Learning Graphical Models
79a3308b13cd31f096d8a4a34f96b66b-Paper.pdf
Questions on whether governments have acted promptly enough, and whether lockdown measures can be lifted soon, have since been central in public discourse. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential for addressing these questions and informing governments on future policy directions.
Depth Uncertainty in Neural Networks
To solve this, we perform probabilistic reasoning over the depth of neural networks.
1e1d184167ca7676cf665225e236a3d2-Reviews.html
First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper presented a method to enable the generation of robust plans with partially specified domain models. The motivation of this research topic is well stated. The main contribution of this work is the formalization of the notion of plan robustness with respect to an incomplete domain model. The paper is clearly written and should the general interest for the broad NIPS audience.
1baff70e2669e8376347efd3a874a341-Reviews.html
First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. COMMENTS BASED ON REVIEWER DISCUSSIONS AND AUTHOR REBUTTAL: I agree with the other reviewers that more could be done to constrain the specifics of the cue integration mechanism. However, I believe that if the data set is expanded, allowing the models to be better constrained, then the paper is appropriate and interesting for the NIPS community. I have left my quality score as it was, but I agree with the other reviewers that the paper merits a ``1'' rather than a ``2'' for impact score. ORIGINAL REVIEW: Summary: This paper extends an existing model for the perception of visual speed that uses a Bayesian observer model acting on the activity of independent spatiotemporal frequency channels. Previously, the model accounted for illusions of perceived speed by postulating the Bayes-optimal combination of noisy sensory representations with a prior for slow speeds.
Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration
Discrete structures play an important role in applications like program language modeling and software engineering.
Reinforcement Learning in Robust Markov Decision Processes
Shiau Hong Lim, Huan Xu, Shie Mannor
Neural Information Processing Systems http://nips.cc/
Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes
Jun Yang, Shengyang Sun, Daniel M. Roy
Neural Information Processing Systems http://nips.cc/
Multi-Prediction Deep Boltzmann Machines
Ian Goodfellow, Mehdi Mirza, Aaron Courville, Yoshua Bengio
Neural Information Processing Systems http://nips.cc/