HyperNetworks. - David Ha; Andrew M. Dai; Quoc V. Le (ICLR (Poster), 2017)

PathNet: Evolution Channels Gradient Descent in Super Neural Networks. - Chrisantha Fernando; Dylan Banarse; Charles Blundell; Yori Zwols; David Ha; Andrei A. Rusu; Alexander Pritzel; Daan Wierstra (CoRR, 2017)

Pommerman: A Multi-Agent Playground. - Cinjon Resnick; Wes Eldridge; David Ha; Denny Britz; Jakob N. Foerster; Julian Togelius; Kyunghyun Cho; Joan Bruna (AIIDE Workshops, 2018)

A Neural Representation of Sketch Drawings. - David Ha; Douglas Eck (ICLR (Poster), 2018)

Learning via social awareness: improving sketch representations with facial feedback. - Natasha Jaques; Jesse H. Engel; David Ha; Fred Bertsch; Rosalind W. Picard; Douglas Eck (ICLR (Workshop), 2018)

Learning via Social Awareness: Improving a Deep Generative Sketching Model with Facial Feedback. - Natasha Jaques; Jennifer McCleary; Jesse H. Engel; David Ha; Fred Bertsch; Douglas Eck; Rosalind W. Picard (AffComp@IJCAI, 2018)

Optimal Classifier Model Status Selection using Bayes boundary uncertainty. - David Ha; Emilie Delattre; Yuya Tomotoshi; Masahiro Senda; Hideyuki Watanabe; Shigeru Katagiri; Miho Ohsaki (MLSP, 2018)

Recurrent World Models Facilitate Policy Evolution. - David Ha; Jürgen Schmidhuber (NeurIPS, 2018)

Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method. - David Ha; Hideyuki Watanabe; Yuya Tomotoshi; Emilie Delattre; Shigeru Katagiri (SPML, 2018)

World Models. - David Ha; Jürgen Schmidhuber (CoRR, 2018)

Deep Learning for Classical Japanese Literature. - Tarin Clanuwat; Mikel Bober-Irizar; Asanobu Kitamoto; Alex Lamb; Kazuaki Yamamoto; David Ha (CoRR, 2018)

collabdraw: An Environment for Collaborative Sketching with an Artificial Agent. - Judith E. Fan; Monica Dinculescu; David Ha (Creativity & Cognition, 2019)

A Learned Representation for Scalable Vector Graphics. - Raphael Gontijo Lopes; David Ha; Douglas Eck; Jonathon Shlens (DGS@ICLR, 2019)

Learning Latent Dynamics for Planning from Pixels. - Danijar Hafner; Timothy P. Lillicrap; Ian Fischer; Ruben Villegas; David Ha; Honglak Lee; James Davidson (ICML, 2019)

Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers. - Yuya Tomotoshi; David Ha; Emilie Delattre; Hideyuki Watanabe; Xugang Lu; Shigeru Katagiri; Miho Ohsaki (IUKM, 2019)

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. - C. Daniel Freeman; David Ha; Luke Metz (NeurIPS, 2019)

Weight Agnostic Neural Networks. - Adam Gaier; David Ha (NeurIPS, 2019)

Maximum Bayes Boundary-Ness Training For Pattern Classification. - Masahiro Senda; David Ha; Hideyuki Watanabe; Shigeru Katagiri; Miho Ohsaki (SPML, 2019)

Reinforcement Learning for Improving Agent Design. - David Ha (Artif. Life, 2019)

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error. - Miguel González Duque; Rasmus Berg Palm; David Ha; Sebastian Risi (CoG, 2020)

Neuroevolution for deep reinforcement learning problems. - David Ha (GECCO Companion, 2020)

Neuroevolution of self-interpretable agents. - Yujin Tang; Duong Nguyen; David Ha (GECCO, 2020)

Scones: towards conversational authoring of sketches. - Forrest Huang; Eldon Schoop; David Ha; John F. Canny (IUI, 2020)

SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks. - Alex Lamb; Sherjil Ozair; Vikas Verma; David Ha (WACV, 2020)

A Practical Method Based on Bayes Boundary-Ness for Optimal Classifier Parameter Status Selection. - David Ha; Yuya Tomotoshi; Masahiro Senda; Hideyuki Watanabe; Shigeru Katagiri; Miho Ohsaki (J. Signal Process. Syst., 2020)

World Models and Attention for Reinforcement Learning. - David Ha (ALIFE, 2021)

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. - Yujin Tang; David Ha (NeurIPS, 2021)

Sketch-based Creativity Support Tools using Deep Learning. - Forrest Huang; Eldon Schoop; David Ha; Jeffrey Nichols 001; John F. Canny (CoRR, 2021)

Collective Intelligence for Deep Learning: A Survey of Recent Developments. - David Ha; Yujin Tang (CoRR, 2021)

An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation. - David Ha; Shigeru Katagiri; Hideyuki Watanabe; Miho Ohsaki (J. Signal Process. Syst., 2021)

Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts. - Yingtao Tian; David Ha (EvoMUSART, 2022)

Evolving modular soft robots without explicit inter-module communication using local self-attention. - Federico Pigozzi; Yujin Tang; Eric Medvet; David Ha (GECCO, 2022)

EvoJAX: hardware-accelerated neuroevolution. - Yujin Tang; Yingtao Tian; David Ha (GECCO Companion, 2022)

Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport. - Yingtao Tian; Marco Cuturi; David Ha (ICCC, 2022)

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery. - Chris Lu 001; Cong Lu; Robert Tjarko Lange; Jakob N. Foerster; Jeff Clune; David Ha (CoRR, 2024)

Automating the Search for Artificial Life with Foundation Models. - Akarsh Kumar; Chris Lu 001; Louis Kirsch; Yujin Tang; Kenneth O. Stanley; Phillip Isola; David Ha (CoRR, 2024)

Learning to Generalize With Object-Centric Agents in the Open World Survival Game Crafter. - Aleksandar Stanic; Yujin Tang; David Ha; Jürgen Schmidhuber (IEEE Trans. Games, 2024)

Evolutionary optimization of model merging recipes. - Takuya Akiba; Makoto Shing; Yujin Tang; Qi Sun; David Ha (Nat. Mac. Intell., 2025)

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search. - Yutaro Yamada; Robert Tjarko Lange; Cong Lu; Shengran Hu; Chris Lu 001; Jakob N. Foerster; Jeff Clune; David Ha (CoRR, 2025)

EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements. - Issa Sugiura; Takashi Ishida 009; Taro Makino; Chieko Tazuke; Takanori Nakagawa; Kosuke Nakago; David Ha (CoRR, 2025)