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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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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 ∙
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