Uncertainty Quantification and Label Error Detection

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Explores projects and challenges in visual intelligence, including hands-on learning and analysis of vision methods.

Emphasizes the need for independent channels when detecting objects in images.

Explores modern approaches and challenges in acquiring data for learning optimal controllers through demonstrations and data-driven methods.

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Delves into choosing a proper feature size for image analysis in life sciences, presenting a Rule Of Thumb for defining object size in pixels.

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.

Covers the basics of computer vision, focusing on image processing techniques and feature detection.

Covers the impact of transformers in computer vision, discussing their architecture, applications, and advancements in various tasks.

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Introduces the LOAFS structure and emphasizes the importance of active learning.