Flipboard

MARCH 13, 2023

RLHF was developed by OpenAI and Google’s DeepMind team in 2017 as a way to improve reinforcement learning when a task involves complex or poorly-defined goals, making it difficult to design a suitable reward function. Periodically, a human evaluator checks ChatGPT responses and chooses those that best reflect the desired behavior.

ChatGPT 172

ChatGPT Large Language Models Neural Network Deep Learning 172

NYU Center for Data Science

AUGUST 24, 2023

Understanding the Math Behind Neural Nets These scientific discussions branch off in many directions, but one common goal within the MaD group is to develop a mathematical understanding of neural networks. “We The idea is to shed light on the “fundamental mathematical principles that explain [neural nets’] behavior.”

Neural Network 70

Neural Network Data Science Machine Learning Explainability 70

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

Unite.AI

MAY 8, 2024

He began his career at Yandex in 2017, concurrently studying at the Yandex School of Data Analysis. The most crucial point during this process was when I learned about neural networks and deep learning. Alexandr Yarats is the Head of Search at Perplexity AI. RAG is a general concept for providing external knowledge to an LLM.

Machine Learning 298

Machine Learning Software Engineer Large Language Models Data Analysis 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

Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

Convolutional Neural Networks 159

Convolutional Neural Networks Neural Network BERT Deep Learning 159

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. In the first step, potential object areas in the image are determined.

Algorithm 90

Algorithm Convolutional Neural Networks Neural Network Computer Vision 90

Heartbeat

OCTOBER 24, 2023

Open Neural Network Exchange, or ONNX, is a free and open-source ecosystem for deep learning model representation. Facebook and Microsoft created this tool in 2017 to make it simpler for academics and engineers to migrate models between various deep-learning frameworks and hardware platforms.

Neural Network 52

Neural Network Deep Learning Data Scientist Machine Learning 52

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

MARCH 26, 2024

described this model in the seminal paper titled “Attention is All You Need” in 2017. Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Vaswani et al.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

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

Explosion

FEBRUARY 12, 2017

In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. An independent representation means that the network can read a text in isolation, and produce a vector representation for it. Most NLP neural networks start with an embedding layer.

Neural Network 40

Neural Network Deep Learning Natural Language Processing NLP 40

Viso.ai

OCTOBER 7, 2024

We will explain its key principles, its applications in computer vision, benefits, and challenges, as well as best practices. Computer vision (CV) models rely on deep learning architectures consisting of artificial neural networks or convolutional neural networks (CNNs). This streamlines training and saves time.

Computer Vision 52

Computer Vision Machine Learning Deep Learning Convolutional Neural Networks 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. “Learning from Human Preferences,” June 13, 2017.

AI 130

AI AI OpenAI Large Language Models 130

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

TheSequence

NOVEMBER 22, 2023

I co-authored my first AI-related paper in 2000 ( using neural networks to manage on-CPU hardware resources ). 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

Unite.AI

JULY 24, 2023

The fundamental concept that the YOLO algorithm proposes is to use an end-to-end neural network using bounding boxes & class probabilities to make predictions in real time. There are three steps that explain how a YOLO algorithm works. The figure above explains the use of a trainable bag of freebies in the YOLOv7 algorithm.

Algorithm 130

Algorithm Computer Vision Explainability Neural Network 130

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). Unsupervised Recurrent Neural Network Grammars Yoon Kim, Alexander Rush, Lei Yu, Adhiguna Kuncoro, Chris Dyer, Gábor Melis. NAACL 2019.

Machine Learning 52

Machine Learning NLP Neural Network BERT 52

Mlearning.ai

MAY 12, 2023

Deployment of deep neural network on mobile phone. (a) NCNN (Neural Network Computing Library) NCNN is a high-performance neural network inference framework developed by Tencent, which is designed specifically for mobile and embedded devices [2]. 2] Tencent, Neural Network Computer Vision, [link] , 2019. [3]

Neural Network 52

Neural Network Deep Learning Computer Vision Large Language Models 52

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. Additional resources: Adrian, R. Deep learning for computer vision with Python. We pay our contributors, and we don’t sell ads.

Computer Vision 97

Computer Vision Robotics Deep Learning Convolutional Neural Networks 97

ML Review

JUNE 4, 2018

2017)[ 51 ] Introduction to Image Captioning Suppose that we asked you to caption an image; that is to describe the image using a sentence. 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. Source: Lu et al.

Neural Network 52

Neural Network Convolutional Neural Networks Computer Vision Deep Learning 52

Applied Data Science

JANUARY 4, 2022

Transformers taking the AI world by storm The family of artificial neural networks (ANNs) saw a new member being born in 2017, the Transformer. Initially introduced for Natural Language Processing (NLP) applications like translation, this type of network was used in both Google’s BERT and OpenAI’s GPT-2 and GPT-3.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Deep 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. The transformer architecture was introduced by Vaswani et al.

BERT 52

BERT Python NLP Neural Network 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. This explains the uncertainty of the model’s classification: the TE is sticking with the SS by design, creating biases in the data. Jay Alammar.

Computer Vision 78

Computer Vision Machine Learning ML Convolutional Neural Networks 78

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

Mlearning.ai

MARCH 2, 2023

Moreover, the most important theoretical foundations for BERT are explained and additional graphics are provided for illustration purposes. The latest achievements regarding the capabilities of modern chat systems still go back to the invention of transformers in 2017.

BERT 52

BERT NLP Deep Learning Neural Network 52

Applied Data Science

MARCH 11, 2022

In contrast to classification, a supervised learning paradigm, generation is most often done in an unsupervised manner: for example an autoencoder , in the form of a neural network, can capture the statistical properties of a dataset. Notice the plural: GANs are not one but two neural networks that are playing a game.

Neural Network 52

Neural Network Data Science AI AI 52

Mlearning.ai

MARCH 9, 2023

Explaining and harnessing adversarial examples. Fully convolutional networks for semantic segmentation. Explaining and harnessing adversarial examples. Neural Networks, 64, 59–63. Neural Networks, 64, 59–63. Intriguing properties of neural networks. 2015; Huang et al., Goodfellow, I.

Computer Vision 52

Computer Vision Deep Learning Machine Learning Algorithm 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

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

Heartbeat

AUGUST 2, 2023

Transformer model used for Language-based tasks [ Source ] The encoder consists of a heap of identical layers, containing two sub-layers: a multi-head self-attention mechanism and a position-wise feed-forward neural network. This architecture comprises two primary components: the encoder and the decoder.

Large Language Models 52

Large Language Models Prompt Engineering Prompt Engineer OpenAI 52

PyImageSearch

JANUARY 2, 2023

Deep neural networks and support vector machines are being explored in developing pre-diabetic screening tools. If healthcare professionals cannot explain how AI systems make diagnoses, this can weaken patients’ trust in the system. In Deep Learning, we need to train Neural Networks. That’s not the case.

Computer Vision 52

Computer Vision Deep Learning Neural Network Machine Learning 52

The Stanford AI Lab Blog

MAY 31, 2022

We will explain this in the next paragraph. 2022. ↩ Strategies for Pre-training Graph Neural Networks. 2017. ↩ For instance, BART and T5. ↩ Training language models for source code data is an active area of research, e.g., CodeX , AlphaCode. ↩ Link-aware LM Pretraining.

BERT 40

BERT Natural Language Processing NLP Neural Network 40

Sebastian Ruder

JANUARY 24, 2022

  While pre-trained transformers will likely continue to be deployed as standard baselines for many tasks, we should expect to see alternative architectures particularly in settings where current models fail short, such as modeling long-range dependencies and high-dimensional inputs or where interpretability and explainability are required.

NLP 52

NLP ML BERT Computational Linguistics 52

O'Reilly Media

JULY 25, 2023

To provide some coherence to the music, I decided to use Taylor Swift songs since her discography covers the time span of most papers that I typically read: Her main albums were released in 2006, 2008, 2010, 2012, 2014, 2017, 2019, 2020, and 2022. This choice also inspired me to call my project Swift Papers.

ChatGPT 98

ChatGPT UX Design AI Tools Metadata 98

Heartbeat

DECEMBER 14, 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. > Finished chain.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Deep Learning Computer Scientist 52

ML Review

MAY 3, 2018

In Deep Learning: Practice and Trends (NIPS 2017) [2] , prominent researchers offered a simple abstraction — that virtually all deep learning approaches can be characterised as either augmenting architectures or loss functions, or applying the previous to new input/output combinations. 2017) present their WLAS Network.

Neural Network 52

Neural Network Computer Vision Deep Learning Convolutional Neural Networks 52