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Found 5 papers, 1 papers with code
Struggle with Adversarial Defense? Try Diffusion
Unlike data-driven classifiers, TMDC, guided by Bayesian principles, utilizes the conditional likelihood from diffusion models to determine the class probabilities of input images, thereby insulating against the influences of data shift and the limitations of adversarial training.
URLBERT:A Contrastive and Adversarial Pre-trained Model for URL Classification
To address this gap, this paper introduces URLBERT, the first pre-trained representation learning model applied to a variety of URL classification or detection tasks.
SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group Sparsity
Our framework consists of two parts: (a) A fine-grained kernel sparsity schema with a sparsity granularity between structured pruning and unstructured pruning.
MIME: Mutual Information Minimisation Exploration
We show that reinforcement learning agents that learn by surprise (surprisal) get stuck at abrupt environmental transition boundaries because these transitions are difficult to learn.
VASE: Variational Assorted Surprise Exploration for Reinforcement Learning
Exploration in environments with continuous control and sparse rewards remains a key challenge in reinforcement learning (RL).
