The top documents tagged [gradient descent methods]

Artificial Neural Networks
Documents Artificial Neural Networks

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A BRIEF INTRODUCTION TO NEURAL NETWORKS Luke Flemmer
Documents A BRIEF INTRODUCTION TO NEURAL NETWORKS Luke Flemmer

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Goman, Khramtsovsky, Kolesnikov (2006) - Computational Framework for Analysis of Aircraft Nonlinear Dynamics & Control Design Based on Qualitative Methods
Technology Goman, Khramtsovsky, Kolesnikov (2006) - Computational Framework for Analysis of Aircraft Nonlinear Dynamics & Control Design Based on Qualitative Methods

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On Quantification of Anchor Placement
Documents On Quantification of Anchor Placement

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High-Order Similarity Relations in Radiative Transfer Shuang Zhao 1, Ravi Ramamoorthi 2, and Kavita Bala 1 1 Cornell University 2 University of California,
Documents High-Order Similarity Relations in Radiative Transfer Shuang Zhao 1, Ravi Ramamoorthi 2, and Kavita Bala 1 1 Cornell University 2 University of California,

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Power Grid Sizing via Convex Programming Peng Du, Shih-Hung Weng, Xiang Hu, Chung-Kuan Cheng University of California, San Diego 1
Documents Power Grid Sizing via Convex Programming Peng Du, Shih-Hung Weng, Xiang Hu, Chung-Kuan Cheng University of California, San Diego 1

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Da Yan, Zhou Zhao and Wilfred Ng The Hong Kong University of Science and Technology
Documents Da Yan, Zhou Zhao and Wilfred Ng The Hong Kong University of Science and Technology

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Chapter 8: Generalization and Function Approximation pLook at how experience with a limited part of the state set be used to produce good behavior over
Documents Chapter 8: Generalization and Function Approximation pLook at how experience with a limited part of the state set be used to produce good behavior over

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On Linking Reinforcement Learning with Unsupervised Learning Cornelius Weber, FIAS presented at Honda HRI, Offenbach, 17 th March 2009
Documents On Linking Reinforcement Learning with Unsupervised Learning Cornelius Weber, FIAS presented at Honda HRI, Offenbach, 17 th March 2009

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Introduction to Location Discovery Lecture 4 September 14, 2004 EENG 460a / CPSC 436 / ENAS 960 Networked Embedded Systems & Sensor Networks Andreas Savvides
Documents Introduction to Location Discovery Lecture 4 September 14, 2004 EENG 460a / CPSC 436 / ENAS 960 Networked Embedded Systems & Sensor Networks Andreas Savvides

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An Introduction to COMPUTATIONAL REINFORCEMENT LEARING Andrew G. Barto Department of Computer Science University of Massachusetts – Amherst Lecture 3 Autonomous
Documents An Introduction to COMPUTATIONAL REINFORCEMENT LEARING Andrew G. Barto Department of Computer Science University of Massachusetts – Amherst Lecture 3 Autonomous

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Goal-Directed Feature and Memory Learning Cornelius Weber Frankfurt Institute for Advanced Studies (FIAS) Sheffield, 3 rd November 2009 Collaborators:
Documents Goal-Directed Feature and Memory Learning Cornelius Weber Frankfurt Institute for Advanced Studies (FIAS) Sheffield, 3 rd November 2009 Collaborators:

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