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What is Machine Learning?
Machine learning aims to produce machines that can learn from their experiences and make predictions based on those experiences and other data they have analyzed. The Machine Learning Center at Georgia Tech (ML@GT) is an Interdisciplinary Research Center that is both a home for thought leaders and practitioners and a training ground for the next generation of pioneers.
The field of machine learning crosses a wide variety of disciplines that use data to find patterns in the ways both living systems, such as the human body and artificial systems, such as robots, are constructed and perform. Whether it’s being applied to analyze and learn from medical data, or to model financial markets, or to create autonomous vehicles, machine learning builds and learns from both algorithm and theory to understand the world around us and create the tools we need and want.
Recent News
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A New Neural Network Makes Decisions Like a Human Would
Neural networks do the opposite, making the same decisions each time. Now, Georgia Tech researchers in Associate Professor Dobromir Rahnev’s lab are training them to make decisions more like humans. This science of human decision-making is only just being applied to machine learning, but developing a neural network even closer to the actual human brain may make it more reliable, according to the researchers.
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New Machine Learning Method Lets Scientists Use Generative AI to Design Custom Molecules and Other Complex Structures
New research from Georgia Tech is giving scientists more control options over generative artificial intelligence (AI) models in their studies. Greater customization from this research can lead to discovery of new drugs, materials, and other applications tailor-made for consumers.
The Tech group dubbed its method PRODIGY (PROjected DIffusion for controlled Graph Generation). PRODIGY enables diffusion models to generate 3D images of complex structures, such as molecules from chemical formulas.
Scientists in pharmacology, materials science, social network analysis, and other fields can use PRODIGY to simulate large-scale networks. By generating 3D molecules from multiple graph datasets, the group proved that PRODIGY could handle complex structures.
In keeping with its name, PRODIGY is the first plug-and-play machine learning (ML) approach to controllable graph generation in diffusion models. This method overcomes a known limitation inhibiting diffusion models from broad use in science and engineering.
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Episode of 'Friends' Inspires New Tool that Provides Human-like Perception to MLLMs
For Jitesh Jain, conducting a simple experiment while watching one of his favorite TV series became the genesis of a paper accepted into a prestigious computer vision conference.
Jain is the creator of VCoder, a new tool that enhances the visual perception capabilities of multimodal large language models (MLLMs). Jain said MLLMs like GPT-4 with vision (GPT-4V) are prone to miss obscure objects that blend in with other objects in an image.
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Davenport Named Associate Chair for Graduate Affairs
Professor Mark Davenport will oversee ECE graduate programs and admissions to further develop the School’s graduate offerings and attract leading Ph.D. candidates.
Events
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Sep 11
ML@GT Seminar Series | Photoshop Fantasies
Featuring Walter Scheirer, University of Notre Dame
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Sep 18
IRIM Fall 2024 Seminar | On Human-Machine Interaction Games
Featuring Sam Burden | University of Washington, School of Electrical and Computer Engineering
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Nov 13
IRIM Fall 2024 Seminar Session VI
Featuring Nima Fazeli | Robotics Institute & Mechanical Engineering Department, University of Michigan
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Oct 30
IRIM Fall 2024 Seminar Session V
Featuring Katia Sycara | The Robotics Institute at Carnegie Mellon University
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Oct 16
IRIM Fall 2024 Seminar Session IV
Featuring Helen Huang | NC State University, Director of the Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) Core
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Oct 2
IRIM Fall 2024 Seminar Session III
Featuring Negar Mehr | U.C. Berkely, Department of Mechanical Engineering