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dselsam
certigrad
Lean2017–2017
dselsam / certigrad

certigrad

Bug-free machine learning on stochastic computation graphs

2Distinct people
2Mentions
0Explicit recommenders
405GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Bug-free machine learning on stochastic computation graphs

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by hour2 dated mentions

Observed mentions: 2017-07-10 20:45:55 UTC to 2017-07-11 02:47:36 UTC.

012017-07-10 20:00:00 UTC to 2017-07-10 21:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJul 10, 20:002017-07-10 21:00:00 UTC to 2017-07-10 22:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-07-10 22:00:00 UTC to 2017-07-10 23:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-07-10 23:00:00 UTC to 2017-07-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJul 10, 23:002017-07-11 00:00:00 UTC to 2017-07-11 01:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-07-11 01:00:00 UTC to 2017-07-11 02:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2017-07-11 02:00:00 UTC to 2017-07-11 03:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 11, 02:00

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Jul 10, 20:002017-07-10 20:00:00 UTC2017-07-10 21:00:00 UTC110
Jul 10, 21:002017-07-10 21:00:00 UTC2017-07-10 22:00:00 UTC000
Jul 10, 22:002017-07-10 22:00:00 UTC2017-07-10 23:00:00 UTC000
Jul 10, 23:002017-07-10 23:00:00 UTC2017-07-11 00:00:00 UTC000
Jul 11, 00:002017-07-11 00:00:00 UTC2017-07-11 01:00:00 UTC000
Jul 11, 01:002017-07-11 01:00:00 UTC2017-07-11 02:00:00 UTC000
Jul 11, 02:002017-07-11 02:00:00 UTC2017-07-11 03:00:00 UTC101

What Hacker News says

2 sources
> The specification is a lot smaller than the code, and so it's easier to read and manually verify that it's correct. >> How is that the case in this specific example? It looks a lot harder to check for correctness. Author here. Here is the specification for the stochastic backpropagation algorithm: https://github.com/dselsam/certigrad/blob/master/src/certigr... The preconditions do not need to be inspected carefully because we prove that the models of interest satisfy them (example: https://github.com/dselsam/certigrad/blob/master/src/certigr...) Here is the part of the specification that needs to be inspected carefully: https://github.com/dselsam/certigrad/blob/master/src/certigr... The syntax may seem strange to those unfamiliar with Lean, and a few of the functions involved may not be self-explanatory without reading their definitions
Read the rest
, but the statement is conceptually simple: the stochastic backpropagation algorithm correctly computes unbiased estimates of the gradient of the expected loss. It is subjective, but I personally think that this specification is vastly easier to understand and to check for correctness than the actual implementation of the stochastic backpropagation algorithm.
In Certigrad: bug-free machine learning on stochastic computation graphs

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