Previous NAS algorithms designed for image search spaces incorporate resource constraints directly into the reinforcement learning (RL) rewards.

Is Q-learningprovablyefficient? InAdvancesin Neural Information Processing Systems, 2018.

Symmetry has been a fundamental tool in the exploration of a broad range of complexsystems.

To address these challenges, we introduce the Block-Level Adaptive STructured (BLAST) matrix, designed to learn and leverage efficient structures prevalent in the weight matrices of linear layers within deep learning models. Compared to existing structured matrices, the BLAST matrix offers substantial flexibility, as it can represent various types of structures that are either learned from data or computed from pre-existing weight matrices.

We first invoke the following lemma which relates the empirical and true cumulative distribution functionsofi.i.d. Steps 1 2 4 8 16 Noregularization 4.17 4.04 4.05 4.04 4.05 L1(λ=10.0) In Figure B.1, we show additional visualizations of regression tasks. C.2 Classification Task Learner The task learner was ResNet-18 [3] for the SVHN and CIFAR-10 datasets, and anMLP with one hidden layer of500nodes and ReLU nonlinearities. TheCNNarchitecture was the4-layer convolutional net in [6] when the task learner was an MLP, and was ResNet-18 otherwise.