Professor Timothy Hospedales
Professor of Artificial Intelligence
Institute of Perception, Action and BehaviourSchool of Informatics
The University of Edinburgh
t.hospedales at ed.ac.uk
Head of Centre, Samsung AI Research Centre, Cambridge
I am a Professor within IPAB in the School of Informatics at the University of Edinburgh, where I head the Machine Intelligence Research group; ELLIS fellow; and Head of Centre, Samsung AI Research Centre, Cambridge where I also direct the Machine Learning & Data Intelligence Programme.
Previously I was Alan Turing Institute Fellow, Reader ('16-'20) at Edinburgh and Senior Lecturer/Lecturer ('12-16) at QMUL within the Risk and Information Management (RIM) group and Centre for Intelligent Sensing, where I founded the Applied Machine Learning Lab. I recieved my PhD in Neuroinformatics from Edinburgh in 2008, working with Sethu Vijayakumar in the Statistical Machine Learning and Motor Control group, and my BA in Computer Science from the University of Cambridge in 2002.
My research focuses on efficient and robust AI using techniques such as meta-learning and lifelong transfer-learning, in both probabilistic and deep learning contexts. I have looked at a variety of application areas including computer vision, vision and language, reinforcement learning for robot control, finance and beyond.
News
10/2017: Program co-chair of BMVC2018!
02/2018: Nine papers accepted at CVPR'18!
07/2018: Our Meta Learning Active Learning paper awarded Best Paper Prize at ICML AutoML'18!
08/2018: Congratulations Kunkun Pang! Dynamic Ensemble Active Learning wins Best Student Paper Award @ ICPR2018!
03/2019: Two papers in ICML'19!
06/2019: Congratulations Carl Allen! Analogies Explained paper wins Honorable Mention @ ICML'19!
08/2019: TuckER model for fast and effective knowledge graph completion accepted at EMNLP2019!
09/2019: Two papers in NeurIPS'19!
12/2019: Joining T-PAMI as AE!
02/2020: Four papers at CVPR'20!
04/2020: Check out our new Survey on meta-learning!
07/2020: Four papers in ECCV'20!
08/2020: Promoted to Full Professor.
09/2020: Paper in Siggraph Asia 2020!
09/2020: Paper on online meta-RL in NeurIPS'20!
09/2020: Paper on neurosymbolic learning at AAAI'21!
02/2021: Our MetaQDA achieves 3rd place in AAAI Meta-Learning competition.
02/2021: Four papers at CVPR'21!
07/2021: Associate Program Co-Chair of AAAI'22!
11/2021: Tim recognised as an ELLIS fellow.
12/2021: Our PMF places second in NeurIPS Meta-learning competition!
01/2022: Three papers in ICLR'22!
04/2022: Check out our new book, Visual Adaptation in the Deep Learning Era!
05/2022: Check out our SPM tutorial on self-supervision!
01/2023: Five papers at ICLR'23!
07/2023: New multi-task meta-learning benchmark at CVPR23: Meta-Omnium.
08/2016: Co-chairing BMVA symposium on Transfer Learning in Computer Vision
09/2016: Guest Editor - IET CV - Special Issue on Deep Learning for Computer Vision
10/2016: Keynote on transfer learning with paramaterised tasks and domains at ECCV 2016 TASK-CV workshop!
10/2016: Giving the zero-shot learning part of ACM Multimedia '16 tutorial Emerging topics in learning from noisy and missing data.
10/2017: Program co-chair of BMVC 2018
11/2017: Hiring one 3.5yr Postdoc on Machine Learning for Robotics & AI. Closing date: 14th Dec 2017. Advert. Apply.
03/2018: Keynote at Vision & Language Conference.
03/2018: Talk on sanity guarantees for deep learning at Deep Learning in Finance Summit London 2018.
09/2018: Hiring a postdoc research associate on deep learning, 2.5yr post, international applicants welcome. Closing date 4th October 2018. Apply.
05/2019: Teaching the Advanced Topics in Deep Learning Summer School, May 2019 in Verona.
05/2020: Keynote @ DIRA workshop in CVPR2020
08/2020: Tutorial on Domain Adaptation @ ECCV2020
09/2020: Open positions for PhD and Postdoc researchers.
10/2020: Check out our Special Issue on Learning with Fewer Labels in IEEE PAMI.
12/2020: Invited Talk at the Meta Learning Workshop in NeurIPS-20
02/2021: Co-organizing learning-to-learn workshop at ICLR-21! Submission deadline Feb 26.
02/2021: Co-organizing learning-to-learn workshop at ICRA-21!
01/2022: Talk at IBM Neuro-symbolic AI workshop 2022.
10/2022: Seminar on meta-learning at Deep Learn 2022 Summer School.
12/2022: Co-organizing Domain Generalisation Workshop at ICLR23.
Sample code for active learning & discovery (PAKDD'11,TKDE'11,ECCV'12)
Unsegmented sports news dataset available (ICDM'11). Includes reference extracted STIP features and ground-truth multi-label annotation.
Sample code for MCTM (ICCV'09, IJCV'11) and WSJTM (ACCV'10, PAMI'11)
Attribute Dataset for Heterogeneous Face Recognition (ACCV'14)
Code and Data for BMVC04 Fine-grained SBIR paper.
02/2015: Code for the famous ELF descriptor in re-id. First ever public release!
06/2015: Ground-truth Anomaly Annotation for ICCV'09 and IJCV'11 papers.
07/2015: Code for Sketch-a-Net BMVC'15 paper.
09/2015: Code for our ICLR'15 Multi-Task/Multi-domain paper
09/2015: Multi-camera video surveillance dataset from our TCSVT'15 paper
03/2016: 800K image+social metadata dataset from our ACM DH'16 paper
05/2017: LIMA dataset for re-id and tracking at CVPR'17
01/2018: Demo code CVPR'18 learning to compare few-shot learning
07/2018: Neural Decision tree Example Code.
05/2019: Hypernetwork KB Completion Code.
05/2019: Tucker Decomposition Link Prediction Code.
06/2021: How Well do Self-Supervised models Transfer? Code.
12/2021: Meta Channel Coding. A new benchmark for meta-learning!
04/2022: P>M>F: State of the art Few-Shot Learning Code.
09/2022: Meta-audio blog and code. Check if your meta-learner works outside of computer vision!
07/2023: Meta-Omnium multi-task meta-learning benchmark.
Publications
Visit my Google Scholar Profile.
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
Meta Learning
Lifelong and Transfer Learning
Zero-Shot Learning
Learning for Robotics and Control
Finance
Active Learning
Behaviour and Action Understanding
Person re-identification
Visual Attribute Learning
Deep Learning
Machine Learning
Zero-shot learning, Vision and Language
Sketches
Computer Vision
Neuroscience
Research Group
Senior Members
- Machine Learning, Finance
- Reliable Machine Learning, ML Theory
- Meta-Learning, Domain Generalisation
PhD Students
- Medical Image Analysis, Cross-Domain
- Multi-view self-supervision
- Multi-Task Meta-Learning
- Meta-Learning, Data Distillation, Cross-Domain
- Meta-Learning, Self-Supervised Learning
- Reinforcement Learning, Adversarial Robustness
- Unsupervised Deep Learning, Meta-Learning
Visitors
- Few-shot regression.
PhD Students, Co-supervisor
Postdocs, Alum
- Person Re-identification, Transfer Learning
- Lifelong learning for Robotics and Control
- Active Learning & Perception
PhD Students, Graduated
→ ETH
→ Postdoc @ Edinburgh → Faculty @ Surrey → Faculty @ QMUL
→ Deep Mind
→ Postdoc at University of Cambridge
→ Postdoc at University of Edinburgh
→ Postdoc at University of Edinburgh → FAIR
→ Postdoc at Edinburgh
→ Faculty at Fudan University, PRC
→ Goldsmiths → Faculty at NUDT, PRC
→ Imperial College London
→ National University of Singapore
Past Visitors
- Structured Video Analysis, Generative Adversarial Nets
- Person Identification
- Generative, Graphical Models in Vision
- Domain Generalisation. Deep RL.
Teaching
Classes
- Introduction to Vision and Robotics, Image and Vision Computing
- Introduction to Vision and Robotics, Image and Vision Computing
- Inf1 - Object Oriented Programming
- Data Mining, Machine Learning, Procedural Programming
- Data Mining, Procedural Programming
- Java (BUPT)
- Data Mining
- Semantic Web
- Information Systems
- Semantic Web