4 accepted papers
Scaling factors in residual branches have emerged as a prevalent method for boosting neural network performance, especially in normalization-free architectures. While prior work has primarily examined scaling effects from an optimization perspective, this paper investigates their role in residual ar…
Large vision-language models have been shown to perform complex tasks. However, aligning language instructions with object visual information to enable general inference for robotic grasping poses a significant challenge. To tackle this issue, we introduce GraspControl, a method that leverages grasp…
Large vision-language models have been shown to perform complex tasks. However, aligning language instructions with object visual information to enable general inference for robotic grasping poses a significant challenge. To tackle this issue, we introduce GraspControl, a method that leverages grasp
This paper primarily investigates the convergence of the Neural Tangent Kernel (NTK) in classification problems. This study firstly show the strictly positive definiteness of NTK of multi-layer fully connected neural networks and residual neural networks. Then, through a contradiction argument, it…