Search Results for author:
Found 7 papers, 2 papers with code
A Framework for Real-time Safeguarding the Text Generation of Large Language Model
However, existing methods have limitations, including the need for training specific control models and proactive intervention during text generation, that lead to quality degradation and increased computational overhead.
Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers
We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning.
Rethinking Software Engineering in the Foundation Model Era: A Curated Catalogue of Challenges in the Development of Trustworthy FMware
Finally, we (i) show how the unique properties of FMArts enabled us to design and develop a complex FMware for a large customer in a timely manner and (ii) discuss the lessons that we learned in doing so.
Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting
This classifier is then used to detect if a trained model is overfit.
Towards Training Reproducible Deep Learning Models
However, DL models are challenging to be reproduced due to issues like randomness in the software (e. g., DL algorithms) and non-determinism in the hardware (e. g., GPU).
Towards a consistent interpretation of AIOps models
Artificial Intelligence for IT Operations (AIOps) has been adopted in organizations in various tasks, including interpreting models to identify indicators of service failures.
Can I use this publicly available dataset to build commercial AI software? -- A Case Study on Publicly Available Image Datasets
Publicly available datasets are one of the key drivers for commercial AI software.
