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Found 10 papers, 0 papers with code
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences.
Reinforced UI Instruction Grounding: Towards a Generic UI Task Automation API
In this work, we build a multimodal model to ground natural language instructions in given UI screenshots as a generic UI task automation executor.
Intestinal Microecology in Pediatric Surgery-Related Gastrointestinal Diseases Current Insights and Future Perspectives
We review the relationship between pathogenesis, diagnosis and treatment of pediatric surgery-related gastrointestinal diseases and intestinal microecology, in order to provide new ideas and methods for clinical diagnosis, treatment and research.
Responsible Task Automation: Empowering Large Language Models as Responsible Task Automators
In specific, we present Responsible Task Automation (ResponsibleTA) as a fundamental framework to facilitate responsible collaboration between LLM-based coordinators and executors for task automation with three empowered capabilities: 1) predicting the feasibility of the commands for executors; 2) verifying the completeness of executors; 3) enhancing the security (e. g., the protection of users' privacy).
Unifying Layout Generation with a Decoupled Diffusion Model
Since different attributes have their individual semantics and characteristics, we propose to decouple the diffusion processes for them to improve the diversity of training samples and learn the reverse process jointly to exploit global-scope contexts for facilitating generation.
Understanding Mobile GUI: from Pixel-Words to Screen-Sentences
A dataset, RICO-PW, of screenshots with Pixel-Words annotations is built based on the public RICO dataset, which will be released to help to address the lack of high-quality training data in this area.
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?
However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks.
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?
However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks.
Realistic Large-Scale Fine-Depth Dehazing Dataset from 3D Videos
We select a large number of high-quality frames of real outdoor scenes and render haze on them using depth from stereo.
Tracing State-Level Obesity Prevalence from Sentence Embeddings of Tweets: A Feasibility Study
Twitter data has been shown broadly applicable for public health surveillance.
