c0deb0t
Joined 7 years ago
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100
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46
Member since
Oct 1
Recent submissions
Comment · 2 years ago
Comment · 5 years ago
Adversarial attacks is a super interesting field, but unfortunately I feel that a lot of papers are just incremental attack or defense improvements like a cat-and-mouse game. I originally did some research on 3D point cloud attacks, but later stopped because making super successful attacks (eg., attacks with higher success rates than all the previous techniques for some very specific task) don't really help us understand that much more about neural nets, its just optimizing a metric for publishing papers. This kind of research is quite common, even at top conferences.
Despite this, recently, we made a 1 minute explainer video introducing adversarial attacks on neural nets as a submission for the Veritasium contest: https://youtu.be/hNuhdf-fL_g Give it a watch!
Comment · 5 years ago
Yes, this will uwuify your text at high speeds. It reached 2.3 GB/s on my 8-core macbook pro, while uwu-ing the first 1 GB of english Wikipedia. This Rust command-line tool takes advantage of SSE4.1 SIMD vectorization and multithreading (exploit all your CPU cores for this!) to be almost as fast as simply copying a file. Installing it is simply cargo install uwuify, assuming you already have Rust installed. It is on crate.io too: https://crates.io/crates/uwuify
In case you don't know what uwu'd text looks like, here's an example:
hey... i think i w-weawwy wuv you. (⑅˘꒳˘) d-do you want a headpat?
Comment · 5 years ago
Yes, this will uwuify your text at high speeds. It reached 2.3 GB/s on my 8-core macbook pro, while uwu-ing the first 1 GB of english Wikipedia. This Rust command-line tool takes advantage of SSE4.1 SIMD vectorization and multithreading to be almost as fast as simply copying a file. Installing it is simply cargo install uwuify, assuming you already have Rust installed. It is on crate.io: https://crates.io/crates/uwuify
Comment · 6 years ago
For binary trees, indexing can be done by saving the subtree size of each node and doing a sort of binary search. Not sure if this is fast for B-trees that have more than 2 children nodes, though.
Comment · 6 years ago
I'm a high school student that is quite new to Rust, and I am liking it so far. I learned a lot from this project and I hope that it can be a good learning resource for others.
In addition to this, I am also working on a SIMD edit distance library in Rust.
Comment · 6 years ago
Since I am storing the entire DP matrix as diagonal vectors that are flattened, I don't think there will be many cache misses. Each diagonal only depends on its previous two diagonals, and each diagonal is stored contiguously in memory.
The problem with handling diagonals is that indexing cells and comparing characters on the diagonal becomes complex. Dealing with this without many branches (less branch mispredictions) is the hard part.
Comment · 6 years ago
I took a look at the code, and read the paper. It seems that they directly calculate the entire 2D DP array, but use SIMD to allow each cell to contain multiple values, one for each query string. My approach uses anti-diagonals instead, but it is fast for one vs one comparisons, instead of handling multiple query strings.
Regardless, my goal was to learn some SIMD and Rust (first time for both), so I did not read many background papers.
What I'm wondering is why the Kraken2 probabilistic hash table doesn't work. It uses 32 bits per element in an open addressing hash table. For 1 billion k-mers and 19 bits for the value, 32 - 19 = 13 bits of the key hash can be stored alongside the value, helping disambiguate hash collisions. If the load factor is 1.25x, then that's 4 * 10^9 * 1.25 = 5GB total, better than ~7GB. Also, this is only one cache miss (+ linear probing that can be SIMD accelerated) per lookup.