no code implementations • 14 May 2024 • Francisco Eiras, Aleksander Petrov, Bertie Vidgen, Christian Schroeder, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Aaron Purewal, Csaba Botos, Fabro Steibel, FAZEL KESHTKAR, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Imperial, Juan Arturo Nolazco, Lori Landay, Matthew Jackson, Phillip H. S. Torr, Trevor Darrell, Yong Lee, Jakob Foerster
Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education.
no code implementations • 25 Apr 2024 • Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob Foerster
In the next few years, applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education.
no code implementations • 20 Oct 2023 • Francisco Eiras, Kemal Oksuz, Adel Bibi, Philip H. S. Torr, Puneet K. Dokania
Referring Image Segmentation (RIS) - the problem of identifying objects in images through natural language sentences - is a challenging task currently mostly solved through supervised learning.
no code implementations • 7 Jun 2023 • Tom A. Lamb, Rudy Brunel, Krishnamurthy Dj Dvijotham, M. Pawan Kumar, Philip H. S. Torr, Francisco Eiras
To address these questions, we introduce a faithful imitation framework to discuss the relative calibration of confidences and provide empirical and certified methods to evaluate the relative calibration of a student w. r. t.
no code implementations • 17 May 2023 • Francisco Eiras, Adel Bibi, Rudy Bunel, Krishnamurthy Dj Dvijotham, Philip Torr, M. Pawan Kumar
Recent work provides promising evidence that Physics-informed neural networks (PINN) can efficiently solve partial differential equations (PDE).
no code implementations • 25 Apr 2023 • Aleksandar Petrov, Francisco Eiras, Amartya Sanyal, Philip H. S. Torr, Adel Bibi
Improving and guaranteeing the robustness of deep learning models has been a topic of intense research.
1 code implementation • 9 Jul 2021 • Francisco Eiras, Motasem Alfarra, M. Pawan Kumar, Philip H. S. Torr, Puneet K. Dokania, Bernard Ghanem, Adel Bibi
Randomized smoothing has recently emerged as an effective tool that enables certification of deep neural network classifiers at scale.
no code implementations • 1 Nov 2020 • Henry Pulver, Francisco Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
In this paper, we present PILOT -- a planning framework that comprises an imitation neural network followed by an efficient optimiser that actively rectifies the network's plan, guaranteeing fulfilment of safety and comfort requirements.
2 code implementations • 6 Feb 2020 • Stefano V. Albrecht, Cillian Brewitt, John Wilhelm, Francisco Eiras, Mihai Dobre, Subramanian Ramamoorthy
The ability to predict the intentions and driving trajectories of other vehicles is a key problem for autonomous driving.
Robotics
1 code implementation • 7 Jan 2020 • Edward Ayers, Francisco Eiras, Majd Hawasly, Iain Whiteside
Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation.
no code implementations • CVPR 2018 • Pedro Miraldo, Francisco Eiras, Srikumar Ramalingam
Vanishing points and vanishing lines are classical geometrical concepts in perspective cameras that have a lineage dating back to 3 centuries.