Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Page not in menu
This is a page not in th emain menu
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
TabTransformer Keras Implementation
Implementation of the TabTransformer Model in Keras.
Reinforcement Learning with Iterated Prisonner’s Dilemma
Observing the evolution of strategies in prisonner’s dilemma using reinforcement learning.
Optiver Quantitative Researcher Sample Questions
My solutions to the sample questions provided by Optiver for their Quantitive Researcher position
DegreeGuru - A ChatBot App
Creating a RAG Chatbot Template using Vercel AI SDK, Langchain, Upstash Vector and OpenAI.
Elixir/Phoenix App using Redis on Fly
Date of writing: 30.8.2022
Poetry No such file or directory: 'python' Error on MacOS
Recently, I had to do some work with LangChain and wanted to create an environment
with the poetry install command. When I ran the command, I got the following
error:
publications
ArTu: A Tool for Generating Goal Models from User Stories
Published in 2021 IEEE 29th International Requirements Engineering Conference (RE), 2021
User stories are widely used to capture the desires of the users in agile development. A set of user stories is easy to read and write but incapable of representing the hierarchical relations and synergies among the user stories. By contrast, goal models are uncommon in industrial projects however they can express the structure and other relations among requirements captured as goals. This paper presents ArTu, a tool for generating goal models from user stories to effortlessly benefit from both. Given a set of user stories, our tool generates goal models with different structures depending on the heuristic selected by the user. Users can import, edit, and export model data in different formats.
Recommended citation: T. Günes, C.A. Öz, F.B. Aydemir, "ArTu: A Tool for Generating Goal Models from User Stories", Proc. IEEE 29th Int. Requirements Eng. Conf. (RE), pp. 436-437, Spt. 2021. https://ieeexplore.ieee.org/abstract/document/9604615/
A Comprehensive Analysis of Static Word Embeddings for Turkish
Submitted to Expert Systems with Applications (Under Review)
Word embeddings are word representations that are used in natural language processing (NLP) applications. There are basically two types of word embedding models which are non-contextual (static) models and contextual models. While the former one generates a single embedding for a word independent of the context, the latter one generates a different embedding for a word in different contexts. There are plenty of works that compare contextual and non-contextual embedding models within their respective groups in different languages. However, the number of studies that compare the models in these two groups with each other is very few and there is no such study in Turkish. This process necessitates converting contextual embeddings into static embeddings. In this paper, we compare and evaluate the performance of several contextual and non-contextual models in both intrinsic and extrinsic evaluation settings for Turkish. We make a fine-grained comparison by analyzing the syntactic and semantic capabilities of the models separately. The results of the analyses provide insights about the suitability of different embedding models in different types of NLP tasks. We also build a Turkish word embedding repository comprising the embedding models used in this work, which may serve as a valuable resource for researchers and practitioners in the field of Turkish NLP. We make the word embeddings, scripts, and evaluation datasets publicly available.
Download here
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.