Search Results for author:
Found 25 papers, 19 papers with code
HyperNetworks
This work explores hypernetworks: an approach of using a one network, also known as a hypernetwork, to generate the weights for another network.
Ranked #14 on
Language Modelling
on Penn Treebank (Character Level)
PathNet: Evolution Channels Gradient Descent in Super Neural Networks
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.
Ranked #5 on
Continual Learning
on F-CelebA (10 tasks)
A Neural Representation of Sketch Drawings
We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects.
Learning via social awareness: Improving a deep generative sketching model with facial feedback
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.
World Models
We explore building generative neural network models of popular reinforcement learning environments.
Recurrent World Models Facilitate Policy Evolution
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 for Improving Agent Design
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.
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environment dynamics.
Ranked #2 on
Continuous Control
on DeepMind Walker Walk (Images)
Deep Learning for Classical Japanese Literature
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.
Ranked #4 on
Image Classification
on Kuzushiji-MNIST
(Error metric)
A Learned Representation for Scalable Vector Graphics
Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world.
Weight Agnostic Neural Networks
We demonstrate that our method can find minimal neural network architectures that can perform several reinforcement learning tasks without weight training.
Learning to Predict Without Looking Ahead: World Models Without Forward Prediction
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
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
In this work we focus on their ability to have invariance towards the presence or absence of details.
Neuroevolution of Self-Interpretable Agents
Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight.
Scones: Towards Conversational Authoring of Sketches
Iteratively refining and critiquing sketches are crucial steps to developing effective designs.
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
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.
The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning
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.
Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts
Evolutionary algorithms have been used in the digital art scene since the 1970s.
Sketch-based Creativity Support Tools using Deep Learning
Sketching is a natural and effective visual communication medium commonly used in creative processes.
Collective Intelligence for Deep Learning: A Survey of Recent Developments
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.
EvoJAX: Hardware-Accelerated Neuroevolution
Evolutionary computation has been shown to be a highly effective method for training neural networks, particularly when employed at scale on CPU clusters.
Evolving Modular Soft Robots without Explicit Inter-Module Communication using Local Self-Attention
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.
Simultaneous Multiple-Prompt Guided Generation Using Differentiable Optimal Transport
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.
Learning to Generalize with Object-centric Agents in the Open World Survival Game Crafter
We show that current agents struggle to generalize, and introduce novel object-centric agents that improve over strong baselines.
Evolutionary Optimization of Model Merging Recipes
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.
