164 citations · 291 across the 40 of their papers we have counts for
5 papers · 1 filter
Momentum-based Gradient Methods in Multi-Objective Recommendation
Blagoj Mitrevski, Milena Filipovic, Diego Antognini +3
Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender…
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
Kirtan Padh, Diego Antognini, Emma Lejal Glaude +2
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One…
Overcoming Multi-Model Forgetting
Yassine Benyahia, Kaicheng Yu, Kamil Bennani-Smires +4
We identify a phenomenon, which we refer to as multi-model forgetting, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performan…
Evaluating the Search Phase of Neural Architecture Search
Kaicheng Yu, Christian Sciuto, Martin Jaggi +2
Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and val…
GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining
Kamil Bennani-Smires, Claudiu Musat, Andreea Hossmann +1
The dramatic success of deep neural networks across multiple application areas often relies on experts painstakingly designing a network architecture specific to each task. To simp…