2 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Visual Autoregressive Transformers Must Use Memory
Yang Cao, Xiaoyu Li, Yekun Ke +3
A fundamental challenge in Visual Autoregressive models is the substantial memory overhead required during inference to store previously generated representations. Despite various…
On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis
Yekun Ke, Xiaoyu Li, Yingyu Liang +3
Recently, Visual Autoregressive () Models introduced a groundbreaking advancement in the field of image generation, offering a scalable approach through a coarse-to-f…
On Computational Limits of FlowAR Models: Expressivity and Efficiency
Yang Cao, Chengyue Gong, Yekun Ke +5
The expressive power and computational complexity of deep visual generative models, such as flow-based and autoregressive (AR) models, have gained considerable interest for their w…
Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
Yekun Ke, Yingyu Liang, Zhenmei Shi +2
The application of transformer-based models on time series forecasting (TSF) tasks has long been popular to study. However, many of these works fail to beat the simple linear resid…
Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
Yekun Ke, Xiaoyu Li, Yingyu Liang +2
Recent empirical studies have identified fixed point iteration phenomena in deep neural networks, where the hidden state tends to stabilize after several layers, showing minimal ch…