Features of both words and sentences will be aggregated to enhance the representation ability.

Our method's efficacy is demonstrated on the class-conditional ImageNet generation benchmark, where

Vision transformers have contributed greatly to advancements in the computer vision domain, demonstrating state-of-the-art performance in diverse tasks ( e.g.,

In this work, we study the performance of byte-level and subword-level autoregressive models when trained using a fixed compute budget.

Figure 1: Overview of the Transformer block used in the PromptIR framework. As mentioned in section 3.1.2 Bias-free convolutions are utilized within this submodule. After MDT A Module the features are processed through the GDFN module. Our method effectively removes haze to produce visually better images.

We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models and optimized data-efficiently for spoken language tasks.

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience.