TY - JOUR
TI - Probabilistic machine learning and artificial intelligence
AU - Ghahramani, Zoubin
T2 - Nature
DA - 2015/05//
PY - 2015
DO - 10/gdxwhq
DP - Crossref
VL - 521
IS - 7553
SP - 452
EP - 459
LA - en
SN - 0028-0836, 1476-4687
UR - http://www.nature.com/articles/nature14541
Y2 - 2019/11/28/12:16:49
KW - Bayesian inference
KW - Classical ML
KW - Machine learning
KW - Probabilistic programming
ER -
TY - JOUR
TI - Deep Probabilistic Programming
AU - Tran, Dustin
AU - Hoffman, Matthew D.
AU - Saurous, Rif A.
AU - Brevdo, Eugene
AU - Murphy, Kevin
AU - Blei, David M.
T2 - arXiv:1701.03757 [cs, stat]
AB - We propose Edward, a Turing-complete probabilistic programming language. Edward defines two compositional representations---random variables and inference. By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning. For flexibility, Edward makes it easy to fit the same model using a variety of composable inference methods, ranging from point estimation to variational inference to MCMC. In addition, Edward can reuse the modeling representation as part of inference, facilitating the design of rich variational models and generative adversarial networks. For efficiency, Edward is integrated into TensorFlow, providing significant speedups over existing probabilistic systems. For example, we show on a benchmark logistic regression task that Edward is at least 35x faster than Stan and 6x faster than PyMC3. Further, Edward incurs no runtime overhead: it is as fast as handwritten TensorFlow.
DA - 2017/03/07/
PY - 2017
DP - arXiv.org
UR - http://arxiv.org/abs/1701.03757
Y2 - 2019/11/27/23:15:14
KW - Bayesian inference
KW - Implementation
KW - Machine learning
KW - Probabilistic programming
ER -
TY - BOOK
TI - Model-Based Machine Learning
AU - Winn, John Michael
AB - This book is unusual for a machine learning text book in that the authors do not review dozens of different algorithms. Instead they introduce all of the key ideas through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter therefore introduces one case study which is drawn from a real-world application that has been solved using a model-based approach.
DA - 2019/06//
PY - 2019
DP - Google Books
SP - 400
LA - en
PB - Taylor & Francis Incorporated
SN - 978-1-4987-5681-5
KW - Bayesian inference
KW - Classical ML
KW - Implementation
ER -