Bayesian Inference
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov
Neural Information Processing Systems http://nips.cc/
Adversarial Surrogate Losses for Ordinal Regression
Rizal Fathony, Mohammad Ali Bashiri, Brian Ziebart
Neural Information Processing Systems http://nips.cc/
Multi-view Matrix Factorization for Linear Dynamical System Estimation
Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvari
Maximum likelihood for LDS estimation, on the other hand, has several advantages.
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes
Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng
This game continues until the agent reaches a final state and receives a terminal reward .
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms
There is rapidly growing interest in using Bayesian optimization to tune model and inference hyperparameters for machine learning algorithms that take a long time to run.
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Nicolò Colombo, Ricardo Silva, Soong Moon Kang
Based on the reconstruction of the users' travel time distribution, the model is
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E.J. Chichilnisky, Liam Paninski
However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal.
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J. Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, Jakob H. Macke
Neural Information Processing Systems http://nips.cc/
Predicting User Activity Level In Point Processes With Mass Transport Equation
Yichen Wang, Xiaojing Ye, Hongyuan Zha, Le Song
However, existing works are mostly problem specific, use heuristics, or simplify the stochastic nature of point processes.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem.