Machine Learning Deep Reinforcement Learning with Feedback-based Exploration (1903.06151v1) Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober 2019-03-14 Deep Reinforcement Learning has enabled the control

Machine Learning Deep Reinforcement Learning with Feedback-based Exploration (1903.06151v1) Jan Scholten, Daan Wout, Carlos Celemin, Jens Kober 2019-03-14 Deep Reinforcement Learning has enabled the control

Machine Learning Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach (1903.05635v1) Jaeseok Kim, Nino Cauli, Pedro Vicente, Bruno Damas, Alexandre Bernardino, José

Machine Learning Communication-efficient distributed SGD with Sketching (1903.04488v1) Nikita Ivkin, Daniel Rothchild, Enayat Ullah, Vladimir Braverman, Ion Stoica, Raman Arora 2019-03-12 Large-scale

Machine Learning Revisiting clustering as matrix factorisation on the Stiefel manifold (1903.04479v1) Stéphane Chrétien, Benjamin Guedj 2019-03-11 This paper studies clustering for possibly high dimensional

Machine Learning Understanding Sparse JL for Feature Hashing (1903.03605v1) Meena Jagadeesan 2019-03-08 Feature hashing and more general projection schemes are commonly used in machine learning to reduce

Machine Learning Accurate inference of crowdsourcing properties when using efficient allocation strategies (1903.03104v1) Abigail Hotaling, James P. Bagrow 2019-03-07 Allocation strategies improve the

Machine Learning Accurate inference of crowdsourcing properties when using efficient allocation strategies (1903.03104v1) Abigail Hotaling, James P. Bagrow 2019-03-07 Allocation strategies improve the

Machine Learning Relational Pooling for Graph Representations (1903.02541v1) Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro 2019-03-06 This work generalizes graph neural networks

Machine Learning Data Amplification: Instance-Optimal Property Estimation (1903.01432v2) Yi Hao, Alon Orlitsky 2019-03-04 The best-known and most commonly used distribution-property estimation technique

Machine Learning Optimistic Adaptive Acceleration for Optimization (1903.01435v1) Jun-Kun Wang, Xiaoyun Li, Ping Li 2019-03-04 We consider a new variant of \textsc{AMSGrad}. AMSGrad \cite{RKK18} is a popular

Machine Learning Multiple Causal Inference with Latent Confounding (1805.08273v3) Rajesh Ranganath, Adler Perotte 2018-05-21 Causal inference from observational data requires assumptions. These assumptions

Machine Learning Insertion-based Decoding with automatically Inferred Generation Order (1902.01370v2) Jiatao Gu, Qi Liu, Kyunghyun Cho 2019-02-04 Conventional neural autoregressive decoding commonly assumes

Machine Learning Insertion-based Decoding with automatically Inferred Generation Order (1902.01370v2) Jiatao Gu, Qi Liu, Kyunghyun Cho 2019-02-04 Conventional neural autoregressive decoding commonly assumes

Machine Learning Insertion-based Decoding with automatically Inferred Generation Order (1902.01370v2) Jiatao Gu, Qi Liu, Kyunghyun Cho 2019-02-04 Conventional neural autoregressive decoding commonly assumes

Want to be update of the latest papers in arxiv on the topic of machine learning? This account can help you. Actually, I created this one to help me scratch my own itch. I'm a heavy consumer of machine