GPU Accelerated Deep Learning for Scala 3 with Storch
Discover Storch, a new PyTorch-based library for GPU-accelerated deep learning in Scala 3. Learn about tensor operations, automatic differentiation, and neural network APIs for building enterprise-grade machine learning applications.
Artificial Intelligence for Geometry Processing
Explores deep learning techniques for processing irregular 3D geometric data, addressing challenges in surface reconstruction and geometric modeling. Highlights potential applications and future research directions.
Self-supervising 3D Scene Representations for Perception and Control
Explore 3D scene representation models that learn object permanence, emergence, and dynamics without annotations, supporting language grounding and generalizing across scenes and cameras.
Fast and Accurate Deep Neural Networks Training
Exploring LARS and LAMB optimizers for scaling deep learning on supercomputers, enabling faster training and higher accuracy in AI applications across various industries.
Learning 3D Perception from Single Images - Unifying Learning and Geometry
Exploring unified learning approaches for single image 3D understanding, combining 2D perception with 3D world modeling to enable more comprehensive computer vision systems.
Deep Learning IV - Advanced Topics in Neural Networks
Explore advanced deep learning concepts with NYU professor Joan Bruna, covering cutting-edge techniques and applications in neural networks and machine learning.
Reinforcement Learning IV - Advanced Concepts and Applications
Explore advanced reinforcement learning concepts with Stanford's Emma Brunskill, covering cutting-edge techniques and applications in AI decision-making.
Deep Learning III - Advanced Topics in Neural Networks
Explore advanced deep learning concepts with NYU professor Joan Bruna, covering cutting-edge techniques and applications in neural networks and machine learning.
Reinforcement Learning III - Advanced Concepts and Applications
Explore advanced reinforcement learning concepts with Stanford's Emma Brunskill, covering cutting-edge techniques and applications in AI decision-making.
Research in Data Management - Challenges and Projects
Explore cutting-edge data management research, including VR/AR video databases, in-memory storage optimization, MapReduce frameworks, cardinality estimation, bias detection in OLAP queries, and automated query reasoning.
Provably Secure Machine Learning Algorithms - Designing Reliable AI Systems
Exploring vulnerabilities in machine learning systems and developing provably secure algorithms to defend against statistical attacks in high-dimensional settings and non-convex models.
Compare, Contrast, Contribute: Model Deployment Standards and How You Can Help
Explore ML model deployment standards like PMML, PFA, and ONNX. Learn their features, use cases, and how to contribute to open-source initiatives advancing AI/ML interoperability.
AI Model Efficiency Toolkit
Explore cutting-edge techniques for optimizing AI models with Qualcomm's toolkit, enhancing performance and efficiency in machine learning applications.
Imaging from Deepfake Data - Applications in Seismic Imaging
Exploring neural networks' role in imaging from sensor data, focusing on seismic applications like bandwidth extension and virtual source data generation for enhanced inversion techniques.
Normalization Effects on Neural Networks: Generalization and High-Dimensional Applications - SIAM FME Talk
Exploring neural network performance, normalization effects, and high-dimensional PDE solving using deep learning. Insights on generalization properties and applications in mathematical finance.
Fusing Biophysics and Machine Learning for Computational Oncology
Innovative approach combining biophysics and machine learning to enhance understanding of cancer dynamics, utilizing clinical data to extract disease patterns and improve oncology research.
Online and Hybrid System Identification Directly From Raw Sensory Signals
Explores neural networks with physical model biases for efficient system identification from sensory data, applicable to robotics, molecular modeling, and X-ray scattering inversion.
Generating Approximate Ground States of Molecules Using Quantum Machine Learning
Explore quantum machine learning for generating molecular ground states, focusing on potential energy surfaces and efficient state preparation using variational quantum circuits and neural networks.
Scala and Spark in the New Deep Learning Era
Explore integrating large language models into big data infrastructures using Apache Spark, Scala, and BigDL. Learn distributed fine-tuning for pre-trained models and their representation as Spark estimators.
Learning of Neural Networks with Quantum Computers and Learning of Quantum States with Graphical Models
Explores quantum algorithms for neural network learning and quantum state learning using graphical models, showcasing advancements in computational efficiency and sample complexity for specific quantum states.
Stable Diffusion: Worldbuilding with 3D and AI Using MVDream - Part 1
Explore MVDream for 3D AI worldbuilding, from setup to mesh generation. Learn to create textures, design interiors, and integrate AI-generated assets into your creative workflow.