❤ مجموعه جلسات «گذر»

💠عنوان:
"Probabilistic Programming for Machine Learning"

🎙 ارائه‌دهنده:
امیرعباس اسدی

🔻توضیحات:
Bayesian Learning provides a natural framework for approaching Machine Learning problems. For a long time, due to the significant computational cost of Bayesian inference, this framework was limited to simple models and problems with a small amount of data. Probabilistic Programming is the fruit of many years of research in approximate Bayesian inference aiming to address these limitations. This presentation is a friendly introduction to Probabilistic Programming. We will explore how modern inference methods and recent advances in Differentiable Programming can help us unlock the full potential of Bayesian Machine Learning.

Presentation outline:
- Bayesian Learning and Probabilistic Programs
- Probabilistic Programming in Julia
- Approximate Bayesian Inference
-- Markov Chain Monte Carlo
-- Variational Inference
- Differentiable Programming
- Discussing some examples:
-- Bayesian Deep Learning
-- Bayesian Neural Differential Equations
-- Inverse Optimization

پیشنیاز های علمی:  آمار و احتمال مقدماتی، آشنایی با Deep Learning



🌐 فرم ثبت‌نام

⏲ مهلت ثبت‌نام : ۱۵ مهر
🗓 زمان: چهارشنبه ۱۸ مهر - ساعت ۱۶:۰۰
📍مکان: به صورت هیبرید - کلاس ۱۰۹ دانشکده ریاضی


🚀 @Gozar_SUT ❤
🚀 @hamband_sut ❤

❤ انجمن علمی دانشکده علوم‌ریاضی دانشگاه صنعتی شریف (همبند) برگزار می‌کند:

❤ مجموعه جلسات «گذر»

💠 عنوان:
How to Count with Polynomials?

🎙 ارائه‌دهنده:
میلاد برزگر، محقق پسادکتری پژوهشگاه دانش‌های بنیادی (IPM)

🔻 توضیحات:
Counting perfect matchings in bipartite graphs is a fundamental problem in theoretical computer science (TCS) and combinatorial optimization. In TCS, the goal is to find (approximation) algorithms, and in combinatorics, the aim is to bound the number of perfect matchings in a specific class of graphs. In this talk, I will focus on regular bipartite graphs and discuss (1) deterministic approximation algorithms for the number of perfect matchings in these graphs, and (2) the Schrijver-Valiant conjecture, which determines the minimum number of perfect matchings in d-regular bipartite graphs of a given size. This conjecture was proposed by Schrijver and Valiant in 1980 and resolved by Schrijver in 1998. Schrijver’s proof is considered to be one of the most complicated and least understood arguments in graph theory!

One of the high points of matching counting (!) is Leonid Gurvits’ ingenious work in the early 2000s. He came up with a neat elementary argument for both (1) and (2). In fact, he created a machinery known as the “capacity method” that has since found many more applications. Gurvits’ approach is based on the “geometry of polynomials,” which is the study of the analytic properties of (multivariate) polynomials with complex or real coefficients. This work kick started a new trend known as the “polynomial paradigm.” Over the last two decades, people have used tools from the geometry of polynomials to solve a number of notorious open problems in mathematics and TCS. In this talk, I will go through Gurvits’ argument and the consequences of his ideas. In particular, I will try to highlight the importance of the notion of “capacity” and its applications in counting and optimization.

پیشنیاز های علمی: آشنایی با جبرخطی و احتمال

📍 امکان شرکت حضوری در این رویداد برای دانشجویان غیرشریفی نیز مهیا ست.

🌐 فرم ثبت‌نام

⏲ مهلت ثبت‌نام : ۵ آبان‌ماه
🗓 زمان: سه‌شنبه ۸ آبان‌ماه - ساعت ۱۵:۰۰
📍مکان: به صورت حضوری _ کلاس ۱۰۹ دانشکده ریاضی

🚀 @Gozar_SUT ❤
🚀 @hamband_sut ❤