Synthesis data in YOLO format given background and object images
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Updated
May 8, 2024 - Python
Synthesis data in YOLO format given background and object images
Since the times of d'Alembert, Lagrange and Euler humans like to add fictitious dimensions to their real-world physical and mathematical problems. This art was perfected in the XX-th century by Heisenberg, Pauli and Dirac in their 'matrix mechanics'. In the XXI-st century we can contribute to this proud tradition too, we have computers! :)
Comprehensive reproduction of the paper "BNT162b2 mRNA Covid-19 Vaccine in a Nationwide Mass Vaccination Setting" by Noa Dagan, MD, et al., assisted by Professor Yair Goldberg. This statistical project explores vaccination's multifaceted impact on infection rates, employing synthetic data, advanced matching, and sophisticated statistical analysis.
Repository for Slide Deck and Code Examples for talk at SDP Convening 2023
Synthesize, analyze, and visualize biological oceanography data
Build machine learning image classifiers and summarize large image datasets from the Imaging FlowCytobot (IFCB)
Website of the ready4 suite of tools for data synthesis and modelling in mental health
data synthesis for simulation of pen-based interaction
FMRI data augmentation via synthesis, The IEEE International Symposium on Biomedical Imaging (ISBI'19)
Data Utility Improvement Experiment for DECAF
For this project, I aimed to perform sentiment analysis on IMDB movie reviews. My dataset consisted of over 36,000 reviews, each accompanied by movie ratings ranging from 0 to 10. The primary objective was to construct a machine learning model capable of categorizing reviews into three sentiment classes: negative, neutral, and positive.
A repository for synthesizing and simulating MRI images
echoseq R package - Synthetic-data generator: replication and simulation of molecular and clinical data
Blender Python Package for extracting internal data from blender scenes for 3d related data generation purposes.
This GitHub repository showcases my bachelor thesis which is focused on exploring the application and comparison of various deep generative models for synthetic image augmentation in manufacturing domain.
Official implementaion of EMNLP 2022 paper "Generate, Discriminate, and Contrast: A Semi-Supervised Sentence Representation Learning Framework"
Customizable Embodied Multi-modal Perturbations for SLAM Robustness Benchmarking
Boosting Document Intelligence
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