6 papers · 1 filter
Landscaper: Understanding Loss Landscapes Through Multi-Dimensional Topological Analysis
Jiaqing Chen, Nicholas Hadler, Tiankai Xie +8
Loss landscapes are a powerful tool for understanding neural network optimization and generalization, yet traditional low-dimensional analyses often miss complex topological featur…
Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing
Tommaso Baldi, Javier Campos, Olivia Weng +4
In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensin…
LossLens: Diagnostics for Machine Learning through Loss Landscape Visual Analytics
Tiankai Xie, Jiaqing Chen, Yaoqing Yang +8
Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with…
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…
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…