Market impact analysis via deep learned architectures
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3415-3
Abstract: How to deeply process market data sources and build systems to process accurate market impact analysis is an attractive problem. In this paper, we build up a system that exploits deep learning architecture to improve… read more here.
Keywords: market impact; deep learned; market; impact analysis ... See more keywords
Deep learned finite elements
Sign Up to like & getrecommendations! Published in 2020 at "Computer Methods in Applied Mechanics and Engineering"
DOI: 10.1016/j.cma.2020.113401
Abstract: Abstract In this paper, we propose a method that employs deep learning, an artificial intelligence technique, to generate stiffness matrices of finite elements. The proposed method is used to develop 4- and 8-node 2D solid… read more here.
Keywords: finite elements; learned finite; elements deep; deep learned ... See more keywords
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
Sign Up to like & getrecommendations! Published in 2025 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.5c01728
Abstract: Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions,… read more here.
Keywords: chemistry; multitask; data fusion; deep learned ... See more keywords
Deep-Learned Perceptual Quality Control for Intelligent Video Communication
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Consumer Electronics"
DOI: 10.1109/tce.2022.3206114
Abstract: With the development of video technology, a large amount of video data generated from video conferences, sports events, live broadcasts and network classes flows into our daily lives. However, ultra-high-definition video transmission is still a… read more here.
Keywords: deep learned; quality; learned perceptual; quality control ... See more keywords
Deep-Learned Regularization and Proximal Operator for Image Compressive Sensing
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2021.3088611
Abstract: Deep learning has recently been intensively studied in the context of image compressive sensing (CS) to discover and represent complicated image structures. These approaches, however, either suffer from nonflexibility for an arbitrary sampling ratio or… read more here.
Keywords: proximal operator; learned regularization; image; deep learned ... See more keywords
Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
Sign Up to like & getrecommendations! Published in 2025 at "Science Advances"
DOI: 10.1126/sciadv.aea8716
Abstract: Aerial insects exhibit agile maneuvers such as sharp braking, saccades, and body flips under disturbances; in contrast, insect-scale aerial robots are limited to tracking smooth trajectories with small acceleration. To achieve similar flight capabilities, insect-scale… read more here.
Keywords: aerial robots; insect scale; deep learned; tube model ... See more keywords