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Upcoming Seminars
A Data-Driven Framework for Flood Mitigation Using Transformers and Reinforcement Learning
Speaker: Shaoping Xiao, Department of Mechanical Engineering
Colloquium - A Hessian View of Fine-tuning, Task Attribution, and Reinforcement Learning: Three Vignettes in Modern Machine Learning
Past Seminars
Colloquium - Programming Languages Techniques for Controlling Generalization Errors in Adaptive Data Analysis
Marco Gaboardi (Boston University)
AbstractData analysts aim at guaranteeing that the result of a data analysis run on sample data does not differ too much from the result one would achieve by running the analysis over the entire population. To achieve this goal, they have developed several techniques to control the generalization errors of their data analyses. In this talk, I will discuss how programming language techniques can help data analysts to design adaptive data analyses...
Colloquium - On Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features
Yingyu Liang
AbstractAn important characteristic of neural networks is their ability to learn representations of the input data with effective features for prediction, which is believed to be a key factor to their superior empirical performance. To better understand the source and benefit of feature learning in neural networks, we consider learning problems motivated by practical data, where the labels are determined by a set of class relevant patterns and the inputs are generated...
GAUSS Seminar: Puzzles, Ice, & Grothendieck Polynomials [hybrid]
We introduce quivers, path algebras and their representations. Then, in the case when our ground field is algebraically closed, we discuss a particular Morita invariant of path algebras arising from finite quivers, the Ext quiver of the category. Through examples we see how to compute the Ext quiver using quiver representations and techniques from linear algebra. We aim to keep the talk accessible to undergraduate and graduate students alike.
SpeakerRyan Bianconi UI Mathematics PhD...
GAUSS Seminar: Puzzles, Ice, & Grothendieck Polynomials
From a summer REU at the University of Minnesota, we constructed a solvable lattice model for the dual weak symmetric Grothendieck polynomials in hopes of using such a model to prove related properties of these polynomials, including Cauchy identities and branching rules. We also considered a similar lattice model construction for the weak symmetric Grothendieck polynomials in hopes of proving a Cauchy identity, concluding with a negative result. Moreover, we expand on previous work by...
GAUSS Seminar: Rotation Symmetric Boolean Functions and its Matrix
Digital signatures are an important feature in any encryption/decryption scheme, as it provides a message with integrity, authenticity, and nonrepudiation. The problem occurs when long messages are being exchanged and signatures that are just as long need to be verified. By using hash functions, a ”fingerprint” of the message can be used instead of the message itself for verification, making the process computationally inexpensive. If we consider a single iteration of a general hashing...