Deriving the latent state and belief state view through Bayesian filtering. Prediction vs correction as distinct operations, and why the Kalman filter is exact Bayesian filtering under linear-Gaussian assumptions.
We don't need a model, we need a state. Exploring belief states, sufficient statistics, and why end-to-end model-free RL struggles with partial observability.
Build log for constructing a world model from first principles. Connecting Bayesian filtering, latent dynamical systems, ELBO, imagination rollouts, and control.
From isolated models to measured systems. How my research approach shifted from optimizing for local novelty to building ideas that survive contact with real system constraints.
An annotated breakdown of Horace He's deep dive into determinism in language models, with side quests into kernels, atomic adds, and batch invariance.
Exploring how game theory principles apply to autonomous vehicle decision-making and negotiation strategies.
Understanding NP-hard problems and practical approaches to tackle computationally intractable challenges.
Exploring the connections between functions, variables, and matrix completion techniques in mathematical modeling.
Exploring the complexities and challenges in multi-modal image fusion, discussing why this seemingly straightforward task requires careful consideration and advanced techniques.
A deep dive into the world of tokenizers, exploring their importance in natural language processing and how they shape the way machines understand human language.
Understanding how Principal Component Analysis (PCA) can be applied to image recognition tasks, exploring dimensionality reduction and feature extraction techniques.
Exploring the mathematical foundations of the PageRank algorithm through the lens of linear algebra, understanding how it revolutionized web search.