5 papers
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…
From Grunts to Lexicons: Emergent Language from Cooperative Foraging
Maytus Piriyajitakonkij, Rujikorn Charakorn, Weicheng Tao +4
Language is a powerful communicative and cognitive tool. It enables humans to express thoughts, share intentions, and reason about complex phenomena. Despite our fluency in using a…
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…
DroneDiffusion: Robust Quadrotor Dynamics Learning with Diffusion Models
Avirup Das, Rishabh Dev Yadav, Sihao Sun +3
An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances. These factors hinder performance and compromise the stability of the system, mak…