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Found 13 papers, 6 papers with code
Measuring Stochastic Data Complexity with Boltzmann Influence Functions
Estimating the uncertainty of a model's prediction on a test point is a crucial part of ensuring reliability and calibration under distribution shifts.
Improving Black-box Robustness with In-Context Rewriting
Machine learning models for text classification often excel on in-distribution (ID) data but struggle with unseen out-of-distribution (OOD) inputs.
Blind Biological Sequence Denoising with Self-Supervised Set Learning
This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence.
If Influence Functions are the Answer, Then What is the Question?
Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters.
Predicting Out-of-Domain Generalization with Neighborhood Invariance
Developing and deploying machine learning models safely depends on the ability to characterize and compare their abilities to generalize to new environments.
Localization dynamics in a centrally coupled system
At longer time scales, we see slow growth of the entanglement, which may arise from dephasing mechanisms in the localized system or long-range interactions mediated by the central degree of freedom.
Disordered Systems and Neural Networks
Improving Dialogue Breakdown Detection with Semi-Supervised Learning
In ablations on DBDC4 data from 2019, our semi-supervised learning methods improve the performance of a baseline BERT model by 2\% accuracy.
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples.
Simple and Effective Noisy Channel Modeling for Neural Machine Translation
Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence.
Facebook FAIR's WMT19 News Translation Task Submission
This paper describes Facebook FAIR's submission to the WMT19 shared news translation task.
Ranked #1 on
Machine Translation
on WMT2019 English-German
Embryo staging with weakly-supervised region selection and dynamically-decoded predictions
First, noting that in each image the embryo occupies a small subregion, we jointly train a region proposal network with the downstream classifier to isolate the embryo.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks.
Predicting Surgery Duration with Neural Heteroscedastic Regression
Scheduling surgeries is a challenging task due to the fundamental uncertainty of the clinical environment, as well as the risks and costs associated with under- and over-booking.
