machine-learning-research
There are 8 repositories under machine-learning-research topic.
benedekrozemberczki/awesome-decision-tree-papers
A collection of research papers on decision, classification and regression trees with implementations.
crunchiness/lernd
Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
techandy42/FinancialBERT
Stock price prediction model built using BERT and regression model trained on textual financial news data.
speediedan/deep_classiflie
Deep Classiflie is a framework for developing ML models that bolster fact-checking efficiency. As a POC, the initial alpha release of Deep Classiflie generates/analyzes a model that continuously classifies a single individual's statements (Donald Trump) using a single ground truth labeling source (The Washington Post). For statements the model deems most likely to be labeled falsehoods, the @DeepClassiflie twitter bot tweets out a statement analysis and model interpretation "report"
sutd-visual-computing-group/research-reproducibility-guide-book
This Guide book is written with the intention of helping researchers and engineers working in machine learning domains to publish reproducible research.
zaaachos/Thesis-Diagnostic-Captioning
B.Sc. Thesis Deep Learning & NLP research on Medical Image Captioning
Awni00/abstract_transformer
This is the project repo associated with the paper "Disentangling and Integrating Relational and Sensory Information in Transformer Architectures" by Awni Altabaa, John Lafferty
speediedan/deep_classiflie_db
Deep_classiflie_db is the backend data system for managing Deep Classiflie metadata, analyzing Deep Classiflie intermediate datasets and orchestrating Deep Classiflie model training pipelines. Deep_classiflie_db includes data scraping modules for the initial model data sources. Deep Classiflie depends upon deep_classiflie_db for much of its analytical and dataset generation functionality but the data system is currently maintained as a separate repository here to maximize architectural flexibility. Depending on how Deep Classiflie evolves (e.g. as it supports distributed data stores etc.), it may make more sense to integrate deep_classiflie_db back into deep_classiflie. Currently, deep_classiflie_db releases are synchronized to deep_classiflie releases. To learn more, visit deepclassiflie.org.