Dylan R. Ashley

According to our database, Dylan R. Ashley authored at least 16 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute.

Aleksandar Stanic, Dylan R. Ashley, Oleg Serikov, Louis Kirsch, Francesco Faccio, Jürgen Schmidhuber, Thomas Hofmann, Imanol Schlag

CoRR, 2023

Mindstorms in Natural Language-Based Societies of Mind.

Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piekos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanic, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber

CoRR, 2023

2022

On Narrative Information and the Distillation of Stories.

Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Jürgen Schmidhuber

CoRR, 2022

Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets.

Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava

CoRR, 2022

Learning Relative Return Policies With Upside-Down Reinforcement Learning.

Dylan R. Ashley, Kai Arulkumaran, Jürgen Schmidhuber, Rupesh Kumar Srivastava

CoRR, 2022

All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL.

Kai Arulkumaran, Dylan R. Ashley, Jürgen Schmidhuber, Rupesh Kumar Srivastava

CoRR, 2022

Reward-Weighted Regression Converges to a Global Optimum.

Miroslav Strupl, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Automatic Embedding of Stories Into Collections of Independent Media.

Dylan R. Ashley, Vincent Herrmann, Zachary Friggstad, Kory W. Mathewson, Jürgen Schmidhuber

CoRR, 2021

Back to Square One: Superhuman Performance in Chutes and Ladders Through Deep Neural Networks and Tree Search.

Dylan R. Ashley, Anssi Kanervisto, Brendan Bennett

CoRR, 2021

Does Standard Backpropagation Forget Less Catastrophically Than Adam?

Dylan R. Ashley, Sina Ghiassian, Richard S. Sutton

CoRR, 2021

2020

Universal Successor Features for Transfer Reinforcement Learning.

Chen Ma, Dylan R. Ashley, Junfeng Wen, Yoshua Bengio

CoRR, 2020

2019

Learning to select mates in artificial life.

Dylan R. Ashley, Valliappa Chockalingam, Braedy Kuzma, Vadim Bulitko

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019

Learning to Select Mates in Evolving Non-playable Characters.

Dylan R. Ashley, Valliappa Chockalingam, Braedy Kuzma, Vadim Bulitko

Proceedings of the IEEE Conference on Games, 2019

2018

Directly Estimating the Variance of the λ-Return Using Temporal-Difference Methods.

Craig Sherstan, Brendan Bennett, Kenny Young, Dylan R. Ashley, Adam White, Martha White, Richard S. Sutton

CoRR, 2018

Comparing Direct and Indirect Temporal-Difference Methods for Estimating the Variance of the Return.

Craig Sherstan, Dylan R. Ashley, Brendan Bennett, Kenny Young, Adam White, Martha White, Richard S. Sutton

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

The Alberta Workloads for the SPEC CPU 2017 Benchmark Suite.

José Nelson Amaral, Edson Borin, Dylan R. Ashley, Caian Benedicto, Elliot Colp, Joao Henrique Stange Hoffmam, Marcus Karpoff, Erick Ochoa, Morgan Redshaw, Raphael Ernani Rodrigues

Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software, 2018