43 citations · 173 across the 33 of their papers we have counts for
6 papers · 1 filter
Magneton: Optimizing Energy Efficiency of ML Systems via Differential Energy Debugging
Yi Pan, Wenbo Qian, Dedong Xie +3
The training and deployment of machine learning (ML) models have become extremely energy-intensive. While existing optimization efforts focus primarily on hardware energy efficienc…
E-Pruner: Towards Efficient, Economical, and Effective Layer Pruning for Large Language Models
Tao Yuan, Haoli Bai, Yinfei Pan +5
With the increasing size of large language models, layer pruning has gained increased attention as a hardware-friendly approach for model compression. However, existing layer pruni…
MoMoE: A Mixture of Expert Agent Model for Financial Sentiment Analysis
Peng Shu, Junhao Chen, Zhengliang Liu +8
We present a novel approach called Mixture of Mixture of Expert (MoMoE) that combines the strengths of Mixture-of-Experts (MoE) architectures with collaborative multi-agent framewo…
Bridging Classical and Quantum Computing for Next-Generation Language Models
Yi Pan, Hanqi Jiang, Junhao Chen +6
Integrating Large Language Models (LLMs) with quantum computing is a critical challenge, hindered by the severe constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, incl…
AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs
Nicholas E. Corrado, Julian Katz-Samuels, Adithya Devraj +6
When aligning large language models (LLMs), their performance on various tasks (such as being helpful, harmless, and honest) depends heavily on the composition of their training da…
MolQAE: Quantum Autoencoder for Molecular Representation Learning
Yi Pan, Hanqi Jiang, Wei Ruan +5
We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to qu…