✌[ICLR 2024] Class Incremental Learning via Likelihood Ratio Based Task Prediction
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Updated
Oct 29, 2024 - Python
✌[ICLR 2024] Class Incremental Learning via Likelihood Ratio Based Task Prediction
A simple framework for ANOVA on various types of Julia statistical models
A statistical model-based Voice Activity Detector
[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic
A new metric to measure multi-class imbalance degree of data using likelihood ratio test based on the paper LRID by Rui Zhu et. al.
Conduct one-way and multi-way anova in Julia with GLM.jl
This package implements hypothesis testing procedures that can be used to identify the number of regimes in a Markov-Switching model.
[AISTATS 2025] "A Likelihood Based Approach for Watermark Detection"
[EMNLP 2023] FLatS: Principled Out-of-Distribution Detection with Feature-Based Likelihood Ratio Score
Measuring the axion mass with a helioscope
A consistent API for hypothesis testing in R. Provides generic methods for p-values, test statistics, degrees of freedom, and significance testing. Includes LRT and Wald test implementations. Available on CRAN.
Convex optimization over classes of multiparticle entanglement
Goodness-of-fit tests for categorical response models
R Shiny App to determine the factors that are most influential in patients’ survival of CHD. I created a Logistic Regression model in R using RStudio to predict the survival of CHD patients. Retrieved the data from the PHIS database using SQL & built tableau dashboards. The model predicted the survival of CHD with an AUC of over .90 and indicate…
Computing a Likelihood Ratio Test on Wisconsin Breast Cancer Dataset to detect the most relevant features to infer wether a tumor is Malignous or Benign
Multiscale change point detection
Conduct one-way and multi-way anova in Julia with FixedEffectModels.jl
This is the repository for the RMediation package.
This workflow identifies stage-specific transcription factor expression modules in time-series bulk RNA-seq data. The goal is to characterize the "waves" of TF expression that define each stage of a differentiation time-course, then use these control TF clusters as a baseline for assessing how experimental conditions alter this expression
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