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Found 5 papers, 3 papers with code
Evaluating XGBoost for Balanced and Imbalanced Data: Application to Fraud Detection
Random search fine-tuning provides consistent improvement for large datasets of 100 thousand samples, not so for medium and small datasets of 10 and 1 thousand samples, respectively.
Forecasting with Deep Learning
This paper presents a method for time series forecasting with deep learning and its assessment on two datasets.
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series Forecasting
We release the datasets used as well as the implementation with all experiments performed to enable future comparisons and to make our research reproducible.
Benchmarking Algorithms for Automatic License Plate Recognition
We evaluated the algorithm on two datasets, one composed of real license plate images and the other of synthetic license plate images.
HEAR: Holistic Evaluation of Audio Representations
The aim of the HEAR benchmark is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios.
