Statistics
9631 papers in Statistics
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TITLE
DATE
VIEWS
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
By Nat Dilokthanakul, Pedro A.M. Mediano, Marta Garnelo · ArXiv: 1611.02648
2017-01-16
0
A Random Dot Product Model for Weighted Networks
By Daryl R. DeFord, Daniel N. Rockmore · ArXiv: 1611.02530
2016-11-09
0
Combining observational and experimental data to find heterogeneous treatment effects
By Alex, er Peysakhovich, Akos Lada · ArXiv: 1611.02385
2016-11-09
0
Neural Taylor Approximations: Convergence and Exploration in Rectifier Networks
By David Balduzzi, Brian McWilliams, Tony Butler-Yeoman · ArXiv: 1611.02345
2018-06-07
0
Adversarial Ladder Networks
By Juan Maro~nas Molano, Alberto Albiol Colomer, Roberto Paredes
Palacios · ArXiv: 1611.02320
2018-04-30
0
Learning from Untrusted Data
By Moses Charikar, Jacob Steinhardt, Gregory Valiant · ArXiv: 1611.02315
2017-06-13
0
Learning Influence Functions from Incomplete Observations
By Xinran He, Ke Xu, David Kempe · ArXiv: 1611.02305
2016-11-09
0
Normalizing Flows on Riemannian Manifolds
By Mevlana C. Gemici, Danilo Rezende, Shakir Mohamed · ArXiv: 1611.02304
2016-11-10
1
Optimal Binary Autoencoding with Pairwise Correlations
By Akshay Balsubramani · ArXiv: 1611.02268
2016-11-08
0
Gaussian Attention Model and Its Application to Knowledge Base Embedding and Question Answering
By Liwen Zhang, John Winn, Ryota Tomioka · ArXiv: 1611.02266
2016-12-01
0
A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications
By Zheng Zhang, Tsui-Wei Weng, Luca Daniel · ArXiv: 1611.02256
2016-11-08
0
Distributed Coordinate Descent for Generalized Linear Models with Regularization
By Ilya Trofimov, Alex, er Genkin · ArXiv: 1611.02101
2017-06-28
0
Reinforcement-based Simultaneous Algorithm and its Hyperparameters Selection
By Valeria Efimova, Andrey Filchenkov, Anatoly Shalyto · ArXiv: 1611.02053
2016-11-08
0
Reinforcement Learning Approach for Parallelization in Filters Aggregation Based Feature Selection Algorithms
By Ivan Smetannikov, Ilya Isaev, Andrey Filchenkov · ArXiv: 1611.02047
2016-11-08
0
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning
By Oron Anschel, Nir Baram, Nahum Shimkin · ArXiv: 1611.01929
2017-03-13
0
An Information-Theoretic Framework for Fast and Robust Unsupervised Learning via Neural Population Infomax
By Wentao Huang, Kechen Zhang · ArXiv: 1611.01886
2017-03-13
0
Learning to Perform Physics Experiments via Deep Reinforcement Learning
By Misha Denil, Pulkit Agrawal, Tejas D Kulkarni · ArXiv: 1611.01843
2017-08-21
0
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
By Feras Saad, Vikash Mansinghka · ArXiv: 1611.01708
2018-04-03
0
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
By Adji B. Dieng, Chong Wang, Jianfeng Gao · ArXiv: 1611.01702
2017-02-28
0
Combining policy gradient and Q-learning
By Brendan ODonoghue, Remi Munos, Koray Kavukcuoglu · ArXiv: 1611.01626
2017-04-10
0
Estimating Causal Direction and Confounding of Two Discrete Variables
By Krzysztof Chalupka, Frederick Eberhardt, Pietro Perona · ArXiv: 1611.01504
2016-11-07
0
Understanding Deep Neural Networks with Rectified Linear Units
By Raman Arora, Amitabh Basu, Poorya Mianjy · ArXiv: 1611.01491
2018-03-01
0
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling
By Hakan Inan, Khashayar Khosravi, Richard Socher · ArXiv: 1611.01462
2017-03-14
0
Ways of Conditioning Generative Adversarial Networks
By Hanock Kwak, Byoung-Tak Zhang · ArXiv: 1611.01455
2016-11-07
0
Adversarial Machine Learning at Scale
By Alexey Kurakin, Ian Goodfellow, Samy Bengio · ArXiv: 1611.01236
2017-02-14
0
Bayesian Optical Flow with Uncertainty Quantification
By Jie Sun, Fern, o J. Quevedo · ArXiv: 1611.01230
2018-08-21
0
Combating Reinforcement Learnings Sisyphean Curse with Intrinsic Fear
By Zachary C. Lipton, Kamyar Azizzadenesheli, Abhishek Kumar · ArXiv: 1611.01211
2018-03-15
0
PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers
By Wei Xie, Yang Wang, Steven M. Boker · ArXiv: 1611.01170
2016-11-07
0
Finding Approximate Local Minima Faster than Gradient Descent
By Naman Agarwal, Zeyuan Allen-Zhu, Brian Bullins · ArXiv: 1611.01146
2017-04-26
0