NeuroTree - A Differentiable Tree Operator for Tabular Data
Explore NeuroTree, a differentiable tree operator for tabular data, addressing traditional tree limitations through simultaneous node learning and built-in ensemble techniques.
Swift for TensorFlow - Google I/O 2019
Explore Swift for TensorFlow, a platform integrating deep neural networks with traditional software development. Learn its benefits for advanced machine learning research and why fast.ai chose it for their deep learning course.
Differentiable Programming for Data-driven Modeling, Optimization, and Control
Explore data-driven modeling, optimization, and control through differentiable programming techniques in this symposium talk by Jan Drgona from Pacific Northwest National Laboratory.
Learning From Ranks, Learning to Rank - Jean-Philippe Vert, Google Brain
Explore differentiable approaches to integrate permutations, sorting, and ranking in machine learning, focusing on embedding techniques and relaxation of ranking operators.
A Fast and Flexible CFD Solver with Heterogeneous Execution - JuliaCon 2024
Explore WaterLily.jl: a fast, flexible CFD solver for CPUs and GPUs. Learn about its meta-programming approach, performance, and potential for ML integration and differentiability.
Qadence: A Differentiable Interface for Digital-Analog Quantum Programs
Explore Qadence, a high-level interface for digital-analog quantum programs. Learn about its flexible design, native differentiability, and focus on real device execution for quantum machine learning algorithms.
Gradients Are Not All You Need - Machine Learning Research Paper Explained
Explores limitations of differentiable programming in machine learning, focusing on chaos-based failures in various systems. Discusses alternatives to backpropagation for gradient estimation in complex, stochastic environments.
Differentiable Programming for End-to-end Optimization of Experiments - Lecture 6
Explore differentiable programming for optimizing experiments in high-energy physics, focusing on end-to-end solutions and advanced machine learning techniques.
How Hard Is It to Train Variational Quantum Circuits?
Explores challenges in training variational quantum circuits, comparing them to classical neural networks and examining quantum generative models, with a focus on robust training methods and circuit compression.
Tropical Tensor Networks
Unified tensor network approach for spin glasses using tropical algebra, enabling ground state energy computation, optimal configuration identification, and solution counting. Combines graphical models and quantum circuit simulation.
Differentiable Programming for End-to-end Optimization of Experiments - Lecture 5
Explore differentiable programming for optimizing experiments in high-energy physics. Learn advanced techniques to enhance data analysis and improve experimental design in particle physics research.
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous Models
Explore advanced scientific machine learning, extending differentiable simulation to complex models like jump diffusions and agent-based systems, addressing challenges in implementation and numerical stability.
Differentiable Programming for End-to-End Optimization of Experiments - Lecture 7
Explore differentiable programming for optimizing experiments in high-energy physics. Learn advanced techniques to enhance data analysis and improve experimental design in particle physics research.
Machine Learning Gradients in Molecular Simulations
Explore ML gradients in molecular simulations, including surrogate functions, active learning, and differentiable programming for enhanced scientific modeling and optimization.
Scallop: A Language for Neurosymbolic Programming
Explore Scallop, a programming language for neurosymbolic learning that combines classical algorithms and deep learning, offering efficient solutions for complex machine learning challenges.
Learning Logical Relationships with Neural Networks - Part 3
Explore experimental evaluation of Differentiable Inductive Logic Programming for Structured Examples, examining its application in learning logical relationships using neural networks.
Neuromancer: Differentiable Programming Library for Data-driven Modeling and Control
Explore NeuroMANCER, an open-source library for integrating machine learning with scientific computing, solving optimization problems, and creating differentiable models with physics-based constraints.
NeuroMANCER - Differentiable Programming Library for Data-driven Modeling and Control
Explore NeuroMANCER: an open-source library for integrating machine learning with scientific computing, focusing on parametric optimization, system identification, and optimal control.
Differentiable Programming for Modeling and Control of Dynamical Systems
Explore differentiable programming for optimal control of dynamical systems, focusing on differentiable predictive control and its applications in building control and power systems optimization.
DLSS 5 - Нейрослоп или революция? | Стрим, общение
Новый курс Супер Blender https://cloudlessons.ru/v/550/ Курс по нейросетям https://cloudlessons.ru/v/540/ Бесплатный мини-курс по созданию игровой 3D-модели с нуля - https://cloudlessons.ru/mc/3d-mini-v2/?camp_id=26408 Курс "Профессия 3D-художник в геймдеве". Полный пайплайн, от моделирования в Bl