Category: Machine Learning

Orchestrating ML Success on Your Own Terms: The Benefits of RoadMentor’s Customer Infrastructure

The RoadMentor platform is an innovative machine learning operations (MLOps) platform specifically designed for advanced driver assistance systems (ADAS). This platform leverages the latest in infrastructure orchestration technologies to provide customers with a powerful and flexible solution for managing their ML models and workflows. In this blog, we will delve into the key features of…
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Navigating the World with Words: How Large Language Models Power Autonomous Systems

HD maps are a critical component of autonomous vehicle technology, providing information on the physical environment in which the vehicle operates. The semantic layer of an HD map is a critical component of this information, representing the meaning and context of the physical environment. One of the most effective ways to encode this information is…
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Revolutionizing the Road: How Hyperspec AI’s Fleet Learning Approach is Changing the Game for Detecting Lane Merges on the Highway

Automating the process of data annotation has been a long-standing challenge in the field of machine learning, especially when it comes to detecting lane merges on the highway. The traditional approach of manually labeling data is not only costly in terms of time and resources, but it also hinders the ability to quickly detect lane…
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Navigating the complexities of coordinate frames: A guide to understanding the differences in Three.js, ROS, and Unreal Engine

Coordinate frames are an important aspect of robotics and computer graphics, as they determine the position and orientation of objects in 3D space. However, different platforms and software libraries use different conventions for their coordinate frames, which can lead to confusion and errors when working with multiple systems. Three.js, ROS, and Unreal Engine are all…
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Unsupervised Machine Learning and Speed Ups in Labeling

Unsupervised machine learning is a type of machine learning that involves training a model on a dataset without providing it with labeled examples. Instead, the model is asked to discover the underlying structure of the data on its own. One popular technique for unsupervised machine learning is clustering, which involves grouping similar data points together.…
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Why Overfitting Data is a Handicap for ADAS Deployment

ADAS enabled vehicles are most commonly driven on highways and main roads for a number of reasons. One reason is that these types of roads tend to have less complex driving scenarios compared to residential areas or side roads. Highways and main roads often have fewer intersections and more predictable traffic patterns, which can make…
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Integrating Federated Learning into CVAT & MLFlow

Federated learning is a machine learning technique that enables the training of models on decentralized data, without the need for the data to be centralized in one location. Instead, data is distributed across a number of different devices or edge devices, such as smartphones or IoT devices, and the model is trained by aggregating updates…
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WebGPU and Federated Learning with FedML, a Killer Combo

WebGPU is a new technology that allows developers to take advantage of the power of the GPU (graphics processing unit) in modern browsers. It allows for faster and more efficient processing of complex tasks, including machine learning algorithms. One of the key benefits of WebGPU is its ability to support federated learning. Federated learning is…
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Training a Model in MLFlow from CVAT label data

CVAT (Computer Vision Annotation Tool) is an open source tool developed by Intel that allows users to label and annotate images and video data for training machine learning models. MLFlow is an open source platform for managing the end-to-end machine learning lifecycle. It provides tools for tracking experiment runs, organizing code, and reproducing runs, among…
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Integrating CVAT annotation into MLFlow

CVAT (Computer Vision Annotation Tool) is an open-source annotation tool for computer vision tasks that allows users to label and manage large datasets quickly and efficiently. Integrating CVAT with an MLFlow framework can streamline the data labeling process and make it easier to track and analyze the performance of your machine learning models. Here is…
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Dimensionality reduction and how it helps reduce the search space by leveraging known information

Removing dimensions or making invariant features is a technique used to reduce the search space in a problem by eliminating certain variables or making them irrelevant. This can be especially useful in LiDAR slam, which has a 6Dof search space (x, y, z, roll, pitch, yaw). By reducing the dimensions, the number of permutations in…
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Data balancing to remove data bias, do a deep dive on different approaches

Data balancing is the process of ensuring that a machine learning dataset is representative of the real-world population from which it is drawn. This is important because if a dataset is biased, then the machine learning model that is trained on that dataset will also be biased. Bias in machine learning models can lead to…
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How data structures impact time complexity of code

Data structures are the foundation of efficient algorithms and play a crucial role in determining the time complexity of a piece of code. Time complexity refers to the amount of time it takes for an algorithm to complete, and it is a measure of how the runtime of an algorithm grows as the input size…
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Techniques to Boost True Positive Rates using Independent Combinatorics

True positive rates, or the proportion of positive cases that are correctly identified, are an important consideration in many areas. One way to boost true positive rates is to utilize independent combinatorics, a set of techniques that involve combining multiple independent pieces of information or evidence to make a decision. Here are some specific techniques…
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Improving Model Performance from 99.9% to 99.999999%

Artificial intelligence (AI) has come a long way in recent years, with many industries adopting it to improve efficiency and productivity. However, there is always room for improvement, and one area where AI can be further enhanced is in terms of accuracy. Currently, many AI systems have an accuracy rate of around 99.9%, which is…
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Message Queues in Multi-Threaded Applications

Message queues are a software component that allow different parts of a system, or different systems, to communicate with each other by passing messages. They are often used in architectures that are distributed, meaning that they consist of multiple independent systems that need to communicate with each other. One common use case for message queues…
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Validating Search Algorithms with a Dimensionality Analysis

Brute forcing is a method of searching through a search space by testing every possible solution. This method can be useful for validating search algorithms, as it provides a means of comparing the results of the search algorithm to the true solution. One way to validate search algorithms using brute forcing is to quantify the…
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Scope of noise and how to expand it

Noise is an inherent part of any machine learning (ML) model development process. It refers to any random or unpredictable variations in the data that can impact the accuracy of the model. Noise can come from a variety of sources, including measurement errors, data quality issues, and even the sampling process itself. In the process…
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Balancing Entropy and Recall Rates for AutoEncoders

Autoencoders are neural network architectures that are used to learn a compact representation of input data, called the encoding, and then reconstruct the input data from this encoding. Autoencoders can be used to process point cloud data, which is a set of points in space that represent the surface of an object, as well as…
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