A type of artificial intelligence that focuses on designing systems that can learn from and make decisions based on data.
This paper introduces a novel approach to sequential recommendation systems, a critical component of online platforms and services. The authors propose the application of the Fisher-Merging method to Sequential Recommendation…
Continue readingThis study investigates the potential of unsupervised domain adaptation (UDA) methods in improving the accuracy and fairness of skin lesion classification. The researchers leverage multiple skin lesion datasets to assess…
Continue readingThe Focused Transformer (FOT) is a new approach designed to tackle the challenge of scaling the context length in language models. Large language models have the capability to incorporate new…
Continue readingThis paper presents a novel framework, Concept Influence for Fairness (CIF), to understand and address the issue of unfairness in machine learning models. The authors propose a method to quantify…
Continue readingThe paper “The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks” dives into the fascinating subject of how neural networks, when trained on algorithmic tasks that…
Continue readingThe paper introduces GIMLET, a unified graph-text model designed to address the challenges of molecule property prediction. The primary issue in this field is the lack of sufficient labels due…
Continue readingThis paper presents a low-bandwidth, decentralized language model fine-tuning approach that leverages shared randomness. The method is an extension of the memory-efficient Simultaneous Perturbation Stochastic Approximation (SPSA) and uses shared…
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