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Found 5 papers, 1 papers with code
Hessian-Aware Bayesian Optimization for Decision Making Systems
Derivative-free approaches such as Bayesian Optimization mitigate the dependency on the quality of gradient feedback, but are known to scale poorly in the high-dimension setting of complex decision making systems.
Balancing training time vs. performance with Bayesian Early Pruning
Pruning is an approach to alleviate overparameterization of deep neural networks (DNN) by zeroing out or pruning DNN elements with little to no efficacy at a given task.
A Unifying Framework of Bilinear LSTMs
This paper presents a novel unifying framework of bilinear LSTMs that can represent and utilize the nonlinear interaction of the input features present in sequence datasets for achieving superior performance over a linear LSTM and yet not incur more parameters to be learned.
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces.
Not all bytes are equal: Neural byte sieve for fuzzing
Fuzzing is a popular dynamic program analysis technique used to find vulnerabilities in complex software.
