Seminars

Seminar by Nathan Silberman on “TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow” Thursday Sep 7 2017, 4:30 pm – 5:45 pm in Clough 144

ML@GT Seminar and Guest Speaker for CS 7643 Deep Learning Title: TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow Speaker: Nathan Silberman Date/Time: Thursday Sep 7 2017, 4:30 pm – 5:45 pm Location: Clough 144 Abstract: TF-Slim is a TensorFlow-based library with various components. These include modules for easily defining neural network models… Continue reading Seminar by Nathan Silberman on “TF-Slim: A Lightweight Library for Defining, Training and Evaluating Complex Models in TensorFlow” Thursday Sep 7 2017, 4:30 pm – 5:45 pm in Clough 144

Seminars

ML/Statistics Seminar by Xiaodong Li on “Convex Relaxation for Community Detection”

Machine Learning/Statistics Seminar Series Date/Time: Thursday Sep 7 2017, 11:00 pm – 12:00 pm Location: Advisory Boardroom, #402 Groseclose Speaker: Xiaodong Li Title: Convex Relaxation for Community Detection Abstract: Cluster structures are ubiquitous for large data, and community detection has recently attracted much research attention in applied physics, sociology, computer science and statistics due to… Continue reading ML/Statistics Seminar by Xiaodong Li on “Convex Relaxation for Community Detection”

Seminars

ML/Statistics Seminar by Jason Lee on “Matrix Completion, Saddlepoints, and Gradient Descent”

ML/Statistics Seminar Series Date/Time: Wed. Sep 6 2017, 3:00 pm – 4:00 pm Location: Advisory Boardroom, #402 Groseclose Speaker: Jason Lee Title: Matrix Completion, Saddlepoints, and Gradient Descent Abstract: Matrix completion is a fundamental machine learning problem with wide applications in collaborative filtering and recommender systems. Typically, matrix completion are solved by non-convex optimization procedures,… Continue reading ML/Statistics Seminar by Jason Lee on “Matrix Completion, Saddlepoints, and Gradient Descent”

Seminars

Seminar by Soumith Chintala on “An Overview of Deep Learning Frameworks and an Introduction to PyTorch” 9/6/17, 12:30, Nano 1118

ML@GT Seminar Series Title: An Overview of Deep Learning Frameworks and an Introduction to PyTorch Speaker: Soumith Chintala, Facebook Date/Time: Wednesday Sep 6 2017, 12:30 pm – 1:30 pm Location: Marcus Nanotechnology Building, Room 1118 Abstract: In this talk, you will get an exposure to the various types of deep learning frameworks – declarative and imperative frameworks such… Continue reading Seminar by Soumith Chintala on “An Overview of Deep Learning Frameworks and an Introduction to PyTorch” 9/6/17, 12:30, Nano 1118

Awards · Events · Seminars

Devi Parikh gives a seminar to celebrate her IJCAI 2017 Computers and Thought Award.

ML@GT hosted a special a seminar & a reception to celebrate Devi’ Parikh’s IJCAI’s 2017 Computers and Thought Award. She will give a talk about her work and there will be a reception to celebrate her award. Date/Time: 9/5/2017 @ 3-4pm with a reception following to celebrate this award. Location: The Engineered Biosystems Building (EBB),… Continue reading Devi Parikh gives a seminar to celebrate her IJCAI 2017 Computers and Thought Award.

Seminars

ML/Statistics Seminar by Junwei Lu on “Topological Inference on Large Scale Graphon”

Machine Learning/Statistics Seminar Series Date/Time: Thursday Aug 31 2017, 11:00 pm – 12:00 pm Location: Advisory Board Room, #402 Groseclose Speaker: Junwei Lu Title: Topological Inference on Large Scale Graphon Abstract: We propose to test the topological structures of complex networks under the graphon model. Graphon is a nonparametric model for large scale stochastic graphs.… Continue reading ML/Statistics Seminar by Junwei Lu on “Topological Inference on Large Scale Graphon”

Seminars

ML/Statistics Seminar by Mengyang Gu on “An improved approach to Bayesian computer model calibration and prediction”

Statistics/Machine Learning Seminar Series Date/Time: Tuesday Aug 15 2017, 3:00 pm – 4:00 pm Location: Advisory Board Room, #402 Groseclose Speaker: Mengyang Gu Title: An improved approach to Bayesian computer model calibration and prediction Abstract: We consider the problem of calibrating inexact computer models using experimental data. To compensate for the misspecification of the computer… Continue reading ML/Statistics Seminar by Mengyang Gu on “An improved approach to Bayesian computer model calibration and prediction”