1k citations · 2.5k across the 23 of their papers we have counts for
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i-Code: An Integrative and Composable Multimodal Learning Framework
Ziyi Yang, Yuwei Fang, Chenguang Zhu +17
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to…
P2L: Predicting Transfer Learning for Images and Semantic Relations
Bishwaranjan Bhattacharjee, John R. Kender, Matthew Hill +7
Transfer learning enhances learning across tasks, by leveraging previously learned representations -- if they are properly chosen. We describe an efficient method to accurately est…
Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning
Noel C. F. Codella, Michael Hind, Karthikeyan Natesan Ramamurthy +5
Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility f…
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
Pouya Bashivan, Irina Rish, Mohammed Yeasin +1
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to inter- and intra-subject differences, as we…