Unite.AI

AUGUST 1, 2024

Can you explain the key features and benefits of Pimloc's Secure Redact privacy platform? These deep learning algorithms are trained on domain-specific videos from sources like CCTV, body-worn cameras, and road survey footage. Pimloc’s AI models accurately detect and redact PII even under challenging conditions.

Machine Learning 147

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ML @ CMU

NOVEMBER 7, 2024

For the Risk Modeling component, we designed a novel interpretable deep learning tabular model extending TabNet. Formally, we use the risk scores (r_i) estimated by our trained deep learning model to compute proxies for the benefit of demining candidate grid cell (i) with centroid ((x_i,y_i)).

Machine Learning 61

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Viso.ai

MAY 7, 2024

GoogLeNet’s deep learning model was deeper than all the previous models released, with 22 layers in total. Increasing the depth of the Machine Learning model is intuitive, as deeper models tend to have more learning capacity and as a result, this increases the performance of a model.

Explainability 52

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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TheSequence

MARCH 9, 2025

One of RL's most notable early successes was demonstrated by Google DeepMind's AlphaGo, which defeated world-class human Go players in 2016 and 2017. This achievement highlighted RL's potential when combined with deep learning techniques, paving the way for deep reinforcement learning.

LLM 52

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Viso.ai

NOVEMBER 27, 2024

When the first YOLO was developed by Joseph Redmon and Ali Farhadi back in 2016, it overcame most problems with traditional object detection algorithms, with a new and enhanced architecture. Improved Explainability : Making the model’s decision-making process more transparent. Architecture The Architecture of YOLOv1.

Explainability 52

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. For visual question answering in Deep Learning using NLP, public datasets play a crucial role. Is aqua the maximum?

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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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NVIDIA

JUNE 5, 2023

Opening the Lab Doors In 2016, Girone was named CTO of CERN openlab, a group that gathers academic and industry researchers to accelerate innovation and tackle future computing challenges. In their presentations, physicists explained the challenges ahead. “By Industry participation was strong and enthusiastic about the technology.

AI 101

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NVIDIA

JANUARY 16, 2024

So began the organization’s trustworthy AI efforts, in 2016, to accelerate its work using ASR. “No To collect speech data in a transparent, ethically compliant, community-oriented way, Te Hiku Media began by explaining its cause to elders, garnering their support and asking them to come to the station to read phrases aloud. “It

AI Modeling 104

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AWS Machine Learning Blog

MAY 10, 2023

For example, using a large language model, Jupyter AI can help a programmer generate, debug, and explain their source code. This distribution includes deep learning frameworks like PyTorch, TensorFlow, and Keras; popular Python packages like NumPy, scikit-learn, and pandas; and IDEs like JupyterLab and the Jupyter Notebook.

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ML Review

MAY 3, 2018

Much the same way we iterate, link and update concepts through whatever modality of input our brain takes — multi-modal approaches in deep learning are coming to the fore. While an oversimplification, the generalisability of current deep learning approaches is impressive.

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Marktechpost

FEBRUARY 17, 2025

The study’s bibliometric analysis revealed a steady increase in AI safety research since 2016, driven by advancements in deep learning. A word cloud analysis highlighted key themes such as safe reinforcement learning, adversarial robustness, and domain adaptation.

Categorization 48

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AWS Machine Learning Blog

JANUARY 17, 2024

The underlying Deep Learning Container (DLC) of the deployment is the Large Model Inference (LMI) NeuronX DLC. He retired from EPFL in December 2016.nnIn He focuses on developing scalable machine learning algorithms. Qing has in-depth knowledge on the infrastructure optimization and Deep Learning acceleration.

Auto-complete 130

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Explosion

JANUARY 12, 2018

We founded Explosion in October 2016, so this was our first full calendar year in operation. Highlights included: Developed new deep learning models for text classification, parsing, tagging, and NER with near state-of-the-art accuracy. spaCy’s Machine Learning library for NLP in Python. Here’s what we got done.

NLP 52

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Pickl AI

APRIL 8, 2024

Example In 2016, an investigation by ProPublica revealed that a risk assessment algorithm used in US courts to predict recidivism rates was biased against Black defendants. Example In 2016, a chatbot developed by Microsoft called Tay was launched on Twitter. How Can We Ensure the Transparency of AI Systems?

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Heartbeat

JUNE 14, 2023

1] explain that the reliability diagram is plotted as follows. Conclusion In this article, we introduced the concept of calibration in deep neural networks. Finally, we explained about a few calibration techniques that can enable neural networks to output reliable and interpretable confidence estimates. 6] Zhang, Hongyi, et al.

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ML Review

JUNE 4, 2018

These ideas also move in step with the explainability of results. However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance. Source : Britz (2016)[ 62 ] CNNs can encode abstract features from images. 2014)[ 73 ] and Donahue et al.

Neural Network 52

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Heartbeat

MAY 11, 2023

The first version of YOLO was introduced in 2016 and changed how object detection was performed by treating object detection as a single regression problem. But just because we have all these YOLOs doesn’t mean that deep learning for object detection is a dormant area of research. We pay our contributors, and we don’t sell ads.

Deep Learning 75

Deep Learning Computer Vision Data Scientist ML 75

AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

Large Language Models 85

Large Language Models NLP LLM Deep Learning 85

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. TheSequence is a reader-supported publication.

LLM 64

LLM Natural Language Processing Machine Learning Deep Learning 64

Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., Explaining and harnessing adversarial examples. 2018; Sitawarin et al., Goodfellow, I.

Deep Learning 52

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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. With deep learning, the results were impressively good.

Convolutional Neural Networks 52

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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. International Conference on Machine Learning. PMLR, 2018. [2]

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Algorithm Deep Learning Neural Network ML 86

deepsense.ai

APRIL 16, 2023

This allows for the efficient processing of large amounts of data and can significantly reduce the time required for training deep learning models. First, we will explain the MLP block. 2016 ), only the activations at the boundaries of each partition are saved and shared between workers during training.

Large Language Models 52

Large Language Models Neural Network Deep Learning Explainability 52

Pickl AI

JULY 24, 2024

The advent of big data, coupled with advancements in Machine Learning and deep learning, has transformed the landscape of AI. Techniques such as neural networks, particularly deep learning, have enabled significant breakthroughs in image and speech recognition, natural language processing, and autonomous systems.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Natural Language Processing Neural Network 52

deepsense.ai

MAY 7, 2023

We talked about diffusion in deep learning, models that utilize it to generate images, and several ways of fine-tuning it to customize your generative model. We also explained the building blocks of Stable Diffusion and highlighted why its release last year was such a groundbreaking achievement. But don’t worry!

Neural Network 52

Neural Network Convolutional Neural Networks Explainability Deep Learning 52

Mlearning.ai

MARCH 2, 2023

Moreover, the most important theoretical foundations for BERT are explained and additional graphics are provided for illustration purposes. Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I. Finally, the impact of the paper and applications of BERT are evaluated from today’s perspective. Benchmark Results V.

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Heartbeat

AUGUST 23, 2023

Natural language processing and machine learning for law and policy texts. Artificial intelligence in law: The state of play 2016. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

NLP 52

NLP Natural Language Processing Algorithm Categorization 52

AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

NLP 52

NLP LLM Deep Learning ML 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. The SNLI dataset is over 100x larger than previous similar resources, allowing current deep-learning models to be applied to the problem.

Neural Network 40

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Heartbeat

FEBRUARY 2, 2024

In xxAI — Beyond Explainable AI Chapter. Deep residual learning for image recognition. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. Salewski, L.,

Computer Vision 52

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Sebastian Ruder

MAY 22, 2020

While a PhD encourages you to delve deep into a specific topic, you can add value by making connections between different topics or entirely different fields. Many ideas in deep learning take inspiration from other fields such as biology ( Hinton et al., 2016 ), physics ( Cohen et al., 2014 ), neuroscience ( Wang et al.,

Natural Language Processing 52

Natural Language Processing NLP ML Deep Learning 52

Snorkel AI

MAY 3, 2023

This is a model trained on the Berkeley Deep Drive-100k dataset , performing bounding box tracking on the road. Whenever we see videos like this, we may get this overly positive impression of how remarkable deep learning models are, which is true in some cases. To explain what I mean, let’s revisit this example.

Machine Learning 52

Machine Learning Neural Network Explainability Data Scientist 52

Snorkel AI

MAY 3, 2023

This is a model trained on the Berkeley Deep Drive-100k dataset , performing bounding box tracking on the road. Whenever we see videos like this, we may get this overly positive impression of how remarkable deep learning models are, which is true in some cases. To explain what I mean, let’s revisit this example.

Machine Learning 52

Machine Learning Neural Network Explainability Data Scientist 52

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

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Probably Approximately a Scientific Blog

AUGUST 14, 2018

Since I’ve started this blog 3 years ago, I’ve been refraining from writing about deep learning (DL), with the exception of occasionally discussing a method that uses it, without going into details. It’s a challenge to explain deep learning using simple concepts and without the caveat of remaining at a very high level.

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