From 069bfa57bb109ea6c19bfcfe8e38e2c5cb5f3302 Mon Sep 17 00:00:00 2001
From: ptimons44 <103891668+ptimons44@users.noreply.github.com>
Date: Tue, 12 Dec 2023 22:54:24 -0500
Subject: [PATCH 1/2] everything
---
...riables with VAEs despite Training Bias.md | 164 ++++++++++++++++++
...iables with VAEs despite Training Bias.bib | 86 +++++++++
.../projection head training curve.html | 1 +
.../Linear Projection Training Curve.png | Bin 0 -> 404190 bytes
.../MSE generating on test set.png | Bin 0 -> 54665 bytes
.../MSE projection head.png | Bin 0 -> 48736 bytes
.../approximate MI.png | Bin 0 -> 54130 bytes
.../beta = 1.png | Bin 0 -> 1181056 bytes
.../beta = 10.png | Bin 0 -> 1174594 bytes
.../beta = 5.png | Bin 0 -> 1185883 bytes
.../beta=1 mu=[0,2] train curves.png | Bin 0 -> 276324 bytes
.../beta=1, mu=[2] training curves.png | Bin 0 -> 235319 bytes
.../example test images.png | Bin 0 -> 5947 bytes
.../example training noised.png | Bin 0 -> 255162 bytes
.../example training.png | Bin 0 -> 76154 bytes
.../mu=[0] training curves.png | Bin 0 -> 395865 bytes
bin/dockerhub_run.sh | 4 +-
17 files changed, 253 insertions(+), 2 deletions(-)
create mode 100644 _posts/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias.md
create mode 100644 assets/bibliography/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias.bib
create mode 100644 assets/html/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/projection head training curve.html
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/Linear Projection Training Curve.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/MSE generating on test set.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/MSE projection head.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/approximate MI.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/beta = 1.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/beta = 10.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/beta = 5.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/beta=1 mu=[0,2] train curves.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/beta=1, mu=[2] training curves.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/example test images.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/example training noised.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/example training.png
create mode 100644 assets/img/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias/mu=[0] training curves.png
diff --git a/_posts/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias.md b/_posts/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias.md
new file mode 100644
index 00000000..55ce818f
--- /dev/null
+++ b/_posts/2023-12-12-Recovering Latent Variables with VAEs despite Training Bias.md
@@ -0,0 +1,164 @@
+---
+layout: distill
+title: Recovering Latent Variables with VAEs despite Training Bias
+description: Final Project Blog
+date: 2022-12-01
+htmlwidgets: true
+
+# Anonymize when submitting
+# authors:
+# - name: Anonymous
+
+authors:
+ - name: Patrick Timons
+ url: "https://en.wikipedia.org/wiki/Albert_Einstein"
+ affiliations:
+ name: MIT
+
+# must be the exact same name as your blogpost
+bibliography: 2023-12-12-Recovering Latent Variables with VAEs despite Training Bias.bib
+
+# Add a table of contents to your post.
+# - make sure that TOC names match the actual section names
+# for hyperlinks within the post to work correctly.
+toc:
+ - name: Introduction
+ - name: Background
+ subsections:
+ - name: Data
+ subsections:
+ - name: Training Data
+ - name: Test Dataset
+ - name: Training
+ - name: Related Work
+ - name: Set-up and Methods
+ - name: Results
+ subsections:
+ - name: Training Observations
+ - name: Evaluation
+ - name: Conclusion
+
+
+# Below is an example of injecting additional post-specific styles.
+# This is used in the 'Layouts' section of this post.
+# If you use this post as a template, delete this _styles block.
+_styles: >
+ .fake-img {
+ background: #bbb;
+ border: 1px solid rgba(0, 0, 0, 0.1);
+ box-shadow: 0 0px 4px rgba(0, 0, 0, 0.1);
+ margin-bottom: 12px;
+ }
+ .fake-img p {
+ font-family: monospace;
+ color: white;
+ text-align: left;
+ margin: 12px 0;
+ text-align: center;
+ font-size: 16px;
+ }
+---
+
+## Introduction
+
+In this age of abundant unlabeled data, unsupervised learning is capitalizing to power the generative models that are eating the world. Large Language Models and Diffusion models are focalizing most of the mainstream hype and therefore siphoning attention from other generative models. In particular, the Variational Autoencoders (VAE) is a model architecture that has been arguably overlooked in the recent onslaught for scaling transformer and diffusion-based models. VAEs are a promising latent variable model that allows for the learning of disentangled latent variables that model data.
+
+As models scale in size, so is concern for the lack of interpretability associated with Neural Networks. Latent variable models offer a solution to this problem since they can learn variables that factorize the data generation process. VAEs are particularly well suited for learning latent variables in an unsupervised setting since they use an unsupervised learning objective and are regularized to learn disentangled encodings of our data. VAEs have been applied in a broad breadth of settings, such as classifying out-of-distribution data
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