Atilim Gunes Baydin

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CSIC ∙ University of Oxford ∙ Maynooth University

Postdoctoral Researcher at University of Oxford

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel framework that enables efficient probabilistic infere...

07/20/2018 ∙ by Atilim Gunes Baydin, et al. ∙ 4 ∙ share

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A semantic network-based evolutionary algorithm for computational creativity

We introduce a novel evolutionary algorithm (EA) with a semantic network...

04/30/2014 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Online Learning Rate Adaptation with Hypergradient Descent

We introduce a general method for improving the convergence rate of grad...

03/14/2017 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Automated Generation of Cross-Domain Analogies via Evolutionary Computation

Analogy plays an important role in creativity, and is extensively used i...

04/11/2012 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Evolution of Ideas: A Novel Memetic Algorithm Based on Semantic Networks

This paper presents a new type of evolutionary algorithm (EA) based on t...

01/12/2012 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Tricks from Deep Learning

The deep learning community has devised a diverse set of methods to make...

11/10/2016 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Inference Compilation and Universal Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cos...

10/31/2016 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

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Using Synthetic Data to Train Neural Networks is Model-Based Reasoning

We draw a formal connection between using synthetic training data to opt...

03/02/2017 ∙ by Tuan Anh Le, et al. ∙ 0 ∙ share

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Automatic Differentiation of Algorithms for Machine Learning

Automatic differentiation---the mechanical transformation of numeric com...

04/28/2014 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation

Mediation is an important method in dispute resolution. We implement a c...

07/30/2011 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabili...

12/21/2017 ∙ by Mario Lezcano Casado, et al. ∙ 0 ∙ share

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DiffSharp: Automatic Differentiation Library

In this paper we introduce DiffSharp, an automatic differentiation (AD) ...

11/24/2015 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

Over the past decade, the study of exoplanets has shifted from their det...

11/08/2018 ∙ by Frank Soboczenski, et al. ∙ 0 ∙ share

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DiffSharp: An AD Library for .NET Languages

DiffSharp is an algorithmic differentiation or automatic differentiation...

11/10/2016 ∙ by Atilim Gunes Baydin, et al. ∙ 0 ∙ share

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Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning

As a part of NASA's Heliophysics System Observatory (HSO) fleet of satel...

11/10/2019 ∙ by Brad Neuberg, et al. ∙ 0 ∙ share

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Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona

Understanding and monitoring the complex and dynamic processes of the Su...

11/10/2019 ∙ by Valentina Salvatelli, et al. ∙ 0 ∙ share

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Atilim Gunes Baydin

Featured Co-authors

Frank Wood

Yarin Gal

Mark Schmidt

Gilles Louppe

Prabhat

Kyle Cranmer

Jeffrey Mark Siskind

Tuan Anh Le

Ramon Lopez de Mantaras

Santiago Ontañón

Bradley Gram-Hansen

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

A semantic network-based evolutionary algorithm for computational creativity

Online Learning Rate Adaptation with Hypergradient Descent

Automated Generation of Cross-Domain Analogies via Evolutionary Computation

Evolution of Ideas: A Novel Memetic Algorithm Based on Semantic Networks

Tricks from Deep Learning

Inference Compilation and Universal Probabilistic Programming

Using Synthetic Data to Train Neural Networks is Model-Based Reasoning

Automatic Differentiation of Algorithms for Machine Learning

CBR with Commonsense Reasoning and Structure Mapping: An Application to Mediation

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

DiffSharp: Automatic Differentiation Library

Bayesian Deep Learning for Exoplanet Atmospheric Retrieval

DiffSharp: An AD Library for .NET Languages

Auto-Calibration of Remote Sensing Solar Telescopes with Deep Learning

Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona