Quinn Xing @QuinnXing
A small ML scientist🐋 | A knowledge builder😼 论文好多读不完啊; paperlist is not fulfilled. Joined January 2021-
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这几天体验了一下Follow 个人感觉是目前个人用过的最好用的rss阅读器了 不仅UI好看 而且可以看个位都关注了什么有趣的源 比自建miniflux+rsshub确实更舒服 期待移动端 顺手抽个邀请码 参与条件: 关注 @follow_app_ 和 @DIYgod 且点赞并转发这条贴文 开奖时间:10.1
Hey folks! 在这里介绍我们过去十个月在开发的下一代 AI 生产力工具 flowith! @flowith_ai 🫡 这是一款基于二维画布、节点式、支持动态 UI 的 AI 内容生成工具。让你在创造内容时大幅度提升效率、并更容易进入心流的状态。 - 基于画布的交互:不同于传统聊天式 UI,你可以在一个类 Figma…
On Limitations of the Transformer Architecture ift.tt/bR6gyfD
[LG] Universal Neural Functionals A Zhou, C Finn, J Harrison [Google DeepMind & Stanford University] (2024) arxiv.org/abs/2402.05232 - The paper proposes an algorithm to automatically construct permutation equivariant models, called universal neural functionals (UNFs), that can…
How do modern RNNs/SSMs such as Mamba perform on in-context learning tasks? How do they relate to attention-based models like Transformers? We find that modern RNNs can implement attention and that they leverage it to solve ICL tasks in an attention-based manner! (1/6)
[CV] Blue noise for diffusion models arxiv.org/abs/2402.04930 The paper introduces a novel approach to diffusion models by incorporating correlated noise, specifically blue noise, into the training process. This alternative to using Gaussian noise aims to improve the…
Towards Understanding Inductive Bias in Transformers: A View From Infinity ift.tt/gZmVQWx
1/8 How do transformers learn? In our new work, we find that transformers develop in-context learning in discrete stages that can be automatically discovered. 🧵 arxiv.org/abs/2402.02364 Joint work w/ @georgeyw_, Matthew Farrugia-Roberts, @lemmykc, Susan Wei, @danielmurfet
[LG] Neural Networks Learn Statistics of Increasing Complexity N Belrose, Q Pope, L Quirke, A Mallen, X Fern [EleutherAI & Oregon State University] (2024) arxiv.org/abs/2402.04362 - The paper presents new evidence for the distributional simplicity bias (DSB) hypothesis, which…
最近看到几次推友吐槽公司日本同事用git的事,我之前有整理过一个旨在从入门到应付日常工作的Git指南给新同事,内部评价还不错,也在这里分享给大家。打开这个链接可以直接看到完整的 markdown 文档「Git从入门到应付日常工作.md」jmp.sh/s/P42MV2FmKfnz…
每周至少四次,每次至少二十分钟,温度在179华氏度(82摄氏度)或更高的干蒸桑拿可以降低患阿尔茨海默病风险约65%以及降低动脉粥样硬化风险50%。 来自《outlive the science art of longevity 》,非常值得推荐的一本书。
每周至少四次,每次至少二十分钟,温度在179华氏度(82摄氏度)或更高的干蒸桑拿可以降低患阿尔茨海默病风险约65%以及降低动脉粥样硬化风险50%。 来自《outlive the science art of longevity 》,非常值得推荐的一本书。 https://t.co/OWAzazlZFA
More information (paper, code, blogpost) on our #ICLR2024 spotlight paper "Fantastic Gains and Where to Find Them - On the Existence and Prospect of General Knowledge Transfer": eml-munich.de/publication/Fa…
More information (paper, code, blogpost) on our #ICLR2024 spotlight paper "Fantastic Gains and Where to Find Them - On the Existence and Prospect of General Knowledge Transfer": eml-munich.de/publication/Fa…
Cool post from my MATS scholars @Connor_Kissane & Rob Krzyzanowski: Attention SAEs scale to GPT2 Small! We find that training sparse auto encoders on the attention outputs continues to work on GPT2 Small, and open source SAEs on every layer! We hope this is a useful resource
[LG] Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks arxiv.org/abs/2402.04248 This study investigates the in-context learning (ICL) capabilities of language models based on state-space models (SSMs) and compares them with Transformer models. The…
[LG] Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers A R Bambhaniya, A Yazdanbakhsh, S Subramanian, S Kao, S Agrawal, U Evci, T Krishna [Georgia Institute of Technology & Google DeepMind & Google] (2024) arxiv.org/abs/2402.04744 - N:M structured…
❓Are attention-based models needed for In-Context Learning(ICL)? 🤔Can emerging architectures perform ICL? 🎉Check out our #ICLR2024 paper "Exploring the Relationship Between Model Architecture and In-Context Learning Ability" 🎉 #LLM Paper: arxiv.org/abs/2310.08049 🧵[1/9]
[LG] A phase transition between positional and semantic learning in a solvable model of dot-product attention H Cui, F Behrens, F Krzakala, L Zdeborová [EPFL] (2024) arxiv.org/abs/2402.03902 - The paper investigates how a dot-product attention layer learns a positional attention…
[LG] Simulated Overparameterization H Mazzawi, P Awasthi, X Gonzalvo, S Ramalingam [Google Research] (2024) arxiv.org/abs/2402.05033 - The paper introduces a new training paradigm called Simulated Overparameterization (SOP). SOP trains models with more parameters than needed for…
[LG] Learning a Decision Tree Algorithm with Transformers Y Zhuang, L Liu, C Singh, J Shang, J Gao [Microsoft Research & UC San Diego] (2024) arxiv.org/abs/2402.03774 - The paper proposes MetaTree, a transformer-based model that learns to generate decision trees. - MetaTree is…

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