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yeesian

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投稿

Goodbye Python-Mip?

oberdieck.dk
1 ポイント·投稿者 yeesian·2 年前·0 コメント

Esmeralda

esmeralda.org
20 ポイント·投稿者 yeesian·2 年前·18 コメント

Coding Patterns – The CP-SAT Primer

d-krupke.github.io
2 ポイント·投稿者 yeesian·2 年前·0 コメント

A Strengthened Implementation of CuPDLP for Linear Programming by C Language

arxiv.org
1 ポイント·投稿者 yeesian·2 年前·0 コメント

Understanding the Decline of Impact Factors in Informs Journals

pubsonline.informs.org
1 ポイント·投稿者 yeesian·2 年前·0 コメント

Towards Large Language Models as Copilots for Theorem Proving in Lean

arxiv.org
3 ポイント·投稿者 yeesian·2 年前·0 コメント

Exploring the Limits of Transfer Learning with a Unified Transformer (2019)

arxiv.org
12 ポイント·投稿者 yeesian·2 年前·1 コメント

How do I get started with Jax on TPU VMs

github.com
23 ポイント·投稿者 yeesian·2 年前·17 コメント

Linearity of Relation Decoding in Transformer Language Models

lre.baulab.info
1 ポイント·投稿者 yeesian·2 年前·0 コメント

LLM Evaluation: Everything You Need to Run, Benchmark Evals

arize.com
2 ポイント·投稿者 yeesian·2 年前·0 コメント

Speech and Language Processing (3rd ed. draft)

web.stanford.edu
214 ポイント·投稿者 yeesian·2 年前·32 コメント

The Greatest Meme Template

readtrung.com
31 ポイント·投稿者 yeesian·2 年前·9 コメント

Coalescence: Making LLM inference 5x faster

blog.dottxt.co
5 ポイント·投稿者 yeesian·2 年前·0 コメント

Fast JSON Decoding for Local LLMs with Compressed Finite State Machine

lmsys.org
1 ポイント·投稿者 yeesian·2 年前·0 コメント

Generative Uncertainty

vaughntan.org
7 ポイント·投稿者 yeesian·3 年前·0 コメント

[untitled]

1 ポイント·投稿者 yeesian·3 年前·0 コメント

My Life as a Con Man (2020)

swyx.io
4 ポイント·投稿者 yeesian·3 年前·0 コメント

Using llama-cpp-Python grammars to generate JSON

til.simonwillison.net
2 ポイント·投稿者 yeesian·3 年前·0 コメント

Finite-State Transducers in Language and Speech Processing (1997)

aclanthology.org
3 ポイント·投稿者 yeesian·3 年前·1 コメント

Finite-State Transducer

en.wikipedia.org
6 ポイント·投稿者 yeesian·3 年前·1 コメント

コメント

yeesian
·3 年前·議論
FST implementation in Rust: https://news.ycombinator.com/item?id=38096511
yeesian
·3 年前·議論
Relatedly (from a data structures perspective): https://news.ycombinator.com/item?id=10551280
yeesian
·3 年前·議論
This was posted in the past (https://news.ycombinator.com/item?id=20502032); just re-posting now in light of their potential application to LLM grammars (https://news.ycombinator.com/item?id=38082219 for context)
yeesian
·3 年前·議論
Relatedly:

Llama: Add grammar-based sampling - https://news.ycombinator.com/item?id=36819906 (105 comments)

ReLLM: Exact Structure for Large Language Model Completions - https://news.ycombinator.com/item?id=35829399 (13 comments)

Show HN: Structured output from LLMs without reprompting - https://news.ycombinator.com/item?id=36750083 (54 comments)

Show HN: LLMs can generate valid JSON 100% of the time - https://news.ycombinator.com/item?id=37125118 (303 comments)

A guidance language for controlling LLMs - https://news.ycombinator.com/item?id=35963936 (190 comments)

LMQL: A query language for programming (large) language models - https://news.ycombinator.com/item?id=35956484 (12 comments)
yeesian
·3 年前·議論
https://archive.ph/hlh06
yeesian
·3 年前·議論
https://cloud.google.com/vertex-ai/docs/start/explore-models...
yeesian
·3 年前·議論
https://cloud.google.com/vertex-ai/docs/start/explore-models...
yeesian
·3 年前·議論
(This is more of a link-dump than a paper discussion --)

For the line of inquiry w.r.t tensor compilers and MLIR/LLVM (linalg, polyhedral, [sparse_]tensor, etc), I personally found the following really helpful: https://news.ycombinator.com/item?id=25545373 (links to a survey), https://github.com/merrymercy/awesome-tensor-compilers

I also have an interest in the community more widely associated with pandas/dataframes-like languages (e.g. modin/dask/ray/polars/ibis) with substrait/calcite/arrow their choice of IR. Some links: https://github.com/modin-project/modin, https://github.com/dask/dask/issues/8980, https://news.ycombinator.com/item?id=16510610, https://news.ycombinator.com/item?id=35521785

I broadly classify them as such since the former has a stronger disposition towards linear/tensor-algebra, while the latter towards relational algebra, and it isn't yet clear (to me) how well innovations in one carry over to the other (if they do), and hence I'm also curious to hear more about proposals for a unified language across linalg and relational alg (e.g. https://news.ycombinator.com/item?id=36349015).

I'm particularly interested in pandas precisely because it seems to be right at the intersection of both forms of algebra (and draws a strong reaction from people who are familiar/comfortable with one community and not the other). See e.g. https://datapythonista.me/blog/pandas-20-and-the-arrow-revol... and https://wesmckinney.com/blog/apache-arrow-pandas-internals/
yeesian
·3 年前·議論
Previous discussion: https://news.ycombinator.com/item?id=12399580
yeesian
·3 年前·議論
That's cool, thanks for sharing! Do you know how close they are to their example use cases [1]? So far I've only been able to find a tool for calcite SQL parsing [2] but not the portion connecting to Arrow C++ compute kernel yet.

[1]: https://substrait.io/#example-use-cases [2]: https://substrait.io/tools/producer_tools/