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20232026
most citedStrategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production

10 citations · 17 across the 17 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

MPSelectTune: Prompt-type Selection for Fine-tuning improves Concept Unlearning in LLMs

Shubhadip Nag, Srinjoy Das, Agniva Saha +5

LLMs can be conveniently adapted to a diverse set of tasks, e.g, prediction, question-answering tasks, etc, using appropriate prompts with few-shot examples. Biased or harmful conc…

cs.LG2025

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness

Md Mushfiqur Rahaman, Elliot Chang, Tasmiah Haque +1

Text classification plays a pivotal role in edge computing applications like industrial monitoring, health diagnostics, and smart assistants, where low latency and high accuracy ar…

cs.LG2025

Path Generation and Evaluation in Video Games: A Nonparametric Statistical Approach

Daniel Campa, Mehdi Saeedi, Ian Colbert +1

Navigation path traces play a crucial role in video game design, serving as a vital resource for both enhancing player engagement and fine-tuning non-playable character behavior. G…

cs.LG2024

A Data-Efficient Sequential Learning Framework for Melt Pool Defect Classification in Laser Powder Bed Fusion

Ahmed Shoyeb Raihan, Austin Harper, Israt Zarin Era +4

Ensuring the quality and reliability of Metal Additive Manufacturing (MAM) components is crucial, especially in the Laser Powder Bed Fusion (L-PBF) process, where melt pool defects…

cs.LG2023★ 2 cited

Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization

Ahmed Shoyeb Raihan, Hamed Khosravi, Srinjoy Das +1

Advancements in materials play a crucial role in technological progress. However, the process of discovering and developing materials with desired properties is often impeded by su…

cs.LG2023★ 10 cited

Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production

Hamed Khosravi, Sarah Farhadpour, Manikanta Grandhi +3

A significant challenge for predictive maintenance in the pulp-and-paper industry is the infrequency of paper breaks during the production process. In this article, operational dat…