Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

The recent boom of linear forecasting models questions the ongoing passion for
architectural modifications of Transformer-based forecasters. These forecasters leverage …

Time series analysis is of immense importance in extensive applications, such as weather
forecasting, anomaly detection, and action recognition. This paper focuses on temporal …

Spatiotemporal predictive learning aims to generate future frames by learning from historical
frames. In this paper, we investigate existing methods and present a general framework of …

Recently, Transformer-based and MLP-based models have emerged rapidly and won
dominance in time series analysis. In contrast, convolution is losing steam in time series …

Since its inception in" Attention Is All You Need", transformer architecture has led to
revolutionary advancements in NLP. The attention layer within the transformer admits a …

In the realm of time series forecasting (TSF), it is imperative for models to adeptly discern
and distill hidden patterns within historical time series data to forecast future states …

Integrating whole-slide images (WSIs) and bulk transcriptomics for predicting patient survival
can improve our understanding of patient prognosis. However this multimodal task is …

While recent Transformer-based approaches have shown impressive performances on
event-based object detection tasks their high computational costs still diminish the low …

Transformers have empowered many milestones across various fields and have recently
been applied to solve partial differential equations (PDEs). However, since PDEs are …