ML @ CMU

NOVEMBER 7, 2024

We also argue how labels should be assigned to predict the results of humanitarian demining operations, rectifying the definition of labels used in previous literature. For the Risk Modeling component, we designed a novel interpretable deep learning tabular model extending TabNet. Validation results in Colombia.

Clustering 154

Clustering Cross Validation Machine Learning Machine Learning 154

AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of relying on predefined, rigid definitions, our approach follows the principle of understanding a set. Its important to note that the learned definitions might differ from common expectations. Instead of relying solely on compressed definitions, we provide the model with a quasi-definition by extension.

Algorithm 77

Algorithm Machine Learning Machine Learning K-nearest Neighbors 77

DrivenData Labs

APRIL 10, 2024

Model architectures : All four winners created ensembles of deep learning models and relied on some combination of UNet, ConvNext, and SWIN architectures. In the modeling phase, XGBoost predictions serve as features for subsequent deep learning models. Test-time augmentations were used with mixed results.

Deep Learning 130

Deep Learning Deep Learning Machine Learning Machine Learning 130

DrivenData Labs

JANUARY 11, 2023

Feature engineering vs. neural network feature learning : The top performing solutions included deep learning models that used image or sequence representations of the data as inputs and feature engineering to capture the mass spectrograms. All winners who used deep learning fine-tuned pre-trained models.

Data Science 130

Data Science Deep Learning Deep Learning Machine Learning 130

PyImageSearch

JANUARY 23, 2023

In this tutorial, you will learn the magic behind the critically acclaimed algorithm: XGBoost. Firstly, we have the definition of the training set, which is refers to the training sample , which has features and labels. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

Deep Learning 109

Deep Learning Deep Learning Algorithm Decision Trees 109

Pickl AI

JULY 26, 2023

Understanding these concepts is paramount for any data scientist, machine learning engineer, or researcher striving to build robust and accurate models. To mitigate variance in machine learning, techniques like regularization, cross-validation, early stopping, and using more diverse and balanced datasets can be employed.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

JULY 12, 2024

Summary: This guide explores Artificial Intelligence Using Python, from essential libraries like NumPy and Pandas to advanced techniques in machine learning and deep learning. This section delves into its foundational definitions, types, and critical concepts crucial for comprehending its vast landscape.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Python Natural Language Processing 52