213 citations · 226 across the 8 of their papers we have counts for
11 papers
Toto 2.0: Time Series Forecasting Enters the Scaling Era
Emaad Khwaja, Chris Lettieri, Gerald Woo +10
We show that time series foundation models scale: a single training recipe produces reliable forecast-quality improvements from 4M to 2.5B parameters. We release Toto 2.0, a family…
MultIOD: Rehearsal-free Multihead Incremental Object Detector
Eden Belouadah, Arnaud Dapogny, Kevin Bailly
Class-Incremental learning (CIL) refers to the ability of artificial agents to integrate new classes as they appear in a stream. It is particularly interesting in evolving environm…
PlaStIL: Plastic and Stable Memory-Free Class-Incremental Learning
Grégoire Petit, Adrian Popescu, Eden Belouadah +2
Plasticity and stability are needed in class-incremental learning in order to learn from new data while preserving past knowledge. Due to catastrophic forgetting, finding a comprom…
A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning
Umang Aggarwal, Adrian Popescu, Eden Belouadah +1
Deep learning approaches are successful in a wide range of AI problems and in particular for visual recognition tasks. However, there are still open problems among which is the cap…
Dataset Knowledge Transfer for Class-Incremental Learning without Memory
Habib Slim, Eden Belouadah, Adrian Popescu +1
Incremental learning enables artificial agents to learn from sequential data. While important progress was made by exploiting deep neural networks, incremental learning remains ver…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks
Eden Belouadah, Adrian Popescu, Ioannis Kanellos
The ability of artificial agents to increment their capabilities when confronted with new data is an open challenge in artificial intelligence. The main challenge faced in such cas…
Initial Classifier Weights Replay for Memoryless Class Incremental Learning
Eden Belouadah, Adrian Popescu, Ioannis Kanellos
Incremental Learning (IL) is useful when artificial systems need to deal with streams of data and do not have access to all data at all times. The most challenging setting requires…
Active Class Incremental Learning for Imbalanced Datasets
Eden Belouadah, Adrian Popescu, Umang Aggarwal +1
Incremental Learning (IL) allows AI systems to adapt to streamed data. Most existing algorithms make two strong hypotheses which reduce the realism of the incremental scenario: (1)…
ScaIL: Classifier Weights Scaling for Class Incremental Learning
Eden Belouadah, Adrian Popescu
Incremental learning is useful if an AI agent needs to integrate data from a stream. The problem is non trivial if the agent runs on a limited computational budget and has a bounde…
DeeSIL: Deep-Shallow Incremental Learning
Eden Belouadah, Adrian Popescu
Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to work on a budget. This is especially true when IL is modeled using a deep learning approach,…