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Found 32 papers, 10 papers with code
PCDCNet: A Surrogate Model for Air Quality Forecasting with Physical-Chemical Dynamics and Constraints
Air quality forecasting (AQF) is critical for public health and environmental management, yet remains challenging due to the complex interplay of emissions, meteorology, and chemical transformations.
Preserving Privacy and Utility in LLM-Based Product Recommendations
To address this, we propose a hybrid privacy-preserving recommendation framework which separates sensitive from nonsensitive data and only shares the latter with the cloud to harness LLM-powered recommendations.
SpinML: Customized Synthetic Data Generation for Private Training of Specialized ML Models
Specialized machine learning (ML) models tailored to users needs and requests are increasingly being deployed on smart devices with cameras, to provide personalized intelligent services taking advantage of camera data.
Multi-Omics Fusion with Soft Labeling for Enhanced Prediction of Distant Metastasis in Nasopharyngeal Carcinoma Patients after Radiotherapy
In order to overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology that mitigates the disparities inherent in omics data.
Quantifying system-environment synergistic information by effective information decomposition
Currently, many theories have attempted to explain the most essential difference between living systems and general systems, such as the self-organization theory and the free energy principle, but there is a lack of a reasonable indicator that can measure to what extent a system can be regarded as a system with life characteristics, especially the lack of attention to the dynamic characteristics of life systems.
Predicting Company Growth by Econophysics informed Machine Learning
Predicting company growth is crucial for strategic adjustment, operational decision-making, risk assessment, and loan eligibility reviews.
Differentially Private Federated Learning without Noise Addition: When is it Possible?
However, we further demonstrate that in practice, these conditions are almost unlikely to hold and hence additional noise added in model updates is still required in order for SA in FL to achieve DP.
Efficient Toxic Content Detection by Bootstrapping and Distilling Large Language Models
Furthermore, student LMs fine-tuned with rationales extracted via DToT outperform baselines on all datasets with up to 16. 9\% accuracy improvement, while being more than 60x smaller than conventional LLMs.
Neural Network Pruning by Gradient Descent
The rapid increase in the parameters of deep learning models has led to significant costs, challenging computational efficiency and model interpretability.
Data driven modeling for self-similar dynamics
For deterministic dynamics, our framework can discern whether the dynamics are self-similar.
Finding emergence in data by maximizing effective information
Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level.
Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions
Previous GCNs-based methods usually require providing spatial correlation graph structure of observation sites in advance.
Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction
To improve the discriminative ability of the loss function, we incorporate a margin into the contrastive learning.
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee?
While SA ensures no additional information is leaked about the individual model update beyond the aggregated model update, there are no formal guarantees on how much privacy FL with SA can actually offer; as information about the individual dataset can still potentially leak through the aggregated model computed at the server.
Completing Networks by Learning Local Connection Patterns
And experiments show that our model perform better on a network with higher Reachable CC.
Neural Information Squeezer for Causal Emergence
We also show how our framework can extract the dynamics on different levels and identify causal emergence from the data on several exampled systems.
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation
Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network performance.
Location Leakage in Federated Signal Maps
We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices.
HARPO: Learning to Subvert Online Behavioral Advertising
Online behavioral advertising, and the associated tracking paraphernalia, poses a real privacy threat.
Tensor networks for unsupervised machine learning
Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks.
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.
Fairness-guided SMT-based Rectification of Decision Trees and Random Forests
Data-driven decision making is gaining prominence with the popularity of various machine learning models.
Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs
In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints.
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
When predicting PM2. 5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period.
Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network
We carried out experiments on discrete and continuous time series data.
Understanding the mesoscopic scaling patterns within cities
Understanding quantitative relationships between urban elements is crucial for a wide range of applications.
Physics and Society
Link Prediction via Graph Attention Network
Link prediction aims to infer missing links or predicting the future ones based on currently observed partial networks, it is a fundamental problem in network science with tremendous real-world applications.
Gumbel-softmax Optimization: A Simple General Framework for Combinatorial Optimization Problems on Graphs
Many problems in real life can be converted to combinatorial optimization problems (COPs) on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints.
A General Deep Learning Framework for Network Reconstruction and Dynamics Learning
We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.
A Novel Learning-based Global Path Planning Algorithm for Planetary Rovers
Autonomous path planning algorithms are significant to planetary exploration rovers, since relying on commands from Earth will heavily reduce their efficiency of executing exploration missions.
A Novel Deep Neural Network Architecture for Mars Visual Navigation
In this paper, emerging deep learning techniques are leveraged to deal with Mars visual navigation problem.
Complex Network Classification with Convolutional Neural Network
Classifying large scale networks into several categories and distinguishing them according to their fine structures is of great importance with several applications in real life.
