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Efficient Queries Transformer Neural Processes
Leo Feng · Hossein Hajimirsadeghi · Yoshua Bengio · Mohamed Osama Ahmed
Event URL: https://openreview.net/forum?id=_3FyT_W1DW »

Neural Processes (NPs) are popular methods in meta-learning that can estimate predictive uncertainty on target datapoints by conditioning on a context dataset. Previous state-of-the-art method Transformer Neural Processes (TNPs) achieve strong performance but require quadratic computation with respect to the number of context datapoints per query, limiting its applications. Conversely, existing sub-quadratic NP variants perform significantly worse than that of TNPs. Tackling this issue, we propose Efficient Queries Transformer Neural Processes (EQTNPs), a more computationally efficient NP variant. The model encodes the context dataset into a set of vectors that is linear in the number of context datapoints. When making predictions, the model retrieves higher-order information from the context dataset via multiple cross-attention mechanisms on the context vectors. We empirically show that EQTNPs achieve results competitive with the state-of-the-art.

Author Information

Leo Feng (Borealis AI & Mila)
Hossein Hajimirsadeghi (Borealis AI)
Yoshua Bengio (Mila / U. Montreal)

Yoshua Bengio is Full Professor in the computer science and operations research department at U. Montreal, scientific director and founder of Mila and of IVADO, Turing Award 2018 recipient, Canada Research Chair in Statistical Learning Algorithms, as well as a Canada AI CIFAR Chair. He pioneered deep learning and has been getting the most citations per day in 2018 among all computer scientists, worldwide. He is an officer of the Order of Canada, member of the Royal Society of Canada, was awarded the Killam Prize, the Marie-Victorin Prize and the Radio-Canada Scientist of the year in 2017, and he is a member of the NeurIPS advisory board and co-founder of the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains. His goal is to contribute to uncover the principles giving rise to intelligence through learning, as well as favour the development of AI for the benefit of all.

Mohamed Osama Ahmed (Borealis AI)

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