195 citations · 261 across the 8 of their papers we have counts for
5 papers · 1 filter
Exploring the Limits of Large Scale Pre-training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur +1
Recent developments in large-scale machine learning suggest that by scaling up data, model size and training time properly, one might observe that improvements in pre-training woul…
Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
Samira Abnar, Rianne van den Berg, Golnaz Ghiasi +3
We focus on the problem of domain adaptation when the goal is shifting the model towards the target distribution, rather than learning domain invariant representations. It has been…
Long Range Arena: A Benchmark for Efficient Transformers
Yi Tay, Mostafa Dehghani, Samira Abnar +7
Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transf…
Transferring Inductive Biases through Knowledge Distillation
Samira Abnar, Mostafa Dehghani, Willem Zuidema
Having the right inductive biases can be crucial in many tasks or scenarios where data or computing resources are a limiting factor, or where training data is not perfectly represe…
Quantifying Attention Flow in Transformers
Samira Abnar, Willem Zuidema
In the Transformer model, "self-attention" combines information from attended embeddings into the representation of the focal embedding in the next layer. Thus, across layers of th…