68 citations · 218 across the 63 of their papers we have counts for
9 papers · 1 filter
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…
DeepConsensus: using the consensus of features from multiple layers to attain robust image classification
Yuchen Li, Safwan Hossain, Kiarash Jamali +1
We consider a classifier whose test set is exposed to various perturbations that are not present in the training set. These test samples still contain enough features to map them t…
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…
Augmenting word2vec with latent Dirichlet allocation within a clinical application
Akshay Budhkar, Frank Rudzicz
This paper presents three hybrid models that directly combine latent Dirichlet allocation and word embedding for distinguishing between speakers with and without Alzheimer's diseas…
Dropout during inference as a model for neurological degeneration in an image captioning network
Bai Li, Ran Zhang, Frank Rudzicz
We replicate a variation of the image captioning architecture by Vinyals et al. (2015), then introduce dropout during inference mode to simulate the effects of neurodegenerative di…
Detecting cognitive impairments by agreeing on interpretations of linguistic features
Zining Zhu, Jekaterina Novikova, Frank Rudzicz
Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of l…
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…