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ORCIDTameem Adel
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- affiliation: National Physical Laboratory (NPL), Cambridge, United Kingdom
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2020 – today
- 2025
[j3]Tameem Adel
:
Adaptive Few-Shot Class-Incremental Learning via Latent Variable Models. J. Artif. Intell. Res. 82: 1807-1843 (2025)- [c25]Tameem Adel:
Adversarial Robustness. ICAAI 2025: 6-10 - [c24]Tameem Adel:
Uncertainty Estimates for Support Vector Machines in the Transfer Learning Paradigm. ICAAI 2025: 16-20 - [c23]Cayla Harris, Umar Abubacar, Ryan Bournes, Tameem Adel, Alireza Tamaddoni-Nezhad, Roman Bauer:
Agent-Based Modelling of Retinal Development. IWBBIO (2) 2025: 56-70 - [i9]Tameem Adel:
The Bayesian Approach to Continual Learning: An Overview. CoRR abs/2507.08922 (2025) - 2024
- [j2]Tameem Adel:
Similarity-Based Adaptation for Task-Aware and Task-Free Continual Learning. J. Artif. Intell. Res. 80: 377-417 (2024) - [c22]Tameem Adel:
Explaining Time Series Predictions via Relevant Shapelets. ICAAI 2024: 212-217 - [c21]Tameem Adel:
Automated Class-Imbalanced Learning by Bayesian Optimisation. ICAAI 2024: 231-237 - [c20]Tameem Adel:
Adversarial Learning of Summary Statistics for Approximate Bayesian Computation. ICAAI 2024: 256-267 - [i8]Tameem Adel, Sam Bilson, Mark Levene, Andrew Thompson:
Trustworthy Artificial Intelligence in the Context of Metrology. CoRR abs/2406.10117 (2024) - 2023
- [j1]Tameem Adel, Mark Levene:
A General Model for Side Information in Neural Networks. Algorithms 16(11): 526 (2023) - [c19]Tameem Adel:
Probabilistic Adaptation for Meta-Learning. ESANN 2023 - 2021
- [c18]Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato:
Getting a CLUE: A Method for Explaining Uncertainty Estimates. ICLR 2021 - 2020
- [c17]Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon:
Conditional Learning of Fair Representations. ICLR 2020 - [c16]Tameem Adel, Han Zhao, Richard E. Turner:
Continual Learning with Adaptive Weights (CLAW). ICLR 2020 - [i7]Javier Antorán, Umang Bhatt, Tameem Adel, Adrian Weller, José Miguel Hernández-Lobato:
Getting a CLUE: A Method for Explaining Uncertainty Estimates. CoRR abs/2006.06848 (2020)
2010 – 2019
- 2019
- [c15]Tameem Adel, Isabel Valera, Zoubin Ghahramani, Adrian Weller:
One-Network Adversarial Fairness. AAAI 2019: 2412-2420 - [c14]Tameem Adel, Adrian Weller:
TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning. ICML 2019: 71-81 - [c13]Raymond R. Bond, Maurice D. Mulvenna, Hui Wan, Dewar D. Finlay, Alexander Wong, Ansgar Koene, Rob Brisk, Jennifer Boger, Tameem Adel:
Human Centered Artificial Intelligence: Weaving UX into Algorithmic Decision Making. RoCHI 2019: 2-9 - [i6]Han Zhao, Amanda Coston, Tameem Adel, Geoffrey J. Gordon:
Conditional Learning of Fair Representations. CoRR abs/1910.07162 (2019) - [i5]Tameem Adel, Han Zhao, Richard E. Turner:
Continual Learning with Adaptive Weights (CLAW). CoRR abs/1911.09514 (2019) - 2018
- [c12]Tameem Adel, Zoubin Ghahramani, Adrian Weller:
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models. ICML 2018: 50-59 - 2017
- [c11]Tameem Adel, Cassio P. de Campos:
Learning Bayesian Networks with Incomplete Data by Augmentation. AAAI 2017: 1684-1690 - [c10]Tameem Adel, Han Zhao, Alexander Wong:
Unsupervised Domain Adaptation with a Relaxed Covariate Shift Assumption. AAAI 2017: 1691-1697 - [c9]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. ICLR (Poster) 2017 - [i4]Luisa M. Zintgraf, Taco S. Cohen, Tameem Adel, Max Welling:
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis. CoRR abs/1702.04595 (2017) - 2016
- [c8]Han Zhao, Tameem Adel, Geoffrey J. Gordon, Brandon Amos:
Collapsed Variational Inference for Sum-Product Networks. ICML 2016: 1310-1318 - [i3]Tameem Adel, Cassio P. de Campos:
Learning Bayesian Networks without Assuming Missing at Random. CoRR abs/1608.07734 (2016) - 2015
- [c7]Tameem Adel, Alexander Wong:
A Probabilistic Covariate Shift Assumption for Domain Adaptation. AAAI 2015: 2476-2482 - [c6]Tameem Adel, David Balduzzi, Ali Ghodsi:
Learning the Structure of Sum-Product Networks via an SVD-based Algorithm. UAI 2015: 32-41 - [i2]Tameem Adel, Alexander Wong, Daniel W. Stashuk:
A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping. CoRR abs/1508.00507 (2015) - 2013
- [c5]Tameem Adel, Benn Smith, Ruth Urner, Daniel W. Stashuk, Daniel J. Lizotte:
Generative Multiple-Instance Learning Models For Quantitative Electromyography. UAI 2013 - [i1]Tameem Adel, Benn Smith, Ruth Urner, Daniel W. Stashuk, Daniel J. Lizotte:
Generative Multiple-Instance Learning Models For Quantitative Electromyography. CoRR abs/1309.6811 (2013) - 2012
- [c4]Tameem Adel, Benn Smith, Daniel W. Stashuk:
Muscle categorization using PDF estimation and Naive Bayes classification. EMBC 2012: 2619-2622 - [c3]Hossein Parsaei, Daniel W. Stashuk, Tameem Adel:
Decomposition of intramuscular EMG signals using a knowledge -based certainty classifier algorithm. EMBC 2012: 6208-6211 - 2011
- [c2]Stefano Rovetta, Francesco Masulli, Tameem Adel:
Tuning Graded Possibilistic Clustering by Visual Stability Analysis. WILF 2011: 164-171 - 2010
- [c1]Tameem Adel, Mohamed Ismail:
ASCM: An accelerated soft c-means clustering algorithm. ISDA 2010: 1142-1147
Coauthor Index
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