MATRIX Spring Seminar Series – Dr. Amey Kulkarni

On Friday, March 5th, MATRIX is pleased to host Dr. Amey Kulkarni from NVIDIA as part of our seminar series.  All spring seminars will be hosted via Zoom. Full details regarding the seminar may be found below –

Demystifying Deep Learning Inference on Embedded Platforms

Dr. Amey Kulkarni

NVIDIA

Friday, March 5, 2021

11 AM -12 PM CST

tinyurl.com/MATRIXSpringSeminar

Implementing deep learning inference on resource constraint embedded platforms is challenging. In recent years, we are witnessing artificial intelligence being deployed on embedded platforms in our everyday life, such as keywords detection on voice assistant devices, voice and image recognition on cell phones, robotics platforms performing food delivery and smart security systems etc. However, successful deployment of highly complex deep learning models require optimization at both the hardware and the software levels. In this talk, I will share my experience on enabling efficient inference on embedded systems using two widely adopted software techniques i.e. quantization and pruning. Performing both quantization and pruning is challenging due to accuracy vs complexity trade-off. Additionally, we will review some of the NVIDIA tools to improve end-to-end deep learning workflow with examples and their performance evaluations on NVIDIA’s Jetson embedded platform.

MATRIX Spring Seminar Series – Dr. Pradeep Ravikumar

On Friday, February 5th, MATRIX is pleased to host Dr. Pradeep Ravikumar from Carnegie Mellon University for our spring seminar series. This seminar will be held via Zoom. Additional information about the seminar may be found below –

Graceful Statistical Machine Learning

Dr. Pradeep Ravikumar

Machine Learning Department, School of Computer Science, Carnegie Mellon University

February 5, 2021, 11 AM – Noon CST

tinyurl.com/MATRIXSpringSeminar

Modern statistical machine learning systems have achieved tremendous empirical successes in recent years. But there is an increasing realization that these successes are in largely sanitized settings where the training and test environments are largely similar, and the desiderata for these systems are largely based on some well-defined expected
performance measures. When these conditions fail to hold, these systems frequently exhibit a lack of graceful behavior.

We provide two vignettes of our recent research into more graceful machine learning systems. In the first, we provide a new computationally-efficient class of machine learning estimators that are robust under varied settings, including arbitrary training data contamination, and where the training data is heavy-tailed (so that the sample data is not “representative” of the true distribution). Our workhorse is a novel robust variant of gradient descent, and we provide conditions under which our gradient descent variant provides accurate estimators in a general convex risk minimization problem. These results provide some of the first computationally tractable and provably robust estimators for general statistical models. In the second vignette, we re-consider the standard workhorse in statistical machine learning of empirical risk minimization, which aims to measure the expected risk with respect to some loss function for a given task. However, seminal results in behavioral economics have shown that human decision-making is based on different risk measures than the expectation of any given loss function. In contrast to minimizing expected loss, could we minimize a better human-aligned risk measure? While this might not seem natural at first glance, we analyze the properties of such a revised “human aligned” risk measure, and surprisingly show that it also better aligns with varied additional desiderata such as fairness.

Speaker Biography:

Pradeep Ravikumar is an Associate Professor in the Machine Learning Department, School of Computer Science at Carnegie Mellon University. His thesis has received honorable mentions in the ACM SIGKDD Dissertation award and the CMU School of Computer Science Distinguished Dissertation award. He is a Sloan Fellow, a Siebel Scholar, a recipient of the NSF CAREER Award. He is Associate Editor-in-Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), editor for the Machine Learning journal, and action editor for the Journal of Machine Learning Research, and was Program Chair for the International Conference on Artificial Intelligence and Statistics (AISTATS) in 2013.

MATRIX Spring Seminar Series – Matthew Mattina

On Friday, January 29th, MATRIX is pleased to host Matthew Mattina from ARM for our initial spring seminar. Mr. Mattina is the ARM Distinguished Engineer and Senior Director of their Machine Learning Research Lab. He is also a member of the MATRIX advisory board. This seminar will be held via Zoom at the following link. Additional information about the seminar may be found below –

Tiny but powerful: Hardware for High Performance, Low Power Machine Learning

Matthew Mattina

Arm

January 29, 2021, 11 AM – Noon CST

tinyurl.com/MATRIXSpringSeminar

This talk will cover the emerging “tinyml” paradigm and give some tinyml example applications and benchmarks. We’ll look at what makes TinyML particularly challenging, and how neural network models and hardware can be co-designed to meet these challenges.

Speaker Biography:

Matthew Mattina is the Distinguished Engineer and Senior Director of Machine Learning Research at ARM, where he leads a global team of machine learning researchers developing advanced hardware, software, and algorithms. Prior to joining ARM in 2015, Matt was Chief Technology Officer at Tilera, where he was responsible for overall company technology, processor architecture, and strategy. Prior to Tilera, Matt was a CPU architect at Intel and invented and designed the Intel Ring Uncore Architecture. Matt has been granted over 30 patents relating to CPU design, multicore processors, on-chip interconnects, and cache coherence protocols. Matt holds a BS in Computer and Systems Engineering from Rensselaer Polytechnic Institute and an MS in Electrical Engineering from Princeton University.

ACM UTSA Student Chapter Open House

Please join the ACM Student Chapter at UTSA on Wednesday, 1/27 at 5:15 PM CST for their spring semester open house event. The event will be held via Zoom at the following link. You can find out more information regarding the ACM student organization in this video. Please contact Tiba Al Anssari with any further questions at [email protected] 

The Past, Present, and Future of VR: Grandfather of VR Dr. Thomas Furness

Please join the Youth AI Lab on Sunday, 1/10 at 7:30 PM CST for a presentation by Dr. Thomas Furness. Entitled “The Past, Present, and Future of VR: Grandfather of VR Dr. Thomas Furness,” the presentation will cover Dr. Furness’ personal VR journey and his vision for the future of the technology. More details may be found at the following link.

TACC@MATIRX – Deep Learning Workshop