InstaDeep maintains its strong commitment to open research with six papers accepted for presentation at the 2024 ICLR conference being held in Vienna this week. The accepted papers cover a diverse range of subjects including Decision-making AI and Machine Learning for biology. Detailed summaries of each paper and their respective presentation slots are provided below…. Read more »
InstaDeep is pleased to continue its support for up and coming AI talent in Africa by once again sponsoring the UmojaHack Africa machine learning hackathon. This year – 2023 – is a pleasing multiplicity of 3s: 3000+ students, hailing from more than 300 universities and 30 countries across the continent, will compete in real-world machine… Read more »
InstaDeep is pleased to announce that a record five of its AI research papers have been accepted for presentation at the Thirty-sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022) with two papers in the main track and three workshop papers, including one authored in collaboration with BioNTech as part of our joint AI Innovation… Read more »
Each year, the Genetic and Evolutionary Computation Conference (GECCO) gathers the leading global experts in the domain of genetic and evolutionary computing. This year, InstaDeep’s research team presented two main conference papers and one workshop paper at the event, which took place in Boston, MA between 9-13 July. The three works result from a close… Read more »
InstaDeep today announces that it has had three papers accepted for presentation at the 2021 Annual Conference on Neural Information Processing Systems (NeurIPS 2021), including one authored in collaboration with Google Research. NeurIPS2021 is the 35th edition of the highly prestigious annual machine learning conference, with sessions and workshop tracks presenting the latest research in… Read more »
With the continuous growth of the global economy and markets, resource imbalance has risen to be one of the central issues in real logistic scenarios. In particular, we focus our study on the marine transportation domain and show how Multi-Agent Reinforcement Learning can be used to learn cooperative policies that limit the severe disparity in… Read more »
(Part 2/4) In our introduction to meta-reinforcement learning, we presented the main concepts of meta-RL: Meta-Environments are associated with a distribution of distinct MDPs called tasks. The goal of Meta-RL is to learn to leverage prior experience to adapt quickly to new tasks. In Meta-RL, we learn an algorithm during a step called meta-training. At meta-testing, we apply this… Read more »
(Part 1/4) The recent developments in Reinforcement Learning (RL) have shown the incredible capacity of computers to outperform human performance in many environments such as Atari Games [1], Go, chess, shogi [2], Starcraft II [3]. This performance results from the development of Deep Learning and Reinforcement Learning methods like Deep Q-Networks (DQN) [4] and actor-critic methods [5, 6, 7, 8]. However, one essential advantage of… Read more »
LONDON, 28 NOVEMBER, 2019 — AI product company InstaDeep, announced this week the launch of its new decision-making routing platform, DeepPCB™. The fully scalable Printed Circuit Board (PCB) router is an entirely automated, no-human-in-the-loop cloud-native product. It allows the user to upload their own board and get results in less than 24 hours, accelerating customer… Read more »
