68 citations · 218 across the 63 of their papers we have counts for
14 papers · 1 filter
MLHOps: Machine Learning for Healthcare Operations
Faiza Khan Khattak, Vallijah Subasri, Amrit Krishnan +4
Machine Learning Health Operations (MLHOps) is the combination of processes for reliable, efficient, usable, and ethical deployment and maintenance of machine learning models in he…
OOD-Probe: A Neural Interpretation of Out-of-Domain Generalization
Zining Zhu, Soroosh Shahtalebi, Frank Rudzicz
The ability to generalize out-of-domain (OOD) is an important goal for deep neural network development, and researchers have proposed many high-performing OOD generalization method…
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations
Aparna Balagopalan, Haoran Zhang, Kimia Hamidieh +3
Machine learning models in safety-critical settings like healthcare are often blackboxes: they contain a large number of parameters which are not transparent to users. Post-hoc exp…
Improving greedy core-set configurations for active learning with uncertainty-scaled distances
Yuchen Li, Frank Rudzicz
We scale perceived distances of the core-set algorithm by a factor of uncertainty and search for low-confidence configurations, finding significant improvements in sample efficienc…
Challenges for Reinforcement Learning in Healthcare
Elsa Riachi, Muhammad Mamdani, Michael Fralick +1
Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an…
BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data
Demetres Kostas, Stephane Aroca-Ouellette, Frank Rudzicz
Deep neural networks (DNNs) used for brain-computer-interface (BCI) classification are commonly expected to learn general features when trained across a variety of contexts, such t…
Sequential Explanations with Mental Model-Based Policies
Arnold YS Yeung, Shalmali Joshi, Joseph Jay Williams +1
The act of explaining across two parties is a feedback loop, where one provides information on what needs to be explained and the other provides an explanation relevant to this inf…
Variations on the Chebyshev-Lagrange Activation Function
Yuchen Li, Frank Rudzicz, Jekaterina Novikova
We seek to improve the data efficiency of neural networks and present novel implementations of parameterized piece-wise polynomial activation functions. The parameters are the y-co…
Centroid-based deep metric learning for speaker recognition
Jixuan Wang, Kuan-Chieh Wang, Marc Law +2
Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker re…
Robustness against the channel effect in pathological voice detection
Yi-Te Hsu, Zining Zhu, Chi-Te Wang +3
Many people are suffering from voice disorders, which can adversely affect the quality of their lives. In response, some researchers have proposed algorithms for automatic assessme…
The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech
Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz +1
Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, mod…
ChainGAN: A sequential approach to GANs
Safwan Hossain, Kiarash Jamali, Yuchen Li +1
We propose a new architecture and training methodology for generative adversarial networks. Current approaches attempt to learn the transformation from a noise sample to a generate…
Deconfounding age effects with fair representation learning when assessing dementia
Zining Zhu, Jekaterina Novikova, Frank Rudzicz
One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. Howev…
Semi-supervised classification by reaching consensus among modalities
Zining Zhu, Jekaterina Novikova, Frank Rudzicz
Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data. Recently, Consensus Networks (CNs) were proposed to alleviate da…