Freddie Kalaitzis

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Freddie Kalaitzis

Freddie Kalaitzis

@alkalait

snr scientist @aspiaspace snr res fellow @oatml_oxford #ai4eo • ml ∧ #CliffordAlgebras • ml lead @fdl_ai

London | 🇬🇧🌐🇬🇷 Katılım Temmuz 2009
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Freddie Kalaitzis
Freddie Kalaitzis@alkalait·
It took me a while, but I'm very excited about this one–that I've joined @AspiaSpace Watch this space.
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Sasha Luccioni, PhD 🦋🌎✨🤗
The amount of backlash I get when I refuse to do free work (e.g. a keynote presentation, a "quick chat") is really shocking to me. I'm sorry, but why would I provide free labor to for-profit entities? (And don't say "exposure")
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aj@anndvision·
thrilled to share that I will be joining David Blei and Donald Green at @Columbia in July for a postdoc exploring scalable machine learning for field experimentation in the social sciences! looking forward to working with everyone at @blei_lab and learning from the NYC community
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University of Oxford
University of Oxford@UniofOxford·
👽 | A new study involving Oxford researchers has demonstrated that AI and machine learning methods can support the search for extraterrestrial life by identifying hidden patterns within geographical data. ⬇ ox.ac.uk/news/2023-03-2…
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Nature Astronomy
Nature Astronomy@NatureAstronomy·
Kimberley Warren-Rhodes & colleagues present an adaptable framework that couples statistical ecology with deep learning to recognize and predict biosignature patterns. Such a framework might be applied to (e.g.) drone flight imagery of planetary surfaces. nature.com/articles/s4155…
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Freddie Kalaitzis
Freddie Kalaitzis@alkalait·
This work shows the first framework for searching for life on another planet in a systematic data-driven fashion based on good-ol' statistics and A.I. starting from an orbital vantage point and all the way through to a rover's camera.
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Freddie Kalaitzis
Freddie Kalaitzis@alkalait·
Variants a) Selection can be based on the max, or the soft-max of dot products. The latter is soft-attention. b) When A=B, this is self-attention. 3/n
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Freddie Kalaitzis
Freddie Kalaitzis@alkalait·
5. We can match keys via the dot product with a "key estimate"—a QUERY vector 6. We can inform selections on objects of a set A based on the QUERY of an object from set B 2/n
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Freddie Kalaitzis
Freddie Kalaitzis@alkalait·
Attention in 6 sentences 1. Each of N objects is encoded by a VALUE vector 2. Representing an object has different requirements to addressing it 3. To decouple the two tasks each object is addressed by a separate KEY vector 4. KEY matching is easier than selection 1/n
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Freddie Kalaitzis
Freddie Kalaitzis@alkalait·
if the Earth's friekin core can take a break, then my brother you deserve one too!
Freddie Kalaitzis tweet media
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