Category: Conferences

ML@GT to Present Nine Papers at Competitive Machine Learning Conference

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The International Conference on Machine Learning (ICML) received nearly 5,000 submissions for its 2020 conference and accepted 1,088 papers. Machine Learning Center at Georgia Tech (ML@GT) researchers authored nine accepted papers. The papers explore topics like privacy, semantics in predictive agents, data science, and artificial intelligence. One paper, Boosting Frank-Wolfe by Chasing Gradients, proposes a new state-of-the-art algorithm for constrained… Continue reading ML@GT to Present Nine Papers at Competitive Machine Learning Conference

ML@GT to Present Diverse Research Interests at CVPR 2020

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For computer vision fans, the Computer Vision and Pattern Recognition (CVPR) conference is a significant annual event, regularly drawing thousands of attendees and paper submissions. The  Georgia Institute of Technology had nine papers by 33 authors accepted in the conference, taking place online this year starting June 14. Accepted papers covered a wide range of… Continue reading ML@GT to Present Diverse Research Interests at CVPR 2020

Georgia Tech Researchers Presenting Work Virtually at Top AI Conference Due to COVID-19

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Due to the rapid spread of coronavirus (COVID-19) and resulting travel restrictions, Georgia Tech students and faculty will now be presenting their research virtually at the International Conference on Learning Representations (ICLR), one of the biggest artificial intelligence (AI) conferences in the world, April 25 through 30. With 17 papers to present, researchers will create a… Continue reading Georgia Tech Researchers Presenting Work Virtually at Top AI Conference Due to COVID-19

Working Towards Explainable and Data-efficient Machine Learning Models via Symbolic Reasoning

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By Yuan Yang In recent years, we have experienced the success of modern machine learning (ML) models. Many of them have led to unprecedented breakthroughs in a wide range of applications, such as AlphaGo beating a world champion human player or the introduction of autonomous vehicles. There has been a continuous effort, both from industry… Continue reading Working Towards Explainable and Data-efficient Machine Learning Models via Symbolic Reasoning

Escaping Saddle Points Faster with Stochastic Momentum

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By Jun-Kun Wang, Chi-Heng Lin, and Jacob Abernethy SGD with stochastic momentum (see Figure 1 below) has been the de facto training algorithm in nonconvex optimization and deep learning. It has been widely adopted for training neural nets in various applications. Modern techniques in computer vision (e.g.[1,2]), speech recognition (e.g. [3]), natural language processing (e.g.… Continue reading Escaping Saddle Points Faster with Stochastic Momentum

Learning to Cooperate in Multi-Agent Environments

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By Jiachen Yang Over the years, human intelligence has evolved to work within a shared environment with other humans to do more than play Atari games or solve Rubik’s cubes alone in our rooms. The presence of other people demands our ability to handle a wide spectrum of complex interactions — we cooperate with colleagues… Continue reading Learning to Cooperate in Multi-Agent Environments

Snapshots from NeurIPS2019

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What once started as a small conference with a few hundred people interested in neural information processing systems has over the years turned into one of the largest artificial intelligence conferences in the world. This year, over 13,000 people attended the 33rd conference on Neural Information Processing Systems (NeurIPS) in Vancouver, British Columbia. Over the… Continue reading Snapshots from NeurIPS2019

Explaining Blended Matching Pursuit: A Multi-Purpose AI Algorithm

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By Cyrille Combettes This is an informal summary of our recent paper Blended Matching Pursuit with my advisor Sebastian Pokutta. It will be presented at the Conference on Neural Information Processing Systems (NeurIPS) in Vancouver, British Columbia, Dec. 8-14, 2019. In this post, we motivate and explain the main ideas behind the design of our… Continue reading Explaining Blended Matching Pursuit: A Multi-Purpose AI Algorithm

Making Artificial Intelligence Work in a Changing Environment

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By Adrian Rivera Cardoso and He Wang Machine learning (ML) is changing our lives. We can instantly translate from one language to another, search entire libraries in a matter of seconds, and even prevent credit card fraud. ML’s success is mostly due to the power of artificial neural networks — a machine learning model inspired… Continue reading Making Artificial Intelligence Work in a Changing Environment

Explaining Nonparametric Regression on Low Dimensional Manifolds using Deep Neural Networks

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By Minshuo Chen Background and Motivation Deep learning has made significant breakthroughs in various real-world applications, such as computer vision, natural language processing, healthcare, robotics, etc. In image classification, the winner of the $latex 2017$ ImageNet challenge retained a top-$latex 5$ error rate of $latex 2.25\% $ [1], while the data set consists of about… Continue reading Explaining Nonparametric Regression on Low Dimensional Manifolds using Deep Neural Networks