Explorations in Exploration: Deep Learning Meets Value of Information for Sequential Experimental Design
Exploring machine learning's role in sequential experimental design, focusing on quantifying information value and improving flexibility in planning future experiments.
Calibration and Generalizability of Probabilistic Models on Low-Data Chemical Datasets
Explore calibration and generalizability of probabilistic models on small chemical datasets. Gain insights into model selection and feature choice for molecular property prediction and design.
Modeling Human Communication Dynamics - 2016
Explore computational models for analyzing and predicting human communication behaviors, with applications in healthcare, education, business, and social media.
A Stochastic Feedback Model for Image Retrieval
Explore a stochastic feedback model for image retrieval, presented by Donald Geman from Johns Hopkins University's Center for Language & Speech Processing.
Essentials of Math Modeling - Session 5 - Probabilistic Models
Explore probabilistic models, including Markov chains and Metropolis-Hastings algorithm, with practical exercises and problem-solving sessions using MATLAB for mathematical modeling applications.
Magical NumPy with JAX
Explore JAX's NumPy-compatible API for performant numerical modeling and gradient-based optimization. Learn functional programming, deterministic random number generation, and neural network integration.
Generative vs. Discriminative AI - A Comparative Analysis
Explore the key distinctions between generative and discriminative AI approaches, understanding their unique characteristics and applications.
Modeling Communicating Systems in a Functional Style: 30+ Years of Evolution
Explore the evolution of modeling communicating systems using functional programming over 30+ years, from telecoms protocols to probabilistic models and Bigraphs, with insights on future challenges.
Multi-Time Scale World Models
Explore multi-time scale world models for intelligent agents, focusing on probabilistic formalism, computational aspects, and action-conditional future predictions in complex environments.
Recent Progress on Grokking and Probabilistic Federated Learning
Explore recent advancements in grokking phenomenon and probabilistic federated learning. Gain insights into neural networks, Gaussian processes, and Bayesian inference for machine learning applications.
A View in Old and New Machine Learning
Explore old and new machine learning approaches, comparing hierarchical probabilistic models with deep neural networks and examining their unique capabilities and potential combinations.
Probabilistic Graphical Models and Mean Field Inference - Lecture 20
Dive into advanced data science concepts including local version inference, alternating updates, and mean field version updates for practical applications in graphic models and word distribution.
Heuristics for the Arithmetic of Elliptic Curves and Abelian Varieties
Explore probabilistic models for elliptic curve arithmetic, examining theoretical evidence and predictions for higher-dimensional abelian varieties.
Tutorial on Hidden Markov Models - Part 2
Explore advanced concepts of Hidden Markov Models, including applications and algorithms, in this in-depth continuation of Jason Eisner's tutorial series.
Integrating Evidence Over Time: Conditional Models for Speech and Audio Processing
Explore conditional random fields for speech recognition and auditory scene analysis, integrating local acoustic evidence over time sequences to improve event detection and classification.
Probabilistic Models of Relational Domains
Explore probabilistic models for relational domains with Stanford's Daphne Koller, covering advanced techniques for representing and reasoning about complex, interconnected data structures.
Mathematical Problems in Machine Learning - Lecture 3/4
Explore mathematical challenges in machine learning with Andrea Montanari, delving into advanced concepts and problem-solving techniques.
Gaussian Processes
Explore Gaussian Processes with Daniel Hernández-Lobato, covering key concepts, applications, and advanced techniques in probabilistic machine learning.
Introduction to Probabilistic Models - Probabilistic AI School
Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.
Introduction to Probabilistic Models - Lecture 1
Explore probabilistic models, their applications, and fundamental concepts in this comprehensive introduction by Silja Renooij at the Nordic Probabilistic AI School.
Diffusion Models - Lecture 18
Dive into the fundamentals of diffusion models with MIT Prof. Song Han, exploring their architecture, principles, and applications in machine learning and AI generation.