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University of Oxford Department of Computer Science
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Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) is concerned with getting computers to perform tasks that currently are only feasible for humans. Within AI, Machine Learning aims to build computers that can learn how to make decisions or carry out tasks without being explicitly told how to do so. We have research strengths across a wide spectrum of AI and ML techniques. In ML, we develop fundamental ML techniques such as reinforcement learning and deep learning and build applications of these techniques in linguistics, robotics and information retrieval. Our researchers in knowledge representation develop techniques that allow us to capture knowledge about our world in a form that computers can process and reason about. In the multi-agent systems domain, we develop techniques that will enable computers to autonomously collaborate on complex problems. We work with industrial partners such as DeepMind to develop the applications of our work, and the theme has spawned a number of spinoff companies, including Dark Blue Labs (http://darkbluelabs.com), Morpheus Labs (http://morpheuslabs.co.uk), and Oxonomy (http://oxonomy.com).

Related seminar series

All Activities

Activities

Knowledge Representation and Reasoning The Knowledge Representation and Reasoning Group conducts research in...

Read more about Knowledge Representation and Reasoning

Complexity and equilibria Study of the computational costs (i.e., complexity) of computing diff...

Read more about Complexity and equilibria

Logics for strategic reasoning Study of logical languages to reason about properties of games.

Read more about Logics for strategic reasoning

All Projects

Projects

ERC AdG WhiteMech WhiteMech aims at developing the science and the tools for mechanisms that are able to pr...

Read more about ERC AdG WhiteMech

Real World Benchmarks for Deep Probabilistic AI

Read more about Real World Benchmarks for Deep Probabilistic AI

All Publications

Publications

RandLA−Net: Efficient Semantic Segmentation of Large−Scale Point Clouds

Read more about RandLA−Net: Efficient Semantic Segmentation of Large−Scale Point Clouds

Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

Read more about Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds

Robust Attentional Aggregation of Deep Feature Sets for Multi−view 3D Reconstruction

Read more about Robust Attentional Aggregation of Deep Feature Sets for Multi−view 3D Reconstruction

Selective Sensor Fusion for Neural Visual Inertial Odometry

Read more about Selective Sensor Fusion for Neural Visual Inertial Odometry

Transferring Physical Motion Between Domains for Neural Inertial Tracking

Read more about Transferring Physical Motion Between Domains for Neural Inertial Tracking

Ontological Query Answering under Many−Valued Group Preferences in Datalog+⁄−

Read more about Ontological Query Answering under Many−Valued Group Preferences in Datalog+⁄−

Derivation Reduction of Metarules in Meta−interpretive Learning

Read more about Derivation Reduction of Metarules in Meta−interpretive Learning

Location−Aware News Recommendation Using Deep Localized Semantic Analysis

Read more about Location−Aware News Recommendation Using Deep Localized Semantic Analysis

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