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Found 13 papers, 10 papers with code
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
However, we argue that these benchmarks fail to address many issues that these algorithms would face in real-world applications.
Explaining and Harnessing Adversarial Examples
Several machine learning models, including neural networks, consistently misclassify adversarial examples---inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence.
Ranked #57 on
Image Classification
on MNIST
On distinguishability criteria for estimating generative models
However, we show that recovering MLE for a learned generator requires departing from the distinguishability game.
Qualitatively characterizing neural network optimization problems
Training neural networks involves solving large-scale non-convex optimization problems.
Generative Adversarial Networks
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Super-Resolution
Time-Series Few-Shot Learning with Heterogeneous Channels
An empirical analysis of dropout in piecewise linear networks
The recently introduced dropout training criterion for neural networks has been the subject of much attention due to its simplicity and remarkable effectiveness as a regularizer, as well as its interpretation as a training procedure for an exponentially large ensemble of networks that share parameters.
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Catastrophic forgetting is a problem faced by many machine learning models and algorithms.
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels.
Ranked #30 on
Image Classification
on SVHN
On the Challenges of Physical Implementations of RBMs
Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC.
Challenges in Representation Learning: A report on three machine learning contests
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge.
Ranked #12 on
Facial Expression Recognition (FER)
on FER2013
Maxout Networks
We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout.
Ranked #34 on
Image Classification
on MNIST
Joint Training Deep Boltzmann Machines for Classification
We introduce a new method for training deep Boltzmann machines jointly.
