RHO-Loss
perceiver-ar
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RHO-Loss | perceiver-ar | |
---|---|---|
1 | 3 | |
143 | 176 | |
3.5% | 8.0% | |
5.4 | 5.3 | |
5 months ago | about 1 month ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
RHO-Loss
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[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end
RHO-LOSS - Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt - Trains Models 18x faster with higher accuracy Paper: https://arxiv.org/abs/2206.07137 Github: https://github.com/OATML/RHO-Loss
perceiver-ar
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[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end
General-purpose, long-context autoregressive modeling with Perceiver AR - Deepmind 2022 Paper: https://arxiv.org/abs/2202.07765 Deepmind: https://www.deepmind.com/publications/perceiver-ar-general-purpose-long-context-autoregressive-generation Code: https://github.com/google-research/perceiver-ar
What are some alternatives?
flash-attention-jax - Implementation of Flash Attention in Jax
flash-attention - Fast and memory-efficient exact attention
CodeRL - This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22).
EfficientZero - Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.
google-research - Google Research
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
msn - Masked Siamese Networks for Label-Efficient Learning (https://arxiv.org/abs/2204.07141)