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Found 9 papers, 3 papers with code
An Effective Baseline for Robustness to Distributional Shift
In this work we present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention: when encountering a sample from an unseen class, the desired behavior is to abstain from predicting.
Ranked #1 on
Out-of-Distribution Detection
on CIFAR-100
(using extra training data)
A Simple and Effective Baseline for Out-of-Distribution Detection using Abstention
In this work we present a simple, but highly effective approach to deal with out-of-distribution detection that uses the principle of abstention: when encountering a sample from an unseen class, the desired behavior is to abstain from predicting.
Autonomous Control of a Particle Accelerator using Deep Reinforcement Learning
We describe an approach to learning optimal control policies for a large, linear particle accelerator using deep reinforcement learning coupled with a high-fidelity physics engine.
Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports
We demonstrate an abstaining classifier in a multitask setting for classifying cancer pathology reports from the NCI SEER cancer registries on six tasks of interest.
On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
In this work, we discuss a hitherto untouched aspect of mixup training -- the calibration and predictive uncertainty of models trained with mixup.
Ranked #1 on
Out-of-Distribution Detection
on STL-10
Combating Label Noise in Deep Learning Using Abstention
In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise.
Knows When it Doesn’t Know: Deep Abstaining Classifiers
We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training.
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs
We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or distributed setting.
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization
We describe a graph-based semi-supervised learning framework in the context of deep neural networks that uses a graph-based entropic regularizer to favor smooth solutions over a graph induced by the data.
