Experiments on Input Functions

This is a summary of papers on experimental investigations of gene’s input function, especially about Diverse Two-Dimensional Input Functions Control Bacterial Sugar Genes (2008) and Plasticity of the cis-Regulatory Input Function of a Gene (2006).

Living cells are capable of responding to the environment conditions, or signals, in various ways. One important choice is regulating gene expression. The relation between the level of input signals and the transcription rate of the gene is called the gene’s input function. Clarifying the form of input functions is crucial in understanding the gene regulation and further evolution. As most genes are regulated by multiple signals, these papers reported well-designed experimental platforms to present the diversity of two-dimensional input functions, providing solid background for further theoretical abstrction.


The Thermodynamic Variational Objective and more

This is a reading note centering around paper Vaden Masrani, et al. (2019) The Thermodynamic Variational Objective. This paper provides a way to construct a tighter bound, called TVO, to observed likelihood in VAE than ELBO (my note about ELBO), base on a statistical physics intuition. There are two main points I want to make here:

  1. Mordern statistical machine learning could borrow a lot from statistical physics field, as they generally share many similar questions;
  2. A tighter bound of learning objective does not always guarantee a better performance. Yes, a beautiful heuristic math sometimes doesn’t produce better result.

This is also what we choose to study on final project of Harvard 20 Fall AM207 course, we test this novel TVO on some sepcially-designed toy datesets. You can find our report here


A Theoretical Connection Between Statistical Physics and Reinforcement Learning

This is a reading note centering around paper Jad Rahme and Ryan Adams (2019) A Theoretical Connection Between Statistical Physics and Reinforcement Learning. I would rather call it a framework to understand RL centering partition function. It is a very heuristic work (but again, seems not very useful at the moment), as we long have the feeling that RL and statistical mechanics deal with the similar issue about integrating all phase space. So this paper provide a good example about how we can start from that intuition and establish more. Similarly, I could also imagine that there is a way to understand the RL in a path intergral framework.

TBC


菜谱:口水鸡

My recipe of a Sichuan food, steamed chicken with chili oil. Most ingredients can be found in American markets.

Configure A macOS with Intel chip From Scratch

A walk-through note on how to configure my familiar working system from a brand new macOS system, including Git token, Homebrew, Terminal color theme, Oh-my-zsh plugins, miniconda and jupyter kernel.