Browse by UCL people
Group by: Type | Date
Number of items: 37.
Article
Cemgil, AT;
Kappen, HJ;
Barber, D;
(2006)
A generative model for music transcription.
IEEE Transactions on Audio, Speech and Language Processing
, 14
(2)
pp. 679-694.
10.1109/TSA.2005.852985.
|
 |
|
Chiappa, S;
Barber, D;
(2007)
Bayesian factorial linear Gaussian state-space models for biosignal decomposition.
IEEE Signal Processing Letters
, 14
(4)
pp. 267-270.
10.1109/LSP.2006.881515.
|
|
|
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.
|
|
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.
|
 |
|
Lu, Yaozhi;
Aslani, Shahab;
Zhao, An;
Shahin, Ahmed;
Barber, David;
Emberton, Mark;
Alexander, Daniel C;
(2023)
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study.
Heliyon
, 9
(8)
, Article e18695. 10.1016/j.heliyon.2023.e18695.
|
|
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.
|
|
|
Shahin, AH;
Zhao, A;
Whitehead, AC;
Alexander, DC;
Jacob, J;
Barber, D;
(2023)
CenTime: Event-conditional modelling of censoring in survival analysis.
Medical Image Analysis
, 91
, Article 103016. 10.1016/j.media.2023.103016.
|
|
|
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.
|
|
|
Zhao, A;
Gudmundsson, E;
Mogulkoc, N;
Jones, MG;
Van Moorsel, C;
Corte, TJ;
Romei, C;
... Jacob, J; + view all
(2021)
Mortality in combined pulmonary fibrosis and emphysema patients is determined by the sum of pulmonary fibrosis and emphysema.
ERJ Open Research
, 7
(3)
, Article 00316-2021. 10.1183/23120541.00316-2021.
|
|
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.
|
|
|
Barber, D;
(2003)
Learning in spiking neural assemblies.
In:
|
|
Bird, T;
Kingma, FH;
Barber, D;
(2021)
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks.
In:
ICLR 2021 - 9th International Conference on Learning Representations.
ICLR: Vienna, Austria.
|
|
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.
|
|
|
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.
|
|
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.
|
|
|
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.
|
|
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
|
|
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
|
|
|
Gaujac, B;
Feige, I;
Barber, D;
(2021)
Learning Disentangled Representations with the Wasserstein Autoencoder.
In:
Machine Learning and Knowledge Discovery in Databases. Research Track.
(pp. pp. 69-84).
Springer: Switzerland, Cham.
|
|
|
Gaujac, B;
Feige, I;
Barber, D;
(2021)
Improving Gaussian mixture latent variable model convergence with Optimal Transport.
In:
Proceedings of Machine Learning Research (PMLR).
(pp. pp. 737-752).
MLResearch Press
|
|
|
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
|
|
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.
|
|
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.
|
|
|
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).
|
|
|
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.
|
|
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.
|
|
|
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.
|
|
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.
|
|
|
Shah, H;
Barber, D;
Botev, A;
(2017)
Overdispersed Variational Autoencoders.
In:
2017 International Joint Conference on Neural Networks (IJCNN).
(pp. pp. 1109-1116).
IEEE
|
|
|
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
|
|
Shahin, Ahmed H;
Jacob, Joseph;
Alexander, Daniel C;
Barber, David;
(2022)
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data.
In: Konukoglu, Ender and Menze, Bjoern and Venkataraman, Archana and Baumgartner, Christian and Dou, Qi and Albarqouni, Shadi, (eds.)
Proceedings of Machine Learning Research: PMLR.
(pp. pp. 1057-1074).
PMLR: Zurich, Switzerland.
|
|
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.
|
|
Yap, Pauching;
Ritter, Hippolyt;
Barber, David;
(2021)
Addressing Catastrophic Forgetting in Few-Shot Problems.
In: Meila, M and Zhang, T, (eds.)
Proceedings of the 38th International Conference on Machine Learning.
(pp. pp. 1-11).
PMLR
|
|
Zhang, M;
Hayes, P;
Barber, D;
(2022)
Generalization Gap in Amortized Inference.
In:
Advances in Neural Information Processing Systems.
NIPS
|
|
Zhao, A;
Shahin, AH;
Zhou, Y;
Gudmundsson, E;
Szmul, A;
Mogulkoc, N;
van Beek, F;
... Alexander, DC; + view all
(2022)
Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis.
In: Wang, L and Dou, Q and Fletcher, PT and Speidel, S and Li, S, (eds.)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
(pp. pp. 223-233).
Springer: Cham, Switzerland.
|
Working / discussion paper
|
Barber, David;
(2023)
A note on Double Descent.
(Research Note
).
UCL Centre for Artificial Intelligence: London, UK.
|
|
|
Zhang, Mingtian;
Key, Oscar;
Hayes, Peter;
Barber, David;
Paige, Brooks;
Briol, François-Xavier;
(2022)
Towards Healing the Blindness of Score Matching.
arXiv: Ithaca (NY), USA.
|
