2 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Visualizing Loss Functions as Topological Landscape Profiles
Caleb Geniesse, Jiaqing Chen, Tiankai Xie +7
In machine learning, a loss function measures the difference between model predictions and ground-truth (or target) values. For neural network models, visualizing how this loss cha…
Evaluating Loss Landscapes from a Topology Perspective
Tiankai Xie, Caleb Geniesse, Jiaqing Chen +5
Characterizing the loss of a neural network with respect to model parameters, i.e., the loss landscape, can provide valuable insights into properties of that model. Various methods…
AlphaPruning: Using Heavy-Tailed Self Regularization Theory for Improved Layer-wise Pruning of Large Language Models
Haiquan Lu, Yefan Zhou, Shiwei Liu +3
Recent work on pruning large language models (LLMs) has shown that one can eliminate a large number of parameters without compromising performance, making pruning a promising strat…
Mitigating Memorization In Language Models
Mansi Sakarvadia, Aswathy Ajith, Arham Khan +6
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that d…
Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
Haiquan Lu, Xiaotian Liu, Yefan Zhou +6
Recent studies on deep ensembles have identified the sharpness of the local minima of individual learners and the diversity of the ensemble members as key factors in improving test…