189 citations · 582 across the 33 of their papers we have counts for
10 papers · 1 filter
Domain Invariant Representation Learning with Domain Density Transformations
A. Tuan Nguyen, Toan Tran, Yarin Gal +1
Domain generalization refers to the problem where we aim to train a model on data from a set of source domains so that the model can generalize to unseen target domains. Naively tr…
Technology Readiness Levels for Machine Learning Systems
Alexander Lavin, Ciarán M. Gilligan-Lee, Alessya Visnjic +12
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of dilig…
Prediction of GNSS Phase Scintillations: A Machine Learning Approach
Kara Lamb, Garima Malhotra, Athanasios Vlontzos +7
A Global Navigation Satellite System (GNSS) uses a constellation of satellites around the earth for accurate navigation, timing, and positioning. Natural phenomena like space weath…
Amortized Rejection Sampling in Universal Probabilistic Programming
Saeid Naderiparizi, Adam Ścibior, Andreas Munk +9
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampli…
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Atılım Güneş Baydin, Lei Shao, Wahid Bhimji +14
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models. However, applications to science remai…
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji +12
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which all…
Using Synthetic Data to Train Neural Networks is Model-Based Reasoning
Tuan Anh Le, Atilim Gunes Baydin, Robert Zinkov +1
We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a…
Online Learning Rate Adaptation with Hypergradient Descent
Atilim Gunes Baydin, Robert Cornish, David Martinez Rubio +2
We introduce a general method for improving the convergence rate of gradient-based optimizers that is easy to implement and works well in practice. We demonstrate the effectiveness…
Tricks from Deep Learning
Atılım Güneş Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind
The deep learning community has devised a diverse set of methods to make gradient optimization, using large datasets, of large and highly complex models with deeply cascaded nonlin…
Automatic Differentiation of Algorithms for Machine Learning
Atilim Gunes Baydin, Barak A. Pearlmutter
Automatic differentiation---the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately---dates to the origin of the computer age…