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Found 11 papers, 5 papers with code
Semantic Aligned Multi-modal Transformer for Vision-LanguageUnderstanding: A Preliminary Study on Visual QA
Recent vision-language understanding approaches adopt a multi-modal transformer pre-training and finetuning paradigm.
Large Language Models in Finance: A Survey
In this paper, we provide a practical survey focused on two key aspects of utilizing LLMs for financial tasks: existing solutions and guidance for adoption.
Instance segmentation based 6D pose estimation of industrial objects using point clouds for robotic bin-picking
3D object pose estimation for robotic grasping and manipulation is a crucial task in the manufacturing industry.
Cross-corpus Readability Compatibility Assessment for English Texts
(3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework.
Self-Supervised Image Representation Learning: Transcending Masking with Paired Image Overlay
Self-supervised learning has become a popular approach in recent years for its ability to learn meaningful representations without the need for data annotation.
DeceFL: A Principled Decentralized Federated Learning Framework
Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client.
Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding
While training an end-to-end navigation network in the real world is usually of high cost, simulation provides a safe and cheap environment in this training stage.
Machine Discovery of Partial Differential Equations from Spatiotemporal Data
The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data.
A General End-to-end Diagnosis Framework for Manufacturing Systems
A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications.
A deep learning-based remaining useful life prediction approach for bearings
In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs).
Data-driven Discovery of Cyber-Physical Systems
Cyber-physical systems (CPSs) embed software into the physical world.
