Publications by Young Jun Ko | EPFL Graph Search

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We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...

Catalytic reduction of CO2 to valuable products is an attractive route for CO2 recycling. CeO2-supported Cu catalysts have shown high activity and selectivity for the hydrogenation of CO2 to CO. To uncover the origin of their high performance, we prepared ...

Natural image statistics exhibit hierarchical dependencies across multiple scales. Representing such prior knowledge in non-factorial latent tree models can boost performance of image denoising, inpainting, deconvolution or reconstruction substantially, be ...

Bilinear models of count data with Poisson distribution are popular in applications such as matrix factorization for recommendation systems, modeling of receptive fields of sensory neurons, and modeling of neural-spike trains. Bayesian inference in such mo ...

The Poisson likelihood with rectified linear function as non-linearity is a physically plausible model to discribe the stochastic arrival process of photons or other particles at a detector. At low emission rates the discrete nature of this process leads t ...

Young Jun Ko

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Applications of Approximate Learning and Inference for Probabilistic Models

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Revealing the Surface Chemistry for CO2 Hydrogenation on Cu/CeO2–x Using Near-Ambient-Pressure X-ray Photoelectron Spectroscopy

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models

Variational Gaussian Inference for Bilinear Models of Count Data

Expectation Propagation for Rectified Linear Poisson Regression