What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Convolutional neural network and Segmentation.
Artificial intelligence is closely attributed to Machine learning in his study.
The various areas that he examines in his Pattern recognition study include Contextual image classification, Pose, Edge detection and Reproducing kernel Hilbert space.
His Contextual image classification research is multidisciplinary, relying on both Artificial neural network and Feature.
His studies deal with areas such as Perception and Pattern recognition as well as Computer vision.
Alan L. Yuille focuses mostly in the field of Convolutional neural network, narrowing it down to topics relating to Graphical model and, in certain cases, CRFS.
His most cited work include:
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs (6128 citations)
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs (2202 citations)
- Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation (1812 citations)
What are the main themes of his work throughout his whole career to date?
Alan L. Yuille focuses on Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Machine learning.
His Artificial intelligence study is mostly concerned with Convolutional neural network, Image segmentation, Object, Object detection and Image.
His Pattern recognition research integrates issues from Contextual image classification, Artificial neural network, Pascal and Robustness.
Computer vision is frequently linked to Algorithm in his study.
His research in Segmentation is mostly focused on Scale-space segmentation.
His Machine learning study combines topics from a wide range of disciplines, such as Adversarial system, Training set, Inference and Bayesian inference.
He most often published in these fields:
- Artificial intelligence (77.24%)
- Pattern recognition (36.32%)
- Computer vision (25.67%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (77.24%)
- Pattern recognition (36.32%)
- Segmentation (22.15%)
In recent papers he was focusing on the following fields of study:
Alan L. Yuille mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Convolutional neural network and Deep learning.
Alan L. Yuille interconnects Machine learning and Computer vision in the investigation of issues within Artificial intelligence.
His Computer vision study combines topics in areas such as Perspective, Representation and Inference.
His research in Pattern recognition intersects with topics in Feature, Margin and Contextual image classification, Image, Real image.
He has included themes like Convolution, Minimum bounding box, Voxel and Code in his Segmentation study.
His biological study spans a wide range of topics, including Cognitive neuroscience of visual object recognition and Feature learning.
Between 2018 and 2021, his most popular works were:
- Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation (445 citations)
- Feature Denoising for Improving Adversarial Robustness (308 citations)
- Improving Transferability of Adversarial Examples With Input Diversity (175 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Artificial intelligence, Pattern recognition, Segmentation, Deep learning and Robustness are his primary areas of study.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Computer vision.
Alan L. Yuille combines subjects such as Context model and Detector with his study of Computer vision.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Contextual image classification, Object, Normalization and Feature.
His Segmentation research includes elements of Transformer, Pancreatic ductal adenocarcinoma, Radiology, Algorithm and Convolution.
His Deep learning research includes themes of Network architecture, Contrast, Abdomen, Categorization and Question answering.
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