no code implementations • 14 Dec 2023 • Kieran Didi, Francisco Vargas, Simon V Mathis, Vincent Dutordoir, Emile Mathieu, Urszula J Komorowska, Pietro Lio
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision.
1 code implementation • 3 Jul 2023 • Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken
Connecting optimal transport and variational inference, we present a principled and systematic framework for sampling and generative modelling centred around divergences on path space.
no code implementations • 16 May 2023 • Francisco Vargas, Teodora Reu, Anna Kerekes
Denoising diffusion models are a class of generative models which have recently achieved state-of-the-art results across many domains.
no code implementations • 15 Apr 2023 • Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D. Lawrence
Dimensionality reduction (DR) algorithms compress high-dimensional data into a lower dimensional representation while preserving important features of the data.
no code implementations • 27 Feb 2023 • Francisco Vargas, Will Grathwohl, Arnaud Doucet
Denoising Diffusion Samplers (DDS) are obtained by approximating the corresponding time-reversal.
1 code implementation • 28 Jan 2022 • Shauli Ravfogel, Francisco Vargas, Yoav Goldberg, Ryan Cotterell
One prominent approach for the identification of concepts in neural representations is searching for a linear subspace whose erasure prevents the prediction of the concept from the representations.
no code implementations • pproximateinference AABI Symposium 2022 • David Lopes Fernandes, Francisco Vargas, Carl Henrik Ek, Neill D. F. Campbell
We present a variational inference scheme to learn a model that solves the Schrödinger Bridge Problem (SBP).
no code implementations • pproximateinference AABI Symposium 2022 • Francisco Vargas, Andrius Ovsianas, David Fernandes, Mark Girolami, Neil D. Lawrence, Nikolas Nüsken
In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control (i. e. Schr\"odinger bridges).
no code implementations • 15 Oct 2021 • Han Xuanyuan, Francisco Vargas, Stephen Cummins
Others have proposed the use of model pruning and efficient data representations to reduce the number of HE operations required.
1 code implementation • 3 Jun 2021 • Francisco Vargas, Pierre Thodoroff, Neil D. Lawrence, Austen Lamacraft
The Schr\"odinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution.
1 code implementation • EMNLP 2020 • Francisco Vargas, Ryan Cotterell
Their method takes pre-trained word representations as input and attempts to isolate a linear subspace that captures most of the gender bias in the representations.
1 code implementation • ACL 2019 • Francisco Vargas, Kamen Brestnichki, Alex Papadopoulos-Korfiatis, Nils Hammerla
In this work we approach the task of learning multilingual word representations in an offline manner by fitting a generative latent variable model to a multilingual dictionary.
1 code implementation • 30 Apr 2019 • Francisco Vargas, Kamen Brestnichki, Nils Hammerla
We introduce a probabilistic framework for quantifying the semantic similarity between two groups of embeddings.
no code implementations • 20 Mar 2016 • Alexander Gomez, Augusto Salazar, Francisco Vargas
Non intrusive monitoring of animals in the wild is possible using camera trapping framework, which uses cameras triggered by sensors to take a burst of images of animals in their habitat.
no code implementations • 11 Feb 2016 • Carlos Palma, Augusto Salazar, Francisco Vargas
Automatic recognition of the quality of movement in human beings is a challenging task, given the difficulty both in defining the constraints that make a movement correct, and the difficulty in using noisy data to determine if these constraints were satisfied.