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ORCIDTom Diethe
Person information
- affiliation: AstraZeneca, Cambridge, UK
- affiliation (former): University of Bristol, UK
- affiliation (former): University College London, UK
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2020 – today
- 2024
[c26]Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander:
Tackling Structural Hallucination in Image Translation with Local Diffusion. ECCV (81) 2024: 87-103- [c25]Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare:
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning. ICML 2024 - [c24]Cédric Malherbe, Emilio Domínguez-Sánchez, Merwan Barlier, Igor Colin, Haitham Bou-Ammar, Tom Diethe:
Measures of diversity and space-filling designs for categorical data. ICML 2024 - [c23]Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni:
Improving Antibody Humanness Prediction using Patent Data. ICML 2024 - [i21]Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni:
Improving Antibody Humanness Prediction using Patent Data. CoRR abs/2401.14442 (2024) - [i20]Seunghoi Kim, Chen Jin, Tom Diethe, Matteo Figini
, Henry F. J. Tregidgo, Asher Mullokandov, Philip Teare, Daniel C. Alexander:
Tackling Structural Hallucination in Image Translation with Local Diffusion. CoRR abs/2404.05980 (2024) - 2023
- [i19]Sylwia Majchrowska, Anders Hildeman, Philip Teare, Tom Diethe:
Unlocking the Heart Using Adaptive Locked Agnostic Networks. CoRR abs/2309.11899 (2023) - [i18]Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare:
An Image is Worth Multiple Words: Learning Object Level Concepts using Multi-Concept Prompt Learning. CoRR abs/2310.12274 (2023) - 2021
- [i17]Yu Chen, Song Liu, Tom Diethe, Peter A. Flach:
Continual Density Ratio Estimation in an Online Setting. CoRR abs/2103.05276 (2021) - 2020
- [j7]Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle:
Automatic Discovery of Privacy-Utility Pareto Fronts. Proc. Priv. Enhancing Technol. 2020(4): 5-23 (2020) - [c22]Jeremias Knoblauch, Hisham Husain, Tom Diethe:
Optimal Continual Learning has Perfect Memory and is NP-hard. ICML 2020: 5327-5337 - [c21]Tom Diethe, Oluwaseyi Feyisetan:
Preserving Privacy in Analyses of Textual Data. PrivateNLP@WSDM 2020: 1-3 - [c20]Oluwaseyi Feyisetan, Borja Balle, Tom Diethe, Thomas Drake:
Calibrating Mechanisms for Privacy Preserving Text Analysis. PrivateNLP@WSDM 2020: 8-11 - [c19]Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake:
Hyperbolic Embeddings for Preserving Privacy and Utility in Text. PrivateNLP@WSDM 2020: 39-40 - [c18]Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, Tom Diethe:
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. WSDM 2020: 178-186 - [i16]Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas C. Damianou:
Similarity of Neural Networks with Gradients. CoRR abs/2003.11498 (2020) - [i15]Jeremias Knoblauch, Hisham Husain, Tom Diethe:
Optimal Continual Learning has Perfect Memory and is NP-hard. CoRR abs/2006.05188 (2020) - [i14]Yu Chen, Tom Diethe, Peter A. Flach:
Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning. CoRR abs/2006.11234 (2020) - [i13]Charlie Dickens, Eric Meissner, Pablo G. Moreno, Tom Diethe:
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams. CoRR abs/2008.01505 (2020)
2010 – 2019
- 2019
- [j6]Niall Twomey, Haoyan Chen, Tom Diethe, Peter A. Flach:
An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. Neurocomputing 342: 152-163 (2019) - [c17]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
$β^3$-IRT: A New Item Response Model and its Applications. AISTATS 2019: 1013-1021 - [c16]Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake:
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. ICDM 2019: 210-219 - [c15]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution calibration for regression. ICML 2019: 5897-5906 - [i12]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
β3-IRT: A New Item Response Model and its Applications. CoRR abs/1903.04016 (2019) - [i11]Tom Diethe, Tom Borchert, Eno Thereska, Borja Balle, Neil Lawrence:
Continual Learning in Practice. CoRR abs/1903.05202 (2019) - [i10]Oluwaseyi Feyisetan, Thomas Drake, Borja Balle, Tom Diethe:
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification. CoRR abs/1903.11112 (2019) - [i9]Yu Chen, Tom Diethe, Neil Lawrence:
Facilitating Bayesian Continual Learning by Natural Gradients and Stein Gradients. CoRR abs/1904.10644 (2019) - [i8]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution Calibration for Regression. CoRR abs/1905.06023 (2019) - [i7]Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle:
Automatic Discovery of Privacy-Utility Pareto Fronts. CoRR abs/1905.10862 (2019) - [i6]Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach:
HyperStream: a Workflow Engine for Streaming Data. CoRR abs/1908.02858 (2019) - [i5]Oluwaseyi Feyisetan, Borja Balle, Thomas Drake, Tom Diethe:
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations. CoRR abs/1910.08902 (2019) - [i4]Oluwaseyi Feyisetan, Tom Diethe, Thomas Drake:
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text. CoRR abs/1910.08917 (2019) - 2018
- [j5]Niall Twomey, Tom Diethe, Xenofon Fafoutis, Atis Elsts, Ryan McConville, Peter A. Flach, Ian Craddock:
A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics 5(2): 27 (2018) - [c14]Haoyan Chen, Tom Diethe, Niall Twomey, Peter A. Flach:
Anomaly detection in star light curves using hierarchical Gaussian processes. ESANN 2018 - [c13]Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach:
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD 2018: 243-252 - 2017
- [j4]Niall Twomey, Tom Diethe, Ian Craddock, Peter A. Flach:
Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing 234: 93-106 (2017) - [i3]Tom Diethe, Niall Twomey, Meelis Kull, Peter A. Flach, Ian Craddock:
Probabilistic Sensor Fusion for Ambient Assisted Living. CoRR abs/1702.01209 (2017) - 2016
- [j3]Niall Twomey, Tom Diethe, Peter A. Flach:
On the need for structure modelling in sequence prediction. Mach. Learn. 104(2-3): 291-314 (2016) - [c12]Tom Diethe, Niall Twomey, Peter A. Flach:
Active transfer learning for activity recognition. ESANN 2016 - [c11]Yu Chen, Tom Diethe, Peter A. Flach:
ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. IJCAI 2016: 1404-1410 - [c10]Tom Diethe, Niall Twomey, Peter A. Flach:
BDL.NET: Bayesian dictionary learning in Infer.NET. MLSP 2016: 1-6 - [i2]Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion L. Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter A. Flach, Ian Craddock:
The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. CoRR abs/1603.00797 (2016) - 2015
- [j2]Ni Zhu, Tom Diethe, Massimo Camplani, Lili Tao, Alison Burrows, Niall Twomey, Dritan Kaleshi, Majid Mirmehdi, Peter A. Flach, Ian Craddock:
Bridging e-Health and the Internet of Things: The SPHERE Project. IEEE Intell. Syst. 30(4): 39-46 (2015) - [c9]Tom Diethe, Niall Twomey, Peter A. Flach:
Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics. ECML/PKDD (2) 2015: 279-294 - [c8]Xenofon Fafoutis, Evangelos Mellios, Niall Twomey, Tom Diethe, Geoffrey S. Hilton, Robert J. Piechocki:
An RSSI-based wall prediction model for residential floor map construction. WF-IoT 2015: 357-362 - 2013
- [j1]Tom Diethe, Mark A. Girolami:
Online Learning with (Multiple) Kernels: A Review. Neural Comput. 25(3): 567-625 (2013) - 2012
- [c7]Guy Lever, Tom Diethe, John Shawe-Taylor:
Data dependent kernels in nearly-linear time. AISTATS 2012: 685-693 - 2011
- [c6]Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca:
Preface. WAPA 2011: 1-4 - [e2]Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca:
Proceedings of the Second Workshop on Applications of Pattern Analysis, WAPA 2011, Castro Urdiales, Spain, October 19-21, 2011. JMLR Proceedings 17, JMLR.org 2011 [contents] - [i1]Guy Lever, Tom Diethe, John Shawe-Taylor:
Data-dependent kernels in nearly-linear time. CoRR abs/1110.4416 (2011) - 2010
- [b1]Thomas Robert Diethe:
Sparse machine learning methods with applications in multivariate signal processing. University College London, UK, 2010 - [c5]Tom Diethe, David R. Hardoon, John Shawe-Taylor:
Constructing Nonlinear Discriminants from Multiple Data Views. ECML/PKDD (1) 2010: 328-343 - [c4]Tom Diethe, Nello Cristianini, John Shawe-Taylor:
Preface. WAPA 2010: 1-3 - [e1]Tom Diethe, Nello Cristianini, John Shawe-Taylor:
Proceedings of the First Workshop on Applications of Pattern Analysis, WAPA 2010, Cumberland Lodge, Windsor, UK, September 1-3, 2010. JMLR Proceedings 11, JMLR.org 2010 [contents]
2000 – 2009
- 2009
- [c3]Tom Diethe, Zakria Hussain:
Kernel Polytope Faces Pursuit. ECML/PKDD (1) 2009: 290-301 - [c2]Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor:
Matching Pursuit Kernel Fisher Discriminant Analysis. AISTATS 2009: 121-128 - 2007
- [c1]Tom Diethe, Peter J. Bentley:
Modelling the Effects of Colony Age on the Foraging Behaviour of Harvester Ants. ECAL 2007: 706-715
Coauthor Index
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last updated on 2024-11-11 21:31 CET by the dblp team
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