Name: XAIENCE 2019
Start: 2019-11-07T08:00:00+09:00
End: 2019-11-08T20:00:00+09:00
Location: Seoul National University, College of Natural Sciences, Building 25-1

Adjourn (in session "Concluding remarks")

AI Applications for Satellite-based Disaster Monitoring and Prediction (in session "Invited talks - Earth and Environmental Sciences 1")

AI-based Smart Molecular Design (in session "Invited talks - Chemistry 2")

Application of Artificial Intelligence techniques to Weather Forecasting (in session "Invited talks - Earth and Environmental Sciences 2")

Application of Machine Learning for Big Data Analysis in Astronomy (천문학 자료분석에서의 기계학습의 활용) (in session "Invited talks - Physics and Astronomy 1-1")

Application of Neural Network Model to Predict and to Evaluate the Groundwater Level Fluctuation (in session "Contributed talks 2")

Architecture of Neuromorphic Hardware with Embedded Learning (in session "Invited talks - Physics and Astronomy 2")

Clustering weather maps for high PM10 events in Seoul using Self Organization Maps (in session "Invited talks - Earth and Environmental Sciences 2")

Connectome Project & Neuroscience with AI (in session "Invited talks - Biological sciences, Neurosciences")

Deep learning for multi-year ENSO forecasts (in session "Invited talks - Earth and Environmental Sciences 1")

Deep Learning for Sea Ice Classification and Ship Detection using Satellite Synthetic Aperture Radar (in session "Invited talks - Earth and Environmental Sciences 1")

Delfos: deep learning model for prediction of solvation free energies in generic organic solvents (in session "Invited talks - Chemistry 2")

Discovering Novel Fluorescent Molecules by Combining Machine-learning and Global Optimization (in session "Invited talks - Chemistry 1")

Exploration of energy surfaces and conformation of molecules and solids by utilizing a machine-learning technique (in session "Invited talks - Physics and Astronomy 2")

Fleming : AI-driven Integrated Drug Discovery Platform (in session "Invited talks - Chemistry 1")

Harmonic Data Analysis for Shape and Centrality (in session "Invited talks - Math & Stats 2")

Hoping for Better AI: Advice from the Brain (in session "Invited talks - Biological sciences, Neurosciences")

Information in Unsupervised Learning (in session "Invited talks - Physics and Astronomy 2")

Learning QCD Jet Flavors in search for Invisible Higgs Decays (in session "Invited talks - Physics and Astronomy 1-2")

Lessons Learned from Running Interdisciplinary Computational Sciences Program for Undergraduate at Seoul National University on the Future of Scientific Computing Education (in session "Invited talks - Earth and Environmental Sciences 1")

Machine Learning In Astrophysics: Estimating Galactic Baryonic Properties from Their Dark Matter (in session "Invited talks - Physics and Astronomy 1-1")

Nonconvex Sparse Regularization For Deep Neural Networks and its Optimal Property (in session "Contributed talks 1")

On Casting Importance Weighted Autoencoder to an EM Algorithm to Learn Deep Generative Models (in session "Contributed talks 1")

Predicting location of suitable groundwater for brewing coffee using tree-based ensemble machine learning (in session "Contributed talks 2")

Prediction and Taking Insight of Molecular Quantum Property by Random Forest Regression (in session "Contributed talks 2")

Prediction of Protein Structure and Interaction by Physics and Informatics (in session "Invited talks - Chemistry 1")

Probing Trilinear Higgs Self–coupling at the HL-LHC with Machine Learning (in session "Contributed talks 2")

Projection onto Minkowski Sums with Application to Constrained Learning (in session "Invited talks - Math & Stats 1")

Quantum AI, Quantum Machine Learning (in session "Invited talks - Math & Stats 2")

Quantum Algorithms and Quantum Machine Learning (in session "Invited talks - Physics and Astronomy 2")

Recent Developments of Deep Learning in Experimental High Energy Physics (in session "Invited talks - Physics and Astronomy 1-2")

Searching for Axions and new concepts in fundamental physics with Machine Learning (in session "Invited talks - Physics and Astronomy 1-2")

Theoretical Advantages of Deep Neural Networks (in session "Invited talks - Math & Stats 1")

Welcome remarks on the 1st XAIENCE Conference (in session "Welcome remarks on the 1st XAIENCE conference")

What Can We Learn with ML in Particle Physics, and How? (in session "Invited talks - Physics and Astronomy 1-2")

데이터의 프라이버시를 보존하는 기계학습 (Safe Machine Learning toward Private AI) (in session "Invited talks - Math & Stats 2")

복소 산술 신경망을 통한 물리적 기계학습모형의 건설과 물리적 불변량의 추출 (in session "Contributed talks 2")

XAIENCE 2019

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