Heartbeat

JUNE 6, 2023

GCNs use a combination of graph-based representations and convolutional neural networks to analyze large amounts of textual data. A GCN consists of multiple layers, each of which applies a graph convolution operation to the input graph. References Paperwithcode | Graph Convolutional Network Kai, S.,

NLP 59

NLP Convolutional Neural Networks Neural Network Natural Language Processing 59

Google Research AI blog

FEBRUARY 23, 2023

Commensurate with our mission to demonstrate these societal benefits , Google Research’s programs in applied machine learning (ML) have helped place Alphabet among the top five most impactful corporate research institutions in the health and life sciences publications on the Nature Impact Index in every year from 2019 through 2022.

Convolutional Neural Networks 104

Convolutional Neural Networks ML Neural Network Deep Learning 104

AWS Machine Learning Blog

FEBRUARY 10, 2023

We describe how we designed an accurate, explainable ML model to make coverage classification from player tracking data, followed by our quantitative evaluation and model explanation results. This explains the uncertainty of the model’s classification: the TE is sticking with the SS by design, creating biases in the data.

Computer Vision 64

Computer Vision Machine Learning ML Convolutional Neural Networks 64

Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). NAACL 2019. ArXiv 2019. NAACL 2019. NAACL 2019. They cover a wide range of different topics, authors and venues.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

Flipboard

JULY 31, 2023

Nowadays, with the advent of deep learning and convolutional neural networks, this process can be automated, allowing the model to learn the most relevant features directly from the data. a convolutional neural network), which then learns to map the features of each image to its correct label.

Machine Learning 104

Machine Learning Computer Vision Deep Learning Convolutional Neural Networks 104

Snorkel AI

MARCH 1, 2023

This can make it challenging for businesses to explain or justify their decisions to customers or regulators. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Radford et al. The company has partnered with Microsoft since 2019. Attention Is All You Need Vaswani et al.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

ML Review

FEBRUARY 11, 2019

Part 1 (2019). If you want to apply for Part 2 (2019) as a diversity fellow or sponsor here is the link. I am not going to be able to explain all of these exceptional works except for ULMFIT. First, my aim is not to explain how or why ULMFIT became to be a state-of-the-art model because there are many resources like this fast.ai

Neural Network 52

Neural Network NLP Explainability Convolutional Neural Networks 52

ML Review

MAY 3, 2018

22] On a high level in the architecture, the frames extracted from a video sequence are processed in small sets within a Convolutional Neural Network (CNN), [23] while an LSTM-variant runs on the CNN output sequentially to generate output characters. An Intuitive Explanation of Convolutional Neural Networks.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52