Interested in revealing hidden biological signals in your single-cell data? ๐ช
Make sure you add our manuscript (w/ @mor_nitzan) on SiFT, Signal Filtering for single-cell data, to your reading list๐, and also check > https://t.co/9fAnbmWxif
Most active learning methods work well in high budgets, and fail in low budgets. But.. why? (Accepted to #ICML2022 ๐)
๐ขActive learning on a Budget โ Opposite Strategies Suit High and Low Budgets
Paper: https://t.co/IOCSo9BvPd
Joint work with @GHacohen and Daphna Weinshall
Recycling Finetuned models, it works!
Finetuned models lie everywhere,
there must be a way to use the data and compute invested in them.
Apparently averaging their weights is such a method.
3 papers & A๐งต
https://t.co/c8YIYzssT0
About generalization of different networks
Main finding: Generalization in pretraining follows a single dimension
Different networks, architectures, seeds, sizes but:
Similar performance โ similar linguistic capabilities
@aclmeeting accepted (#NLProc)
Summary & story ๐งต
Excited to present my latest work with @HyadataLab at @CIKM2021!
8:40pm today and 8:40am tomorrow EDT.
Full text: https://t.co/eLmMyaaTR7
We suggest a general approach to summarize procedural texts (e.g., recipes, manuals, how-to instructions, etc.) into an intuitive graph.
Semantics in Machine Translation?
We present the first semantically-aware transformer-based NMT model.
#ML#NMT#NLP#semantic#Transformers@LChoshen@AbendOmri
Paper: https://t.co/qP28esE2a5
In addition, hereโs a short video summarizing our paper:
https://t.co/rlJeCwT5nS
You were very patient, so without further ado, I'm happy to announce the new semester of the #MachineLearningClub, starting with a great talk by @GHacohen. Join us this Thu 10:30 IST, more info: https://t.co/7ZioRbUSGS
#ML@CseHuji
LMs learn generalizations in the same order (which?).
Same acquisition of grammatical phenomena capabilities,
regardless of data, seed, architecture.
Humbly, fascinating work with @saksheli Weinhsall @AbendOmri
https://t.co/uBxI3BlvNl
#NLProc#MachineLearning#deepRead
1/n
Published new paper at ICML 2020!
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
https://t.co/tXxWYTnvgN
See video:
https://t.co/ecnMs4Tpko
or come to the zoom session starting in 30 min:
https://t.co/i0vvzxJlnn