Check out our new paper ‘One-Line-of-Code Data Mollification Improves Optimization of Likelihood-bas
Published:
Check out our new paper “One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models” (link)
- Thuwal, Saudi Arabia
- KAUST
- Google Scholar
- GitHub
You May Also Enjoy
Check out our new book on ‘Bayesian Deep Learning’
less than 1 minute read
Published:
Our new “Handbook of Bayesian Deep Learning” is now available online (link)
Talk at the SMILES summer schools ‘Bayesian Deep Learning’
less than 1 minute read
Published:
Invited talk at the ‘SMILES summer school of machine learning’ in Nanjing University: “Bayesian Deep Learning” (link)
Talk at ISBA ‘Model Selection for Over-Parameterized Models’
less than 1 minute read
Published:
Invited talk at the ‘Calibrated Bayes: Model Design and Adaptation Under Limited Resources’ session at ISBA in Nagoya: “Model Selection for Over-Parameterized Models” (link)
Paper ‘Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions’ accepted in DeCarbon
less than 1 minute read
Published:
Our paper “Review of timescale distributions for electrochemical impedance spectroscopy analysis: Advantages, synergies, and future directions” has been accepted for publication in the journal DeCarbon (link)