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Found 7 papers, 2 papers with code
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape
This paper delves into the critical area of deep learning robustness, challenging the conventional belief that classification robustness and explanation robustness in image classification systems are inherently correlated.
Privacy-preserving Fine-tuning of Large Language Models through Flatness
The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT.
When eBPF Meets Machine Learning: On-the-fly OS Kernel Compartmentalization
Compartmentalization effectively prevents initial corruption from turning into a successful attack.
Uncertainty Regularized Evidential Regression
The Evidential Regression Network (ERN) represents a novel approach that integrates deep learning with Dempster-Shafer's theory to predict a target and quantify the associated uncertainty.
Open-TI: Open Traffic Intelligence with Augmented Language Model
Transportation has greatly benefited the cities' development in the modern civilization process.
Federated Learning with Projected Trajectory Regularization
Specifically, FedPTR allows local clients or the server to optimize an auxiliary (synthetic) dataset that mimics the learning dynamics of the recent model update and utilizes it to project the next-step model trajectory for local training regularization.
Learning Sparsity and Randomness for Data-driven Low Rank Approximation
With the learned value and fixed non-zero positions for sketch matrices from learning-based algorithms, these matrices can reduce the test error of low rank approximation significantly.
