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GNN prompting aims to adapt models across tasks and graphs without requiring extensive retraining. However, most existing graph prompt methods still require task-specific parameter updates and face the issue of generalizing across graphs,…

Machine Learning · Computer Science 2026-04-02 Yaqi Chen , Shixun Huang , Ryan Twemlow , Lei Wang , John Le , Sheng Wang , Willy Susilo , Jun Yan , Jun Shen

Machine learning is increasingly used to improve decisions within branch-and-bound algorithms for mixed-integer programming. Many existing approaches rely on deep learning, which often requires very large training datasets and substantial…

Machine Learning · Computer Science 2026-04-02 Selin Bayramoğlu , George L Nemhauser , Nikolaos V Sahinidis

Due to the high diversity of image styles, the scalability to various styles plays a critical role in real-world applications. To accommodate a large amount of styles, previous multi-style transfer approaches rely on enlarging the model…

Computer Vision and Pattern Recognition · Computer Science 2025-03-27 Hongda Liu , Longguang Wang , Weijun Guan , Ye Zhang , Yulan Guo

Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A…

Software Engineering · Computer Science 2015-06-26 Saiqa Aleem , Luiz Fernando Capretz , Faheem Ahmed

Millions of learners worldwide are now using intelligent tutoring systems (ITSs). At their core, ITSs rely on machine learning algorithms to track each user's changing performance level over time to provide personalized instruction.…

Machine Learning · Computer Science 2022-02-09 Robin Schmucker , Tom M. Mitchell

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we…

Software Engineering · Computer Science 2019-05-22 Michele Tufano , Cody Watson , Gabriele Bavota , Massimiliano Di Penta , Martin White , Denys Poshyvanyk

Artificial Intelligence has gained a lot of traction in the recent years, with machine learning notably starting to see more applications across a varied range of fields. One specific machine learning application that is of interest to us…

Software Engineering · Computer Science 2023-05-10 Teodor Rares Begu

Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other than language modeling further improve performance? In this…

Computation and Language · Computer Science 2020-10-08 Tu Vu , Tong Wang , Tsendsuren Munkhdalai , Alessandro Sordoni , Adam Trischler , Andrew Mattarella-Micke , Subhransu Maji , Mohit Iyyer

This paper addresses the issues of parameter redundancy, rigid structure, and limited task adaptability in the fine-tuning of large language models. It proposes an adapter-based fine-tuning method built on a structure-learnable mechanism.…

Computation and Language · Computer Science 2025-09-04 Ming Gong , Yingnan Deng , Nia Qi , Yujun Zou , Zhihao Xue , Yun Zi

Despite a growing body of work at the intersection of deep learning and formal languages, there has been relatively little systematic exploration of transformer models for reasoning about typed lambda calculi. This is an interesting area of…

Programming Languages · Computer Science 2023-04-21 Brando Miranda , Avi Shinnar , Vasily Pestun , Barry Trager

Probabilistic graphical models (PGMs) are widely used to discover latent structure in data, but their success hinges on selecting an appropriate model design. In practice, model specification is difficult and often requires iterative…

Machine Learning · Computer Science 2026-04-08 Kevin Zhang , Yixin Wang

Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a novel speedup technique: we change…

Computation and Language · Computer Science 2007-05-23 Joshua Goodman

We propose a novel application of self-attention networks towards grammar induction. We present an attention-based supertagger for a refined type-logical grammar, trained on constructing types inductively. In addition to achieving a high…

Computation and Language · Computer Science 2020-09-30 Konstantinos Kogkalidis , Michael Moortgat , Tejaswini Deoskar

Generalized Additive Models (GAMs) have quickly become the leading choice for inherently-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they lack expressive power and easy scalability, and are hence…

Machine Learning · Computer Science 2022-10-20 Abhimanyu Dubey , Filip Radenovic , Dhruv Mahajan

Recently, large language models (LLMs) have demonstrated excellent performance, inspiring researchers to explore their use in automating register transfer level (RTL) code generation and improving hardware design efficiency. However, the…

Computation and Language · Computer Science 2025-04-24 Peiyang Wu , Nan Guo , Xiao Xiao , Wenming Li , Xiaochun Ye , Dongrui Fan

We present a technique for automatically weaving structural invariant checks into an existing collection of classes. Using variations on existing design patterns, we use a concise specification to generate from this collection a new set of…

Programming Languages · Computer Science 2013-01-21 John Lasseter , John Cipriano

In this study, we aim to identify the most effective machine learning model for accurately classifying Myers-Briggs Type Indicator (MBTI) types from Reddit posts and a Kaggle data set. We apply multi-label classification using the Binary…

Machine Learning · Computer Science 2024-05-08 Siana Kong , Marina Sokolova

Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations. However, a limitation of these methods is their lack of interpretability, particularly in…

Machine Learning · Computer Science 2025-07-21 Wenliang Liu , Danyang Li , Erfan Aasi , Daniela Rus , Roberto Tron , Calin Belta

Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples. Prevailing fine-tuning methods could potentially contaminate pre-trained features by comparably high…

Machine Learning · Computer Science 2019-07-15 Farshid Varno , Behrouz Haji Soleimani , Marzie Saghayi , Lisa Di Jorio , Stan Matwin

The overarching goal of Explainable AI is to develop systems that not only exhibit intelligent behaviours, but also are able to explain their rationale and reveal insights. In explainable machine learning, methods that produce a high level…

Artificial Intelligence · Computer Science 2020-05-06 Xiuyi Fan , Siyuan Liu , Thomas C. Henderson