David Ha

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 59

Top Topics

Reinforcement Learning

Conversation Quanta

Vector Graphics

Top Venues

GECCO (Companion)

Publications

Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha
Evolutionary Optimization of Model Merging Recipes. CoRR (2024)
Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber
Learning to Generalize With Object-Centric Agents in the Open World Survival Game Crafter. IEEE Trans. Games 16 (2) (2024)
Yujin Tang, Yingtao Tian, David Ha
EvoJAX: Hardware-Accelerated Neuroevolution. CoRR (2022)
Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter. CoRR (2022)
Yujin Tang, Yingtao Tian, David Ha
EvoJAX: hardware-accelerated neuroevolution. GECCO Companion (2022)
Federico Pigozzi, Yujin Tang, Eric Medvet, David Ha
Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention. CoRR (2022)
Federico Pigozzi, Yujin Tang, Eric Medvet, David Ha
Evolving modular soft robots without explicit inter-module communication using local self-attention. GECCO (2022)
Yingtao Tian, Marco Cuturi, David Ha
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport. CoRR (2022)
Yingtao Tian, Marco Cuturi, David Ha
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport. ICCC (2022)
Yingtao Tian, David Ha
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts. EvoMUSART (2022)
Yingtao Tian, David Ha
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts. CoRR (2021)
Yujin Tang, David Ha
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. NeurIPS (2021)
Yujin Tang, David Ha
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. CoRR (2021)
David Ha, Yujin Tang
Collective Intelligence for Deep Learning: A Survey of Recent Developments. CoRR (2021)
David Ha
World Models and Attention for Reinforcement Learning. ALIFE (2021)
Forrest Huang, Eldon Schoop, David Ha, Jeffrey Nichols, John F. Canny
Sketch-based Creativity Support Tools using Deep Learning. CoRR (2021)
David Ha, Shigeru Katagiri, Hideyuki Watanabe, Miho Ohsaki
An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation. J. Signal Process. Syst. 93 (9) (2021)
Miguel González Duque, Rasmus Berg Palm, David Ha, Sebastian Risi
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error. CoG (2020)
Yujin Tang, Duong Nguyen, David Ha
Neuroevolution of Self-Interpretable Agents. CoRR (2020)
Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. WACV (2020)
Forrest Huang, Eldon Schoop, David Ha, John F. Canny
Scones: Towards Conversational Authoring of Sketches. CoRR (2020)
Forrest Huang, Eldon Schoop, David Ha, John F. Canny
Scones: towards conversational authoring of sketches. IUI (2020)
David Ha, Yuya Tomotoshi, Masahiro Senda, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki
A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection. J. Signal Process. Syst. 92 (2) (2020)
David Ha
Neuroevolution for deep reinforcement learning problems. GECCO Companion (2020)
Miguel González Duque, Rasmus Berg Palm, David Ha, Sebastian Risi
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error. CoRR (2020)
Yujin Tang, Duong Nguyen, David Ha
Neuroevolution of self-interpretable agents. GECCO (2020)
Masahiro Senda, David Ha, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki
Maximum Bayes Boundary-Ness Training For Pattern Classification. SPML (2019)
Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. CoRR (2019)
Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens
A Learned Representation for Scalable Vector Graphics. CoRR (2019)
Adam Gaier, David Ha
Weight Agnostic Neural Networks. NeurIPS (2019)
David Ha
Reinforcement Learning for Improving Agent Design. Artif. Life 25 (4) (2019)
Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens
A Learned Representation for Scalable Vector Graphics. ICCV (2019)
C. Daniel Freeman, David Ha, Luke Metz
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. NeurIPS (2019)
Yuya Tomotoshi, David Ha, Emilie Delattre, Hideyuki Watanabe, Xugang Lu, Shigeru Katagiri, Miho Ohsaki
Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers. IUKM (2019)
Adam Gaier, David Ha
Weight Agnostic Neural Networks. CoRR (2019)
Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens
A Learned Representation for Scalable Vector Graphics. DGS@ICLR (2019)
Judith E. Fan, Monica Dinculescu, David Ha
collabdraw: An Environment for Collaborative Sketching with an Artificial Agent. Creativity & Cognition (2019)
C. Daniel Freeman, Luke Metz, David Ha
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. CoRR (2019)
David Ha
Neuroevolution for deep reinforcement learning problems. GECCO (Companion) (2019)
Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
Learning Latent Dynamics for Planning from Pixels. ICML (2019)
David Ha, Jürgen Schmidhuber
Recurrent World Models Facilitate Policy Evolution. NeurIPS (2018)
Natasha Jaques, Jesse H. Engel, David Ha, Fred Bertsch, Rosalind W. Picard, Douglas Eck
Learning via social awareness: improving sketch representations with facial feedback. CoRR (2018)
Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob N. Foerster, Julian Togelius, Kyunghyun Cho, Joan Bruna
Pommerman: A Multi-Agent Playground. AIIDE Workshops (2018)
Natasha Jaques, Jennifer McCleary, Jesse H. Engel, David Ha, Fred Bertsch, Douglas Eck, Rosalind W. Picard
Learning via Social Awareness: Improving a Deep Generative Sketching Model with Facial Feedback. AffComp@IJCAI (2018)
Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
Learning Latent Dynamics for Planning from Pixels. CoRR (2018)
David Ha
Neuroevolution for deep reinforcement learning problems. GECCO (Companion) (2018)
Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob N. Foerster, Julian Togelius, Kyunghyun Cho, Joan Bruna
Pommerman: A Multi-Agent Playground. CoRR (2018)
David Ha, Emilie Delattre, Yuya Tomotoshi, Masahiro Senda, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki
Optimal Classifier Model Status Selection using Bayes boundary uncertainty. MLSP (2018)
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha
Deep Learning for Classical Japanese Literature. CoRR (2018)
David Ha, Douglas Eck
A Neural Representation of Sketch Drawings. ICLR (Poster) (2018)
David Ha, Hideyuki Watanabe, Yuya Tomotoshi, Emilie Delattre, Shigeru Katagiri
Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method. SPML (2018)
David Ha, Jürgen Schmidhuber
Recurrent World Models Facilitate Policy Evolution. CoRR (2018)
David Ha
Reinforcement Learning for Improving Agent Design. CoRR (2018)
Natasha Jaques, Jesse H. Engel, David Ha, Fred Bertsch, Rosalind W. Picard, Douglas Eck
Learning via social awareness: improving sketch representations with facial feedback. ICLR (Workshop) (2018)
David Ha, Jürgen Schmidhuber
World Models. CoRR (2018)
David Ha, Douglas Eck
A Neural Representation of Sketch Drawings. CoRR (2017)
Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra
PathNet: Evolution Channels Gradient Descent in Super Neural Networks. CoRR (2017)
David Ha, Andrew M. Dai, Quoc V. Le
HyperNetworks. ICLR (Poster) (2017)
David Ha, Andrew M. Dai, Quoc V. Le
HyperNetworks. CoRR (2016)