What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Algorithm
Pascal Frossard mostly deals with Artificial intelligence, Robustness, Algorithm, Theoretical computer science and Computer network.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
His Robustness research focuses on Curvature and how it relates to Classifier.
His Algorithm research integrates issues from Upper and lower bounds, Recurrence relation and Mathematical optimization.
He has researched Theoretical computer science in several fields, including Data modeling, Graph theory, Signal processing and Graph.
Pascal Frossard studied Contextual image classification and Convolutional neural network that intersect with Transformation geometry.
His most cited work include:
- The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains (2237 citations)
- DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks (2131 citations)
- Universal Adversarial Perturbations (1095 citations)
What are the main themes of his work throughout his whole career to date?
Pascal Frossard mainly investigates Artificial intelligence, Algorithm, Computer vision, Computer network and Decoding methods.
Pascal Frossard combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
His studies deal with areas such as Graph, Mathematical optimization and Theoretical computer science as well as Algorithm.
His study brings together the fields of Signal processing and Theoretical computer science.
The various areas that he examines in his Computer network study include Distributed computing, Overlay network and Video quality.
The Network packet study which covers Real-time computing that intersects with Scheduling.
He most often published in these fields:
- Artificial intelligence (34.71%)
- Algorithm (22.17%)
- Computer vision (18.96%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (34.71%)
- Graph (10.86%)
- Algorithm (22.17%)
In recent papers he was focusing on the following fields of study:
Pascal Frossard mostly deals with Artificial intelligence, Graph, Algorithm, Graph and Theoretical computer science.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Computer vision and Pattern recognition.
His Algorithm research includes themes of Pixel, Laplacian matrix and Signal processing.
Pascal Frossard works mostly in the field of Theoretical computer science, limiting it down to concerns involving Node and, occasionally, Feature vector.
His studies in Robustness integrate themes in fields like Contextual image classification, Deep learning, Curvature and Decision boundary.
His work carried out in the field of Artificial neural network brings together such families of science as Classifier and Adversarial system.
Between 2017 and 2021, his most popular works were:
- Graph Signal Processing: Overview, Challenges, and Applications (559 citations)
- Analysis of classifiers’ robustness to adversarial perturbations (174 citations)
- Learning Graphs From Data: A Signal Representation Perspective (135 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Algorithm
Pascal Frossard spends much of his time researching Artificial intelligence, Algorithm, Graph, Robustness and Theoretical computer science.
The Artificial intelligence study combines topics in areas such as Computer vision and Complex network.
His work deals with themes such as Depth map, Quadratic programming, Color image, Manifold and Transformation geometry, which intersect with Algorithm.
His research in the fields of Topological graph theory, Laplacian matrix and Graph neural networks overlaps with other disciplines such as Mathematical theory.
His Robustness research incorporates elements of Artificial neural network, Deep learning, Curvature and Decision boundary.
His Theoretical computer science research incorporates themes from Data modeling, Vertex, Linear combination and Dimensionality reduction.
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