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generalization


Cartographic Generalization With A.I and Machine Learning

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The process of cartographic generalization is used to produce a harmonized picture at different scales of geospatial features. Generalization is an essential part of any cartographic production process and is, generally, a process that is still at least partly, manually driven. The move to ENC charting has enabled some degree of automation of chart creation at different scales through the development of features for managing "scale-dependent" features. Database driven production systems, able to store the data for multiple charts in a single database instance, are then able to reuse features for different charts reducing the need for manual intervention. The issue remains though, that many features require extensive manual editing in order to produce generalized products which are acceptable to both cartographer and end-user.


High-frequency component helps explain the generalization of convolutional neural networks

AIHub

There are many works aiming to explain the generalization behavior of neural networks using heavy mathematical machinery, but we will do something different here: with a simple and intuitive twist of data, we will show that many generalization mysteries (like adversarial vulnerability, BatchNorm's efficacy, and the "generalization paradox") might be results of our overconfidence in processing data through naked eyes. The models may have not outsmarted us, but the data has. Let's start with an interesting observation (Figure 1): we trained a ResNet-18 with the Cifar10 dataset, picked a test sample, and plotted the model's prediction confidence for this sample. Then we mapped the sample into the frequency domain through Fourier transform, and cut the frequency representation into its high-frequency component (HFC) and low-frequency component (LFC). Although this phenomenon can only be observed with a subset of samples ( 600 images), it's striking enough to raise an alarm.


Beyond the Turing Test: How One of the Most Important AI Papers of Recent Years Proposes a New…

#artificialintelligence

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Every once in a while, you encounter a research paper that is so simple and yet profound and brilliant that makes you wish you would have written it yourself. That's how I felt when I read François Chollet's On the Measure of Intelligence.


How Artificial Neural Networks Paved the Way For A Dramatic New Theory of Dreams

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Psychologists, neuroscientists and others have pondered the origin and role of dreams for time immemorial. Freud suggested that they were a way of expressing frustrations associated with taboos -- an idea that has long been discredited. Others have suggested dreams are a kind of emotional thermostat that allow us to control and resolve emotional conflicts. However, critics point out that most dreams lack strong emotional content and that emotionally neutral dreams are common. Still others say dreams are part of the process the brain uses to fix memories or to selectively forget unwanted or unneeded memories.


On Learning Invariant Representations for Domain Adaptation

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In domain adaptation the source (training) domain is related to but different from the target (testing) domain. During training, the algorithm can only have access to labeled samples from source domain and unlabeled samples from target domain. The goal is to generalize on the target domain. One of the backbone assumptions underpinning the generalization theory of supervised learning algorithms is that the test distribution should be the same as the training distribution. However in many real-world applications it is usually time-consuming or even infeasible to collect labeled data from all the possible scenarios where our learning system is going to be deployed.


Introduction to Causality in Machine Learning

#artificialintelligence

Despite the hype around AI, most Machine Learning (ML)-based projects focus on predicting outcomes rather than understanding causality. Indeed, after several AI projects, I realized that ML is great at finding correlations in data, but not causation. In our projects, we try to not fall into the trap of equating correlation with causation. This issue significantly limits our ability to rely on ML for decision-making. From a business perspective, we need to have tools that can understand the causal relationships between data and create ML solutions that can generalize well.


Top 10 Deep Learning Tips & Tricks - KDnuggets

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Dr. Arno Candel is Chief Architect at H2O.ai. He is considered one of the leading deep learning experts. He has over a decade of experience in high-performance computing. In past he has designed and implemented high-performance machine learning algorithms. Arno was named 2014 Big Data All-Star by Fortune Magazine.


Traffic Sign Classification Using Deep Learning in Python/Keras

#artificialintelligence

In this Guided Project, you will: … Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators. In this 1-hour long project-based course, you will be able to: – Understand the theory and intuition behind Convolutional Neural Networks (CNNs). Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators.2


Emotion AI: Facial Key-points Detection

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Emotion AI: Facial Key-points Detection Find helpful learner reviews, feedback, and ratings for Emotion AI: Facial Key-points Detection from Coursera Project Network. Build and train a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators. In this 1-hour long project-based course, you will be able to: – Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks.


Using Deep Learning Traffic Sign Classification in Python/Keras

#artificialintelligence

In this Guided Project, you will: … Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators. Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators. In this 1-hour long project-based course, you will be able to: – Understand the theory and intuition behind Convolutional Neural Networks (CNNs).