Machine Learning + X Seminars

Machine Learning + X seminars are held byweekly.

Selected presentations from past seminars are posted blow. Yeonjong Shin

  • When multiscale computation, Parareal and PINN have a party by Zhen Li
  • Single-Particle Diffusion Characterization by Deep Learning by He Li
  • Image analysis and machine learning for detecting malaria by Shaoqing Yu
  • Can semantic inpainting inspire hydrogeologist? by Qiang Zheng

  • Vascular Network Structure and Its Transport Properties in Mouse Retina by Yosuke Hasegawa

  • Paper reviews: 1) Diagnosing Pregnancy Based on Wrist Pulse Wave,  2) Human Pulse Recognition based on Convolutional Neural Networks and 3) Wrist Pulse Signals Analysis based on Deep Convolutional Neural Network by Xiaoli Chen
  • Deep learning observables in computational fluid dynamics by Ameya D. Jagtap
  • Deep Learning for Ocean Remote Sensing: An Application of Convolutional Neural Networks for Super-Resolution on Satellite-Derived SST Data by Xiang Li
  • The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies by Guofei Pang
  • Can PINNs beat FWI by Yiran Xu
  • Graph Embedding method for Network data analysis by Mengjia Xu.
  • Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Pengzhan Jin
  • MoGlow: Probabilistic and controllable motion synthesis using normalising flows.
  • Super-resolution reconstruction of turbulent flows with machine learning.
  • Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam.
  • Bridging Finite Element and Machine Learning Modeling: Stress Prediction of Arterial Walls in Atherosclerosis
  • Deep relaxation: partial differential equations for optimizing deep neural networks.
  • Data driven nonlinear dynamical systems identification using multi-step CLDNN.
  • Recent applications of neural networks in biological systems
  • A synthetic turbulent inflow generator using machine learning
  • Spectral Normalization for Generative Adversarial Networks
  • Generative Modeling using the Sliced Wasserstein Distance.
  • Sliced Wasserstein Generative Models.
  • Understanding training and generalization in deep learning by Fourier analysis.
  • An analysis of training and generalization errors in shallow and deep networks.
  • Deep vs. Shallow Networks: an Approximation Theory Perspective.
  • Predicting the solutions of heterogeneous elliptic PDEs with a confidence interval by probabilistic convolutional neural networks.
  •  Error bounds for approximations with deep ReLU neural networks in W^{s,p} norms.
  • Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations .
  • Assessment of End-to-End and Sequential Data-driven Learning of Fluid Flows.
  • Boost your research through SciCoNet and SumsJob.  Lu Lu
  • An inverse problem framework for extracting phonon properties from thermal spectroscopy measurments .  Mojtaba Forghani
  • A review of definitions of fractional derivatives and other operators.  Ehsan Kharazmi
  • Paper Review: Optimal approximation of continuous functions by very deep ReLU networks.  Dmitry Yarotsky 
  • Mark Thachuk

    Department of Chemistry, University of British Columbia, Canada

    Title: Coarse Graining Independent Particles using Mori-Zwanzig Theory

  • Ameya Jagtap

    Paper Review: 
    Artificial Neural Networks Trained Through Deep Reinforcement Learning Discover Control Strategies for Active Flow Control (arXiv:1808.07664v5)

  • Yeonjong Shin

    Paper Review: Elimination of all bad local minima in deep learning by Kenji Kawaguchi and Leslie Pack Kaelbling

  • Mojtaba Forghani

    Mechanical Engineering Department, MIT

    Title: An inverse problem framework for extracting phonon properties from thermal spectroscopy measurements.

  • Drew Linsley, Junkyung Kim & Thomas Serre

    TITLE: Recurrent neural networks for visual processing

  • Xiang Li

    TITLE: The Acquisition and Uncertainty Quantification of Land Surface Evapotranspiration at the Satellite Pixel Scale

  • Prof. Yosuke Hasegawa

    TITLE: Vascular Network of Zebrafish Brain

  • Ludger Paehler, Technische Universität München
    TITLE: Multifidelity and Machine Learning for Turbulent Flows
  • Alireza Yazdani

    TITLE: Data-Driven Multiscale Modeling in Physical and Biological Systems

  • Dr. Yu-Hang Tang, 
    Lawrence Berkeley National Laboratory

    Prediction of atomization energy using graph kernel and active learning

  • Guofei Pang -  Paper Review:  Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks by John Bradshaw, Alexander G. de G. Matthews, Zoubin Ghahramani
  • Guofei Pang - Image recognition: Defense adversarial attacks using Generative Adversarial Network (GAN)
  • Lu Lu - 4 Years of Generative Adversarial Networks (GANs)
  • Yanhui Su - Approximation theory in neural networks
  • Zhiping Mao - Learning networks of stochastic differential equations
  • Guofei Pang - When a neural net meets a Gaussian process