223,760 research outputs found
Auto-Encoding Sequential Monte Carlo
We build on auto-encoding sequential Monte Carlo (AESMC): a method for model
and proposal learning based on maximizing the lower bound to the log marginal
likelihood in a broad family of structured probabilistic models. Our approach
relies on the efficiency of sequential Monte Carlo (SMC) for performing
inference in structured probabilistic models and the flexibility of deep neural
networks to model complex conditional probability distributions. We develop
additional theoretical insights and introduce a new training procedure which
improves both model and proposal learning. We demonstrate that our approach
provides a fast, easy-to-implement and scalable means for simultaneous model
learning and proposal adaptation in deep generative models
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
Combining deep model-free reinforcement learning with on-line planning is a
promising approach to building on the successes of deep RL. On-line planning
with look-ahead trees has proven successful in environments where transition
models are known a priori. However, in complex environments where transition
models need to be learned from data, the deficiencies of learned models have
limited their utility for planning. To address these challenges, we propose
TreeQN, a differentiable, recursive, tree-structured model that serves as a
drop-in replacement for any value function network in deep RL with discrete
actions. TreeQN dynamically constructs a tree by recursively applying a
transition model in a learned abstract state space and then aggregating
predicted rewards and state-values using a tree backup to estimate Q-values. We
also propose ATreeC, an actor-critic variant that augments TreeQN with a
softmax layer to form a stochastic policy network. Both approaches are trained
end-to-end, such that the learned model is optimised for its actual use in the
tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a
box-pushing task, as well as n-step DQN and value prediction networks (Oh et
al. 2017) on multiple Atari games. Furthermore, we present ablation studies
that demonstrate the effect of different auxiliary losses on learning
transition models
Deep Over-sampling Framework for Classifying Imbalanced Data
Class imbalance is a challenging issue in practical classification problems
for deep learning models as well as traditional models. Traditionally
successful countermeasures such as synthetic over-sampling have had limited
success with complex, structured data handled by deep learning models. In this
paper, we propose Deep Over-sampling (DOS), a framework for extending the
synthetic over-sampling method to exploit the deep feature space acquired by a
convolutional neural network (CNN). Its key feature is an explicit, supervised
representation learning, for which the training data presents each raw input
sample with a synthetic embedding target in the deep feature space, which is
sampled from the linear subspace of in-class neighbors. We implement an
iterative process of training the CNN and updating the targets, which induces
smaller in-class variance among the embeddings, to increase the discriminative
power of the deep representation. We present an empirical study using public
benchmarks, which shows that the DOS framework not only counteracts class
imbalance better than the existing method, but also improves the performance of
the CNN in the standard, balanced settings
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