Lama Nachman

Artificial Intelligence · 2005–2020

A researcher in the Science corpus: 3 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it
• Probabilistic & Bayesian Methods — 23 citations into it

Whom Lama Nachman trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Miklos Maroti

Papers from Lama Nachman's perspective

Ordered by how strongly the researchers Lama Nachman trusts endorse each paper. Same corpus, a different order for every researcher.

1. Bayesian learning for neural networks
2. Pattern Recognition and Machine Learning
3. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
4. Bayesian Learning via Stochastic Gradient Langevin Dynamics
5. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
6. Information Theory, Inference, and Learning Algorithms
7. A Practical Bayesian Framework for Backpropagation Networks
8. Variational Dropout and the Local Reparameterization Trick
9. Weight Uncertainty in Neural Network
10. Keeping the neural networks simple by minimizing the description length of the weights

Their papers

• PIPENET: A Wireless Sensor Network for Pipeline Monitoring (2007) — 584 citations
• Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2020) — 336 citations
• Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea (2005) — 401 citations