CUED Publications database

Items where Division is "Div F > Computational and Biological Learning" and Year is 2016

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Number of items: 86.


Aitchison, L and Lengyel, M (2016) The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics. PLoS Computational Biology, 12.

Alexander, AG and Hensman, J and Turner, RE and Ghahramani, Z (2016) On sparse variational methods and the Kullback-Leibler divergence between stochastic processes. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. pp. 231-239.


Balog, M and Lakshminarayanan, B and Ghahramani, Z and Roy, DM and Teh, YW (2016) The Mondrian Kernel. 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016). pp. 32-41.

Bauer, M and Van Der Wilk, M and Rasmussen, CE (2016) Understanding probabilistic sparse Gaussian Process approximations. Advances in Neural Information Processing Systems. pp. 1533-1541. ISSN 1049-5258

Bolognini, N and Convento, S and Casati, C and Mancini, F and Brighina, F and Vallar, G (2016) Multisensory integration in hemianopia and unilateral spatial neglect: Evidence from the sound induced flash illusion. Neuropsychologia, 87. pp. 134-143. ISSN 0028-3932

Borgström, J and Gordon, AD and Ouyang, L and Russo, C and Ścibior, A and Szymczak, M (2016) Fabular: regression formulas as probabilistic programming. Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages - POPL 2016. pp. 271-283.

Bruinsma, WP and Hes, RP and Bosma, S and Lager, IE and Bentum, MJ (2016) Radiation properties of moving constellations of (nano) satellites: A complexity study. In: UNSPECIFIED.

Bui, TD and Hernández-Lobato, JM and Hernández-Lobato, D and Li, Y and Turner, RE (2016) Deep Gaussian processes for regression using approximate expectation propagation. In: UNSPECIFIED pp. 2187-2208..


Calandra, R and Peters, J and Rasmussen, CE and Deisenroth, MP (2016) Manifold Gaussian Processes for regression. In: International Joint Conference on Neural Networks, -- to -- pp. 3338-3345..

Chen, Y and Ghahramani, Z (2016) Scalable Discrete Sampling as a Multi-Armed Bandit Problem. In: UNSPECIFIED pp. 2492-2501..

Chen, Y and Ghahramani, Z (2016) Scalable discrete sampling as a multi-armed bandit problem. 33rd International Conference on Machine Learning, ICML 2016, 5. pp. 3691-3707.

Cuong, NV and Xu, H (2016) Adaptive maximization of pointwise submodular functions with budget constraint. In: UNSPECIFIED pp. 1252-1260..

Cuong, NV and Ye, N and Lee, WS (2016) Robustness of Bayesian pool-based active learning against prior misspecification. In: UNSPECIFIED pp. 1512-1518..


Echeveste, R and Gros, C (2016) Drifting states and synchronization induced chaos in autonomous networks of excitable neurons. Frontiers in Computational Neuroscience, 10. 98-. ISSN 1662-5188

Einspieler, C and Peharz, R and Marschik, PB (2016) Fidgety movements – tiny in appearance, but huge in impact. Jornal de Pediatria, 92. S64-S70. ISSN 0021-7557


Franklin, DW (2016) Rapid feedback responses arise from precomputed gains. Motor Control, 20. pp. 171-176. ISSN 1087-1640

Franklin, DW and Batchelor, AV and Wolpert, DM (2016) The sensorimotor system can sculpt behaviorally relevant representations for motor learning. eNeuro, 3.

Frellsen, J and Winther, O and Ghahramani, Z and Ferkinghoff-Borg, J (2016) Bayesian generalised ensemble Markov chain Monte Carlo. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, 51. pp. 408-416.

Friedrich, J and Lengyel, M (2016) Goal-directed decision making with spiking neurons. Journal of Neuroscience, 36. pp. 1529-1546. ISSN 0270-6474

Frith, CD and Blakemore, SJ and Wolpert, DM (2016) Abnormalities in the awareness and control of action. In: Discovering the Social Mind: Selected works of Christopher D. Frith. UNSPECIFIED, pp. 64-100.


Gal, Y and Ghahramani, Z (2016) Dropout as a Bayesian Approximation: Appendix. 33rd International Conference on Machine Learning, ICML 2016, 3. pp. 1661-1680.

Gal, Y and Ghahramani, Z (2016) Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In: UNSPECIFIED pp. 1050-1059..

Gal, Y and Ghahramani, Z (2016) Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. 33rd International Conference on Machine Learning, ICML 2016, 3. pp. 1651-1660.

Gal, Y and Ghahramani, Z (2016) A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. In: UNSPECIFIED pp. 1019-1027..

Gal, Y and Ghahramani, Z (2016) A theoretically grounded application of dropout in recurrent neural networks. Advances in Neural Information Processing Systems. pp. 1027-1035. ISSN 1049-5258

Gallivan, JP and Bowman, NAR and Chapman, CS and Wolpert, DM and Flanagan, JR (2016) The sequential encoding of competing action goals involves dynamic restructuring of motor plans in working memory. Journal of Neurophysiology, 115. pp. 3113-3122. ISSN 0022-3077

Gallivan, JP and Logan, L and Wolpert, DM and Flanagan, JR (2016) Parallel specification of competing sensorimotor control policies for alternative action options. Nature Neuroscience, 19. pp. 320-326. ISSN 1097-6256

Ghahramani, Z (2016) Automating machine learning. In: UNSPECIFIED XX-..

Gomersall, PA and Turner, RE and Baguley, DM and Deeks, JM and Gockel, HE and Carlyon, RP (2016) Perception of stochastic envelopes by normal-hearing and cochlear-implant listeners. Hearing Research, 333. pp. 8-24. ISSN 0378-5955

Gros, C and Echeveste, R (2016) Information-theoretical foundations of hebbian learning. In: UNSPECIFIED 560-..

Gu, S and Lillicrap, TP and Ghahramani, Z and Turner, RE and Levine, S (2016) Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic. In: UNSPECIFIED.


Hennequin, G and Ahmadian, Y and Rubin, D and Lengyel, M and Miller, K (2016) Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex.

Hernández-Lobato, D and Hernández-Lobato, JM (2016) Scalable gaussian process classification via expectation propagation. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. pp. 168-176.

Hernández-Lobato, D and Hernández-Lobato, JM and Shah, A and Adams, RP (2016) Predictive entropy search for multi-objective Bayesian optimization. 33rd International Conference on Machine Learning, ICML 2016, 3. pp. 2219-2237.

Hernández-Lobato, JM and Gelbart, MA and Adams, RP and Hoffman, MW and Ghahramani, Z (2016) A General Framework for Constrained Bayesian Optimization using Information-based Search. Journal of Machine Learning Research, 17.

Hernández-Lobato, JM and Li, Y and Rowland, M and Hernández-Lobato, D and Bui, TD and Turner, RE (2016) Black-Box α-divergence minimization. Proceedings of the 33rd International Conference on Machine Learning, 48. pp. 1511-1520.


Iwata, T and Lloyd, JR and Ghahramani, Z (2016) Unsupervised Many-to-Many Object Matching for Relational Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38. pp. 607-617. ISSN 0162-8828


Jefferis, G and Frechter, S and Dolan, M-J and Tootoonian, S and Sutcliffe, B and Lengyel, M (2016) Odour coding and organisational logic of the lateral horn. In: UNSPECIFIED p. 388..


Kang, YHR (2016) Estimation of time-varying decision thresholds from the choice and reaction times with no assumption on the shape.

Koizumi, A and Amano, K and Cortese, A and Shibata, K and Yoshida, W and Seymour, B and Kawato, M and Lau, H (2016) Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure. Nature Human Behaviour, 1.

Korattikara, A and Chen, Y and Welling, M (2016) Sequential tests for large-scale learning. Neural Computation, 28. pp. 45-70. ISSN 0899-7667

Kremer, J and Stensbo-Smidt, K and Gieseke, F and Pedersen, KS and Igel, C (2016) Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy. IEEE Intelligent Systems, 32. pp. 16-22. ISSN 1541-1672


Lawson, RP and Nord, CL and Seymour, B and Thomas, DL and Dayan, P and Pilling, S and Roiser, JP (2016) Disrupted habenula function in major depression. Molecular psychiatry. (Unpublished)

Li, Y and Turner, RE (2016) Rényi Divergence Variational Inference. In: Neural Information Processing Systems (NIPS 2016), 2016-12-5 to 2016-12-10, Barcelona, Spain pp. 1073-1081..


Mancini, F and Dolgevica, K and Steckelmacher, J and Haggard, P and Friston, K and Iannetti, GD (2016) Perceptual learning to discriminate the intensity and spatial location of nociceptive stimuli. Scientific Reports, 6. 39104-.

Mattar, M and Wymbs, N and Bock, A and Aguirre, G and Grafton, S and Bassett, D (2016) Predicting future learning from baseline network architecture.

McNamee, D and Wolpert, D and Lengyel, M (2016) Efficient state-space modularization for planning: Theory, behavioral and neural signatures. In: UNSPECIFIED pp. 4518-4526..


Nazabal, A and Garcia-Moreno, P and Artes-Rodriguez, A and Ghahramani, Z (2016) Human Activity Recognition by Combining a Small Number of Classifiers. IEEE Journal of Biomedical and Health Informatics, 20. pp. 1342-1351. ISSN 2168-2194

Norbury, AE and Valton, V and Rees, G and Roiser, JP and Husain, M (2016) Shared Neural Mechanisms for the Evaluation of Intense Sensory Stimulation and Economic Reward, Dependent on Stimulation-Seeking Behavior. The Journal of Neuroscience, 36. pp. 10026-10038.


Orbán, G and Berkes, P and Fiser, J and Lengyel, M (2016) Neural Variability and Sampling-Based Probabilistic Representations in the Visual Cortex. Neuron, 92. pp. 530-543.


Paige, B and Sejdinovic, D and Wood, F (2016) Super-sampling with Reservoir. In: UNSPECIFIED pp. 567-576..

Paige, B and Wood, F (2016) Inference Networks for Sequential Monte Carlo in Graphical Models. In: UNSPECIFIED pp. 3040-3049..

Pokorny, FB and Peharz, R and Roth, W and Zöhrer, M and Pernkopf, F and Marschik, PB and Schuller, BW (2016) Manual versus automated: The challenging routine of infant vocalisation segmentation in home videos to study neuro(mal)development. In: UNSPECIFIED pp. 2997-3001..

Pradier, MF and Ruiz, FJR and Perez-Cruz, F (2016) Prior design for dependent Dirichlet processes: An application to marathon modeling. PLoS ONE, 11. e0147402-.


Rainforth, T and Naesseth, CA and Lindsten, F and Paige, B and van de Meent, J-W and Doucet, A and Wood, F (2016) Interacting Particle Markov Chain Monte Carlo. In: UNSPECIFIED pp. 2616-2625..

Reagen, B and Whatmough, P and Adolf, R and Rama, S and Lee, H and Lee, SK and Hernandez-Lobato, JM and Wei, GY and Brooks, D (2016) Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators. In: UNSPECIFIED pp. 267-278..

Rudolph, M and Ruiz, FJR and Mandt, S and Blei, DM (2016) Exponential family embeddings. Advances in Neural Information Processing Systems. pp. 478-486. ISSN 1049-5258

Ruiz, FJR and Titsias, MK and Blei, DM (2016) Overdispersed black-box variational inference. 32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016. pp. 647-656.

Ruiz, FJR and Titsias, MK and Blei, DM (2016) The generalized reparameterization gradient. Advances in Neural Information Processing Systems. pp. 460-468. ISSN 1049-5258


Sadabadi, MS and Peaucelle, D (2016) From static output feedback to structured robust static output feedback: A survey. Annual Reviews in Control, 42. pp. 11-26. ISSN 1367-5788

Seymour, B and Barbe, M and Dayan, P and Shiner, T and Dolan, R and Fink, GR (2016) Deep brain stimulation of the subthalamic nucleus modulates sensitivity to decision outcome value in Parkinson's disease. Scientific Reports, 6.

Shadlen, MN and Kiani, R and Newsome, WT and Gold, JI and Wolpert, DM and Zylberberg, A and Ditterich, J and De Lafuente, V and Yang, T and Roitman, J (2016) Comment on "single-trial spike trains in parietal cortex reveal discrete steps during decision-making". Science, 351. 1406-. ISSN 0036-8075

Shah, A and Ghahramani, Z (2016) Markov beta processes for time evolving dictionary learning. In: UNSPECIFIED pp. 676-685..

Shah, A and Ghahramani, Z (2016) Pareto Frontier Learning with Expensive Correlated Objectives. In: UNSPECIFIED pp. 1919-1927..

Shah, A and Ghahramani, Z (2016) Pareto frontier learning with expensive correlated objectives. In: UNSPECIFIED pp. 2839-2849..

Sharmanska, V and Hernandez-Lobato, D and Hernandez-Lobato, JM and Quadrianto, N (2016) Ambiguity Helps: Classification with Disagreements in Crowdsourced Annotations. In: UNSPECIFIED pp. 2194-2202..

Sheahan, HR and Franklin, DW and Wolpert, DM (2016) Motor Planning, Not Execution, Separates Motor Memories. Neuron, 92. pp. 773-779.

Simon-Gabriel, C-J and Ścibior, AM and Tolstikhin, I and Schölkopf, B (2016) Consistent Kernel Mean Estimation for Functions of Random Variables. Proceedings of the Thirtieth Annual Conference on Neural Information Processing Systems. pp. 1740-1748.

Stanciu, O and Lengyel, M and Wolpert, D and Fiser, J (2016) On optimal estimation from correlated samples. In: UNSPECIFIED pp. 228-229..

Stanton, TR and Gilpin, HR and Reid, E and Mancini, F and Spence, C and Moseley, GL (2016) Modulation of pain via expectation of its location. European Journal of Pain (United Kingdom), 20. pp. 753-766. ISSN 1090-3801

Stensbo-Smidt, K and Gieseke, F and Igel, C and Zirm, A and Steenstrup Pedersen, K (2016) Sacrificing information for the greater good: how to select photometric bands for optimal accuracy. Monthly Notices of the Royal Astronomical Society, 464. pp. 2577-2596. ISSN 0035-8711


Therrien, AS and Wolpert, DM and Bastian, AJ (2016) Effective Reinforcement learning following cerebellar damage requires a balance between exploration and motor noise. Brain, 139. pp. 101-114. ISSN 0006-8950

Turner, RE and Frellsen, J and Navarro, A (2016) The Multivariate Generalised von Mises distribution: Inference and applications. In: AAAI 2017, 2017-2-4 to 2017-2-9, San Francisco pp. 2394-2400..


Valera, I and Ruiz, FJR and Olmos, PM and Blanco, C and Perez-Cruz, F (2016) Infinite continuous feature model for psychiatric comorbidity analysis. Neural Computation, 28. pp. 354-381. ISSN 0899-7667

Valera, I and Ruiz, FJR and Perez-Cruz, F (2016) Infinite factorial unbounded-state hidden Markov model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38. pp. 1816-1828. ISSN 0162-8828

Van Den Berg, R and Anandalingam, K and Zylberberg, A and Kiani, R and Shadlen, MN and Wolpert, DM (2016) A common mechanism underlies changes of mind about decisions and confidence. eLife, 5. e12192-.

van de Meent, JW and Paige, B and Tolpin, D and Wood, F (2016) Black-box policy search with probabilistic programs. In: UNSPECIFIED pp. 1195-1204..

van den Berg, R and Zylberberg, A and Kiani, R and Shadlen, MN and Wolpert, DM (2016) Confidence Is the Bridge between Multi-stage Decisions. Current Biology, 26. pp. 3157-3168.


Wolpe, N and Ingram, JN and Tsvetanov, KA and Geerligs, L and Kievit, RA and Henson, RN and Wolpert, DM and Rowe, JB and Tyler, LK and Brayne, C and Bullmore, E and Calder, A and Cusack, R and Dalgleish, T and Duncan, J and Matthews, FE and Marslen-Wilson, W and Shafto, MA and Campbell, K and Cheung, T and Davis, S and McCarrey, A and Mustafa, A and Price, D and Samu, D and Taylor, JR and Treder, M and van Belle, J and Williams, N and Bates, L and Emery, T and Erzinclioglu, S and Gadie, A and Gerbase, S and Georgieva, S and Hanley, C and Parkin, B and Troy, D and Auer, T and Correia, M and Gao, L and Green, E and Henriques, R and Allen, J and Amery, G and Amunts, L and Barcroft, A and Castle, A and Dias, C and Dowrick, J and Fair, M and Fisher, H and Goulding, A and Grewal, A and Hale, G and Hilton, A and Johnson, F and Johnston, P and Kavanagh-Williamson, T and Kwasniewska, M and McMinn, A and Norman, K and Penrose, J and Roby, F and Rowland, D and Sargeant, J and Squire, M and Stevens, B and Stoddart, A and Stone, C and Thompson, T and Yazlik, O and Barnes, D and Dixon, M and Hillman, J and Mitchell, J and Villis, L (2016) Ageing increases reliance on sensorimotor prediction through structural and functional differences in frontostriatal circuits. Nature Communications, 7. 13034-.

Wolpert, DM and Flanagan, JR (2016) Computations underlying sensorimotor learning. Current Opinion in Neurobiology, 37. pp. 7-11. ISSN 0959-4388


Yanagisawa, T and Fukuma, R and Seymour, B and Hosomi, K and Kishima, H and Shimizu, T and Yokoi, H and Hirata, M and Yoshimine, T and Kamitani, Y and Saitoh, Y (2016) Induced sensorimotor brain plasticity controls pain in phantom limb patients. Nature Communications, 7.

Yang, SCH and Lengyel, M and Wolpert, DM (2016) Active sensing in the categorization of visual patterns. eLife, 5.

Yang, SCH and Wolpert, DM and Lengyel, M (2016) Theoretical perspectives on active sensing. Current Opinion in Behavioral Sciences, 11. pp. 100-108.

Yeo, S-H and Franklin, DW and Wolpert, DM (2016) When Optimal Feedback Control Is Not Enough: Feedforward Strategies Are Required for Optimal Control with Active Sensing. PLoS Computational Biology, 12.


Zhang, S and Mano, H and Ganesh, G and Robbins, T and Seymour, B (2016) Dissociable Learning Processes Underlie Human Pain Conditioning. Current Biology, 26. pp. 52-58. ISSN 0960-9822

Zhe, S and Zhang, K and Wang, P and Lee, KC and Xu, Z and Qi, Y and Gharamani, Z (2016) Distributed flexible nonlinear tensor factorization. Advances in Neural Information Processing Systems. pp. 928-936. ISSN 1049-5258

This list was generated on Tue Oct 27 20:14:10 2020 GMT.