How to Increase Interest in Studying Functional Programming via Interdisciplinary Application

• URL: http://arxiv.org/abs/2007.11070v2
• Date: Mon, 24 Aug 2020 09:19:09 GMT
• Title: How to Increase Interest in Studying Functional Programming via Interdisciplinary Application
• Authors: Pedro Figueir\^edo (E\"otv\"os Lor\'and University), Yuri Kim (E\"otv\"os Lor\'and University), Nghia Le Minh (E\"otv\"os Lor\'and University), Evan Sitt (E\"otv\"os Lor\'and University), Xue Ying (E\"otv\"os Lor\'and University), Vikt\'oria Zs\'ok (E\"otv\"os Lor\'and University)
• Abstract summary: The state of the art in functional programming reports an increasing number of methodologies in this paradigm. Our goal is to increase student interest in pursuing further studies in functional programming with the use of an application: the ray tracer.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Functional programming represents a modern tool for applying and implementing software. The state of the art in functional programming reports an increasing number of methodologies in this paradigm. However, extensive interdisciplinary applications are missing. Our goal is to increase student interest in pursuing further studies in functional programming with the use of an application: the ray tracer. We conducted a teaching experience, with positive results and student feedback, described here in this paper.

Related papers

• Action Research with Industrial Software Engineering -- An Educational Perspective [0.5257115841810258]
  Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings. It requires a different kind of interaction with the software development organisation. This chapter is intended to support learning and teaching action research.
  arXiv  Detail & Related papers  (2024-07-05T17:05:06Z)
• Towards Coarse-to-Fine Evaluation of Inference Efficiency for Large Language Models [95.96734086126469]
  Large language models (LLMs) can serve as the assistant to help users accomplish their jobs, and also support the development of advanced applications. For the wide application of LLMs, the inference efficiency is an essential concern, which has been widely studied in existing work. We perform a detailed coarse-to-fine analysis of the inference performance of various code libraries.
  arXiv  Detail & Related papers  (2024-04-17T15:57:50Z)
• Application-Driven Innovation in Machine Learning [56.85396167616353]
  We describe the paradigm of application-driven research in machine learning. We show how this approach can productively synergize with methods-driven work. Despite these benefits, we find that reviewing, hiring, and teaching practices in machine learning often hold back application-driven innovation.
  arXiv  Detail & Related papers  (2024-03-26T04:59:27Z)
• De-fine: Decomposing and Refining Visual Programs with Auto-Feedback [75.62712247421146]
  De-fine is a training-free framework that decomposes complex tasks into simpler subtasks and refines programs through auto-feedback. Our experiments across various visual tasks show that De-fine creates more robust programs.
  arXiv  Detail & Related papers  (2023-11-21T06:24:09Z)
• Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs [58.94569213396991]
  We propose a hierarchical programmatic reinforcement learning framework to produce program policies. By learning to compose programs, our proposed framework can produce program policies that describe out-of-distributionally complex behaviors. The experimental results in the Karel domain show that our proposed framework outperforms baselines.
  arXiv  Detail & Related papers  (2023-01-30T14:50:46Z)
• ProTo: Program-Guided Transformer for Program-Guided Tasks [59.34258016795216]
  We formulate program-guided tasks which require learning to execute a given program on the observed task specification. We propose the Program-guided Transformer (ProTo), which integrates both semantic and structural guidance of a program. ProTo executes a program in a learned latent space and enjoys stronger representation ability than previous neural-symbolic approaches.
  arXiv  Detail & Related papers  (2021-10-02T13:46:32Z)
• Learning functional programs with function invention and reuse [0.0]
  This paper is concerned with a subfield of IP, inductive functional programming (IFP) We explore the idea of generating modular functional programs, and how those allow for function reuse. We show reuse is important (if not crucial) for a variety of problems and distinguished two broad classes of programs that will generally benefit from function reuse.
  arXiv  Detail & Related papers  (2020-11-17T19:11:00Z)
• Curriculum Learning with a Progression Function [6.212955085775758]
  This paper introduces a novel paradigm for curriculum generation based on progression and mapping functions. Our approach's benefits and wide applicability are shown by empirically comparing its performance to two state-of-the-art Curriculum Learning algorithms.
  arXiv  Detail & Related papers  (2020-08-02T16:18:41Z)
• On the Nature of Programming Exercises [0.0]
  It is essential to understand that the nature of a programming exercise is an important factor for the success and consistent learning. This paper explores different approaches on the creation of a programming exercise.
  arXiv  Detail & Related papers  (2020-06-25T15:22:26Z)
• IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis [70.61283188380689]
  We investigate the problem of revealing the functionality of a black-box agent. We do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding an approximately functional equivalent program in 78% of cases.
  arXiv  Detail & Related papers  (2020-06-18T17:50:48Z)
• Genome as a functional program [0.0]
  We introduce a model of learning for some class of functional programs. We consider the approach to Darwinian evolution as a learning problem for functional programming.
  arXiv  Detail & Related papers  (2020-06-05T07:58:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.