9 research outputs found
Towards Task Understanding in Visual Settings
We consider the problem of understanding real world tasks depicted in visual
images. While most existing image captioning methods excel in producing natural
language descriptions of visual scenes involving human tasks, there is often
the need for an understanding of the exact task being undertaken rather than a
literal description of the scene. We leverage insights from real world task
understanding systems, and propose a framework composed of convolutional neural
networks, and an external hierarchical task ontology to produce task
descriptions from input images. Detailed experiments highlight the efficacy of
the extracted descriptions, which could potentially find their way in many
applications, including image alt text generation.Comment: Accepted as Student Abstract at 33rd AAAI Conference on Artificial
Intelligence, 201
Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity
Alzheimer's disease is estimated to affect around 50 million people worldwide
and is rising rapidly, with a global economic burden of nearly a trillion
dollars. This calls for scalable, cost-effective, and robust methods for
detection of Alzheimer's dementia (AD). We present a novel architecture that
leverages acoustic, cognitive, and linguistic features to form a multimodal
ensemble system. It uses specialized artificial neural networks with temporal
characteristics to detect AD and its severity, which is reflected through
Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS
challenge dataset, which is a subject-independent and balanced dataset matched
for age and gender to mitigate biases, and is available through DementiaBank.
Our system achieves state-of-the-art test accuracy, precision, recall, and
F1-score of 83.3% each for AD classification, and state-of-the-art test root
mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our
knowledge, the system further achieves state-of-the-art AD classification
accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt
database. Our work highlights the applicability and transferability of
spontaneous speech to produce a robust inductive transfer learning model, and
demonstrates generalizability through a task-agnostic feature-space. The source
code is available at https://github.com/wazeerzulfikar/alzheimers-dementiaComment: To appear in INTERSPEECH 202
Multimodal inductive transfer learning for detection of Alzheimer's dementia and its severity
Copyright © 2020 ISCA Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia
Robustness to Missing Features using Hierarchical Clustering with Split Neural Networks (Student Abstract)
The problem of missing data has been persistent for a long time and poses a major obstacle in machine learning and statistical data analysis. Past works in this field have tried using various data imputation techniques to fill in the missing data, or training neural networks (NNs) with the missing data. In this work, we propose a simple yet effective approach that clusters similar input features together using hierarchical clustering and then trains proportionately split neural networks with a joint loss. We evaluate this approach on a series of benchmark datasets and show promising improvements even with simple imputation techniques. We attribute this to learning through clusters of similar features in our model architecture.</jats:p
neuronets/nobrainer: 1.1.0
Enhancement
Changes required to support the warmstart guide notebook #266 (@ohinds @satra @pre-commit-ci[bot])
Bug Fix
Fix some typos using codespell #262 (@yarikoptic @satra)
[pre-commit.ci] pre-commit autoupdate #263 (@pre-commit-ci[bot] @satra)
Remove unnecessary keepalive runner #265 (@ohinds)
Dynamically provision self-hosted runner #264 (@ohinds)
Authors: 4
@ohinds
@pre-commit-ci[bot]
Satrajit Ghosh (@satra)
Yaroslav Halchenko (@yarikoptic