7 papers
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 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…
DPBloomfilter: Securing Bloom Filters with Differential Privacy
Yekun Ke, Yingyu Liang, Zhizhou Sha +3
The Bloom filter is a simple yet space-efficient probabilistic data structure that supports membership queries for dramatically large datasets. It is widely utilized and implemente…
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…
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…
Circuit Complexity Bounds for Visual Autoregressive Model
Yekun Ke, Xiaoyu Li, Yingyu Liang +2
Understanding the expressive ability of a specific model is essential for grasping its capacity limitations. Recently, several studies have established circuit complexity bounds fo…