Quantitative analysis of brain disorders such as Autism Spectrum Disorder (ASD) is an ongoing field of research. Machine learning and deep learning techniques have been playing an important role in automating the diagnosis of brain disorders by extracting discriminative features from the brain data. In this study, we propose a model called Auto-ASD-Network in order to classify subjects with Autism disorder from healthy subjects using only fMRI data. Our model consists of a multilayer perceptron (MLP) with two hidden layers. We use an algorithm called SMOTE for performing data augmentation in order to generate artificial data and avoid overfitting, which helps increase the classification accuracy. We further investigate the discriminative power of features extracted using MLP by feeding them to an SVM classifier. In order to optimize the hyperparameters of SVM, we use a technique called Auto Tune Models (ATM) which searches over the hyperparameter space to find the best values of SVM hyperparameters. Our model achieves more than 70% classification accuracy for 4 fMRI datasets with the highest accuracy of 80%. It improves the performance of SVM by 26%, the stand-alone MLP by 16% and the state of the art method in ASD classification by 14%. The implemented code will be available as GPL license on GitHub portal of our lab (https://github.com/PCDS).
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This content will become publicly available on June 26, 2025
Compression with Attention: Learning in Lower Dimensions
With deep learning models ever ballooning in size to push state-ofthe-
art accuracy improvements, efforts to find compact models have
become necessary. To meet such an objective, we propose a novel
operation called Personal Self-Attention (PSA). It is designed specifically
to learn non-linear 1-D functions faster than existing architectures
like Multi-Layer Perceptron (MLP) and Polynomial-based
methods, while being highly compatible with gradient backpropagation.
We show that by stacking and combining these non-linear
functions with linear transformations, we can achieve the same
accuracy as a larger model but with a hidden dimension that is
significantly smaller.
To test our contribution, we implemented PSA on an MLP-based
vision model called ResMLP and tested it against vision classification
tasks on SVHN, and CIFAR-10 datasets. We show how PSA
pushes the pareto-front, achieving the same accuracy with 2 − 6×
smaller hidden-dimension sizes compared to the conventional MLP
structures. Further, by quantizing our non-linear function, the PSA
can be mapped to a simple lookup table, allowing for very efficient
translation to FPGA hardware. We demonstrate this by designing
an unrolled high-throughput accelerator for ResMLP using nearly
1.5× fewer DSPs with PSA compared to a conventional MLP architecture
while achieving the same accuracy of 86% and throughput
of 29k FPS.
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- Award ID(s):
- 2016390
- PAR ID:
- 10533920
- Publisher / Repository:
- Design Automation Conference
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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