Scalable and Cost-Efficient AI Workloads with AWS and Anyscale
Learn to scale and productionize GenAI and LLM workloads cost-effectively using AWS compute instances and Anyscale's capabilities for ambitious AI projects.
Optimizing LLM Inference with AWS Trainium, Ray, vLLM, and Anyscale
Explore cost-effective LLM inference scaling using AWS accelerators, Ray, vLLM, and Anyscale. Learn to build a complete stack on EKS and leverage advanced cluster management for enterprise-grade GenAI workloads.
Probabilistic Inference of the Steady-State Distribution of an Age-Size Structured Population
Explore probabilistic techniques for inferring steady-state distributions of age-size structured populations using single-cell data and stochastic modeling.
New Directions in Correlation Clustering and Biclustering
Explore advanced techniques in correlation clustering and biclustering, focusing on novel approaches and applications in data analysis and machine learning.
Hybrid AI for Knowledge Representation and Model-based Image Understanding
Explore hybrid AI for spatial reasoning and image understanding, combining knowledge representation with data-driven approaches in medical imaging applications.
Federated Learning with Communication Constraints: Challenges and Recent Results
Explore federated learning optimization with compression, focusing on convergence-compression tradeoffs, user-heterogeneity impacts, and a novel random codebook compression scheme.
Photographic Image Priors in the Era of Machine Learning
Explore image processing advancements using machine learning, focusing on implicit priors in neural networks for tasks like denoising, deblurring, and super-resolution.
Interpreting Deep Neural Networks Towards Trustworthiness
Explore methods for interpreting neural networks, including contextual decomposition and adaptive wavelet distillation, to enhance model trustworthiness and performance.
The Quest for Adaptivity in Machine Learning - Comparing Popular Methods
Explore supervised learning algorithms' strengths and weaknesses, comparing k-nearest-neighbor, kernel methods, and neural networks for adaptivity and performance across various problems.
The New Era of Image Denoising
Explore revolutionary image denoising techniques, their applications in inverse problems, and the concept of diverse solutions in image processing.
Geometry, Topology and Discrete Symmetries Revealed by Deep Neural Networks
Explore deep neural networks' ability to reveal geometry, topology, and discrete symmetries in manifolds. Learn about universal approximators and their applications in inverse problems and group actions.
Sketching Semidefinite Programs for Super-Resolution Problems
Explore super-resolution problems, focusing on measure recovery from Fourier coefficients. Learn about sketching approaches to reduce semidefinite program size and algorithmic developments for integral bounds.
A Path Towards Autonomous Machine Intelligence
Explore Yann LeCun's vision for autonomous machine intelligence, delving into cutting-edge AI concepts and potential future developments in the field.
Reliable AI: From Applied Harmonic Analysis to Quantum Computing
Explore reliable AI through generalization in graph neural networks and explainability using applied harmonic analysis. Delve into limitations of digital hardware and connections to quantum computing.
Rapture of the Deep: Highs and Lows of Sparsity in Neural Networks
Explore sparsity in neural networks, its role in interpretability, and innovative optimization techniques beyond gradient descent for learning fast transforms.
Advanced Computational Imaging - Chassez le Naturel, Il Revient au Galop
Explorez les avancées en imagerie computationnelle, en mettant l'accent sur la stabilité et la fiabilité des réseaux neuronaux profonds pour la reconstruction d'images biomédicales.
From Thom's Gradient Cell Decomposition to the Curvature of Milnor Fibers
Explore Thom's gradient cell decomposition and its impact on understanding complex hypersurface singularities, polar curves, and Milnor fibers through algebraic, topological, and Lipschitz-geometric perspectives.
Perception of Visual Art and Topological Data Analysis
Explore topological data analysis applications in visual art perception, comparing eye movements for abstract paintings vs. AI-generated images using algebraic topology methods.
Thirty-Six Entangled Officers of Euler: Quantum Solution to a Classical Problem
Explore quantum combinatorial designs and their application in solving Euler's 36 officers problem, revealing insights into quantum information processing and error detection.
Learning with Missing Values: Theoretical Insights and Application to Health Databases
Explore theoretical insights and practical applications of learning with missing values in health databases, focusing on regression, classification, and neural network solutions.
Weak Signals: Machine Learning Meets Extreme Value Theory
Explore machine learning's intersection with extreme value theory, focusing on angular measure recovery and its applications in classification and anomaly detection.