Liam Peet-Pare

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1ranked-venue papers

0as first author

1since 2021 · last 2022

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

1 paper

Optimization for machine learning · 50% Deep learning architectures and training · 25% Transfer learning and domain adaptation · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Transfer learning and domain adaptation › domain shift covariate shift	0.6	1	2022	Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum · ICLR 2022
Machine learning › Deep learning architectures and training › training dynamics optimization dynamics	0.6	1	2022	Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum · ICLR 2022
Machine learning › Optimization for machine learning stochastic gradient descent	0.6	1	2022	Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum · ICLR 2022
Machine learning › Optimization for machine learning › stochastic gradient descent stochastic gradient descent with momentum	0.6	1	2022	Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum · ICLR 2022

Methods — techniques the papers use, named apart from their topics

stochastic gradient descent with momentum · 0.6

Year	Publication	Venue	Position
2022	Resonance in Weight Space: Covariate Shift Can Drive Divergence of SGD with Momentum Kirby Banman, Liam Peet-Pare, Nidhi Hegde 0001, Alona Fyshe, Martha White	ICLR	2