MPCa.2

Can Rich Embeddings Compensate for Model Simplicity? Exploring the Relationship Between Embedding Quality and Model Complexity

Cate Dunham, Worcester Polytechnic Institute, United States; Thomas Cao, Chia-Wei Tsai, Defense Threat Reduction Agency, United States; Joshua Uzarski, Development Command Soldier Center, United States; Randy Paffenroth, Worcester Polytechnic Institute, United States
Session:
MPCa: Foundations of Adaptive Learning Models Lecture
Track:
Adaptive Systems, Machine Learning, and Data Analytics
Location:
Chapel
Presentation Time:
Mon, 27 Oct, 13:55 - 14:20 PT (UTC -7)
Presentation
Discussion
Resources
No resources available.
Session MPCa
MPCa.1: Bi-Linear Homogeneity Enforced Filtering
Matthias Wagner, Oliver Lang, Johannes Kepler University, Austria
MPCa.2: Can Rich Embeddings Compensate for Model Simplicity? Exploring the Relationship Between Embedding Quality and Model Complexity
Cate Dunham, Worcester Polytechnic Institute, United States; Thomas Cao, Chia-Wei Tsai, Defense Threat Reduction Agency, United States; Joshua Uzarski, Development Command Soldier Center, United States; Randy Paffenroth, Worcester Polytechnic Institute, United States
MPCa.3: Prompt Fairness: Sub-group Disparities in LLMs
Meiyu Zhong, Noel Teku, Ravi Tandon, University of Arizona, United States
MPCa.4: Threshold Effect in Period Estimation for Short Periodic Pulses
Sebastian Schertler, Johannes Kepler University, Austria; Stefan Schuster, Stefan Scheiblhofer, Alexander Haberl, Clemens Staudinger, Johann Reisinger, voestalpine Stahl GmbH, Austria; Oliver Lang, Johannes Kepler University, Austria
Contacts