92 citations · 302 across the 14 of their papers we have counts for
15 papers
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter
Aleksandar Stanić, Yujin Tang, David Ha +1
Reinforcement learning agents must generalize beyond their training experience. Prior work has focused mostly on identical training and evaluation environments. Starting from the r…
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport
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 n…
Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention
Federico Pigozzi, Yujin Tang, Eric Medvet +1
Modularity in robotics holds great potential. In principle, modular robots can be disassembled and reassembled in different robots, and possibly perform new tasks. Nevertheless, ac…
EvoJAX: Hardware-Accelerated Neuroevolution
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. Recent work have also sho…
Sketch-based Creativity Support Tools using Deep Learning
Forrest Huang, Eldon Schoop, David Ha +2
Sketching is a natural and effective visual communication medium commonly used in creative processes. Recent developments in deep-learning models drastically improved machines' abi…
Visual Intelligence through Human Interaction
Ranjay Krishna, Mitchell Gordon, Li Fei-Fei +1
Over the last decade, Computer Vision, the branch of Artificial Intelligence aimed at understanding the visual world, has evolved from simply recognizing objects in images to descr…
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts
Yingtao Tian, David Ha
Evolutionary algorithms have been used in the digital art scene since the 1970s. A popular application of genetic algorithms is to optimize the procedural placement of vector graph…
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
Miguel González-Duque, Rasmus Berg Palm, David Ha +1
Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of…
Scones: Towards Conversational Authoring of Sketches
Forrest Huang, Eldon Schoop, David Ha +1
Iteratively refining and critiquing sketches are crucial steps to developing effective designs. We introduce Scones, a mixed-initiative, machine-learning-driven system that enables…
Neuroevolution of Self-Interpretable Agents
Yujin Tang, Duong Nguyen, David Ha
Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight. It is a consequence of the selective attention in perception that lets us rem…
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Alex Lamb, Sherjil Ozair, Vikas Verma +1
Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In thi…
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
C. Daniel Freeman, Luke Metz, David Ha
Much of model-based reinforcement learning involves learning a model of an agent's world, and training an agent to leverage this model to perform a task more efficiently. While the…
Deep Learning for Classical Japanese Literature
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto +3
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 tas…
Learning via social awareness: Improving a deep generative sketching model with facial feedback
Natasha Jaques, Jennifer McCleary, Jesse Engel +4
In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards.…
HyperNetworks
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. Hypernetworks provide an abstractio…