What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
His main research concerns Artificial intelligence, Computer vision, Facial recognition system, Pattern recognition and Machine learning.
His study in Image segmentation, Segmentation, Feature learning, Contextual image classification and Boosting is carried out as part of his studies in Artificial intelligence.
His Segmentation research includes elements of Structured prediction and Pascal.
His studies in Facial recognition system integrate themes in fields like Classifier, Support vector machine and Word error rate.
His research integrates issues of Divergence and Face in his study of Pattern recognition.
The concepts of his Machine learning study are interwoven with issues in Visualization, Training set and Similarity.
His most cited work include:
- Learning Representations for Automatic Colorization (410 citations)
- Feedforward semantic segmentation with zoom-out features (353 citations)
- Nearest-neighbor methods in learning and vision : theory and practice (333 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Algorithm.
Gregory Shakhnarovich performs integrative Artificial intelligence and Set research in his work.
His Pattern recognition research incorporates elements of Object detection, Representation and Pose.
His research in Computer vision intersects with topics in Path and Benchmark.
His work on Autoencoder and k-nearest neighbors algorithm as part of general Machine learning study is frequently linked to Network architecture, therefore connecting diverse disciplines of science.
His Algorithm research is multidisciplinary, incorporating perspectives in Classifier and Image, Similarity.
He most often published in these fields:
- Artificial intelligence (77.53%)
- Pattern recognition (35.96%)
- Computer vision (28.09%)
What were the highlights of his more recent work (between 2017-2020)?
- Artificial intelligence (77.53%)
- Image (8.99%)
- Algorithm (13.48%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Image, Algorithm, Machine learning and Natural language processing.
His Computer vision research extends to the thematically linked field of Artificial intelligence.
His research in the fields of Motion overlaps with other disciplines such as Frame.
His Machine learning study combines topics from a wide range of disciplines, such as Segmentation and Decoding methods.
The study incorporates disciplines such as Semantics, External image, Speech processing and Visualization in addition to Natural language processing.
His work carried out in the field of Regularization brings together such families of science as Image segmentation and Convolutional neural network.
Between 2017 and 2020, his most popular works were:
- Recurrent Back-Projection Network for Video Super-Resolution (99 citations)
- Discriminability Objective for Training Descriptive Captions (80 citations)
- Discriminability objective for training descriptive captions (57 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Gregory Shakhnarovich focuses on Artificial intelligence, Image, Component, Discriminative model and Property.
Artificial intelligence is closely attributed to Computer vision in his research.
The various areas that Gregory Shakhnarovich examines in his Image study include Algorithm and Control.
His Component study overlaps with BLEU, Natural language processing, Closed captioning and Visualization.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
