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Philosophy, Science and Religion: Religion and Science
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Explore natural language instruction following for robots and collaborative scenarios, featuring new datasets, interpretable models, and learning methods combining supervised and reinforcement approaches.
Explore recent progress in general-purpose language understanding models through GLUE and SuperGLUE benchmarks. Discuss evaluation techniques and future directions in NLP research.
Explore submodular maximization techniques, recent advances, and open questions in combinatorial optimization with Professor Niv Buchbinder's comprehensive theory seminar.
Face numbers in centrally symmetric polytopes and spheres: exploring upper bounds, recent developments, and open problems in combinatorial geometry. Insights into maximum face counts and structural properties.
Explore algorithms and hardness for linear algebra on geometric graphs, focusing on efficient spectral graph theory and its limitations in high-dimensional spaces.
Embedding social impact awareness in CS education: integrating ethics, career reflection, and professional development into technical courses at UC Berkeley.
Explainable AI techniques for human-robot collaboration, focusing on shared expectations, interpretable models, and safe task planning to create trustworthy autonomous teammates in various domains.
Explore cognitive architectures and knowledge representation for advanced robot manipulation, focusing on symbolic reasoning, mental simulation, and real-world applications in autonomous systems.
Explore NVIDIA's AI-driven robotics research, focusing on robot manipulation, physics simulation, and perception. Discover breakthroughs in manufacturing, logistics, and healthcare industries.
Explore drone interactions, user responses to autonomy failures, and communication strategies in public spaces. Insights on novice interactions and applications in various fields.
Explore uncertainty in perception for control systems, covering model-free algorithms, pixel-based control, and machine learning applications in robotics and autonomous systems.
Explore advancements in online linear programming and learning, focusing on resource allocation, competitive ratios, and dynamic learning applications in control systems.
Explore advanced techniques in reinforcement learning, focusing on randomized value functions and their applications in exploration strategies for complex decision-making problems.
Explore batch/counterfactual reinforcement learning techniques to optimize decision-making with minimal data, focusing on policy evaluation, generalization bounds, and applications in healthcare simulations.
Explore advanced Gaussian kernel factorization techniques and their applications in machine learning and control systems, with insights from approximation theory.
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