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Found 25 papers, 3 papers with code
Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning
According to the Markov Equivalence Class (MEC) theory, both the skeleton and the v-structures are identifiable causal structures under the canonical MEC setting, so predictions about skeleton and v-structures do not suffer from the identifiability limit in causal discovery, thus SiCL can avoid the systematic bias in Node-Edge architecture, and enable consistent estimators for causal discovery.
UAV Cognitive Semantic Communications Enabled by Knowledge Graph for Robust Object Detection
To overcome these challenges, a UAV cognitive semantic communication system is proposed by exploiting a knowledge graph.
Data-and-Semantic Dual-Driven Spectrum Map Construction for 6G Spectrum Management
To address the aforementioned challenges, a UNet-based data-and-semantic dual-driven method is proposed by introducing the semantic knowledge of binary city maps and binary sampling location maps to enhance the accuracy of spectrum map construction in complex urban environments with dense communications.
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers
To this end, in this paper, we propose a Progressive Fine-to-Coarse Reconstruction (PFCR) method for accurate PTQ, which significantly improves the performance of low-bit quantized vision transformers.
Relation Learning and Aggregate-attention for Multi-person Motion Prediction
Multi-person motion prediction is an emerging and intricate task with broad real-world applications.
UnityGraph: Unified Learning of Spatio-temporal features for Multi-person Motion Prediction
However, the uncertain compatibility of the two networks brings a challenge for spatio-temporal features fusion and violate the spatio-temporal coherence and coupling of human motions by nature.
Radical-mediated Electrical Enzyme Assay For At-home Clinical Test
To meet the growing demand for accurate, rapid, and cost-effective at-home clinical testing, we developed a radical-mediated enzyme assay (REEA) integrated with a paper fluidic system and electrically read by a handheld field-effect transistor (FET) device.
Control the GNN: Utilizing Neural Controller with Lyapunov Stability for Test-Time Feature Reconstruction
The performance of graph neural networks (GNNs) is susceptible to discrepancies between training and testing sample distributions.
Leveraging Data Mining, Active Learning, and Domain Adaptation in a Multi-Stage, Machine Learning-Driven Approach for the Efficient Discovery of Advanced Acidic Oxygen Evolution Electrocatalysts
The process culminated in the discovery of a promising Ru-Mn-Ca-Pr oxide catalyst.
Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs.
Enhancing Text Authenticity: A Novel Hybrid Approach for AI-Generated Text Detection
Our approach aims to address the challenges associated with detecting AI-generated text by leveraging the strengths of both traditional feature extraction methods and state-of-the-art deep learning models.
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.
A Partially Observable Deep Multi-Agent Active Inference Framework for Resource Allocation in 6G and Beyond Wireless Communications Networks
Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes.
FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction
To address this challenge, we propose FR-GNN, a general framework for GNNs to conduct feature reconstruction.
Novel Online-Offline MA2C-DDPG for Efficient Spectrum Allocation and Trajectory Optimization in Dynamic Spectrum Sharing UAV Networks
Unmanned aerial vehicle (UAV) communication is of crucial importance for diverse practical applications.
Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System
In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts and simplifies the exploration process for users.
f-Betas and Portfolio Optimization with f-Divergence induced Risk Measures
In this paper, we build on using the class of f-divergence induced coherent risk measures for portfolio optimization and derive its necessary optimality conditions formulated in CAPM format.
Curvature regularization for Non-line-of-sight Imaging from Under-sampled Data
Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections.
LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction
The noisy incomplete projection data results in the ill-posedness of the inverse problems.
XInsight: eXplainable Data Analysis Through The Lens of Causality
XInsight is a three-module, end-to-end pipeline designed to extract causal graphs, translate causal primitives into XDA semantics, and quantify the quantitative contribution of each explanation to a data fact.
Data-and-Knowledge Dual-Driven Automatic Modulation Recognition for Wireless Communication Networks
In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations.
A Unified and Fast Interpretable Model for Predictive Analytics
Predictive analytics is human involved, thus the machine learning model is preferred to be interpretable.
ML4C: Seeing Causality Through Latent Vicinity
Starting from seeing that SCL is not better than random guessing if the learning target is non-identifiable a priori, we propose a two-phase paradigm for SCL by explicitly considering structure identifiability.
Machine Learning Applications in Forecasting of COVID-19 Based on Patients' Individual Symptoms
Predicting the COVID-19 outbreak has been studied by many researchers in recent years.
A hybrid deep-learning approach for complex biochemical named entity recognition
In this approach, the MHATT mechanism aims to improve the recognition accuracy of abbreviations to efficiently deal with the problem of inconsistency in full-text labels.
