Superplastic Bulging of Fine-Grained Zirconia

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65850/1/j.1151-2916.1990.tb06585.x.pd

Maximum a Posteriori Adaptation of Network Parameters in Deep Models

We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance. Given an abundance of DNN parameters but with only a limited amount of data, the effectiveness of the adapted DNN model can often be compromised. We formulate maximum a posteriori (MAP) adaptation of parameters of a specially designed CD-DNN-HMM with an augmented linear hidden networks connected to the output tied states, or senones, and compare it to feature space MAP linear regression previously proposed. Experimental evidences on the 20,000-word open vocabulary Wall Street Journal task demonstrate the feasibility of the proposed framework. In supervised adaptation, the proposed MAP adaptation approach provides more than 10% relative error reduction and consistently outperforms the conventional transformation based methods. Furthermore, we present an initial attempt to generate hierarchical priors to improve adaptation efficiency and effectiveness with limited adaptation data by exploiting similarities among senones

Accelerating and Improving AlphaZero Using Population Based Training

AlphaZero has been very successful in many games. Unfortunately, it still consumes a huge amount of computing resources, the majority of which is spent in self-play. Hyperparameter tuning exacerbates the training cost since each hyperparameter configuration requires its own time to train one run, during which it will generate its own self-play records. As a result, multiple runs are usually needed for different hyperparameter configurations. This paper proposes using population based training (PBT) to help tune hyperparameters dynamically and improve strength during training time. Another significant advantage is that this method requires a single run only, while incurring a small additional time cost, since the time for generating self-play records remains unchanged though the time for optimization is increased following the AlphaZero training algorithm. In our experiments for 9x9 Go, the PBT method is able to achieve a higher win rate for 9x9 Go than the baselines, each with its own hyperparameter configuration and trained individually. For 19x19 Go, with PBT, we are able to obtain improvements in playing strength. Specifically, the PBT agent can obtain up to 74% win rate against ELF OpenGo, an open-source state-of-the-art AlphaZero program using a neural network of a comparable capacity. This is compared to a saturated non-PBT agent, which achieves a win rate of 47% against ELF OpenGo under the same circumstances.Comment: accepted by AAAI2020 as oral presentation. In this version, supplementary materials are adde

Giant isotope effect and spin state transition induced by oxygen isotope exchange in (Pr1−xSmx)0.7Ca0.3CoO3Pr_{1-x}Sm_x)_{0.7}Ca_{0.3}CoO_3

We systematically investigate effect of oxygen isotope in $(Pr_{1-x}Sm_x)_{0.7}Ca_{0.3}CoO_3$ which shows a crossover with x from ferromagnetic metal to the insulator with spin-state transition. A striking feature is that effect of oxygen isotope on the ferromagnetic transition is negligible in the metallic phase, while replacing $^{16}O$ with $^{18}O$ leads to a giant up-shift of the spin-state transition temperature ($T_s$) in the insulating phase, especially $T_s$ shifts from 36 to 54 K with isotope component $\alpha_S=-4.7$ for the sample with x=0.175. A metal-insulator transition is induced by oxygen isotope exchange in the sample x=0.172 being close to the insulating phase. The contrasting behaviors observed in the two phases can be well explained by occurrence of static Jahn-Teller distortions in the insulating phase, while absence of them in the metallic phase.Comment: 4 pages, 5 figure