Multiclass Support Vector Machines for Articulatory Feature Classification. - Brian Hutchinson; Jianna Zhang (AAAI, 2006)

Non-segmental duration feature extraction for prosodic classification. - Amy Dashiell; Brian Hutchinson; Anna Margolis; Mari Ostendorf (INTERSPEECH, 2008)

Towards Standardisation of Online Dispute Resolution Tools. - Arno R. Lodder; Andrea Borri; Jacques Gouimenou; Brian Hutchinson; Vincent Tilman (ODR, 2008)

Unsupervised broadcast conversation speaker role labeling. - Brian Hutchinson; Bin Zhang 009; Mari Ostendorf (ICASSP, 2010)

Extracting Phrase Patterns with Minimum Redundancy for Unsupervised Speaker Role Classification. - Bin Zhang 009; Brian Hutchinson; Wei Wu; Mari Ostendorf (HLT-NAACL, 2010)

"What i know is...": establishing credibility on Wikipedia talk pages. - Meghan Oxley; Jonathan T. Morgan; Mark Zachry; Brian Hutchinson (Int. Sym. Wikis, 2010)

Analyzing conversations using rich phrase patterns. - Bin Zhang 009; Alex Marin; Brian Hutchinson; Mari Ostendorf (ASRU, 2011)

Learning non-parametric models of pronunciation. - Brian Hutchinson; Jasha Droppo (ICASSP, 2011)

Low Rank Language Models for Small Training Sets. - Brian Hutchinson; Mari Ostendorf; Maryam Fazel (IEEE Signal Process. Lett., 2011)

A deep architecture with bilinear modeling of hidden representations: Applications to phonetic recognition. - Brian Hutchinson; Li Deng 001; Dong Yu 001 (ICASSP, 2012)

Parallel Training for Deep Stacking Networks. - Li Deng 001; Brian Hutchinson; Dong Yu 001 (INTERSPEECH, 2012)

A Sparse Plus Low Rank Maximum Entropy Language Model. - Brian Hutchinson; Mari Ostendorf; Maryam Fazel (INTERSPEECH, 2012)

Exceptions in language as learned by the multi-factor sparse plus low-rank language model. - Brian Hutchinson; Mari Ostendorf; Maryam Fazel (ICASSP, 2013)

Tensor Deep Stacking Networks. - Brian Hutchinson; Li Deng 001; Dong Yu 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Learning Phrase Patterns for Text Classification. - Bin Zhang 009; Alex Marin; Brian Hutchinson; Mari Ostendorf (IEEE Trans. Speech Audio Process., 2013)

Subword-based modeling for handling OOV words inkeyword spotting. - Yanzhang He; Brian Hutchinson; Peter Baumann 003; Mari Ostendorf; Eric Fosler-Lussier; Janet B. Pierrehumbert (ICASSP, 2014)

The Tensor Deep Stacking Network Toolkit. - David Palzer; Brian Hutchinson (IJCNN, 2015)

A Sparse Plus Low-Rank Exponential Language Model for Limited Resource Scenarios. - Brian Hutchinson; Mari Ostendorf; Maryam Fazel (IEEE ACM Trans. Audio Speech Lang. Process., 2015)

Using Pronunciation-Based Morphological Subword Units to Improve OOV Handling in Keyword Search. - Yanzhang He; Peter Baumann 003; Hao Fang 002; Brian Hutchinson; Aaron Jaech; Mari Ostendorf; Eric Fosler-Lussier; Janet B. Pierrehumbert (IEEE ACM Trans. Audio Speech Lang. Process., 2016)

Predicting User Roles from Computer Logs Using Recurrent Neural Networks. - Aaron Tuor; Samuel Kaplan; Brian Hutchinson; Nicole Nichols 001; Sean Robinson (AAAI, 2017)

Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams. - Aaron Tuor; Samuel Kaplan; Brian Hutchinson; Nicole Nichols 001; Sean Robinson (AAAI Workshops, 2017)

Predicting the Effect of Point Mutations on Protein Structural Stability. - Roshanak Farhoodi; Max Shelbourne; Rebecca Hsieh; Nurit Haspel; Brian Hutchinson; Filip Jagodzinski (BCB, 2017)

Low n-rank tensor log-linear models for classification. - Caleb Nelson; Yulo Leake; Brian Hutchinson (IJCNN, 2017)

Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection. - Aaron Randall Tuor; Ryan Baerwolf; Nicolas Knowles; Brian Hutchinson; Nicole Nichols 001; Robert Jasper (AAAI Workshops, 2018)

Low Rank Smoothed Sampling Methods for Identifying Impactful Pairwise Mutations. - Nicholas Majeske; Filip Jagodzinski; Brian Hutchinson; Tanzima Z. Islam (BCB, 2018)

Deep Learning Approaches to Chemical Property Prediction from Brewing Recipes. - Gracie Ermi; Ellyn Ayton; Nolan Price; Brian Hutchinson (IJCNN, 2018)

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection. - Andy Brown; Aaron Tuor; Brian Hutchinson; Nicole Nichols 001 (CoRR, 2018)

A systematic exploration of ΔΔG cutoff ranges in machine learning models for protein mutation stability prediction. - Richard Olney; Aaron Tuor; Filip Jagodzinski; Brian Hutchinson (J. Bioinform. Comput. Biol., 2018)

Deep Learning for Classroom Activity Detection from Audio. - Robin Cosbey; Allison Wusterbarth; Brian Hutchinson (ICASSP, 2019)

Explaining Multimodal Deceptive News Prediction Models. - Svitlana Volkova; Ellyn Ayton; Dustin Lockhart Arendt; Zhuanyi Huang; Brian Hutchinson (ICWSM, 2019)

Metric-Based Few-Shot Learning for Video Action Recognition. - Chris Careaga; Brian Hutchinson; Nathan Oken Hodas; Lawrence Phillips (CoRR, 2019)

Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers. - Loc Truong; Chace Jones; Brian Hutchinson; Andrew August; Brenda Praggastis; Robert Jasper; Nicole Nichols 001; Aaron Tuor (CVPR Workshops, 2020)

DeepClimGAN: A High-Resolution Climate Data Generator. - Alexandra Puchko; Robert Link; Brian Hutchinson; Ben Kravitz; Abigail Snyder (CoRR, 2020)

A Study of Few-Shot Audio Classification. - Piper Wolters; Chris Careaga; Brian Hutchinson; Lauren Phillips (CoRR, 2020)

Loosely Conditioned Emulation of Global Climate Models With Generative Adversarial Networks. - Alexis Ayala; Christopher Drazic; Brian Hutchinson; Ben Kravitz; Claudia Tebaldi (CoRR, 2021)

Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers. - Piper Wolters; Chris Daw; Brian Hutchinson; Lauren Phillips (CoRR, 2021)

Fine-Grained Classroom Activity Detection from Audio with Neural Networks. - Eric Slyman; Chris Daw; Morgan Skrabut; Ana Usenko; Brian Hutchinson (CoRR, 2021)

RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning. - Sarah Coffland; Katie Christensen; Filip Jagodzinski; Brian Hutchinson (BCB, 2023)

DiffESM: Conditional Emulation of Earth System Models with Diffusion Models. - Seth Bassetti; Brian Hutchinson; Claudia Tebaldi; Ben Kravitz (CoRR, 2023)

Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models. - Katie Christensen; Lyric Otto; Seth Bassetti; Claudia Tebaldi; Brian Hutchinson (CoRR, 2024)

DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models. - Seth Bassetti; Brian Hutchinson; Claudia Tebaldi; Ben Kravitz (CoRR, 2024)

Emulating the Global Change Analysis Model with Deep Learning. - Andrew Holmes; Matt Jensen; Sarah Coffland; Hidemi Mitani Shen; Logan Sizemore; Seth Bassetti; Brenna Nieva; Claudia Tebaldi; Abigail Snyder; Brian Hutchinson (CoRR, 2024)