Theofanis Karaletsos | DeepAI

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∙ 05/30/2023

Contextual Vision Transformers for Robust Representation Learning

We present Contextual Vision Transformers (ContextViT), a method for pro...

14 Yujia Bao, et al. ∙

∙ 11/04/2022

Black-box Coreset Variational Inference

Recent advances in coreset methods have shown that a selection of repres...

3 Dionysis Manousakas, et al. ∙

∙ 10/01/2021

TyXe: Pyro-based Bayesian neural nets for Pytorch

We introduce TyXe, a Bayesian neural network library built on top of Pyt...

4 Hippolyt Ritter, et al. ∙

∙ 06/17/2021

Localized Uncertainty Attacks

The susceptibility of deep learning models to adversarial perturbations ...

3 Ousmane Amadou Dia, et al. ∙

∙ 02/25/2021

Stochastic Aggregation in Graph Neural Networks

Graph neural networks (GNNs) manifest pathologies including over-smoothi...

8 Yuanqing Wang, et al. ∙

∙ 06/09/2020

Variational Auto-Regressive Gaussian Processes for Continual Learning

This paper proposes Variational Auto-Regressive Gaussian Process (VAR-GP...

0 Sanyam Kapoor, et al. ∙

∙ 02/10/2020

Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights

Probabilistic neural networks are typically modeled with independent wei...

10 Theofanis Karaletsos, et al. ∙

∙ 02/08/2020

Generalized Hidden Parameter MDPs Transferable Model-based RL in a Handful of Trials

There is broad interest in creating RL agents that can solve many (relat...

0 Christian F. Perez, et al. ∙

∙ 01/17/2019

Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference

A regression-based BNN model is proposed to predict spatiotemporal quant...

0 Xinyu Hu, et al. ∙

∙ 12/08/2018

Efficient transfer learning and online adaptation with latent variable models for continuous control

Traditional model-based RL relies on hand-specified or learned models of...

0 Christian F. Perez, et al. ∙

∙ 10/18/2018

Pyro: Deep Universal Probabilistic Programming

Pyro is a probabilistic programming language built on Python as a platfo...

0 Eli Bingham, et al. ∙

∙ 10/01/2018

Probabilistic Meta-Representations Of Neural Networks

Existing Bayesian treatments of neural networks are typically characteri...

0 Theofanis Karaletsos, et al. ∙

∙ 06/05/2018

Pathwise Derivatives for Multivariate Distributions

We exploit the link between the transport equation and derivatives of ex...

0 Martin Jankowiak, et al. ∙

∙ 05/23/2018

Likelihood-free inference with emulator networks

Approximate Bayesian Computation (ABC) provides methods for Bayesian inf...

0 Jan-Matthis Lueckmann, et al. ∙

∙ 12/15/2016

Adversarial Message Passing For Graphical Models

Bayesian inference on structured models typically relies on the ability ...

0 Theofanis Karaletsos, et al. ∙

∙ 03/25/2016

Conditional Similarity Networks

What makes images similar? To measure the similarity between images, the...

0 Andreas Veit, et al. ∙

∙ 02/10/2016

Knowledge Transfer with Medical Language Embeddings

Identifying relationships between concepts is a key aspect of scientific...

0 Stephanie L. Hyland, et al. ∙

∙ 10/01/2015

A Generative Model of Words and Relationships from Multiple Sources

Neural language models are a powerful tool to embed words into semantic ...

0 Stephanie L. Hyland, et al. ∙

∙ 06/16/2015

Bayesian representation learning with oracle constraints

Representation learning systems typically rely on massive amounts of lab...

0 Theofanis Karaletsos, et al. ∙

∙ 05/28/2015

Automatic Relevance Determination For Deep Generative Models

A recurring problem when building probabilistic latent variable models i...

0 Theofanis Karaletsos, et al. ∙

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