Best AI papers explained
Ein Podcast von Enoch H. Kang
512 Folgen
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Reliable Statistical Inference with Synthetic Data from Large Language Models
Vom: 11.7.2025 -
Multi-Turn Reinforcement Learning from Human Preference Feedback
Vom: 10.7.2025 -
Provably Learning from Language Feedback
Vom: 9.7.2025 -
Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners
Vom: 5.7.2025 -
Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation
Vom: 5.7.2025 -
Causal Abstraction with Lossy Representations
Vom: 4.7.2025 -
The Winner's Curse in Data-Driven Decisions
Vom: 4.7.2025 -
Embodied AI Agents: Modeling the World
Vom: 4.7.2025 -
Beyond Statistical Learning: Exact Learning Is Essential for General Intelligence
Vom: 4.7.2025 -
What Has a Foundation Model Found? Inductive Bias Reveals World Models
Vom: 4.7.2025 -
Language Bottleneck Models: A Framework for Interpretable Knowledge Tracing and Beyond
Vom: 3.7.2025 -
Learning to Explore: An In-Context Learning Approach for Pure Exploration
Vom: 3.7.2025 -
Human-AI Matching: The Limits of Algorithmic Search
Vom: 25.6.2025 -
Uncertainty Quantification Needs Reassessment for Large-language Model Agents
Vom: 25.6.2025 -
Bayesian Meta-Reasoning for Robust LLM Generalization
Vom: 25.6.2025 -
General Intelligence Requires Reward-based Pretraining
Vom: 25.6.2025 -
Deep Learning is Not So Mysterious or Different
Vom: 25.6.2025 -
AI Agents Need Authenticated Delegation
Vom: 25.6.2025 -
Probabilistic Modelling is Sufficient for Causal Inference
Vom: 25.6.2025 -
Not All Explanations for Deep Learning Phenomena Are Equally Valuable
Vom: 25.6.2025
Cut through the noise. We curate and break down the most important AI papers so you don’t have to.
