Rutgers Machine Learning Group


Names in {___} indicates equal contribution or equal advising

  • SCALOR: Scalable Object-Oriented Sequential Generative Models [pdf] [project]
    {J. Jiang, S. Janghorbani}, G. Melo, and S. Ahn 
    International Conference on Learning Representations (ICLR)

  • SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition [pdf] [project]
    {Z. Lin, Y. Wu, S. Peri, W. Sun,} G. Singh, F. Deng, J. Jiang, S. Ahn
    International Conference on Learning Representations (ICLR)
  • Sequential Neural Processes [pdf] [project] [code]
    {G. Singh, J. Yoon}, Y. Sohn, and S. Ahn 
    Neural Information Processing Systems (NeurIPS) Spotlight (top 2.4% = 164/6743)

  • Variational Temporal Abstraction [pdf] [code]
    T. Kim, {S. Ahn, Y. Bengio} 
    Neural Information Processing Systems (NeurIPS)

  • Neural Multisensory Scene Inference [pdf] [project] [code]
    J. Lim, P. Pinheiro, N. Rostamzadeh, C. Pal, and S. Ahn
    Neural Information Processing Systems (NeurIPS)

  • Learning Single-View 3D Reconstruction with Adversarial Training [pdf]
    P. Pinheiro, N. Rostamzadeh, and S. Ahn
    International Conference on Computer Vision (ICCV) Oral (top 4.3% of all the submitted)

  • Generative Hierarchical Models for Parts, Objects, and Scenes [pdf]
    F. Deng, Z. Zhi, and S. Ahn

  • Reinforced Imitation in Heterogeneous Action Space [pdf]
    K. Zolna, N. Rostamzadeh, Y. Bengio, {S. Ahn, P. O. Pinheiro} 
  • Reinforced Imitation Learning from Observations [pdf]
    K. Żołna, N. Rostamzadeh, Y. Bengio, {S. Ahn, P. Pinheiro} 
    NeurIPS 18 Workshop on Imitation Learning and Its Challenges in Robotics

  • Bayesian Model-Agnostic Meta-Learning [pdf
    {J Yoon, T Kim}, O. Dia, S. Kim, Y. BengioS. Ahn  
    Neural Information Processing Systems (NeurIPS) Spotlight (top 3.5% = 168/4856)
  • Hierarchical Multiscale Recurrent Neural Networks [pdf]
    J. Chung, S. AhnY. Bengio
    International Conference on Learning Representations (ICLR)

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

  • 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
  • A Neural Knowledge Language Model
    S. Ahn, H. Choi, T. Parnamaa, Y. Bengio
    [ArXiv16] [pdf] [dataset]

  • Hierarchical Memory Networks
    S. Chandar, S. Ahn, H. Larochelle, P. Vincent, G. Tasauro, Y. Bengio
    [ArXiv16] [pdf]

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

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

  • 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] [pdf] [dataset

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

  • Scalable Overlapping Community Detection
    I. El-Helw, R. Hofman, W. Li, S. Ahn, M. Welling, H. Bal
  • [ParLearning16] [pdfBest Paper Award
~2015 (selected publications)
  • Stochastic Gradient MCMC: Algorithms and Applications 
    [PhD Dissertation 15] [pdf]

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

  • Distributed Stochastic Gradient MCMC
    S. Ahn, B. Shahbaba, and M. Welling
    [ICML14] [pdf

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

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