Ryota Tomioka

Senior Principal Research Manager

출판물

자료 보기:

- A generative model for inorganic materials design
  
  Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, Tian Xie
  
  Nature | January 2025
  
  Publication
- MatterGen: a generative model for inorganic materials design
  
  Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie
  
  December 2023
  
  PDF Publication 동영상 동영상 동영상 액세스 프로젝트
- Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
  
  Marco Federici, Patrick Forr'e, Ryota Tomioka, Bastiaan S. Veeling
  
  ICLR 2024 | September 2023
  
  DOI Publication
- Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
  
  Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
  
  NeurIPS 2023 | February 2023
  
  Publication
- An Information-theoretic Approach to Distribution Shifts
  
  Marco Federici, Ryota Tomioka, Patrick Forré
  
  2021 Neural Information Processing Systems | November 2021
  
  Publication
- DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks
  
  Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Andrew Fitzgibbon, Tim Harris
  
  EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems | April 2021
  
  Publication Github
- Regularized Policies are Reward Robust
  
  Hisham Husain, Kamil Ciosek, Ryota Tomioka
  
  Proceedings of The 24th International Conference on Artificial Intelligence and Statistics | April 2021
  
  Publication
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
  
  Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk
  
  NeurIPS 2020 | December 2020
  
  Publication
- Conservative Uncertainty Estimation By Fitting Prior Networks
  
  Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
  
  Eighth International Conference on Learning Representations (ICLR) | April 2020
  
  Publication PDF Github 프로젝트 프로젝트
- On Certifying Non-Uniform Bounds against Adversarial Attacks
  
  Chen Liu, Ryota Tomioka, Volkan Cevher
  
  International Conference on Machine Learning (ICML) | June 2019
  
  Publication
- Depth and nonlinearity induce implicit exploration for RL
  
  Justas Dauparas, Ryota Tomioka, Katja Hofmann
  
  ICML workshop on Exploration in RL | July 2018
  
  Publication
- Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
  
  Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
  
  AAAI 2018 | February 2018
  
  Publication 프로젝트
- QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
  
  Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
  
  Advances in Neural Information Processing Systems 30 (NIPS 2017) | December 2017
  
  출판
- AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
  
  Alexander Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster
  
  arXiv preprint arXiv:1705.09786 | May 2017
  
  출판 출판
- Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
  
  Liwen Zhang, John Winn, Ryota Tomioka
  
  MSR-TR-2016-1141 | November 2016
  
  글쓴이 Microsoft
  
  Publication
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
  
  Sebastian Nowozin, Botond Cseke, Ryota Tomioka
  
  Advances in Neural Information Processing Systems 29 (NIPS 2016) | October 2016
  
  출판 출판 출판 프로젝트
- Data-Dependent Path Normalization in Neural Networks
  
  Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
  
  ICLR 2016 | November 2015
  
  출판

- A generative model for inorganic materials design
  
  Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, Tian Xie
  
  Nature | January 2025
  
  Publication
- MatterGen: a generative model for inorganic materials design
  
  Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie
  
  December 2023
  
  PDF Publication 동영상 동영상 동영상 액세스 프로젝트
- Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
  
  Marco Federici, Patrick Forr'e, Ryota Tomioka, Bastiaan S. Veeling
  
  ICLR 2024 | September 2023
  
  DOI Publication
- Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
  
  Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
  
  NeurIPS 2023 | February 2023
  
  Publication
- An Information-theoretic Approach to Distribution Shifts
  
  Marco Federici, Ryota Tomioka, Patrick Forré
  
  2021 Neural Information Processing Systems | November 2021
  
  Publication
- Regularized Policies are Reward Robust
  
  Hisham Husain, Kamil Ciosek, Ryota Tomioka
  
  Proceedings of The 24th International Conference on Artificial Intelligence and Statistics | April 2021
  
  Publication
- DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks
  
  Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Andrew Fitzgibbon, Tim Harris
  
  EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems | April 2021
  
  Publication Github
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
  
  Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk
  
  NeurIPS 2020 | December 2020
  
  Publication
- Conservative Uncertainty Estimation By Fitting Prior Networks
  
  Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
  
  Eighth International Conference on Learning Representations (ICLR) | April 2020
  
  Publication PDF Github 프로젝트 프로젝트
- On Certifying Non-Uniform Bounds against Adversarial Attacks
  
  Chen Liu, Ryota Tomioka, Volkan Cevher
  
  International Conference on Machine Learning (ICML) | June 2019
  
  Publication
- Depth and nonlinearity induce implicit exploration for RL
  
  Justas Dauparas, Ryota Tomioka, Katja Hofmann
  
  ICML workshop on Exploration in RL | July 2018
  
  Publication
- Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
  
  Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
  
  AAAI 2018 | February 2018
  
  Publication 프로젝트
- QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
  
  Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
  
  Advances in Neural Information Processing Systems 30 (NIPS 2017) | December 2017
  
  출판
- AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
  
  Alexander Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster
  
  arXiv preprint arXiv:1705.09786 | May 2017
  
  출판 출판
- Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
  
  Liwen Zhang, John Winn, Ryota Tomioka
  
  MSR-TR-2016-1141 | November 2016
  
  글쓴이 Microsoft
  
  Publication
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
  
  Sebastian Nowozin, Botond Cseke, Ryota Tomioka
  
  Advances in Neural Information Processing Systems 29 (NIPS 2016) | October 2016
  
  출판 출판 출판 프로젝트
- Data-Dependent Path Normalization in Neural Networks
  
  Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
  
  ICLR 2016 | November 2015
  
  출판
- A generative model for inorganic materials design
  
  Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, Tian Xie
  
  Nature | January 2025
  
  Publication

- A generative model for inorganic materials design
  
  Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, Tian Xie
  
  Nature | January 2025
  
  Publication
- AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks
  
  Alexander Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster
  
  arXiv preprint arXiv:1705.09786 | May 2017
  
  출판 출판
- Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
  
  Marco Federici, Patrick Forr'e, Ryota Tomioka, Bastiaan S. Veeling
  
  ICLR 2024 | September 2023
  
  DOI Publication
- Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
  
  Leon Klein, Andrew Y. K. Foong, Tor Erlend Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, Ryota Tomioka
  
  NeurIPS 2023 | February 2023
  
  Publication
- An Information-theoretic Approach to Distribution Shifts
  
  Marco Federici, Ryota Tomioka, Patrick Forré
  
  2021 Neural Information Processing Systems | November 2021
  
  Publication
- Regularized Policies are Reward Robust
  
  Hisham Husain, Kamil Ciosek, Ryota Tomioka
  
  Proceedings of The 24th International Conference on Artificial Intelligence and Statistics | April 2021
  
  Publication
- DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks
  
  Keshav Santhanam, Siddharth Krishna, Ryota Tomioka, Andrew Fitzgibbon, Tim Harris
  
  EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems | April 2021
  
  Publication Github
- On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them
  
  Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk
  
  NeurIPS 2020 | December 2020
  
  Publication
- Conservative Uncertainty Estimation By Fitting Prior Networks
  
  Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
  
  Eighth International Conference on Learning Representations (ICLR) | April 2020
  
  Publication PDF Github 프로젝트 프로젝트
- On Certifying Non-Uniform Bounds against Adversarial Attacks
  
  Chen Liu, Ryota Tomioka, Volkan Cevher
  
  International Conference on Machine Learning (ICML) | June 2019
  
  Publication
- Depth and nonlinearity induce implicit exploration for RL
  
  Justas Dauparas, Ryota Tomioka, Katja Hofmann
  
  ICML workshop on Exploration in RL | July 2018
  
  Publication
- Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations
  
  Diane Bouchacourt, Ryota Tomioka, Sebastian Nowozin
  
  AAAI 2018 | February 2018
  
  Publication 프로젝트
- QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
  
  Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
  
  Advances in Neural Information Processing Systems 30 (NIPS 2017) | December 2017
  
  출판
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
  
  Sebastian Nowozin, Botond Cseke, Ryota Tomioka
  
  Advances in Neural Information Processing Systems 29 (NIPS 2016) | October 2016
  
  출판 출판 출판 프로젝트
- Data-Dependent Path Normalization in Neural Networks
  
  Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
  
  ICLR 2016 | November 2015
  
  출판
- MatterGen: a generative model for inorganic materials design
  
  Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Ryota Tomioka, Tian Xie
  
  December 2023
  
  PDF Publication 동영상 동영상 동영상 액세스 프로젝트
- Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
  
  Liwen Zhang, John Winn, Ryota Tomioka
  
  MSR-TR-2016-1141 | November 2016
  
  글쓴이 Microsoft
  
  Publication

Ryota Tomioka

출판물

A generative model for inorganic materials design

MatterGen: a generative model for inorganic materials design

Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

An Information-theoretic Approach to Distribution Shifts

DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks

Regularized Policies are Reward Robust

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Conservative Uncertainty Estimation By Fitting Prior Networks

On Certifying Non-Uniform Bounds against Adversarial Attacks

Depth and nonlinearity induce implicit exploration for RL

Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Data-Dependent Path Normalization in Neural Networks

A generative model for inorganic materials design

MatterGen: a generative model for inorganic materials design

Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

An Information-theoretic Approach to Distribution Shifts

Regularized Policies are Reward Robust

DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Conservative Uncertainty Estimation By Fitting Prior Networks

On Certifying Non-Uniform Bounds against Adversarial Attacks

Depth and nonlinearity induce implicit exploration for RL

Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Data-Dependent Path Normalization in Neural Networks

A generative model for inorganic materials design

A generative model for inorganic materials design

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks

Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck

Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

An Information-theoretic Approach to Distribution Shifts

Regularized Policies are Reward Robust

DistIR: An Intermediate Representation for Optimizing Distributed Neural Networks

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Conservative Uncertainty Estimation By Fitting Prior Networks

On Certifying Non-Uniform Bounds against Adversarial Attacks

Depth and nonlinearity induce implicit exploration for RL

Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations

QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding

f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

Data-Dependent Path Normalization in Neural Networks

MatterGen: a generative model for inorganic materials design

Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering