Coders Technology’s Post

Our latest work, RuleFuser, demonstrates that intelligently combining learning-based and rule-based planners surpasses the safety performance of either planner individually. Moreover, this fusion approach exhibits improved robustness to distribution shifts. See the post below for more details. You can find the full paper on arxiv: https://lnkd.in/gbytkh_5

𝗖𝘂𝗿𝗶𝗼𝘂𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝘁𝘆𝗽𝗲𝘀 𝗼𝗳 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝘁𝗵𝗲𝗶𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀❓ Machine learning is transforming industries, from social media to autonomous vehicles. Understanding its three main types—supervised, unsupervised, and reinforcement learning—is key for leveraging its potential. 𝗦𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:  Supervised learning is defined as when a model gets trained on a “Labelled Dataset”. Learns from labelled data for tasks like fraud detection. 𝗨𝗻𝘀𝘂𝗽𝗲𝗿𝘃𝗶𝘀𝗲𝗱 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:  Unsupervised Learning Unsupervised learning is a type of machine learning technique in which an algorithm discovers patterns and relationships using unlabeled data. Finds patterns in unlabelled data, useful for customer behaviour analysis. 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:  Reinforcement machine learning algorithm is a learning method that interacts with the environment by producing actions and discovering errors. Mimics human learning through feedback, powering innovations like self-parking cars. With the machine learning market expected to reach $225.91 billion by 2030, now is the time to dive in! 💡 𝗪𝗵𝗮𝘁 𝘁𝘆𝗽𝗲 𝗼𝗳 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝗻𝘁𝗿𝗶𝗴𝘂𝗲𝘀 𝘆𝗼𝘂 𝗺𝗼𝘀𝘁? 𝗦𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀! #MachineLearning #ArtificialIntelligence #Innovation

Can we inject symbolic rules into learning-based planners to improve out-of-distribution robustness? 🤔 We’re excited to introduce RuleFuser, a novel method for integrating traffic rules into IL-based motion planners using evidential deep learning. 🚀 RuleFuser is developed on the key insight that learning-based planners excel in in-distribution (ID) scenarios while rule-based planners provide greater robustness in out-of-distribution (OOD) scenarios. Integrating these two into a framework that strategically deploys each planner in the appropriate regime can enhance overall performance. 🧠 We’ve tested RuleFuser on the nuPlan dataset, where it adapts effectively to OOD situations, achieving safety levels beyond those of IL or rule-based planners alone. 📊 This work is a collaboration between the Autonomous Vehicles Research Group at NVIDIA and The AirLab at Carnegie Mellon University🤝 Congratulations Jay Patrikar Sushant Veer Apoorva Sharma Marco Pavone Sebastian Scherer Interested to learn more? 📎 Paper: “RuleFuser: An Evidential Bayes Approach for Rule Injection in Imitation Learned Planners for Robustness under Distribution Shifts” Link: https://lnkd.in/dYTwv4Q5 #AutonomousDriving #AI #MachineLearning #AVSafety #SafeAI

AI Tip: Did you know that Machine Learning algorithms can be classified into three main types? 1. _Supervised Learning_: The algorithm learns from labeled data, like a teacher guiding a student. Example: Image recognition. 2. _Unsupervised Learning_: The algorithm discovers patterns in unlabeled data, like a detective solving a mystery. Example: Customer segmentation. 3. _Reinforcement Learning_: The algorithm learns through trial and error, like a gamer leveling up. Example: Autonomous vehicles. Understanding these types helps us apply Machine Learning effectively in various industries. Let's dive deeper! What ML applications interest you the most? #MachineLearning #AI #ArtificialIntelligence #DataScience #Innovation

🚀 Excited to share my latest project in computer vision! 📸 Leveraging advanced algorithms and deep learning techniques, we're pushing the boundaries of image recognition and analysis. From autonomous vehicles to healthcare diagnostics, the applications are endless and transformative. 🌐 🔍 Highlights: - Enhanced object detection - Real-time image processing - Improved accuracy in visual data interpretation Let's connect and discuss the future of computer vision and its impact across various industries! 💡 #ComputerVision #DeepLearning #AI #Innovation #TechTrends

I’m excited to share a recent project where I developed an AI simulation for autonomous vehicles. The goal was to teach cars to navigate traffic and avoid obstacles using neural networks and evolutionary algorithms. Each car in the simulation started with a random neural network and used a ray-casting sensor system to detect its surroundings. As they drove, the cars were evaluated on distance traveled, time spent, and avoiding collisions. The best-performing cars’ “brains” were selected for the next generation, with mutations added to improve learning. Over time, the cars evolved, becoming smarter with each generation, and the results were incredible to watch. I implemented this using JavaScript (ES6+), HTML5 Canvas, TensorFlow.js, and custom neural networks, which allowed for real-time visualization of the AI's learning process. This project gave me a hands-on understanding of how AI can mimic natural processes and improve autonomously. Watching the simulation progress from random movements to intelligent decision-making was a rewarding experience, highlighting the potential of AI in self-driving technology. If you're interested in learning more or collaborating, feel free to reach out! #ArtificialIntelligence #SelfDrivingCars #MachineLearning #TensorFlow #Innovation

🚀🔍 Just another day in the life of a Computer Vision Engineer! Today’s highlight: Teaching machines to see and understand the world. From detecting objects in images to recognizing patterns and anomalies, the possibilities are endless! 👁️💡 Working on innovative projects that blend AI with visual data, making strides in fields like Documents, autonomous driving, and augmented reality. It's amazing to witness the magic when technology meets creativity. #TechLife #ComputerVision #AI #MachineLearning #Innovation #FutureTech #EngineerLife #DeepLearning #AutonomousSystems #AugmentedReality #Documentation #CodingLife Stay curious, keep learning, and never stop exploring! 🌟

Deep learning is a cutting-edge subset of artificial intelligence (AI) that focuses on creating models that can automatically learn from large amounts of data, just like the human brain does. At NordStern Consulting, we're thrilled to be working at the forefront of AI innovation in the automotive sector by leveraging Bidirectional LSTMs (BiLSTMs). Unlike regular machine learning models, BiLSTMs process data in two directions—both past to present and future to past. This makes them perfect for understanding complex, time-dependent data like vehicle performance, sensor inputs, and autonomous driving systems. It's an exciting time as we push the boundaries of AI to make the roads safer and the driving experience smarter. #AI #DeepLearning #BiLSTM #AutomotiveInnovation #NordStern #AutonomousVehicles #MachineLearning #ML #Data #DataScience

New article: The inconvenient truth of ground truth errors in automotive datasets and DNN-based detection 👉 https://bit.ly/494XxfX The article highlights the errors in ground truth data used for training deep neural networks in automotive applications, which can affect the accuracy of object detection systems. The authors from the WMG at the University of Warwick propose a method to mitigate these errors and improve the reliability of autonomous vehicles. By Pak Hung Chan, Boda Li, Gabriele Baris, Qasim Sadiq & Valentina Donzella #machinelearning #deepneuralnetworks #automotive

From generating a 3D Avatar to driving an autonomous car or capturing a panorama picture on our phone, all these applications use a classic computer vision technique called feature matching. Surprising, right? Feature Matching is the process that takes two images and matches the similar feature points between the input image pairs. In our latest article, we will also learn: Why is feature matching still relevant in the deep learning era? What are the recent advancements in feature matching? What are the applications of feature matching? How do feature-matching algorithms work in code? Link to the article - https://buff.ly/3Wy94j1