1 citations · 3 across the 12 of their papers we have counts for
10 papers · 1 filter
Integrated electro-optic attention nonlinearities for transformers
Luis Mickeler, Kai Lion, Alfonso Nardi +5
Transformers have emerged as the dominant neural-network architecture, achieving state-of-the-art performance in language processing and computer vision. At the core of these model…
Zeroth-Order Optimization at the Edge of Stability
Minhak Song, Liang Zhang, Bingcong Li +3
Zeroth-order (ZO) methods are widely used when gradients are unavailable or prohibitively expensive, including black-box learning and memory-efficient fine-tuning of large models,…
SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference
Hao Ma, Melis Ilayda Bal, Liang Zhang +4
Modern large language models are increasingly deployed under compute and memory constraints, making flexible control of model capacity a central challenge. While sparse and low-ran…
On the Benefits of Weight Normalization for Overparameterized Matrix Sensing
Yudong Wei, Liang Zhang, Bingcong Li +1
While normalization techniques are widely used in deep learning, their theoretical understanding remains relatively limited. In this work, we establish the benefits of (generalized…
Zeroth-Order Optimization Finds Flat Minima
Liang Zhang, Bingcong Li, Kiran Koshy Thekumparampil +3
Zeroth-order methods are extensively used in machine learning applications where gradients are infeasible or expensive to compute, such as black-box attacks, reinforcement learning…
PoLAR: Polar-Decomposed Low-Rank Adapter Representation
Kai Lion, Liang Zhang, Bingcong Li +1
We show that low-rank adaptation of large-scale models suffers from a low stable rank that is well below the linear algebraic rank of the subspace, degrading fine-tuning performanc…