Optimistic Simulated Exploration as an Incentive for Real Exploration - Ivo Danihelka (CoRR, 2009)

Deep AutoRegressive Networks. - Karol Gregor; Ivo Danihelka; Andriy Mnih; Charles Blundell; Daan Wierstra (ICML, 2014)

Neural Turing Machines. - Alex Graves; Greg Wayne; Ivo Danihelka (CoRR, 2014)

DRAW: A Recurrent Neural Network For Image Generation. - Karol Gregor; Ivo Danihelka; Alex Graves; Danilo Jimenez Rezende; Daan Wierstra (ICML, 2015)

DRAW: A Recurrent Neural Network For Image Generation. - Karol Gregor; Ivo Danihelka; Alex Graves; Daan Wierstra (CoRR, 2015)

Associative Long Short-Term Memory. - Ivo Danihelka; Greg Wayne; Benigno Uria; Nal Kalchbrenner; Alex Graves (ICML, 2016)

One-Shot Generalization in Deep Generative Models. - Danilo Jimenez Rezende; Shakir Mohamed; Ivo Danihelka; Karol Gregor; Daan Wierstra (ICML, 2016)

Towards Conceptual Compression. - Karol Gregor; Frederic Besse; Danilo Jimenez Rezende; Ivo Danihelka; Daan Wierstra (NIPS, 2016)

Memory-Efficient Backpropagation Through Time. - Audrunas Gruslys; Rémi Munos; Ivo Danihelka; Marc Lanctot; Alex Graves (NIPS, 2016)

Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes. - Jack W. Rae; Jonathan J. Hunt; Ivo Danihelka; Timothy Harley; Andrew W. Senior; Gregory Wayne; Alex Graves; Tim Lillicrap (NIPS, 2016)

Grid Long Short-Term Memory. - Nal Kalchbrenner; Ivo Danihelka; Alex Graves (ICLR (Poster), 2016)

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; et al. (Nat., 2016)

Video Pixel Networks. - Nal Kalchbrenner; Aäron van den Oord; Karen Simonyan; Ivo Danihelka; Oriol Vinyals; Alex Graves; Koray Kavukcuoglu (ICML, 2017)

The Cramer Distance as a Solution to Biased Wasserstein Gradients. - Marc G. Bellemare; Ivo Danihelka; Will Dabney; Shakir Mohamed; Balaji Lakshminarayanan; Stephan Hoyer; Rémi Munos (CoRR, 2017)

Comparison of Maximum Likelihood and GAN-based training of Real NVPs. - Ivo Danihelka; Balaji Lakshminarayanan; Benigno Uria; Daan Wierstra; Peter Dayan (CoRR, 2017)

OpenSpiel: A Framework for Reinforcement Learning in Games. - Marc Lanctot; Edward Lockhart; Jean-Baptiste Lespiau; Vinícius Flores Zambaldi; Satyaki Upadhyay; Julien Pérolat; Sriram Srinivasan 005; Finbarr Timbers; Karl Tuyls; Shayegan Omidshafiei; Daniel Hennes; Dustin Morrill; et al. (CoRR, 2019)

Grandmaster level in StarCraft II using multi-agent reinforcement learning. - Oriol Vinyals; Igor Babuschkin; Wojciech M. Czarnecki; Michaël Mathieu; Andrew Dudzik; Junyoung Chung; David H. Choi; Richard Powell; Timo Ewalds; Petko Georgiev; Junhyuk Oh; Dan Horgan; et al. (Nat., 2019)

Causally Correct Partial Models for Reinforcement Learning. - Danilo J. Rezende; Ivo Danihelka; George Papamakarios; Nan Rosemary Ke; Ray Jiang; Theophane Weber; Karol Gregor; Hamza Merzic; Fabio Viola; Jane Wang 001; Jovana Mitrovic; Frederic Besse; et al. (CoRR, 2020)

Muesli: Combining Improvements in Policy Optimization. - Matteo Hessel; Ivo Danihelka; Fabio Viola; Arthur Guez; Simon Schmitt; Laurent Sifre; Theophane Weber; David Silver 001; Hado van Hasselt (ICML, 2021)

Policy improvement by planning with Gumbel. - Ivo Danihelka; Arthur Guez; Julian Schrittwieser; David Silver 001 (ICLR, 2022)

Learning by Directional Gradient Descent. - David Silver 001; Anirudh Goyal; Ivo Danihelka; Matteo Hessel; Hado van Hasselt (ICLR, 2022)