1 code implementation • 13 Feb 2024 • David Brandfonbrener, Simon Henniger, Sibi Raja, Tarun Prasad, Chloe Loughridge, Federico Cassano, Sabrina Ruixin Hu, Jianang Yang, William E. Byrd, Robert Zinkov, Nada Amin
In this paper, we present VerMCTS, an approach to begin to resolve this issue by generating verified programs in Dafny and Coq.
1 code implementation • 20 Oct 2019 • Saeid Naderiparizi, Adam Ścibior, Andreas Munk, Mehrdad Ghadiri, Atılım Güneş Baydin, Bradley Gram-Hansen, Christian Schroeder de Witt, Robert Zinkov, Philip H. S. Torr, Tom Rainforth, Yee Whye Teh, Frank Wood
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance.
no code implementations • pproximateinference AABI Symposium 2019 • Bradley Gram-Hansen, Christian Schroeder de Witt, Robert Zinkov, Saeid Naderiparizi, Adam Scibior, Andreas Munk, Frank Wood, Mehrdad Ghadiri, Philip Torr, Yee Whye Teh, Atilim Gunes Baydin, Tom Rainforth
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i. e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops.
no code implementations • NAACL 2018 • Fatemeh Torabi Asr, Robert Zinkov, Michael Jones
Word embeddings obtained from neural network models such as Word2Vec Skipgram have become popular representations of word meaning and have been evaluated on a variety of word similarity and relatedness norming data.
no code implementations • NeurIPS 2018 • Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Tom Rainforth, Yee Whye Teh, Frank Wood
Inference amortization methods share information across multiple posterior-inference problems, allowing each to be carried out more efficiently.
no code implementations • 2 Mar 2017 • Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov, Frank Wood
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning.
no code implementations • 6 Mar 2016 • Robert Zinkov, Chung-chieh Shan
Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm.