ML Review

MARCH 31, 2019

We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3% This is, clearly, an optical illusion — but for the neural network.

Neural Network 52

Neural Network Machine Learning Deep Learning Explainability 52

Unite.AI

NOVEMBER 14, 2024

Founded in 2017, DeepL today has over 1,000 passionate employees and is supported by world-renowned investors including Benchmark, IVP, and Index Ventures. When I started the company back in 2017, we were at a turning point with deep learning. What began as a fun project quickly evolved into something more significant.

Neural Network 130

Neural Network LLM Large Language Models AI Modeling 130

Marek Rei

JANUARY 2, 2018

Venues First, let’s look at different publication venues between 2012-2017. Most other venues are also growing rapidly, with 2017 being the biggest year ever for ICML, ICLR, EMNLP, EACL and CoNLL. NAACL and COLING were notably missing from 2017, but we can look forward to both of them in 2018. That’s it for 2017.

NLP 52

NLP ML Neural Network Machine Learning 52

Marktechpost

APRIL 24, 2024

Image Source One of the first successful applications of RL with neural networks was TD-Gammon, a computer program developed in 1992 for playing backgammon. The computer player is a neural network trained using a deep RL algorithm, a deep version of Q-learning called deep Q-networks (DQN), with the game score as the reward.

Neural Network 113

Neural Network Deep Learning Algorithm Robotics 113

Unite.AI

MAY 29, 2024

This enhances speed and contributes to the extraction process's overall performance. Adapting to Varied Data Types While some models like Recurrent Neural Networks (RNNs) are limited to specific sequences, LLMs handle non-sequence-specific data, accommodating varied sentence structures effortlessly.

Data Extraction 273

Data Extraction Neural Network Large Language Models NLP 273

Unite.AI

JULY 29, 2024

In fact, traditional NMT models used Recurrent Neural Networks – RNNs – as the core architecture, since they are designed to process sequential data by maintaining a hidden state that evolves as each new input (word or token) is processed.

Neural Network 264

Neural Network Deep Learning Machine Learning Automation 264

NVIDIA

MARCH 14, 2024

Transforming AI: Hear more from Huang as he discusses the origins and impact of transformer neural network architecture with its creators and industry pioneers. It’s a gateway to the next wave of AI innovations.

Neural Network 144

Neural Network Robotics AI AI 144

Explosion

JANUARY 12, 2018

Declined 36 opportunities to “touch base” with investors and other professional networkers, who were confused by our radical we-spend-our-time-working approach. spaCy In 2017 spaCy grew into one of the most popular open-source libraries for Artificial Intelligence. Received $0 of external funding and retained 100% ownership.

NLP 52

NLP Neural Network Machine Learning Python 52

Marktechpost

APRIL 6, 2024

in 2017, marking a departure from the previous reliance on recurrent neural networks (RNNs) and convolutional neural networks (CNNs) for processing sequential data. This includes the weights of the neural network layers and the parameters of the attention mechanisms.

Large Language Models 124

Large Language Models NLP Convolutional Neural Networks Neural Network 124

Towards AI

MARCH 4, 2024

Long before Attention, Recurrent Neural Networks (RNNs) could handle entire sentences, processing them word by word in order and giving a meaningful output based on the task at hand. For instance, if it’s about translation, it would take an English sentence and turn it into Spanish.

Neural Network 95

Neural Network Large Language Models ChatGPT AI 95

Unite.AI

JULY 24, 2024

Over the past decade, advancements in machine learning, Natural Language Processing (NLP), and neural networks have transformed the field. In 2017, Apple introduced Core ML , a machine learning framework that allowed developers to integrate AI capabilities into their apps. Notable acquisitions include companies like Xnor.a

Machine Learning 223

Machine Learning NLP AI AI 223

Towards AI

MARCH 4, 2024

Long before Attention, Recurrent Neural Networks (RNNs) could handle entire sentences, processing them word by word in order and giving a meaningful output based on the task at hand. For instance, if it’s about translation, it would take an English sentence and turn it into Spanish.

Neural Network 87

Neural Network Large Language Models ChatGPT AI 87

Towards AI

JUNE 6, 2023

unsplash Attention-based transformers have revolutionized the AI industry since 2017. Now it’s possible to have deep learning models with no limitation for the input size.

Neural Network 90

Neural Network Computer Vision Deep Learning NLP 90

Marktechpost

MARCH 25, 2024

These models use deep learning techniques, particularly neural networks, to process and produce text that mimics human-like understanding and responses. LLMs Core: Transformer Architecture The transformer architecture, introduced in 2017, is at the core of LLMs. What are Large Language Models?

ChatGPT 133

ChatGPT Neural Network Large Language Models BERT 133

Towards AI

FEBRUARY 20, 2024

The state-of-the-art Natural Language Processing (NLP) models used to be Recurrent Neural Networks (RNN) among others. in their 2017 paper “Attention is All You Need,” Transformers revolutionized NLP by leveraging self-attention mechanisms, allowing the model to learn the relevance and context of all words in a sentence.

Neural Network 79

Neural Network Natural Language Processing NLP AI 79

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

Unite.AI

MARCH 21, 2024

The Origins of Mixture-of-Experts The concept of Mixture-of-Experts (MoE) can be traced back to the early 1990s when researchers explored the idea of conditional computation, where parts of a neural network are selectively activated based on the input data.

Large Language Models 147

Large Language Models Neural Network Natural Language Processing LLM 147

NVIDIA

FEBRUARY 22, 2024

All eight authors of “ Attention Is All You Need ,” the seminal 2017 NeurIPS paper that introduced the trailblazing transformer neural network architecture will appear in person at GTC on a panel hosted by NVIDIA Founder and CEO Jensen Huang. Gomez, Lukasz Kaiser, and Illia Polosukhin.

Neural Network 140

Neural Network Robotics Generative AI AI 140

Marktechpost

MAY 28, 2024

in 2017 , which relies on self-attention mechanisms to process input data in parallel, enhancing computational efficiency and scalability. This parallel processing capability allows Transformers to handle long-range dependencies more effectively than recurrent neural networks (RNNs) or convolutional neural networks (CNNs).

Neural Network 115

Neural Network Convolutional Neural Networks NLP Conversational AI 115

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 158

Convolutional Neural Networks Neural Network BERT Deep Learning 158

Mlearning.ai

APRIL 8, 2023

Over the years, we evolved that to solving NLP use cases by adopting Neural Network-based algorithms loosely based on the structure and function of a human brain. The birth of Neural networks was initiated with an approach akin to structuring solving problems with algorithms modeled after the human brain.

NLP 98

NLP Neural Network Natural Language Processing Convolutional Neural Networks 98

Viso.ai

DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Towards AI

SEPTEMBER 7, 2023

How is attention computed using Recurrent Neural Networks (RNNs)? Machine Translation We will look at Neural machine translation (NMT) as a running example in this article. NMT aims to build and train a single, large neural network that reads a sentence and outputs a correct translation.

Explainability 121

Explainability Neural Network Natural Language Processing NLP 121

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

Towards AI

AUGUST 25, 2023

Deep learning (DL) is a subset of machine learning that uses neural networks which have a structure similar to the human neural system. Packt, ISBN: 978–1787125933, 2017. O’Reilly Media, ISBN: 978–1491957660, 2017. In ML, there are a variety of algorithms that can help solve problems. 3, IEEE, 2014. Klein, and E.

Software Engineer 95

Software Engineer Machine Learning Neural Network Artificial Intelligence 95

Marktechpost

MARCH 26, 2024

Existing IR systems heavily rely on models such as BM25, E5, and various neural network architectures, focusing primarily on enhancing semantic search capabilities through keyword-based queries and short sentences. FOLLOWIR integrates three TREC collections: TREC News 2021, TREC Robust 2004, and TREC Common Core 2017.

Neural Network 104

Neural Network Large Language Models ML AI 104

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 67

Neural Network LLM OpenAI Large Language Models 67

Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. For example, in 2017, the Mask R-CNN algorithm was the fastest real-time object detector on the MS COCO benchmark, with an inference time of 330 ms per frame. How Does Image Classification Work?

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 98

Viso.ai

OCTOBER 10, 2024

We will elaborate on computer vision techniques like Convolutional Neural Networks (CNNs). 2023) utilized Satellite Image Time Series (SITS) with about 50 satellite datasets, gathered in the period 2017-2023. Answer: The most useful convolutional neural network for computer vision models over satellite imagery is ResNet50.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 52

AssemblyAI

MAY 3, 2023

These architectures are based on artificial neural networks , which are computational models loosely inspired by the structure and functioning of biological neural networks, such as those in the human brain. A simple artificial neural network consisting of three layers.

Large Language Models 363

Large Language Models Neural Network LLM ChatGPT 363

Towards AI

AUGUST 8, 2024

These experts are the specialized neural networks. Then, there’s the gating network, like a smart manager who knows exactly which expert to call on based on the task at hand. Transformers architecture, introduced back in 2017, revolutionized AI, particularly in language models.

Neural Network 82

Neural Network ML Algorithm AI 82

Marktechpost

SEPTEMBER 27, 2023

These neural network architectures, introduced in 2017, have revolutionized how machines understand and generate human language. Recent years have shown us how capable artificial neural networks have become in a variety of tasks. They changed the language tasks, vision tasks, etc. Time to meet the answer.

Neural Network 88

Neural Network Artificial Intelligence Artificial Intelligence ChatGPT 88

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

Marktechpost

MARCH 20, 2024

The field has oscillated between Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with unique strengths and limitations. In the evolving landscape of computer vision, the quest for models that adeptly navigate the tightrope between high accuracy and low computational cost has led to significant strides.

Convolutional Neural Networks 105

Convolutional Neural Networks Computer Vision Neural Network AI 105

Viso.ai

JUNE 26, 2024

The Concept of Neuromorphic Engineering [ Source ] Core Principles of Neuromorphic Engineering The core principle of neuromorphic engineering is to develop models that replicate the working mechanism of biological neural networks and process information just like a human brain does.

Neural Network 40

Neural Network Algorithm Robotics Computer Vision 40

Machine Learning Research at Apple

JUNE 19, 2023

2020) consider maps , where is an input convex neural network (ICNN), as defined by Amos et al. 2017, and fit with SGD using… To exploit that result, , Makkuva et al. 2020); Korotin et al.

Neural Network 40

Neural Network Machine Learning 40

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.

Machine Learning 298

Machine Learning Software Engineer Large Language Models Data Analysis 298