Search Results for author:
Found 7 papers, 3 papers with code
Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding
This research aims to accelerate the inference speed of large language models (LLMs) with billions of parameters.
BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
Large language models (LLMs) commonly employ autoregressive generation during inference, leading to high memory bandwidth demand and consequently extended latency.
FaceMap: Towards Unsupervised Face Clustering via Map Equation
Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management.
Enhancing Social Relation Inference with Concise Interaction Graph and Discriminative Scene Representation
There has been a recent surge of research interest in attacking the problem of social relation inference based on images.
Spatio-Temporal Hybrid Graph Convolutional Network for Traffic Forecasting in Telecommunication Networks
We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks.
Efficient and Robust Equilibrium Strategies of Utilities in Day-ahead Market with Load Uncertainty
We consider the scenario where $N$ utilities strategically bid for electricity in the day-ahead market and balance the mismatch between the committed supply and actual demand in the real-time market, with uncertainty in demand and local renewable generation in consideration.
A Parallelizable Acceleration Framework for Packing Linear Programs
This paper presents an acceleration framework for packing linear programming problems where the amount of data available is limited, i. e., where the number of constraints m is small compared to the variable dimension n. The framework can be used as a black box to speed up linear programming solvers dramatically, by two orders of magnitude in our experiments.
