Publications

AIMS students have published their work at leading venues in their field, including CVPR, NIPS, ICCV, AAAI, ICLR and IROS: this is indicative of the high quality of research conducted at AIM.

  • Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal. Inter-domain Deep Gaussian Processes with RKHS Fourier Features. ICML 2020.

  • J. Gutierrez, A Murano, G. Perelli, S Rubin, T. Steeples and M. Wooldridge. Equilibria for Games with Combined Qualative and Quantative Objectives. Springer 2020
  • Multitask Soft Option Learning" Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N Siddharth, Wendelin Böhmer, Shimon Whiteson. UAI 2020 (The Thirty-Sixth Conference on Uncertainty in Artificial Intelligence).

  • Jamaludin, R. Windsor, S. Ather, T. Kadir, A. Zisserman, J. Braun, L. S. Gensler, P. M. Machado, M. Østergaard, D. Poddubnyy, T. Coroller, B. Porter, S. Mpofu, A. Readie. Machine learning based Berlin scoring of magnetic resonance images of the spine in patients with ankylosing spondylitis from the MEASURE 1 study. Annual European Congress of Rheumatology 2020.
  • Oliver Groth, Chia-Man Hung, Andrea Vedaldi, Ingmar Posner. Goal-Conditioned End-to-End Visuomotor Control for Versatile Skill Primitives. Under review for ICML 2020.

  • Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman. "ASR is all you need: Cross-modal distillation for lip reading" ICASSP 2020
  • Henry Kenlay, Dorina Thanou, Xiaowen Dong. On the Stability of Polynomial Spectral Graph Filters.  ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • An Overview of Gaussian Process Regression for Volatility Forecasting, B Liu, I Kiskin, S Roberts, IEEE International Conference on Artificial Intelligence in Information and Communication, 2020, 2020 (Liu et al., 2020).
  • Prannay Kaul, Danielle De Martini, Matthew Gadd and Paul Newman. RSS-Net: Weakley-Supervised Multi-Class Semantic Segmentation with FMCW Radar. IEEE Intelligent Vehicles Symposium (IV) 2020.

  • HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset, I Kiskin, AD Cobb, L Wang, S Roberts, 2019 NeurIPS Machine Learning for the Developing World workshop, 2020 International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019–2020 (Kiskin et al., 2019, 2020). 
  • Yuki M, Asano, Christian Rupprecht and Andrea Vedaldi. Self-labelling via Simultaneous Clustering and Representation Learning. Proc. ICLR 2020.
  • Yuki M. Asano, Christian Rupprecht and Andrea Vedaldi. A Critical Analysis of Self-Supervision, or What We can Learn from a Single Image. Proc. ICLR 2020.
  • Blaas, L Laurenti, A Patane, L Cardelli, M Kwiatkowska and S Roberts. Adversarial Robustness Guarantees for Classification withh Gaussian Processes. AIStats 2020.
  • Siddhant Gangapurwala, Alexander Mitchell and Ioannis Havoutis. Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion. RA-L.
  • Shuyu Lin, Ronald Clark, Robert Birke, Sandro Schonborn, Niki Trigoni and Stephen Roberts. Anomaly Detection for Time Series Using VAE-LSTM Hybrid Model. ICASSP 2020.
  • Yijing Liu*, Shuyu Lin* and Ronals Clark* (*equal contribution). Towards Consistens Cariational Auto-Encoding. AAAI 2020.
  • Rhydian Windsor and Amir Jamaludin. The Ladder Algorithm: Finding Repetitive Structures in Medical Images by Induction. ISBI 2020.
  • L Cardelli, M Kwiatkowska, L Laurenti, N Paoletti, A Patane and M Wicker. Statistical Guarantees for the Robustness of Bayesian Neural Networks. IJCAI 2019.
  • Super-resolution of Time-series Labels for Bootstrapped Event Detection, I Kiskin, U Meepegama, S Roberts, 2019 ICML Time-series workshop, 2019 (Kiskin et al., 2019).
  • R. Fong*, M. Patrick* and A. Vedaldi (*denotes equal contributions). Understanding Deep Networks via Extremal Perturbations and Smooth Masks. ICCV 2019.
  • D Columbo, J Fernandez-Alvarez, A Patane, M Semonella, M Kwiatkowska, A Garcia-Palacios, P Cipresso, G Riva and C Botella. Current State and Future Directions of Technology-Based Ecological Momentary Assessment and Intervention for Major Depressive Disorder: A Systematic Review. Journal of Clinial Medicine 2019.
  • Tim G.J. Rudner, Florian Wenze and Yarin Gal.  The Natural Neural Tangent Kernel: Neural Network: Training Dynamics under Natural Gradient Descent. NeurIPS Workshop on Bayesian Deep Learning, 2019. (Contribted talk, top 6% of submissions).
  • Tim G.J. Rudner, Waseem Aslam, Oscar Key, Tom Rainforth and Yairn Gal. Tighter Variational Bounds for Deep Gaussian Processes. NeurIPS Workshop on Bayesian Deep Learning, 2019.
  • Angelos Filos, Sebastian Farquhar, Aidan N. Gomez, Tim G.J. Rudner, Zachary Kenton, Lewis Smith, Milad Alizadeh, Arnoud de Kroom and Yarin Gal. A Systematic Comparision of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks. NeurIPS Workshop on Bayessian Learning, 2019.
  • Adam Golinski*, Frank Wood, Tom Rainforth*. Amortized Monte Carlo Integration. ICML 2019.
  • Ben Moseley, Valentin Bickel, Jerome Burelbach, Nicole Relatores, Daniel Angerhausen, Frank Soboczenski and Dennis Wingo. Unsupervised Learning for Thermal Detection on the Lunar Surface. NeurIPS 2019.
  • Andreas Kirsch, Joost van Amersfoort and Yarin Gal. BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning, NeurIPS 2019.
  • Farquhar, G., Gustafson, L., Lin, Z., Whiteson, S., Usunier, N., Synnaeve, G. (2019). Growing Action Spaces. Under review, preprint available.
  • Gregory Farquhar, Shimon Whiteson and Jakob Foerster. Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning. NeurIPS 2019
  • Christian Schroeder de Witt, Jakob Foerster, Gregory Farquhar, Philip Torr, Wendelin Boehmer and Shimon Whiteson. Multi-Agent Common Knowledge Reinforcement Learning.  NeurIPS 2019.
  • Maximilian Igl, Kamil Ciosek, Yingzhen Li, Sebastian Tschiatschek, Cheng Zhang, Sam Devlin and Katja Hofmann. Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck. NeurIPS 2019.
  • Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson. VIREL: A Variational Inference Framework for Reinforcement Learning. NeurIPS 2019. (Spotlight Talk, top 3% of submitted papers).
  • Atılım Güneş Baydin, Lukas Heinrich, Wahid Bhimji, Lei Shao, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Prabhat, Kyle Cranmer, Frank Wood. Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. NeurIps2019.
  • Supratik Paul, Vitaly Kurin, Shimon Whiteson. Fast Efficient Hyperparameter Tuning for Policy Gradients.  NeurIps2019.
  • Luketina, J., Nardelli, N., Farquhar, G., Foerster, J., Andreas, J., Grefenstette, E., ... & Rocktäschel, T. (2019). A Survey of Reinforcement Learning Informed by Natural Language. IJCAI 2019.
  • X. Ji, J.F. Henriques and A. Vedaldi, "Invariant Information Clustering for Unsupervised Image Classification and Segmentation"
  • G. I. Parisi, X. Ji, S. Wermter, "On the role of neurogenesis in overcoming catastrophic forgetting"
  • T. Afouras, J. Chung, A. Senior, O. Vinyals and A. Zisserman, "Deep Audio-visual Speech Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Triantafyllos Afouras, Joon Son Chung and Andrew Zisserman. My lips are concealed: Audio-visual speech enhancement through obstructions. INTERSPEECH 2019
  • Adam D. Cobb, Michael D. Himes, Frank Soboczenski, Simone Zorzan, Molly D. O’Beirne, Atilim Gunes Baydin, Yarin Gal, Shawn D. Domagal-Goldman, Giada N. Arney, Daniel Angerhausen and 2018 NASA FDL Astrobiology Team II. An Ensemble of Abyesian Neural Networks for Exoplanetary Atmospheric Retrieval. The Astronomical Journal
  • N. Neverova, J. Thewlis, R. A. Güler, I. Kokkinos, A. Vedaldi. Slim DensePose: Thrifty Learning from Sparse Annotations and Motion Cues. CVPR 2019 (Oral presentation)
  • Rob Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet. Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. ICML2019
  • Rob Cornish, George Deligiannidis, Arnaud Doucet. Robust Out-of-Sample Uncertainty for Neural Networks via Confidence Densities. ICML workshop on Uncertainty and Robustness in Deeep Learning 2019
  • Shuyu Lin, Bo Yang, Robert Birke and Ronald Clarke. Learning Semantically Meaningful Embeddings Using Linear Constraints. Accepted by Explainable AI Workshop at CVPR 2019.
  • Shuyu Lin, Ronald Clark, Robert Birke, Niki Trigoni and Stephen Roberts. WiSE-ALE: Wide Sample Estimator for Aggregate Latent Embedding. Accepted by Deep Generative Models for Highly Structured Data Workshop at ICLR 2019
  • Edward Wagstaff, Fabian. B. Fuchs, Martin Engelcke, Ingmar Posner and Michael Osborne. On the Limitations of Representing Functions on Sets. ICML 2019
  • Mikayel Samvelyan, Tabish Rashid, Christian Schroeder de Witt, Gregory Farquhar, Nantas Nardelli, Tim G. J. Rudner, Chia-Man Hung, Philip H. S. Torr, Jakob Foerster, Shimon Whiteson. The StarCraft Multi-Agent Challenge (Extended Abstract). International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019.
  • K. M. Judd, J. D. Gammell. “SE(3) multimotion estimation through occlusion.” in Proceedings of the long-term human motion prediction (LHMP) workshop, IEEE international conference on robotics and automation (ICRA), 2019.
  • K. M. Judd, J. D. Gammell. “The Oxford Multimotion Dataset: Multiple SE(3) motions with ground truth.” IEEE Robotics and Automation Letters (RA-L), 4(2):800–807, Apr. 2019. Presented at ICRA 2019.
  • Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann and Shimon Whiteson. Fast Context Adaption via Meta-Learning. ICML 2019.
  • Rashid, T., Samvelyan, M., Witt, C.S., Farquhar, G., Foerster, J.N., & Whiteson, S. (2018). QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning. ICML 2018.
  • 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.
  • Semi-separable Hamiltonian Monte Carlo for inference in Bayesian neural networks, A D Cobb, A G Baydin, I Kiskin, A Markham, S J Roberts, 2019 NeurIPS Bayesian Deep Learning Workshop, 2019 (Cobb et al., 2019).
  • 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
  • Foerster, J.N., Farquhar, G., Al-Shedivat, M., Rocktäschel, T., Xing, E.P., & Whiteson, S. (2018). DiCE: The Infinitely Differentiable Monte-Carlo Estimator. ICML 2018.
  • Fast Mosqutio Acoustic Detection With Field Cup Recordings: An Initial Investigation, Y Li, I Kiskin, M Sinka, D Zilli, H Chan, E HerrerosM oya, T Chareonviriyaphap, R Tisgratog, K Willis, S Roberts, Detection and Classification of Acoustic Scenes and Events 2018 (DCASE) 2018 Workshop, (Li et al., 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.
  • 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. Torr. Probabilistic 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 & Applications Special Issue in Deep learning for Music and Audio, 2018 (Kiskin et al., 2018). 
  • 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.
  • S Rosa, A Patane, C X Lu and N Trigoni. Semantic Place Understanding for Human-Robot Coexistence-Toward Intelligent Workplaces. IEEE Transactions on Human-Machine Systems 2018.
  • 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, S. TreeQN 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 and H. 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).
  • Mosquito Detection with Neural Networks: The Buzz of Deep Learning, I Kiskin, B.P. Orozco, T. Windebank, D. Zilli, M. Sinka, K. Willis, S Roberts, arXiv pre-print, submitted to ECML 2017, 2017 (Kiskin et al., 2017).
  • Mosquito detection with low-cost smartphones: Data acquisition for malaria research, Y Li, D Zilli, H Chan, I Kiskin, M Sinka, S Roberts, K Willis,NeurIPS 2017 Workshop on Machine Learning for the Developing World, 2017 (Li et al., 2017b).
  • Cost-sensitive detection with variational autoencoders for environmental acoustic sensing, Y Li, I Kiskin, D Zilli, M Sinka, H Chan, K Willis, S Roberts, NeurIPS 2017 Workshop on Machine Learning for Audio Signal Processing, 2017 (Li et al., 2017a).
  • 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.