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Found 4 papers, 2 papers with code
FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score
Backed by theoretical analysis, this paper advocates for the measurement of the "OOD-ness" of a test case $\boldsymbol{x}$ through the likelihood ratio between out-distribution $\mathcal P_{\textit{out}}$ and in-distribution $\mathcal P_{\textit{in}}$.
Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective
By using circuit complexity theory, we first give impossibility results showing that bounded-depth Transformers are unable to directly produce correct answers for basic arithmetic/equation tasks unless the model size grows super-polynomially with respect to the input length.
Denoising Masked AutoEncoders Help Robust Classification
In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images.
Guided Diffusion Model for Adversarial Purification from Random Noise
In this paper, we propose a novel guided diffusion purification approach to provide a strong defense against adversarial attacks.
