1k citations · 2.5k across the 44 of their papers we have counts for
10 papers · 1 filter
ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Vamsi Aribandi, Yi Tay, Tal Schuster +11
Despite the recent success of multi-task learning and transfer learning for natural language processing (NLP), few works have systematically studied the effect of scaling up the nu…
Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers
Yi Tay, Mostafa Dehghani, Jinfeng Rao +7
There remain many open questions pertaining to the scaling behaviour of Transformer architectures. These scaling decisions and findings can be critical, as training runs often come…
The Benchmark Lottery
Mostafa Dehghani, Yi Tay, Alexey A. Gritsenko +5
The world of empirical machine learning (ML) strongly relies on benchmarks in order to determine the relative effectiveness of different algorithms and methods. This paper proposes…
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption
Dara Bahri, Heinrich Jiang, Yi Tay +1
Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders o…
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization
Yi Tay, Vinh Q. Tran, Sebastian Ruder +7
State-of-the-art models in natural language processing rely on separate rigid subword tokenization algorithms, which limit their generalization ability and adaptation to new settin…
How Reliable are Model Diagnostics?
Vamsi Aribandi, Yi Tay, Donald Metzler
In the pursuit of a deeper understanding of a model's behaviour, there is recent impetus for developing suites of probes aimed at diagnosing models beyond simple metrics like accur…
Are Pre-trained Convolutions Better than Pre-trained Transformers?
Yi Tay, Mostafa Dehghani, Jai Gupta +4
In the era of pre-trained language models, Transformers are the de facto choice of model architectures. While recent research has shown promise in entirely convolutional, or CNN, a…
Rethinking Search: Making Domain Experts out of Dilettantes
Donald Metzler, Yi Tay, Dara Bahri +1
When experiencing an information need, users want to engage with a domain expert, but often turn to an information retrieval system, such as a search engine, instead. Classical inf…
OmniNet: Omnidirectional Representations from Transformers
Yi Tay, Mostafa Dehghani, Vamsi Aribandi +6
This paper proposes Omnidirectional Representations from Transformers (OmniNet). In OmniNet, instead of maintaining a strictly horizontal receptive field, each token is allowed to…
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection
Dara Bahri, Heinrich Jiang, Yi Tay +1
Detecting out-of-distribution (OOD) examples is critical in many applications. We propose an unsupervised method to detect OOD samples using a -NN density estimate with respect…