2,082 research outputs found
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing
This work considers the trade-off between accuracy and test-time
computational cost of deep neural networks (DNNs) via \emph{anytime}
predictions from auxiliary predictions. Specifically, we optimize auxiliary
losses jointly in an \emph{adaptive} weighted sum, where the weights are
inversely proportional to average of each loss. Intuitively, this balances the
losses to have the same scale. We demonstrate theoretical considerations that
motivate this approach from multiple viewpoints, including connecting it to
optimizing the geometric mean of the expectation of each loss, an objective
that ignores the scale of losses. Experimentally, the adaptive weights induce
more competitive anytime predictions on multiple recognition data-sets and
models than non-adaptive approaches including weighing all losses equally. In
particular, anytime neural networks (ANNs) can achieve the same accuracy faster
using adaptive weights on a small network than using static constant weights on
a large one. For problems with high performance saturation, we also show a
sequence of exponentially deepening ANNscan achieve near-optimal anytime
results at any budget, at the cost of a const fraction of extra computation
Evolutionary Algorithms for Reinforcement Learning
There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
Modern predictive models are often deployed to environments in which
computational budgets are dynamic. Anytime algorithms are well-suited to such
environments as, at any point during computation, they can output a prediction
whose quality is a function of computation time. Early-exit neural networks
have garnered attention in the context of anytime computation due to their
capability to provide intermediate predictions at various stages throughout the
network. However, we demonstrate that current early-exit networks are not
directly applicable to anytime settings, as the quality of predictions for
individual data points is not guaranteed to improve with longer computation. To
address this shortcoming, we propose an elegant post-hoc modification, based on
the Product-of-Experts, that encourages an early-exit network to become
gradually confident. This gives our deep models the property of conditional
monotonicity in the prediction quality -- an essential stepping stone towards
truly anytime predictive modeling using early-exit architectures. Our empirical
results on standard image-classification tasks demonstrate that such behaviors
can be achieved while preserving competitive accuracy on average.Comment: NeurIPS 202
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