5 papers · 1 filter
On the Generalization Behavior of Deep Residual Networks From a Dynamical System Perspective
Jinshu Huang, Mingfei Sun, Chunlin Wu
Deep neural networks (DNNs) have significantly advanced machine learning, with model depth playing a central role in their successes. The dynamical system modeling approach has rec…
Rank-1 Approximation of Inverse Fisher for Natural Policy Gradients in Deep Reinforcement Learning
Yingxiao Huo, Satya Prakash Dash, Radu Stoican +2
Natural gradients have long been studied in deep reinforcement learning due to their fast convergence properties and covariant weight updates. However, computing natural gradients…
Gradient Regularized Natural Gradients
Satya Prakash Dash, Hossein Abdi, Wei Pan +2
Gradient regularization (GR) has been shown to improve the generalizability of trained models. While Natural Gradient Descent has been shown to accelerate optimization in the initi…
Bayesian Natural Gradient Fine-Tuning of CLIP Models via Kalman Filtering
Hossein Abdi, Mingfei Sun, Wei Pan
Vision-language pre-trained models, such as CLIP, have established new benchmarks in multimodal data mining. In such models, few-shot fine-tuning is a major challenge to achieve op…
LoKO: Low-Rank Kalman Optimizer for Online Fine-Tuning of Large Models
Hossein Abdi, Mingfei Sun, Andi Zhang +2
Training large models with millions or even billions of parameters from scratch incurs substantial computational costs. Parameter Efficient Fine-Tuning (PEFT) methods, particularly…