What is he best known for?
The fields of study he is best known for:
- Machine learning
- Artificial intelligence
- Artificial neural network
Brian Kulis mainly focuses on Artificial intelligence, Pattern recognition, Machine learning, Theoretical computer science and Locality-sensitive hashing.
Brian Kulis combines Artificial intelligence and Metric in his studies.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Feature, Kernel and Cluster analysis, Fuzzy clustering.
His Machine learning course of study focuses on Cognitive neuroscience of visual object recognition and Visualization, Kernel and Object model.
His Theoretical computer science study combines topics from a wide range of disciplines, such as CURE data clustering algorithm, Constrained clustering, Algorithm, Clustering coefficient and Graph kernel.
His Locality-sensitive hashing research is multidisciplinary, incorporating elements of Feature hashing and Universal hashing.
His most cited work include:
- Information-theoretic metric learning (1617 citations)
- Adapting visual category models to new domains (1539 citations)
- Kernel k-means: spectral clustering and normalized cuts (927 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Cluster analysis, Machine learning, Algorithm and Metric.
His Artificial intelligence study combines topics in areas such as Theoretical computer science and Pattern recognition.
His research investigates the connection between Pattern recognition and topics such as Cognitive neuroscience of visual object recognition that intersect with problems in Support vector machine.
His Cluster analysis research includes themes of Mixture model, Mathematical optimization and Applied mathematics.
His work deals with themes such as Constrained clustering and Canopy clustering algorithm, which intersect with Data stream clustering.
His Locality-sensitive hashing research integrates issues from Feature hashing and Universal hashing.
He most often published in these fields:
- Artificial intelligence (46.25%)
- Cluster analysis (32.50%)
- Machine learning (25.00%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (46.25%)
- Metric (17.50%)
- Artificial neural network (10.00%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Metric, Artificial neural network, Cluster analysis and Applied mathematics.
His Artificial intelligence study integrates concerns from other disciplines, such as Computer vision and Natural language processing.
Artificial neural network is a subfield of Machine learning that Brian Kulis investigates.
His studies deal with areas such as Bregman divergence, Probabilistic logic, Linear separability and Markov chain as well as Cluster analysis.
His Applied mathematics research focuses on Generating function and how it relates to Kullback–Leibler divergence and Mahalanobis distance.
His studies in Deep learning integrate themes in fields like Theoretical computer science and Kernel.
Between 2018 and 2021, his most popular works were:
- Deep Metric Learning to Rank (75 citations)
- Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses (14 citations)
- Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses (7 citations)
In his most recent research, the most cited papers focused on:
- Machine learning
- Artificial intelligence
- Artificial neural network
Brian Kulis focuses on Artificial intelligence, Artificial neural network, Robustness, Machine learning and Natural language processing.
Brian Kulis interconnects Computer vision and Set in the investigation of issues within Artificial intelligence.
Brian Kulis has included themes like Image, Deep learning, State and Heuristic in his Artificial neural network study.
His Robustness study improves the overall literature in Control theory.
He performs multidisciplinary study in the fields of Machine learning and Metric via his papers.
His research ties Word and Natural language processing together.
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