11 citations · 11 across the 10 of their papers we have counts for
10 papers
Low-Rank Adaptation Redux for Large Models
Bingcong Li, Yilang Zhang, Georgios B. Giannakis
Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networ…
Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation
Yilang Zhang, Abraham Jaeger Mountain, Bingcong Li +1
Meta-learning offers a principled framework leveraging \emph{task-invariant} priors from related tasks, with which \emph{task-specific} models can be fine-tuned on downstream tasks…
ANCRe: Adaptive Neural Connection Reassignment for Efficient Depth Scaling
Yilang Zhang, Bingcong Li, Niao He +1
Scaling network depth has been a central driver behind the success of modern foundation models, yet recent investigations suggest that deep layers are often underutilized. This pap…
ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning
Yilang Zhang, Xiaodong Yang, Yiwei Cai +1
As large language models (LLMs) continue to scale in size, the computational overhead has become a major bottleneck for task-specific fine-tuning. While low-rank adaptation (LoRA)…
VASSO: Variance Suppression for Sharpness-Aware Minimization
Bingcong Li, Yilang Zhang, Georgios B. Giannakis
Sharpness-aware minimization (SAM) has well-documented merits in enhancing generalization of deep neural network models. Accounting for sharpness in the loss function geometry, whe…
Learnable Loss Geometries with Mirror Descent for Scalable and Convergent Meta-Learning
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
Utilizing task-invariant knowledge acquired from related tasks as prior information, meta-learning offers a principled approach to learning a new task with limited data records. Sa…
RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit…
Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a Novel Learning Algorithm
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
Targeting solutions over `flat' regions of the loss landscape, sharpness-aware minimization (SAM) has emerged as a powerful tool to improve generalizability of deep neural network…
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror Descent
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
Utilizing task-invariant prior knowledge extracted from related tasks, meta-learning is a principled framework that empowers learning a new task especially when data records are li…
Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
Yilang Zhang, Bingcong Li, Shijian Gao +1
Meta-learning owns unique effectiveness and swiftness in tackling emerging tasks with limited data. Its broad applicability is revealed by viewing it as a bi-level optimization pro…