5 papers
Multiple Descents in Deep Learning as a Sequence of Order-Chaos Transitions in LSTM Networks
Wenbo Wei, Fan Xu, Nicholas Chong Jia Le +2
We observe a novel `multiple-descent' phenomenon during the learning process of a recurrent neural network called long-short-term memory (LSTM) networks during its training on real…
Spatial-aware Vision Language Model for Autonomous Driving
Weijie Wei, Zhipeng Luo, Ling Feng +1
While Vision-Language Models (VLMs) show significant promise for end-to-end autonomous driving by leveraging the common sense embedded in language models, their reliance on 2D imag…
DeepLogit: A sequentially constrained explainable deep learning modeling approach for transport policy analysis
Jeremy Oon, Rakhi Manohar Mepparambath, Ling Feng
Despite the significant progress of deep learning models in multitude of applications, their adaption in planning and policy related areas remains challenging due to the black-box…
Education distillation:getting student models to learn in shcools
Ling Feng, Tianhao Wu, Xiangrong Ren +2
This paper introduces a new knowledge distillation method, called education distillation (ED), which is inspired by the structured and progressive nature of human learning. ED mimi…
Bezier Distillation
Ling Feng, SK Yang
In Rectified Flow, by obtaining the rectified flow several times, the mapping relationship between distributions can be distilled into a neural network, and the target distribution…