6 papers
Riding the Fixed-Point Curve: Image Classification in the Continuous Thought Machine
Thomas Hannagan
The Continuous Thought Machine (CTM) is a recurrent network that attends to inputs and makes predictions based on the synchronization between its neurons. On tasks spanning many do…
The Initialization Determines Whether In-Context Learning Is Gradient Descent
Shifeng Xie, Rui Yuan, Simone Rossi +1
In-context learning (ICL) in large language models (LLMs) is a striking phenomenon, yet its underlying mechanisms remain only partially understood. Previous work connects linear se…
From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning
Zhe Huang, Simone Rossi, Rui Yuan +1
Transformers have become a standard architecture in machine learning, demonstrating strong in-context learning (ICL) abilities that allow them to learn from the prompt at inference…
Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation
Yixiao Wang, Chen Tang, Lingfeng Sun +8
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference steps and high c…
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning
Marc Schachtsiek, Simone Rossi, Thomas Hannagan
Domain adaptive active learning is leading the charge in label-efficient training of neural networks. For semantic segmentation, state-of-the-art models jointly use two criteria of…
On permutation symmetries in Bayesian neural network posteriors: a variational perspective
Simone Rossi, Ankit Singh, Thomas Hannagan
The elusive nature of gradient-based optimization in neural networks is tied to their loss landscape geometry, which is poorly understood. However recent work has brought solid evi…