Rethinking machine unlearning for large language models
We explore machine unlearning (MU) in the domain of large language models (LLMs),
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …
referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence …
On penalty-based bilevel gradient descent method
Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization,
meta-learning and reinforcement learning. However, bilevel problems are difficult to solve …
meta-learning and reinforcement learning. However, bilevel problems are difficult to solve …
Advancing model pruning via bi-level optimization
The deployment constraints in practical applications necessitate the pruning of large-scale
deep learning models, ie, promoting their weight sparsity. As illustrated by the Lottery Ticket …
deep learning models, ie, promoting their weight sparsity. As illustrated by the Lottery Ticket …
Enhancing generalization of universal adversarial perturbation through gradient aggregation
Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an
instance-agnostic perturbation capable of fooling the target model for most samples …
instance-agnostic perturbation capable of fooling the target model for most samples …
Robust mixture-of-expert training for convolutional neural networks
Abstract Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has
demonstrated a great promise to enable high-accuracy and ultra-efficient model inference …
demonstrated a great promise to enable high-accuracy and ultra-efficient model inference …
Text-visual prompting for efficient 2d temporal video grounding
In this paper, we study the problem of temporal video grounding (TVG), which aims to predict
the starting/ending time points of moments described by a text sentence within a long …
the starting/ending time points of moments described by a text sentence within a long …
An Introduction to Bilevel Optimization: Foundations and applications in signal processing and machine learning
Recently, bilevel optimization (BLO) has taken center stage in some very exciting
developments in the area of signal processing (SP) and machine learning (ML). Roughly …
developments in the area of signal processing (SP) and machine learning (ML). Roughly …
Simfbo: Towards simple, flexible and communication-efficient federated bilevel learning
Federated bilevel optimization (FBO) has shown great potential recently in machine learning
and edge computing due to the emerging nested optimization structure in meta-learning …
and edge computing due to the emerging nested optimization structure in meta-learning …
Random sharpness-aware minimization
Abstract Currently, Sharpness-Aware Minimization (SAM) is proposed to seek the
parameters that lie in a flat region to improve the generalization when training neural …
parameters that lie in a flat region to improve the generalization when training neural …
A survey on efficient methods for adversarial robustness
A Muhammad, SH Bae - IEEE Access, 2022 - ieeexplore.ieee.org
Deep learning has revolutionized computer vision with phenomenal success and
widespread applications. Despite impressive results in complex problems, neural networks …
widespread applications. Despite impressive results in complex problems, neural networks …