AI Innovation Institute

The Institute for AI-Driven Discovery and Innovation hosts Dr. Mary
Simoni for a talk on her music and its intersection with AI, as part
of the Music and AI Seminars series.

The event will be held on Thursday, December 10, 2020, at 3:00 PM.

Abstract: Mary Simoni, Dean of Humanities, Arts & Social Sciences at
Rensselaer Polytechnic Institute will discuss her research in the use
of computer algorithms and technology in the composition and
performance of music. The talk will feature compositions inspired by
Augmented Transition Networks (ATNs), employ motion tracking to
control synthesis parameters, and a work in progress that employs
machine learning using training data that juxtaposes classical music
with COVID-19. During this talk, participants will be introduced to
several technologies that support music information retrieval, machine
learning, and algorithmic composition such as jSymbolic, Weka, and
Common Music.

Zoom details below:
https://stonybrook.zoom.us/j/98236706900?pwd=bDFEZFZtaHBWU0cyL0wxK3UrdUpIdz09
Meeting ID: 982 3670 6900
Passcode: 133945  

A talk by Jerome Zhengrong Liang entitled, Machine Learning from Original Images to Texture Patterns: A Paradigm Shift from Non-Medical Application to Medical Diagnosis. Abstract: Artificial intelligence (AI) research for medical diagnosis started soon after human began to use computer, initially called artificial neural network (ANN) and now convolutional neural network (CNN). ANN has been mainly explored to classify the experts' handcrafted features from the original (or raw) images, while CNN has been mainly explored directly on the raw images for both tasks of extracting abstract features and classifying the features. Experimental evidences have been shown that CNN can be trained by a large number of the raw images with experts' scores (or labels) to match or even surpass the experts' performance for both non-medical and medical diagnosis applications. However, the performances of the CNN models as well as the experts on medical diagnosis dropped dramatically when the labels of the raw images were replaced by the corresponding medical pathological reports. Accumulated medical knowledge reveals that the lesion heterogeneity is a footprint of lesion evolution and ecology, and the heterogeneity is an indicator of lesion progress and response to medical intervention. The heterogeneity can be reflected by the image contrast distribution (or texture patterns) across the lesion volume. Image textures have been shown as an effective descriptor of the lesion heterogeneity for computer-aided diagnosis. Can we map the raw images into texture patterns (or images) and train CNN to learn from the texture images? This question is the central theme of this presentation with application to CT Colonography or virtual colonoscopy, a game from AlphaGo to PolypGo. Bio: Jerome Zhengrong Liang, PhD, IEEE Fellow Imaging Research and Informatics Laboratory Department of Radiology, Stony Brook University

Spring 2026, Wednesdays 2 to 3:20 pm, NCS 220 and Zoom link to be announced soon.

The seminar will be jointly taught by Prof. Dimitris Samaras (samaras@cs.stonybrook.edu).

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision.

To enroll in this course, you must either: (1) be in the Ph.D. program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend.

Please note: Exceptionally, the first meeting on 1/28 will be in NCS 120.

TITLE: Towards a Theory of Encode/Decoder Architectures by Andrej Risteski of CMU

ABSTRACT: A common choice of architecture in representation learning (i.e., learning a good embedding of the data) is an encoder/decoder architecture, which tries to map a part of the input into a good latent representation (via an encoder), and predict the remaining part of the input (via a decoder). Two common examples are universal machine translation: where one tries to learn to translate between any pair of a set of languages via a common latent language, given paired up corpora for only a part of the pairs; and contextual encoders -- where one tries to predict a part of the image, given the rest of the image.
 
We will give a framework for analyzing the sample complexity of such architectures -- i.e., how many pairs of languages do we need to have paired up corpora for? How many image prediction tasks do we have to solve to get a good representation?

The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.

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