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Lecture 2 from UW-Madison's Summer 2022 iteration of CS 577: Introduction to Algorithms. This lecture presented the technique ...

Notes: https://drive.google.com/drive/folders/1gdXBCpHF6hGWMjLxu_bPg01J7NyYLle6?usp=drive_link.

Please consider supporting us on Patreon if you enjoy our content: https://www.patreon.com/thesyntheticmind What's the best way ...

Function Approximation, Probabilistic View Point, Deterministic View Point.

This video serves as a gentle introduction to information theory geared towards quants who have little to no familiarity with the ...

The striking resemblance between the behaviour of pseudo-algebraically closed, pseudo real closed and pseudo p-adically fields ...

Title: Faster Combinatorial Algorithms for Bipartite Matching Abstract: Maximum bipartite matching is among the most fundamental ...

In this video we use a simple example of a stochastic optimization problem and show how to solve it using the incremental ...

Recording from the University of Maryland, College Park's Multi-Agent Reinforcement Learning Seminar as presented in March of ...

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Thanks for watching! Timestamps: 0:00 Casas-Alvero conjecture 2:41 Riemann hypothesis 5:08 Navier–Stokes existence and ...

I will overview several practical tricks for handling (interpolating) functions efficiently, and discuss how they can help your ...

Authors: Jason D. Hartline, Aleck Johnsen, Anant Shah Full paper on Arxiv: https://arxiv.org/abs/2403.10451.

Computational Gas Dynamics Course, HIT – Lecture #10, November 7, 2025 ...

A flag is a sequence of nested linear subspaces of increasing dimension. It can also be defined as a sequence of ...

Time: October 1, 2025 Speaker: Jiawei Zhang (UW-Madison) Abstract: It is well known that for nonconvex unconstrained ...

All teaching material is available at: [github](https://github.com/bengsV/OptML) This video is the third exercise session for the ...

We get back to K-means clustering algorithm. This time we define the underlying learning problem through risk formulation.

We see that NNs are universal approximators, i.e., they can approximate any complicated function as accurate as we want if they ...

Numerical mismatch between training and inference is an important but often overlooked issue in large language model ...