Publications and Output

  • Gupta, Ankush and Vedaldi, Andrea and Zisserman, Andrew.  Synthetic Data for Text Localisation in Natural Images The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016
  • Ghoshal, Sid and S. Roberts, Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes, Algorithmic Finance, vol. 5, no. 1-2, pp. 21-30, 2016.
  • Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, Yee Whye Teh, Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server. Journal of Machine Learning Research 18 (2017) 1-37.
  • Learning Grimaces by Watching TV”  – Sam Albanie and Andrea Vedaldi, accepted into BMVC 2016.
  • “James Thewlis, Shuai Zheng, Philip H. S. Torr, Andrea Vedaldi.  Fully-Trainable Deep Matching. British  Machine Vision Conference (BMVC), 2016.
  • Bartlett, O, C. Gurau, L. Marchegiani, and I. Posner, “Enabling Intelligent Energy Management for Robots using Publicly Available Maps,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016. PDF
  • Berrada, Leo, Trusting SVM for Piecewise Linear CNNs” has been accepted at ICLR 2017 (International Conference on Learning Representations). It is available on arXiv at https://arxiv.org/abs/1611.02185
  • Ghoshal, S and Roberts, S. Reading the Tea Leaves: A Neural Network Perspective on Technical Trading. Knowledge Discovery and Data Mining (KDD), 2017.
  • Cobb, A and Markham, A and Roberts, S. Learning from lions: inferring the utility of agents from their trajectories. Available at https://arxiv.org/pdf/1709.02357.pdf
  • Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos, Philip H.S. Torr, Pushmeet Kohli, Shimon Whiteson, Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning.   Accepted at ICML2017. Available at https://arxiv.org/abs/1702.08887
  • Unsupervised object learning from dense invariant image labelling, J.Thewlis and H. Bilen and A. Vedaldi, Proceedings of Advances in Neural Information Processing Systems (NIPS), 2017 (oral presentation)
  • Unsupervised learning of object landmarks by factorized spatial embeddings, J. Thewlis and H. Bilen and A. Vedaldi, Proceedings of the International Conference on Computer Vision (ICCV), 2017 (oral presentation)
  • Zand, Jaleh & Roberts, S. MiDGaP: Mixture Density Gaussian Processes.  NIPS time series workshop 2017.
  • Closed-loop quantitative verification of rate-adaptive pacemakers. N Paoletti, A Patane, M Kwiatkowska. ACM Transactions on Cyber-Physical Systems, to appear, 2018
  • Stefan Webb and Yee Whye Teh. A Tighter Monte Carlo Objective with Renyi divergence Measures. NIPS2016 Workshop in Bayesian Deep Learning.
  • Stefan Webb, Adam Golinski, Robert Zinkov and Frank Wood. Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. NIPS2017 Workshop in Bayesian Deep Learning.
  • Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Yee Whye Teh, and Frank Wood. Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. https://arxiv.org/abs/1712.00287
  • Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. (2018). Counterfactual multi-agent policy gradients. 32nd AAAI Conference on Artificial Intelligence (AAAI’18)
  • Farquhar, G., Rocktäschel, T., Igl, M., & Whiteson, S.  TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. ICLR 2018. (To appear)
  • Border, J. D. Gammell, and P. Newman, “Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations,” ICRA, 2018.
  •  Le, Tuan Anh, Igl, Maximilian, Jin, Tom, Rainforth, Tom, Wood, Frank. Auto-encoding sequential Monte Carlo. In ICLR (To Appear), 2018.
  • D. Novotny*, S. Albanie*, D. Larlus, A. Vedaldi, Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection, CVPR, 2018. (* denotes equal contribution)
  • A. Nagrani, S. Albanie, A. Zisserman, Seeing Voices and Hearing Faces: Cross-modal biometric matching, CVPR, 2018.  (* denotes equal contribution)
  • S. Albanie, H. Shakespeare and T. Gunter, Unknowable Manipulators: Social Network Curator Algorithms, (NIPS 2016 Symposium: Machine Learning and the Law)
  • Cornish, R., Wood, F., & Yang, H. (2017). “Towards a testable notion of generalisation for generative adversarial networks”. In NIPS Workshop on Deep Learning: Bridging Theory and Practice.
  • Baydin, A. G., Cornish, R., Martínez Rubio, D., Schmidt, M., & Wood, F. (2017).  Online Learning Rate Adaptation with Hypergradient Descent.  In Sixth International Conference on Learning Representations (ICLR), Vancouver, Canada, April 30 – May 3, 2018.
  • Adam D. Cobb, Richard Everett, Andrew Markham, Stephen J. Roberts. (2018). Identifying Sources and Sinks in the Presence of Multiple Gaents with Gaussian Process Vector Calculus, https://arxiv.org/abs/1802.10446
  • When Your Fitness Tracker Betrays You: Quantifying the Predictability of Biometric Features Across Contexts. Simon Eberz, Giulio Lovisotto, Andrea Patané, Marta Kwiatkowska, Vincent Lenders, and Ivan Martinovic. IEEE Symposium on Security and Privacy (S&P), San Francisco, CA, USA, May 2018
  • CommonSense: Collaborative learning of scene semantics by robots and humans. Stefano Rosa, Andrea Patanè, Xiaoxuan Lu, and Niki Trigoni. In Proceedings of the 1st International Workshop on Internet of People, Assistive Robots and ThingS (pp. 1-6). ACM. 2018, June.
  • Loss-Calibrated Approximate Inference in Bayesian Neural Betworks. Adam D. Cobb, Stephen J. Roberts, Yarin Gal.  https://arxiv.org/abs/1805.03901
  • T. Afouras, J. S. Chung, A. Zisserman. Deep Lip Reading: a comparison of models and an online application  INTERSPEECH, 2018
  • T. Afouras, J. S. Chung, A. Zisserman. The Conversation: Deep Audio-Visual Speech Enhancement  INTERSPEECH, 2018
  • L. Berrada, A. Zisserman, M. P. Kumar. Smooth Loss Functions for Deep Top-k Classification  International Conference on Learning Representations, 2018
  • A. Nagrani*, S. Albanie*, and A. Zisserman, Learnable PINs: Cross-Modal Embeddings for Person Identity, ECCV 2018
  • D. Novotny*, S. Albanie*, D. Larlus, A. Vedaldi, Semi-convolutional operators for instance segmentation, ECCV 2018