Explores levels of analysis in social psychology and critical thinking, emphasizing cognitive dissonance and the importance of skepticism in decision-making.
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.
Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Explores the influence of computational linguistics on deep learning architectures, covering grammar formalisms, connectionism, variable binding, and future directions.
