Covers the basics of reinforcement learning, including Markov Decision Processes and policy gradient methods, and explores real-world applications and recent advances.
Explores the evolution and frequency analysis of Operational Transconductance Amplifiers, including stability considerations and the impact of feedback.
Explores communicating classes in Markov chains, distinguishing between transient and recurrent classes, and delves into the properties of these classes.
Delves into the geometric insights of deep learning models, exploring their vulnerability to perturbations and the importance of robustness and interpretability.
By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Examines the interplay between sound, architecture, and human evolution, highlighting their interconnectedness in shaping cultural and social dynamics.