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
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
Notebooks developed in Mathematica for my Ph.D. thesis and other resources
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.
Supplementary code for the paper "Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks" - an accepted paper of LOD2019
In this repository you can find the all the ML algorithm's notebook and notes.
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