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B2Opt: Learning to Optimize Black-box Optimization with Little Budget
To achieve this, 1) drawing on the mechanism of genetic algorithm, we propose a deep neural network framework called B2Opt, which has a stronger representation of optimization strategies based on survival of the fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task to guide the design of the efficient optimization strategies.
Thermodynamic limit of Nekrasov partition function for 5-brane web with O5-plane
On the one hand, we discuss Seiberg-Witten curve based on the dual graph of the 5-brane web with $O5$-plane.
High Energy Physics - Theory Mathematical Physics Mathematical Physics
Robustness-aware 2-bit quantization with real-time performance for neural network
The structural information learned from NN not only plays an important role in improving the performance but also allows for further fine tuning of the quantization network by applying the Lipschitz constraint to the structural loss.
Learning Discriminative Features Via Weights-biased Softmax Loss
Loss functions play a key role in training superior deep neural networks.
