NU Bootcamp Module 14

A quick-start project that helps you to perform different types of selection in Vue Grid and know about different modes of selection – Row, Cell and Both. This project contains code snippet about cell, checkbox and toggle selection, and the way to get row index of selected cells using row selection events.

A project to select only part of a PDF file. It's usefull when you want to extract informations with some python library like fitz.

Introducing you to the fundamentals of the quintessential Python data analysis library, pandas, and its core data structures – the Series and DataFrame objects.

This repo contains the code for "Privacy Preserving Data Selection for Bias Mitigation in Speech Models"

Code for Generative Deduplication For Socia Media Data Selection (Findings of EMNLP 2024)

Quilt: Robust Data Segment Selection against Concept Drifts (AAAI 2024)

This repository contains the data and code for the paper "Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation" (EMNLP2022-Findings).

CORE: Mitigating Catastrophic Forgetting in Continual Learning through Cognitive Replay (CogSci 2024 Oral)

Allo-media data selection tool

A Python package for studying neural learning

Code for NeurIPS 2023 Paper (Imitation Learning from Imperfection: Theoretical Justifications and Algorithms)

Dynamic Transfer Learning for Low-Resource Neural Machine Translation

Keras sentence classification

Repository for the experiments in my paper accepted to the CLIN Journal: "Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts"

This is an official repository for "Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources" (NeurIPS 2023).

Enhanced spatio-temporal electric load forecasts with less data using active deep learning

Enhancing Efficiency in Multidevice Federated Learning through Data Selection

[ACL 2023] The code for our ACL'23 paper Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Propagation Approach