Search Results for author:
Found 13 papers, 10 papers with code
Evolutionary rates of information gain and decay in fluctuating environments
In this paper, we wish to investigate the dynamics of information transfer in evolutionary dynamics.
Bootstrapping of memetic from genetic evolution via inter-agent selection pressures
We create an artificial system of agents (attention-based neural networks) which selectively exchange messages with each-other in order to study the emergence of memetic evolution and how memetic evolutionary pressures interact with genetic evolution of the network weights.
BADGER: Learning to (Learn [Learning Algorithms] through Multi-Agent Communication)
In this work, we propose a novel memory-based multi-agent meta-learning architecture and learning procedure that allows for learning of a shared communication policy that enables the emergence of rapid adaptation to new and unseen environments by learning to learn learning algorithms through communication.
Estimating Planetary Mass with Deep Learning
Here, we demonstrate the use of a neural network that models the density of planets in a space of six properties that is then used to impute a probability distribution for missing values.
Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics
Deep learning on butterfly phenotypes tests evolution's oldest mathematical model
Traditional anatomical analyses captured only a fraction of real phenomic information.
Generating the support with extreme value losses
We consider a simple method of summarizing the prediction error, such that the optimal strategy corresponds to outputting a distribution of predictions with a support that matches the support of the distribution of targets --- optimizing against the minimal value of the loss given a set of samples from the prediction distribution, rather than the mean.
On the potential for open-endedness in neural networks
In this paper, we hope to bridge that gap by reviewing common barriers to open-endedness in the evolution-inspired approach and how they are dealt with in the evolutionary case - collapse of diversity, saturation of complexity, and failure to form new kinds of individuality.
Being curious about the answers to questions: novelty search with learned attention
We investigate the use of attentional neural network layers in order to learn a `behavior characterization' which can be used to drive novelty search and curiosity-based policies.
Learning to generate classifiers
We train a network to generate mappings between training sets and classification policies (a 'classifier generator') by conditioning on the entire training set via an attentional mechanism.
Learning body-affordances to simplify action spaces
Controlling embodied agents with many actuated degrees of freedom is a challenging task.
Counterfactual Control for Free from Generative Models
In this method, the problem of action selection is reduced to one of gradient descent on the latent space of the generative model, with the model itself providing the means of evaluating outcomes and finding the gradient, much like how the reward network in Deep Q-Networks (DQN) provides gradient information for the action generator.
Permutation-equivariant neural networks applied to dynamics prediction
The introduction of convolutional layers greatly advanced the performance of neural networks on image tasks due to innately capturing a way of encoding and learning translation-invariant operations, matching one of the underlying symmetries of the image domain.
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks
We present a loss function for neural networks that encompasses an idea of trivial versus non-trivial predictions, such that the network jointly determines its own prediction goals and learns to satisfy them.
