Memory-Based Dual Gaussian Processes for Sequential Learning
- URL: http://arxiv.org/abs/2306.03566v1
- Date: Tue, 6 Jun 2023 10:34:03 GMT
- Title: Memory-Based Dual Gaussian Processes for Sequential Learning
- Authors: Paul E. Chang, Prakhar Verma, S.T. John, Arno Solin, Mohammad Emtiyaz
Khan
- Abstract summary: We present a method to keep all such errors in check using the recently proposed dual sparse variational GP.
Our method enables accurate inference for generic likelihoods and improves learning by actively building and updating a memory of past data.
- Score: 26.22552882103996
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Sequential learning with Gaussian processes (GPs) is challenging when access
to past data is limited, for example, in continual and active learning. In such
cases, errors can accumulate over time due to inaccuracies in the posterior,
hyperparameters, and inducing points, making accurate learning challenging.
Here, we present a method to keep all such errors in check using the recently
proposed dual sparse variational GP. Our method enables accurate inference for
generic likelihoods and improves learning by actively building and updating a
memory of past data. We demonstrate its effectiveness in several applications
involving Bayesian optimization, active learning, and continual learning.
Related papers
- An Effective Dynamic Gradient Calibration Method for Continual Learning [11.555822066922508]
Continual learning (CL) is a fundamental topic in machine learning, where the goal is to train a model with continuously incoming data and tasks.
Due to the memory limit, we cannot store all the historical data, and therefore confront the catastrophic forgetting'' problem.
We develop an effective algorithm to calibrate the gradient in each updating step of the model.
arXiv Detail & Related papers (2024-07-30T16:30:09Z) - Adaptive Rentention & Correction for Continual Learning [114.5656325514408]
A common problem in continual learning is the classification layer's bias towards the most recent task.
We name our approach Adaptive Retention & Correction (ARC)
ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets.
arXiv Detail & Related papers (2024-05-23T08:43:09Z) - Enhancing Consistency and Mitigating Bias: A Data Replay Approach for
Incremental Learning [100.7407460674153]
Deep learning systems are prone to catastrophic forgetting when learning from a sequence of tasks.
To mitigate the problem, a line of methods propose to replay the data of experienced tasks when learning new tasks.
However, it is not expected in practice considering the memory constraint or data privacy issue.
As a replacement, data-free data replay methods are proposed by inverting samples from the classification model.
arXiv Detail & Related papers (2024-01-12T12:51:12Z) - Primal Dual Continual Learning: Balancing Stability and Plasticity through Adaptive Memory Allocation [86.8475564814154]
We show that it is both possible and beneficial to undertake the constrained optimization problem directly.
We focus on memory-based methods, where a small subset of samples from previous tasks can be stored in a replay buffer.
We show that dual variables indicate the sensitivity of the optimal value of the continual learning problem with respect to constraint perturbations.
arXiv Detail & Related papers (2023-09-29T21:23:27Z) - Efficient Meta-Learning for Continual Learning with Taylor Expansion
Approximation [2.28438857884398]
Continual learning aims to alleviate catastrophic forgetting when handling consecutive tasks under non-stationary distributions.
We propose a novel efficient meta-learning algorithm for solving the online continual learning problem.
Our method achieves better or on-par performance and much higher efficiency compared to the state-of-the-art approaches.
arXiv Detail & Related papers (2022-10-03T04:57:05Z) - Learning to Prompt for Continual Learning [34.609384246149325]
This work presents a new paradigm for continual learning that aims to train a more succinct memory system without accessing task identity at test time.
Our method learns to dynamically prompt (L2P) a pre-trained model to learn tasks sequentially under different task transitions.
The objective is to optimize prompts to instruct the model prediction and explicitly manage task-invariant and task-specific knowledge while maintaining model plasticity.
arXiv Detail & Related papers (2021-12-16T06:17:07Z) - Active Learning for Sequence Tagging with Deep Pre-trained Models and
Bayesian Uncertainty Estimates [52.164757178369804]
Recent advances in transfer learning for natural language processing in conjunction with active learning open the possibility to significantly reduce the necessary annotation budget.
We conduct an empirical study of various Bayesian uncertainty estimation methods and Monte Carlo dropout options for deep pre-trained models in the active learning framework.
We also demonstrate that to acquire instances during active learning, a full-size Transformer can be substituted with a distilled version, which yields better computational performance.
arXiv Detail & Related papers (2021-01-20T13:59:25Z) - AdaS: Adaptive Scheduling of Stochastic Gradients [50.80697760166045]
We introduce the notions of textit"knowledge gain" and textit"mapping condition" and propose a new algorithm called Adaptive Scheduling (AdaS)
Experimentation reveals that, using the derived metrics, AdaS exhibits: (a) faster convergence and superior generalization over existing adaptive learning methods; and (b) lack of dependence on a validation set to determine when to stop training.
arXiv Detail & Related papers (2020-06-11T16:36:31Z) - Continual Deep Learning by Functional Regularisation of Memorable Past [95.97578574330934]
Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past.
We propose a new functional-regularisation approach that utilises a few memorable past examples crucial to avoid forgetting.
Our method achieves state-of-the-art performance on standard benchmarks and opens a new direction for life-long learning where regularisation and memory-based methods are naturally combined.
arXiv Detail & Related papers (2020-04-29T10:47:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.