1 citations · 1 across the 4 of their papers we have counts for
6 papers
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…
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…
Auditing the Sensitivity of Graph-based Ranking with Visual Analytics
Tiankai Xie, Yuxin Ma, Hanghang Tong +2
Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall w…
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Yuxin Ma, Tiankai Xie, Jundong Li +1
Machine learning models are currently being deployed in a variety of real-world applications where model predictions are used to make decisions about healthcare, bank loans, and nu…