What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Control theory, Artificial intelligence, Computer vision, Artificial neural network and Synchronization of chaos are his primary areas of study.
His Lyapunov function, Stability theory, Exponential stability and Nonlinear system study, which is part of a larger body of work in Control theory, is frequently linked to Secure communication, bridging the gap between disciplines.
His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Object and Representation.
His work deals with themes such as Anomaly detection, Representation and Feature learning, which intersect with Computer vision.
His work carried out in the field of Artificial neural network brings together such families of science as Periodic orbits, Statistical physics, Click-through rate and Bifurcation.
His most cited work include:
- Chaotic attractors in delayed neural networks (296 citations)
- On stability of nonlinear continuous-time neural networks with delays (117 citations)
- Chaotic behavior in first-order autonomous continuous-time systems with delay (108 citations)
What are the main themes of his work throughout his whole career to date?
Hongtao Lu mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and Image.
Hongtao Lu regularly links together related areas like Machine learning in his Artificial intelligence studies.
Hongtao Lu interconnects Contextual image classification, Facial recognition system and Feature in the investigation of issues within Pattern recognition.
His Computer vision research is multidisciplinary, relying on both Subspace topology and Robustness.
Hongtao Lu combines subjects such as Stability, Control theory, Exponential stability and Mathematical optimization with his study of Artificial neural network.
His Digital watermarking research includes themes of Theoretical computer science and Discrete cosine transform.
He most often published in these fields:
- Artificial intelligence (61.69%)
- Pattern recognition (41.13%)
- Computer vision (21.37%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (61.69%)
- Pattern recognition (41.13%)
- Deep learning (7.26%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Computer vision and Machine learning.
His research in the fields of Image, Feature extraction, Artificial neural network and Leverage overlaps with other disciplines such as Simple.
His studies in Artificial neural network integrate themes in fields like Segmentation and Weighted network.
His Pattern recognition research incorporates elements of Identity, Face and Feature.
His Computational photography study in the realm of Computer vision connects with subjects such as Process.
Hongtao Lu usually deals with Machine learning and limits it to topics linked to Posterior probability and Sequence.
Between 2018 and 2021, his most popular works were:
- Rethinking on Multi-Stage Networks for Human Pose Estimation. (68 citations)
- Attribute-Driven Feature Disentangling and Temporal Aggregation for Video Person Re-Identification (51 citations)
- Look Back and Predict Forward in Image Captioning (32 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of study are Artificial intelligence, Deep learning, Image, Pattern recognition and Computer vision.
His study ties his expertise on Machine learning together with the subject of Artificial intelligence.
His work is dedicated to discovering how Deep learning, Feature are connected with Weighted network, Segmentation and Artificial neural network and other disciplines.
In Image, Hongtao Lu works on issues like Speech recognition, which are connected to Rule-based machine translation and Word.
His Pattern recognition research incorporates themes from Embedding, Texture transfer and Transformer.
His Computer vision study combines topics from a wide range of disciplines, such as Convolution and Leverage.
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