2,354 research outputs found
BayesNAS: A Bayesian Approach for Neural Architecture Search
One-Shot Neural Architecture Search (NAS) is a promising method to
significantly reduce search time without any separate training. It can be
treated as a Network Compression problem on the architecture parameters from an
over-parameterized network. However, there are two issues associated with most
one-shot NAS methods. First, dependencies between a node and its predecessors
and successors are often disregarded which result in improper treatment over
zero operations. Second, architecture parameters pruning based on their
magnitude is questionable. In this paper, we employ the classic Bayesian
learning approach to alleviate these two issues by modeling architecture
parameters using hierarchical automatic relevance determination (HARD) priors.
Unlike other NAS methods, we train the over-parameterized network for only one
epoch then update the architecture. Impressively, this enabled us to find the
architecture on CIFAR-10 within only 0.2 GPU days using a single GPU.
Competitive performance can be also achieved by transferring to ImageNet. As a
byproduct, our approach can be applied directly to compress convolutional
neural networks by enforcing structural sparsity which achieves extremely
sparse networks without accuracy deterioration.Comment: International Conference on Machine Learning 201
Actor-Critic Reinforcement Learning for Control with Stability Guarantee
Reinforcement Learning (RL) and its integration with deep learning have
achieved impressive performance in various robotic control tasks, ranging from
motion planning and navigation to end-to-end visual manipulation. However,
stability is not guaranteed in model-free RL by solely using data. From a
control-theoretic perspective, stability is the most important property for any
control system, since it is closely related to safety, robustness, and
reliability of robotic systems. In this paper, we propose an actor-critic RL
framework for control which can guarantee closed-loop stability by employing
the classic Lyapunov's method in control theory. First of all, a data-based
stability theorem is proposed for stochastic nonlinear systems modeled by
Markov decision process. Then we show that the stability condition could be
exploited as the critic in the actor-critic RL to learn a controller/policy. At
last, the effectiveness of our approach is evaluated on several well-known
3-dimensional robot control tasks and a synthetic biology gene network tracking
task in three different popular physics simulation platforms. As an empirical
evaluation on the advantage of stability, we show that the learned policies can
enable the systems to recover to the equilibrium or way-points when interfered
by uncertainties such as system parametric variations and external disturbances
to a certain extent.Comment: IEEE RA-L + IROS 202
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