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Found 13 papers, 5 papers with code
LD-SDM: Language-Driven Hierarchical Species Distribution Modeling
Further, we propose a novel proximity-aware evaluation metric that enables evaluating species distribution models using any pixel-level representation of ground-truth species range map.
Vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning
In general multi-label learning, a model learns to predict multiple labels or categories for a single input image.
Causality for Inherently Explainable Transformers: CAT-XPLAIN
There have been several post-hoc explanation approaches developed to explain pre-trained black-box neural networks.
Robust Flow-based Conformal Inference (FCI) with Statistical Guarantee
Our approach is applicable and robust when the testing data is contaminated.
Dynamic Image for 3D MRI Image Alzheimer's Disease Classification
We propose to apply a 2D CNN architecture to 3D MRI image Alzheimer's disease classification.
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks
Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks.
Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks
Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios.
Joint 2D-3D Breast Cancer Classification
Breast cancer is the malignant tumor that causes the highest number of cancer deaths in females.
2D Convolutional Neural Networks for 3D Digital Breast Tomosynthesis Classification
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice.
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms
In this article, we develop an asymptotic analysis to derive the distribution of RandNLA sampling estimators for the least-squares problem.
False Discovery Rate Control via Data Splitting
Selecting relevant features associated with a given response variable is an important issue in many scientific fields.
Methodology
Minimax Nonparametric Two-sample Test under Smoothing
Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions.
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent.
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