Iteratively Training Look-Up Tables for Network Quantization. - Fabien Cardinaux; Stefan Uhlich; Kazuki Yoshiyama; Javier Alonso García; Stephen Tiedemann; Thomas Kemp; Akira Nakamura (CoRR, 2018)

Differentiable Quantization of Deep Neural Networks. - Stefan Uhlich; Lukas Mauch; Kazuki Yoshiyama; Fabien Cardinaux; Javier Alonso García; Stephen Tiedemann; Thomas Kemp; Akira Nakamura (CoRR, 2019)

Mixed Precision DNNs: All you need is a good parametrization. - Stefan Uhlich; Lukas Mauch; Fabien Cardinaux; Kazuki Yoshiyama; Javier Alonso García; Stephen Tiedemann; Thomas Kemp; Akira Nakamura (ICLR, 2020)

Efficient Sampling for Predictor-Based Neural Architecture Search. - Lukas Mauch; Stephen Tiedemann; Javier Alonso García; Bac Nguyen Cong; Kazuki Yoshiyama; Fabien Cardinaux; Thomas Kemp (CoRR, 2020)

Iteratively Training Look-Up Tables for Network Quantization. - Fabien Cardinaux; Stefan Uhlich; Kazuki Yoshiyama; Javier Alonso García; Lukas Mauch; Stephen Tiedemann; Thomas Kemp; Akira Nakamura (IEEE J. Sel. Top. Signal Process., 2020)

A Statistical Model for Predicting Generalization in Few-Shot Classification. - Yassir Bendou; Vincent Gripon; Bastien Pasdeloup; Lukas Mauch; Stefan Uhlich; Fabien Cardinaux; Ghouthi Boukli Hacene; Javier Alonso García (CoRR, 2022)

A Statistical Model for Predicting Generalization in Few-Shot Classification. - Yassir Bendou; Vincent Gripon; Bastien Pasdeloup; Giulia Lioi; Lukas Mauch; Stefan Uhlich; Fabien Cardinaux; Ghouthi Boukli Hacene; Javier Alonso García (EUSIPCO, 2023)

On multi-token prediction for efficient LLM inference. - Somesh Mehra; Javier Alonso García; Lukas Mauch (CoRR, 2025)

Global-Order GFlowNets. - Lluís Pastor Pérez; Javier Alonso García; Lukas Mauch (CoRR, 2025)