Introduction to Scilab for Beginners - Arrays, Conditional Statements, Loops, and Functions
Learn Scilab basics: mathematical operations, arrays, matrices, conditional statements, loops, and functions. Ideal for physics, math, and engineering students seeking numerical computation skills.
Master Data Analytics with ChatGPT, Python and Excel
Learn data analytics essentials using ChatGPT, Python, and Excel. Covers data handling, regression modeling, summary statistics, and univariate analysis with practical examples on a car MPG dataset.
NeuroMANCER - Differentiable Programming Library for Data-driven Modeling and Control
Explore NeuroMANCER: an open-source library for integrating machine learning with scientific computing, focusing on parametric optimization, system identification, and optimal control.
A Connection between Probability, Physics and Neural Networks
Explore the intersection of probability, physics, and neural networks, focusing on constructing physics-consistent models using Gaussian processes and the central limit theorem.
PirateNets - Physics-Informed Deep Learning with Residual Adaptive Networks
Explore PirateNets: a novel deep learning architecture for solving PDE-governed problems, offering improved stability and efficiency over traditional physics-informed neural networks.
Kalman-Bucy Informed Neural Networks for System Identification
Combining physics-informed neural networks and Kalman filters to identify parameters in continuous-time systems with noisy measurements, improving model precision for complex systems.
Mathematical Assumptions in Cryptography
Explore cutting-edge mathematical assumptions in cryptography at this Crypto 2024 session, chaired by Takahiro Matsuda, featuring latest research and advancements in the field.
Polynomial Commitments and SNARKs
Explore advanced cryptographic concepts like polynomial commitments and SNARKs in this comprehensive session from Crypto 2024, chaired by Yupeng Zhang.
Isogenies and Elliptic Curves in Cryptography
Explore advanced cryptographic concepts focusing on isogenies and elliptic curves in this comprehensive session from Crypto 2024, chaired by Francisco Rodríguez-Henríquez.
The Geometry of the Deep Linear Network - Part 2
Explore the intricate geometry of deep linear networks, delving into advanced mathematical concepts and their applications in machine learning architectures.
The Platonic Representation Hypothesis
Explore the Platonic Representation Hypothesis with MIT's Phillip Isola, delving into philosophical concepts and their applications in modern computational theory.
Mathematical Structures in Generative Linguistics
Explore mathematical structures in generative linguistics, from formal languages to Hopf algebras and geometric spaces for modeling semantics and syntactic variation across languages.
Geodesic Framework for Vortex Sheets and Generalized Fluid Flows
Explore geodesic frameworks for vortex sheets and generalized fluid flows, examining group-theoretic approaches to ideal hydrodynamics and Lie groupoid symmetries in mathematical physics.
Renormalization Group of the Inviscid Limit
Explore the renormalization group dynamics in multi-scale systems, focusing on hydrodynamic turbulence and the inviscid limit. Gain insights into RG operators, fixed-point attractors, and chaotic dynamics.
Exponential Networks for Linear Partitions
Explore exponential networks and their applications in linear partitioning, uncovering advanced mathematical concepts and computational techniques.
Learning Linear Models In-Context with Transformers
Explore in-context learning of linear models using transformers with UC Davis professor Spencer Frei in this insightful seminar.
AlphaGeometry: Solving IMO Geometry Problems without Human Demonstrations
Explore AlphaGeometry, a groundbreaking theorem prover for Euclidean plane geometry that outperforms previous methods and approaches IMO gold medallist level, solving complex problems without human demonstrations.
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.
Optimal Transport for Distributionally Robust Optimization and Applications in Machine Learning
Explore optimal transport in distributionally robust optimization, focusing on its applications in machine learning, distribution shifts, and responsible AI. Gain insights into solving challenging optimization problems.
Anomalous Skin Effects in Disordered Systems with a Single Non-Hermitian Impurity
Explore anomalous skin effects in disordered systems with non-Hermitian impurities. Discover scale-free localization, boundary accumulation, and potential applications in sensing and control of non-Hermitian systems.
The Complexity of Bohmian Positron Dynamics Inside a Chiral Carbon Nanotube
Explore Bohmian positron dynamics in chiral carbon nanotubes, analyzing classical and quantum treatments, phase singularities, and complex trajectory behaviors.