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Found 7 papers, 2 papers with code
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
To address the growing demand for privacy protection in machine learning, we propose a novel and efficient machine unlearning approach for \textbf{L}arge \textbf{M}odels, called \textbf{LM}Eraser.
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization
Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning, wherein the weak-to-strong consistency regularization framework, popularized by FixMatch, is widely used as a benchmark scheme.
Manipulating the Label Space for In-Context Classification
Specifically, using our approach on ImageNet, we increase accuracy from 74. 70\% in a 4-shot setting to 76. 21\% with just 2 shots.
Machine Unlearning: Solutions and Challenges
Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy breaches, security vulnerabilities, and performance degradation.
Speaker Embeddings as Individuality Proxy for Voice Stress Detection
However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages.
Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings
Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers.
RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System
In this paper, we propose a Robust Transformer-based Intrusion Detection System(RTIDS)reconstructingfeaturerepresentationstomakeatrade-offbetweendimensionalityreduction and feature retention in imbalanced datasets.
