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Found 318 papers, 83 papers with code
Public Sentiment Drift Analysis Based on Hierarchical Variational Auto-encoder
Detecting public sentiment drift is a challenging task due to sentiment change over time.
End-to-end Dynamic Matching Network for Multi-view Multi-person 3d Pose Estimation
To address this phenomenon, we propose a novel end-to-end training scheme that brings the three separate modules into a single model.
Emotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy
Emotion inference in multi-turn conversations aims to predict the participant’s emotion in the next upcoming turn without knowing the participant’s response yet, and is a necessary step for applications such as dialogue planning.
ACFlow: Flow Models for Arbitrary Conditional Likelihoods
However, a majority of generative modeling approaches are focused solely on the joint distribution $p(x)$ and utilize models where it is intractable to obtain the conditional distribution of some arbitrary subset of features $x_u$ given the rest of the observed covariates $x_o$: $p(x_u \mid x_o)$.
Tackling Cooperative Incompatibility for Zero-Shot Human-AI Coordination
Achieving coordination between humans and artificial intelligence in scenarios involving previously unencountered humans remains a substantial obstacle within Zero-Shot Human-AI Coordination, which aims to develop AI agents capable of efficiently working alongside previously unknown human teammates.
PaLI-X: On Scaling up a Multilingual Vision and Language Model
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture.
Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency
Notably, our algorithm has achieved state-of-the-art performance on neuromorphic datasets DVS-CIFAR10 and N-Caltech101, and can achieve superior performance in the test phase with timestep T=1.
Dive into the Power of Neuronal Heterogeneity
The biological neural network is a vast and diverse structure with high neural heterogeneity.
A Sea-Land Clutter Classification Framework for Over-the-Horizon-Radar Based on Weighted Loss Semi-supervised GAN
The semi-supervised classification performance of WL-SSGAN is evaluated on a sea-land clutter dataset.
Feature Chirality in Deep Learning Models
As deep learning applications extensively increase by leaps and bounds, their interpretability has become increasingly prominent.
MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer
With the increasing demand for video understanding, video moment and highlight detection (MHD) has emerged as a critical research topic.
MINN: Learning the dynamics of differential-algebraic equations and application to battery modeling
The concept of integrating physics-based and data-driven approaches has become popular for modeling sustainable energy systems.
OpenBox: A Python Toolkit for Generalized Black-box Optimization
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning.
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.
Road Genome: A Topology Reasoning Benchmark for Scene Understanding in Autonomous Driving
By introducing Road Genome (OpenLane-V2), we intend to shift the community's attention and take a step further beyond perception - to the task of topology reasoning for scene structure.
Topology Reasoning for Driving Scenes
Understanding the road genome is essential to realize autonomous driving.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X
Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.
Ranked #19 on
Code Generation
on HumanEval
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks
The self-adaptation to membrane potential and input allows a timely adjustment of the threshold to fire spike faster and transmit more information.
Improving the Performance of Spiking Neural Networks on Event-based Datasets with Knowledge Transfer
To enhance the generalizability of SNNs on event-based datasets, we propose a knowledge-transfer framework that leverages static images to assist in the training on neuromorphic datasets.
Ensemble Nonlinear Model Predictive Control for Residential Solar-Battery Energy Management
In a dynamic distribution market environment, residential prosumers with solar power generation and battery energy storage devices can flexibly interact with the power grid via power exchange.
Mpox-AISM: AI-Mediated Super Monitoring for Forestalling Monkeypox Spread
The challenge on forestalling monkeypox (Mpox) spread is the timely, convenient and accurate diagnosis for earlystage infected individuals.
Multi-Task Sub-Band Network For Deep Residual Echo Suppression
This paper introduces the SWANT team entry to the ICASSP 2023 AEC Challenge.
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other.
Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks
Instead, we propose two methods to improve the representational power of AGCs by utilizing 1) structural information in a high-dimensional space and 2) multiple attention functions when calculating their weights.
Transfer Learning for Bayesian Optimization: A Survey
A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization.
Cooperative Open-ended Learning Framework for Zero-shot Coordination
However, these approaches can result in a loss of learning and an inability to cooperate with certain strategies within the population, known as cooperative incompatibility.
Rover: An online Spark SQL tuning service via generalized transfer learning
When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production.
DivBO: Diversity-aware CASH for Ensemble Learning
To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems.
HardSATGEN: Understanding the Difficulty of Hard SAT Formula Generation and A Strong Structure-Hardness-Aware Baseline
Industrial SAT formula generation is a critical yet challenging task for heuristic development and the surging learning-based methods in practical SAT applications.
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
Layout design is an important task in various design fields, including user interfaces, document, and graphic design.
Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response
A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms.
HTTE: A Hybrid Technique For Travel Time Estimation In Sparse Data Environments
Travel time estimation is a critical task, useful to many urban applications at the individual citizen and the stakeholder level.
Finding the Most Transferable Tasks for Brain Image Segmentation
Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance.
Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management
Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents.
Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach
Multi-uncertainties from power sources and loads have brought significant challenges to the stable demand supply of various resources at islands.
An Information-Theoretic Approach to Transferability in Task Transfer Learning
Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks.
ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks
In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation.
Super-resolution Probabilistic Rain Prediction from Satellite Data Using 3D U-Nets and EarthFormers
To further improve the model performance, multi-model ensemble and threshold optimization were used to produce the final probabilistic rain prediction.
Learning Cooperative Beamforming with Edge-Update Empowered Graph Neural Networks
However, the current GNNs are only equipped with the node-update mechanism, which restricts it from modeling more complicated problems such as the cooperative beamforming design, where the beamformers are on the graph edges of wireless networks.
A Dual-scale Lead-seperated Transformer With Lead-orthogonal Attention And Meta-information For Ecg Classification
Auxiliary diagnosis of cardiac electrophysiological status can be obtained through the analysis of 12-lead electrocardiograms (ECGs).
Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams
The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks.
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network
On account that previous studies either lacks variance analysis or only focus on a particular sampling paradigm, we firstly propose an unified node sampling variance analysis framework and analyze the core challenge "circular dependency" for deriving the minimum variance sampler, i. e., sampling probability depends on node embeddings while node embeddings can not be calculated until sampling is finished.
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of Intelligence
We compare the prediction performance for different intelligence measures based on static FC, dynamic FC, and region level time series acquired from the Adolescent Brain Cognitive Development (ABCD) study involving close to 7000 individuals.
CCPrompt: Counterfactual Contrastive Prompt-Tuning for Many-Class Classification
The sharing task description is unable to stimulate the unique task-related information in each training sample, especially for tasks with the finite-label space.
A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR
The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature-based method for current sensor fault diagnosis in three-phase PWM VSR systems.
Factorized Blank Thresholding for Improved Runtime Efficiency of Neural Transducers
We show how factoring the RNN-T's output distribution can significantly reduce the computation cost and power consumption for on-device ASR inference with no loss in accuracy.
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
To handle the data privacy and openness, we propose a forecasting scheme that combines federated learning and deep reinforcement learning (DRL) for ultra-short-term wind power forecasting, called federated deep reinforcement learning (FedDRL).
Review on Monitoring, Operation and Maintenance of Smart Offshore Wind Farms
In recent years, with the development of wind energy, the number and scale of wind farms are developing rapidly.
GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs
However, it remains challenging to model the negation and union operator.
Self-supervised Graph-based Point-of-interest Recommendation
In order to counteract the scarcity and incompleteness of POI check-ins, we propose a novel self-supervised learning paradigm in \ssgrec, where the trajectory representations are contrastively learned from two augmented views on geolocations and temporal transitions.
Understanding Embodied Reference with Touch-Line Transformer
Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line.
Contrastive Bayesian Analysis for Deep Metric Learning
Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other.
Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus
Specifically, we enhance a vision-language model that only takes the screenshot of the UI and a region of interest on the screen -- the focus -- as the input.
MUG: Interactive Multimodal Grounding on User Interfaces
To investigate the problem, we create a new dataset that consists of 77, 820 sequences of human user-agent interaction on mobile interfaces in which 20% involves multiple rounds of interactions.
Controlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning
The mean exit time escaping basin of attraction in the presence of white noise is of practical importance in various scientific fields.
Optimal dispatch of low-carbon integrated energy system considering nuclear heating and carbon trading
The development of miniaturized nuclear power (NP) units and the improvement of the carbon trading market provide a new way to realize the low-carbon operation of integrated energy systems (IES).
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning
In this paper, we investigate the joint offloading, communication and computation resource allocation for IRS-assisted NOMA MEC system.
Enabling Conversational Interaction with Mobile UI using Large Language Models
This paper investigates the feasibility of enabling versatile conversational interactions with mobile UIs using a single LLM.
SDFE-LV: A Large-Scale, Multi-Source, and Unconstrained Database for Spotting Dynamic Facial Expressions in Long Videos
In this paper, we present a large-scale, multi-source, and unconstrained database called SDFE-LV for spotting the onset and offset frames of a complete dynamic facial expression from long videos, which is known as the topic of dynamic facial expression spotting (DFES) and a vital prior step for lots of facial expression analysis tasks.
Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
Deep Learning-Based Automatic Diagnosis System for Developmental Dysplasia of the Hip
To the best of our knowledge, this is the first study for objective DDH diagnosis by leveraging deep learning keypoint detection and integrating different anatomical measurements, which can provide reliable and explainable support for clinical decision-making.
Video-based Cross-modal Auxiliary Network for Multimodal Sentiment Analysis
Multimodal sentiment analysis has a wide range of applications due to its information complementarity in multimodal interactions.
Distance-Aware Occlusion Detection with Focused Attention
In this work, (1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection.
Human-Object Interaction Detection
Relationship Detection
+1
Resilience assessment and improvement for electric power transmission systems against typhoon disasters: A data-model hybrid driven approach
In response to the damage to electric power transmission systems caused by typhoon disasters in coastal areas, a planning-targeted resilience assessment framework that considers the impact of multiple factors is established to accurately find the weak links of the transmission system and improve the system resilience.
GraphTTA: Test Time Adaptation on Graph Neural Networks
In this paper, we present a novel test time adaptation strategy named Graph Adversarial Pseudo Group Contrast (GAPGC), for graph neural networks TTA, to better adapt to the Out Of Distribution (OOD) test data.
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment
In this work, we guide the curriculum reinforcement learning results towards a preferred performance level that is neither too hard nor too easy via learning from the human decision process.
A Real-time Edge-AI System for Reef Surveys
Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels.
A Universal PINNs Method for Solving Partial Differential Equations with a Point Source
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs)method emerges to be a promising method for solving both forward and inverse PDE problems.
BrainCog: A Spiking Neural Network based Brain-inspired Cognitive Intelligence Engine for Brain-inspired AI and Brain Simulation
These brain-inspired AI models have been effectively validated on various supervised, unsupervised, and reinforcement learning tasks, and they can be used to enable AI models to be with multiple brain-inspired cognitive functions.
Transferability-Guided Cross-Domain Cross-Task Transfer Learning
We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more transferable representations for cross-domain cross-task transfer learning.
Generalizing to Unseen Domains with Wasserstein Distributional Robustness under Limited Source Knowledge
Domain generalization aims at learning a universal model that performs well on unseen target domains, incorporating knowledge from multiple source domains.
An Unsupervised STDP-based Spiking Neural Network Inspired By Biologically Plausible Learning Rules and Connections
By integrating the above three adaptive mechanisms and STB-STDP, our model greatly accelerates the training of unsupervised spiking neural networks and improves the performance of unsupervised SNNs on complex tasks.
Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation
Spiking neural network (SNN) has been attached to great importance due to the properties of high biological plausibility and low energy consumption on neuromorphic hardware.
Auto-Encoder-Extreme Learning Machine Model for Boiler NOx Emission Concentration Prediction
An automatic encoder (AE) extreme learning machine (ELM)-AE-ELM model is proposed to predict the NOx emission concentration based on the combination of mutual information algorithm (MI), AE, and ELM.
Warped Convolutional Networks: Bridge Homography to sl(3) algebra by Group Convolution
In this paper, we propose Warped Convolution Networks (WCN) to effectively learn and represent the homography by SL(3) group and sl(3) algebra with group convolution.
Efficient End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
DFG-NAS: Deep and Flexible Graph Neural Architecture Search
Graph neural networks (GNNs) have been intensively applied to various graph-based applications.
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
First, GNNs can learn higher-order structural information by stacking more layers but can not deal with large depth due to the over-smoothing issue.
Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot
Equipped with a wide span of sensors, predominant autonomous driving solutions are becoming more modular-oriented for safe system design.
Graph Attention Multi-Layer Perceptron
Graph neural networks (GNNs) have achieved great success in many graph-based applications.
Solving the Spike Feature Information Vanishing Problem in Spiking Deep Q Network with Potential Based Normalization
Brain inspired spiking neural networks (SNNs) have been successfully applied to many pattern recognition domains.
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important.
Transfer Learning based Search Space Design for Hyperparameter Tuning
The extensive experiments show that our approach considerably boosts BO by designing a promising and compact search space instead of using the entire space, and outperforms the state-of-the-arts on a wide range of benchmarks, including machine learning and deep learning tuning tasks, and neural architecture search.
Data Encryption based on 9D Complex Chaotic System with Quaternion for Smart Grid
In this paper, a new 9D complex chaotic system with quaternion is proposed for the encryption of smart grid data.
Do Deep Neural Networks Always Perform Better When Eating More Data?
Data has now become a shortcoming of deep learning.
Non-rigid Point Cloud Registration with Neural Deformation Pyramid
Non-rigid point cloud registration is a key component in many computer vision and computer graphics applications.
Demand Response Method Considering Multiple Types of Flexible Loads in Industrial Parks
With the rapid development of the energy internet, the proportion of flexible loads in smart grid is getting much higher than before.
Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications
The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback.
A BCS-GDE Multi-objective Optimization Algorithm for Combined Cooling, Heating and Power Model with Decision Strategies
The simulation results show that the model established in this paper can reduce economic cost by 72%, primary energy consumption by 73%, and pollutant emission by 88%.
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech
Modelling prosody variation is critical for synthesizing natural and expressive speech in end-to-end text-to-speech (TTS) systems.
Deep-Attack over the Deep Reinforcement Learning
Recent adversarial attack developments have made reinforcement learning more vulnerable, and different approaches exist to deploy attacks against it, where the key is how to choose the right timing of the attack.
Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks.
Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikes
Spiking neural network (SNN), as a brain-inspired energy-efficient neural network, has attracted the interest of researchers.
Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI
Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach
This paper tackles this issue by proposing a reinforcement learning assisted deep learning framework for the probabilistic EVCS charging power forecasting to capture its uncertainties.
Chinese Idiom Paraphrasing
Idioms, are a kind of idiomatic expression in Chinese, most of which consist of four Chinese characters.
GMSS: Graph-Based Multi-Task Self-Supervised Learning for EEG Emotion Recognition
GMSS has the ability to learn more general representations by integrating multiple self-supervised tasks, including spatial and frequency jigsaw puzzle tasks, and contrastive learning tasks.
Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis
We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view hierarchies required by previous work.
Data and Physics Driven Learning Models for Fast MRI -- Fundamentals and Methodologies from CNN, GAN to Attention and Transformers
Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e. g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning.
Preventing Over-Smoothing for Hypergraph Neural Networks
In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships.
Multi-source data processing and fusion method for power distribution internet of things based on edge intelligence
In this paper, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data.
PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark
Methods for 3D lane detection have been recently proposed to address the issue of inaccurate lane layouts in many autonomous driving scenarios (uphill/downhill, bump, etc.).
Ranked #5 on
3D Lane Detection
on Apollo Synthetic 3D Lane
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging
In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks.
Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices
Nevertheless, the implementation of the VMM needs complex peripheral circuits and the complexity further increases since non-idealities of memristive devices prevent precise conductance tuning (especially for the online training) and largely degrade the performance of the deep neural networks (DNNs).
A Robust Approach for the Decomposition of High-Energy-Consuming Industrial Loads with Deep Learning
The commonly used parameters in a conventional method are however inapplicable in high-energy-consuming industrial loads.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.
Smart Tracking Tray System for A Smart and Sustainable Wet Lab Community
Positive feedback has been collected from pilot tests in several labs.
PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs.
Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification
Unsupervised learning technology has caught up with or even surpassed supervised learning technology in general object classification (GOC) and person re-identification (re-ID).
Active and Passive Hybrid Detection Method for Power CPS False Data Injection Attacks with Improved AKF and GRU-CNN
Taking account of the fact that the existing knowledge-driven detection process for FDIAs has been in a passive detection state for a long time and ignores the advantages of data-driven active capture of features, an active and passive hybrid detection method for power CPS FDIAs with improved adaptive Kalman filter (AKF) and convolutional neural networks (CNN) is proposed in this paper.
Wind power ramp prediction algorithm based on wavelet deep belief network
To improve the ramp prediction accuracy, a hybrid wavelet deep belief network algorithm with adaptive feature selection (WDBNAFS) is proposed.
Robust Dynamic State Estimator of Integrated Energy Systems based on Natural Gas Partial Differential Equations
A novel robust dynamic state estimation methodology for integrated natural gas and electric power systems is proposed based on Kalman filter.
Probabilistic load flow calculation of AC/DC hybrid system based on cumulant method
The operating conditions of the power system have become more complex and changeable.
Improved normal-boundary intersection algorithm: a method for energy optimization strategy in smart buildings
With the widespread use of distributed energy sources, the advantages of smart buildings over traditional buildings are becoming increasingly obvious.
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck.
Joint Planning of Distributed Generations and Energy Storage in Active Distribution Networks: A Bi-Level Programming Approach
In this model, the upper-level aims to seek the optimal location and capacity of DGs and energy storage, while the lower-level optimizes the operation of energy storage devices.
Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale
The layout of a mobile screen is a critical data source for UI design research and semantic understanding of the screen.
Swin Transformer for Fast MRI
The IM and OM were 2D convolutional layers and the FEM was composed of a cascaded of residual Swin transformer blocks (RSTBs) and 2D convolutional layers.
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal sparse few-shot learning
Few-shot learning (learning with a few samples) is one of the most important cognitive abilities of the human brain.
A review of data-driven short-term voltage stability assessment of power systems: Concept, principle, and challenges
With the rapid growth of power market reform and power demand, the power transmission capacity of a power grid is approaching its limit, and the secure and stable operation of power systems becomes increasingly important.
Learning to Model the Relationship Between Brain Structural and Functional Connectomes
Recent advances in neuroimaging along with algorithmic innovations in statistical learning from network data offer a unique pathway to integrate brain structure and function, and thus facilitate revealing some of the brain's organizing principles at the system level.
Homography Decomposition Networks for Planar Object Tracking
Planar object tracking plays an important role in AI applications, such as robotics, visual servoing, and visual SLAM.
Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game
An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets.
Progressive Graph Convolution Network for EEG Emotion Recognition
Moreover, motivated by the observation of the relationship between coarse- and fine-grained emotions, we adopt a dual-head module that enables the PGCN to progressively learn more discriminative EEG features, from coarse-grained (easy) to fine-grained categories (difficult), referring to the hierarchical characteristic of emotion.
Dynamic Exploitation Gaussian Bare-Bones Bat Algorithm for Optimal Reactive Power Dispatch to Improve the Safety and Stability of Power System
DeGBBBA is an advanced variant of GBBBA in which a modified Gaussian distribution is introduced so as to allow the dynamic adaptation of exploitation and exploitation in the proposed algorithm.
Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT
Correlations between imaging findings and clinical lab tests suggested the value of this system as a potential tool to assess disease severity of COVID-19.
VUT: Versatile UI Transformer for Multi-Modal Multi-Task User Interface Modeling
Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.
A Deep-Learning Intelligent System Incorporating Data Augmentation for Short-Term Voltage Stability Assessment of Power Systems
Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on a small training dataset is a challenging and urgent problem.
Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networks
In this paper, we propose two methods to improve the performance of GCs: 1) Utilizing structural information in the feature space, and 2) exploiting the multi-hop information in one GC step.
Iterative Connecting Probability Estimation for Networks
Starting at a random initial point or an existing estimate, our method iteratively updates the pairwise vertex distances, the sets of similar vertices, and connecting probabilities to improve the precision of the estimate.
Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation
In this setting, we embed an additional pair of “latent-latent” to reduce the domain gap between the source and different latent domains, allowing the model to adapt well on multiple target domains simultaneously.
Stochastic optimal scheduling of demand response-enabled microgrids with renewable generations: An analytical-heuristic approach
In order to solve this model, this research combines Jaya algorithm and interior point method (IPM) to develop a hybrid analysis-heuristic solution method called Jaya-IPM, where the lower- and upper- levels are respectively addressed by the IPM and the Jaya, and the scheduling scheme is obtained via iterations between the two levels.
Lepard: Learning partial point cloud matching in rigid and deformable scenes
We present Lepard, a Learning based approach for partial point cloud matching in rigid and deformable scenes.
Ranked #1 on
Partial Point Cloud Matching
on 4DMatch
Unsupervised COVID-19 Lesion Segmentation in CT Using Cycle Consistent Generative Adversarial Network
The lung volume volume was firstly delineated using a pre-trained U-net and worked as the input for the later network.
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i. e., PDEs with different physical parameters, boundary conditions, shapes of computation domains, etc.
Spiking CapsNet: A Spiking Neural Network With A Biologically Plausible Routing Rule Between Capsules
Our Spiking CapsNet fully combines the strengthens of SNN and CapsNet, and shows strong robustness to noise and affine transformation.
DSBERT:Unsupervised Dialogue Structure learning with BERT
Unsupervised dialogue structure learning is an important and meaningful task in natural language processing.
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a promising method for solving both forward and inverse PDE problems.
A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges
Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems.
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup
In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2).
Data Driven based Dynamic Correction Prediction Model for NOx Emission of Coal Fired Boiler
The real-time prediction of NOx emissions is of great significance for pollutant emission control and unit operation of coal-fired power plants.
Path-Enhanced Multi-Relational Question Answering with Knowledge Graph Embeddings
The multi-relational Knowledge Base Question Answering (KBQA) system performs multi-hop reasoning over the knowledge graph (KG) to achieve the answer.
Knowledge Base Question Answering
Knowledge Graph Embedding
+1
Node Dependent Local Smoothing for Scalable Graph Learning
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs).
Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End Displacement and Strain Measurement
StrainNet predicts the strain field directly from the image input without relying on the displacement prediction, which significantly improves the strain prediction accuracy.
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies
Designing neural architectures requires immense manual efforts.
Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models
The design process of user interfaces (UIs) often begins with articulating high-level design goals.
Transferability Estimation for Semantic Segmentation Task
Recent analytical transferability metrics are mainly designed for image classification problem, and currently there is no specific investigation for the transferability estimation of semantic segmentation task, which is an essential problem in autonomous driving, medical image analysis, etc.
Selective transfer learning with adversarial training for stock movement prediction
To validate our method, we perform the back-testing on the historical data of two public datasets and a newly constructed dataset.
Bitcoin Transaction Strategy Construction Based on Deep Reinforcement Learning
Then an automatically-generating transaction strategy is constructed building on PPO with LSTM as the basis to construct the policy.
Extracting stochastic dynamical systems with $α$-stable Lévy noise from data
Despite the wide applications of non-Gaussian fluctuations in numerous physical phenomena, the data-driven approaches to extract stochastic dynamical systems with (non-Gaussian) L\'evy noise are relatively few so far.
VUT: Versatile UI Transformer for Multimodal Multi-Task User Interface Modeling
Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.
Improving Generative Adversarial Networks via Adversarial Learning in Latent Space
Although many efforts have been made in terms of backbone architecture design, loss function, and training techniques, few results have been obtained on how the sampling in latent space can affect the final performance, and existing works on latent space mainly focus on controllability.
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision
It consists of (1) a pairwise type-enriched sentence encoding module injecting both context-free and -related backgrounds to alleviate sentence-level wrong labeling, and (2) a hierarchical type-sentence alignment module enriching a sentence with the triple fact's basic attributes to support long-tail relations.
Class-conditioned Domain Generalization via Wasserstein Distributional Robust Optimization
Given multiple source domains, domain generalization aims at learning a universal model that performs well on any unseen but related target domain.
Non-intrusive load decomposition based on CNN-LSTM hybrid deep learning model
In this paper, the proposed decomposition method is compared with the existing traditional deep learning load decomposition method.
Extracting Stochastic Governing Laws by Nonlocal Kramers-Moyal Formulas
In this work, we propose a data-driven approach to extract stochastic governing laws with both (Gaussian) Brownian motion and (non-Gaussian) L\'evy motion, from short bursts of simulation data.
Lightweight Self-Attentive Sequential Recommendation
Modern deep neural networks (DNNs) have greatly facilitated the development of sequential recommender systems by achieving state-of-the-art recommendation performance on various sequential recommendation tasks.
Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments
The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention.
A BCS-GDE Algorithm for Multi-objective Optimization of Combined Cooling, Heating and Power Model
District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand.
Optimal Scheduling of Isolated Microgrids Using Automated Reinforcement Learning-based Multi-period Forecasting
In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads.
Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning
Modeling tap or click sequences of users on a mobile device can improve our understandings of interaction behavior and offers opportunities for UI optimization by recommending next element the user might want to click on.
HelpViz: Automatic Generation of Contextual Visual MobileTutorials from Text-Based Instructions
We present HelpViz, a tool for generating contextual visual mobile tutorials from text-based instructions that are abundant on the web.
Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning
Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be useful for many language-based application scenarios.
Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms
Aiming at the problem that delay time is difficult to determine and prediction accuracy is low in building prediction model of SCR system, a dynamic modeling scheme based on a hybrid of multiple data-driven algorithms was proposed.
Frequency support Scheme based on parametrized power curve for de-loaded Wind Turbine under various wind speed
However, for the existing frequency regulation scheme of wind turbines, the control gains in the auxiliary frequency controller are difficult to set because of the compromise of the frequency regulation performance and the stable operation of wind turbines, especially when the wind speed remains variable.
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets.
ROD: Reception-aware Online Distillation for Sparse Graphs
Unfortunately, many real-world networks are sparse in terms of both edges and labels, leading to sub-optimal performance of GNNs.
Extracting Governing Laws from Sample Path Data of Non-Gaussian Stochastic Dynamical Systems
Advances in data science are leading to new progresses in the analysis and understanding of complex dynamics for systems with experimental and observational data.
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
Coordinating Flexible Demand Response and Renewable Uncertainties for Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station: A Bi-level Approach
A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties.