Desarrollos con Modelos de IA Generativa de Hugging Face
Descubre cómo implementar y utilizar los modelos de IA generativa de Hugging Face para crear aplicaciones innovadoras y soluciones tecnológicas avanzadas.
Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation
Explore coverage-guided fuzzing for tensor compilers using joint IR-pass mutation, enhancing reliability and detecting bugs in deep learning systems.
Input-Relational Verification of Deep Neural Networks
Explore novel input-relational verification techniques for deep neural networks using difference tracking and the DiffPoly abstract domain. Gain insights into improving precision for various properties.
Incremental Verification of Neural Networks
Explore a novel framework for efficient, incremental, and complete verification of updated deep neural networks, improving scalability and speed in determining trustworthy properties.
Architecture-Preserving Provable Repair of Deep Neural Networks
Explore architecture-preserving V-polytope provable repair of deep neural networks. Learn about a novel approach that guarantees specification satisfaction while preserving DNN architecture, supporting various layers and activation functions.
Personalizing Your Images with AI-Powered Features - Conf42 Python 2024
Explore AI-powered image personalization techniques, from handling large image sets to implementing automated management solutions. Includes practical demo and resources for further learning.
Future of LLMs and Machine Learning Productionization - Conf42 LLMs 2024
Explore the future of LLM deployment, traditional AI models in production, and generative AI application development using Langchain frameworks. Gain insights into MLOps and ChatGPT's impact.
Deep Learning for Protein Structure Prediction
Explore deep learning techniques for predicting protein structures, from traditional methods to AlphaFold's advancements. Gain insights into molecular dynamics, homology modeling, and cutting-edge AI approaches in structural biology.
Moving Beyond Screening via Generative Machine Learning Models for Materials Discovery
Explore generative machine learning models for materials discovery, moving beyond screening to generate novel structures and chemistries using advanced algorithms and techniques.
Brain-Inspired Multimodal Deep Learning
Explore brain-inspired approaches to multimodal deep learning, enhancing AI systems with cognitive-like capabilities.
Multimodality and Data Fusion Techniques in Deep Learning
Explore multimodal deep learning and data fusion techniques, understanding their applications and potential in AI through real-world examples and future developments.
Advancing Personalized Medicine and Radiotherapy through AI-Enabled Computer Vision
Explore AI and computer vision's impact on medical imaging for personalized medicine and radiotherapy, covering research, tools, and real-world applications.
What Does the AI Revolution Mean for Open Source, Open Tech and Open Societies
Explore the impact of AI on open source, tech, and society. Examine challenges like access, innovation, discrimination, and environmental concerns. Discuss ethical AI solutions.
Vision-Based Activity Detection Inside Vehicles - Automotive In-Cabin Sensing
Explore vision-based activity detection for in-vehicle safety, covering data collection, algorithm development, and SoC integration. Learn to create robust systems for monitoring driver behavior and reducing distraction-related accidents.
Synthetic Data for Computer Vision Problems When Real Annotation Is Too Expensive
Explore synthetic data generation for computer vision tasks when labeled data is scarce. Learn tools and approaches to build effective models using primarily synthetic data.
Capsule Network-based Framework for Identification of COVID Cases from X-ray Images
Explore a Capsule Network framework for COVID-19 detection in X-ray images, addressing small dataset challenges and outperforming CNN models.
Prompt Optimization and Parameter Efficient Fine Tuning for Large Language Models
Explore state-of-the-art prompt optimization techniques and parameter efficient fine-tuning methods for large language models to enhance performance on various tasks.
Efficient Inference of Extremely Large Transformer Models
Explore techniques for optimizing large transformer models, including compression, efficient attention, and parallelism for faster, smaller, and cost-effective inference.
Declarative Machine Learning Systems - Ludwig and Predibase
Explore declarative machine learning systems, focusing on Ludwig framework and Predibase enterprise solution for flexible, simplified ML infrastructure.
Deep Learning-Based Lung Ultrasound Video Classification to Predict COVID-19
Explore deep learning methods for COVID-19 detection using lung ultrasound videos, focusing on results, methodology, and explainability of the approach.
Productionizing Deep Learning Models at Scale
Explore emerging patterns and best practices for deploying machine learning and deep learning models in production environments at scale.