51 citations · 86 across the 12 of their papers we have counts for
5 papers · 1 filter
The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation
Alexander Xiong, Xuandong Zhao, Aneesh Pappu +1
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet they also exhibit memorization of their training data. This phenomenon rais…
Measuring memorization in RLHF for code completion
Aneesh Pappu, Billy Porter, Ilia Shumailov +1
Reinforcement learning with human feedback (RLHF) has become the dominant method to align large models to user preferences. Unlike fine-tuning, for which there are many studies reg…
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
Aneesh Pappu, Brooks Paige
Graph neural networks have become very popular for machine learning on molecules due to the expressive power of their learnt representations. However, molecular machine learning is…
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg +5
Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make…
Low Data Drug Discovery with One-shot Learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S. Pappu +1
Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts i…