Cued multimodal learning in infancy: a neuro-computational model. - Thomas Hannagan; Rachel Wu (CogSci, 2011)

Holographic String Encoding. - Thomas Hannagan; Emmanuel Dupoux; Anne Christophe (Cogn. Sci., 2011)

Broken Symmetries in a Location-Invariant Word Recognition Network. - Thomas Hannagan; Frédéric Dandurand; Jonathan Grainger (Neural Comput., 2011)

A time-invariant connectionist model of spoken word recognition. - Thomas Hannagan; James Magnusson; Jonathan Grainger (CogSci, 2012)

Protein Analysis Meets Visual Word Recognition: A Case for String Kernels in the Brain. - Thomas Hannagan; Jonathan Grainger (Cogn. Sci., 2012)

Computational models of location-invariant orthographic processing. - Frédéric Dandurand; Thomas Hannagan; Jonathan Grainger (Connect. Sci., 2013)

Learning Spatial Invariance with the Trace Rule in Nonuniform Distributions. - Jasmin Léveillé; Thomas Hannagan (Neural Comput., 2013)

The Lazy Visual Word Form Area: Computational Insights into Location-Sensitivity. - Thomas Hannagan; Jonathan Grainger (PLoS Comput. Biol., 2013)

Continuous Pruning of Deep Convolutional Networks Using Selective Weight Decay. - Hugo Tessier; Vincent Gripon; Mathieu Léonardon; Matthieu Arzel; Thomas Hannagan; David Bertrand (CoRR, 2020)

Leveraging Structured Pruning of Convolutional Neural Networks. - Hugo Tessier; Vincent Gripon; Mathieu Léonardon; Matthieu Arzel; David Bertrand; Thomas Hannagan (SiPS, 2022)

Energy Consumption Analysis of Pruned Semantic Segmentation Networks on an Embedded GPU. - Hugo Tessier; Vincent Gripon; Mathieu Léonardon; Matthieu Arzel; David Bertrand; Thomas Hannagan (SYSINT, 2022)

Rethinking Weight Decay for Efficient Neural Network Pruning. - Hugo Tessier; Vincent Gripon; Mathieu Léonardon; Matthieu Arzel; Thomas Hannagan; David Bertrand (J. Imaging, 2022)

On permutation symmetries in Bayesian neural network posteriors: a variational perspective. - Simone Rossi 001; Ankit Singh; Thomas Hannagan (NeurIPS, 2023)

Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning. - Marc Schachtsiek; Simone Rossi 001; Thomas Hannagan (CoRR, 2023)

Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation. - Yixiao Wang; Chen Tang 001; Lingfeng Sun; Simone Rossi 001; Yichen Xie 002; Chensheng Peng; Thomas Hannagan; Stefano Sabatini; Nicola Poerio; Masayoshi Tomizuka; Wei Zhan (ECCV (29), 2024)

From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning. - Zhe Huang; Simone Rossi 001; Rui Yuan; Thomas Hannagan (AABI, 2025)