2 citations · 2 across the 6 of their papers we have counts for
6 papers
Pruned Adaptation Modules: A Simple yet Strong Baseline for Continual Foundation Models
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
The continual learning literature has rapidly shifted from traditional class incremental learning (CIL) techniques to foundation model (FM)-based CIL methods without a clear unders…
Automated Machine Learning for Unsupervised Tabular Tasks
Prabhant Singh, Pieter Gijsbers, Elif Ceren Gok Yildirim +2
In this work, we present LOTUS (Learning to Learn with Optimal Transport for Unsupervised Scenarios), a simple yet effective method to perform model selection for multiple unsuperv…
Unlocking [CLS] Features for Continual Post-Training
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Joaquin Vanschoren
Continual learning requires models to integrate new classes or domains over time while preserving previously acquired knowledge. Within this paradigm, foundation models often achie…
Continual Learning on a Data Diet
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
Continual Learning (CL) methods usually learn from all available data. However, this is not the case in human cognition which efficiently focuses on key experiences while disregard…
Self-Regulated Neurogenesis for Online Data-Incremental Learning
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Decebal Constantin Mocanu +1
Neural networks often struggle with catastrophic forgetting when learning sequences of tasks or data streams, unlike humans who can continuously learn and consolidate new concepts…
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Ghada Sokar +2
Continual learning (CL) refers to the ability of an intelligent system to sequentially acquire and retain knowledge from a stream of data with as little computational overhead as p…