1 citations · 3 across the 12 of their papers we have counts for
12 papers
A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes
Alper Sahistan, Haichao Miao, Zhimin Li +3
Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray computed tomography (CT), are o…
Error Aware Distribution Prediction for Lightweight Implicit Neural Representations
Zhimin Li, Jake D. Balla, Joshua A. Levine
Implicit neural representations (INRs) offer compact encoding of volumes, but as lossy approximators, inevitably have prediction errors. We consider INRs that can simultaneously en…
Quantifying the Impact of Lossy Compression on Neural Generative Surrogate Modeling
Zhimin Li, Harshitha Menon, Charles Jekel +2
Neural networks are used as generative surrogate models for scientific discovery, which are trainable approximations of scientific simulations. These models enable users to replace…
Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks
Haichao Miao, Zhimin Li, Kuangshi Ai +4
The ability to inspect, interpret, and communicate complex data is crucial for virtually any scientific endeavor, but often requires significant expertise outside the core domain r…
Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting
Ishrat Jahan Eliza, Xuan Huang, Aashish Panta +4
Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive d…
An Evaluation-Centric Paradigm for Scientific Visualization Agents
Kuangshi Ai, Haichao Miao, Zhimin Li +2
Recent advances in multi-modal large language models (MLLMs) have enabled increasingly sophisticated autonomous visualization agents capable of translating user intentions into dat…
Seeing the Many: Exploring Parameter Distributions Conditioned on Features in Surrogates
Xiaohan Wang, Zhimin Li, Joshua A. Levine +1
Recently, neural surrogate models have emerged as a compelling alternative to traditional simulation workflows. This is accomplished by modeling the underlying function of scientif…
See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs
Zhimin Li, Haichao Miao, Xinyuan Yan +3
Recent developments in multimodal large language models (MLLM) have equipped language models to reason about vision and language jointly. This permits MLLMs to both perceive and an…
Visualization Literacy of Multimodal Large Language Models: A Comparative Study
Zhimin Li, Haichao Miao, Valerio Pascucci +1
The recent introduction of multimodal large language models (MLLMs) combine the inherent power of large language models (LLMs) with the renewed capabilities to reason about the mul…
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
Shusen Liu, Haichao Miao, Zhimin Li +3
With recent advances in multi-modal foundation models, the previously text-only large language models (LLM) have evolved to incorporate visual input, opening up unprecedented oppor…
Instance-wise Linearization of Neural Network for Model Interpretation
Zhimin Li, Shusen Liu, Kailkhura Bhavya +3
Neural network have achieved remarkable successes in many scientific fields. However, the interpretability of the neural network model is still a major bottlenecks to deploy such t…
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
Zhimin Li, Shusen Liu, Xin Yu +5
Deep learning approaches have provided state-of-the-art performance in many applications by relying on large and overparameterized neural networks. However, such networks have been…