Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201

Pressure- and temperature-induced structural phase transitions of CaFe2_2As2_2 and BaFe2_2As2_2 studied in the Hund's rule correlation picture

With the proposed Hund's rule correlation picture, i.e. the fluctuating Fe local moments with the As-bridged antiferromagnetic superexchange interactions, the exceptional collapsed tetragonal phase and related phase transitions observed in CaFe$_2$As$_2$ are well understood. With the same framework, a pressure-temperature phase diagram is predicted for BaFe$_2$As$_2$ as well, in which a paramagnetic tetragonal and a collinear antiferromagnetic orthorhombic structures to nonmagnetic tetragonal structure transitions take place around 4-8 GPa and 10-15 GPa respectively, and a nonmagnetic tetragonal to a nonmagnetic collapsed tetragonal structure transition takes place over 26 GPa. Our study helps better understand the complex correlation among crystal structure, magnetism, and electronic structure in pnictides, a precondition to understand the superconductivity in pnictides.Comment: 5 pages and 4 figure