Explosion

NOVEMBER 9, 2016

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. This post explains the components of this new approach, and shows how they’re put together in two recent systems. 2016) introduce an attention mechanism that takes a single matrix and outputs a single vector.

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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.

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Unite.AI

JULY 24, 2023

The YOLO concept was first introduced in 2016 by Joseph Redmon, and it was the talk of the town almost instantly because it was much quicker, and much more accurate than the existing object detection algorithms. There are three steps that explain how a YOLO algorithm works. What is YOLO? How Does YOLO Work?

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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. Preprint posted online June 21, 2016. New York, NY: W.W. link] 7 Ngo R.

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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?

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Explosion

JANUARY 12, 2018

We founded Explosion in October 2016, so this was our first full calendar year in operation. In August 2016, Ines wrote a post on how AI developers could benefit from better tooling and more careful attention to interaction design. The DarkNet code base is a great way to learn about implementing neural networks from scratch.

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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.

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Probably Approximately a Scientific Blog

MAY 25, 2016

I'll explain it shortly: I'm working on automated methods to recognize that a certain term's meaning (word or multi-word expression) can be inferred from another's. We started by improving path representation, using a recurrent neural network. Now is such a time.* so let me share that with you.

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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.

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Dlabs.ai

AUGUST 10, 2020

One of the first widely discussed chatbots was the one deployed by SkyScanner in 2016. We asked our AI researchers to explain: Our AI team has developed a model with intent accuracy recognition at 98% – exceeding previously existing solutions, which was shown on the well known ATIS dataset and featured as number 1 on Papers with Code.

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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. Johnson et al. What is Perceptual Loss?

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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!

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TheSequence

DECEMBER 13, 2023

My path to working in AI is somewhat unconventional and began when I was wrapping up a postdoc in theoretical particle physics around 2016. I was surprised to learn that a few lines of code could outperform features that had been carefully designed by physicists over many years. Why another Transformers book, and what sets this one apart?

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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. 2018; Papernot et al., Goodfellow, I.

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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. arXiv preprint arXiv:1609.04836 (2016). [3] PMLR, 2018. [2]

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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.

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John Snow Labs

MAY 23, 2023

VQA frameworks combine two Deep Learning architectures to deliver the final answer: Convolutional Neural Networks (CNN) for image recognition and Recurrent Neural Network (RNN) (and its special variant Long Short Term Memory networks or LSTM) for NLP processing.

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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.

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Snorkel AI

MAY 3, 2023

A fun story that I want to share—I remember back in 2016-2017ish when we started working on this problem and submitted one of our first papers on OOD detection called Odin to the conference. Before we get into the methodology, I wanted to spend a couple of slides explaining “the why”. Out-of-distribution detection is a hard problem.

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Snorkel AI

MAY 3, 2023

A fun story that I want to share—I remember back in 2016-2017ish when we started working on this problem and submitted one of our first papers on OOD detection called Odin to the conference. Before we get into the methodology, I wanted to spend a couple of slides explaining “the why”. Out-of-distribution detection is a hard problem.

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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.

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ML Review

MAY 3, 2018

Similar to the advancements seen in Computer Vision, NLP as a field has seen a comparable influx and adoption of deep learning techniques, especially with the development of techniques such as Word Embeddings [6] and Recurrent Neural Networks (RNNs) [7]. 2016) — “ LipNet: End-to-End Sentence-level Lipreading.” [17]

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Probably Approximately a Scientific Blog

AUGUST 14, 2018

It’s a challenge to explain deep learning using simple concepts and without the caveat of remaining at a very high level. Going Deep Representation Learning Recurrent Neural Networks What is not yet working perfectly? A deep neural network is a network that contains one or more hidden layers, which are also learned.

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deepsense.ai

MARCH 28, 2023

In the case of diffusion, the encoder’s job is overtaken by a mathematical process – the neural network approach is redundant. This denoising process is performed by a neural network, and arguably this is the place where most of the heavy lifting is done. Below we will explain each of these in a bit more detail.

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Unite.AI

NOVEMBER 28, 2024

The invention of the backpropagation algorithm in 1986 allowed neural networks to improve by learning from errors. In 2016, DeepMind's AlphaGo defeated Lee Sedol, one of the world’s top Go players, in a game renowned for its strategic depth and complexity. The post Has AI Taken Over the World?

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NVIDIA

NOVEMBER 18, 2024

Milestones like Tokyo Tech’s Tsubame supercomputer in 2008, the Oak Ridge National Laboratory’s Titan supercomputer in 2012 and the AI-focused NVIDIA DGX-1 delivered to OpenAI in 2016 highlight NVIDIA’s transformative role in the field. Since CUDA’s inception, we’ve driven down the cost of computing by a millionfold,” Huang said.

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PyImageSearch

MAY 1, 2023

And, of course, all of this wouldn’t have been possible without the power of Deep Neural Networks (DNNs) and the massive computation by NVIDIA GPUs. 2016) published the YOLO research community gem, “ You Only Look Once: Unified, Real-Time Object Detection, ” at the CVPR (Computer Vision and Pattern Recognition) Conference.

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Bugra Akyildiz

NOVEMBER 17, 2024

They have shown impressive performance in various computer vision tasks, often outperforming traditional convolutional neural networks (CNNs). 2020) EBM : Explainable Boosting Machine (Nori, et al. Positional embeddings : Added to the patch embeddings to retain positional information. 2019; Lou, et al.

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Mlearning.ai

FEBRUARY 10, 2023

Back in 2016 I was trying to explain to software engineers how to think about machine learning models from a software design perspective; I told them that they should think of a database. How are neural networks like databases? And are there new questions we could answer if we stored the data differently?

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