Y
Yori Zwols
Researcher at Google
Publications - 28
Citations - 2566
Yori Zwols is an academic researcher from Google. The author has contributed to research in topics: Chordal graph & Pathwidth. The author has an hindex of 13, co-authored 28 publications receiving 2060 citations. Previous affiliations of Yori Zwols include McGill University & Columbia University.
Papers
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Journal ArticleDOI
Hybrid computing using a neural network with dynamic external memory
Alex Graves,Greg Wayne,Malcolm Reynolds,Tim Harley,Ivo Danihelka,Agnieszka Grabska-Barwinska,Sergio Gomez Colmenarejo,Edward Grefenstette,Tiago Ramalho,John P. Agapiou,Adrià Puigdomènech Badia,Karl Moritz Hermann,Yori Zwols,Georg Ostrovski,Adam Cain,Helen King,Christopher Summerfield,Phil Blunsom,Koray Kavukcuoglu,Demis Hassabis +19 more
TL;DR: A machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer.
Posted Content
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
Chrisantha Fernando,Dylan Banarse,Charles Blundell,Yori Zwols,David Ha,Andrei Rusu,Alexander Pritzel,Daan Wierstra +7 more
TL;DR: Successful transfer learning is demonstrated; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning.
Proceedings Article
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search
Book
Linear and integer optimization: Theory and practice
Gerard Sierksma,Yori Zwols +1 more
TL;DR: Basic Concepts of Linear Optimization The Company Dovetail Definition of an LO- model Alternatives of the Standard LO-Model Solving LO-Models Using a Computer Package Linearizing Nonlinear Functions Examples of Linearoptimization Models Building and Implementing Mathematical Models Exercises.
Posted Content
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
TL;DR: The new agent's superiority over agents that either ignore the combinatorial or sequential long-term value aspect is demonstrated on a range of environments with dynamics from a real-world recommendation system.