Pre-Rendered Regularization Images fou use with fine-tuning, especially for the current implementation of "Dreambooth"
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Updated
Dec 26, 2022
Pre-Rendered Regularization Images fou use with fine-tuning, especially for the current implementation of "Dreambooth"
Applied Sparse regularization (L1), Weight decay regularization (L2), ElasticNet, GroupLasso and GroupSparseLasso to Neuronal Network.
A Julia package to perform Tikhonov regularization for small to moderate size problems.
PyInvGeo: Regularized Inversion Techniques
Python source code for EMNLP 2020 Findings paper: "Domain Adversarial Fine-Tuning as an Effective Regularizer".
All my Machine Learning Projects from A to Z in (Python & R)
Here, we implement regularized linear regression to predict the amount of water flowing out of a dam using the change of water level in a reservoir. In the next half, we go through some diagnostics of debugging learning algorithms and examine the effects of bias v.s. variance.
fdaPDE: Physics-Informed Spatial and Functional Data Analysis
A vast assortment of class regularization images in sets of 1500
Notebooks developed in Mathematica for my Ph.D. thesis and other resources
Implementation of all basic algorithms needed in Deep Learning
System developed by team datamafia in WNUT 2020 Task 2: Identification of informative COVID-19 English Tweets
Regularized Levenberg-Marquardt algorithm for nonlinear regression on small size datasets
Code and Data sets for the EMNLP-2021-Findings Paper "ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection"
All about machine learning
Sparse Gaussian graphical models with Sorted L-One Penalized Estimation
This project propose the loss landscape analysis as effective methodology to understand the robustness against natural perturbation of QNN.
In this repository you can find the all the ML algorithm's notebook and notes.
Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019
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