Unite.AI

NOVEMBER 8, 2023

Recurrent Neural Networks (RNNs) became the cornerstone for these applications due to their ability to handle sequential data by maintaining a form of memory. Functionality : Each encoder layer has self-attention mechanisms and feed-forward neural networks. However, RNNs were not without limitations.

BERT 298

BERT NLP Neural Network Natural Language Processing 298

NVIDIA

AUGUST 29, 2024

Soon after those papers were published in 2017 and 2018, Kiela and his team of AI researchers at Facebook, where he worked at that time, realized LLMs would face profound data freshness issues. In addition to greater accuracy, its offering lowers latency thanks to fewer API calls between the RAG’s and LLM’s neural networks.

Neural Network 72

Neural Network LLM OpenAI Large Language Models 72

Unite.AI

AUGUST 1, 2023

Prompt 1 : “Tell me about Convolutional Neural Networks.” ” Response 1 : “Convolutional Neural Networks (CNNs) are multi-layer perceptron networks that consist of fully connected layers and pooling layers. They are commonly used in image recognition tasks. .”

Prompt Engineering 246

Prompt Engineering Prompt Engineer ChatGPT Convolutional Neural Networks 246

IBM Journey to AI blog

AUGUST 1, 2023

A foundation model is built on a neural network model architecture to process information much like the human brain does. An open-source model, Google created BERT in 2018. The term “foundation model” was coined by the Stanford Institute for Human-Centered Artificial Intelligence in 2021.

Generative AI 189

Generative AI BERT Data Scientist Machine Learning 189

Heartbeat

FEBRUARY 6, 2023

In the second step, these potential fields are classified and corrected by the neural network model. R-CNN (Regions with Convolutional Neural Networks) and similar two-stage object detection algorithms are the most widely used in this regard. YOLOv3 is a newer version of YOLO and was released in 2018.

Algorithm 90

Algorithm Convolutional Neural Networks Neural Network Computer Vision 90

Heartbeat

OCTOBER 18, 2023

By leveraging the power of neural networks, deep learning techniques breathe new life into recommendation algorithms, empowering them to handle complex data and surpass the limitations of their predecessors. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Deep Learning 52

Deep Learning Neural Network Machine Learning Algorithm 52

Mlearning.ai

JANUARY 17, 2024

The foundations for today’s generative language applications were elaborated in the 1990s ( Hochreiter , Schmidhuber ), and the whole field took off around 2018 ( Radford , Devlin , et al.). Complex ML problems can only be solved in neural networks with many layers. Deep learning neural network.

Generative AI 98

Generative AI Prompt Engineering Prompt Engineer Large Language Models 98

Flipboard

JULY 10, 2023

An autoencoder is an artificial neural network used for unsupervised learning tasks (i.e., Sequence-to-Sequence Autoencoder Also known as a Recurrent Autoencoder, this type of autoencoder utilizes recurrent neural network (RNN) layers (e.g., What Are Autoencoders? They seek to: Accept an input set of data (i.e.,

Computer Vision 132

Computer Vision Deep Learning Neural Network Machine Learning 132

Marek Rei

NOVEMBER 12, 2019

ArXiv 2018. Unsupervised Recurrent Neural Network Grammars Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gábor Melis. link] Extending recurrent neural network grammars to the unsupervised setting, discovering constituency parses only from plain text. EMNLP 2018. NAACL 2018.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. Object detection is no different. 2015 ; He et al.,

Convolutional Neural Networks 119

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 119

Mlearning.ai

MARCH 9, 2023

2018; Sitawarin et al., 2018; Papernot et al., 2018) investigated the vulnerability of deep learning models to adversarial attacks in medical image segmentation tasks, and proposed a method to improve their robustness. 2018; Pang et al., Explaining and harnessing adversarial examples. For instance, Xu et al.

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 52

AI Impacts

FEBRUARY 1, 2023

But this model, on its own, is inadequate for AI, for reasons I will explain in the next section. 5 I will not explain this problem in detail, but I will list some aspects of it here, along with real-world examples, and you can read more about it elsewhere. arXiv preprint arXiv:1803.03453 (2018). ” Cimpanu C.

AI 130

AI AI OpenAI Large Language Models 130

deepsense.ai

APRIL 16, 2023

Pipeline Parallelism Since deep neural networks typically have multiple layers stacked on top of each other, the naive approach to model parallelism involves dividing a large model into smaller parts, with a few consecutive layers grouped together and assigned to a separate device, with the output of one stage serving as the input to the next stage.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

Heartbeat

DECEMBER 8, 2023

is well known for his work on optical character recognition and computer vision using convolutional neural networks (CNN), and is a founding father of convolutional nets. in 1998, In general, LeNet refers to LeNet-5 and is a simple convolutional neural network. We pay our contributors, and we don’t sell ads.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning LLM 52

Pickl AI

JULY 24, 2024

Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems. ” 1986: A resurgence in neural networks occurs with the introduction of the backpropagation algorithm, revitalising AI research.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Natural Language Processing Neural Network 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 ).

Machine Learning 52

Machine Learning BERT Neural Network OpenAI 52

Mlearning.ai

MARCH 2, 2023

Moreover, the most important theoretical foundations for BERT are explained and additional graphics are provided for illustration purposes. In particular, the architecture of transformers and the attention mechanism, as well as the idea behind unsupervised transfer learning, especially with fine-tuning, are explained.

BERT 52

BERT NLP Deep Learning Neural Network 52

Marek Rei

NOVEMBER 7, 2024

A recent survey paper by Calderon and Reichart [10] found that less than 10% of NLP interpretability papers consider inherently interpretable self-explaining models, with the authors advocating for more research on causal based interpretability methods. Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic.

Explainability 90

Explainability BERT NLP Large Language Models 90

Dlabs.ai

AUGUST 20, 2020

Interestingly, one of the authors – Yoshua Bengio won the 2018 Turing Award (the Nobel Prize of computing) for his work in deep learning. A Guide for Making Black Box Models Explainable Author: Christoph Molnar If you’re looking to learn how to make machine learning decisions interpretable, this is the eBook for you!

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Deep Learning 52

ML @ CMU

JUNE 30, 2023

Object Goal Navigation We instantiate semantic navigation with the Object Goal navigation task [ Anderson 2018 ], where a robot starts in a completely unseen environment and is asked to find an instance of an object category, let’s say a toilet.

Robotics 79

Robotics Neural Network Explainability Machine Learning 79

Heartbeat

MARCH 15, 2023

An open-source machine learning model called BERT was developed by Google in 2018 for NLP, but this model had some limitations, and due to this, a modified BERT model called RoBERTa (Robustly Optimized BERT Pre-Training Approach) was developed by the team at Facebook in the year 2019. Here’s a high-level overview of how it works.

BERT 52

BERT NLP Deep Learning Neural Network 52

Heartbeat

JANUARY 29, 2024

Memory-Augmented Neural Networks (MANNs) The path that led to Memory-Augmented Neural Networks started with conceptualizing the Neural Turing Machine (NTM). NTMs introduced the revolutionary concept of incorporating dynamic memory storage into neural networks. & Schulman, J.

Machine Learning 52

Machine Learning Neural Network Natural Language Processing Algorithm 52

TheSequence

NOVEMBER 22, 2023

I co-authored my first AI-related paper in 2000 ( using neural networks to manage on-CPU hardware resources ). In 2018, when I moved into Azure, my team started building custom AI supercomputers at scale. Understanding biological neural networks is one current focus. One is a more formal view of explainability.

Neural Network 40

Neural Network Generative AI AI AI 40

Viso.ai

JULY 17, 2024

Output from Neural Style Transfer – source Neural Style Transfer Explained Neural Style Transfer follows a simple process that involves: Three images, the image from which the style is copied, the content image, and a starting image that is just random noise. What is Perceptual Loss?

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 52

Viso.ai

JUNE 4, 2024

Many of these methods utilize deep learning and Convolutional Neural Networks (CNNs) to create point cloud processing. This approach is straightforward and allows the use of variable point cloud sizes for designing neural networks similar to 2D CNNs. The reason for that is the constraints of the undefined discriminator.

Computer Vision 52

Computer Vision Convolutional Neural Networks Deep Learning Neural Network 52

Sebastian Ruder

JANUARY 6, 2020

 State-of-the-art neural networks are getting larger and more expensive to train and to use for prediction. Investigating what makes winning tickets special should also help us gain a better understanding of the initialisation and learning dynamics of neural networks. 3) The Neural Tangent Kernel What happened?  

NLP 52

NLP ML Neural Network BERT 52

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. Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation.

Computer Vision 78

Computer Vision Machine Learning ML Convolutional Neural Networks 78

Heartbeat

MARCH 14, 2023

Some of the methods used for scene interpretation include Convolutional Neural Networks (CNNs) , a deep learning-based methodology, and more conventional computer vision-based techniques like SIFT and SURF. A combination of simulated and real-world data was used to train the system, enabling it to generalize to new objects and tasks.

Computer Vision 97

Computer Vision Robotics Deep Learning Convolutional Neural Networks 97

deepsense.ai

MAY 7, 2023

We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. Source: [ 2 ] In the previous post, we explained the importance of Stable Diffusion [ 3 ]. Next, we embed the images using an Inception-based [ 5 ] neural network. But don’t worry!

Neural Network 52

Neural Network Convolutional Neural Networks Explainability Deep Learning 52

Chatbots Life

MARCH 17, 2023

NLP, Natural Language Processing, and deep neural networks are the core building blocks of the technology, which allows our machines, appliances, and IOT devices to understand human language at ease. Simply put, GPTs are machine learning models based on the neural network architecture that mimics the human brain.

Conversational AI 52

Conversational AI ChatGPT Neural Network Chatbots 52

Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MAY 25, 2023

The potential of these enormous neural networks has both excited and frightened the public; the same technology that promises to help you digest long email chains also threatens to dethrone the essay as the default classroom assignment. Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment.

Large Language Models 52

Large Language Models BERT Neural Network LLM 52

Snorkel AI

MARCH 1, 2023

Model architectures that qualify as “supervised learning”—from traditional regression models to random forests to most neural networks—require labeled data for training. This can make it challenging for businesses to explain or justify their decisions to customers or regulators. What are some examples of Foundation Models?

BERT 83

BERT Natural Language Processing Large Language Models Neural Network 83

ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. If language grounding is achieved, then the network tells me how a decision was reached. In image captioning a network is not only required to classify objects, but instead to describe objects (including people and things) and their relations in a given image.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). There are a number of theories that try to explain this effect: When tensor updates are big in size, traffic between workers and the parameter server can get congested. PMLR, 2018. [2] arXiv preprint arXiv:1810.03264 (2018). [4]

Algorithm 78

Algorithm Deep Learning Neural Network Machine Learning 78

PyImageSearch

JANUARY 2, 2023

Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 ( Figure 1 ). Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools. That’s not the case.

Computer Vision 52

Computer Vision Deep Learning Neural Network Machine Learning 52

John Snow Labs

JUNE 6, 2023

Transformers are defined as a specific type of neural network architecture that have proven to be particularly effective for sequence classification tasks, thanks to their ability to capture long-term dependencies and contextual relationships in the data. Also the relationship with his wife is weird. Is she bad, or just weak?"

BERT 52

BERT Python NLP Neural Network 52