University of Illinois Urbana-Champaign

Wake-sleep Bayesian program synthesis applications in bioinformatics

Khirwar, Madhav

Content Files
FA21-ECE499Thesis-Khirwar, Madhav (1).pdf

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https://hdl.handle.net/2142/113505

Description

Title
Wake-sleep Bayesian program synthesis applications in bioinformatics
Author(s)
Khirwar, Madhav
Contributor(s)
Bhargava, Rohit
Issue Date
2021-12
Keyword(s)
  • Program synthesis
  • Bayesian wake-sleep Learning
  • Bioinformatics
  • Cancer ii
Date of Ingest
2022-02-27T23:11:53Z
Abstract
Program synthesis is the process of learning mappings between sets of inputs and outputs in a way that generalizes to new inputs. Contrary to deep learning in the gradient descent sense, the goal of program induction is not to 'converge' to a correct solution by performing gradient descent on millions of parameters - rather it is to generate and search for discrete programs that are expressed as combinations of a library of known 'concepts' that will solve the given problem. The goal of this thesis is to explore the portability of program induction onto the bioinformatics domain – specifically the problem of tumor grade prediction. Programs enumerated to predict tumor grade from a data set of colon cancer were up to 76% accurate when the library of primitives was limited to arithmetic, exponential and logarithmic operations. Further work will involve building in models for solving differential equations (another success was to induce Dreamcoder to discover the forward Euler method for solving PDEs), as well as building conceptual representations of n-dimensional spatial data such as images. data such as images.
Type of Resource
text
Genre of Resource
dissertation/thesis
Language
en
Permalink
http://hdl.handle.net/2142/113505

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