Deep Learning
GEPS: Boosting Generalization in Parametric PDE Neural Solvers through Adaptive Conditioning
We demonstrate the versatility of our approach for both fully data-driven and for physics-aware neural solvers.
CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics Fatih Dinc Department of Applied Physics Stanford University Stanford, CA94305 Adam Shai
A promising way to extract computational principles from these large datasets is to train data-constrained recurrent neural networks (dRNNs).
The geometry of hidden representations of large transformer models
By analyzing the intrinsic dimension (ID) and neighbor composition, we find that the representations evolve similarly in transformers trained on protein language tasks and image reconstruction tasks.
MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion Shitao T ang
MVDiffusion processes perspective images in parallel with a pre-trained text-to-image diffusion model, while integrating novel correspondence-aware attention layers to facilitate cross-view interactions.
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism Chengcheng Wang Wei He Ying Nie Jianyuan Guo Chuanjian Liu Kai Han Yunhe Wang
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection.
