Research output per year
Research output per year
- 1 - 50 out of 59 results
Search results
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2023
Nudging neural click prediction models to pay attention to position
Karra, E., Zhao, W., Murray, I. & Pellegrini, R., 5 Aug 2023, (Accepted/In press) Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM ’23). p. 1-10Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2022
Don’t recommend the obvious: Estimate probability ratios
Pellegrini, R., Zhao, W. & Murray, I., 13 Sept 2022, Proceedings of the 16th ACM Conference on Recommender Systems. Golbeck, J., Maxwell Harper, F., Murdock, V., Ekstrand, M., Shapira, B., Basilico, J., Lundgaard, K. & Oldridge, E. (eds.). ACM Association for Computing Machinery, p. 188-197 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2021
Maximum Likelihood Training of Score-Based Diffusion Models
Song, Y., Durkan, C., Murray, I. & Ermon, S., 6 Dec 2021, Advances in Neural Information Processing Systems 34. Neural Information Processing Systems Foundation, Inc, 14 p. (Advances in Neural Information Processing Systems).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Regularising Fisher Information Improves Cross-lingual Generalisation
Cooper Stickland, A. & Murray, I., 11 Nov 2021, Proceedings of the 1st Workshop on Multilingual Representation Learning. Punta Cana, Dominican Republic: Association for Computational Linguistics, p. 238-241 4 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting
Zhang, C., Fiore, M., Murray, I. & Patras, P., 18 May 2021, The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). AAAI Press, p. 10851 - 10858 8 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 35, no. 12).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Lossless compression with state space models using bits back coding
Townsend, J. & Murray, I., 7 May 2021, Neural Compression Workshop at ICLR 2021. 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2020
Density Deconvolution with Normalizing Flows
Dockhorn, T., Ritchie, J. A., Yu, Y. & Murray, I., 18 Jul 2020. 8 p.Research output: Contribution to conference › Paper › peer-review
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On Contrastive Learning for Likelihood-free Inference
Durkan, C., Murray, I. & Papamakarios, G., 18 Jul 2020, Proceedings of the 37th International Conference on Machine Learning. PMLR, p. 2771-2781 15 p. (Proceedings of Machine Learning Research; vol. 119).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Ordering Dimensions with Nested Dropout Normalizing Flows
Bekasovs, A. & Murray, I., 18 Jul 2020. 6 p.Research output: Contribution to conference › Paper › peer-review
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Diverse Ensembles Improve Calibration
Cooper Stickland, A. & Murray, I., 17 Jul 2020. 6 p.Research output: Contribution to conference › Paper › peer-review
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2019
Neural Spline Flows
Durkan, C., Bekasovs, A., Murray, I. & Papamakarios, G., 14 Dec 2019, Advances in Neural Information Processing Systems 32 (NeurIPS 2019). Neural Information Processing Systems Foundation, Inc, Vol. 32. p. 7511-7522 12 p. (Advances in Neural Information Processing Systems; vol. 32).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Scalable Extreme Deconvolution
Ritchie, J. & Murray, I., 14 Dec 2019. 7 p.Research output: Contribution to conference › Poster › peer-review
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BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
Cooper Stickland, A. & Murray, I., 3 Jul 2019, (E-pub ahead of print) Proceedings of the 36th International Conference on Machine Learning (ICML). Chaudhuri, K. & Salakhutdinov, R. (eds.). Long Beach, USA: PMLR, Vol. 97. p. 5986-5995 12 p. (Proceedings of Machine Learning Research; vol. 97).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Cubic-Spline Flows
Durkan, C., Bekasovs, A., Murray, I. & Papamakarios, G., 15 Jun 2019, (E-pub ahead of print) First workshop on Invertible Neural Networks and Normalizing Flows: at ICML 2019. 7 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Model Criticism in Latent Space
Seth, S., Murray, I. & Williams, C. K. I., 11 Jun 2019, In: Bayesian analysis. 14, 3, p. 703-725 23 p.Research output: Contribution to journal › Article › peer-review
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Mode Normalization
Deecke, L., Murray, I. & Bilen, H., 9 May 2019, (E-pub ahead of print) Proceedings of the Seventh International Conference on Learning Representations (ICLR 2019). New Orleans, Louisiana, USA, 12 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows
Papamakarios, G., Sterratt, D. C. & Murray, I., 18 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019. Naha, Okinawa, Japan: PMLR, p. 837-848 12 p. (Proceedings of Machine Learning Research; vol. 89).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Dynamic Evaluation of Transformer Language Models
Krause, B., Mbabazi, E., Murray, I. & Renals, S., 17 Apr 2019, 6 p.Research output: Working paper
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2018
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting
Bekasovs, A. & Murray, I., 16 Nov 2018, (Accepted/In press) p. 1-6. 6 p.Research output: Contribution to conference › Paper › peer-review
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Dynamic Evaluation of Neural Sequence Models
Krause, B., Kahembwe, E., Murray, I. & Renals, S., 15 Jul 2018, Proceedings of the 35th International Conference on Machine Learning. Dy, J. & Krause, A. (eds.). Stockholmsmässan, Stockholm Sweden: PMLR, p. 2766-2775 10 p. (Proceedings of Machine Learning Research; vol. 80).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Sequential Neural Methods for Likelihood-free Inference
Durkan, C., Papamakarios, G. & Murray, I., 2018, p. 1-9. 9 p.Research output: Contribution to conference › Paper › peer-review
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2017
Masked Autoregressive Flow for Density Estimation
Papamakarios, G., Pavlakou, T. & Murray, I., 9 Dec 2017, Advances in Neural Information Processing Systems 30. Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. & Garnett, R. (eds.). Long Beach, United States: Curran Associates Inc, p. 2335-2344 10 p. (Advances in Neural Information Processing Systems; vol. 30).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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A determinant-free method to simulate the parameters of large Gaussian fields
Ellam, L., Strathmann, H., Girolami, M. & Murray, I., Aug 2017, In: Stat. 6, 1, p. 271-281 11 p.Research output: Contribution to journal › Article › peer-review
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Multiplicative LSTM for sequence modelling
Krause, B., Murray, I., Renals, S. & LU, L., 26 Apr 2017, International Conference on Learning Representations - ICLR 2017 - Workshop Track. p. 2872-2880 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Markov Chain Truncation for Doubly-Intractable Inference
Wei, C. & Murray, I., 15 Apr 2017, In: Journal of Machine Learning Research: Workshop and Conference Proceedings. 54, p. 776-784 9 p.Research output: Contribution to journal › Article › peer-review
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Aye or naw, whit dae ye hink? Scottish independence and linguistic identity on social media
Shoemark, P., Sur, D., Shrimpton, L., Murray, I. & Goldwater, S., 7 Apr 2017, European Chapter of the Association for Computational Linguistics (EACL 2017). Valencia, Spain : Association for Computational Linguistics, p. 1239–1248 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2016
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
Papamakarios, G. & Murray, I., 10 Dec 2016, Advances in Neural Information Processing Systems 29 (NIPS 2016). Barcelona, Spain: Neural Information Processing Systems Foundation, Inc, p. 1028-1036 9 p. (Advances in Neural Information Processing Systems; vol. 29).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Neural Autoregressive Distribution Estimation
Uria, B., Côté, M-A., Gregor, K., Murray, I. & Larochelle, H., 1 Sept 2016, In: Journal of Machine Learning Research. 17, 205, p. 1-37 37 p.Research output: Contribution to journal › Article › peer-review
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Pseudo-Marginal Slice Sampling
Murray, I. & Graham, M., May 2016, Proceedings of the 19th International Conference on Artificial Intelligence and Statistics 2016. Cadiz, Spain: PMLR, Vol. 51. p. 911-919 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2015
On the Efficiency of Recurrent Neural Network Optimization Algorithms
Krause, B., Lu, L., Murray, I. & Renals, S., Dec 2015, OPT2015 Optimization for Machine Learning at the Neural Information Processing Systems Conference, 2015. 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Dissecting Magnetar Variability with Bayesian Hierarchical Models
Huppenkothen, D., Brewer, B. J., Hogg, D. W., Murray, I., Frean, M., Elenbaas, C., Watts, A. L., Levin, Y., Van Der Horst, A. J. & Kouveliotou, C., 1 Sept 2015, In: Astrophysical Journal. 810, 1, 21 p.Research output: Contribution to journal › Article › peer-review
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Distilling Intractable Generative Models
Papamakarios, G. & Murray, I., 2 Aug 2015, (Accepted/In press) Probabilistic Integration Workshop at the Neural Information Processing Systems Conference, 2015. 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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MADE: Masked Autoencoder for Distribution Estimation
Germain, M., Gregor, K., Murray, I. & Larochelle, H., 7 Jul 2015, Proceedings of the 32nd International Conference on Machine Learning. Bach, F. & Blei, D. (eds.). PMLR, p. 881-889 9 p. (Proceedings of Machine Learning Research; vol. 37).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE
Uria, B., Murray, I., Renals, S., Valentini-Botinhao, C. & Bridle, J., 2015, Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on . IEEE Signal Processing Society Press, p. 4465-4469 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2014
A Deep and Tractable Density Estimator
Uria, B., Murray, I. & Larochelle, H., 24 Jun 2014, Proceedings of the 31st International Conference on Machine Learning. PMLR, p. 467-475 9 p. (Journal of Machine Learning Research: Workshops & Conference Proceedings; vol. 32, no. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Parallel MCMC with Generalized Elliptical Slice Sampling
Nishihara, R., Murray, I. & Adams, R. P., 1 Jun 2014, In: Journal of Machine Learning Research. 15, p. 2087-2112 26 p., 61.Research output: Contribution to journal › Article › peer-review
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2013
Estimation Bias in Maximum Entropy Models
Macke, J. H., Murray, I. & Latham, P. E., Aug 2013, In: Entropy. 15, 8, p. 3109-3129 21 p.Research output: Contribution to journal › Article › peer-review
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Attention as Reward-Driven Optimization of Sensory Processing: Neural Computation
Chalk, M., Murray, I. & Seriés, P., 18 Jun 2013, In: Neural Computation. 25, 11, p. 2904-2933 30 p.Research output: Contribution to journal › Article › peer-review
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A Composable Strategy for Shredded Document Reconstruction
Ranca, R. & Murray, I., 2013, Computer Analysis of Images and Patterns. Wilson, R., Hancock, E., Bors, A. & Smith, W. (eds.). Springer-Verlag GmbH, p. 324-331 8 p. (Lecture Notes in Computer Science; vol. 8048).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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A Framework for Evaluating Approximation Methods for Gaussian Process Regression
Chalupka, K., Williams, C. K. I. & Murray, I., 2013, In: Journal of Machine Learning Research. 14, p. 333-350 18 p.Research output: Contribution to journal › Article › peer-review
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RNADE: The real-valued neural autoregressive density-estimator
Uria, B., Murray, I. & Larochelle, H., 2013, Advances in Neural Information Processing Systems 26. p. 2175-2183 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2012
Deep Architectures for Articulatory Inversion
Uria, B., Murray, I., Renals, S. & Richmond, K., Sept 2012, INTERSPEECH 2012 13th Annual Conference of the International Speech Communication Association. ISCA, p. 867-870 4 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2011
How biased are maximum entropy models?
Macke, J. H., Murray, I. & Latham, P. E., 2011, Advances in Neural Information Processing Systems 24. Shawe-Taylor, J., Zemel, R. S., Bartlett, P., Pereira, F. C. N. & Weinberger, K. Q. (eds.). p. 2034-2042 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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The Neural Autoregressive Distribution Estimator
Larochelle, H. & Murray, I., 2011, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS). Vol. 15. p. 29-37 9 p. ( JMLR: Workshop and Conference Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2010
Dynamical Inference from a Kinematic Snapshot: The Force Law in the Solar System
Bovy, J., Murray, I. & Hogg, D. W., Mar 2010, In: Astrophysical Journal. 711, 2, p. 1157-1167 11 p.Research output: Contribution to journal › Article › peer-review
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Elliptical slice sampling
Murray, I., Adams, R. P. & MacKay, D. J. C., 2010, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). Journal of Machine Learning Research: Workshop and Conference Proceedings, p. 541-548 8 p. (Journal of Machine Learning Research: Workshop and Conference Proceedings; vol. 9).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Incorporating side information into probabilistic matrix factorization using Gaussian Processes
Adams, R. P., Dahl, G. E. & Murray, I., 2010, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010). 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Slice sampling covariance hyperparameters of latent Gaussian models
Murray, I. & Adams, R. P., 2010, Neural Information Processing Systems Conference 2010. 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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2009
Characterizing response behavior in multisensory perception with conflicting cues
Natarajan, R., Murray, I., Shams, L. & Zemel, R., 2009, Advances in Neural Information Processing Systems 21. Koller, D., Schuurmans, D., Bengio, Y. & Bottou, L. (eds.). p. 1153-1160 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Evaluating probabilities under high-dimensional latent variable models
Murray, I. & Salakhutdinov, R., 2009, Advances in Neural Information Processing Systems 21. Koller, D., Schuurmans, D., Bengio, Y. & Bottou, L. (eds.). p. 1137-1144 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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