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Theodore L. Willke

6 accepted papers

2025

AutoParLLM: GNN-guided Context Generation for Zero-Shot Code Parallelization using LLMs

NAACL 2025long

In-Context Learning (ICL) has been shown to be a powerful technique to augment the capabilities of LLMs for a diverse range of tasks. This work proposes AutoParLLM, a novel way to generate context using guidance from graph neural networks (GNNs) to generate efficient parallel codes. We evaluate Auto…

2024

A Structure-Aware Framework for Learning Device Placements on Computation Graphs

NeurIPS 2024poster

Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous…

2024

Structure Guided Prompt: Instructing Large Language Model in Multi-Step Reasoning by Exploring Graph Structure of the Text

EMNLP 2024main

Although Large Language Models (LLMs) excel at addressing straightforward reasoning tasks, they frequently struggle with difficulties when confronted by more complex multi-step reasoning due to a range of factors. Firstly, natural language often encompasses complex relationships among entities, maki…

Cited by 6SourcePDFScholar
2018

Capturing Shared and Individual Information in fMRI Data

ICASSP 2018accepted

Cognitive neuroscience seeks to explain the organization of the brain, but typically focuses on aspects that are shared across people rather than those that vary across individuals. Here, we present a new method for analyzing brain imaging data that captures both shared and individual components of…

Cited by 0SourceScholar
2018

Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers

ECCV 2018poster

As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-distribution (OOD) inputs while employing these algorithms. In this work, we propose an OOD detection algorithm which com…

Cited by 305SourcePDFScholar
2017

A semi-supervised method for multi-subject FMRI functional alignment

ICASSP 2017accepted

Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods s…

Cited by 0SourceScholar