CUED Publications database

Items where Division is "Div F > Computational and Biological Learning" and Year is 2019

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Number of items: 59.

A

Adel, T and Valera, I and Ghahramani, Z and Weller, A (2019) One-network adversarial fairness. In: UNSPECIFIED pp. 2412-2420..

Arakaki, T and Barello, G and Ahmadian, Y (2019) Inferring neural circuit structure from datasets of heterogeneous tuning curves. PLoS Comput Biol, 15. e1006816-.

B

Bakkour, A and Palombo, DJ and Zylberberg, A and Kang, YHR and Reid, A and Verfaellie, M and Shadlen, MN and Shohamy, D (2019) The hippocampus supports deliberation during value-based decisions. eLife, 8.

Balog, M and Gaunt, AL and Brockschmidt, M and Nowozin, S and Tarlow, D (2019) DeepCoder: Learning to write programs. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.

Bhardwaj, K and Havasi, M and Yao, Y and Brooks, DM and Lobato, JMH and Wei, GY (2019) Determining Optimal Coherency Interface for Many-Accelerator SoCs Using Bayesian Optimization. IEEE Computer Architecture Letters, 18. pp. 119-123. ISSN 1556-6056

Bradshaw, J and Kusner, MJ and Paige, B and Segler, MHS and Hernández-Lobato, JM (2019) Generating molecules via chemical reactions. In: UNSPECIFIED.

Bradshaw, J and Kusner, MJ and Paige, B and Segler, MHS and Hernández-Lobato, JM (2019) A generative model for electron paths. In: UNSPECIFIED.

Bradshaw, J and Kusner, MJ and Paige, B and Segler, MHS and Hernández-Lobato, JM (2019) A generative model for electron paths. 7th International Conference on Learning Representations, ICLR 2019.

Bradshaw, J and Paige, B and Kusner, MJ and Segler, MHS and Hernández-Lobato, JM (2019) A model to search for synthesizable molecules. Advances in Neural Information Processing Systems, 32. ISSN 1049-5258

C

Cave, S and Nyrup, R and Vold, K and Weller, A (2019) Motivations and Risks of Machine Ethics. Proceedings of the IEEE, 107. pp. 562-574. ISSN 0018-9219

carroll, T and McNamee, D and Ingram, J and Wolpert, DM (2019) Rapid visuomotor responses reflect value-based decisions. Journal of Neuroscience, 1934. pp. 3906-3920. ISSN 1529-2401 (Unpublished)

D

Depeweg, S and Hernández-Lobato, JM and Doshi-Velez, F and Udluft, S (2019) Learning and policy search in stochastic dynamical systems with Bayesian neural networks. 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings.

Depeweg, S and Hernández-Lobato, JM and Doshi-Velez, F and Udluft, S (2019) Learning and policy search in stochastic dynamical systems with Bayesian neural networks. In: UNSPECIFIED.

Doiron, B and Lengyel, M (2019) Editorial overview: Computational neuroscience. Current Opinion in Neurobiology, 58. iii-vii. ISSN 0959-4388

da Fonseca, M and Vattuone, N and Clavero, F and Echeveste, R and Samengo, I (2019) The subjective metric of remembered colors: A Fisher-information analysis of the geometry of human chromatic memory. PLoS ONE, 14. e0207992-.

E

Echeveste, R and Aitchison, L and Hennequin, G and Lengyel, M (2019) Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference.

G

Garriga-Alonso, A and Aitchison, L and Rasmussen, CE (2019) Deep convolutional networks as shallow Gaussian processes. In: UNSPECIFIED.

Gong, W and Li, Y and Hernández-Lobato, JM (2019) Meta-learning for stochastic gradient MCMC. 7th International Conference on Learning Representations, ICLR 2019.

Gong, W and Li, Y and Hernández-Lobato, JM (2019) Meta-learning for stochastic gradient MCMC. In: UNSPECIFIED.

Gong, W and Tschiatschek, S and Turner, RE and Nowozin, S and Hernández-Lobato, JM and Zhang, C (2019) Icebreaker: Element-wise efficient information acquisition with a Bayesian deep latent gaussian model. Advances in Neural Information Processing Systems, 32. ISSN 1049-5258

Gáspár, ME and Polack, PO and Golshani, P and Lengyel, M and Orbán, G (2019) Representational untangling by the firing rate nonlinearity in V1 simple cells. eLife, 8.

H

Havasi, M and Peharz, R and Hernández-Lobato, JM (2019) Minimal random code learning: Getting bits back from compressed model parameters. In: UNSPECIFIED.

Havasi, M and Peharz, R and Hernández-Lobato, JM (2019) Minimal random code learning: Getting bits back from compressed model parameters. In: UNSPECIFIED.

Havasi, M and Peharz, R and Hernández-Lobato, JM (2019) Minimal random code learning: Getting bits back from compressed model parameters. 7th International Conference on Learning Representations, ICLR 2019.

I

Ialongo, AD and van der Wilk, M and Hensman, J and Rasmussen, CE (2019) Overcoming mean-field approximations in recurrent Gaussian process models. In: UNSPECIFIED pp. 5223-5232..

Iigaya, K and Ahmadian, Y and Sugrue, LP and Corrado, GS and Loewenstein, Y and Newsome, WT and Fusi, S (2019) Deviation from the matching law reflects an optimal strategy involving learning over multiple timescales. Nat Commun, 10. 1466-.

J

Janz, D and Hron, J and Mazur, P and Hofmann, K and Hernández-Lobato, JM and Tschiatschek, S (2019) Successor uncertainties: Exploration and uncertainty in temporal difference learning. Advances in Neural Information Processing Systems, 32. ISSN 1049-5258

Jercog, PE and Ahmadian, Y and Woodruff, C and Deb-Sen, R and Abbott, LF and Kandel, ER (2019) Heading direction with respect to a reference point modulates place-cell activity. Nat Commun, 10. 2333-.

Johansen, JP and Seymour, B (2019) Editorial overview: Pain and aversive motivation. Current Opinion in Behavioral Sciences, 26. iii-v.

K

Kao, T-C and Hennequin, G (2019) Neuroscience out of control: control-theoretic perspectives on neural circuit dynamics. Curr Opin Neurobiol, 58. pp. 122-129.

Kimura, A and Ghahramani, Z and Takeuchi, K and Iwata, T and Ueda, N (2019) Few-shot learning of neural networks from scratch by pseudo example optimization. British Machine Vision Conference 2018, BMVC 2018.

Kimura, A and Ghahramani, Z and Takeuchi, K and Iwata, T and Ueda, N (2019) Few-shot learning of neural networks from scratch by pseudo example optimization. In: UNSPECIFIED.

L

Lee, JH and Seymour, B and Leibo, JZ and An, SJ and Lee, SW (2019) Toward high-performance, memory-efficient, and fast reinforcement learning—Lessons from decision neuroscience. Science Robotics, 4.

Lengyel, G and Žalalytė, G and Pantelides, A and Ingram, JN and Fiser, J and Lengyel, M and Wolpert, DM (2019) Unimodal statistical learning produces multimodal object-like representations. eLife, 8. ISSN 2050-084X (Unpublished)

Levitin, HM and Yuan, J and Cheng, YL and Ruiz, FJR and Bush, EC and Bruce, JN and Canoll, P and Iavarone, A and Lasorella, A and Blei, DM and Sims, PA (2019) De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization. Molecular Systems Biology, 15. e8557-.

Lomeli, M and Rowland, M and Gretton, A and Ghahramani, Z (2019) Antithetic and Monte Carlo kernel estimators for partial rankings. Stat. Comput., 29. pp. 1127-1147.

Lomelí, M and Rowland, M and Gretton, A and Ghahramani, Z (2019) Antithetic and Monte Carlo kernel estimators for partial rankings. Statistics and Computing, 29. pp. 1127-1147. ISSN 0960-3174

M

Ma, C and Li, Y and Hernández-Lobato, JM (2019) Variational implicit processes. In: UNSPECIFIED pp. 7464-7482..

Ma, C and Tschiatschek, S and Palla, K and Hernández-Lobato, JM and Nowozin, S and Zhang, C (2019) EdDI: Efficient dynamic discovery of high-value information with partial VAE. 36th International Conference on Machine Learning, ICML 2019, 2019-J. pp. 7483-7504.

Mancini, F and Wang, AP and Schira, MM and Isherwood, ZJ and McAuley, JH and Iannetti, GD and Sereno, MI and Moseley, GL and Rae, CD (2019) Fine-Grained Mapping of Cortical Somatotopies in Chronic Complex Regional Pain Syndrome. The Journal of neuroscience : the official journal of the Society for Neuroscience, 39. pp. 9185-9196.

McNamee, D and Wolpert, DM (2019) Internal Models in Biological Control. Annual Review of Control, Robotics, and Autonomous Systems, 2. pp. 339-364. (Unpublished)

N

Nalisnick, E and Hernández-Lobato, JM and Smyth, P (2019) Dropout as a structured shrinkage prior. 36th International Conference on Machine Learning, ICML 2019, 2019-J. pp. 8273-8283.

P

Peharz, R and Vergari, A and Stelzner, K and Molina, A and Shao, X and Trapp, M and Kersting, K and Ghahramani, Z (2019) Random sum-product networks: A simple and effective approach to probabilistic deep learning. In: 35th Conference on Uncertainty in Artificial Intelligence, UAI 2019, 2019-7-22 to 2019-7-25, Tel Aviv, Israel. (Unpublished)

Pinsler, R and Gordon, J and Nalisnick, E and Hernández-Lobato, JM (2019) Bayesian batch active learning as sparse subset approximation. In: UNSPECIFIED.

Proud, K and Heald, JB and Ingram, JN and Gallivan, JP and Wolpert, DM and Flanagan, JR (2019) Separate motor memories are formed when controlling different implicitly specified locations on a tool. Journal of Neurophysiology, 121. pp. 1342-1351. ISSN 0022-3077

S

Sadeghi, M and Sheahan, HR and Ingram, JN and Wolpert, DM (2019) The visual geometry of a tool modulates generalization during adaptation. Scientific Reports, 9. 2731-.

Sengupta, U and Carballo-Pacheco, M and Strodel, B (2019) Automated Markov state models for molecular dynamics simulations of aggregation and self-assembly. The Journal of Chemical Physics, 150. p. 115101. ISSN 0021-9606

Seymour, B (2019) Pain: A Precision Signal for Reinforcement Learning and Control. Neuron, 101. pp. 1029-1041. ISSN 0896-6273

Seymour, B and Lee, SW (2019) Decision-making in brains and robots - the case for an interdisciplinary approach. Current Opinion in Behavioral Sciences, 26. pp. 137-145. ISSN 2352-1546 (Unpublished)

Shergill, SS and White, TP and Joyce, DW and Bays, PM and Wolpert, DM and Frith, CD (2019) Corrigendum to “Modulation of somatosensory processing by action” [Elsevier, 70 (2013) 356-362](S1053811912012293)(10.1016/j.neuroimage.2012.12.043). NeuroImage, 197. 827-. ISSN 1053-8119

Stroud, JP and Porter, MA and Hennequin, G and Vogels, TP (2019) Publisher Correction: Motor primitives in space and time via targeted gain modulation in cortical networks (Nature Neuroscience, (2018), 21, 12, (1774-1783), 10.1038/s41593-018-0276-0). Nature Neuroscience, 22. 504-. ISSN 1097-6256

T

Takenaka, S and Kan, S and Seymour, B and Makino, T and Sakai, Y and Kushioka, J and Tanaka, H and Watanabe, Y and Shibata, M and Yoshikawa, H and Kaito, T (2019) Towards prognostic functional brain biomarkers for cervical myelopathy: A resting-state fMRI study. Scientific Reports, 9.

Tourigny, DS and Karim, MKA and Echeveste, R and Kotter, MRN and O’Neill, JS (2019) Energetic substrate availability regulates synchronous activity in an excitatory neural network. PLoS ONE, 14.

Trapp, M and Peharz, R and Ge, H and Pernkopf, F and Ghahramani, Z (2019) Bayesian learning of sum-product networks. Advances in Neural Information Processing Systems, 32. ISSN 1049-5258

V

Vergari, A and Peharz, R and Di Mauro, N and Esposito, F (2019) Encoding and decoding representations with sum- And max-product networks. In: UNSPECIFIED.

W

Weller, A (2019) Transparency: Motivations and Challenges. pp. 23-40. ISSN 0302-9743

Wu, A and Nowozin, S and Meeds, E and Turner, RE and Hernández-Lobato, JM and Gaunt, AL (2019) Deterministic variational inference for robust Bayesian neural networks. 7th International Conference on Learning Representations, ICLR 2019.

Wu, A and Nowozin, S and Meeds, E and Turner, RE and Hernández-Lobato, JM and Gaunt, AL (2019) Deterministic variational inference for robust Bayesian neural networks. In: UNSPECIFIED.

Wu, A and Nowozin, S and Meeds, E and Turner, RE and Hernández-Lobato, JM and Gaunt, AL (2019) Deterministic variational inference for robust Bayesian neural networks. In: UNSPECIFIED.

This list was generated on Tue Oct 27 20:13:35 2020 GMT.