思想与社会2023 · 分享于 Hacker News

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

探索《Mamba: Linear-Time Sequence Modeling with Selective State Spaces》的原文与 Hacker News 社区讨论,查看来源并加入阅读清单。

阅读原文

看看社区如何讨论这个想法。

来自 Hacker News 的原始讨论。不同的视角、疑问,以及值得继续思考的线索。

最多展示 100 条相关讨论。论文可能出现在评论中,并非原帖投稿内容。

在讨论中发现的引用

按 HN 条目顺序展示最近的 50 条引用来源。

Ytodsacerdoti ·

Our next-generation model: Gemini 1.5

1,244 赞同588 评论
Ymarc__1 ·

Claude 3 model family

1,016 赞同683 评论
YPhilpax ·

GPT-5: Key characteristics, pricing and system card

643 赞同295 评论
Ybeefman ·

Were RNNs all we needed?

520 赞同260 评论
Ytosh ·

Codestral Mamba

485 赞同138 评论
YLaserPineapple ·

Understand how transformers work by demystifying the math behind them

470 赞同137 评论
Yguybedo ·

Eagle 7B: Soaring past Transformers

400 赞同81 评论
Ygnyeki ·

My Python code is a neural network

331 赞同67 评论
Yjackcook ·

Mamba: The Easy Way

279 赞同60 评论
Yyuhongsun ·

Launch HN: Danswer (YC W24) – Open-source AI search and chat over private data

231 赞同129 评论
Ytaiboku256 ·

Ask HN: Is anybody building an alternative transformer?

148 赞同93 评论
Yjulius ·

A pure NumPy implementation of Mamba

100 赞同35 评论
YByteAtATime ·

Cerebras achieves 2,500T/s on Llama 4 Maverick (400B)

93 赞同93 评论
Ymilliondreams ·

Generative AI and the big buzz about small language models

13 赞同5 评论
Ymatt_d ·

FlashAttention-T: Towards Tensorized Attention

12 赞同0 评论
Ybehnamoh ·

Mamba outperforms transformers "everywhere we tried"

7 赞同0 评论
YMAXPOOL ·

Introduction to State Space Models (SSM)

4 赞同1 评论
Yjohnma2006 ·

Minimal implementation of Mamba, the new LLM architecture, in 1 file of PyTorch

3 赞同0 评论
Yanigbrowl ·

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

2 赞同0 评论
Ykvee ·

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

1 赞同0 评论
Yxenova ·

Mamba: New SSM arch with linear-time scaling that outperforms Transformers

1 赞同1 评论
Yhahnchen ·

Ask HN: AI/ML papers to catch up with current state of AI?

1 赞同0 评论

一个想法,引向另一个。

发现更多论文