | https://openreview.net/forum?id=Jt8FYFnyTLR | https://openreview.net/pdf?id=Jt8FYFnyTLR | null | ['additive-models', 'interpretable-machine-learning'] | ['methodology', 'methodology'] | [ 2.87317425e-01 7.11804628e-01 -8.92102540e-01 -5.54455280e-01
-1.18462443e+00 -8.54198992e-01 2.50269175e-01 4.00461793e-01
-1.16304405e-01 9.28760231e-01 4.75873291e-01 -8.50129187e-01
-7.58736551e-01 -4.71086472e-01 -8.32263231e-01 -5.91992974e-01
8.74636695e-03 3.37318212e-01 -7.06706405e-01 2.79284209... | [8.575719833374023, 5.40773868560791] |
7c730ed3-2f27-44dd-ab5c-65756f13ebd4 | offline-rl-with-no-ood-actions-in-sample | 2303.15810 | null | https://arxiv.org/abs/2303.15810v1 | https://arxiv.org/pdf/2303.15810v1.pdf | Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization | Most offline reinforcement learning (RL) methods suffer from the trade-off between improving the policy to surpass the behavior policy and constraining the policy to limit the deviation from the behavior policy as computing $Q$-values using out-of-distribution (OOD) actions will suffer from errors due to distributional... | ['Xianyuan Zhan', 'Victor Wai Kin Chan', 'Zhaoran Wang', 'Zhuoran Yang', 'Jianxiong Li', 'Li Jiang', 'Haoran Xu'] | 2023-03-28 | null | null | null | null | ['q-learning', 'offline-rl', 'd4rl'] | ['methodology', 'playing-games', 'robots'] | [-1.87841758e-01 5.51631488e-02 -7.67552793e-01 -3.72783005e-01
-9.83183086e-01 -7.29295492e-01 3.26562911e-01 9.56037268e-02
-7.20174491e-01 9.68017220e-01 6.83153272e-02 -6.08193159e-01
-5.51983714e-01 -8.54492962e-01 -1.03634179e+00 -8.68896961e-01
-9.15125534e-02 3.82940233e-01 -9.52486917e-02 -2.18225881... | [4.098418235778809, 2.2984869480133057] |
878b2243-63d8-4340-85b1-120a20185149 | personalize-segment-anything-model-with-one | 2305.03048 | null | https://arxiv.org/abs/2305.03048v1 | https://arxiv.org/pdf/2305.03048v1.pdf | Personalize Segment Anything Model with One Shot | Driven by large-data pre-training, Segment Anything Model (SAM) has been demonstrated as a powerful and promptable framework, revolutionizing the segmentation models. Despite the generality, customizing SAM for specific visual concepts without man-powered prompting is under explored, e.g., automatically segmenting your... | ['Hongsheng Li', 'Peng Gao', 'Hao Dong', 'Junting Pan', 'Shilin Yan', 'Ziyu Guo', 'Zhengkai Jiang', 'Renrui Zhang'] | 2023-05-04 | null | null | null | null | ['personalized-segmentation', 'video-object-segmentation', 'video-semantic-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.40343386e-01 -5.32843880e-02 -3.65131408e-01 -4.21336204e-01
-7.71856785e-01 -7.45221317e-01 3.27385128e-01 -3.36077124e-01
-6.03103042e-01 3.71454090e-01 -1.37982666e-01 -1.61782175e-01
1.17279142e-01 -4.37623024e-01 -7.99325705e-01 -6.19174600e-01
3.34715486e-01 4.40249383e-01 6.88159108e-01 -6.86848611... | [9.537130355834961, 0.17527590692043304] |
930dbe2b-de5f-4ed4-9fcc-d3fff76de0d5 | single-image-reflection-removal-exploiting | 1904.00637 | null | http://arxiv.org/abs/1904.00637v1 | http://arxiv.org/pdf/1904.00637v1.pdf | Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements | Removing undesirable reflections from a single image captured through a glass
window is of practical importance to visual computing systems. Although
state-of-the-art methods can obtain decent results in certain situations,
performance declines significantly when tackling more general real-world cases.
These failures s... | ['Jiaolong Yang', 'David Wipf', 'Kaixuan Wei', 'Hua Huang', 'Ying Fu'] | 2019-04-01 | single-image-reflection-removal-exploiting-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wei_Single_Image_Reflection_Removal_Exploiting_Misaligned_Training_Data_and_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wei_Single_Image_Reflection_Removal_Exploiting_Misaligned_Training_Data_and_Network_CVPR_2019_paper.pdf | cvpr-2019-6 | ['reflection-removal'] | ['computer-vision'] | [ 7.33111560e-01 8.34999681e-02 2.29710788e-01 -4.73207623e-01
-9.97935534e-01 -4.16034758e-01 4.99591917e-01 -3.03126127e-01
-3.94868135e-01 4.04186159e-01 3.26814383e-01 -2.69125700e-01
1.82075072e-02 -4.04842764e-01 -8.85809481e-01 -6.81686819e-01
5.52130342e-02 -5.71518466e-02 1.70635089e-01 -1.90963700... | [10.137740135192871, -2.80237078666687] |
3ebfa638-3125-45bb-b029-b928fe44901b | adversarial-robustness-of-prompt-based-few | 2306.11066 | null | https://arxiv.org/abs/2306.11066v2 | https://arxiv.org/pdf/2306.11066v2.pdf | Adversarial Robustness of Prompt-based Few-Shot Learning for Natural Language Understanding | State-of-the-art few-shot learning (FSL) methods leverage prompt-based fine-tuning to obtain remarkable results for natural language understanding (NLU) tasks. While much of the prior FSL methods focus on improving downstream task performance, there is a limited understanding of the adversarial robustness of such metho... | ['Srijan Kumar', 'Subhabrata Mukherjee', 'Gaurav Verma', 'Venkata Prabhakara Sarath Nookala'] | 2023-06-19 | null | null | null | null | ['adversarial-robustness', 'few-shot-learning'] | ['adversarial', 'methodology'] | [ 2.97467470e-01 5.52743673e-02 -7.01010600e-02 -9.65660438e-02
-9.58955824e-01 -8.44924688e-01 1.00923419e+00 -6.85950145e-02
-5.80728710e-01 6.13116443e-01 4.76973772e-01 -4.26291198e-01
3.31630148e-02 -7.06578195e-01 -9.35599685e-01 -3.39938343e-01
1.61619693e-01 2.21329704e-01 4.88714784e-01 -7.03637004... | [6.148858547210693, 8.15090560913086] |
58624016-c4cf-4977-9480-f8e9d7229284 | yaso-a-new-benchmark-for-targeted-sentiment | 2012.14541 | null | https://arxiv.org/abs/2012.14541v2 | https://arxiv.org/pdf/2012.14541v2.pdf | YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews | Current TSA evaluation in a cross-domain setup is restricted to the small set of review domains available in existing datasets. Such an evaluation is limited, and may not reflect true performance on sites like Amazon or Yelp that host diverse reviews from many domains. To address this gap, we present YASO - a new TSA e... | ['Noam Slonim', 'Yoav Katz', 'Ranit Aharonov', 'Artem Spector', 'Orith Toledo-Ronen', 'Matan Orbach'] | 2020-12-29 | null | https://aclanthology.org/2021.emnlp-main.721 | https://aclanthology.org/2021.emnlp-main.721.pdf | emnlp-2021-11 | ['aspect-extraction'] | ['natural-language-processing'] | [-2.62119502e-01 -1.59571156e-01 -5.27067721e-01 -5.64661980e-01
-1.11336517e+00 -1.01444042e+00 4.84679788e-01 3.63337487e-01
-1.96256623e-01 7.16782331e-01 2.56374627e-01 -2.81246752e-01
1.97140425e-01 -3.89478147e-01 -2.62096733e-01 -8.04917067e-02
3.90351176e-01 7.11367488e-01 2.76837051e-01 -7.63539910... | [11.29773998260498, 6.839864253997803] |
83722873-364c-421d-91cc-7b3336d34e37 | scprisma-infers-filters-and-enhances | null | null | https://www.nature.com/articles/s41587-023-01663-5 | https://www.nature.com/articles/s41587-023-01663-5.pdf | scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching | Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance... | ['Mor Nitzan', 'Yonathan Bornfeld', 'Jonathan Karin'] | 2023-02-27 | null | null | null | nature-biotechnology-2023-2 | ['template-matching'] | ['computer-vision'] | [ 5.56653976e-01 -5.13314903e-01 2.69416034e-01 1.62746698e-01
-4.06661570e-01 -1.28765893e+00 8.31075907e-01 3.69534105e-01
-3.21069390e-01 9.92191792e-01 4.37123477e-01 -2.04985082e-01
-3.41604501e-01 -3.02693337e-01 -4.83603567e-01 -1.22289336e+00
-1.62436843e-01 5.70399165e-01 1.80221438e-01 1.34218428... | [6.569747447967529, 5.156620025634766] |
9a58a0b7-06c5-439d-a3a8-0728eccea907 | multi-center-anatomical-segmentation-with | 2211.07395 | null | https://arxiv.org/abs/2211.07395v1 | https://arxiv.org/pdf/2211.07395v1.pdf | Multi-center anatomical segmentation with heterogeneous labels via landmark-based models | Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are only annotated in images coming from particular medical centers, but not in the full database. Here we first show how state-of-the-art pixe... | ['Enzo Ferrante', 'Diego H. Milone', 'Maria Vakalopoulou', 'Nicolás Gaggion'] | 2022-11-14 | null | null | null | null | ['landmark-based-segmentation'] | ['computer-vision'] | [ 4.83180076e-01 6.20532632e-01 -5.61912298e-01 -4.53766435e-01
-1.26605749e+00 -8.30072701e-01 2.87251890e-01 5.64809680e-01
-3.23237598e-01 6.29056334e-01 4.80105340e-01 -3.28429818e-01
-3.60322684e-01 -7.29456961e-01 -6.17192864e-01 -7.07818389e-01
-1.16941765e-01 8.77745688e-01 2.96940237e-01 1.96185425... | [14.743453025817871, -2.1816747188568115] |
5dead5f7-7faa-4a05-9549-119269c36ed1 | categorisation-of-bulgarian-legislative | null | null | https://aclanthology.org/2020.clib-1.6 | https://aclanthology.org/2020.clib-1.6.pdf | Categorisation of Bulgarian Legislative Documents | The paper presents the categorisation of Bulgarian MARCELL corpus in toplevel EuroVoc domains. The Bulgarian MARCELL corpus is part of a recently developed multilingual corpus representing the national legislation in seven European countries. We performed several experiments with JEX Indexer, with neural networks and w... | ['Svetla Koeva', 'Martin Yalamov', 'Nikola Obreshkov'] | null | null | null | null | clib-2020-9 | ['term-extraction'] | ['natural-language-processing'] | [-6.26926720e-02 2.72486538e-01 -2.60690749e-01 -3.48916322e-01
-7.71712542e-01 -9.65974987e-01 1.07621372e+00 7.56076813e-01
-1.01914454e+00 8.14869761e-01 7.25628734e-01 -8.95552158e-01
-6.16271436e-01 -5.50557375e-01 -4.16417569e-01 -3.02162081e-01
1.61903962e-01 9.62272227e-01 -1.34863853e-01 -6.04133129... | [10.337769508361816, 10.164125442504883] |
594fe800-7f50-4f98-a117-d103b74827ea | two-birds-one-stone-jointly-learning-binary | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Li_Two_Birds_One_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Li_Two_Birds_One_ICCV_2015_paper.pdf | Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction | We address the challenging large-scale content-based face image retrieval problem, intended as searching images based on the presence of specific subject, given one face image of him/her. To this end, one natural demand is a supervised binary code learning method. While the learned codes might be discriminating, people... | ['Xilin Chen', 'Haomiao Liu', 'Yan Li', 'Ruiping Wang', 'Shiguang Shan', 'Huajie Jiang'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['face-image-retrieval'] | ['computer-vision'] | [ 1.58488885e-01 -3.31626743e-01 -2.82435238e-01 -8.23201835e-01
-4.32316929e-01 -5.92173874e-01 5.70450485e-01 8.91573951e-02
-1.54201344e-01 4.85337406e-01 -2.17487980e-02 6.02501370e-02
-2.47385129e-01 -6.85819387e-01 -4.38659191e-01 -7.19361246e-01
1.04779616e-01 4.43339795e-01 -3.63026172e-01 1.33223198... | [13.353414535522461, 0.7183812260627747] |
273af3ab-3b7e-4eee-a724-913dbf1f9357 | should-i-run-offline-reinforcement-learning | null | null | https://openreview.net/forum?id=AP1MKT37rJ | https://openreview.net/pdf?id=AP1MKT37rJ | Should I Run Offline Reinforcement Learning or Behavioral Cloning? | Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing only previously collected experience, without any online interaction. While it is widely understood that offline RL is able to extract good policies even from highly suboptimal data, in practice offline RL is often used with dat... | ['Sergey Levine', 'Anikait Singh', 'Joey Hong', 'Aviral Kumar'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['robot-manipulation'] | ['robots'] | [-2.80037103e-03 1.05898477e-01 -3.61812085e-01 4.18649577e-02
-9.56294954e-01 -9.18866634e-01 2.75467873e-01 1.30903602e-01
-9.22075748e-01 1.33958888e+00 -2.60018706e-01 -3.65975082e-01
-2.98982054e-01 -5.25635064e-01 -1.09653246e+00 -8.63065779e-01
-5.56475639e-01 6.28526807e-01 -1.11053422e-01 -4.95812118... | [4.186164379119873, 1.9033386707305908] |
599733ab-8899-4a58-b519-0fff54897721 | jepoo-highly-accurate-joint-estimation-of | 2306.01304 | null | https://arxiv.org/abs/2306.01304v2 | https://arxiv.org/pdf/2306.01304v2.pdf | JEPOO: Highly Accurate Joint Estimation of Pitch, Onset and Offset for Music Information Retrieval | Melody extraction is a core task in music information retrieval, and the estimation of pitch, onset and offset are key sub-tasks in melody extraction. Existing methods have limited accuracy, and work for only one type of data, either single-pitch or multipitch. In this paper, we propose a highly accurate method for joi... | ['Gang Wang', 'Yueguo Chen', 'Rui Zhang', 'Jun Yuan', 'Haojie Wei'] | 2023-06-02 | null | null | null | null | ['melody-extraction', 'music-information-retrieval', 'information-retrieval'] | ['music', 'music', 'natural-language-processing'] | [-2.82160550e-01 -8.33639920e-01 -3.74038011e-01 2.95607448e-01
-1.32432783e+00 -7.61260986e-01 -1.29565373e-01 1.39548123e-01
-3.93990248e-01 5.30360341e-01 5.59437498e-02 3.42822254e-01
-5.18294394e-01 -2.05022678e-01 -2.27504596e-01 -6.39229417e-01
-2.48361975e-01 3.63587499e-01 1.60507392e-02 -2.69685596... | [15.824742317199707, 5.389732837677002] |
d3127fde-86c5-4a5e-81f9-3dbcdec36370 | improving-sketch-colorization-using | 2301.08590 | null | https://arxiv.org/abs/2301.08590v1 | https://arxiv.org/pdf/2301.08590v1.pdf | Improving Sketch Colorization using Adversarial Segmentation Consistency | We propose a new method for producing color images from sketches. Current solutions in sketch colorization either necessitate additional user instruction or are restricted to the "paired" translation strategy. We leverage semantic image segmentation from a general-purpose panoptic segmentation network to generate an ad... | ['Pinar Duygulu', 'Emre Akbas', 'Nermin Samet', 'Samet Hicsonmez'] | 2023-01-20 | null | null | null | null | ['panoptic-segmentation', 'colorization'] | ['computer-vision', 'computer-vision'] | [ 5.37732720e-01 -9.35761258e-02 -1.79819882e-01 -3.43337685e-01
-9.53579485e-01 -1.08845413e+00 7.03545809e-01 -5.66820383e-01
-1.32191673e-01 6.42693758e-01 -3.33712071e-01 -3.38462383e-01
4.25096005e-01 -7.94321775e-01 -7.94511795e-01 -4.86796886e-01
4.84977841e-01 3.87444705e-01 -9.40388441e-02 -1.66828617... | [11.699539184570312, -0.6074205040931702] |
33cb7f0b-a5cd-48ba-8b3f-30dac3a6f7c2 | a-lightweight-network-for-photovoltaic-cell | 2302.07455 | null | https://arxiv.org/abs/2302.07455v1 | https://arxiv.org/pdf/2302.07455v1.pdf | A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation | Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters ... | ['Kanjian Zhang', 'Haikun Wei', 'Xinyi Chen', 'Jinxia Zhang'] | 2023-02-15 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [-8.24731961e-02 -4.49130714e-01 1.70413658e-01 9.99300331e-02
-3.52554739e-01 -2.16113850e-01 -1.21069655e-01 -1.12550840e-01
1.90636832e-02 6.78970933e-01 -5.75241029e-01 -2.32815519e-01
-7.16753155e-02 -1.00603211e+00 -6.41907334e-01 -1.17834818e+00
2.54946768e-01 1.32701576e-01 1.92008048e-01 2.07550541... | [7.337219715118408, 1.8341957330703735] |
cfb5f781-df1e-40a0-91da-3211f83826e2 | iteratively-coupled-multiple-instance | 2303.15749 | null | https://arxiv.org/abs/2303.15749v1 | https://arxiv.org/pdf/2303.15749v1.pdf | Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification | Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSIs are usually classified as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding process and a b... | ['Hao Chen', 'Lanfen Lin', 'Hongjie Hu', 'Yen-Wei Chen', 'Ruofeng Tong', 'Fang Wang', 'Luyang Luo', 'Hongyi Wang'] | 2023-03-28 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.37201440e-01 -6.33961111e-02 -4.55843925e-01 -5.73845029e-01
-1.51620746e+00 -5.77400804e-01 4.55373019e-01 4.90249485e-01
-1.79897264e-01 6.14483714e-01 -9.06981379e-02 -2.28245035e-02
5.85193224e-02 -9.72675681e-01 -7.40351260e-01 -9.61210549e-01
2.01819390e-01 3.77822429e-01 4.24088567e-01 2.71122932... | [15.09524917602539, -2.7476806640625] |
fa299140-0ee4-45ea-94db-2e5e05b43584 | interactive-removal-and-ground-truth-for | 1608.00762 | null | http://arxiv.org/abs/1608.00762v1 | http://arxiv.org/pdf/1608.00762v1.pdf | Interactive Removal and Ground Truth for Difficult Shadow Scenes | A user-centric method for fast, interactive, robust and high-quality shadow
removal is presented. Our algorithm can perform detection and removal in a
range of difficult cases: such as highly textured and colored shadows. To
perform detection an on-the-fly learning approach is adopted guided by two
rough user inputs fo... | ['Han Gong', 'Darren P. Cosker'] | 2016-08-02 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 8.59075189e-01 -2.26960167e-01 4.33357120e-01 -3.92069280e-01
-4.45509881e-01 -5.79111040e-01 5.17217875e-01 -8.91917348e-02
-2.38362849e-01 8.83284628e-01 2.81475447e-02 -3.86218816e-01
2.44914427e-01 -3.76037657e-01 -2.87836730e-01 -9.34368849e-01
-8.96410272e-02 5.06206632e-01 8.67603123e-01 -3.00287336... | [10.816753387451172, -4.068728923797607] |
4bb00577-a473-45ac-8597-8aef1e861c09 | tensor-variable-elimination-for-plated-factor | 1902.03210 | null | https://arxiv.org/abs/1902.03210v2 | https://arxiv.org/pdf/1902.03210v2.pdf | Tensor Variable Elimination for Plated Factor Graphs | A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do not provide a compact way to express repeated structure when compared to plate diagrams for directed graphical models. To exploit efficient t... | ['Martin Jankowiak', 'Justin Chiu', 'Alexander Rush', 'Neeraj Pradhan', 'Eli Bingham', 'Noah Goodman', 'Fritz Obermeyer'] | 2019-02-08 | null | null | null | null | ['music-modeling'] | ['music'] | [ 1.05401553e-01 2.36732721e-01 -3.37063640e-01 -1.76674396e-01
-2.31596455e-01 -9.77289498e-01 5.53070366e-01 -2.02638850e-01
2.95632601e-01 3.80635589e-01 -1.37156770e-01 -8.13306391e-01
-5.60060441e-01 -9.47389960e-01 -7.56075084e-01 -6.10996068e-01
-7.26362109e-01 7.39511430e-01 -3.00512969e-01 6.04775473... | [7.1196770668029785, 4.783234596252441] |
340f35e7-f814-47ce-b3bf-4711deff7292 | in-domain-representation-learning-for-remote-1 | 1911.06721 | null | https://arxiv.org/abs/1911.06721v1 | https://arxiv.org/pdf/1911.06721v1.pdf | In-domain representation learning for remote sensing | Given the importance of remote sensing, surprisingly little attention has been paid to it by the representation learning community. To address it and to establish baselines and a common evaluation protocol in this domain, we provide simplified access to 5 diverse remote sensing datasets in a standardized form. Specific... | ['Neil Houlsby', 'Maxim Neumann', 'Xiaohua Zhai', 'Andre Susano Pinto'] | 2019-11-15 | null | https://openreview.net/forum?id=BJx_JAVKDB | https://openreview.net/pdf?id=BJx_JAVKDB | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 7.06460476e-01 -1.69212192e-01 -4.60060358e-01 -4.73903775e-01
-1.02600467e+00 -5.93550801e-01 8.50737214e-01 1.39393643e-01
-1.41414121e-01 7.29490638e-01 4.06904668e-01 -6.37433648e-01
-5.52246511e-01 -1.17907012e+00 -2.98402369e-01 -5.63714206e-01
-4.28434044e-01 1.76679760e-01 -2.55886048e-01 -3.96961540... | [9.638164520263672, -1.4078093767166138] |
3642863a-0a09-45e4-b5be-a9c6e2c5eb18 | bccwj-timebank-temporal-and-event-information | null | null | https://aclanthology.org/Y13-1019 | https://aclanthology.org/Y13-1019.pdf | BCCWJ-TimeBank: Temporal and Event Information Annotation on Japanese Text | null | ['Kikuo Maekawa', 'Hikari Konishi', 'Sachi Yasuda', 'Mizuho Imada', 'Masayuki Asahara'] | 2013-11-01 | bccwj-timebank-temporal-and-event-information-1 | https://aclanthology.org/O14-4001 | https://aclanthology.org/O14-4001.pdf | paclic-2013-11 | ['temporal-information-extraction'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.24799108505249, 3.8141849040985107] |
a9dee558-e3ed-4f10-8378-fcd085b8ae67 | automatic-discourse-segmentation-an | 2002.04095 | null | https://arxiv.org/abs/2002.04095v2 | https://arxiv.org/pdf/2002.04095v2.pdf | Automatic Discourse Segmentation: an evaluation in French | In this article, we describe some discursive segmentation methods as well as a preliminary evaluation of the segmentation quality. Although our experiment were carried for documents in French, we have developed three discursive segmentation models solely based on resources simultaneously available in several languages:... | ['Juan-Manuel Torres-Moreno', 'Andréa Carneiro Linhares', 'Alejandro Molina-Villegas', 'Rémy Saksik'] | 2020-02-10 | null | null | null | null | ['discourse-segmentation'] | ['natural-language-processing'] | [-1.00463659e-01 3.21765393e-01 -1.41099840e-01 -5.03277957e-01
-9.11119938e-01 -9.00483489e-01 8.32217813e-01 3.84641081e-01
-9.68365848e-01 1.03411174e+00 3.24470073e-01 -3.88543993e-01
-2.89451759e-02 -4.52664584e-01 -2.05869824e-01 -2.96159983e-01
1.62750050e-01 1.15354967e+00 7.81773269e-01 -2.20408395... | [10.405887603759766, 10.137604713439941] |
82639f2b-55c7-49d8-90d8-720bdec8b061 | ood-augmentation-may-be-at-odds-with-open-set | 2206.04242 | null | https://arxiv.org/abs/2206.04242v1 | https://arxiv.org/pdf/2206.04242v1.pdf | OOD Augmentation May Be at Odds with Open-Set Recognition | Despite advances in image classification methods, detecting the samples not belonging to the training classes is still a challenging problem. There has been a burst of interest in this subject recently, which is called Open-Set Recognition (OSR). In OSR, the goal is to achieve both the classification and detecting out-... | ['Mohammad Hossein Rohban', 'Mohammad Azizmalayeri'] | 2022-06-09 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 4.79394317e-01 4.98541929e-02 -2.04809323e-01 -4.58576083e-01
-7.71428585e-01 -4.36160713e-01 6.86177850e-01 -1.38027489e-01
-5.47688782e-01 5.28263390e-01 -1.25514984e-01 -3.40114057e-01
1.76325455e-01 -5.43248653e-01 -7.84722626e-01 -8.20497096e-01
3.88126448e-02 3.86096448e-01 5.28683484e-01 -3.11284602... | [9.460394859313965, 2.866537094116211] |
03afc3e4-308d-4205-b3f3-7b69c57f81ab | causality-in-neural-networks-an-extended | 2106.05842 | null | https://arxiv.org/abs/2106.05842v1 | https://arxiv.org/pdf/2106.05842v1.pdf | Causality in Neural Networks -- An Extended Abstract | Causal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and reliability. Introducing the ideas of causality to machine learning helps in providing better learning and explainable models. Explainability... | ['Abbavaram Gowtham Reddy'] | 2021-06-03 | null | null | null | null | ['explainable-models'] | ['computer-vision'] | [-2.62060583e-01 5.02411067e-01 -8.29190314e-01 -8.80750000e-01
4.42154557e-01 -1.72295704e-01 9.25454021e-01 1.90873146e-01
2.10157350e-01 1.15746653e+00 6.38655245e-01 -6.37440979e-01
-6.15973294e-01 -8.39848578e-01 -5.03327906e-01 -2.93828905e-01
-3.96724999e-01 4.91800040e-01 -2.20278397e-01 -1.25186458... | [8.686554908752441, 5.659814357757568] |
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