Skip to content
seqamlab
seqamlab

seqamlab

Sequence Analysis and Modeling Lab

  • About Us
  • Research
  • Publications
  • Teaching
  • Software and Data
Scroll down to content

Posts

Posted on September 6, 2019October 7, 2019

Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement – ICCV’19 Oral

Our new paper on Bayesian representation learning was accepted as Oral at ICCV 2019. Congratulations to Minyoung, Yuting, and Pritish!

Abstract

We propose a family of novel hierarchical Bayesian deep auto-encoder models capable of identifying disentangled factors of variability in … Read the rest

Posted on August 11, 2019October 7, 2019

The Art of Food: Meal Image Synthesis from Ingredients

The task is to generate a meal image given a set of ingredients

Fangda Han
2019-08-11

Abstract

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

Posted on July 9, 2019October 2, 2019

Unsupervised Visual Domain Adaptation:A Deep Max-Margin Gaussian Process Approach : Oral Paper at CVPR 2019

This is a joint work by Minyoung Kim, Pritish Sahu, Behnam Gholami, Vladimir Pavlovic.

Abstract

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

Posted on May 3, 2018

cs535 special permission or prerequisite override requests for Fall 2018

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

Posted on November 9, 2017November 9, 2017

New AAAI’18 paper on Crowd Modeling

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

Posted on November 9, 2017November 9, 2017

Rutgers CS ranked #9 in the US in Computer Vision / Machine Learning / IR

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

Posted on October 30, 2017October 30, 2017

ICCV 2017 – Wrap Up

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

Posted on October 30, 2017

cs536 – Machine Learning, Spring 2018 – Registration Requests

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

Posted on September 29, 2017September 29, 2017

“Unsupervised Domain Adaptation with Copula Models” presented at MLSP’17

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

Posted on September 29, 2017

Chapter on “Machine Learning Methods for Social Signal Procesing” in Social Signal Processing book

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.

References

[1] O. Rudovic, M.
… Read the rest

Posts navigation

Previous page Page 1 Page 2 Page 3 … Page 7 Next page

Recent Posts

  • Keynote at 1st Chengdu International Forum on Frontier Medicine
  • Bayes-Factor-VAE, ICCV’19 – Presentation Slides & Video
  • Deep Cooking: Predicting Relative Food Ingredient Amounts from Images
  • Task-Discriminative Domain Alignment for Unsupervised Domain Adaptation
  • Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach

Recent Comments

    Archives

    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • May 2018
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • November 2016
    • August 2016
    • January 2016

    Categories

    • Conference Paper
    • Journal Paper
    • Presentation Slides
    • Presentation Video
    • Teaching
    • Uncategorized

    Meta

    • Log in
    • Entries feed
    • Comments feed
    • WordPress.org

    Calendar

    December 2019
    M T W T F S S
    « Nov    
     1
    2345678
    9101112131415
    16171819202122
    23242526272829
    3031  

    Contact us

    SEQAM Lab
    CBIM Center
    Rutgers University
    617 Bowser Road
    Piscataway, New Jersey 08854
    United States of America
    Phone: +1 (848) 445-8846
    Fax:  +1 (732) 445-0537

    Proudly powered by WordPress