# NeurIPS 2018

In this paper, we develop improved techniques for defending against adversarial examples at scale.

54,125

# PointCNN: Convolution On $\mathcal{X}$-Transformed Points

The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.

788

# Pelee: A Real-Time Object Detection System on Mobile Devices

In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.

701

# Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?

This paper seeks to answer the question: as the (near-) orthogonality of weights is found to be a favorable property for training deep convolutional neural networks, how can we enforce it in more effective and easy-to-use ways?

38

# Learning long-range spatial dependencies with horizontal gated-recurrent units

As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks.

9

# Discretely Relaxing Continuous Variables for tractable Variational Inference

We explore a new research direction in Bayesian variational inference with discrete latent variable priors where we exploit Kronecker matrix algebra for efficient and exact computations of the evidence lower bound (ELBO).

8

# Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $β$-Divergences

The resulting inference procedure is doubly robust for both the parameter and the changepoint (CP) posterior, with linear time and constant space complexity.

7

# A Simple Cache Model for Image Recognition

We propose to extract this extra class-relevant information using a simple key-value cache memory to improve the classification performance of the model at test time.

2

# Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling

We also show that under the assumption of power-law decay of ridge leverage scores, this deterministic algorithm is provably as accurate as randomized algorithms.

2

# Computing Kantorovich-Wasserstein Distances on $d$-dimensional histograms using $(d+1)$-partite graphs

This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of $d$-dimensional histograms having $n$ bins each.

1