Deep Learning | Learning, Intelligence + Signal Processing Lab

Applying Manifold Learning Technique to Design Recurrent Architecture for Low Dimension Classification

Wed, 03 Aug 2022 19:59:08 -0500

Deep Neural Networks (DNNs) can have very high performance in a visual recognition task but are prone to noise and adversarial attacks. One main problem of training a DNN is the input often lay in very high dimensional space which leads to a high number of parameters to train. This raises the question of reducing the number of dimensions of the dataset. Given a high dimension dataset such as a visual dataset, how can we find a lower dimension representation that keeps the essential information of the images? With a low dimension representation, we can hopefully use a more shallow/simple architecture that can decently classify high dimensional datasets.

Complex Valued Neural Networks

Wed, 03 Aug 2022 19:59:08 -0500

Current neural network models that deal with data on the spectral plane (magnitude and phase) only take as input the magnitude, and do not incorporate, in a meaningful way, the phase information. Research has shown that the output of biological neurons is affected by the phase of its inputs.

In order to bridge this separation between artificial neurons and biological neurons, I am experimenting with the effectiveness and implementation of complex valued neural networks, which would integrate the phase information meaningfully. In particular, in order to take advantage of the popular neural network software package and framework, PyTorch, I am working to simulate complex valued neural network operations through real valued neural networks. I hope that by implementing complex valued neural networks through this framework, it will be easy for other researchers to use and experiment on.

Efficient Neural Networks: Reducing Network Architecture Size

Wed, 03 Aug 2022 22:03:18 -0500

Neural networks are an immensely powerful tool for many difficult problems, but often require computational power beyond that of small devices such as embedded systems, Internet of Things devices, and mobile phones. In this project we aim to create computationally efficient neural networks, networks with smaller memory and compute footprints without losing objective functionality.

🎉 Laura Greige's Successful Ph.D. Thesis Defense 🎉

Sun, 07 Aug 2022 20:17:27 -0500

Congratulations to Dr. Greige! Laura has successfully defended her PhD thesis entitled “Anomaly Detection in Competitive Multiplayer Games”.

🎉 Yida Xin's Successful Ph.D. Thesis Defense 🎉

Sun, 07 Aug 2022 20:17:27 -0500

Congratulations to Dr. Xin! Yida has successfully defended his PhD thesis entitled “Disambiguating Natural Language via Aligning Meaningful Descriptions”.

🎉 Xiao Zhou's Successful Ph.D. Thesis Defense 🎉

Wed, 01 Dec 2021 03:35:06 -0500

Congratulations to Dr. Zhou! Xiao has successfully defended his PhD thesis entitled “Competitive Deep Learning for Imaging Applications”.

🎉 Louis Jensen's Successful Ph.D. Thesis Defense 🎉

Fri, 06 Aug 2021 03:35:06 -0500

Congratulations to Dr. Jensen! Louis has successfully defended his PhD thesis entitled “Minimalism in Deep Learning”.

🎉 Kieran Wang's Successful Ph.D. Thesis Defense 🎉

Wed, 16 Dec 2020 03:35:06 -0500

Congratulations to Dr. Wang! Kieran has successfully defended his PhD thesis entitled “Topics of Deep Learning in Security and Compression”.

🎉 Ken Zhou's Successful M.S. Thesis Defense 🎉

Tue, 07 Aug 2018 21:56:00 -0500

Congratulations to Ken Zhou! Ken has successfully defended his MS thesis entitled “Bidirectional Long Short-term Memory Network For Proto-object Representation”.

Deep learning-based classification and anomaly detection of side-channel signals

Mon, 01 Jan 2018 00:00:00 +0000

🎉 Tao Xiong's Successful Ph.D. Thesis Defense 🎉

Mon, 07 Aug 2017 20:17:27 -0500

Congratulations to Dr. Xiong! Tao has successfully defended his PhD thesis entitled “Data-Driven Representation, Learning and Applications: From Compressed Sensing to Deep Neural Networks”.