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Found 26 papers, 4 papers with code
Causal Structure Learning Supervised by Large Language Model
Causal discovery from observational data is pivotal for deciphering complex relationships.
Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields
With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation.
From Query Tools to Causal Architects: Harnessing Large Language Models for Advanced Causal Discovery from Data
In this paper, we advance the current research of LLM-driven causal discovery by proposing a novel framework that combines knowledge-based LLM causal analysis with data-driven causal structure learning.
Mitigating Prior Errors in Causal Structure Learning: Towards LLM driven Prior Knowledge
LLM presents strong capability in discovering causal relationships between variables with the "text" inputs defining the investigated variables, leading to a potential new hierarchy and new ladder of causality.
GIMM: InfoMin-Max for Automated Graph Contrastive Learning
Many GCL methods with automated data augmentation face the risk of insufficient information as they fail to preserve the essential information necessary for the downstream task.
Long-Term Value of Exploration: Measurements, Findings and Algorithms
We conduct live experiments on one of the largest short-form video recommendation platforms that serves billions of users to validate the new experiment designs, quantify the long-term values of exploration, and to verify the effectiveness of the adopted neural linear bandit algorithm for exploration.
A Graph Neural Network with Negative Message Passing for Graph Coloring
In this paper, we propose a graph network model for graph coloring, which is a class of representative heterophilous problems.
ImmFusion: Robust mmWave-RGB Fusion for 3D Human Body Reconstruction in All Weather Conditions
However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images.
mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar
The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected.
Distributed Representations of Emotion Categories in Emotion Space
Emotion category is usually divided into different ones by human beings, but it is indeed difficult to clearly distinguish and define the boundaries between different emotion categories.
SE-Harris and eSUSAN: Asynchronous Event-Based Corner Detection Using Megapixel Resolution CeleX-V Camera
The proposed eSUSAN extracts the univalue segment assimilating nucleus from the circle kernel based on the similarity across timestamps and distinguishes corner events by the number of pixels in the nucleus area.
Unsupervised Change Detection in Satellite Images with Generative Adversarial Network
Detecting changed regions in paired satellite images plays a key role in many remote sensing applications.
Proximal Alternating Direction Network: A Globally Converged Deep Unrolling Framework
Deep learning models have gained great success in many real-world applications.
Boosting Variational Inference
Variational inference (VI) provides fast approximations of a Bayesian posterior in part because it formulates posterior approximation as an optimization problem: to find the closest distribution to the exact posterior over some family of distributions.
Unsupervised Cross-Media Hashing with Structure Preservation
Here, given a query of any media type, cross-media retrieval seeks to find relevant results of different media types from heterogeneous data sources.
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
We unify slice sampling and Hamiltonian Monte Carlo (HMC) sampling, demonstrating their connection via the Hamiltonian-Jacobi equation from Hamiltonian mechanics.
DECOrrelated feature space partitioning for distributed sparse regression
The dataset can be partitioned either horizontally (in the sample space) or vertically (in the feature space).
A Direct Approach for Sparse Quadratic Discriminant Analysis
Formulated in a simple and coherent framework, DA-QDA aims to directly estimate the key quantities in the Bayes discriminant function including quadratic interactions and a linear index of the variables for classification.
Parallelizing MCMC with Random Partition Trees
The new algorithm applies random partition trees to combine the subset posterior draws, which is distribution-free, easy to resample from and can adapt to multiple scales.
No penalty no tears: Least squares in high-dimensional linear models
Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size.
High-dimensional Ordinary Least-squares Projection for Screening Variables
Variable selection is a challenging issue in statistical applications when the number of predictors $p$ far exceeds the number of observations $n$.
Protecting Against Screenshots: An Image Processing Approach
We model this blending of information by an additive process, and exploit this to design a visual contents distortion algorithm that supports real-time contents recovery by the human visual system.
On the consistency theory of high dimensional variable screening
Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection.
Median Selection Subset Aggregation for Parallel Inference
For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs.
Parallelizing MCMC via Weierstrass Sampler
With the rapidly growing scales of statistical problems, subset based communication-free parallel MCMC methods are a promising future for large scale Bayesian analysis.
