Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift

no code implementations • 29 Dec 2023 • Benjamin Eyre, Elliot Creager, David Madras, Vardan Papyan, Richard Zemel

Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research.

Out-of-Distribution Generalization regression

Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach

no code implementations • EMNLP (WNUT) 2020 • Benjamin Eyre, Aparna Balagopalan, Jekaterina Novikova

Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains common in fields such as cognitive impairment (CI) detection.

BIG-bench Machine Learning Feature Engineering +1

To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection

no code implementations • 26 Jul 2020 • Aparna Balagopalan, Benjamin Eyre, Frank Rudzicz, Jekaterina Novikova

Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods.

Alzheimer's Disease Detection