Research

Articles

InstaDeep, Imperial College London and Sorbonne joint research accepted for ICLR 2022 workshop

InstaDeep proudly announces that a co-authored research paper on Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization has been accepted by ICLR and will be presented at a workshop during the week-long event.  This latest acceptance – on the tenth anniversary of ICLR – continues InstaDeep’s track record of producing high quality research in collaboration… Read more »

InstaDeep launches dedicated Quantum Machine Learning team, as first QML research paper is published in Nature Machine Intelligence

InstaDeep is pleased to announce the formation of a new Quantum Machine Learning (QML) team within the company’s well-established research department. The announcement coincides with the publication of the “Autoregressive neural-network wavefunctions for ab initio quantum chemistry” research paper in the leading science journal, Nature Machine Intelligence.  Publication in Nature Machine Intelligence The paper was… Read more »

InstaDeep and Oxford University Research Collaboration Accepted to ICLR

This research was authored by Scott Cameron at the University of Oxford and InstaDeep, in collaboration with Prof Stephen Roberts, Dr Arnu Pretorius of InstaDeep and Tyron Cameron of Discovery Insure South Africa.  It is Cameron’s first publication from his PhD, which is funded by InstaDeep.  Stochastic differential equations (SDEs) have been consistently used across… Read more »

BioNTech and InstaDeep Developed and Successfully Tested Early Warning System to Detect Potential High-Risk SARS-CoV-2 Variants

Early Warning System combines Spike protein structural modeling with artificial intelligence (AI) to detect and monitor high-risk SARS-CoV-2 variants, identifying >90% of WHO-designated variants on average two months prior to officially receiving the designation Study introduces a new method of combining publicly available SARS-CoV-2 sequence information with predictive analytics to effectively detect and monitor potential… Read more »

An early detection system for desert locust outbreaks in Africa, in collaboration with Google AI

Our collaboration with Google AI, “On pseudo-absence generation and machine learning for locust breeding ground prediction in Africa”, describes an early detection system for desert locust outbreaks across the African continent. This research will be presented at two NeurIPS 2021 machine learning conference workshops: Artificial Intelligence for Humanitarian Assistance and Disaster Response (AI+HADR) and Machine… Read more »