205 citations · 861 across the 59 of their papers we have counts for
11 papers · 1 filter
Continual Reinforcement Learning via Autoencoder-Driven Task and New Environment Recognition
Zeki Doruk Erden, Donia Gasmi, Boi Faltings
Continual learning for reinforcement learning agents remains a significant challenge, particularly in preserving and leveraging existing information without an external signal to i…
A Proposal for Networks Capable of Continual Learning
Zeki Doruk Erden, Boi Faltings
We analyze the ability of computational units to retain past responses after parameter updates, a key property for system-wide continual learning. Neural networks trained with grad…
Directed Structural Adaptation to Overcome Statistical Conflicts and Enable Continual Learning
Zeki Doruk Erden, Boi Faltings
Adaptive networks today rely on overparameterized fixed topologies that cannot break through the statistical conflicts they encounter in the data they are exposed to, and are prone…
Representation Memorization for Fast Learning New Knowledge without Forgetting
Fei Mi, Tao Lin, Boi Faltings
The ability to quickly learn new knowledge (e.g. new classes or data distributions) is a big step towards human-level intelligence. In this paper, we consider scenarios that requir…
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…