Papers
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira
Self-Imitation Learning
Junhyuk Oh, Yijie Guo, Satinder Singh et al.
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh, Efstratios Gavves, Max Welling
Learning in Reproducing Kernel Kreı̆n Spaces
Dino Oglic, Thomas Gaertner
Is Generator Conditioning Causally Related to GAN Performance?
Augustus Odena, Jacob Buckman, Catherine Olsson et al.
The Uncertainty Bellman Equation and Exploration
Brendan O’Donoghue, Ian Osband, Remi Munos et al.
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic et al.
Functional Gradient Boosting based on Residual Network Perception
Atsushi Nitanda, Taiji Suzuki
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Weili Nie, Yang Zhang, Ankit Patel
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae, Andre Martins, Mathieu Blondel et al.
State Space Gaussian Processes with Non-Gaussian Likelihood
Hannes Nickisch, Arno Solin, Alexander Grigorevskiy
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Maximillian Nickel, Douwe Kiela
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh Nguyen, Akshay Soni, Chinmay Hegde
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Phuc Nguyen, Deva Ramanan, Charless Fowlkes
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Lam Nguyen, PHUONG HA NGUYEN, Marten Dijk et al.
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
Optimization Landscape and Expressivity of Deep CNNs
Quynh Nguyen, Matthias Hein
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Seth Neel, Aaron Roth
Stochastic Proximal Algorithms for AUC Maximization
Michael Natole, Yiming Ying, Siwei Lyu
Nearly Optimal Robust Subspace Tracking
Praneeth Narayanamurthy, Namrata Vaswani
Smoothed Action Value Functions for Learning Gaussian Policies
Ofir Nachum, Mohammad Norouzi, George Tucker et al.
Fitting New Speakers Based on a Short Untranscribed Sample
Eliya Nachmani, Adam Polyak, Yaniv Taigman et al.
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Stephen Mussmann, Percy Liang
Rapid Adaptation with Conditionally Shifted Neurons
Tsendsuren Munkhdalai, Xingdi Yuan, Soroush Mehri et al.
Kernelized Synaptic Weight Matrices
Lorenz Muller, Julien Martel, Giacomo Indiveri