Search Results for author:
Found 3 papers, 1 papers with code
Exact Learning Dynamics of In-Context Learning in Linear Transformers and Its Application to Non-Linear Transformers
Transformer models exhibit remarkable in-context learning (ICL), adapting to novel tasks from examples within their context, yet the underlying mechanisms remain largely mysterious.
Transformers represent belief state geometry in their residual stream
What computational structure are we building into large language models when we train them on next-token prediction?
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation
In this paper, we propose a depth completion and uncertainty estimation approach that better handles the challenges of aerial platforms, such as large viewpoint and depth variations, and limited computing resources.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
