Variational Auto-Regressive Gaussian Processes for Continual Learning
Abstrak
Through sequential construction of posteriors on observing data online, Bayes' theorem provides a natural framework for continual learning. We develop Variational Auto-Regressive Gaussian Processes (VAR-GPs), a principled posterior updating mechanism to solve sequential tasks in continual learning. By relying on sparse inducing point approximations for scalable posteriors, we propose a novel auto-regressive variational distribution which reveals two fruitful connections to existing results in Bayesian inference, expectation propagation and orthogonal inducing points. Mean predictive entropy estimates show VAR-GPs prevent catastrophic forgetting, which is empirically supported by strong performance on modern continual learning benchmarks against competitive baselines. A thorough ablation study demonstrates the efficacy of our modeling choices.
Penulis (3)
Sanyam Kapoor
Theofanis Karaletsos
Thang D. Bui
Akses Cepat
- Tahun Terbit
- 2020
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓