Cross-Lingual Transfer with MAML on Trees

no code implementations • EACL (AdaptNLP) 2021 • Jezabel Garcia, Federica Freddi, Jamie McGowan, Tim Nieradzik, Feng-Ting Liao, Ye Tian, Da-Shan Shiu, Alberto Bernacchia

In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related.

Cross-Lingual Natural Language Inference Cross-Lingual Transfer +1

Meta-Learning with MAML on Trees

no code implementations • 8 Mar 2021 • Jezabel R. Garcia, Federica Freddi, Feng-Ting Liao, Jamie McGowan, Tim Nieradzik, Da-Shan Shiu, Ye Tian, Alberto Bernacchia

We show that TreeMAML improves the state of the art results for cross-lingual Natural Language Inference.

Cross-Lingual Natural Language Inference Cross-Lingual Transfer +2

Model agnostic meta-learning on trees

no code implementations • 1 Jan 2021 • Jezabel Garcia, Federica Freddi, Jamie McGowan, Tim Nieradzik, Da-Shan Shiu, Ye Tian, Alberto Bernacchia

In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related, and sharing information between unrelated tasks might hurt performance.

Meta-Learning

Cyclic orthogonal convolutions for long-range integration of features

no code implementations • NeurIPS Workshop SVRHM 2021 • Federica Freddi, Jezabel R Garcia, Michael Bromberg, Sepehr Jalali, Da-Shan Shiu, Alvin Chua, Alberto Bernacchia

We propose a novel architecture that allows flexible information flow between features $z$ and locations $(x, y)$ across the entire image with a small number of layers.

Image Classification Pathfinder