[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/MachineLearning

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  • google-research

    Google Research

    An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems - Google 2022 – Pathways - Jeff Dean! - Network grows with amount of tasks and data! Paper: https://arxiv.org/abs/2205.12755 Github: https://github.com/google-research/google-research/tree/master/muNet

  • EfficientZero

    Open-source codebase for EfficientZero, from "Mastering Atari Games with Limited Data" at NeurIPS 2021.

    Mastering Atari Games with Limited Data – EfficientZero ( Human sample -efficiency! ) Paper: https://arxiv.org/abs/2111.00210 Lesswrong article about the paper: https://www.lesswrong.com/posts/mRwJce3npmzbKfxws/efficientzero-how-it-works Github: https://github.com/YeWR/EfficientZero

  • Zigi

    Close all those tabs. Zigi will handle your updates.. Zigi monitors Jira and GitHub updates, pings you when PRs need approval and lets you take fast actions - all directly from Slack! Plus it reduces cycle time by up to 75%.

  • msn

    Masked Siamese Networks for Label-Efficient Learning (https://arxiv.org/abs/2204.07141)

    Masked Siamese Networks for Label-Efficient Learning similar to EfficientZero in structure Paper: https://arxiv.org/abs/2204.07141 Github: https://github.com/facebookresearch/msn

  • flash-attention

    Fast and memory-efficient exact attention

    FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness Paper: https://arxiv.org/abs/2205.14135 Github: https://github.com/HazyResearch/flash-attention and https://github.com/lucidrains/flash-attention-jax

  • flash-attention-jax

    Implementation of Flash Attention in Jax

    FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness Paper: https://arxiv.org/abs/2205.14135 Github: https://github.com/HazyResearch/flash-attention and https://github.com/lucidrains/flash-attention-jax

  • DeepSpeed

    DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

    DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale - Microsoft 2022 Paper: https://arxiv.org/pdf/2207.00032.pdf Github: https://github.com/microsoft/DeepSpeed

  • perceiver-ar

    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

  • InfluxDB

    Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Data Platform where developers build real-time applications for analytics, IoT and cloud-native services in less time with less code.

  • RHO-Loss

    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

  • CodeRL

    This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22).

    CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning Paper: https://arxiv.org/pdf/2207.01780.pdf Github: https://github.com/salesforce/CodeRL

  • XMem

    [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model

    XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model ( Added because of the Atkinson-Shiffrin Memory Model ) Paper: https://arxiv.org/abs/2207.07115 Github: https://github.com/hkchengrex/XMem

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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