Sequence Analysis and Modeling Lab
Our new paper on Bayesian representation learning was accepted as Oral at ICCV 2019. Congratulations to Minyoung, Yuting, and Pritish!
We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in … Read the rest
Fangda Han
2019-08-11
In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual … Read the rest
This is a joint work by Minyoung Kim, Pritish Sahu, Behnam Gholami, Vladimir Pavlovic.
For unsupervised domain adaptation, the target domain error can be provably reduced by having a shared input representation that makes the source and target domains … Read the rest
If you are interested in registering for my Fall 2018 cs535 Pattern Recognition and do not have the prerequisites or for some other reason need a Special Permission (SPN) or Prerequisite Override, you will need to fill out a request … Read the rest
Our paper “The Role of Data Driven Priors in Multi-agent Crowd Trajectory Estimation” has been accepted to AAAI’18.
Congratulations to Gang on his hard work!… Read the rest
We recently received some exceptionally encouraging news: Rutgers CS is ranked #9 in the US in the combined categories of Computer Vision / Machine Learning and Data Mining / Information Retrieval.
This is quite an accomplishment. Congratulations to all!… Read the rest
Last week was the time for ICCV 2017 in Venice, Italy. Setting aside my personal indifference to Venice (overcrowded and overpriced, uninspiring food), ICCV was an interesting meeting. It took place on Lido, at the same venue as the Venice … Read the rest
If you are interested in registering for my Spring 2018 Machine Learning course (01:198:536) and do not have the prerequisites or for some other reason need a Special Permission (SPN) or Prerequisite Override, you will need to fill out a … Read the rest
Our paper “Unsupervised Domain Adaptation with Copula Models”[1] was presented this week at the IEEE Int’l Workshop on Machine Learning for Signal Processing in Tokyo, Japan.
It was an exciting meeting; unlike the now mega-conferences of CVPR and … Read the rest
Social Signal Processing book by Cambridge University Press was finally published this July. Read our chapter on “Machine Learning Methods for Social Signal Processing”[1] on p. 234 of this collection of outstanding articles.
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