6 papers
Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space
Yue Cao, Jianyang Gu, Vyacheslav Kungurtsev +4
Dataset distillation (DD) has proven to reduce training cost while preserving accuracy. While promising, the factors that make one distilled dataset more effective than another rem…
Symplectic Neural Networks for Learning Non-Separable Hamiltonians
Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta +2
Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency. Identifying the…
CONCORD: Concept-Informed Diffusion for Dataset Distillation
Jianyang Gu, Haonan Wang, Ruoxi Jia +4
Dataset distillation (DD) has witnessed significant progress in creating small datasets that encapsulate rich information from large original ones. Particularly, methods based on g…
Dataset Distillation from First Principles: Integrating Core Information Extraction and Purposeful Learning
Vyacheslav Kungurtsev, Yuanfang Peng, Jianyang Gu +4
Dataset distillation (DD) is an increasingly important technique that focuses on constructing a synthetic dataset capable of capturing the core information in training data to achi…
Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal +3
In this work, we demonstrate the Empirical Bayes approach to learning a Dynamic Bayesian Network. By starting with several point estimates of structure and weights, we can use a da…
Learning Dynamic Bayesian Networks from Data: Foundations, First Principles and Numerical Comparisons
Vyacheslav Kungurtsev, Fadwa Idlahcen, Petr Rysavy +2
In this paper, we present a guide to the foundations of learning Dynamic Bayesian Networks (DBNs) from data in the form of multiple samples of trajectories for some length of time.…