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  • title: Practical Real Time Recurrent Learning with a Sparse Approximation
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            Practical Real Time Recurrent Learning with a Sparse Approximation
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            Practical Real Time Recurrent Learning with a Sparse Approximation

            May 3, 2021

            Speakers

            JM

            Jacob Menick

            Speaker · 0 followers

            EE

            Erich Elsen

            Speaker · 1 follower

            UE

            Utku Evci

            Speaker · 1 follower

            About

            Recurrent neural networks are usually trained with backpropagation through time, which requires storing a complete history of network states, and prohibits updating the weights "online" (after every timestep). Real Time Recurrent Learning (RTRL) eliminates the need for history storage and allows for online weight updates, but does so at the expense of computational costs that are quartic in the state size. This renders RTRL training intractable for all but the smallest networks, even ones that a…

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            About ICLR 2021

            The International Conference on Learning Representations (ICLR) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning. ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

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