Generative Models: Advancements in Molecular Design

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Deep Generative Models in Drug Discovery

Explores the application of deep generative models in drug discovery, focusing on designing small molecules and optimizing molecular structures.

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Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Decentralized Optimization

Explores decentralized optimization in machine learning, emphasizing robustness, privacy, and fairness in collaborative learning.

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Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Car-Parrinello molecular dynamics

Explores Car-Parrinello molecular dynamics, a unified approach combining molecular dynamics and density-functional theory for simulating various systems, with a focus on historical background, technical details, and challenges in atomistic simulations.

Drug Discovery: Computational Approaches

Explores the EXSCALATE4COV project, focusing on computational drug discovery for COVID-19 treatments and the collaboration between academia and industry.

Optimization Techniques: Convexity in Machine Learning

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.