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Found 13 papers, 4 papers with code
Localized Physics-informed Gaussian Processes with Curriculum Training for Topology Optimization
We introduce a simultaneous and meshfree topology optimization (TO) framework based on physics-informed Gaussian processes (GPs).
Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image Classification
X-ray imaging is pivotal in medical diagnostics, offering non-invasive insights into a range of health conditions.
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies
We first construct a large-scale 3D scene dataset based on 3DGS, dubbed \textbf{SegGaussian}, which provides detailed semantic and instance annotations for both Gaussian points and multi-view images.
Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets.
Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
With model compression techniques such as quantization and entropy coding, we consistently show over 25x reduced storage and enhanced rendering speed compared to 3DGS for static scenes, while maintaining the quality of the scene representation.
Counterfactual Explanations for Multivariate Time-Series without Training Datasets
However, most existing CFE methods require access to the model's training dataset, few methods can handle multivariate time-series, and none can handle multivariate time-series without training datasets.
Deblurring 3D Gaussian Splatting
However, it suffers from severe degradation in the rendering quality if the training images are blurry.
Compact 3D Gaussian Representation for Radiance Field
On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality.
Ranked #9 on
Novel View Synthesis
on Mip-NeRF 360
ChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions
Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming.
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning
We propose a Represent And Mimic (RAMi) framework for training 1) an identifiable latent representation to capture the independent factors of variation for the objects and 2) a mimic tree that extracts the causal impact of the latent features on DRL action values.
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and instantaneous (intra-slice) relations among variables.
Cracking the Black Box: Distilling Deep Sports Analytics
This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics.
