12 citations · 53 across the 37 of their papers we have counts for
10 papers · 1 filter
Completion of partial structures using Patterson maps with the CrysFormer machine learning model
Tom Pan, Evan Dramko, Mitchell D. Miller +2
Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been appli…
TwIST: Rigging the Lottery in Transformers with Independent Subnetwork Training
Michael Menezes, Barbara Su, Xinze Feng +3
We introduce TwIST, a distributed training framework for efficient large language model (LLM) sparsification. TwIST trains multiple subnetworks in parallel, periodically aggregates…
Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest
Yuqian Huo, David Quiroga, Anastasios Kyrillidis +1
Variational quantum algorithms (VQAs) have the potential to demonstrate quantum utility on near-term quantum computers. However, these algorithms often get executed on the highest-…
Guided by the Experts: Provable Feature Learning Dynamic of Soft-Routed Mixture-of-Experts
Fangshuo Liao, Anastasios Kyrillidis
Mixture-of-Experts (MoE) architectures have emerged as a cornerstone of modern AI systems. In particular, MoEs route inputs dynamically to specialized experts whose outputs are agg…
ADAPT: Lightweight, Long-Range Machine Learning Force Fields Without Graphs
Evan Dramko, Yihuang Xiong, Yizhi Zhu +4
Point defects play a central role in driving the properties of materials. First-principles methods are widely used to compute defect energetics and structures, including at scale f…
One Rank at a Time: Cascading Error Dynamics in Sequential Learning
Mahtab Alizadeh Vandchali, Fangshuo, Liao +1
Sequential learning -- where complex tasks are broken down into simpler, hierarchical components -- has emerged as a paradigm in AI. This paper views sequential learning through th…
Exploring How LLMs Capture and Represent Domain-Specific Knowledge
Mirian Hipolito Garcia, Camille Couturier, Daniel Madrigal Diaz +5
We study whether Large Language Models (LLMs) inherently capture domain-specific nuances in natural language. Our experiments probe the domain sensitivity of LLMs by examining thei…
Quantum EigenGame for excited state calculation
David Quiroga, Jason Han, Anastasios Kyrillidis
Computing the excited states of a given Hamiltonian is computationally hard for large systems, but methods that do so using quantum computers scale tractably. This problem is equiv…
RecCrysFormer: Refined Protein Structural Prediction from 3D Patterson Maps via Recycling Training Runs
Tom Pan, Evan Dramko, Mitchell D. Miller +2
Determining protein structures at an atomic level remains a significant challenge in structural biology. We introduce , a hybrid model that exploits the str…
Provable Model-Parallel Distributed Principal Component Analysis with Parallel Deflation
Fangshuo Liao, Wenyi Su, Anastasios Kyrillidis
We study a distributed Principal Component Analysis (PCA) framework where each worker targets a distinct eigenvector and refines its solution by updating from intermediate solution…