Search Results for author: Atılım Güneş Baydin

Found 28 papers, 15 papers with code

Gradients without Backpropagation

2 code implementations17 Feb 2022 Atılım Güneş Baydin, Barak A. Pearlmutter, Don Syme, Frank Wood, Philip Torr

Using backpropagation to compute gradients of objective functions for optimization has remained a mainstay of machine learning.

Detecting and Quantifying Malicious Activity with Simulation-based Inference

no code implementations6 Oct 2021 Andrew Gambardella, Bogdan State, Naeemullah Khan, Leo Tsourides, Philip H. S. Torr, Atılım Güneş Baydin

We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm.

Probabilistic Programming

KL Guided Domain Adaptation

1 code implementation ICLR 2022 A. Tuan Nguyen, Toan Tran, Yarin Gal, Philip H. S. Torr, Atılım Güneş Baydin

A common approach in the domain adaptation literature is to learn a representation of the input that has the same (marginal) distribution over the source and the target domain.

Domain Adaptation

Domain Invariant Representation Learning with Domain Density Transformations

1 code implementation NeurIPS 2021 A. Tuan Nguyen, Toan Tran, Yarin Gal, Atılım Güneş Baydin

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.

Domain Generalization Representation Learning

Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly using Machine Learning

1 code implementation27 Dec 2020 Luiz F. G. dos Santos, Souvik Bose, Valentina Salvatelli, Brad Neuberg, Mark C. M. Cheung, Miho Janvier, Meng Jin, Yarin Gal, Paul Boerner, Atılım Güneş Baydin

Our approach establishes the framework for a novel technique to calibrate EUV instruments and advance our understanding of the cross-channel relation between different EUV channels.

BIG-bench Machine Learning Camera Calibration

Towards Automated Satellite Conjunction Management with Bayesian Deep Learning

1 code implementation23 Dec 2020 Francesco Pinto, Giacomo Acciarini, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, Atılım Güneş Baydin

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.

Management Time Series +1

Simulation-Based Inference for Global Health Decisions

2 code implementations14 May 2020 Christian Schroeder de Witt, Bradley Gram-Hansen, Nantas Nardelli, Andrew Gambardella, Rob Zinkov, Puneet Dokania, N. Siddharth, Ana Belen Espinosa-Gonzalez, Ara Darzi, Philip Torr, Atılım Güneş Baydin

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 makers about suitable prevention and containment strategies.

Bayesian Inference Epidemiology

Black-Box Optimization with Local Generative Surrogates

1 code implementation NeurIPS 2020 Sergey Shirobokov, Vladislav Belavin, Michael Kagan, Andrey Ustyuzhanin, Atılım Güneş Baydin

To address such cases, we introduce the use of deep generative models to iteratively approximate the simulator in local neighborhoods of the parameter space.

Bayesian Optimization

Transflow Learning: Repurposing Flow Models Without Retraining

no code implementations29 Nov 2019 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.

Bayesian Inference Style Transfer

Attention for Inference Compilation

no code implementations25 Oct 2019 William Harvey, Andreas Munk, Atılım Güneş Baydin, Alexander Bergholm, Frank Wood

We present a new approach to automatic amortized inference in universal probabilistic programs which improves performance compared to current methods.

Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo

1 code implementation14 Oct 2019 Adam D. Cobb, Atılım Güneş Baydin, Andrew Markham, Stephen J. Roberts

We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians.

Bayesian Inference

Flood Detection On Low Cost Orbital Hardware

no code implementations4 Oct 2019 Gonzalo Mateo-Garcia, Silviu Oprea, Lewis Smith, Josh Veitch-Michaelis, Guy Schumann, Yarin Gal, Atılım Güneş Baydin, Dietmar Backes

Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding.

Hijacking Malaria Simulators with Probabilistic Programming

no code implementations29 May 2019 Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atılım Güneş Baydin

Epidemiology simulations have become a fundamental tool in the fight against the epidemics of various infectious diseases like AIDS and malaria.

Epidemiology Probabilistic Programming

An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

1 code implementation25 May 2019 Adam D. Cobb, Michael D. Himes, Frank Soboczenski, Simone Zorzan, Molly D. O'Beirne, Atılım Güneş Baydin, Yarin Gal, Shawn D. Domagal-Goldman, Giada N. Arney, Daniel Angerhausen

We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra.

BIG-bench Machine Learning Retrieval

Alpha MAML: Adaptive Model-Agnostic Meta-Learning

no code implementations17 May 2019 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.

Few-Shot Learning General Classification

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

3 code implementations NeurIPS 2019 Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood

We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.

Probabilistic Programming

Tricks from Deep Learning

no code implementations10 Nov 2016 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 nonlinearities, practical.

Machine Translation speech-recognition +1

DiffSharp: An AD Library for .NET Languages

no code implementations10 Nov 2016 Atılım Güneş Baydin, Barak A. Pearlmutter, Jeffrey Mark Siskind

DiffSharp is an algorithmic differentiation or automatic differentiation (AD) library for the . NET ecosystem, which is targeted by the C# and F# languages, among others.

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