Publications and Output

  • Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann and Shimon Whiteson. Fast Context Adaption via Meta-Learning. ICML 2019.
  • Leonard Berrada, Andrew Zisserman and M Pawan Kumar. Deep Frank-Wolfe for Neural Network Optimization. ICLR 2019.
  • Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski. Multi³Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. AAAI Conference on Artificial Intelligence, 2019.
  • Webb, Stefan, Rainforth, Tom, Teh, Yee Whye, and Pawan Kumar, M. A Statistical Approach to Assesing Neural Network Robustness. To appear in the Proceedings of the Seventh International Conference on Learning Representations (ICLR2019), New Orleans.
  • Gram-Hansen, P. Helber, I. Varatharajan, F. Azam, A. Coca-Castro, V. Kopackova and P. Bilinski, Mapping Informal Settlements in Developing Countries using Machine Learning and Low-Resolution Multi-spectral Data .The AAAI/ACM International Conference on AI Ethics and Society, 2019
  • Gram-Hansen, Y, Zhou, T. Kohn, T. Rainforth, H. Yang and F. Wood, A Low-Level Probabilistic Programming Language for Non-Differentiable Models. Under review for AISTATS 2019
  • Gram-Hansen, Y, Zhou, T. Kohn, H. Yang and F. Wood, Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities. The International Conference on Probabilistic Programming 2018
  • Gram-Hansen, P. Helber, I. Varatharajan, F. Azam, A. Coca-Castro, V. Kopackova and P. Bilinski, Generating Material Maps to Map Informal Settlements Machine Learning for the Developing World .Workshop at the 32nd Conference for Neural Information Processing Systems 2018
  • Baydin, L. Heinrich, W. Bhimji, B. Gram-Hansen, G. Louppe, L. Shao, K, Cranmer and F.Wood, Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model,  arXiv preprint arXiv: 1807.07706, 2018
  • Tim G. J. Rudner, Vincent Fortuin, Yee Whye Teh, Yarin Gal. On the Connection between Neural Processes and Gaussian Processes with Deep Kernels. NeurIPS Workshop on Bayesian Deep Learning, 2018.
  • Tim G. J. Rudner, Marc Rußwurm, Jakub Fil, Ramona Pelich, Benjamin Bischke, Veronika Kopackova, Piotr Bilinski. Rapid Computer Vision-Aided Disaster Response via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery. NeurIPS Workshop on AI for Social Good, 2018.
  • Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson. VIREL: A Variational Inference Framework for Reinforcement Learning. NeurIPS Workshop on Probabilistic Reinforcement Learning and Structured Control, 2018.
  • Maximilian Igl, Wendelin Boehmer, Andrew Gambardella, Nantas Nardelli, Siddharth Narayanaswamy and Shimon Whiteson. Bayesian hierarchical multitask learning. NeurIPS workshop 2018.
  • Oliver E Bent, Sekou L Remy, Nelson K Bore. A machine learning environment to determine novel malaria policies. NeurIPS Demonstration, 2018.
  • Kevin M. Judd, Jonathan D. Gammell and Paul Newman. Multimotion Visual Odometry (MVO): Simultaneous Estimation of Camera and Third-Party Motions. IROS2018.
  • Kyriacos Shiarlis, Markus Wulfmeier, Sasha Salter, Shimon Whiteson, Ingmar Posner. TACO: Learning Task Decomposition via Temporal Alignment for Control.
  • Fabian B. Fuchs, Oliver Groth, Adam R. Kosiorek, Alex Bewley, Markus Wulfmeier, Andrea Vedaldi, Ingmar Posner. Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing.
  • Jack Hunt, Victor A. Prisacariu, Stuart Golodetz, Tommaso Cavallari, Nicholas A. Lord and Philip H. S. TorrProbabilistic Object Reconstruction with Online Global Model Correction. Proceedings of the 5th International Conference on 3D Vision. 3DV 2017.
  • A Patane, S Ghiasi, E P Scilingo, M Kwiatkowska. Automated Recognition of Sleep Arousal using Multimodal and Personalized Deep Ensembles of Neural Networks. Computing in Cardiology 2018.
  • A Patane, M Kwiatkowska. Calibrating the Classifier: Siamese Neural Network Architecture for End-to-End Arousal Recognition from ECG. The Fourth International Conference on Machine Learning, Optimization, and Data Science. 2018.
  • L Cardelli, M Kwiatkowska, L Laurenti, A Patane. Robustness Guarantees for Bayesian Inference with Gaussian Processes.
  • Ankush Gupta, Andrea Vedaldi, Andrew Zisserman. Learning to Read by Spelling: Towards Unsupervised Text Recognition
  • Aravindh Mahendran, James Thewlis, Andrea Vedaldi. Self-Supervised Segmentation by Grouping Optical-Flow. POCV Workshop on Action, Perception and Organization, ECCV 2018 Workshops.
  • Xu Ji, Joao F. Henriques and Andrea Vedaldi. Invariant Information Distillation for Unsupervised Image Segmentation and Clustering.
  • Aravindh Mahendran, James Thewlis, Andrea Vedaldi. Cross Pixel Optical Flow Similarity for Self-Supervised Learning. ACCV 2018.
  • Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Tom Rainforth, Yee Whye Teh and Frank Wood. Faithdul Inversion of Generative Models for Effective Amortized Inference. NIPS2018
  • James Thewlis, Hakan Bilen and Andea Vedaldi. Modelling and unsupervised of symmetric deformable object categories.  NIPS2018.
  • Tomas Jakab*, Ankush Gupta*, Hakan Bilen, Andrea Vedaldi. Conditional Image Generation for Learning the Structure of Visual. NIPS2018 (* indicates equal contribution)
  • Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J Maddision, Maximilian Igl, Frank Wood and Yee Whye Teh. Tighter variational bounds are not necessarily better. International Conference on Machine Learning. ICML 2018.
  • M. Igl, L. Zintgraf, TA. Le, F. Wood and S. Whiteson. Deep variational reinforcement learning for POMDP. ICML 2018.
  • Benjamin Moseley, Andrew Markham, Tarje Nissen-Meyer. Fast approximate simulation of seismic waves with deep learning.
  • Oliver Groth, Fabian B. Fuchs, Ingmar Posner, Andrea Vedaldi. ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking. ECCV 2018.
  • Ivan Kiskin, Davide Zilli, Yunpeng Li, Marianne Sinka, Kathy Willis, Stephen Roberts. Bioacoustic detection with wavelet-conditioned convolutional neural networks. Springer, Neural Computing and Applications.
  • Golinski, Adam, Teh, Yee Whye, Wood Frank and Rainforth, Tom. Amortized Monte Carlo Integration. UAI 2018 Workshop on Uncertainity in Deep Learning.
  • Gupta, Ankush, Vedaldi, Andrea and Zisserman, Andrew. Inductive Visual Localisation: Factorised Training for Superior Generalisation. Proceedings of the British Machine Vision Conference (BMVC), 2018.
  • Smith, Lewis and Gal, Yarin. Understanding Measures of Uncertainty for Adversarial Example Detection. UAI 2018.
  • Ghoshal, S. and Roberts, S. Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting. Data Science in Fintech, KDD 2018.
  • Tim G. J. Rudner and Dino Sejdinovic. Inter-domain Deep Gaussian Processes. Bayesian Deep Learning. NIPS Workshop, 2017.
  • Rainforth, T., Cornish, R., Yang, H., Warrington, A., & Wood, F. On Nesting Monte Carlo Estimators. International Conference on Machine Learning. ICML2018.
  • Novotny*, S. Albanie*, D. Larlus, A. Vedaldi. Semi-convolutional operators for instance segmentation, ECCV 2018.
  • Nagrani*, S. Albanie*, and A. Zisserman. Learnable PINs: Cross-Modal Embeddings for Person Identit., ECCV 2018.
  • Berrada, L, A. Zisserman, M. P. Kumar. Smooth Loss Functions for Deep Top-k Classification.  International Conference on Learning Representations, 2018.
  • T. Afouras, J. S. Chung, A. Zisserman. The Conversation: Deep Audio-Visual Speech Enhancement.  INTERSPEECH, 2018
  • T. Afouras, J. S. Chung, A. Zisserman. Deep Lip Reading: a comparison of models and an online application.  INTERSPEECH, 2018
  • Adam D. Cobb, Stephen J. Roberts, Yarin Gal. Loss-Calibrated Approximate Inference in Bayesian Neural Betworks.
  • Stefano Rosa, Andrea Patanè, Xiaoxuan Lu, and Niki Trigoni. CommonSense: Collaborative learning of scene semantics by robots and humans. In Proceedings of the 1st International Workshop on Internet of People, Assistive Robots and ThingS (pp. 1-6). ACM. 2018, June.
  • Simon Eberz, Giulio Lovisotto, Andrea Patané, Marta Kwiatkowska, Vincent Lenders, and Ivan Martinovic. When Your Fitness Tracker Betrays You: Quantifying the Predictability of Biometric Features Across Contexts. IEEE Symposium on Security and Privacy (S&P), San Francisco, CA, USA, May 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.
  • 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.
  • 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.
  • S. Albanie, H. Shakespeare and T. Gunter. Unknowable Manipulators: Social Network Curator Algorithms. (NIPS 2016 Symposium: Machine Learning and the Law).
  • Nagrani, S. Albanie, A. Zisserman. Seeing Voices and Hearing Faces: Cross-modal biometric matching, CVPR, 2018.  (* denotes equal contribution)
  • Novotny*, S. Albanie*, D. Larlus, A. Vedaldi. Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection. CVPR, 2018. (* denotes equal contribution)
  • Le, Tuan Anh, Igl, Maximilian, Jin, Tom, Rainforth, Tom, Wood, Frank. Auto-encoding sequential Monte Carlo. ICLR 2018.
  • Border, R. D. Gammell, and P. Newman. Surface Edge Explorer (SEE): Planning Next Best Views Directly from 3D Observations. ICRA 2018.
  • Farquhar, G., Rocktäschel, T., Igl, M., & Whiteson, STreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning. ICLR 2018.
  • Foerster, J., Farquhar, G., Afouras, T., Nardelli, N., & Whiteson, S. Counterfactual multi-agent policy gradients. 32nd AAAI Conference on Artificial Intelligence (AAAI’18).
  • Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Tom Rainforth, Yee Whye Teh and Frank Wood. Faithful Model Inversion Substantially Improves Auto-encoding Variational Inference. NIPS2017 Workshop in Bayesian Deep Learning.
  • Stefan Webb and Yee Whye Teh. A Tighter Monte Carlo Objective with Renyi divergence Measures. NIPS 2016 Workshop in Bayesian Deep Learning.
  • N Paoletti, A Patane, M Kwiatkowska. Closed-loop quantitative verification of rate-adaptive pacemakers. ACM Transactions on Cyber-Physical Systems, to appear, 2018.
  • Zand, Jaleh & Roberts, S. MiDGaP: Mixture Density Gaussian Processes.  NIPS time series workshop 2017.
  • J. Thewlis andH. Bilen and A. Vedaldi. Unsupervised learning of object landmarks by factorized spatial embeddings. Proceedings of the International Conference on Computer Vision. ICCV 2017, (oral presentation).
  • J. Thewlis and H. Bilen and A. Vedaldi. Unsupervised object learning from dense invariant image labellin., Proceedings of Advances in Neural Information Processing Systems. NIPS 2017 (oral presentation).
  • Jakob Foerster, Nantas Nardelli, Gregory Farquhar, Triantafyllos Afouras, Philip H.S. Torr. Pushmeet Kohli, Shimon Whiteson. Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning. ICML 2017.
  • Cobb, A, Markham, A and Roberts, S. Learning from lions: inferring the utility of agents from their trajectories.
  • Ghoshal, S and Roberts, S. Reading the Tea Leaves: A Neural Network Perspective on Technical Trading. Knowledge Discovery and Data Mining (KDD), 2017.
  • Gram-Hansen and S.J Roberts, Multi-layer Stacked Gaussian Processes, preprint: http://www.robots.ox.ac.uk/~bradley/papers/mp1.pdf , 2017
  • Berrada, Leo. Trusting SVM for Piecewise Linear CNNs. ICLR 2017 (International Conference on Learning Representations).
  • Bartlett, O, C. Gurau, L. Marchegiani, and I. Posner. Enabling Intelligent Energy Management for Robots using Publicly Available Maps. Iin IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016. PDF
  • James Thewlis, Shuai Zheng, Philip H. S. Torr, Andrea Vedaldi. Fully-Trainable Deep Matching. British  Machine Vision Conference (BMVC), 2016.
  • Sam Albanie and Andrea Vedaldi. Learning Grimaces by Watching TV. BMVC 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.
  • 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.
  • 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.