David Ha

research scientist at google brain 🧠 in tokyo🗼

Featured Co-authors

Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter

Reinforcement learning agents must generalize beyond their training.

Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport

Recent advances in deep learning, such as powerful generative models and...

Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention

Modularity in robotics holds great potential.

EvoJAX: Hardware-Accelerated Neuroevolution

Evolutionary computation has been shown to be a highly effective method.

Collective Intelligence for Deep Learning: A Survey of Recent Developments

In the past decade, we have witnessed the rise of deep learning to dominate.

Sketch-based Creativity Support Tools using Deep Learning

Sketching is a natural and effective visual communication medium.

Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts

Evolutionary algorithms have been used in the digital art scene since.

The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning

In complex systems, we often observe complex global behavior.

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

Methods for dynamic difficulty adjustment allow games to be tailored.

Scones: Towards Conversational Authoring of Sketches

Iteratively refining and critiquing sketches are crucial steps.

Neuroevolution of Self-Interpretable Agents

Inattentional blindness is a psychological phenomenon that causes.

SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks

Deep networks have achieved excellent results in perceptual tasks.

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Much of model-based reinforcement learning involves learning a model.

Weight Agnostic Neural Networks

Not all neural network architectures are created equal.

A Learned Representation for Scalable Vector Graphics

Dramatic advances in generative models have resulted in near photographic.

Deep Learning for Classical Japanese Literature

Much of machine learning research focuses on producing models.

Learning Latent Dynamics for Planning from Pixels

Planning has been very successful for control tasks.

Reinforcement Learning for Improving Agent Design

In many reinforcement learning tasks, the goal is to learn a policy.

Pommerman: A Multi-Agent Playground

We present Pommerman, a multi-agent environment based on the classic.

Recurrent World Models Facilitate Policy Evolution

A generative recurrent neural network is quickly trained in.

World Models

We explore building generative neural network models of popular.

Learning via social awareness: improving sketch representations with facial feedback

In the quest towards general artificial intelligence (AI).

A Neural Representation of Sketch Drawings

We present sketch-rnn, a recurrent neural network (RNN) able to construct.

PathNet: Evolution Channels Gradient Descent in Super Neural Networks

For artificial general intelligence (AGI) it would be efficient.