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). I’m also affiliated with Center for Cognitive Science. My research focus is how an AI-agent can learn to build and represent models of the world in an unsupervised and compositional way. My approach to achieving this is based on deep learning, Bayesian 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

Publications / Google Scholar

2020

Generative Neurosymbolic Machines
J. Jiang and S. Ahn
NeurIPS-20, [arxiv] Spotlight (top 4% = 395/9454 submissions)

Learning to Infer 3D Object Models from Images
{C. Chen, F. Deng}, S. Ahn
Preprint [arxiv] [project]

Improving Generative Imagination in Object-Centric World Models
Z. Lin, Y. Wu, S. Peri, B. Fu, J. Jiang, S. Ahn
ICML-20 [pdf] [project] [code]

Robustifying Sequential Neural Processes
J. Yoon, G. Singh, and S. Ahn
ICML-20 [arxiv]

SCALOR: Generative World Models with Scalable Object Representations
{J. Jiang, S. Janghorbani}, G. Melo, and S. Ahn
ICLR-20 [arxiv] [project] [code]

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] [code]

Hierarchical Decomposition and Generation of Scenes with Compositional Objects
F. Deng, Z. Zhi, S. Ahn
ICML-20, Workshop on Object-Oriented Learning Spotlight [pdf]

Generating Stochastic Object Dynamics in Scenes
Z. Lin, Y. Wu, S. Peri, B. Fu, J. Jiang, and S. Ahn
ICML-20, Workshop on Object-Oriented Learning [pdf]

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
ICLR-17

Denoising Criterion for Variational Auto-Encoding Framework
D. Im, S. Ahn, R. Memisevic, Y. Bengio
AAAI-17

SENA: Preserving Social Structure for Network Embedding
S. Hong, T. Chakraborty, S. Ahn, G. Husari and N. Park
ACM Hypertext and Social Media 17

2016

Pointing the Unknown Words
C. Gulcehre, S. Ahn, R. Nallapati, B. Zhou, Y. Bengio
ACL-16

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
ACL-16

Scalable MCMC for Mixed Membership Stochastic Blockmodels
{W. Li, S. Ahn}, and M. Welling
AISTATS-16

Scalable Overlapping Community Detection
I. El-Helw, R. Hofman, W. Li, S. Ahn, M. Welling, H. Bal
ParLearning-16, Best Paper Award

Learning Latent Multiscale Structure using Recurrent Neural Networks
J. Chung, S. Ahn, Y. Bengio
NIPS-16 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
KDD-15

Distributed Stochastic Gradient MCMC
S. Ahn, B. Shahbaba, and M. Welling
ICML-14

Distributed and Adaptive Darting Monte Carlo through Regenerations
S. Ahn, Y. Chen, and M. Welling
AISTATS-13

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
S. Ahn, A. Korattikara, and M. Welling
ICML-12 Best Paper Award