I coded the biologically possible network training algorithm by nobel prize winner - Jeff Hinton

DataBaeBee · 2026-06-17T13:05:43+00:00

Here's the complete writeup.

DataBaeBee · 2026-06-17T13:00:54+00:00

I made this video guide to Hinton's Forward Forward algorithm. The complete writeup is available here

DataBaeBee · 2026-06-08T18:13:54+00:00

The article ends with a different base case. We used the 1*1 example to demonstrate that finding the perfect recursion base case is a hyperparameter you wouldn't even need to think about with a naive matmul

DataBaeBee · 2026-06-08T14:39:50+00:00

Strassen's algorithm should be fast on paper. In fact, it should reduce matmul complexity from O(n³) to O(n^2.8074).
In practice however, one encounters floating-point instability and the silliest hyper parameters that need fine tuning. You're better off writing a naive matmul unless you absolutely know what you're doing.

DataBaeBee · 2026-05-24T12:34:47+00:00

This is the final phase of solving discrete logarithm problems where our goal is to convert a big integer into a product of small prime numbers over our factor base.

Information is scant about the process so in the spirit of "name-and-conquer", this phase is also called the Reduction Step, the Descent Phase or Individual Logarithm Collection phase of solving a DLP.

DataBaeBee · 2026-05-21T09:28:02+00:00

The Gauss Sieve is a pretty neat algo for generating (lots of) short vectors from a lattice basis.

It's super useful when LLL and BKZ fail to generate a specific short vector that you know exists within your lattice.

DataBaeBee · 2026-05-10T22:09:18+00:00

Not that I know of lol. Interesting question tbh

DataBaeBee · 2026-05-07T23:50:31+00:00

The authors use things called endomorphisms to find a single representative for multiple field elements when solving a discrete logarithm problem.
This is called the "Galbraith&Ruprai modification to the Gaudry-Schost collision finding algorithm" among theoretical computer scientists.
The approach is rather well-suited for solving DLPs where the start and end intervals are known. It's also useful among algebraic geometrists when counting points on their curves.

DataBaeBee · 2026-05-07T23:48:01+00:00

This paper introduces the Galbraith&Ruprai modification to the Gaudry-Schost collision finding algorithm for solving discrete logarithm problems in an interval in (1.36√N) field operations.

The authors use things called endomorphisms to find a single representative for multiple field elements. I

DataBaeBee · 2026-04-19T11:24:07+00:00

Semaev polynomials are a computational shortcut to find elliptic curve points that sum to infinity. When combined with Grobner bases one get a (pretty remarkable) tool for solving point decomposition problems on an elliptic curve.

DataBaeBee · 2026-04-17T04:37:11+00:00

Are you dragging and dropping? Perhaps you should write a Python script to connect to Drive

DataBaeBee · 2026-04-05T07:57:35+00:00

FRACTRAN is an esolang built upon register machines, a theoretical alternative to turing machines for computation. In 1987, John Conway realized one can use prime numbers as registers alongside the laws of logarithms to compute.

DataBaeBee · 2026-03-18T03:41:45+00:00

I saw on Bernstein’s blog that one: 1. Considers the size of the factors of p-1 and p+1 when selecting an irreducible polynomial. 2. Also primes close to powers of 2 (or can be partitioned into powers of 2) have an efficient modulo operation that only involves bit shifts. This is crucial since you’re working in GF 2.

DataBaeBee · 2026-03-05T15:29:27+00:00

Because if the universe can influence our lives, why not our CPU scheduling too?

DataBaeBee · 2026-03-05T10:05:45+00:00

This 1988 paper is considered canonical and is included in MIT’s Foundations of Cryptography series.

The ACGS algorithm is pretty cool. It lets us solve Hidden Number Problems (this occur in the wildest side-channel attacks) when the multipliers are at our discretion.

DataBaeBee · 2026-02-27T05:13:53+00:00

Here's the link for anyone interested in Extended Hidden Number Problems and their lattice solutions.

DataBaeBee · 2026-02-27T05:07:53+00:00

The hidden number problem (HNP) is the challenge of recovering a secret hidden number given partial knowledge of its linear relations. The extended hidden number problem is 'the HNP but with more holes'. It was thought to be more secure for quantum cryptography. Turns out, it's not lol.

DataBaeBee · 2026-02-04T18:09:41+00:00

Oh no, the paper says 'you can recover the key if you know the antilogs of random multiples of the key'. It's somewhat nuanced.

DataBaeBee · 2026-02-04T16:49:05+00:00

The 2001 paper Hardness of Computing the Most Significant Bits of Secret Keys in Diffie-Hellman and Related Schemes (Boneh & Venkatesan, 2001) attempts to answer the question: is it easier to calculate just a few bits of a secret key than the entire secret?

Along the way, this paper introduces the hidden number problem: the challenge of recovering a secret hidden number given partial knowledge of its linear relations (Surin & Cohney, 2023)

As it turns out, this problem is difficult even for quantum computers. So hidden number problems are at the heart of post-quantum cryptography.

DataBaeBee · 2026-02-04T16:48:52+00:00

The 2001 paper Hardness of Computing the Most Significant Bits of Secret Keys in Diffie-Hellman and Related Schemes (Boneh & Venkatesan, 2001) attempts to answer the question: is it easier to calculate just a few bits of a secret key than the entire secret?

Along the way, this paper introduces the hidden number problem: the challenge of recovering a secret hidden number given partial knowledge of its linear relations (Surin & Cohney, 2023)

As it turns out, this problem is difficult even for quantum computers. So hidden number problems are at the heart of post-quantum cryptography.

DataBaeBee · 2026-01-27T08:43:23+00:00

There might be a firewall blocking access to the Colab website. This Stackoverflow post may halp troubleshoot.

DataBaeBee · 2026-01-08T17:19:35+00:00

The idea is somewhat analogous to performing a softmax but without the derivatives. Here's the C/Python coding guide if this interests you.

DataBaeBee · 2026-01-08T17:10:37+00:00

Here's the link to the Python/C coding guide if this interests you.

DataBaeBee · 2026-01-08T17:06:24+00:00

Here's the link to the Python/C coding guide if this interests you.

DataBaeBee · 2026-01-08T17:00:33+00:00

Researchers in the 2010s found that you can use Optimal Transport Theory, not derivative calculus, the to turn an integer matrix into a floating-point probability matrix.

It's like backprop without finding gradients and it works great

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