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Found 7 papers, 1 papers with code
Transformers for Supervised Online Continual Learning
Inspired by the in-context learning capabilities of transformers and their connection to meta-learning, we propose a method that leverages these strengths for online continual learning.
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models
We consider the problem of online fine tuning the parameters of a language model at test time, also known as dynamic evaluation.
Towards Robust and Efficient Continual Language Learning
As the application space of language models continues to evolve, a natural question to ask is how we can quickly adapt models to new tasks.
Kalman Filter for Online Classification of Non-Stationary Data
Non-stationarity over the linear predictor weights is modelled using a parameter drift transition density, parametrized by a coefficient that quantifies forgetting.
Towards Compute-Optimal Transfer Learning
The field of transfer learning is undergoing a significant shift with the introduction of large pretrained models which have demonstrated strong adaptability to a variety of downstream tasks.
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks.
On the Role of Optimization in Double Descent: A Least Squares Study
Empirically it has been observed that the performance of deep neural networks steadily improves as we increase model size, contradicting the classical view on overfitting and generalization.
