Statistical Learning
Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates
In particular, we establish the surprising result that: F or any constant learning rate η > 0, the stochastic gradient bandit algorithm is guaranteed to converge to the globally optimal policy almost surely.
SODAI: Multi-Modal Maritime Object Detection Dataset With RGB and Hyperspectral Image Sensors
Notwithstanding astonishing advances in computer vision technologies, detecting ships and floating matters in these images is challenging due to factors such as object distance.
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Specifically, we pose and answer the following questions: Q1. How do the learned spatial and temporal representations vary based on different VSSL pretrain-ing methodologies? How robust are these representations to different distribution shifts?
Diffusion Representation for Asymmetric Kernels via Magnetic Transform
In DM, kernels play an important role for capturing the nonlinear relationship of data.
