Machine Learning Diaries

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Everything you need to know about identifying hallucinations by LLMs

4 implied HN points • 02 Jan 24

🕹 Technology Machine Learning Artificial Intelligence

Identifying hallucinations in news headlines is crucial due to recent advancements in language models.
Common methods to generate news headlines can lead to misleading or false information.
The ExHalder framework addresses the challenge of detecting headline hallucinations using reasoning and explanation-enhanced classifiers.

How to apply Boosting when the Data Labels are Noisy and Uncertain

0 implied HN points • 28 Feb 24

🔬 Science Machine Learning Model Evaluation

Boosting algorithms can struggle when dealing with noisy and uncertain data labels.
Weakly supervised learning (WSL) is gaining attention as a way to handle noisy and weak data labels more effectively than fully-supervised methods.
The LocalBoost approach aims to address challenges by iteratively and adaptively enhancing boosting in a weakly supervised setting.

DiffGrad : Is it the right optimization method for training your CNNs?

0 implied HN points • 25 Sep 23

🔬 Science Optimization Machine Learning Neural Networks Research

Optimizing neural networks with DiffGrad may prevent slow learning and jittering effects in training
DiffGrad adjusts learning rates based on gradient behavior for each parameter, leading to improved optimization
Comparisons suggest that DiffGrad outperformed Adam optimizer in terms of avoiding overshooting global minima