dCpp

Automatic differentiation in C++; infinite differentiability of conditionals, loops, recursion and all things C++

AI & Machine Learning Learning & Reference

Explore on GitHub Read Hacker News discussions

3Distinct people

3Mentions

0Explicit recommenders

151GitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

Automatic differentiation in C++; infinite differentiability of conditionals, loops, recursion and all things C++

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

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 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 hour3 dated mentions

Observed mentions: 2016-12-30 00:39:18 UTC to 2016-12-30 05:33:26 UTC.

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.
Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
Dec 30, 00:00	2016-12-30 00:00:00 UTC	2016-12-30 01:00:00 UTC	1	1
Dec 30, 01:00	2016-12-30 01:00:00 UTC	2016-12-30 02:00:00 UTC	0	0
Dec 30, 02:00	2016-12-30 02:00:00 UTC	2016-12-30 03:00:00 UTC	0	0
Dec 30, 03:00	2016-12-30 03:00:00 UTC	2016-12-30 04:00:00 UTC	0	0
Dec 30, 04:00	2016-12-30 04:00:00 UTC	2016-12-30 05:00:00 UTC	1	1
Dec 30, 05:00	2016-12-30 05:00:00 UTC	2016-12-30 06:00:00 UTC	1	1

What Hacker News says

3 sources

kxyvr·Dec 30, 2016

Actually, what's funny is that this is probably the most succinct explanation of an operator overloading approach to AD that I've heard. But, from what I can tell as well, yes. Most of the action is in var.cpp: https://github.com/ZigaSajovic/dCpp/blob/master/src/var.cpp It looks like the AST is held by a collection of maps that contain pointers and other variables. Outside of this paper, most of my time on writing AD codes has been literally just trying to do things on ASTs really, really fast, which is why your comment is really apt.

In Operational calculus on programming spaces and generalized tensor networks

hcarvalhoalves·Dec 30, 2016

> What's not clear to me in this paper is the practical efficiency of their technique especially for high-order derivatives. On section "5.3.1 Order reduction for nested applications": > We gained the ability of writing a differentiable program acting on derivatives of another program, stressed as crucial (but lacking in most models) by other authors [18]. See http://docs.lib.purdue.edu/cgi/viewcontent.cgi?article=1370&... for [18]. --- > Right now, there exist good AD tools that work just fine with control structures, recursion, and composition. See their example implementation (https://github.com/ZigaSajovic/dCpp) to understand what "control structures, recursion and composition" mean in this context.

In Operational calculus on programming spaces and generalized tensor networks

ilaksh·Dec 30, 2016

Weird I was just looking at this last night. They implemented it: https://github.com/ZigaSajovic/dCpp I got onto that based on this fantasy I was having. Basically I keep thinking that the main advantage deep learning has is probably not actually the neural nets, but the fact that they are highly parallel and very general computation techniques. So if it doesn't really have to be a NN, maybe there could be a way to more efficiently achieve the same level of results by using something more like evolved parallel programs. Maybe have a universal virtual sensor grounding. Then if you find a useful/general procedure/dataset you could distribute it to improve lots of diverse agent's knowledge/skills immediately. But to evolve these supposed universal function-approximating parallel programs that are more efficient and exchangable than deep NN

Read the rest

s, you would need something like backpropagation, except for some arbitrary program structures. So when reading about backpropagation on wikipedia they mention the generalization is https://en.m.wikipedia.org/wiki/Automatic_differentiation Anyway I don't grock all the details fully but my interpretation of all of this is that I think things like backpropagation with deep NNs are truly general purpose and when combined with grounding such as streams of virtual/real senses and training across widely varied tasks can actually produce general human and animal/like intelligence. I think the problem is it just takes a ton of computing power, iterations and time. If we wait 2-10 years that probably won't matter. But if we don't want to wait we can probably do something similar and more efficient/readily distributable if we can use algorithms that aren't NNs but are general and highly parallel and can be adjusted via AD to learn. Based on this I personally believe we will see human/animal like general intelligence systems before 2020.

In Operational calculus on programming spaces and generalized tensor networks