16k
55 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
ImageNet Training in Minutes
If we can make full use of the supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in one minute.
Detecting Offensive Language in Tweets Using Deep Learning
This paper addresses the important problem of discerning hateful content in social media.
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
sweta20/Detecting-Cyberbullying-Across-SMPs
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We show that deep learning based models can overcome all three bottlenecks.
Author Profiling for Abuse Detection
The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of hateful and offensive language on the Internet.
Nonlinear Conjugate Gradients For Scaling Synchronous Distributed DNN Training
apple/ml-ncg
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In this work, we propose and evaluate the stochastic preconditioned nonlinear conjugate gradient algorithm for large scale DNN training tasks.
Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach
Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians.
TabFact: A Large-scale Dataset for Table-based Fact Verification
wenhuchen/Table-Fact-Checking
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ICLR 2020
To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.
The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection
vlkniaz/MAGritte
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NeurIPS 2019
We collected large-scale manipulated image dataset to train our model.
Classifying the classifier: dissecting the weight space of neural networks
of neural network classifiers, and train a large number of models to represent the weight space.
MorphoCluster: Efficient Annotation of Plankton images by Clustering
By aggregating similar images into clusters, our novel approach to image annotation increases consistency, multiplies the throughput of an annotator and allows experts to adapt the granularity of their sorting scheme to the structure in the data.
