Search Results for author:
Found 14 papers, 11 papers with code
Detecting Dementia through Retrospective Analysis of Routine Blog Posts by Bloggers with Dementia
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.
Generating and Evaluating Summaries for Partial Email Threads: Conversational Bayesian Surprise and Silver Standards
We define and motivate the problem of summarizing partial email threads.
The Thermodynamic Variational Objective
We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models.
Improved Few-Shot Visual Classification
Few-shot learning is a fundamental task in computer vision that carries the promise of alleviating the need for exhaustively labeled data.
Ranked #2 on
Few-Shot Image Classification
on Mini-Imagenet 10-way (5-shot)
(using extra training data)
Planning as Inference in Epidemiological Models
In this work we demonstrate how to automate parts of the infectious disease-control policy-making process via performing inference in existing epidemiological models.
All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference
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.
Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective
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.
Annealed Importance Sampling with q-Paths
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.
q-Paths: Generalizing the Geometric Annealing Path using Power Means
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.
Proof of the impossibility of probabilistic induction
In this short note I restate and simplify the proof of the impossibility of probabilistic induction from Popper (1992).
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
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.
Flexible Diffusion Modeling of Long Videos
We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments.
GOLD: Generalized Knowledge Distillation via Out-of-Distribution-Guided Language Data Generation
Knowledge distillation from LLMs is essential for the efficient deployment of language models.
All in the (Exponential) Family: Information Geometry and Thermodynamic Variational Inference
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.
