Training and exploration of linear probes into Othello-GPT by Li et al. (2022)
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Updated
Jun 29, 2023 - Jupyter Notebook
Training and exploration of linear probes into Othello-GPT by Li et al. (2022)
Optimizing Mind static website v1
Neural model interpretation on MRI data
Creating the model and approach to manage and adjust the process/equipment
where I learn and explore mechanistic interpretability of transformers
Techniques for interpreting ConvNets
Code for the paper: PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification
Feature selection is widely used in nearly all data science pipelines. Hence I have created functions that do a form of backward stepwise selection based on the XGBoost classifier feature importance and a set of other input values with the goal to return the number of features to keep in regard to a prefered AUC-score.
This Alignment Jam Hackathon project explores whether the concept of "logit lens" applies to the encoder and decoder layers in Whisper, an end-to-end speech recognition model.
StellarGraph - Machine Learning on Graphs
A Quick Look at B-cos Nets' Adversarial Robustness
This code is part of the paper: "A Deep Dive Into Neural Synchrony Evaluation for Audio-visual Translation" published at ACM ICMI 2022.
Technical audit of Automated Decision System for Fairness and Bias
In this repo. I apply several variation of GradCAM including GradCAM, GradCAMPlusPlus, EigenCAM and etc on a pretrained Resnet model and report ROAD as an evaluation metric for interpretability.
COMP 551: Applied Machine Learning — Project #4
Goal: create and implement metrics to measure Transparency and Trustworthiness
A unified approach to explain the output of any machine learning model.
Exploring feature contributions to outliers, feature importances, and image recognition features
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