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Found 11 papers, 6 papers with code
On Breaking Deep Generative Model-based Defenses and Beyond
The idea is to view the inversion phase as a dynamical system, through which we extract the gradient with respect to the input by tracing its recent trajectory.
On Evaluating LLMs' Capabilities as Functional Approximators: A Bayesian Perspective
Recent works have successfully applied Large Language Models (LLMs) to function modeling tasks.
On the Weaknesses of Backdoor-based Model Watermarking: An Information-theoretic Perspective
Based on this discovery, we propose a novel model watermarking scheme, In-distribution Watermark Embedding (IWE), to overcome the limitations of existing method.
Mutual Information Multinomial Estimation
Estimating mutual information (MI) is a fundamental yet challenging task in data science and machine learning.
Scalable Infomin Learning
The task of infomin learning aims to learn a representation with high utility while being uninformative about a specified target, with the latter achieved by minimising the mutual information between the representation and the target.
A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication
To enable the discrimination of RFF from both known and unknown devices, we propose a new end-to-end deep learning framework for extracting RFFs from raw received signals.
Do Concept Bottleneck Models Learn as Intended?
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets.
Neural Approximate Sufficient Statistics for Likelihood-free Inference
We consider the fundamental problem of how to automatically construct summary statistics for likelihood-free inference where the evaluation of likelihood function is intractable but sampling / simulating data from the model is possible.
Neural Approximate Sufficient Statistics for Implicit Models
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable, but sampling data from the model is possible.
Adaptive Gaussian Copula ABC
Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible.
A Deep, Information-theoretic Framework for Robust Biometric Recognition
Deep neural networks (DNN) have been a de facto standard for nowadays biometric recognition solutions.
