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Found 6 papers, 3 papers with code
Query-Efficient Black-Box Red Teaming via Bayesian Optimization
To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations.
Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming
We propose a subset selection problem that replaces inefficient activation layers with identity functions and optimally merges consecutive convolution operations into shallow equivalent convolution operations for efficient end-to-end inference latency.
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization
We focus on the problem of adversarial attacks against models on discrete sequential data in the black-box setting where the attacker aims to craft adversarial examples with limited query access to the victim model.
Optimal channel selection with discrete QCQP
We first show the greedy approach of recent channel pruning methods ignores the inherent quadratic coupling between channels in the neighboring layers and cannot safely remove inactive weights during the pruning procedure.
Succinct Network Channel and Spatial Pruning via Discrete Variable QCQP
Reducing the heavy computational cost of large convolutional neural networks is crucial when deploying the networks to resource-constrained environments.
Critical phenomena of a hybrid phase transition in cluster merging dynamics
In spite of considerable effort to develop the theory of HPT, it is still incomplete, particularly when the transition is induced by cluster merging dynamics.
Statistical Mechanics
