NeurIPS-2011 Paper Digest

ID	Paper	Authors	Source	Date
1	Algorithms for Hyper-Parameter Optimization IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs).	James S. Bergstra; R�mi Bardenet; Yoshua Bengio et al.	nips	2011-12-30
2	An Empirical Evaluation of Thompson Sampling IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We present here some empirical results using Thompson sampling on simulated and real data, and show that it is highly competitive.	Olivier Chapelle; Lihong Li;	nips	2011-12-30
3	Im2Text: Describing Images Using 1 Million Captioned Photographs IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: We develop and demonstrate automatic image description methods using a large captioned photo collection.	Vicente Ordonez; Girish Kulkarni; Tamara L. Berg;	nips	2011-12-30
4	Practical Variational Inference for Neural Networks IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This paper introduces an easy-to-implement stochastic variational method (or equivalently, minimum description length loss function) that can be applied to most neural networks.	Alex Graves;	nips	2011-12-30
5	Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent IF:9 Related Papers Related Patents Related Grants Related Venues Related Experts View Save Highlight: This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented without any locking.	Benjamin Recht; Christopher Re; Stephen Wright et al.	nips	2011-12-30

NeurIPS-2011 Paper Digest

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