Frank D. Wood

According to our database1, Frank D. Wood authored at least 85 papers between 1996 and 2019.

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Bibliography

2019
Improved Few-Shot Visual Classification.
CoRR, 2019

Attention for Inference Compilation.
CoRR, 2019

Deep Probabilistic Surrogate Networks for Universal Simulator Approximation.
CoRR, 2019

Amortized Rejection Sampling in Universal Probabilistic Programming.
CoRR, 2019

Safer End-to-End Autonomous Driving via Conditional Imitation Learning and Command Augmentation.
CoRR, 2019

The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging.
CoRR, 2019

Near-Optimal Glimpse Sequences for Improved Hard Attention Neural Network Training.
CoRR, 2019

Imitation Learning of Factored Multi-agent Reactive Models.
CoRR, 2019

Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Etalumis: bringing probabilistic programming to scientific simulators at scale.
Proceedings of the International Conference for High Performance Computing, 2019

The Thermodynamic Variational Objective.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Amortized Monte Carlo Integration.
Proceedings of the 36th International Conference on Machine Learning, 2019

LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
An Introduction to Probabilistic Programming.
CoRR, 2018

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.
CoRR, 2018

Revisiting Reweighted Wake-Sleep.
CoRR, 2018

Discontinuous Hamiltonian Monte Carlo for Probabilistic Programs.
CoRR, 2018

High Throughput Synchronous Distributed Stochastic Gradient Descent.
CoRR, 2018

Faithful Inversion of Generative Models for Effective Amortized Inference.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Bayesian Distributed Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Tighter Variational Bounds are Not Necessarily Better.
Proceedings of the 35th International Conference on Machine Learning, 2018

Deep Variational Reinforcement Learning for POMDPs.
Proceedings of the 35th International Conference on Machine Learning, 2018

Auto-Encoding Sequential Monte Carlo.
Proceedings of the 6th International Conference on Learning Representations, 2018

Online Learning Rate Adaptation with Hypergradient Descent.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes.
J. Mach. Learn. Res., 2017

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.
CoRR, 2017

Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference.
CoRR, 2017

Updating the VESICLE-CNN Synapse Detector.
CoRR, 2017

Interpreting Lion Behaviour as Probabilistic Programs.
Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Learning Disentangled Representations with Semi-Supervised Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Using synthetic data to train neural networks is model-based reasoning.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Inference Compilation and Universal Probabilistic Programming.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Spreadsheet Probabilistic Programming.
CoRR, 2016

Inducing Interpretable Representations with Variational Autoencoders.
CoRR, 2016

Probabilistic structure discovery in time series data.
CoRR, 2016

Super-Sampling with a Reservoir.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Bayesian Optimization for Probabilistic Programs.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints.
Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science, 2016

Nonparametric Bayesian models for unsupervised activity recognition and tracking.
Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016

Design and Implementation of Probabilistic Programming Language Anglican.
Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages, 2016

Interacting Particle Markov Chain Monte Carlo.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Inference Networks for Sequential Monte Carlo in Graphical Models.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Black-Box Policy Search with Probabilistic Programs.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Automatic Sampler Discovery via Probabilistic Programming and Approximate Bayesian Computation.
Proceedings of the Artificial General Intelligence - 9th International Conference, 2016

2015
Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Path Finding under Uncertainty through Probabilistic Inference.
CoRR, 2015

Adaptive Scheduling in MCMC and Probabilistic Programming.
CoRR, 2015

Canonical Correlation Forests.
CoRR, 2015

Data-driven Sequential Monte Carlo in Probabilistic Programming.
CoRR, 2015

Maximum a Posteriori Estimation by Search in Probabilistic Programs.
Proceedings of the Eighth Annual Symposium on Combinatorial Search, 2015

Probabilistic Programming in Anglican.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Particle Gibbs with Ancestor Sampling for Probabilistic Programs.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Mixed Membership Classification for Documents with Hierarchically Structured Labels.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Diagnosis code assignment: models and evaluation metrics.
JAMIA, 2014

Learning Probabilistic Programs.
CoRR, 2014

Asynchronous Anytime Sequential Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

A Compilation Target for Probabilistic Programming Languages.
Proceedings of the 31th International Conference on Machine Learning, 2014

Improved activity recognition via Kalman smoothing and multiclass linear discriminant analysis.
Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014

A New Approach to Probabilistic Programming Inference.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data.
Proceedings of the 30th International Conference on Machine Learning, 2013

A Joint Learning Model of Word Segmentation, Lexical Acquisition, and Phonetic Variability.
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013

2012
Inference in Hidden Markov Models with Explicit State Duration Distributions.
IEEE Signal Process. Lett., 2012

Low rank continuous-space graphical models.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Unsupervised Detection and Tracking of Arbitrary Objects with Dependent Dirichlet Process Mixtures
CoRR, 2012

2011
Discussion of "The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling".
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

The sequence memoizer.
Commun. ACM, 2011

Hierarchically Supervised Latent Dirichlet Allocation.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Deplump for Streaming Data.
Proceedings of the 2011 Data Compression Conference (DCC 2011), 2011

2010
Probabilistic Deterministic Infinite Automata.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Lossless Compression Based on the Sequence Memoizer.
Proceedings of the 2010 Data Compression Conference (DCC 2010), 2010

2009
A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

A stochastic memoizer for sequence data.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Dependent Dirichlet Process Spike Sorting.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Characterizing neural dependencies with copula models.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
Biological parametric mapping: A statistical toolbox for multimodality brain image analysis.
NeuroImage, 2007

2006
Discovering natural kinds of robot sensory experiences in unstructured environments.
J. Field Robotics, 2006

A Non-Parametric Bayesian Method for Inferring Hidden Causes.
Proceedings of the UAI '06, 2006

Particle Filtering for Nonparametric Bayesian Matrix Factorization.
Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005
Modeling Neural Population Spiking Activity with Gibbs Distributions.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

2004
On the variability of manual spike sorting.
IEEE Trans. Biomed. Engineering, 2004

1996
WorkSpace and the study of Chagas' disease.
IEEE Computer Graphics and Applications, 1996


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