Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

Modern detection transformers (DETRs) use a set of object queries to predict a list of
bounding boxes, sort them by their classification confidence scores, and select the top …

Open-domain textual question answering (QA), which aims to answer questions from large
data sources like Wikipedia or the web, has gained wide attention in recent years. Recent …

In this study, we introduce adaptive KV cache compression, a plug-and-play method that
reduces the memory footprint of generative inference for Large Language Models (LLMs) …

This paper identifies significant redundancy in the query-key interactions within self-attention
mechanisms of diffusion transformer models, particularly during the early stages of …

Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Oriented object detection, an emerging task in recent years, aims to identify and locate
objects across varied orientations. This requires the detector to accurately capture the …

Transformer architecture has become the de-facto model for many machine learning tasks
from natural language processing and computer vision. As such, improving its computational …

One unintended consequence of the Electronic Health Records (EHR) implementation is the
overuse of content-importing technology, such as copy-and-paste, that creates “bloated” …