“Can machines categorize new things by learning how to group similar things together?” The following describes work by Yen-Chang Hsu, Zhaoyang Lv, and Zsolt Kira, which will be presented at the 2018 International Conference on Learning Representations (ICLR) in Vancouver. Read the paper here. Clustering is the task of partitioning data into groups, so that… Continue reading Learning to Cluster
The following describes work by Ching-An Cheng and Byron Boots, which was awarded Best Paper at the Further details and proofs are available at The 21st International Conference on Artificial Intelligence and Statistics (AISTATS). The paper can be found here: https://arxiv.org/abs/1801.07292. Learning to make sequential decisions is a fundamental topic in designing automatic agents with artificial intelligence.… Continue reading Convergence of Value Aggregation for Imitation Learning
Video understanding tasks such as action recognition and caption generation are crucial for various real-world applications in surveillance, video retrieval, human behavior understanding, etc. In this work, we present a generic recurrent module to detect relationships and interactions between arbitrary object groups for fine-grained video understanding. Our work is applicable to various open domain video… Continue reading From Object Interactions to Fine-grained Video Understanding
A fundamental question in Natural Language Processing (NLP) is how to represent words. If we have a paragraph we want to translate, or a product review we want to determine whether is positive or negative, or a question we want to answer, ultimately the easiest building block to start from is the individual word. The… Continue reading Learning to Represent Words by how They’re Spelled
Everyday skills, such as making your bed or even pressing a doorbell, might seem trivial to us, but are actually quite complicated for today’s robots. Think about your performance the first time you tried a sport. Did you seek help from a peer or coach? Did you perform better after that? Most probably you answered yes. It… Continue reading Robust Skill Generalization Using Probabilistic Inference
This post summarizes the latest joint research between researchers at Georgia Tech and Facebook on using visualization to make sense of deep learning models, published at IEEE VIS’17, a top visualization conference. While powerful deep learning models have significantly improved prediction accuracy, understanding these models remains a big challenge. Deep learning models are more difficult… Continue reading Visualizing Deep Learning Models at Facebook
Advances in deep learning of representation have resulted in powerful generative approaches on modeling continuous data like time series and images, but it is still challenging to correctly deal with discrete structured data, such as chemical molecules and computer programs. To tackle these challenges, there has been many improvements in formalization of structure generation that… Continue reading Syntax-Directed Variational Autoencoder for Structured Data
The list of accepted papers at ICLR 2018 was released last week and Machine Learning at Georgia Tech (ML@GT) had a strong presence. Out of 935 submissions, 23 oral and 314 conference papers were accepted (roughly 36%). We are pleased to announce that Georgia Tech had 10 conference papers this year, with 1 of them… Continue reading ICLR 2018 accepted papers and ML@GT
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