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Found 8 papers, 3 papers with code
Learning Language-Specific Layers for Multilingual Machine Translation
Multilingual Machine Translation promises to improve translation quality between non-English languages.
State Spaces Aren't Enough: Machine Translation Needs Attention
Structured State Spaces for Sequences (S4) is a recently proposed sequence model with successful applications in various tasks, e. g. vision, language modeling, and audio.
Non-Autoregressive Neural Machine Translation: A Call for Clarity
Non-autoregressive approaches aim to improve the inference speed of translation models by only requiring a single forward pass to generate the output sequence instead of iteratively producing each predicted token.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Explainability-aided Domain Generalization for Image Classification
Traditionally, for most machine learning settings, gaining some degree of explainability that tries to give users more insights into how and why the network arrives at its predictions, restricts the underlying model and hinders performance to a certain degree.
Collaborative Filtering under Model Uncertainty
In their work, Dean, Rich, and Recht create a model to research recourse and availability of items in a recommender system.
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers
Choosing the optimizer is considered to be among the most crucial design decisions in deep learning, and it is not an easy one.
Recurrent Neural Networks (RNNs): A gentle Introduction and Overview
State-of-the-art solutions in the areas of "Language Modelling & Generating Text", "Speech Recognition", "Generating Image Descriptions" or "Video Tagging" have been using Recurrent Neural Networks as the foundation for their approaches.
