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Number of items: 25.

Article

Cemgil, AT; Kappen, HJ; Barber, D; (2006) A generative model for music transcription. IEEE Trans. Speech Audio Process. , 14 (2) pp. 679-694. Green open access
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Chiappa, S; Barber, D; (2007) Bayesian factorial linear Gaussian state-space models for biosignal decomposition. IEEE Signal Processing Letters , 14 (4) pp. 267-270. Green open access
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Furmston, T; Lever, G; Barber, D; (2016) Approximate Newton Methods for Policy Search in Markov Decision Processes. Journal of Machine Learning Research , 17 , Article 227. Green open access
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Kunze, J; Kirsch, L; Ritter, H; Barber, D; (2019) Gaussian mean field regularizes by limiting learned information. Entropy , 21 (8) , Article 758. 10.3390/e21080758. Green open access
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Mansbridge, A; Fierimonte, R; Feige, I; Barber, D; (2019) Improving latent variable descriptiveness by modelling rather than ad-hoc factors. Machine Learning , 108 (8-9) pp. 1601-1611. 10.1007/s10994-019-05830-1. Green open access
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Wu, X; Kim, GH; Salisbury, ML; Barber, D; Bartholmai, BJ; Brown, KK; Conoscenti, CS; ... Walsh, SLF; + view all (2019) Computed Tomographic Biomarkers in Idiopathic Pulmonary Fibrosis: The Future of Quantitative Analysis. American Journal of Respiratory and Critical Care Medicine , 199 (1) pp. 12-21. 10.1164/rccm.201803-0444PP. Green open access
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Proceedings paper

Anthony, T; Tian, Z; Barber, D; (2017) Thinking Fast and Slow with Deep Learning and Tree Search. In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.) Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings. NIPS Proceedings: Long Beach, CA, USA. Green open access
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Barber, D; (2003) Learning in spiking neural assemblies. In: (pp. pp. 165-172).

Botev, A; Lever, G; Barber, D; (2017) Nesterov's accelerated gradient and momentum as approximations to regularised update descent. In: Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN). IEEE: Anchorage, AK, USA. Green open access
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Botev, A; Ritter, J; Barber, D; (2017) Practical Gauss-Newton Optimisation for Deep Learning. In: Precup, D and Teh, YW, (eds.) Proceedings of the 34th International Conference on Machine Learning. (pp. pp. 557-565). Proceedings of Machine Learning Research: Sydney, Australia. Green open access
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Botev, A; Zheng, B; Barber, D; (2017) Complementary sum sampling for likelihood approximation in large scale classification. In: Singh, Aarti and Zhu, Jerry, (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS 2017). PMLR (Proceedings of Machine Learning Research): Fort Lauderdale, FL, USA. Green open access
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Bracegirdle, C; Barber, D; (2012) Bayesian conditional cointegration. In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012). (pp. 1095 - 1102). International Conference on Machine Learning: Edinburgh, UK. Green open access
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Challis, E; Barber, D; (2011) Concave Gaussian variational approximations for inference in large-scale Bayesian linear models. In: Gordon, G and Dunson, D, (eds.) Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. (pp. 199 - 207). Journal of Machine Learning Research Green open access
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Furmston, T; Barber, D; (2012) A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes. In: Advances in Neural Information Processing Systems 25 (NIPS 2012). (pp. 2726 - 2734). Neural Information Processing Systems Foundation Green open access
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Habib, R; Barber, D; (2019) Auxiliary variational MCMC. In: Proceedings of the 7th International Conference on Learning Representations, ICLR 2019. (pp. pp. 1-13). International Conference on Learning Representations Green open access
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He, Z; Gao, S; Xiao, L; Barber, D; (2017) Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.) Advances in Neural Information Processing Systems 30 (NIPS 2017) pre-proceedings. NIPS Proceedings: Long Beach, CA, USA. Green open access
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He, Z; Li, J; Liu, D; He, H; Barber, D; (2020) Tracking by animation: Unsupervised learning of multi-object attentive trackers. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (pp. pp. 1318-1327). IEEE: Long Beach, CA, USA. Green open access
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Innes, M; Karpinski, S; Shah, V; Barber, D; Saito Stenetorp, PLEPS; Besard, T; Bradbury, J; ... Yuret, D; + view all (2018) On Machine Learning and Programming Languages. In: (Proceedings) SysML 2018. Association for Computing Machinery (ACM) (In press). Green open access
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Kirsch, L; Kunze, J; Barber, D; (2018) Modular Networks: Learning to Decompose Neural Computation. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R, (eds.) Advances in Neural Information Processing Systems 31 (NIPS 2018). NIPS Proceedings: Montreal, Canada. Green open access
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Ritter, H; Botev, A; Barber, D; (2018) Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.) Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS 2018). Neural Information Processing Systems (NIPS): Montréal, Canada. Green open access
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Ritter, H; Botev, A; Barber, D; (2018) A Scalable Laplace Approximation for Neural Networks. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings. International Conference on Representation Learning: Vancouver, Canada. Green open access
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Shah, H; Barber, D; (2018) Generative Neural Machine Translation. In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and Cesa-Bianchi, N and Garnett, R, (eds.) Advances in Neural Information Processing Systems 31 (NIPS 2018). NIPS Proceedings: Montreal, Canada. Green open access
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Shah, H; Barber, D; Botev, A; (2017) Overdispersed Variational Autoencoders. In: 2017 International Joint Conference on Neural Networks (IJCNN). (pp. pp. 1109-1116). IEEE Green open access
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Shah, H; Zheng, B; Barber, D; (2018) Generating Sentences Using a Dynamic Canvas. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). AAAI Association for the Advancement of Artificial Intelligence Green open access
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Townsend, J; Bird, T; Barber, D; (2019) Practical lossless compression with latent variables using bits back coding. In: Proceedings of the Seventh International Conference on Learning Representations (ICLR 2019). International Conference on Learning Representations (ICLR): New Orleans, LA, USA. Green open access
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This list was generated on Fri Jul 10 07:26:10 2020 BST.