Results 41 to 50 of about 792,416 (284)
, 2018 We present Residual Policy Learning (RPL): a simple method for improving nondifferentiable policies using model-free deep reinforcement learning. RPL thrives in complex robotic manipulation tasks where good but imperfect controllers are available. In these tasks, reinforcement learning from scratch remains data-inefficient or intractable, but learning ...
Silver, Tom +3 more
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Residual Reinforcement Learning from Demonstrations
, 2021 Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a reward signal. We extend the residual formulation to learn from visual inputs and sparse rewards using demonstrations.
Alakuijala, Minttu +4 more
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Periodic residual learning for crowd flow forecasting
Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022 Crowd flow forecasting, which aims to predict the crowds entering or leaving certain regions, is a fundamental task in smart cities. One of the key properties of crowd flow data is periodicity: a pattern that occurs at regular time intervals, such as a weekly pattern.
Wang, Chengxin, Liang, Yuxuan, Tan, Gary
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Dog Nose-Print Identification Using Deep Neural Networks
IEEE Access, 2021 Recently, there has been rapid growth in the number of people who own companion pets (cats and dogs) due to low birth rates, an increasingly aging population, and an increasing number of single-person households.
Han Byeol Bae, Daehyun Pak, Sangyoun Lee
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Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
, 2018 Leveraging on the recent developments in convolutional neural networks (CNNs), matching dense correspondence from a stereo pair has been cast as a learning problem, with performance exceeding traditional approaches.
Pang, Jiahao +4 more
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Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network
IEEE Access, 2018 The features produced by the layers of a neural network become increasingly more sparse as the network gets deeper and consequently, the learning capability of the network is not further enhanced as the number of layers is increased.
Alireza Esmaeilzehi +2 more
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MRU-NET: A U-Shaped Network for Retinal Vessel Segmentation
Applied Sciences, 2020 Fundus blood vessel image segmentation plays an important role in the diagnosis and treatment of diseases and is the basis of computer-aided diagnosis.
Hongwei Ding +3 more
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Learning Residual Images for Face Attribute Manipulation
, 2017 Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a ...
Liu, Rujie, Shen, Wei
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Applied Sciences, 2021 Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and ...
Chang-Hwan Son
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Learning Residual Finite-State Automata Using Observation Tables
, 2010 We define a two-step learner for RFSAs based on an observation table by using an algorithm for minimal DFAs to build a table for the reversal of the language in question and showing that we can derive the minimal RFSA from it after some simple ...
Anna Kasprzik +2 more
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