1.7k citations · 2.2k across the 42 of their papers we have counts for
15 papers · 1 filter
Optimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets
Gwen Legate, Rahaf Aljundi, Eugene Belilovsky
Large language models (LLMs) trained on vast amounts of data have demonstrated impressive performance over a range of tasks, but future performance gains will require increasing am…
Continual Self-Improvement with Lightweight Experiential Latent Memories
Vaggelis Dorovatas, Nancy Kalaj, Rahaf Aljundi
Large language models achieve strong reasoning performance by scaling inference-time compute, yet remain fundamentally stateless, discarding the rich, self-produced reasoning trace…
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
Christian Gumbsch, Leonardo Barcellona, Lennard Schünemann +7
Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics. Recent work has explored…
Position: Modular Memory is the Key to Continual Learning Agents
Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov +21
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these…
When Data Falls Short: Grokking Below the Critical Threshold
Vaibhav Singh, Eugene Belilovsky, Rahaf Aljundi
In this paper, we investigate the phenomenon of grokking, where models exhibit delayed generalization following overfitting on training data. We focus on data-scarce regimes where…
Dual-Phase Continual Learning: Supervised Adaptation Meets Unsupervised Retention
Vaibhav Singh, Rahaf Aljundi, Eugene Belilovsky
Foundational Vision-Language Models (VLMs) excel across diverse tasks, but adapting them to new domains without forgetting prior knowledge remains a critical challenge. Continual L…
OOD Aware Supervised Contrastive Learning
Soroush Seifi, Daniel Olmeda Reino, Nikolay Chumerin +1
Out-of-Distribution (OOD) detection is a crucial problem for the safe deployment of machine learning models identifying samples that fall outside of the training distribution, i.e.…
Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning
Nader Asadi, MohammadReza Davari, Sudhir Mudur +2
In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent best-performing methods utilize various…
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi +3
In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small sub…
Probing Representation Forgetting in Supervised and Unsupervised Continual Learning
MohammadReza Davari, Nader Asadi, Sudhir Mudur +2
Continual Learning research typically focuses on tackling the phenomenon of catastrophic forgetting in neural networks. Catastrophic forgetting is associated with an abrupt loss of…
New Insights on Reducing Abrupt Representation Change in Online Continual Learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi +3
In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small sub…
Identifying Wrongly Predicted Samples: A Method for Active Learning
Rahaf Aljundi, Nikolay Chumerin, Daniel Olmeda Reino
State-of-the-art machine learning models require access to significant amount of annotated data in order to achieve the desired level of performance. While unlabelled data can be l…
Continual Learning in Neural Networks
Rahaf Aljundi
Artificial neural networks have exceeded human-level performance in accomplishing several individual tasks (e.g. voice recognition, object recognition, and video games). However, s…
Online Continual Learning with Maximally Interfered Retrieval
Rahaf Aljundi, Lucas Caccia, Eugene Belilovsky +4
Continual learning, the setting where a learning agent is faced with a never ending stream of data, continues to be a great challenge for modern machine learning systems. In partic…
Gradient based sample selection for online continual learning
Rahaf Aljundi, Min Lin, Baptiste Goujaud +1
A continual learning agent learns online with a non-stationary and never-ending stream of data. The key to such learning process is to overcome the catastrophic forgetting of previ…