Search Results for author:
Found 9 papers, 3 papers with code
A Speed Odyssey for Deployable Quantization of LLMs
The large language model era urges faster and less costly inference.
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
In this study, we propose a novel W4A8 post-training quantization method for the available open-sourced LLMs, which combines the advantages of both two recipes.
SLAMB: Accelerated Large Batch Training with Sparse Communication
Distributed training of large deep neural networks requires frequent exchange of massive data between machines, thus communication efficiency is a major concern.
Stochastic Ratios Tracking Algorithm for Large Scale Machine Learning Problems
In this paper, we propose a novel algorithm for adaptive step length selection in the classical SGD framework, which can be readily adapted to other stochastic algorithms.
Match Cutting: Finding Cuts with Smooth Visual Transitions
A match cut is a transition between a pair of shots that uses similar framing, composition, or action to fluidly bring the viewer from one scene to the next.
Constrained and Composite Optimization via Adaptive Sampling Methods
The motivation for this paper stems from the desire to develop an adaptive sampling method for solving constrained optimization problems in which the objective function is stochastic and the constraints are deterministic.
A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization
This paper describes an extension of the BFGS and L-BFGS methods for the minimization of a nonlinear function subject to errors.
Optimization and Control
A Theoretical Analysis of Deep Q-Learning
Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood.
Contrastive Learning from Pairwise Measurements
In this paper, we study a semiparametric model where the pairwise measurements follow a natural exponential family distribution with an unknown base measure.
