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The dark side of Graph Neural Networks
The current limitations of Graph Neural Networks. We continue our two part series on ML on Graphs, by asking: could graphs replace other domain specific formats and algorithms, such as Computer Vision (CV) or Natural Language Processing (NLP)?
Daniel Szemerey
and
Mark Aron Szulyovszky
Jun 27, 2022
3
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The dark side of Graph Neural Networks
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Zen and the Art of Generalisation
Is it enough to report performance on a single dataset? Can we trust the reported improvement of new model architectures? We created 144 experiments for…
Daniel Szemerey
and
Mark Aron Szulyovszky
Oct 6, 2022
1
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Zen and the Art of Generalisation
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How I Learned to Stop Worrying and Love Graphs
Why Machine Learning on Graphs could be the future of AI. Many researchers are doubling down on scaling models like GPT-3 on an ever bigger corpus of…
Daniel Szemerey
and
Mark Aron Szulyovszky
Jun 13, 2022
3
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How I Learned to Stop Worrying and Love Graphs
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Two Mortals’ Journey into a Deep Reinforcement Learning Project
Deep Reinforcement Learning is about learning from mistakes - although all machine learning algorithms depend on making sense of the magnitude of error…
Daniel Szemerey
and
Mark Aron Szulyovszky
May 4, 2021
2
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Two Mortals’ Journey into a Deep Reinforcement Learning Project
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Welcome to Applied Exploration
This is Applied Exploration, a newsletter about SOTA Machine Learning in practice.
Daniel Szemerey
and
Mark Aron Szulyovszky
May 1, 2021
2
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Welcome to Applied Exploration
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Open-Core Libraries
Fold
A Time Series Cross-Validation library that lets you build, deploy and update composite models easily.
Krisi
Lightweight Evaluation of Time Series Forecasting (Classification and Regression) with powerful Reporting.
Fold-Models
Models and wrappers for 3rd party models to be used with `fold` (github.com/dream-faster/fold)
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