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Found 9 papers, 4 papers with code
Benchmarking Domain Generalization on EEG-based Emotion Recognition
Specifically, numerous domain adaptation (DA) algorithms have been exploited in the past five years to enhance the generalization of emotion recognition models across subjects.
Joining datasets via data augmentation in the label space for neural networks
Most, if not all, modern deep learning systems restrict themselves to a single dataset for neural network training and inference.
A critical look at the current train/test split in machine learning
The establishment of these split protocols are based on two assumptions: (i)-fixing the dataset to be eternally static so we could evaluate different machine learning algorithms or models; (ii)-there is a complete set of annotated data available to researchers or industrial practitioners.
Levenshtein Transformer
We further confirm the flexibility of our model by showing a Levenshtein Transformer trained by machine translation can straightforwardly be used for automatic post-editing.
Ranked #7 on
Machine Translation
on WMT2016 Romanian-English
GLoMo: Unsupervised Learning of Transferable Relational Graphs
We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden units), or embedding-free units such as image pixels.
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden unit), or embedding-free units such as image pixels.
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples
We propose a retrieval-augmented convolutional network and propose to train it with local mixup, a novel variant of the recently proposed mixup algorithm.
Adversarially Regularized Autoencoders
This adversarially regularized autoencoder (ARAE) allows us to generate natural textual outputs as well as perform manipulations in the latent space to induce change in the output space.
End to End Learning for Self-Driving Cars
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
