no code implementations • 20 Jun 2023 • Christopher T. Small, Ivan Vendrov, Esin Durmus, Hadjar Homaei, Elizabeth Barry, Julien Cornebise, Ted Suzman, Deep Ganguli, Colin Megill
In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements.
1 code implementation • 13 Jul 2022 • Julien Cornebise, Ivan Oršolić, Freddie Kalaitzis
We hereby hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop from free public low-resolution Sentinel2 imagery the same power of analysis allowed by costly private high-resolution imagery.
no code implementations • 1 Apr 2020 • Lachlan Kermode, Jan Freyberg, Alican Akturk, Robert Trafford, Denis Kochetkov, Rafael Pardinas, Eyal Weizman, Julien Cornebise
We introduce a machine learning workflow to search for, identify, and meaningfully triage videos and images of munitions, weapons, and military equipment, even when limited training data exists for the object of interest.
2 code implementations • 15 Feb 2020 • Michel Deudon, Alfredo Kalaitzis, Israel Goytom, Md Rifat Arefin, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on Multi-Frame Super-Resolution on PROBA-V
1 code implementation • ICLR 2020 • Michel Deudon, Alfredo Kalaitzis, Md Rifat Arefin, Israel Goytom, Zhichao Lin, Kris Sankaran, Vincent Michalski, Samira E. Kahou, Julien Cornebise, Yoshua Bengio
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on Multi-Frame Super-Resolution on PROBA-V
no code implementations • 21 Sep 2019 • Thomas Boquet, Laure Delisle, Denis Kochetkov, Nathan Schucher, Parmida Atighehchian, Boris Oreshkin, Julien Cornebise
Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance.
no code implementations • 31 Jan 2019 • Laure Delisle, Alfredo Kalaitzis, Krzysztof Majewski, Archy de Berker, Milena Marin, Julien Cornebise
We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse against women on Twitter.
no code implementations • 28 Dec 2015 • Joel Z. Leibo, Julien Cornebise, Sergio Gómez, Demis Hassabis
This paper describes a framework for modeling the interface between perception and memory on the algorithmic level of analysis.
37 code implementations • 20 May 2015 • Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.
no code implementations • 30 Jun 2011 • Sarah Filippi, Chris Barnes, Julien Cornebise, Michael P. H. Stumpf
Here we discuss how to construct the perturbation kernels that are required in ABC SMC approaches, in order to construct a set of distributions that start out from a suitably defined prior and converge towards the unknown posterior.
Computation