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Adaptive Batch Size for Safe Policy Gradients

Neural Information Processing Systems

Policy gradient methods are among the best Reinforcement Learning (RL) techniques to solve complex control problems.

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  Technology: Information Technology > Artificial Intelligence > Machine Learning > Reinforcement Learning (0.49)

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Trimmed Density Ratio Estimation

Song Liu, Akiko Takeda, Taiji Suzuki, Kenji Fukumizu

Neural Information Processing Systems

Neural Information Processing Systems http://nips.cc/

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Simple strategies for recovering inner products from coarsely quantized random projections

Ping Li, Martin Slawski

Neural Information Processing Systems

The idea of achieving further data compression by means of subsequent scalar quantization of the projected data has been considered for a while.

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A Learning Error Analysis for Structured Prediction with Approximate Inference

Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang

Neural Information Processing Systems

Neural Information Processing Systems http://nips.cc/

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ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization

Yi Xu, Mingrui Liu, Qihang Lin, Tianbao Yang

Neural Information Processing Systems

In particular, it remains an open question how to quantify the improvement in ADMM's theoretical convergence by using adaptive penalty parameters. Of course, the answer to this question depends on the adaptive scheme being used.

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Approximation Algorithms for $\ell_0$-Low Rank Approximation

Karl Bringmann, Pavel Kolev, David Woodruff

Neural Information Processing Systems

Neural Information Processing Systems http://nips.cc/

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Deep Supervised Discrete Hashing

Qi Li, Zhenan Sun, Ran He, Tieniu Tan

Neural Information Processing Systems

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years.

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QMDP-Net: Deep Learning for Planning under Partial Observability

Peter Karkus, David Hsu, Wee Sun Lee

Neural Information Processing Systems

This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning.

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  Genre: Research Report (0.46)
  Industry: Education (0.68)
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Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos

Gerasimos Palaiopanos, Ioannis Panageas, Georgios Piliouras

Neural Information Processing Systems

Neural Information Processing Systems http://nips.cc/

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Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

Geoffrey Roeder, Yuhuai Wu, David K. Duvenaud

Neural Information Processing Systems

Neural Information Processing Systems http://nips.cc/

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  Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (1.00)

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