1k citations · 1.1k across the 5 of their papers we have counts for
6 papers
fastai: A Layered API for Deep Learning
Jeremy Howard, Sylvain Gugger
fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning dom…
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
Julian Martin Eisenschlos, Sebastian Ruder, Piotr Czapla +3
Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of co…
Applying a Pre-trained Language Model to Spanish Twitter Humor Prediction
Bobak Farzin, Piotr Czapla, Jeremy Howard
Our entry into the HAHA 2019 Challenge placed in the classification task and in the regression task. We describe our system and innovations, as well as comparing…
Universal Language Model Fine-Tuning with Subword Tokenization for Polish
Piotr Czapla, Jeremy Howard, Marcin Kardas
Universal Language Model for Fine-tuning [arXiv:1801.06146] (ULMFiT) is one of the first NLP methods for efficient inductive transfer learning. Unsupervised pretraining results in…
The Matrix Calculus You Need For Deep Learning
Terence Parr, Jeremy Howard
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learne…
Universal Language Model Fine-tuning for Text Classification
Jeremy Howard, Sebastian Ruder
Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Uni…