Ankesh Anand

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 23

Top Topics

Normalized Correlation

Scientific Disciplines

Reinforcement Learning

Top Venues

Publications

Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy P. Lillicrap, Jean-Baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, Ioannis Antonoglou, Rohan Anil, Sebastian Borgeaud, Andrew M. Dai, Katie Millican, Ethan Dyer, Mia Glaese, Thibault Sottiaux, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, James Molloy, Jilin Chen, Michael Isard, Paul Barham, Tom Hennigan, Ross McIlroy, Melvin Johnson, Johan Schalkwyk, Eli Collins, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, Clemens Meyer, Gregory Thornton, Zhen Yang, Henryk Michalewski, Zaheer Abbas, Nathan Schucher, Ankesh Anand, Richard Ives, James Keeling, Karel Lenc, Salem Haykal, Siamak Shakeri, Pranav Shyam, Aakanksha Chowdhery, Roman Ring, Stephen Spencer, Eren Sezener, et al.
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. CoRR (2024)
Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. Trans. Mach. Learn. Res. 2024 (2024)
Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie C. Y. Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle
Many-Shot In-Context Learning. CoRR (2024)
Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron Parisi, Abhishek Kumar, Alex Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin F. Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. CoRR (2023)
Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Théophane Weber, Jessica B. Hamrick
Investigating the role of model-based learning in exploration and transfer. CoRR (2023)
Jacob C. Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Theophane Weber, Jessica B. Hamrick
Investigating the Role of Model-Based Learning in Exploration and Transfer. ICML (2023)
Ankesh Anand, Jacob C. Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Theophane Weber, Jessica B. Hamrick
Procedural generalization by planning with self-supervised world models. ICLR (2022)
Ankesh Anand, Jacob Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Théophane Weber, Jessica B. Hamrick
Procedural Generalization by Planning with Self-Supervised World Models. CoRR (2021)
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
Data-Efficient Reinforcement Learning with Self-Predictive Representations. ICLR (2021)
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
Pretraining Representations for Data-Efficient Reinforcement Learning. CoRR (2021)
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
Pretraining Representations for Data-Efficient Reinforcement Learning. NeurIPS (2021)
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
Data-Efficient Reinforcement Learning with Momentum Predictive Representations. CoRR (2020)
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
Unsupervised State Representation Learning in Atari. CoRR (2019)
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
Unsupervised State Representation Learning in Atari. NeurIPS (2019)
Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee, Jaegul Choo, David Keetae Park, Tanmoy Chakraborty, Hongkyu Park, Youngmin Kim
MMGAN: Manifold-Matching Generative Adversarial Networks. ICPR (2018)
Ankesh Anand, Kshitij Gorde, Joel Ruben Antony Moniz, Noseong Park, Tanmoy Chakraborty, Bei-Tseng Chu
Phishing URL Detection with Oversampling based on Text Generative Adversarial Networks. IEEE BigData (2018)
Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron C. Courville
Blindfold Baselines for Embodied QA. CoRR (2018)
Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron C. Courville
HoME: a Household Multimodal Environment. ICLR (Workshop) (2018)
Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron C. Courville
HoME: a Household Multimodal Environment. CoRR (2017)
Noseong Park, Ankesh Anand, Joel Ruben Antony Moniz, Kookjin Lee, Tanmoy Chakraborty, Jaegul Choo, Hongkyu Park, Youngmin Kim
MMGAN: Manifold Matching Generative Adversarial Network for Generating Images. CoRR (2017)
Ankesh Anand, Tanmoy Chakraborty, Noseong Park
We Used Neural Networks to Detect Clickbaits: You Won't Believe What Happened Next! ECIR (2017)
Ankesh Anand, Tanmoy Chakraborty, Amitava Das
FairScholar: Balancing Relevance and Diversity for Scientific Paper Recommendation. ECIR (2017)
Ankesh Anand, Tanmoy Chakraborty, Noseong Park
We used Neural Networks to Detect Clickbaits: You won't believe what happened Next! CoRR (2016)