Mitigating Catastrophic Forgetting in Continual Learning for Natural ...

Catastrophic forgetting remains a critical challenge in continual learning scenarios for Natural Language Processing (NLP) tasks, to which ...

Catastrophic forgetting remains a critical challenge in continual learning scenarios for Natural Language Processing (NLP) tasks, to which this research.

CORE: Mitigating Catastrophic Forgetting in Continual Learning ...

Abstract:This paper introduces a novel perspective to significantly mitigate catastrophic forgetting in continuous learning (CL), ...

Understanding and Mitigating Catastrophic Forgetting in Machine ...

Lifelong Learning Challenges: In scenarios where models are required to learn from a continuous stream of tasks, catastrophic forgetting can ...

Mitigating Catastrophic Forgetting in Large Language Models with ...

When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non- ...

Overcoming catastrophic forgetting in neural networks - PNAS

Continual learning poses particular challenges for artificial neural networks due to the tendency for knowledge of the previously learned task(s) (e.g., task A) ...

Preventing Catastrophic Forgetting in Continual Learning of New ...

Multi-Task Learning (MTL) is widely-accepted in Natural Language. Processing as a standard technique for learning multiple related tasks in one model. Training ...

Preventing Catastrophic Forgetting in Continual Learning of New ...

Multi-Task Learning (MTL) is widely-accepted in Natural Language Processing as a standard technique for learning multiple related tasks in ...

Natural Mitigation of Catastrophic Interference: Continual Learning ...

Humans learn to perform new skills throughout their lifetime without appreciable forgetting of old skills. Within the context of machine learning (ML) ...

Consistency is the Key to Further Mitigating Catastrophic Forgetting ...

Proceedings of The 1st Conference on Lifelong Learning Agents, PMLR 199:1195-1212, 2022. Abstract. Deep neural networks struggle to continually learn ...

Mitigating Forgetting in Online Continual Learning with Neuron ...

Overall the memory-based approaches address catastrophic forgetting issue from training with past data but their performance could easily. 2. Page 3. be ...

Mitigating Catastrophic Forgetting in Large Language Models with ...

Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training ...

Reducing Catastrophic Forgetting With Associative Learning

Since learning in the natural world often occurs sequentially, the past few years have witnessed an explosion of brain-inspired continual ...

Mitigating Forgetting in Online Continual Learning with Neuron...

This paper develops an approach to mitigate catastrophic forgetting in continual learning ... naturally result in better performance ...

Mitigating Forgetting in Online Continual Learning via Instance ...

... learn from a continuous stream of data without revisiting any previously encountered data instances. The phenomenon of catastrophic forgetting is worsened ...

Regularizing Trajectories to Mitigate Catastrophic Forgetting

We derive a new co-natural gradient update rule for continual learning whereby the new task gradients are preconditioned with the empirical Fisher ...

Mitigating Forgetting in Online Continual Learning via Instance ...

The phenomenon of catastrophic forgetting is worsened since the model should not only address the forgetting at the task-level but also at the data instance- ...

Mitigating Catastrophic Forgetting in Large-Scale Models with ...

4. Replay-Based Methods. One of the effective methods for combating catastrophic forgetting in continuous learning models is the use of replay- ...

Catastrophic Forgetting in Neural Networks - DEV

Lifelong learning, or the continual adjustment of provided knowledge ... mitigating catastrophic forgetting during neural network training.

Mitigating Catastrophic Forgetting in Spiking Neural Networks...

This paper proposes a voltage threshold modulation method to mitigate the catastrophic forgetting problem in SNN during continual learning. The ...