Before applying data analytics or machine learning to a data set, a vita...
The immense evolution in Large Language Models (LLMs) has underscored th...
Large language models (LLMs) have recently demonstrated remarkable
capab...
LLMs have demonstrated great capabilities in various NLP tasks. Differen...
Large vision-language models (LVLMs) have recently witnessed rapid
advan...
Large language models (LLMs) have emerged as a new paradigm for Text-to-...
We introduce the Qwen-VL series, a set of large-scale vision-language mo...
Foundation language models obtain the instruction-following ability thro...
Medical image analysis using deep learning is often challenged by limite...
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in whi...
Lung cancer is a leading cause of death worldwide and early screening is...
Estimating displacement vector field via a cost volume computed in the
f...
With the rapid evolution of large language models (LLMs), there is a gro...
Liver tumor segmentation and classification are important tasks in compu...
Gastric cancer is the third leading cause of cancer-related mortality
wo...
Radiotherapists require accurate registration of MR/CT images to effecti...
The pursuit of controllability as a higher standard of visual content
cr...
As Federated Learning (FL) has gained increasing attention, it has becom...
In this work, we explore a scalable way for building a general represent...
In this paper, we present ChatPLUG, a Chinese open-domain dialogue syste...
Real-world graphs are constantly evolving, which demands updates of the
...
Real-world medical image segmentation has tremendous long-tailed complex...
Federated Learning (FL) aims to train high-quality models in collaborati...
Foundation models are pre-trained on massive data and transferred to
dow...
Human brains respond to semantic features of presented stimuli with diff...
Pancreatic cancer is one of the leading causes of cancer-related death.
...
Parameter-efficient transfer learning (PETL) based on large-scale pre-tr...
Recent large-scale generative models learned on big data are capable of
...
Recent years have witnessed a big convergence of language, vision, and
m...
Deep learning empowers the mainstream medical image segmentation methods...
Human readers or radiologists routinely perform full-body multi-organ
mu...
Generalist models, which are capable of performing diverse multi-modal t...
In this paper, we propose a novel multi-modal multi-task encoder-decoder...
Diffusion models, which learn to reverse a signal destruction process to...
This work presents two astonishing findings on neural networks learned f...
The tremendous success of CLIP (Radford et al., 2021) has promoted the
r...
On-device machine learning enables the lightweight deployment of
recomme...
Big data processing at the production scale presents a highly complex
en...
In the past few years, transformer-based pre-trained language models hav...
In data mining, estimating the number of distinct values (NDV) is a
fund...
Large-scale pretrained foundation models have been an emerging paradigm ...
Human can extrapolate well, generalize daily knowledge into unseen scena...
Industrial recommender systems have been growing increasingly complex, m...
The incredible development of federated learning (FL) has benefited vari...
Although remarkable progress has been made by the existing federated lea...
In this paper, we focus on the unsupervised Video Object Segmentation (V...
In this work, we pursue a unified paradigm for multimodal pretraining to...
Estimating the number of distinct values (NDV) in a column is useful for...
Influenced by the great success of deep learning via cloud computing and...
Recent expeditious developments in deep learning algorithms, distributed...