Underthisassumption,weintroduce IMED-RLandprove that its regret upper bound asymptotically matches the regret lower bound.

Distinct from traditional heuristic solvers, this paper on one hand proposes an RL-based model for mixed-size macro placement, which differs from existing learning-based placers that often consider the macro by coarse grid-based mask. While the standard cells are placed via gradient-based GPU acceleration. On the other hand, a one-shot conditional generative routing model, which is composed of a special-designed input-size-adapting generator and a bi-discriminator, is devised to perform one-shot routing to the pins within each net, and the order of nets to route is adaptively learned.

In this work, we identify dropout induced sparsity forLSTMs asasuitable mode ofcomputation reduction. Dropout isawidely usedregularization mechanism, which randomly drops computed neuron values during each iteration of training.

We present a novel compression algorithm for reducing the storage of LiDAR sensor data streams.

The nineteenth century saw the emergence of proofs so involved that they could only be verified by a handful of specialists.

Then, we consider sufficient assumptions under which learning good policies requires polynomial number of episodes.