Search Results for author:
Found 16 papers, 6 papers with code
Gradient-based inference of abstract task representations for generalization in neural networks
We show that gradients backpropagated through a neural network to a task representation layer are an efficient heuristic to infer current task demands, a process we refer to as gradient-based inference (GBI).
Continually Learn to Map Visual Concepts to Large Language Models in Resource-constrained Environments
This work proposes Continual Visual Mapping (CVM), an approach that continually ground vision representations to a knowledge space extracted from a fixed Language model.
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
This paper aims to explore the role of hyperparameter selection in continual learning and the necessity of continually and automatically tuning them according to the complexity of the task at hand.
In-context Interference in Chat-based Large Language Models
However, one limitation of this scenario is that users cannot modify the internal knowledge of the model, and the only way to add or modify internal knowledge is by explicitly mentioning it to the model during the current interaction.
A Comprehensive Empirical Evaluation on Online Continual Learning
Online continual learning aims to get closer to a live learning experience by learning directly on a stream of data with temporally shifting distribution and by storing a minimum amount of data from that stream.
Studying Generalization on Memory-Based Methods in Continual Learning
One of the objectives of Continual Learning is to learn new concepts continually over a stream of experiences and at the same time avoid catastrophic forgetting.
Continual Learning for Predictive Maintenance: Overview and Challenges
In this work, we present a brief introduction to predictive maintenance, non-stationary environments, and continual learning, together with an extensive review of the current state of applying continual learning in real-world applications and specifically in predictive maintenance.
Class-Incremental Learning with Repetition
We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters.
PIVOT: Prompting for Video Continual Learning
In this paper, we address the problem of continual learning for video data.
How Relevant is Selective Memory Population in Lifelong Language Learning?
Lifelong language learning seeks to have models continuously learn multiple tasks in a sequential order without suffering from catastrophic forgetting.
A Study on the Predictability of Sample Learning Consistency
Curriculum Learning is a powerful training method that allows for faster and better training in some settings.
Memory Population in Continual Learning via Outlier Elimination
This paper introduces Memory Outlier Elimination (MOE), a method for identifying and eliminating outliers in the memory buffer by choosing samples from label-homogeneous subpopulations.
Entropy-based Stability-Plasticity for Lifelong Learning
The ability to continuously learn remains elusive for deep learning models.
Sequoia: A Software Framework to Unify Continual Learning Research
We propose a taxonomy of settings, where each setting is described as a set of assumptions.
Optimizing Reusable Knowledge for Continual Learning via Metalearning
On the other hand, a set of trainable masks provides the key mechanism to selectively choose from the KB relevant weights to solve each task.
Catching the Long Tail in Deep Neural Networks
As a working hypothesis, we speculate that during learning some weights focus on mining patterns from frequent examples while others are in charge of memorizing rare long-tail samples.
