Hierarchical PAC-Bayes Bounds via Deep Probabilistic Programming
J Warrell, MB Gerstein (2019). Bayesian Deep Learning Workshop at NeurIPS.

Knowledge Based Factorized High Order Sparse Learning Models
S Purushotham, MR Min, CJ Kuo, M Gerstein (2015). NIPS Workshop on Machine Learning in Computational Biology.

Ensemble Learning Based Sparse High-Order Boltzmann Machine for Unsupervised Feature Interaction Identification
MR Min, X Ning, Y Qi, C Cheng, A Bonner, M Gerstein (2014). NIPS Workshop on Machine Learning in Computational Biology.

Interpretable Sparse High-Order Boltzmann Machines
MR Min, X Ning, C Cheng, M Gerstein (2014). JMLR W&CP 33:614-622 (AISTATS 2014).

Interpretable Sparse High-Order Boltzmann Machines for Transcription Factor Interaction Identification
MR Min, X Ning, C Cheng, M Gerstein (2013). NIPS Workshop on Machine Learning in Computational Biology.

Inferring Protein-Protein Interactions Using Interaction Network Topologies
A Paccanaro, V Trifonov, H Yu, M Gerstein (2005). International Joint Conference on Neural Networks (IJCNN, Jul. 31-Aug. 4, Montreal, Canada), pages 161 - 166, vol. 1

An XML-Based Approach to Integrating Heterogeneous Yeast Genome Data
KH Cheung, D Pan, A Smith, M Seringhaus, SM Douglas, M Gerstein (2004). International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences (METMBS); pp 236-242

Using iterative dynamic programming to obtain accurate pairwise and multiple alignments of protein structures.
M Gerstein, M Levitt (1996). Proc Int Conf Intell Syst Mol Biol 4: 59-67.


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