What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Zaid Harchaoui focuses on Artificial intelligence, Machine learning, Pattern recognition, Contextual image classification and Training set.
Artificial intelligence is often connected to Computer vision in his work.
Many of his research projects under Machine learning are closely connected to Network architecture with Network architecture, tying the diverse disciplines of science together.
Zaid Harchaoui studied Pattern recognition and Kernel that intersect with Convolutional neural network, MNIST database, Kernel and Invariant.
His research integrates issues of Embedding, Sequence, Hidden Markov model and Visualization in his study of Contextual image classification.
His Training set study integrates concerns from other disciplines, such as World Wide Web, Mobile device and Federated learning.
His most cited work include:
- DeepFlow: Large Displacement Optical Flow with Deep Matching (640 citations)
- Advances and Open Problems in Federated Learning (571 citations)
- EpicFlow: Edge-preserving interpolation of correspondences for optical flow (565 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Algorithm, Machine learning, Mathematical optimization and Pattern recognition.
Artificial intelligence and Computer vision are frequently intertwined in his study.
His work on Motion and Optical flow as part of general Computer vision study is frequently linked to Detector, bridging the gap between disciplines.
His work on Regularization as part of general Algorithm research is frequently linked to Gaussian, bridging the gap between disciplines.
His work on Coordinate descent and Dynamic programming as part of general Mathematical optimization research is frequently linked to Rate of convergence and Set, thereby connecting diverse disciplines of science.
Zaid Harchaoui works mostly in the field of Pattern recognition, limiting it down to topics relating to Kernel and, in certain cases, Scale-invariant feature transform and Image retrieval.
He most often published in these fields:
- Artificial intelligence (44.19%)
- Algorithm (27.91%)
- Machine learning (21.71%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (44.19%)
- Algorithm (27.91%)
- Hilbert space (4.65%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Algorithm, Hilbert space, Smoothness and Approximation error.
His studies in Artificial intelligence integrate themes in fields like Smoothing, Differentiable function, Machine learning and Pattern recognition.
His Smoothing research focuses on Function and how it connects with Training set.
His Algorithm study combines topics in areas such as Function space, Kernel, Functional decomposition, Elementary function and Spherical harmonics.
The Hilbert space study combines topics in areas such as Kernel, Power series, Eigenvalues and eigenvectors and Dot product.
His Smoothness study which covers Applied mathematics that intersects with Data point, Markov chain, Entropy, Finite state and Quadratic cost.
Between 2019 and 2021, his most popular works were:
- Advances and Open Problems in Federated Learning (28 citations)
- On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points (8 citations)
- Device Heterogeneity in Federated Learning: A Superquantile Approach. (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Zaid Harchaoui mostly deals with Stationary point, Parameterized complexity, Artificial intelligence, Machine learning and Training set.
His biological study spans a wide range of topics, including Algorithm, Quadratic equation and Exponential function.
Zaid Harchaoui interconnects Artificial neural network, Distribution and Linear model in the investigation of issues within Parameterized complexity.
His Supervised learning study in the realm of Artificial intelligence connects with subjects such as Risk measure.
His Machine learning research integrates issues from Smoothing, Differentiable function and Point estimation.
Zaid Harchaoui combines subjects such as World Wide Web, Mobile device and Federated learning with his study of Training set.
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