71 citations · 218 across the 16 of their papers we have counts for
17 papers
Gradients without Backpropagation
Atılım Güneş Baydin, Barak A. Pearlmutter, Don Syme +2
Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation, is…
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles
Bernard Benson, Edward Brown, Stefano Bonasera +7
Solar radio flux along with geomagnetic indices are important indicators of solar activity and its effects. Extreme solar events such as flares and geomagnetic storms can negativel…
Simulation Intelligence: Towards a New Generation of Scientific Methods
Alexander Lavin, David Krakauer, Hector Zenil +21
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of comput…
Detecting and Quantifying Malicious Activity with Simulation-based Inference
Andrew Gambardella, Bogdan State, Naeemullah Khan +3
We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm. Probabilistic programming provides num…
KL Guided Domain Adaptation
A. Tuan Nguyen, Toan Tran, Yarin Gal +2
Domain adaptation is an important problem and often needed for real-world applications. In this problem, instead of i.i.d. training and testing datapoints, we assume that the sourc…
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning
Luiz F. G. dos Santos, Souvik Bose, Valentina Salvatelli +7
Solar activity plays a quintessential role in influencing the interplanetary medium and space-weather around the Earth. Remote sensing instruments onboard heliophysics space missio…
Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
Francesco Pinto, Giacomo Acciarini, Sascha Metz +7
After decades of space travel, low Earth orbit is a junkyard of discarded rocket bodies, dead satellites, and millions of pieces of debris from collisions and explosions. Objects i…
Spacecraft Collision Risk Assessment with Probabilistic Programming
Giacomo Acciarini, Francesco Pinto, Sascha Metz +7
Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dea…
AutoSimulate: (Quickly) Learning Synthetic Data Generation
Harkirat Singh Behl, Atılım Güneş Baydin, Ran Gal +2
Simulation is increasingly being used for generating large labelled datasets in many machine learning problems. Recent methods have focused on adjusting simulator parameters with t…
Simulation-Based Inference for Global Health Decisions
Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli +8
The COVID-19 pandemic has highlighted the importance of in-silico epidemiological modelling in predicting the dynamics of infectious diseases to inform health policy and decision m…
Black-Box Optimization with Local Generative Surrogates
Sergey Shirobokov, Vladislav Belavin, Michael Kagan +2
We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many proce…
Transflow Learning: Repurposing Flow Models Without Retraining
Andrew Gambardella, Atılım Güneş Baydin, Philip H. S. Torr
It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space. Recently, archi…
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Xavier Gitiaux, Shane A. Maloney, Anna Jungbluth +7
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific do…
Attention for Inference Compilation
William Harvey, Andreas Munk, Atılım Güneş Baydin +2
We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to current methods. Our approach is a variation o…
Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
Adam D. Cobb, Atılım Güneş Baydin, Andrew Markham +1
We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians. We show its relevance to Riemannian manifold H…
Probabilistic Surrogate Networks for Simulators with Unbounded Randomness
Andreas Munk, Berend Zwartsenberg, Adam Ścibior +5
We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of stochastic simulators. Unlike traditional approaches to surrogate mod…
Alpha MAML: Adaptive Model-Agnostic Meta-Learning
Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr
Model-agnostic meta-learning (MAML) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks…