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David Ha
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2020 – today
- 2024
- [j4]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): 384-395 (2024) - [i27]Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha:
Evolutionary Optimization of Model Merging Recipes. CoRR abs/2403.13187 (2024) - [i26]Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, David Ha:
The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery. CoRR abs/2408.06292 (2024) - 2022
- [c29]Yingtao Tian, David Ha:
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts. EvoMUSART 2022: 275-291 - [c28]Federico Pigozzi, Yujin Tang, Eric Medvet, David Ha:
Evolving modular soft robots without explicit inter-module communication using local self-attention. GECCO 2022: 148-157 - [c27]Yujin Tang, Yingtao Tian, David Ha:
EvoJAX: hardware-accelerated neuroevolution. GECCO Companion 2022: 308-311 - [c26]Yingtao Tian, Marco Cuturi, David Ha:
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport. ICCC 2022: 324-332 - [i25]Yujin Tang, Yingtao Tian, David Ha:
EvoJAX: Hardware-Accelerated Neuroevolution. CoRR abs/2202.05008 (2022) - [i24]Federico Pigozzi, Yujin Tang, Eric Medvet, David Ha:
Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention. CoRR abs/2204.06481 (2022) - [i23]Yingtao Tian, Marco Cuturi, David Ha:
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport. CoRR abs/2204.08472 (2022) - [i22]Aleksandar Stanic, Yujin Tang, David Ha, Jürgen Schmidhuber:
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter. CoRR abs/2208.03374 (2022) - 2021
- [j3]David Ha, Shigeru Katagiri, Hideyuki Watanabe, Miho Ohsaki:
An Improved Boundary Uncertainty-Based Estimation for Classifier Evaluation. J. Signal Process. Syst. 93(9): 1057-1084 (2021) - [c25]David Ha:
World Models and Attention for Reinforcement Learning. ALIFE 2021: 8 - [c24]Yujin Tang, David Ha:
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. NeurIPS 2021: 22574-22587 - [i21]Yujin Tang, David Ha:
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. CoRR abs/2109.02869 (2021) - [i20]Yingtao Tian, David Ha:
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts. CoRR abs/2109.08857 (2021) - [i19]Forrest Huang, Eldon Schoop, David Ha, Jeffrey Nichols, John F. Canny:
Sketch-based Creativity Support Tools using Deep Learning. CoRR abs/2111.09991 (2021) - [i18]David Ha, Yujin Tang:
Collective Intelligence for Deep Learning: A Survey of Recent Developments. CoRR abs/2111.14377 (2021) - 2020
- [j2]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): 135-151 (2020) - [c23]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: 503-510 - [c22]David Ha:
Neuroevolution for deep reinforcement learning problems. GECCO Companion 2020: 404-427 - [c21]Yujin Tang, Duong Nguyen, David Ha:
Neuroevolution of self-interpretable agents. GECCO 2020: 414-424 - [c20]Forrest Huang, Eldon Schoop, David Ha, John F. Canny:
Scones: towards conversational authoring of sketches. IUI 2020: 313-323 - [c19]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: 952-961 - [i17]Yujin Tang, Duong Nguyen, David Ha:
Neuroevolution of Self-Interpretable Agents. CoRR abs/2003.08165 (2020) - [i16]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 abs/2005.07677 (2020) - [i15]Forrest Huang, Eldon Schoop, David Ha, John F. Canny:
Scones: Towards Conversational Authoring of Sketches. CoRR abs/2005.07781 (2020)
2010 – 2019
- 2019
- [j1]David Ha:
Reinforcement Learning for Improving Agent Design. Artif. Life 25(4): 352-365 (2019) - [c18]Judith E. Fan, Monica Dinculescu, David Ha:
collabdraw: An Environment for Collaborative Sketching with an Artificial Agent. Creativity & Cognition 2019: 556-561 - [c17]David Ha:
Neuroevolution for deep reinforcement learning problems. GECCO (Companion) 2019: 550-593 - [c16]Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens:
A Learned Representation for Scalable Vector Graphics. ICCV 2019: 7929-7938 - [c15]Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens:
A Learned Representation for Scalable Vector Graphics. DGS@ICLR 2019 - [c14]Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson:
Learning Latent Dynamics for Planning from Pixels. ICML 2019: 2555-2565 - [c13]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: 295-307 - [c12]Adam Gaier, David Ha:
Weight Agnostic Neural Networks. NeurIPS 2019: 5365-5379 - [c11]C. Daniel Freeman, David Ha, Luke Metz:
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. NeurIPS 2019: 5380-5391 - [c10]Masahiro Senda, David Ha, Hideyuki Watanabe, Shigeru Katagiri, Miho Ohsaki:
Maximum Bayes Boundary-Ness Training For Pattern Classification. SPML 2019: 18-28 - [i14]Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens:
A Learned Representation for Scalable Vector Graphics. CoRR abs/1904.02632 (2019) - [i13]Adam Gaier, David Ha:
Weight Agnostic Neural Networks. CoRR abs/1906.04358 (2019) - [i12]C. Daniel Freeman, Luke Metz, David Ha:
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction. CoRR abs/1910.13038 (2019) - [i11]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 abs/1912.11570 (2019) - 2018
- [c9]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 - [c8]David Ha:
Neuroevolution for deep reinforcement learning problems. GECCO (Companion) 2018: 421-431 - [c7]David Ha, Douglas Eck:
A Neural Representation of Sketch Drawings. ICLR (Poster) 2018 - [c6]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 - [c5]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: 1-9 - [c4]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: 1-6 - [c3]David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. NeurIPS 2018: 2455-2467 - [c2]David Ha, Hideyuki Watanabe, Yuya Tomotoshi, Emilie Delattre, Shigeru Katagiri:
Optimality Analysis of Boundary-Uncertainty-Based Classifier Model Parameter Status Selection Method. SPML 2018: 107-114 - [i10]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 abs/1802.04877 (2018) - [i9]David Ha, Jürgen Schmidhuber:
World Models. CoRR abs/1803.10122 (2018) - [i8]David Ha, Jürgen Schmidhuber:
Recurrent World Models Facilitate Policy Evolution. CoRR abs/1809.01999 (2018) - [i7]Cinjon Resnick, Wes Eldridge, David Ha, Denny Britz, Jakob N. Foerster, Julian Togelius, Kyunghyun Cho, Joan Bruna:
Pommerman: A Multi-Agent Playground. CoRR abs/1809.07124 (2018) - [i6]David Ha:
Reinforcement Learning for Improving Agent Design. CoRR abs/1810.03779 (2018) - [i5]Danijar Hafner, Timothy P. Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson:
Learning Latent Dynamics for Planning from Pixels. CoRR abs/1811.04551 (2018) - [i4]Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha:
Deep Learning for Classical Japanese Literature. CoRR abs/1812.01718 (2018) - 2017
- [c1]David Ha, Andrew M. Dai, Quoc V. Le:
HyperNetworks. ICLR (Poster) 2017 - [i3]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 abs/1701.08734 (2017) - [i2]David Ha, Douglas Eck:
A Neural Representation of Sketch Drawings. CoRR abs/1704.03477 (2017) - 2016
- [i1]David Ha, Andrew M. Dai, Quoc V. Le:
HyperNetworks. CoRR abs/1609.09106 (2016)
Coauthor Index
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last updated on 2024-10-22 20:16 CEST by the dblp team
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