Search Results for author: David Ha

Found 25 papers, 19 papers with code

Evolutionary Optimization of Model Merging Recipes

1 code implementation19 Mar 2024 Takuya Akiba, Makoto Shing, Yujin Tang, Qi Sun, David Ha

Surprisingly, our Japanese Math LLM achieved state-of-the-art performance on a variety of established Japanese LLM benchmarks, even surpassing models with significantly more parameters, despite not being explicitly trained for such tasks.

Evolutionary Algorithms Math

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

1 code implementation5 Aug 2022 Aleksandar Stanić, Yujin Tang, David Ha, Jürgen Schmidhuber

We show that current agents struggle to generalize, and introduce novel object-centric agents that improve over strong baselines.

Meta-Learning

Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport

no code implementations18 Apr 2022 Yingtao Tian, Marco Cuturi, David Ha

Recent advances in deep learning, such as powerful generative models and joint text-image embeddings, have provided the computational creativity community with new tools, opening new perspectives for artistic pursuits.

Diversity Image Generation

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

2 code implementations13 Apr 2022 Federico Pigozzi, Yujin Tang, Eric Medvet, David Ha

We show experimentally that the evolved robots are effective in the task of locomotion: thanks to self-attention, instances of the same controller embodied in the same robot can focus on different inputs.

Inductive Bias

EvoJAX: Hardware-Accelerated Neuroevolution

1 code implementation10 Feb 2022 Yujin Tang, Yingtao Tian, David Ha

Evolutionary computation has been shown to be a highly effective method for training neural networks, particularly when employed at scale on CPU clusters.

Collective Intelligence for Deep Learning: A Survey of Recent Developments

no code implementations29 Nov 2021 David Ha, Yujin Tang

In this review, we will provide a historical context of neural network research's involvement with complex systems, and highlight several active areas in modern deep learning research that incorporate the principles of collective intelligence to advance its current capabilities.

Sketch-based Creativity Support Tools using Deep Learning

no code implementations19 Nov 2021 Forrest Huang, Eldon Schoop, David Ha, Jeffrey Nichols, John Canny

Sketching is a natural and effective visual communication medium commonly used in creative processes.

Retrieval

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

3 code implementations NeurIPS 2021 Yujin Tang, David Ha

In complex systems, we often observe complex global behavior emerge from a collection of agents interacting with each other in their environment, with each individual agent acting only on locally available information, without knowing the full picture.

reinforcement-learning Reinforcement Learning (RL)

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

1 code implementation15 May 2020 Miguel González-Duque, Rasmus Berg Palm, David Ha, Sebastian Risi

The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.

AI Agent Bayesian Optimization

Scones: Towards Conversational Authoring of Sketches

no code implementations12 May 2020 Forrest Huang, Eldon Schoop, David Ha, John Canny

Iteratively refining and critiquing sketches are crucial steps to developing effective designs.

Neuroevolution of Self-Interpretable Agents

3 code implementations18 Mar 2020 Yujin Tang, Duong Nguyen, David Ha

Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight.

Reinforcement Learning (RL)

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

1 code implementation25 Dec 2019 Alex Lamb, Sherjil Ozair, Vikas Verma, David Ha

In this work we focus on their ability to have invariance towards the presence or absence of details.

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

2 code implementations NeurIPS 2019 C. Daniel Freeman, Luke Metz, David Ha

That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances.

Model-based Reinforcement Learning reinforcement-learning +1

Weight Agnostic Neural Networks

1 code implementation NeurIPS 2019 Adam Gaier, David Ha

We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training.

Car Racing Image Classification

A Learned Representation for Scalable Vector Graphics

2 code implementations ICCV 2019 Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens

Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world.

Vector Graphics

Deep Learning for Classical Japanese Literature

10 code implementations3 Dec 2018 Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, David Ha

Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks.

BIG-bench Machine Learning Image Classification

Reinforcement Learning for Improving Agent Design

1 code implementation9 Oct 2018 David Ha

In this work, we explore the possibility of learning a version of the agent's design that is better suited for its task, jointly with the policy.

OpenAI Gym reinforcement-learning +1

Recurrent World Models Facilitate Policy Evolution

no code implementations NeurIPS 2018 David Ha, Jürgen Schmidhuber

A generative recurrent neural network is quickly trained in an unsupervised manner to model popular reinforcement learning environments through compressed spatio-temporal representations.

reinforcement-learning Reinforcement Learning (RL)

World Models

22 code implementations27 Mar 2018 David Ha, Jürgen Schmidhuber

We explore building generative neural network models of popular reinforcement learning environments.

Car Racing reinforcement-learning +1

Learning via social awareness: Improving a deep generative sketching model with facial feedback

no code implementations13 Feb 2018 Natasha Jaques, Jennifer McCleary, Jesse Engel, David Ha, Fred Bertsch, Rosalind Picard, Douglas Eck

We use a Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small group of viewers, by optimizing the model to produce sketches that it predicts will lead to more positive facial expressions.

AI Agent

A Neural Representation of Sketch Drawings

18 code implementations ICLR 2018 David Ha, Douglas Eck

We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects.

PathNet: Evolution Channels Gradient Descent in Super Neural Networks

1 code implementation30 Jan 2017 Chrisantha Fernando, Dylan Banarse, Charles Blundell, Yori Zwols, David Ha, Andrei A. Rusu, Alexander Pritzel, Daan Wierstra

It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks.

Continual Learning reinforcement-learning +2

HyperNetworks

8 code implementations27 Sep 2016 David Ha, Andrew Dai, Quoc V. Le

This work explores hypernetworks: an approach of using a one network, also known as a hypernetwork, to generate the weights for another network.

Handwriting generation Language Modelling +2

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