4 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - Provable Tensor-Train Format Tensor Completion by Riemannian Optimization
The tensor train (TT) format enjoys appealing advantages in handling structural high-order tensors. The recent decade has witnessed the wide applications of TT-format tensors from diverse disciplines, among which tensor completion has drawn considerable attention.
4 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks
Pruning is a model compression method that removes redundant parameters and accelerates the inference speed of deep neural networks while maintaining accuracy. Most available pruning methods impose various conditions on parameters or features directly.
4 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - Highest weight crystals for Schur Q-functions
In 1990s, Kashiwara and Lusztig defined crystals as abstraction of crystal bases of quantum group representations.
4 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - A Moving Mesh Finite Element Method for Topology Optimization
Many partial differential equations may have solutions with nearly singular behaviors, such as shock waves and boundary layers.
3 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - Apply threshold dynamics algorithm to minimal compliance problem in topology optimization
Inspired by the simple two-step threshold dynamics algorithm which iteratively does convolution and thresholding to simulate the motion of grain boundaries, we developed an algorithm to approach the minimal compliance problem in topology optimization with
2 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - Application of Reinforcement Learning to High-frequency Market Making Strategy
With the increasing usage of the electronic limit order book (LOB) in modern financial markets, high-frequency algorithmic trading has captured over 70 percent of the whole trading volume in various financial markets.
2 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - SDE-based deep generative model
Deep generative models are a category of machine learning models that utilizes deep neural networks to model data distributions and generate new samples.
2 May 2022
Seminar, Lecture, Talk
Department of Mathematics - PhD Student Seminar - Data Adaptive Early Stopping in Split LBI: towards Controlling the False Discovery Rate
Early stopping is a widely-used regularization technique to avoid overfitting in iterative algorithms.
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