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Found 17 papers, 8 papers with code
APART: Diverse Skill Discovery using All Pairs with Ascending Reward and DropouT
This problem is formulated as mutual training of skills using an intrinsic reward and a discriminator trained to predict a skill given its trajectory.
Entropic Desired Dynamics for Intrinsic Control
An agent might be said, informally, to have mastery of its environment when it has maximised the effective number of states it can reliably reach.
Rapid training of deep neural networks without skip connections or normalization layers using Deep Kernel Shaping
Using an extended and formalized version of the Q/C map analysis of Poole et al. (2016), along with Neural Tangent Kernel theory, we identify the main pathologies present in deep networks that prevent them from training fast and generalizing to unseen data, and show how these can be avoided by carefully controlling the "shape" of the network's initialization-time kernel function.
Reward is enough for convex MDPs
Maximising a cumulative reward function that is Markov and stationary, i. e., defined over state-action pairs and independent of time, is sufficient to capture many kinds of goals in a Markov decision process (MDP).
Behavior Priors for Efficient Reinforcement Learning
In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts.
Importance Weighted Policy Learning and Adaptation
The ability to exploit prior experience to solve novel problems rapidly is a hallmark of biological learning systems and of great practical importance for artificial ones.
Information asymmetry in KL-regularized RL
In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning.
Understanding disentangling in $β$-VAE
We present new intuitions and theoretical assessments of the emergence of disentangled representation in variational autoencoders.
Overcoming catastrophic forgetting in neural networks
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence.
Ranked #3 on
Continual Learning
on F-CelebA (10 tasks)
Progressive Neural Networks
Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Policy Distillation
Policies for complex visual tasks have been successfully learned with deep reinforcement learning, using an approach called deep Q-networks (DQN), but relatively large (task-specific) networks and extensive training are needed to achieve good performance.
Natural Neural Networks
We introduce Natural Neural Networks, a novel family of algorithms that speed up convergence by adapting their internal representation during training to improve conditioning of the Fisher matrix.
Compress and Control
This paper describes a new information-theoretic policy evaluation technique for reinforcement learning.
Deep Tempering
Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning.
Disentangling Factors of Variation via Generative Entangling
Seen from a generative perspective, the multiplicative interactions emulates the entangling of factors of variation.
On Tracking The Partition Function
In this paper, we exploit the gradient descent training procedure of restricted Boltzmann machines (a type of MRF) to {\bf track} the log partition function during learning.
