Related
I have set of short phrases and a set of texts. I want to predict if a phrase is related to an article. A phrase that isn't appearing in the article may still be related.
Some examples of annotated data (not real) is like this:
Example 1
Phrase: Automobile
Text: Among the more affordable options in the electric-vehicle marketplace, the 2021 Tesla Model 3 is without doubt the one with the
most name recognition. It borrows some styling cues from the company's
Model S sedan and Model X SUV, but goes its own way with a unique
interior design and an all-glass roof. Acceleration is quick, and the
Model 3's chassis is playful as well—especially the Performance
model's, which receives a sportier suspension and a track driving
mode. But EV buyers are more likely interested in driving range than
speediness or handling, and the Model 3 delivers there too. The base
model offers up to 263 miles of driving range according to the EPA,
and the more expensive Long Range model can go up to 353 per charge.
Label: Related (PS: For a given text, one and only one phrase is labeled 'Related' with it. All others are 'Unrelated')
Example 2
Phrase: Programming languages
Text: Python 3.9 uses a new parser, based on PEG instead of LL(1). The new parser’s performance is roughly comparable to that of the old
parser, but the PEG formalism is more flexible than LL(1) when it
comes to designing new language features. We’ll start using this
flexibility in Python 3.10 and later.
The ast module uses the new parser and produces the same AST as the
old parser.
In Python 3.10, the old parser will be deleted and so will all
functionality that depends on it (primarily the parser module, which
has long been deprecated). In Python 3.9 only, you can switch back to
the LL(1) parser using a command line switch (-X oldparser) or an
environment variable (PYTHONOLDPARSER=1).
Label: Related(i.e. all other phrases are 'Unrelated')
I think I may have to use, for example, pre-trained BERT, because this kind of prediction needs additional knowledge. But this does not seem like a standard classification problem so I can't find out-of-the-box codes. May I have some advice on how to combine existing wheels and train it?
Native support for differential programming has been added to Swift for the Swift for Tensorflow project. Julia has similar with Zygote.
What exactly is differentiable programming?
what does it enable? Wikipedia says
the programs can be differentiated throughout
but what does that mean?
how would one use it (e.g. a simple example)?
and how does it relate to automatic differentiation (the two seem conflated a lot of the time)?
I like to think about this question in terms of user-facing features (differentiable programming) vs implementation details (automatic differentiation).
From a user's perspective:
"Differentiable programming" is APIs for differentiation. An example is a def gradient(f) higher-order function for computing the gradient of f. These APIs may be first-class language features, or implemented in and provided by libraries.
"Automatic differentiation" is an implementation detail for automatically computing derivative functions. There are many techniques (e.g. source code transformation, operator overloading) and multiple modes (e.g. forward-mode, reverse-mode).
Explained in code:
def f(x):
return x * x * x
∇f = gradient(f)
print(∇f(4)) # 48.0
# Using the `gradient` API:
# ▶ differentiable programming.
# How `gradient` works to compute the gradient of `f`:
# ▶ automatic differentiation.
I never heard the term "differentiable programming" before reading your question, but having used the concepts noted in your references, both from the side of creating code to solve a derivative with Symbolic differentiation and with Automatic differentiation and having written interpreters and compilers, to me this just means that they have made the ability to calculate the numeric value of the derivative of a function easier. I don't know if they made it a First-class citizen, but the new way doesn't require the use of a function/method call; it is done with syntax and the compiler/interpreter hides the translation into calls.
If you look at the Zygote example it clearly shows the use of prime notation
julia> f(10), f'(10)
Most seasoned programmers would guess what I just noted because there was not a research paper explaining it. In other words it is just that obvious.
Another way to think about it is that if you have ever tried to calculate a derivative in a programming language you know how hard it can be at times and then ask yourself why don't they (the language designers and programmers) just add it into the language. In these cases they did.
What surprises me is how long it to took before derivatives became available via syntax instead of calls, but if you have ever worked with scientific code or coded neural networks at at that level then you will understand why this is a concept that is being touted as something of value.
Also I would not view this as another programming paradigm, but I am sure it will be added to the list.
How does it relate to automatic differentiation (the two seem conflated a lot of the time)?
In both cases that you referenced, they use automatic differentiation to calculate the derivative instead of using symbolic differentiation. I do not view differentiable programming and automatic differentiation as being two distinct sets, but instead that differentiable programming has a means of being implemented and the way they chose was to use automatic differentiation, they could have chose symbolic differentiation or some other means.
It seems you are trying to read more into what differential programming is than it really is. It is not a new way of programming, but just a nice feature added for doing derivatives.
Perhaps if they named it differentiable syntax it might have been more clear. The use of the word programming gives it more panache than I think it deserves.
EDIT
After skimming Swift Differentiable Programming Mega-Proposal and trying to compare that with the Julia example using Zygote, I would have to modify the answer into parts that talk about Zygote and then switch gears to talk about Swift. They each took a different path, but the commonality and bottom line is that the languages know something about differentiation which makes the job of coding them easier and hopefully produces less errors.
About the Wikipedia quote that
the programs can be differentiated throughout
At first reading it seems nonsense or at least lacks enough detail to understand it in context which is why I am sure you asked.
In having many years of digging into what others are trying to communicate, one learns that unless the source has been peer reviewed to take it with a grain of salt, and unless it is absolutely necessary to understand, then just ignore it. In this case if you ignore the sentence most of what your reference makes sense. However I take it that you want an answer, so let's try and figure out what it means.
The key word that has me perplexed is throughout, but since you note the statement came from Wikipedia and in Wikipedia they give three references for the statement, a search of the word throughout appears only in one
∂P: A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
Thus, since our ∂P system does not require primitives to handle new
types, this means that almost all functions and types defined
throughout the language are automatically supported by Zygote, and
users can easily accelerate specific functions as they deem necessary.
So my take on this is that by going back to the source, e.g. the paper, you can better understand how that percolated up into Wikipedia, but it seems that the meaning was lost along the way.
In this case if you really want to know the meaning of that statement you should ask on the Wikipedia talk page and ask the author of the statement directly.
Also note that the paper referenced is not peer reviewed, so the statements in there may not have any meaning amongst peers at present. As I said, I would just ignore it and get on with writing wonderful code.
You can guess its definition by application of differentiability.
It's been used for optimization i.e. to calculate minimum value or maximum value
Many of these problems can be solved by finding the appropriate function and then using techniques to find the maximum or the minimum value required.
People like Alexander Stepanov and Sean Parent vote for a formal and abstract approach on software design.
The idea is to break complex systems down into a directed acyclic graph and hide cyclic behaviour in nodes representing that behaviour.
Parent gave presentations at boost-con and google (sheets from boost-con, p.24 introduces the approach, there is also a video of the google talk).
While i like the approach and think its a neccessary development, i have a problem with imagining how to handle subsystems with amorphous behaviour.
Imagine for example a common pattern for state-machines: using an interface which all states support and having different behaviour in concrete implementations for the states.
How would one solve that?
Note that i am just looking for an abstract approach.
I can think of hiding that behaviour behind a node and defining different sub-DAGs for the states, but that complicates the design considerately if you want to influence the behaviour of the main DAG from a sub-DAG.
Your question is not clear. Define amorphous subsystems.
You are "just looking for an abstract approach" but then you seem to want details about an implementation in a conventional programming language ("common pattern for state-machines"). So, what are you asking for? How to implement nested finite state-machines?
Some more detail will help the conversation.
For a real abstract approach, look at something like Stream X-Machines:
... The X-machine model is structurally the
same as the finite state machine, except
that the symbols used to label the machine's
transitions denote relations of type X→X. ...
The Stream X-Machine differs from Eilenberg's
model, in that the fundamental data type
X = Out* × Mem × In*,
where In* is an input sequence,
Out* is an output sequence, and Mem is the
(rest of the) memory.
The advantage of this model is that it
allows a system to be driven, one step
at a time, through its states and
transitions, while observing the
outputs at each step. These are
witness values, that guarantee that
particular functions were executed on
each step. As a result, complex
software systems may be decomposed
into a hierarchy of Stream
X-Machines, designed in a top-down
way and tested in a bottom-up way.
This divide-and-conquer approach to
design and testing is backed by
Florentin Ipate's proof of correct
integration, which proves how testing
the layered machines independently is
equivalent to testing the composed
system. ...
But I don't see how the presentation is related to this. He seems to speak about a quite mainstream approach to programming, nothing similar to X-Machines. Anyway, the presentation is quite confusing and I have no time to see the video right now.
First impression of the talk, reading the slides only
The author touches haphazardly on numerous fields/problems/solutions, apparently without recognizing it: from Peopleware (for example Psychology of programming), to Software Engineering (for example software product lines), to various programming techniques.
How the various parts are linked and what exactly he is advocating is not clear at all (I'm accustomed to just reading slides and they are usually consequential):
Dataflow programming?
Constraints solving for User Interfaces? For practical implementations, see Garnet for Common Lisp, Amulet/OpenAmulet for C++.
What advantages gives us this "new" concept-based generic programming with respect to well-known approaches (for example, tools based on Hoare logic pre/post conditions and invariants or, better, Hoare's Communicating Sequential Processes (CSP) or Hehner's Practical Theory of Programming or some programming language with a sophisticated type-system like ATS, Qi or Epigram and so on)? It seems to me that introducing "concepts" - which, as-is, are specific to C++ - is not more simple than using the alternatives. Is it just about jargon and "politics"? (Finally formal methods... but disguised).
Why organizing program modules as a DAG and not as a tree, like David Parnas advocated decades ago in Designing software for ease of extension and contraction? (here a directly accessible .pdf and here slides from a lecture). The work on X-Machines probably is an answer to this question (going even beyond DAGs), but, again, the author seems to speak about a quite conventional program development regime in which Parnas' approach is the only sensible.
If/when I will see the video I will update this answer.
I have been doing a little reading on Flow Based Programming over the last few days. There is a wiki which provides further detail. And wikipedia has a good overview on it too. My first thought was, "Great another proponent of lego-land pretend programming" - a concept harking back to the late 80's. But, as I read more, I must admit I have become intrigued.
Have you used FBP for a real project?
What is your opinion of FBP?
Does FBP have a future?
In some senses, it seems like the holy grail of reuse that our industry has pursued since the advent of procedural languages.
1. Have you used FBP for a real project?
We've designed and implemented a DF server for our automation project (dispatcher, component iterface, a bunch of components, DF language, DF compiler, UI). It is written in bare C++, and runs on several Unix-like systems (Linux x86, MIPS, avr32 etc., Mac OSX). It lacks several features, e.g. sophisticated flow control, complex thread control (there is only a not too advanced component for it), so it is just a prototype, even it works. We're now working on a full-featured server. We've learnt lot during implementing and using the prototype.
Also, we'll make a visual editor some day.
2. What is your opinion of FBP?
2.1. First of all, dataflow programming is ultimate fun
When I met dataflow programming, I was feel like 20 years ago, when I met programming first. Altough, DF programming differs from procedural/OOP programming, it's just a kind of programming. There are lot of things to discover, even sooo simple ones! It's very funny, when, as an experienced programmer, you met a DF problem, which is a very-very basic thing, but it was completely unknown for you before. So, if you jump into DF programming, you will feel like a rookie programmer, who first met the "cycle" or "condition".
2.2. It can be used only for specific architectures
It's just a hammer, which are for hammering nails. DF is not suitable for UIs, web server and so on.
2.3. Dataflow architecture is optimal for some problems
A dataflow framework can make magic things. It can paralellize procedures, which are not originally designed for paralellization. Components are single-threaded, but when they're organized into a DF graph, they became multi-threaded.
Example: did you know, that make is a DF system? Try make -j (see man, what -j is used for). If you have multi-core machine, compile your project with and without -j, and compare times.
2.4. Optimal split of the problem
If you're writing a program, you often split up the problem for smaller sub-problems. There are usual split points for well-known sub-problems, which you don't need to implement, just use the existing solutions, like SQL for DB, or OpenGL for graphics/animation, etc.
DF architecture splits your problem a very interesting way:
the dataflow framework, which provides the architecture (just use an existing one),
the components: the programmer creates components; the components are simple, well-separated units - it's easy to make components;
the configuration: a.k.a. dataflow programming: the configurator puts the dataflow graph (program) together using components provided by the programmer.
If your component set is well-designed, the configurator can build such system, which the programmer has never even dreamed about. Configurator can implement new features without disturbing the programmer. Customers are happy, because they have personalised solution. Software manufacturer is also happy, because he/she don't need to maintain several customer-specific branches of the software, just customer-specific configurations.
2.5. Speed
If the system is built on native components, the DF program is fast. The only time loss is the message dispatching between components compared to a simple OOP program, it's also minimal.
3. Does FBP have a future?
Yes, sure.
The main reason is that it can solve massive multiprocessing issues without introducing brand new strange software architectures, weird languages. Dataflow programming is easy, and I mean both: component programming and dataflow configuration building. (Even dataflow framework writing is not a rocket science.)
Also, it's very economic. If you have a good set of components, you need only put the lego bricks together. A DF program is easy to maintain. The DF config building requires no experienced programmer, just a system integrator.
I would be happy, if native systems spread, with doors open for custom component creating. Also there should be a standard DF language, which means that it can be used with platform-independent visual editors and several DF servers.
Interesting discussion! It occurred to me yesterday that part of the confusion may be due to the fact that many different notations use directed arcs, but use them to mean different things. In FBP, the lines represent bounded buffers, across which travel streams of data packets. Since the components are typically long-running processes, streams may comprise huge numbers of packets, and FBP applications can run for very long periods - perhaps even "perpetually" (see a 2007 paper on a project called Eon, mostly by folks at UMass Amherst). Since a send to a bounded buffer suspends when the buffer is (temporarily) full (or temporarily empty), indefinite amounts of data can be processed using finite resources.
By comparison, the E in Grafcet comes from Etapes, meaning "steps", which is a rather different concept. In this kind of model (and there are a number of these out there), the data flowing between steps is either limited to what can be held in high-speed memory at one time, or has to be held on disk. FBP also supports loops in the network, which is hard to do in step-based systems - see for example http://www.jpaulmorrison.com/cgi-bin/wiki.pl?BrokerageApplication - notice that this application used both MQSeries and CORBA in a natural way. Furthermore, FBP is natively parallel, so it lends itself to programming of grid networks, multicore machines, and a number of the directions of modern computing. One last comment: in the literature I have found many related projects, but few of them have all the characteristics of FBP. A list that I have amassed over the years (a number of them closer than Grafcet) can be found in http://www.jpaulmorrison.com/cgi-bin/wiki.pl?FlowLikeProjects .
I do have to disagree with the comment about FBP being just a means of implementing FSMs: I think FSMs are neat, and I believe they have a definite role in building applications, but the core concept of FBP is of multiple component processes running asynchronously, communicating by means of streams of data chunks which run across what are now called bounded buffers. Yes, definitely FSMs are one way of building component processes, and in fact there is a whole chapter in my book on FBP devoted to this idea, and the related one of PDAs (1) - http://www.jpaulmorrison.com/fbp/compil.htm - but in my opinion an FSM implementing a non-trivial FBP network would be impossibly complex. As an example the diagram shown in
is about 1/3 of a single batch job running on a mainframe. Every one of those blocks is running asynchronously with all the others. By the way, I would be very interested to hearing more answers to the questions in the first post!
1: http://en.wikipedia.org/wiki/Pushdown_automaton Push-down automata
Whenever I hear the term flow based programming I think of LabView, conceptually. Ie component processes who's scheduling is driven primarily by a change to its input data. This really IS lego programming in the sense that the labview platform was used for the latest crop of mindstorm products. However I disagree that this makes it a less useful programming model.
For industrial systems which typically involve data collection, control, and automation, it fits very well. What is any control system if not data in transformed to data out? Ie what component in your control scheme would you not prefer to represent as a black box in a bigger picture, if you could do so. To achieve that level of architectural clarity using other methodologies you might have to draw a data domain class diagram, then a problem domain run time class relationship, then on top of that a use case diagram, and flip back and forth between them. With flow driven systems you have the luxury of being able to collapse a lot of this information together accurately enough that you can realistically design a system visually once the components are build and defined.
One question I never had to ask when looking at an application written in labview is "What piece of code set this value?", as it was inherent and easy to trace backwards from the data, and also mistakes like multiple untintended writers were impossible to create by mistake.
If only that was true of code written in a more typically procedural fashion!
1) I build a small FBP framework for an anomaly detection project, and it turns out to have been a great idea.
You can also have a look at some of the KNIME videos, that give a good idea of what a flow based framework feels like when the framework is put together by a great team. Admittedly, it is batch based and not created for continuous operation.
By far the best example of flow based programming, however, is UNIX pipes which is one of the oldest, most overlooked FBP framework. I don't think I have to elaborate on the power of nix pipes...
2) FBP is a very powerful tool for a large set of problems. The intrinsic parallelism is a great advantage, and any FBP framework can be made completely network transparent by using adapter modules. Smart frameworks are also absurdly fault tolerant, and able to dynamically reload crashed modules when necessary. The conceptual simplicity also allows cleaner communication with everybody involved in a project, and much cleaner code.
3) Absolutely! Pipes are here to stay, and are one of the most powerful feature of unix. The power inherent in a FBP framework compared to a static program are many, and trivialise change, to the point where some frameworks can be reconfigured while running with no special measures.
FBP FTW! ;-)
In automotive development, they have a language agnostic messaging protocol which is part of the MOST specification (Media Oriented Systems Transport), this was designed to communicate between components over a network or within the same device. Systems usually have both a real and visualized message bus - therefore you effectively have a form of flow based programming.
That was what made the light bulb go on for me several years ago and brought me here. It really is a fantastic way to work and so much more fun than conventional programming. The message catalog form the central specification and point of reference. It works well for both developers and management. i.e. Management are able to browse the message catalog instead of looking at source.
With integrated logging also referencing the catalog to produce intelligible analysis things can get really productive. I have real world experience of developing commercial products in this way. I am interested in taking things further, particularly with regards to tools and IDEs. Unfortunately I think many people within the automotive sector have missed the point about how great this is and have failed to build on it. They are now distracted by other fads and failed to realize that there was far more to most development than the physical bus.
I've used Spring Web Flow extensively in Java Web applications to model (typically) application processes, which tend to be complex wizard-like affairs with lots of conditional logic as to which pages to display. Its incredibly powerful. A new product was added and I managed to recut the existing pieces into a completely new application process in an hour or two (with adding a couple of new views/states).
I also looked into using OS Workflow to model business processes but that project got canned for various reasons.
In the Microsoft world you have Windows Workflow Foundation ("WWF"), which is becoming more popular, particularly in conjunction with Sharepoint.
FBP is just a means of implementing a finite state machine. It's nothing new.
I realize that it is not exactly the same thing, but this model has been used for years in PLC programming. ISO calls it Sequential Flow Chart, but many people call it Grafcet after a popular implementation. It offers parallel processing and defines transitions between states.
It's being used in the Business Intelligence world these days to mashup and process data. Data processing steps like ETL, querying, joining , and producing reports can be done by the end-user. I'm a developer on an open system - ComposableAnalytics.com In CA, the flow-based apps can be shared and executed via the browser.
This is what MQ Series, MSMQ and JMS are for.
This is cornerstone of Web Services and Enterprise Service Bus implementations.
Products like TIBCO and Sun's JCAPS are basically flow-based without using this particular buzz-word.
Most of the work of the application is done with small modules that pass messages through a processing network.
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Closed 10 years ago.
When you are learning a new language, what is there a particularly good/effective exercise to help get the hang of it? And why?
EDIT:
Preferably looking for things that are more complicated that 'Hello World'.
I usually do the following (in the order presented):
Print a pyramid with height provided by the user (checks basic I/O, conditionals and loops)
Write a class hierarchy with polymorphism etc... (checks OO concepts)
Convert decimals to roman numerals (checks enums and basic data structures)
Write a linkedlist implementation (checks memory allocation/deallocation)
Write clones of JUnit and JMock (checks refelction/metaprogramming)
Write a console based chat system (checks basic networking)
Modify (6) to support group chat via multicasting (checks advanced networking)
Write a GUI for (7) (checks GUI library)
After that its on to a real project...
other than hello world, I try to port one of the existing programs to the new languange. this will challenge me to learn some good old techniques in the new language and help me build a new library of classes or helpers..
Larry O'Brien had a great series of blogs titled '15 Exercises to know A programming Language' Part 1 Part 2 Part 3
See Larry's Blog for the details.
Part 1. Calculations
Write a program that takes as its first argument one of the words 'sum,' 'product,' 'mean,' or 'sqrt' and for further arguments a series of numbers. The program applies the appropriate function to the series.
Write a program that calculates a Haar wavelet on an array of numbers. .
Write a program that takes as its arguments a the name of a bitmapped image. Apply the Haar wavelet to the pixel values. Save the results to a file.
Using the outputs of the previous exercise file, write a GUI program that reconstitutes the original bitmap (N.B.: The Haar wavelet is lossless).
Write a GUI program that deals with bitmaps images
Part 2. Data Structures
Write a class (or module or what-have-you: please map OOP terminology into whatever paradigm appropriate) that only stores objects of the same type as the first object placed in it and raises an exception if a non-compatible type is added.
Using the language's idioms, implement a tree-based datastructure (splay, AVL, or red-black).
Create a new type that uses a custom comparator (i.e., overrides "Equals"). Place more of these objects than can fit in memory into the datastructure created above as well as into standard libraries, put more objects into it than can fit in memory. Compare performance of the standard libraries with your own implementation.
Implement an iterator for your datastructure. Consider multithreading issues.
Write a multithreaded application that uses your data structure, comparable types, and iterators to implement the type-specific storage functionality as described in Exercise 6. How do you deal with concurrent inserts and traversals?
Part 3. Libraries
Write a program that outputs the current date and time to a Web page as a reversed ISO 8601-formatted value (i.e.: "2006-06-16T13:15:30Z" becomes "Z03:51:31T61-60-6002"). Create an XML interface (either POX or WS-*) to the same.
Write a client-side program that can both scrape the above Web page and the XML return and redisplays the date in a different format.
Write a daemon program that monitors an email account. When a strongly-encoded email arrives that decrypts to a valid ISO 8601 time, the program sets the system time to that value.
Write a program that connects to your mail client, performs a statistical analysis of its contents (see A Plan for Spam ) and stores the results in a database.
Using previous Exercise, write a spam filter, including moving messages within your mail client
If you can do all these things in 2 languages, I'm sure google has a job for you
'hello world!'
I really do think this a good place to start. Its basic and only takes a few seconds but you make sure your compiler is running and you have everything in place. Once you have that done you can keep going. Add a variable, print to database, print to file. Make sure you know how to leave comments. This could all take a mater of 5 minutes. But its important stuff.
Connect to data somehow, whether it be a database, file or other...
Red-Black tree.
I usually don't do very well with it unless I have a "real" project to apply it to. Even made up ones get boring fast. In fact, I find it helpful to throw yourself in the middle of a bigger project and make small changes to something that already works.
YMMV
My equivalent of a hello world is to do the following:
Retrieve multiple inputs (ie, parms from command line, text boxes on a gui)
Manipulate that input (ie, do math on numbers and manipulate text)
On a gui use a list box.
read and write files.
I feel after doing the above I get a good feel for the language and a good introduction to the IDE and how easy (or really how difficult) it is to work with the language and the environment it runs in.
After that if I want to go further I will use the language in a real project that I need to do (probably a utility of some kind).
Personally I like to make a simple echo server and client to get the hang of network programming with that language.
Ray tracer.
I like to learn a new language by doing a "real" task (for "personal" use)
My first java program was a client for an online multiplayer game (that I then released into public domain)
My first vb.net program was a front-end for my digital video recorder
My first VHDL "program" was a 64x32 led array controller
Often I'll implement the k-means clustering algorithm.
Drag-and-drop image gallery.
When I was cutting my teeth on Win32 and MFC, this was one of my first projects. Pretty quickly I ported all my code into ActiveX controls. Then I rewrote the thing in Java. For kicks, I rewrote it again in pure Javascript. When I broke into .Net, I rewrote the thing again in C#. Last but not least, I used it as an exercise for learning Objective-C and UIKit.
Why? It's a visually appealing toy, for one thing. It's nice to get instant gratification from your code, I think, and working with images is one of the most gratifying things I can think of.
Console based Tetris
I like games for learning programming because the business rules are carefully delineated. The first three programs I write in a new language are Ro-Sham-Bo, Blackjack, and Video Poker.
Pick a task(s) that you already understand. That way you limit the amount of "new stuff" you need to assimilate.
I think, for me, learning by porting existing code (for example, from another platform) is always a challenge and fun. just simple demos, boardgames, etc.
Mandelbrot set.