What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Elisa Ricci focuses on Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Feature.
Her work in the fields of Artificial intelligence, such as Motion, Support vector machine and Noise reduction, overlaps with other areas such as Multi-task learning and Event.
Her Pattern recognition study combines topics from a wide range of disciplines, such as Deep learning and Data mining.
Her Computer vision research is multidisciplinary, relying on both Proxemics, Generator and Extraversion and introversion.
Her study in the field of Bayesian multivariate linear regression, Transduction and Transfer of learning also crosses realms of A priori and a posteriori.
The Feature study combines topics in areas such as Embedding, Adaptation and Visualization.
Her most cited work include:
- Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification (581 citations)
- Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (245 citations)
- Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation (241 citations)
What are the main themes of her work throughout her whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Computer vision and Deep learning.
Her Artificial intelligence and Support vector machine, Segmentation, Benchmark, Feature and Object investigations all form part of her Artificial intelligence research activities.
Elisa Ricci combines subjects such as Noise reduction and Adaptation with her study of Feature.
Her work on Transfer of learning, Data stream mining and Stability as part of general Machine learning research is frequently linked to Multi-task learning and Key, bridging the gap between disciplines.
Her work in Pattern recognition tackles topics such as Image which are related to areas like State and Representation.
Her Computer vision study incorporates themes from Generator, Facial expression and Pattern recognition.
She most often published in these fields:
- Artificial intelligence (98.19%)
- Machine learning (38.55%)
- Pattern recognition (32.53%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (98.19%)
- Pattern recognition (32.53%)
- Machine learning (38.55%)
In recent papers she was focusing on the following fields of study:
Elisa Ricci spends much of her time researching Artificial intelligence, Pattern recognition, Machine learning, Segmentation and Object.
Elisa Ricci integrates Artificial intelligence with Overhead in her study.
The various areas that she examines in her Pattern recognition study include Pixel, Lasso, Feature and Visualization.
When carried out as part of a general Machine learning research project, her work on Continual learning, Data stream mining, Regularization and Stability is frequently linked to work in Key, therefore connecting diverse disciplines of study.
In Object, Elisa Ricci works on issues like Motion, which are connected to Supervised learning, Generator, Representation and Animation.
Her work on Inpainting as part of general Computer vision research is frequently linked to Block, thereby connecting diverse disciplines of science.
Between 2019 and 2021, her most popular works were:
- Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (92 citations)
- Towards Recognizing Unseen Categories in Unseen Domains (9 citations)
- Towards Recognizing Unseen Categories in Unseen Domains (9 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Artificial intelligence, Pattern recognition, Machine learning, Benchmark and Object detection are her primary areas of study.
Elisa Ricci specializes in Artificial intelligence, namely Image.
Her work carried out in the field of Image brings together such families of science as Classifier, Pixel, Representation and Pattern recognition.
Elisa Ricci interconnects Feature and Kernel in the investigation of issues within Pattern recognition.
In most of her Object detection studies, her work intersects topics such as Monocular.
Her study in Object is interdisciplinary in nature, drawing from both Visual appearance, Stereo camera and Set.
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