Search Results for author: Vaden Masrani

Found 12 papers, 9 papers with code

All in the (Exponential) Family: Information Geometry and Thermodynamic Variational Inference

no code implementations ICML 2020 Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan

While the Evidence Lower Bound (ELBO) has become a ubiquitous objective for variational inference, the recently proposed Thermodynamic Variational Objective (TVO) leverages thermodynamic integration to provide a tighter and more general family of bounds.

Variational Inference

Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning

2 code implementations13 Jan 2022 Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Jan-Willem van de Meent, Leonid Sigal, Frank Wood

The first method, Simple CNAPS, employs a hierarchically regularized Mahalanobis-distance based classifier combined with a state of the art neural adaptive feature extractor to achieve strong performance on Meta-Dataset, mini-ImageNet and tiered-ImageNet benchmarks.

Active Learning Continual Learning +1

Proof of the impossibility of probabilistic induction

no code implementations1 Jul 2021 Vaden Masrani

In this short note I restate and simplify the proof of the impossibility of probabilistic induction from Popper (1992).

q-Paths: Generalizing the Geometric Annealing Path using Power Means

1 code implementation1 Jul 2021 Vaden Masrani, Rob Brekelmans, Thang Bui, Frank Nielsen, Aram Galstyan, Greg Ver Steeg, Frank Wood

Many common machine learning methods involve the geometric annealing path, a sequence of intermediate densities between two distributions of interest constructed using the geometric average.

Bayesian Inference

Annealed Importance Sampling with q-Paths

1 code implementation NeurIPS Workshop DL-IG 2020 Rob Brekelmans, Vaden Masrani, Thang Bui, Frank Wood, Aram Galstyan, Greg Ver Steeg, Frank Nielsen

Annealed importance sampling (AIS) is the gold standard for estimating partition functions or marginal likelihoods, corresponding to importance sampling over a path of distributions between a tractable base and an unnormalized target.

Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective

1 code implementation NeurIPS 2020 Vu Nguyen, Vaden Masrani, Rob Brekelmans, Michael A. Osborne, Frank Wood

Achieving the full promise of the Thermodynamic Variational Objective (TVO), a recently proposed variational lower bound on the log evidence involving a one-dimensional Riemann integral approximation, requires choosing a "schedule" of sorted discretization points.

All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference

1 code implementation1 Jul 2020 Rob Brekelmans, Vaden Masrani, Frank Wood, Greg Ver Steeg, Aram Galstyan

We propose to choose intermediate distributions using equal spacing in the moment parameters of our exponential family, which matches grid search performance and allows the schedule to adaptively update over the course of training.

Variational Inference

Planning as Inference in Epidemiological Models

1 code implementation30 Mar 2020 Frank Wood, Andrew Warrington, Saeid Naderiparizi, Christian Weilbach, Vaden Masrani, William Harvey, Adam Scibior, Boyan Beronov, John Grefenstette, Duncan Campbell, Ali Nasseri

In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models.

Probabilistic Programming

Improved Few-Shot Visual Classification

2 code implementations CVPR 2020 Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal

Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data.

Classification Few-Shot Image Classification +2

The Thermodynamic Variational Objective

1 code implementation NeurIPS 2019 Vaden Masrani, Tuan Anh Le, Frank Wood

We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models.

Variational Inference

Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia

1 code implementation WS 2017 Vaden Masrani, Gabriel Murray, Thalia Field, Giuseppe Carenini

We investigate if writers with dementia can be automatically distinguished from those without by analyzing linguistic markers in written text, in the form of blog posts.

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