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Session Panel: Learning with Physical Systems
ML4Science
May 29, 2019
Reducing simulation dependence with deep learning
ML4Science
May 29, 2019
Reinforcement Learning, Control, and Inference
ML4Science
May 29, 2019
Reinforcement Learning for Materials Synthesis
ML4Science
May 29, 2019
Noise2Self: Blind Denoising by Self-Supervision
ML4Science
May 29, 2019
Spectral Inference Networks: Unifying Deep and Spectral Learning
ML4Science
May 29, 2019
Physics-constrained Computational Imaging
ML4Science
May 29, 2019
Laura Waller
Session Panel: Generative Models
ML4Science
May 29, 2019
Hybrid Physical - Deep Learning Models for Astronomical Inverse Problems
ML4Science
May 29, 2019
François Lanusse
Towards a cosmology emulator using Generative Adversarial Networks
ML4Science
May 29, 2019
Improved learning for materials and chemical structures through symmetry, hierarchy and similarity
ML4Science
May 29, 2019
Deducing Inference from Hyperspectral Imaging of Materials Using Deep Recurrent Neural Networks
ML4Science
May 29, 2019
Putting Non-Euclidean Geometry to Work in ML: Hyperbolic and Product Manifold Embeddings
ML4Science
May 29, 2019
Flow-based generative models for lattice field theory
ML4Science
May 29, 2019
Generative models as priors for signal denoising
ML4Science
May 29, 2019
Session Panel: Incorporating Physics directly into the Models
ML4Science
May 29, 2019
Cosmology for Machine Learning
ML4Science
May 29, 2019
Uroš Seljak
Physics Constrained Fluid Flow Prediction using Lyapunov's Method
ML4Science
May 29, 2019
FPGA-accelerated machine learning inference as a service for particle physics computing
ML4Science
May 29, 2019
Machine learning in high-energy particle physics experiments, from simulation, through reconstruction to physics analysis
ML4Science
May 29, 2019
Physics informed Machine Learning
ML4Science
May 29, 2019
Machine learning for lattice gauge theory
ML4Science
May 29, 2019
Session Panel: Producing & Discovering Dynamical Models
ML4Science
May 29, 2019
Solving Astrophysical PDEs with Deep Neural Networks and TensorFlow
ML4Science
May 29, 2019
Data Driven Discretization for Partial Differential Equations
ML4Science
May 29, 2019
Learning physical interaction in many ways
ML4Science
May 29, 2019
AI Feynman: a Physics-Inspired Method for Symbolic Regression
ML4Science
May 29, 2019
Data-driven methods for the discovery of governing equations
ML4Science
May 29, 2019
Welcome and Introductory Remarks - Physics in Machine Learning Workshop
ML4Science
May 29, 2019
Hate speech, algorithms, and digital connectivity
Lecture
May 8, 2019
Chris Kennedy
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