Snorkel AI

APRIL 11, 2023

Now the key insight that we had in solving this is that we noticed that unseen concepts are actually well clustered by pre-trained deep learning models or foundation models. And effectively in the latent space, they form kind of tight clusters for these unseen concepts that are very well-connected components. of the unlabeled data.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

DagsHub

JUNE 24, 2024

This can lead to enhancing accuracy but also increasing the efficiency of downstream tasks such as classification, retrieval, clusterization, and anomaly detection, to name a few. This can lead to higher accuracy in tasks like image classification and clusterization due to the fact that noise and unnecessary information are reduced.

Clustering 40

Clustering Machine Learning Machine Learning K-nearest Neighbors 40

Mlearning.ai

JULY 6, 2023

The sub-categories of this approach are negative sampling, clustering, knowledge distillation, and redundancy reduction. Some common quantitative evaluations are linear probing , K nearest neighbors (KNN), and fine-tuning. They have done a very comprehensive study regarding this topic so lot of things we can talk about.

Supervised Learning 52

Supervised Learning Deep Learning Deep Learning K-nearest Neighbors 52

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. For unSupervised Learning tasks (e.g.,

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Dataconomy

MARCH 28, 2025

Definition and importance of machine learning algorithms The core value of machine learning algorithms lies in their capacity to process and analyze vast amounts of data efficiently. Common types include: K-means clustering: Groups similar data points together based on specific metrics.

Machine Learning 91

Machine Learning Machine Learning Algorithm K-nearest Neighbors 91

Dataconomy

MARCH 7, 2025

Definition and structure of feature vectors A feature vector contains numerical values that represent the attributes of an observed phenomenon. This can be useful in clustering algorithms where distance metrics help define groups. Each feature corresponds to a specific measurable element, allowing for detailed comparative analysis.

K-nearest Neighbors 91

K-nearest Neighbors Machine Learning Machine Learning ML 91

AWS Machine Learning Blog

NOVEMBER 1, 2024

We tried different methods, including k-nearest neighbor (k-NN) search of vector embeddings, BM25 with synonyms , and a hybrid of both across fields including API routes, descriptions, and hypothetical questions. The request arrives at the microservice on our existing Amazon Elastic Container Service (Amazon ECS) cluster.

Python 103

Python AI AI K-nearest Neighbors 103