1 code implementation • 18 May 2023 • Javier E Santos, Zachary R. Fox, Nicholas Lubbers, Yen Ting Lin
Generalizing from specific (Gaussian) forward processes to discrete-state processes without a variational approximation sheds light on how to interpret diffusion models, which we discuss.
Ranked #13 on Image Generation on CelebA 64x64
1 code implementation • 20 Jul 2020 • Yen Ting Lin, Jacob Neumann, Ely Miller, Richard G. Posner, Abhishek Mallela, Cosmin Safta, Jaideep Ray, Gautam Thakur, Supriya Chinthavali, William S. Hlavacek
To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population.
1 code implementation • 21 Jan 2022 • Xiaoyu Ma, Sylvain Sardy, Nick Hengartner, Nikolai Bobenko, Yen Ting Lin
To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability in certain regimes even if the sample size is smaller than the dimension of the input vector (haystack).
1 code implementation • 21 Mar 2022 • Yen Ting Lin, Nicolas E. Buchler
Here, we show that gene expression noise counter-intuitively accelerates the evolution of a biological oscillator and, thus, can impart a benefit to living organisms.
no code implementations • 7 Jun 2020 • Sylvain Sardy, Nicolas W Hengartner, Nikolai Bonenko, Yen Ting Lin
Using a sparsity inducing penalty in artificial neural networks (ANNs) avoids over-fitting, especially in situations where noise is high and the training set is small in comparison to the number of features.
no code implementations • 4 Sep 2020 • Afroza Shirin, Yen Ting Lin, Francesco Sorrentino
We then introduce a time-varying control input that represents the level of social distancing imposed on the population of a given area and solve an optimal control problem with the goal of minimizing the impact of social distancing on the economy in the presence of relevant constraints, such as a desired level of suppression for the epidemics at a terminal time.
no code implementations • 29 Sep 2021 • Jacob Neumann, Yen Ting Lin, Abhishek Mallela, Ely F. Miller, Joshua Colvin, Abell T. Duprat1, Ye Chen, William S. Hlavacek, Richard G. Posner
Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable.
no code implementations • 10 May 2022 • Yen Ting Lin, Yifeng Tian, Danny Perez, Daniel Livescu
We propose to adopt statistical regression as the projection operator to enable data-driven learning of the operators in the Mori--Zwanzig formalism.
1 code implementation • 9 Jun 2023 • Van A. Ngo, Yen Ting Lin, Danny Perez
It has become common to perform kinetic analysis using approximate Koopman operators that transforms high-dimensional time series of observables into ranked dynamical modes.
no code implementations • 29 Nov 2023 • Javier E. Santos, Yen Ting Lin
The aim of this short note is to show that Denoising Diffusion Probabilistic Model DDPM, a non-homogeneous discrete-time Markov process, can be represented by a time-homogeneous continuous-time Markov process observed at non-uniformly sampled discrete times.