ML Review

DECEMBER 16, 2019

NeurIPS’18 presented several papers with deep theoretical studies of building hyperbolic neural nets. Source: Chami et al Chami et al present Hyperbolic Graph Convolutional Neural Networks (HGCN) and Liu et al propose Hyperbolic Graph Neural Networks (HGNN).

Machine Learning 52

Machine Learning Neural Network Explainability NLP 52

Heartbeat

SEPTEMBER 20, 2023

Object Detection Image from a personal computer Convolutional neural networks (CNNs) are utilized in object detection algorithms to identify and locate objects based on their visual attributes accurately. These algorithms can learn and extract intricate features from input images by using convolutional layers.

Natural Language Processing 52

Natural Language Processing Computer Vision NLP Algorithm 52

Viso.ai

MARCH 26, 2024

Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Source ) This has led to groundbreaking models like GPT for generative tasks and BERT for understanding context in Natural Language Processing ( NLP ).

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Mlearning.ai

JUNE 8, 2023

Vector Embeddings for Developers: The Basics | Pinecone Used geometry concept to explain what is vector, and how raw data is transformed to embedding using embedding model. Pinecone Used a picture of phrase vector to explain vector embedding. What are Vector Embeddings? using its Spectrogram ). All we need is the vectors for the words.

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

Snorkel AI

MARCH 1, 2023

BERT BERT, an acronym that stands for “Bidirectional Encoder Representations from Transformers,” was one of the first foundation models and pre-dated the term by several years. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. On the other hand, NLP frameworks like BERT help in understanding the context and content of documents. Overcoming the ‘black box’ nature of AI for transparent and explainable AI systems.

Computer Vision 52

Computer Vision Neural Network Machine Learning Convolutional Neural Networks 52

Heartbeat

FEBRUARY 8, 2024

Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively. Building the Model Deep learning techniques have proven to be highly effective in performing cross-modal retrieval.

Neural Network 52

Neural Network Deep Learning Convolutional Neural Networks Computer Vision 52

Heartbeat

OCTOBER 12, 2023

One of the standout achievements in this domain is the development of models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). They owe their success to many factors, including substantial computational resources, vast training data, and sophisticated architectures.

Large Language Models 59

Large Language Models Neural Network Artificial Intelligence Artificial Intelligence 59

Marek Rei

NOVEMBER 12, 2019

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. link] Constructing a system for NLI that explains its decisions by pointing to the most relevant parts of the input. NAACL 2019. Cambridge, Amazon. NAACL 2019. Tison, Codie Bourn, Mintu P.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

ML Review

FEBRUARY 11, 2019

If you don’t know it already, NLP had a huge hype of transfer learning in this past 1 year, starting with ULMFit , ELMo , GLoMo , OpenAI transformer , BERT and recently Transformer-XL for further improving language modeling capabilities of the current state of the art. Apply random dropout per input sequence.

Neural Network 52

Neural Network NLP Explainability Convolutional Neural Networks 52