35 citations · 38 across the 11 of their papers we have counts for
13 papers
BlockMGARD: Accelerating Adaptive Scientific Data Reduction with Region-of-Interest Error Control on GPUs
Yanliang Li, Qian Gong, Qing Liu +5
The growing scale of scientific data makes lossy compression essential for reducing data volume under controllable error. Transformation-based compressors using multilevel decompos…
QoI-Aware Provisional Rollout and Retrospective Reconciliation for Reduced-State Scientific Twins
Liangji Zhu, Scott Klasky, Jaemoon Lee +3
Scientific twins may need to continue operating when updates from an authoritative primary system are temporarily unavailable. Once synchronization resumes, the new boundary can al…
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill Diversification
Geonwoo Cho, Jaemoon Lee, Jaegyun Im +3
Skill-based reinforcement learning (SBRL) enables rapid adaptation in environments with sparse rewards by pretraining a skill-conditioned policy. Effective skill learning requires…
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression
Jaemoon Lee, Xiao Li, Liangji Zhu +2
This paper proposes a new compression paradigm -- Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) -- for lossy scientific data compression. The framework is based o…
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module.…
Attention Based Machine Learning Methods for Data Reduction with Guaranteed Error Bounds
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
Scientific applications in fields such as high energy physics, computational fluid dynamics, and climate science generate vast amounts of data at high velocities. This exponential…
Machine Learning Techniques for Data Reduction of Climate Applications
Xiao Li, Qian Gong, Jaemoon Lee +3
Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data. Often, QoI are linked to specific features, regions, or time intervals…
Machine Learning Techniques for Data Reduction of CFD Applications
Jaemoon Lee, Ki Sung Jung, Qian Gong +5
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics…
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Qian Gong, Jieyang Chen, Ben Whitney +13
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capa…
Scalable Hybrid Learning Techniques for Scientific Data Compression
Tania Banerjee, Jong Choi, Jaemoon Lee +5
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific…
Expressing linear equality constraints in feedforward neural networks
Anand Rangarajan, Pan He, Jaemoon Lee +2
We seek to impose linear, equality constraints in feedforward neural networks. As top layer predictors are usually nonlinear, this is a difficult task if we seek to deploy standard…
Hybrid Generative Models for Two-Dimensional Datasets
Hoda Shajari, Jaemoon Lee, Sanjay Ranka +1
Two-dimensional array-based datasets are pervasive in a variety of domains. Current approaches for generative modeling have typically been limited to conventional image datasets an…
A Hidden Variables Approach to Multilabel Logistic Regression
Jaemoon Lee, Hoda Shajari
Multilabel classification is an important problem in a wide range of domains such as text categorization and music annotation. In this paper, we present a probabilistic model, Mult…