Arbre de décision à gradient amplifié extrême

Définition

Le nom propre XGBoost (pour Extreme Gradient Boosting) est une implantation populaire d'un algorithme d'arbres de décision à gradient amplifié qualifiée d'extrême. XGBoost est basé sur l'amplification de gradient (en anglais, gradient boosting) pour la construction de modèles à base d'ensembles d'arbres de décision.

Compléments

L'algorithme XGBoost, s'est distingué dans de nombreux défis en sciences des données comme Kaggle. XGBoost est maintenant un choix presque automatique pour les problèmes de type classification ayant un nombre relativement petit de données.

Français

arbre / arbres de décision à gradient amplifié extrême

arbre / arbres à gradient amplifié extrême

arbre / arbres de décision à amplification de gradient extrême

arbre / arbres à amplification de gradient extrême

renforcement XG (Attention! Confusion possible avec l'apprentissage par renforcement)

XGBoost

Anglais

XGBoost

Extreme Gradient Boosting

Sources

Source : Accenture - applied intelligence glossary

[How Do Humanoid Robots Work? Humanoid robots are learning and adapting faster than ever before, using artificial intelligence models to perceive, sense, plan, and autonomously perform complex tasks in wide range of settings. These robots are equipped with sophisticated actuators, sensors, and on-robot compute and software that help them move and interact to mimic human dexterity, and even self-navigate. Robots are taught various movements and responses within simulated environments so they can handle the unpredictability of real-world scenarios. 

After rigorous AI training, optimized models and software workflows are deployed on the robot’s onboard computing systems. The combination of effective on-chip compute, AI, actuators, sensors, manipulation, dexterity, and locomotion policies makes humanoid robots highly versatile with the potential of taking on a variety of tasks.

Because our world is built for humans by humans, humanoid robots shine in their ability to operate efficiently in human-centric environments with minimal adjustments.

How Do You Train Humanoid Robots? Robot learning is driven by adaptive algorithms and comprehensive training in both virtual and real-world settings. This lets humanoid robots acquire and refine intricate skills like bipedal locomotion, object manipulation, and social interactions. 

Developers use an optimized software stack that includes data ingestion and processing pipelines, training frameworks, and containerized microservices to power scalable and efficient training. AI foundation models, simulation environments, synthetic data, and specialized learning techniques such as reinforcement learning and imitation learning are used to train robots to perform tasks like grasping objects or navigating obstacles in different scenes.

Training uses digital twins that accurately simulate real scenarios, providing a risk-free environment for robot models to learn and improve. This eliminates the risk of physical damage and enables faster iteration by training many different models simultaneously. In simulations, operators can easily introduce variability and noise into scenes, giving robot models a richer set of experience data to learn from.

Once the robot’s skills are adequately refined in the digital world, the models can be deployed on the real robot. In some cases, training continues with the robot operating and practicing in the real world.    

Important emerging humanoid robot training techniques include: 

Machine Learning: Humanoid robots are equipped with machine learning algorithms that let them analyze data to learn from past actions and process data from sensors to make informed decisions in real time.  Imitation Learning: Robots can acquire new skills by replicating movements demonstrated by humans. These actions are captured by sensors or cameras, then translated into robotic commands that mimic the observed behaviors. This approach is especially useful for teaching robots nuanced, complex tasks that are difficult to codify with traditional programming methods. Reinforcement Learning: In this technique, an algorithm uses a mathematical equation to reward robots for correct actions and penalize them for incorrect actions. Through trial and error and the associated reward system, the robot adapts and improves its performance over time. https://www.nvidia.com/en-us/glossary/humanoid-robot/

Numba Numba is an open-source, just-in-time compiler for Python code that developers can use to accelerate numerical functions on both CPUs and GPUs using standard Python functions.

https://www.nvidia.com/en-us/glossary/numba/

NumPy

NumPy is a powerful, well-optimized, free open-source library for the Python programming language, adding support for large, multi-dimensional arrays (also called matrices or tensors). NumPy also comes equipped with a collection of high-level mathematical functions to work in conjunction with these arrays. These include basic linear algebra, random simulation, Fourier transforms, trigonometric operations, and statistical operations.

NumPy stands for ‘numerical Python’, and builds on the early work of the Numeric and Numarray libraries with the goal to give fast numeric computation to Python.  Today NumPy has numerous contributors and is sponsored by NumFOCUS.

As the core library for scientific computing, NumPy is the base for libraries such as Pandas, Scikit-learn, and SciPy. It’s widely used for performing optimized mathematical operations on large arrays.

https://www.nvidia.com/en-us/glossary/numpy/

Polars

Polars is an open-source DataFrame library for data manipulation and analysis. It is implemented in Rust and uses Apache Arrow’s columnar memory format for efficient data processing. The library provides a structured and typed API, enabling users to perform a wide range of data transformations. Polars is designed to maximize computational efficiency and supports various file formats and data storage layers, making it compatible with modern workflows.

https://www.nvidia.com/en-us/glossary/polars/

Sentiment Analysis

Sentiment analysis is the automated interpretation and classification of emotions (usually positive, negative, or neutral) from textual data such as written reviews and social media posts.

https://www.nvidia.com/en-us/glossary/sentiment-analysis/

Mixed Integer Programming?

Mixed integer programming (MIP) is a mathematical optimization technique that solves problems involving a mix of continuous variables (which can have any value, including decimals and fractions), discrete variables (which must be countable whole numbers), and binary variables (which can only take values 0 or 1).

https://www.nvidia.com/en-us/glossary/mixed-integer-programming/

Stream Processing

Stream processing is the continuous processing of new data events as they’re received.

https://www.nvidia.com/en-us/glossary/stream-processing/

Synthetic Data

Synthetic data is artificially generated data used to accelerate AI model training across many domains, such as robotics and autonomous vehicles.

What Is Synthetic Data Generation (SDG)?

Synthetic data generation is the creation of text, 2D or 3D images, and videos in the visual and non-visual spectrum using computer simulations, generative AI models, or a combination of the two. This technique can be used for structured and unstructured data and is often applied to fields where original data is scarce, sensitive, or difficult to collect.

How Does Synthetic Data Generation Work?

Building artificial intelligence models that deliver accuracy and performance depends on high-quality, diverse datasets with careful labeling. However, real-world data is often limited, unrepresentative of the desired sample, or unavailable due to data protection standards. Due to these limitations, acquiring and labeling original data is a time-consuming, costly process that can delay the progress of AI development.

Synthetic data addresses these challenges with artificially generated data created based on rules, algorithms, or simulations that mimic the statistical properties of real data. Developers and researchers can use this synthetic data to conduct robust testing and training of models without the constraints or privacy concerns associated with using actual data.

https://www.nvidia.com/en-us/glossary/synthetic-data-generation/

World Foundation Models

World foundation models (WFMs) are neural networks that simulate real-world environments as videos and predict accurate outcomes based on text, image, or video input. Physical AI developers use WFMs to generate custom synthetic data or downstream AI models for training robots and autonomous vehicles.

What Is a World Model?

World models are generative AI models that understand the dynamics of the real world, including physics and spatial properties. They use input data, including text, image, video, and movement, to generate videos. They understand the physical qualities of real-world environments by learning to represent and predict dynamics like motion, force, and spatial relationships from sensory data.

Generative Foundation Models

Foundation models are AI neural networks trained on massive unlabeled datasets to generate new data based on input data. Because of their generalizability, they can greatly accelerate the development of a wide range of generative AI applications. Developers can fine-tune these pretrained models on smaller, task-specific datasets for a custom domain-specific model.

Developers can tap into the power of foundation models to generate high-quality data for training AI models in industrial and robotics applications, such as factory robots, warehouse automation, and autonomous vehicles on highways or on difficult terrains. Physical AI systems require large-scale, visually, spatially and physically accurate data for learning through realistic simulations. World foundation models generate this data efficiently at scale.

There can be different types of WMFs:

Prediction Models – These models predict world generation and synthesize continuous motion based on a text prompt, input video, or by interpolating between two images. They enable realistic, temporally coherent scene generation, making them valuable for applications like video synthesis, animation, and robotic motion planning.Style Transfer Models – These models guide outputs based on specific inputs using ControlNet, a model network that conditions a model’s generation based on structured guidance such as segmentation maps, lidar scans, depth maps, or edge detection. By mirroring input instructions visually, these models can control layout and motion while producing diverse, photorealistic results grounded in a text prompt. This makes them useful for applications requiring structured image or video synthesis, like digital twin simulations and environment reconstruction.Reasoning Models – These models take multimodal inputs and analyze them over time and space. They use chain-of-thought reasoning based on reinforcement learning to understand what’s happening and decide the best actions. These models help AI tackle complex tasks like distinguishing real vs. synthetic data, selecting useful training data for robots or games, predicting robotic actions, and optimizing autonomous system logistics. What Are the Real-World Applications of World Foundation Models?

World models, when used with 3D simulators, serve as virtual environments to safely streamline and scale training for autonomous machines. With the ability to generate, curate, and encode video data, developers can better train autonomous machines to sense, perceive, and interact with dynamic surroundings.

Autonomous Vehicles

WFMs bring significant benefits to every stage of the autonomous vehicle (AV) pipeline. With pre-labeled, encoded video data, developers can curate and train the AV stack to recognize the behavior of vehicles, pedestrians, and objects more accurately. These models can also generate new scenarios, such as different traffic patterns, road conditions, weather, and lighting, to fill training gaps and expand testing coverage. They can also create predictive video simulations based on text and visual inputs, accelerating virtual training and testing.

Robotics

WFMs generate photorealistic synthetic data and predictive world states to help robots develop spatial intelligence. Using virtual simulations powered by physical simulators, these models let robots practice tasks safely and efficiently, accelerating learning through rapid testing and training. They help robots adapt to new situations by learning from diverse data and experiences.

Modified world models enhance planning by simulating object interactions, predicting human behavior, and guiding robots to reach goals accurately. They also improve decision-making by running multiple simulations and learning from feedback. With virtual simulations, developers can reduce real-world testing risks, cutting time, costs, and resources.

What Are the Benefits of World Foundation Models?

Building a world model for a physical AI system, like a self-driving car, is resource-and time-intensive. First, gathering real-world datasets from driving around the globe in various terrains and conditions requires petabytes of data and millions of hours of simulation footage. Next, filtering and preparing this data demands thousands of hours of human effort. Finally, training these large models costs millions of dollars in GPU compute and requires many GPUs.

WFMs aim to capture the underlying structure and dynamics of the world, enabling more sophisticated reasoning and planning capabilities. Trained on vast amounts of curated, high-quality, real-world data, these neural networks serve as visually, spatially, and physically aware synthetic data generators for physical AI systems.

WFMs allow developers to extend generative AI beyond the confines of 2D software and bring its capabilities into the real world while reducing the need for real-world trials. While AI’s power has traditionally been harnessed in digital domains, world models will unlock AI for tangible, real-world experiences.

Realistic Video Generation

World models can create more realistic and physically accurate visual content by understanding the underlying principles of how objects move and interact. These models can generate realistic 3D worlds on demand for many uses, including video games and interactive experiences. In certain cases, outputs from highly accurate world models can take the form of synthetic data, which can be leveraged for training perception AI.

Current AI video generation can struggle with complex scenes and has limited understanding of cause and effect. However, world models paired with 3D simulation platforms and software are showing the potential to demonstrate a deeper understanding of cause and effect in visual scenarios, such as simulating a painter leaving brush strokes on a canvas.

Predictive Intelligence

WFMs help physical AI systems learn, adapt, and make better decisions by simulating real-world actions and predicting outcomes. They allow systems to "imagine" different scenarios, test actions, and learn from virtual feedback—just like a self-driving car practicing in a simulator to handle sudden obstacles or bad weather. By predicting possible outcomes, an autonomous machine can plan smarter actions without needing real-world trials, saving time and reducing risk.

When combined with large language models (LLMs), world models help AI understand instructions in natural language and interact more effectively. For example, a delivery robot could interpret a spoken request to "find the fastest route" and simulate different paths to determine the best one.

This predictive intelligence makes physical AI models more efficient, adaptable, and safer—helping robots, autonomous vehicles, and industrial machines operate smarter in complex, real-world environments.

Improved Policy Learning

Policy learning entails exploring strategies to find the best actions. A policy model helps a system, like a robot, decide the best action to take based on its current state and the broader state of the world. It links the system’s state (e.g., position) to an action (e.g., movement) to achieve a goal or improve performance. A policy model can be derived from fine-tuning a model. Policy models are commonly used in reinforcement learning, where they learn through interaction and feedback.

Optimizing for Efficiency, Accuracy, and Feasibility

Use a reasoning WFM to filter and critique synthetic data, improving quality and relevance at speed.

World models enable strategy exploration, rewarding the most effective outcomes. Add a reward module to run simulations and build cost models that track resource use—boosting both performance and efficiency for real-world tasks.

https://www.nvidia.com/en-us/glossary/world-models/

XGBoost

XGBoost is an open-source software library that implements optimized distributed gradient boosting machine learning algorithms under the Gradient Boosting framework.

Source : nvidia