4 accepted papers
Little research explores the correlation between the expressive ability and generalization ability of the low-rank adaptation (LoRA). Sharpness-Aware Minimization (SAM) improves model generalization for both Convolutional Neural Networks (CNNs) and Transformers by encouraging convergence to locally
Advanced end-to-end autonomous driving systems predict other vehicles
Current linear State-Space Models for 3D point clouds typically rely on 1D serialization (e.g., Hilbert curves) for global modeling. Such rigid ordering disrupts spatial continuity in dense scenes, introducing what we term Serialization Bias. We propose AnIsoNet, a framework that decouples anisotrop…
Sharpness-Aware Minimization (SAM) has emerged as a promising approach for effectively reducing the generalization error. However, SAM incurs twice the computational cost compared to the base optimizer (e.g., SGD). We propose Asymptotic Unbiased data sampling to accelerate SAM (AUSAM), which maintai…