25 Sep 2020
Seminar, Lecture, Talk
MATH - Seminar on Pure Mathematics - Introduction to Coulomb branches and their cluster structure
Braverman, Finkelberg and Nakajima have recently proposed a mathematical definition of the Coulomb branch of a 3d N=4 gauge theory of cotangent type, associating to each such theory a family of associative algebras deforming the algebra of functions on an affine Poisson
11 Sep 2020
Seminar, Lecture, Talk
MATH - Seminar on Scientific Computation - Towards the high-fidelity computation and modeling of compressible turbulent flows
In this talk, we will focus on the high-fidelity computations of turbulence from the perspective of modeling and numeric.
28 Aug 2020
Seminar, Lecture, Talk
MATH - Seminar on Statistics and Machine Learning - ROOT-SGD: Sharp Nonasymptotics and Asymptotic Efficiency in a Single Algorithm
The theory and practice of stochastic optimization has focused on stochastic gradient descent (SGD) in recent years, retaining the basic first-order stochastic nature of SGD while aiming to improve it via mechanisms such as averaging, momentum, and variance reduction.
28 Aug 2020
MPhil in Chemistry - Analysis of Polycyclic Aromatic Hydrocarbons and Derivatives in Ambient Aerosols using 1-D and 2-D Gas Chromatography-Mass Spectrometry Methods
Candidate: Mr. REN, Zhong
27 Aug 2020
Seminar, Lecture, Talk
MATH - PhD Student Seminar - An adaptive iterative convolution thresholding method for topology optimization
Topology optimization has become progressively important as a tool for engineering structural designs due to advancing manufacturing technology and increasing computational power.
27 Aug 2020
MPhil in Chemistry - Structure and Statistics Based Analysis of Cas9 Protospacer Adjacent Motif (PAM) Recognition and Diversity
Candidate: Mr. WANG, Yu
26 Aug 2020
PhD in Chemistry - Enantioselective Synthesis of Chromanone and Thiochromanone Derivatives
Candidate: Miss MENG, Ling
21 Aug 2020
Seminar, Lecture, Talk
MATH - Seminar on Data Science and Applied Math - Towards Better Global Landscape of GAN: How Two Lines of Code Change Makes a Difference
Our understanding of GAN (generative adversarial net) training is still very limited since it is a non-convex-non-concave min-max optimization. As a result, most recent studies focused on local analysis. In this talk, we discuss how to perform a global analysis of GANs.
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