26 citations · 98 across the 31 of their papers we have counts for
43 papers
Generalization behavior of OPTQ and the role of regularization
Erin George, Rayan Saab
Large neural networks can be compressed by rounding or "quantizing" their weights to numbers that admit representations with fewer bits. One algorithm for quantization, OPTQ, progr…
WaterKron and FlipFlop Hessian: Information-Theoretically Grounded Quantization with Kronecker-factored Hessians
Johann Birnick, Rayan Saab
How should a Kronecker-factored Hessian approximation be chosen for post-training quantization? We address this question through WaterKron, which combines two-sided GPTQ with row-…
FastKron: Efficient Quantization with Kronecker-Factored Hessians
Johann Birnick, Rayan Saab
We accelerate a family of algorithms for neural network quantization which utilize a two-sided version of the GPTQ/LDLQ algorithm. Standard GPTQ-style adaptive rounding uses one-si…
GPTQ-intrinsic LoRA: A Near-optimal Algorithm for Low-precision Quantization with Low-rank Adaptation
Shihao Zhang, Rayan Saab
Post-training quantization is widely used for compressing large neural networks, but aggressive low-bit quantization can significantly degrade model quality. A common remedy is to…
Low-Bit Quantization of Bandlimited Graph Signals via Iterative Methods
Felix Krahmer, He Lyu, Rayan Saab +3
We study the quantization of real-valued bandlimited signals on graphs, focusing on low-bit representations. We propose iterative noise-shaping algorithms for quantization, includi…
Beacon: Post-Training Quantization with Integrated Grid Selection
Shihao Zhang, Rayan Saab
Quantization is a widely used compression technique for reducing the memory and computation costs of large pre-trained models. A key challenge in per-channel post-training quantiza…