Sungjin Ahn


Assistant Professor
Department of Computer Science
Center for Cognitive Science
Rutgers University




I’m an Assistant Professor of Computer Science at Rutgers University where I lead the Rutgers Machine Learning Group (RUML). My current research focus is how an AI-agent can learn to build models of the world in an unsupervised and compositional way. My approach to achieving this is based on deep learning, probabilistic modeling, reinforcement learning, and inspiration from cognitive & neuroscience. I received my Ph.D. at the University of California, Irvine on the study of scalable approximate Bayesian inference under the supervision of Prof. Max Welling. I did my postdoc working on deep learning at MILA under Prof. Yoshua Bengio. Then, I joined Rutgers University in Fall 2018.

Email: sjn.[last_name] at gmail
Address: CBIM 9, 617 Bowser Rd, Piscataway, NJ 08854

News

  • I’ll be visiting DeepMind in March to give a talk
  • 2 papers accepted in ICLR 2020
  • Teaching CS 536: Machine Learning in Spring 2020
  • New paper on Generative Hierarchical Models for Parts, Objects, and Scenes is now in arXiv
  • 3 papers accepted in NeurIPS 2019 including one spotlight paper
  • Learning Single-View 3D Reconstruction with Adversarial Training is accepted to ICCV 2019 as an oral presentation
  • New paper on Sequential Neural Processes is in arXiv

Publications / Google Scholar

2020

SCALOR: Scalable Object-Oriented Sequential Generative Models
{J. Jiang, S. Janghorbani}, G. Melo, and S. Ahn
ICLR 20 [pdf] [project]

SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
{Z. Lin, Y. Wu, S. Peri}, W. Sun, G. Singh, F. Deng, J. Jiang, S. Ahn
ICLR 20 [pdf] [project]

2019

Sequential Neural Processes
{G. Singh, J. Yoon}, Y. Sohn, and S. Ahn
NeurIPS 19, Spotlight (top 2.4% = 164/6743), pdf, project, code

Variational Temporal Abstraction
T. Kim, {S. Ahn, Y. Bengio}
NeurIPS 19

Neural Multisensory Scene Inference
J. Lim, P. Pinheiro, N. Rostamzadeh, C. Pal, and S. Ahn
NeurIPS 19

Learning Single-View 3D Reconstruction with Adversarial Training
P. Pinheiro, N. Rostamzadeh, and S. Ahn
ICCV 19, Oral (top 4.3% of all the submitted)

Generative Hierarchical Models for Parts, Objects, and Scenes
F. Deng, Z. Zhi, and S. Ahn
arXiv

Reinforced Imitation in Heterogeneous Action Space
K. Zolna, N. Rostamzadeh, Y. Bengio, {S. Ahn, P. O. Pinheiro}
arXiv

2018

Bayesian Model-Agnostic Meta-Learning
{J Yoon, T Kim}, O. Dia, S. Kim, Y. Bengio, S. Ahn
NeurIPS 18, Spotlight (top 3.5% = 168/4856)

Reinforced Imitation Learning from Observations
K. Żołna, N. Rostamzadeh, Y. Bengio, {S. Ahn, P. Pinheiro}
NeurIPS 18 Workshop on Imitation Learning and Its Challenges in Robotics

2017

Hierarchical Multiscale Recurrent Neural Networks
J. Chung, S. Ahn, Y. Bengio
International Conference on Learning Representations (ICLR)

Denoising Criterion for Variational Auto-Encoding Framework
D. Im, S. Ahn, R. Memisevic, Y. Bengio
AAAI Conferenceon Artificial Intelligence (AAAI)

SENA: Preserving Social Structure for Network Embedding
S. Hong, T. Chakraborty, S. Ahn, G. Husari and N. Park
ACM Conference on Hypertext and Social Media

2016

Pointing the Unknown Words
C. Gulcehre, S. Ahn, R. Nallapati, B. Zhou, Y. Bengio
ACL16

Generating Factoid Questions with Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
{I. V. Serban, A. G. Duran}, C. Gulcehre, S. Ahn, S. Chandar, A. Courville, Y. Bengio
ACL16

Scalable MCMC for Mixed Membership Stochastic Blockmodels
{W. Li, S. Ahn}, and M. Welling
AISTATS16

Scalable Overlapping Community Detection
I. El-Helw, R. Hofman, W. Li, S. Ahn, M. Welling, H. Bal
ParLearning16, Best Paper Award

Learning Latent Multiscale Structure using Recurrent Neural Networks
J. Chung, S. Ahn, Y. Bengio
NIPS 2016 Workshop on Neural Abstract Machines & Program Induction (NAMPI)

A Neural Knowledge Language Model
S. Ahn, H. Choi, T. Parnamaa, Y. Bengio
arXiv

Hierarchical Memory Networks
S. Chandar, S. Ahn, H. Larochelle, P. Vincent, G. Tasauro, Y. Bengio
arXiv

~2015 (selected publications)

Stochastic Gradient MCMC: Algorithms and Applications
PhD Dissertation

Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC
S. Ahn, A. Korattikara, N. Liu, S. Rajan, and M. Welling
KDD15

Distributed Stochastic Gradient MCMC
S. Ahn, B. Shahbaba, and M. Welling
ICML14

Distributed and Adaptive Darting Monte Carlo through Regenerations
S. Ahn, Y. Chen, and M. Welling
AISTATS13

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
S. Ahn, A. Korattikara, and M. Welling
ICML12 Best Paper Award