sarosh
Joined 19 years ago
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Comment · 1 year ago
Comment · 1 year ago
This is the PDF of the following 2011 book focused on FFTs and fast arithmetic for both real numbers and finite fields. https://www.amazon.com/Matters-Computational-Ideas-Algorithm...
The author is Jörg Arndt: born 1964 in Berlin, Germany. Study of theoretical physics at the University of Bayreuth, and the Technical University of Berlin, Diploma in 1995. PhD in Mathematics, supervised by Richard Brent, at the Australian National University, Canberra, in 2010.
Comment · 1 year ago
Interesting that the underlying model, a LoRA fine-tune of Qwen2.5-Coder-32B, relies on synthetic data from Claude[1]:
But we had a classic chicken-and-egg problem—we needed data to train the model, but we didn't have any real examples yet. So we started by having Claude generate about 50 synthetic examples that we added to our dataset. We then used that initial fine-tune to ship an early version of Zeta behind a feature flag and started collecting examples from our own team's usage.
...
This approach let us quickly build up a solid dataset of around 400 high-quality examples, which improved the model a lot!
[1]: https://zed.dev/blog/edit-prediction [2]: https://huggingface.co/datasets/zed-industries/zeta
Comment · 2 years ago
Defer to other experts, but (briefly) normalizing flows are a method for constructing complex distributions by transforming a probability density through a series of invertible transformations. Normalizing flows are trained using
a plain log-likelihood function, and they are capable of exact density evaluation and efficient sampling. See:
Danilo Rezende and Shakir Mohamed. Variational inference with normalizing flows. In ICML, 2015. Link: https://bigdata.duke.edu/wp-content/uploads/2022/08/1505.057...
Laurent Dinh, David Krueger, and Yoshua Bengio. Nice: Non-linear independent components estimation. In ICLR Workshop, 2015. Link: https://arxiv.org/pdf/1410.8516
And for your direct question, the following paper "Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods" appears upon a superficial glance to be relevant. Link: https://arxiv.org/pdf/2107.08001
Comment · 2 years ago
This actually seems like just a very clever market segmentation solution since the GPU was already limited to 8x PCIe lanes (its a laptop GPU see https://www.notebookcheck.net/NVIDIA-GeForce-RTX-4060-Laptop...). The 'addition' of the M.2 SSD makes it a unique offering. Limiting it to only one drive is another way to keep the thermal envelope down. Kudos to the Asus design and product development folks.
Comment · 3 years ago
The paper, Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks, is here https://arxiv.org/abs/2303.08812.
The crucial advantage of SSCNN are that they "replaces traditional convolutions with sparse submanifold convolutions" which operate "only on the non-zero elements" and creates efficiencies.
Comment · 3 years ago
Looking at
https://research.wmz.ninja/projects/phd/rulesets/default/eve... provides most of the key 'game loops', i.e.
# idea -> prelim -> major -> 2 figures -> submitted paper
interesting to see the hypothesis about reading more papers being borne out:
# increase the success rate as the player reads more papers probability: 0.60 + player.readPapers / 100 - itemCount('idea') / 20
Also interesting to see that passing the qualification exam provides the largest player.hope boost (+10)
Was fun to see the TooManyIdeas random event - now to actually get it to trigger.
Comment · 3 years ago
A (now 6 year old) discussion that might be helpful: Anders Hejlsberg on Modern Compiler Construction: https://www.youtube.com/watch?v=wSdV1M7n4gQ
Comment · 3 years ago
The author, David Mumford, is known for his distinguished work in algebraic geometry, and was awarded the Fields Medal in 1974. [2]
Pattern theory was formulated by Ulf Grenander to describe knowledge of the world as patterns. [3]
Prof. Mumford explains that "[s]everal essential ideas brought me to realize how Grenander's Pattern Theory was the right way to understand almost all cognitive skills and especially vision. One was the emphasis on pattern synthesis as well as pattern analysis." [0]. Second, "was that natural signals given by functions f vary not only by random additive perturbations but often by composition with random rearrangements of their domain. The resulting probability distribution in the vector space of signals is nothing like Gaussian. Its support is usually a twisted snakey submanifold. This puts the lie to all simplistic Gaussian pattern recognition algorithms." [0]. And third, "graphical structures were everywhere in the representations of ideas in cognitive domains" [0].
His most recent work seems to be "Pattern Theory, the Stochastic Analysis of Real World Signals" [1]
[0] https://www.dam.brown.edu/people/mumford/vision/pattern.html
[1] https://www.amazon.com/dp/1568815794/ref=sr_1_1?ie=UTF8&qid=...
Comment · 3 years ago
Heres the Arxiv link with the PDF since the Science link isn't working. https://arxiv.org/pdf/2203.07814v1.pdf
*See p. 21 for a list of benchmarks, including the results of a finetuned GPT-3 for coding.
Comment · 4 years ago
The repo from the paper: https://github.com/mlpen/Nystromformer
Comment · 4 years ago
A more detailed source: https://www.pulitzer.org/winners/staff-quanta-magazine-new-y..., explaining "For coverage that revealed the complexities of building the James Webb Space Telescope, designed to facilitate groundbreaking astronomical and cosmological research."
Comment · 4 years ago
The pre-print referred to in this article itself is here: https://arxiv.org/abs/2111.15323
Comment · 4 years ago
An interesting take from Mike Pondsmith given his heavy involvement in the venture at the end of (at least in my opinion) well-written article: "[C]omparing the tabletop experience with its video-game incarnation, he noted that the latter doesn’t really compare to the former when it comes to self-expression. “You could be you in a tabletop game and bring all the stuff that you wanted to bring into it,” he said. “A tabletop game is limitless. A video game, by its very nature of how it’s designed, has some limits.” "
Francis Bach, the author, makes a good faith effort to explain exactly why this material is beneficial (see https://francisbach.com/my-book-is-out/):
"Why yet another book on learning theory? ...the main reason is that I felt that the current trend in the mathematical analysis of machine learning was leading to overly complicated arguments and results that are often not relevant to practitioners. Therefore, my aim was to propose the simplest formulations that can be derived from first principles, trying to remain rigorous without overwhelming readers with more powerful results that require too much mathematical sophistication."
From my own reading and experience on the mathematical analysis approach of this "training goes brrr" approach, I thought the material in Chapter 12, Overparameterized Models, was interesting and coherent with 12.2.4 Linear Regression with Gaussian Projections being an especially elegant explanation. It would be interesting to hear if you had read/skimmed/purused this section and found it wanting etc.