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Found 20 papers, 6 papers with code
Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization
In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks.
Visualization Literacy of Multimodal Large Language Models: A Comparative Study
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 multimodal context.
AVA: Towards Autonomous Visualization Agents through Visual Perception-Driven Decision-Making
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 opportunities for various applications in visualization.
Transformer-Powered Surrogates Close the ICF Simulation-Experiment Gap with Extremely Limited Data
Recent advances in machine learning, specifically transformer architecture, have led to significant advancements in commercial domains.
Instance-wise Linearization of Neural Network for Model Interpretation
For a neural network model, the non-linear behavior is often caused by non-linear activation units of a model.
Topological Data Analysis Guided Segment Anything Model Prompt Optimization for Zero-Shot Segmentation in Biological Imaging
Emerging foundation models in machine learning are models trained on vast amounts of data that have been shown to generalize well to new tasks.
Cross-GAN Auditing: Unsupervised Identification of Attribute Level Similarities and Differences between Pretrained Generative Models
To this end, we introduce Cross-GAN Auditing (xGA) that, given an established "reference" GAN and a newly proposed "client" GAN, jointly identifies intelligible attributes that are either common across both GANs, novel to the client GAN, or missing from the client GAN.
On-the-fly Object Detection using StyleGAN with CLIP Guidance
We present a fully automated framework for building object detectors on satellite imagery without requiring any human annotation or intervention.
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches
We decomposed and evaluated a set of critical geometric concepts from the common adopted classification loss, and used them to design a visualization system to compare and highlight the impact of pruning on model performance and feature representation.
Reliable Graph Neural Network Explanations Through Adversarial Training
Graph neural network (GNN) explanations have largely been facilitated through post-hoc introspection.
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Actionable Attribution Maps for Scientific Machine Learning
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the process.
Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion.
Function Preserving Projection for Scalable Exploration of High-Dimensional Data
We present function preserving projections (FPP), a scalable linear projection technique for discovering interpretable relationships in high-dimensional data.
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization.
Generative Counterfactual Introspection for Explainable Deep Learning
In this work, we propose an introspection technique for deep neural networks that relies on a generative model to instigate salient editing of the input image for model interpretation.
Visual Interrogation of Attention-Based Models for Natural Language Inference and Machine Comprehension
Neural networks models have gained unprecedented popularity in natural language processing due to their state-of-the-art performance and the flexible end-to-end training scheme.
Distinguishing Unitary Gates on the IBM Quantum Processor
We program these two schemes on the \emph{ibmqx4}, a $5$-qubit superconducting quantum processor via IBM cloud, with the help of the $QSI$ modules [S. Liu et al.,~arXiv:1710. 09500, 2017].
Quantum Physics
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
Furthermore, we introduce a new approach to discover a diverse set of high quality linear projections and show that in practice the information of $k$ linear projections is often jointly encoded in $\sim k$ axis aligned plots.
