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Found 434 papers, 124 papers with code
MSPKmerCounter: A Fast and Memory Efficient Approach for K-mer Counting
For the task of k-mer counting, MSPKmerCounter offers a very fast and memory-efficient solution.
Reliable Patch Trackers: Robust Visual Tracking by Exploiting Reliable Patches
Most modern trackers typically employ a bounding box given in the first frame to track visual objects, where their tracking results are often sensitive to the initialization.
3D Model-Based Continuous Emotion Recognition
We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications.
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
And the dynamic features are formed by using the information of its neighbor frames in the sequence.
Person Re-Identification With Discriminatively Trained Viewpoint Invariant Dictionaries
This paper introduces a new approach to address the person re-identification problem in cameras with non-overlapping fields of view.
Improving Deep Neural Network with Multiple Parametric Exponential Linear Units
In this paper, we first propose a new activation function, Multiple Parametric Exponential Linear Units (MPELU), aiming to generalize and unify the rectified and exponential linear units.
Gland Instance Segmentation by Deep Multichannel Side Supervision
In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images.
Gland Instance Segmentation by Deep Multichannel Neural Networks
Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images.
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking
To enable the study of this problem, there exist a vast number of action datasets, which are recorded under controlled laboratory settings, real-world surveillance environments, or crawled from the Internet.
Recurrent Regression for Face Recognition
To address the sequential changes of images including poses, in this paper we propose a recurrent regression neural network(RRNN) framework to unify two classic tasks of cross-pose face recognition on still images and video-based face recognition.
Gland Instance Segmentation Using Deep Multichannel Neural Networks
Methods: We leverage the idea of image-to-image prediction in recent deep learning by designing an algorithm that automatically exploits and fuses complex multichannel information - regional, location, and boundary cues - in gland histology images.
Hashtag Recommendation with Topical Attention-Based LSTM
Microblogging services allow users to create hashtags to categorize their posts.
Spatial-Temporal Recurrent Neural Network for Emotion Recognition
Then a bi-directional temporal RNN layer is further used to learn discriminative temporal dependencies from the sequences concatenating spatial features of each time slice produced from the spatial RNN layer.
Time-Dependent Representation for Neural Event Sequence Prediction
Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization.
Detection of Moving Object in Dynamic Background Using Gaussian Max-Pooling and Segmentation Constrained RPCA
Due to its efficiency and stability, Robust Principal Component Analysis (RPCA) has been emerging as a promising tool for moving object detection.
Disentangled Variational Auto-Encoder for Semi-supervised Learning
The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE.
Robust Estimation of Similarity Transformation for Visual Object Tracking
In order to efficiently search in such a large 4-DoF space in real-time, we formulate the problem into two 2-DoF sub-problems and apply an efficient Block Coordinates Descent solver to optimize the estimation result.
Satellite-relayed intercontinental quantum network
This was on the one hand the transmission of images in a one-time pad configuration from China to Austria as well as from Austria to China.
Quantum Physics
Nonlinear Online Learning with Adaptive Nyström Approximation
Use of nonlinear feature maps via kernel approximation has led to success in many online learning tasks.
PotentialNet for Molecular Property Prediction
The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales.
Tensor graph convolutional neural network
For cross graph convolution, a parameterized Kronecker sum operation is proposed to generate a conjunctive adjacency matrix characterizing the relationship between every pair of nodes across two subgraphs.
Progressive Neural Networks for Image Classification
The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images.
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Model compression is essential for serving large deep neural nets on devices with limited resources or applications that require real-time responses.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e. g., object detection) of hazy images.
Guess Me if You Can: Acronym Disambiguation for Enterprises
The framework takes the enterprise corpus as input and produces a high-quality acronym disambiguation system as output.
Collaborative Learning for Extremely Low Bit Asymmetric Hashing
Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression.
Look Deeper See Richer: Depth-aware Image Paragraph Captioning
Existing image paragraph captioning methods give a series of sentences to represent the objects and regions of interests, where the descriptions are essentially generated by feeding the image fragments containing objects and regions into conventional image single-sentence captioning models.
Boosted Convolutional Neural Networks for Motor Imagery EEG Decoding with Multiwavelet-based Time-Frequency Conditional Granger Causality Analysis
Decoding EEG signals of different mental states is a challenging task for brain-computer interfaces (BCIs) due to nonstationarity of perceptual decision processes.
Area Attention
We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e. g., spatially for a 2D memory such as images, or temporally for a 1D memory such as natural language sentences.
Learning to Screen for Fast Softmax Inference on Large Vocabulary Neural Networks
The algorithm achieves an order of magnitude faster inference than the original softmax layer for predicting top-$k$ words in various tasks such as beam search in machine translation or next words prediction.
Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates
The performance of deep neural networks crucially depends on good hyperparameter configurations.
An amplitudes-perturbation data augmentation method in convolutional neural networks for EEG decoding
Brain-Computer Interface (BCI) system provides a pathway between humans and the outside world by analyzing brain signals which contain potential neural information.
Implementation of Robust Face Recognition System Using Live Video Feed Based on CNN
In this way, the advantages of multiple models can be combined to mention the recognition accuracy.
You Look Twice: GaterNet for Dynamic Filter Selection in CNNs
The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sample it is processing.
Ensemble Feature for Person Re-Identification
However, deep feature extracted by a single CNN network is not robust enough in testing stage.
Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning
Furthermore, we also fuse phonetic features with textual and visual features in order to mimic the way humans read and understand Chinese text.
Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning
Sensor fusion has wide applications in many domains including health care and autonomous systems.
A Forest from the Trees: Generation through Neighborhoods
In this work, we propose to learn a generative model using both learned features (through a latent space) and memories (through neighbors).
Exchangeable Generative Models with Flow Scans
In this work, we develop a new approach to generative density estimation for exchangeable, non-i. i. d.
The Complexity of Morality: Checking Markov Blanket Consistency with DAGs via Morality
A family of Markov blankets in a faithful Bayesian network satisfies the symmetry and consistency properties.
Proving the NP-completeness of optimal moral graph triangulation
Moral graphs were introduced in the 1980s as an intermediate step when transforming a Bayesian network to a junction tree, on which exact belief propagation can be efficiently done.
Pose Graph Optimization for Unsupervised Monocular Visual Odometry
In this paper, we propose to leverage graph optimization and loop closure detection to overcome limitations of unsupervised learning based monocular visual odometry.
SADIH: Semantic-Aware DIscrete Hashing
Due to its low storage cost and fast query speed, hashing has been recognized to accomplish similarity search in large-scale multimedia retrieval applications.
Accurate and Fast reconstruction of Porous Media from Extremely Limited Information Using Conditional Generative Adversarial Network
To overcome this shortcoming, in this study we proposed a deep learning-based framework for reconstructing full image from its much smaller sub-area(s).
$L_0$-ARM: Network Sparsification via Stochastic Binary Optimization
Thanks to the flexibility of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid or hard sigmoid, can be used to parameterize this binary optimization problem and the unbiasness of the ARM estimator is retained, while the hard concrete estimator has to rely on the hard sigmoid function to achieve conditional computation and thus accelerated training.
An Information-Theoretic Metric of Transferability for Task Transfer Learning
An important question in task transfer learning is to determine task transferability, i. e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task.
Integrating Tensor Similarity to Enhance Clustering Performance
The performance of most the clustering methods hinges on the used pairwise affinity, which is usually denoted by a similarity matrix.
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].
Ranked #1 on
Node Classification
on Amazon2M
Saliency detection based on structural dissimilarity induced by image quality assessment model
Similar to IQA models, the structural dissimilarity is computed based on the correlation of the structural features.
Event Detection without Triggers
The goal of event detection (ED) is to detect the occurrences of events and categorize them.
Robustness Verification of Tree-based Models
We show that there is a simple linear time algorithm for verifying a single tree, and for tree ensembles, the verification problem can be cast as a max-clique problem on a multi-partite graph with bounded boxicity.
Bayesian Optimization with Directionally Constrained Search
This constraint demands an optimizer to be aware of its remaining budget and able to spend it wisely, in order to return as better a point as possible.
Inferring Latent dimension of Linear Dynamical System with Minimum Description Length
This paper addresses the issue and proposes a criterion for linear dynamical system based on the principle of minimum description length.
Proximal Policy Optimization with Mixed Distributed Training
Instability and slowness are two main problems in deep reinforcement learning.
Neural Plasticity Networks
To the best of our knowledge, this is the first learning framework that unifies network sparsification and network expansion in an end-to-end training pipeline.
What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning
We propose a hybrid hardware-software framework that has the potential to significantly reduce the computational complexity and memory requirements of on-device machine learning.
Snowball: Iterative Model Evolution and Confident Sample Discovery for Semi-Supervised Learning on Very Small Labeled Datasets
In this work, we develop a joint sample discovery and iterative model evolution method for semi-supervised learning on very small labeled training sets.
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
Specifically, we start by learning the structure of the graph that parsimoniously represents the spatial dependency between the data at different locations.
SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation with Surgical Robotics
In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control.
Flow Models for Arbitrary Conditional Likelihoods
Understanding the dependencies among features of a dataset is at the core of most unsupervised learning tasks.
A Hybrid Architecture for On-Device Compressive Machine Learning
By processing the data on the device that has collected the data, we can dramatically increase the level of privacy.
Meta-Neighborhoods
Making an adaptive prediction based on one's input is an important ability for general artificial intelligence.
Attribute-aware Pedestrian Detection in a Crowd
To tackle this critical problem, we propose an attribute-aware pedestrian detector to explicitly model people's semantic attributes in a high-level feature detection fashion.
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning
In this paper, we proposed a general framework for data poisoning attacks to graph-based semi-supervised learning (G-SSL).
Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.
Auto Completion of User Interface Layout Design Using Transformer-Based Tree Decoders
While we focus on interface layout prediction, our model can be generally applicable for other layout prediction problems that involve tree structures and 2-dimensional placements.
Illumination adaptive person reid based on teacher-student model and adversarial training
Most existing works in Person Re-identification (ReID) focus on settings where illumination either is kept the same or has very little fluctuation.
Deep Learning-based End-to-end Diagnosis System for Avascular Necrosis of Femoral Head
To the best of our knowledge, this study is the first research on the prospective use of a deep learning-based diagnosis system for AVNFH by conducting two pilot studies representing real-world application scenarios.
Differential Evolution with Individuals Redistribution for Real Parameter Single Objective Optimization
Versions of the ten DE algorithms based on individuals redistribution are compared with not only original version but also version based on complete restart, where individuals redistribution and complete restart are based on the same entry criterion.
SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction
Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue.
FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow
Dynamic environments are challenging for visual SLAM since the moving objects occlude the static environment features and lead to wrong camera motion estimation.
Defense Through Diverse Directions
In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training.
Learning to Optimize Non-Rigid Tracking
One of the widespread solutions for non-rigid tracking has a nested-loop structure: with Gauss-Newton to minimize a tracking objective in the outer loop, and Preconditioned Conjugate Gradient (PCG) to solve a sparse linear system in the inner loop.
Anomalous Motion of Particle Levitated by Laguerre-Gaussian beam
We calculated the scattering force distribution of a micro-particle trapped in an optical tweezers formed by the strongly focused LG beam, and showed that there exist stable trajectories of the particle that controlled by the negative torque.
Optics
SSIM-Based CTU-Level Joint Optimal Bit Allocation and Rate Distortion Optimization
This problem is due to the lack of an accurate R-$D_\text{SSIM}$ model that can be used uniformly in both OBA and RDO.
Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
We describe the annotation process in detail and compare it with other similar evaluation systems.
A Data-Driven Approach for Discovering Stochastic Dynamical Systems with Non-Gaussian Levy Noise
We then design a numerical algorithm to compute the drift, diffusion coefficient and jump measure, and thus extract a governing stochastic differential equation with Gaussian and non-Gaussian noise.
Mapping Natural Language Instructions to Mobile UI Action Sequences
We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it.
A heterogeneous branch and multi-level classification network for person re-identification
A new heterogeneous branch, SE-Res-Branch, is proposed based on the SE-Res module, which consists of the Squeeze-and-Excitation block and the residual block.
Grafted network for person re-identification
In order to relieve this problem, we propose a novel grafted network (GraftedNet), which is designed by grafting a high-accuracy rootstock and a light-weighted scion.
Dynamic Feature Acquisition with Arbitrary Conditional Flows
To trade off the improvement with the cost of acquisition, we leverage an information theoretic metric, conditional mutual information, to select the most informative feature to acquire.
GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals
To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.
Attention-based Graph ResNet for Motor Intent Detection from Raw EEG signals
In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
Probabilistic Classification Vector Machine for Multi-Class Classification
The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems.
SplitFusion: Simultaneous Tracking and Mapping for Non-Rigid Scenes
We present SplitFusion, a novel dense RGB-D SLAM framework that simultaneously performs tracking and dense reconstruction for both rigid and non-rigid components of the scene.
RGB-IR Cross-modality Person ReID based on Teacher-Student GAN Model
To solve this challenge, we proposed a Teacher-Student GAN model (TS-GAN) to adopt different domains and guide the ReID backbone to learn better ReID information.
Multi-Stage Influence Function
With this score, we can identify the pretraining examples in the pretraining task that contribute most to a prediction in the finetuning task.
A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News
This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep learning model to predict future stock movement.
A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges
Question Answering (QA) over Knowledge Base (KB) aims to automatically answer natural language questions via well-structured relation information between entities stored in knowledge bases.
Exchangeable Neural ODE for Set Modeling
Reasoning over an instance composed of a set of vectors, like a point cloud, requires that one accounts for intra-set dependent features among elements.
Unsupervised Deep Metric Learning with Transformed Attention Consistency and Contrastive Clustering Loss
To further enhance the inter-class discriminative power of the feature generated by this network, we adapt the concept of triplet loss from supervised metric learning to our unsupervised case and introduce the contrastive clustering loss.
Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019
All methods were based on deep learning and categorized into two groups: multi-model method and single model method.
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition
So it is necessary to give more attention to the EEG samples with strong transferability rather than forcefully training a classification model by all the samples.
A Unified Plug-and-Play Framework for Effective Data Denoising and Robust Abstention
The success of Deep Neural Networks (DNNs) highly depends on data quality.
A Machine Learning Framework for Computing the Most Probable Paths of Stochastic Dynamical Systems
The emergence of transition phenomena between metastable states induced by noise plays a fundamental role in a broad range of nonlinear systems.
Active Feature Acquisition with Generative Surrogate Models
Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data.
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention
Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations.
Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements
We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces.
Interpretable Models in ANNs
Artificial neural networks are often very complex and too deep for a human to understand.
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.
Efficient Automatic CASH via Rising Bandits
In this framework, the BO methods are used to solve the HPO problem for each ML algorithm separately, incorporating a much smaller hyperparameter space for BO methods.
InvertGAN: Reducing mode collapse with multi-dimensional Gaussian Inversion
Generative adversarial networks have shown their ability in capturing high-dimensional complex distributions and generating realistic data samples e. g. images.
Multitask Learning for Emotion and Personality Detection
In recent years, deep learning-based automated personality trait detection has received a lot of attention, especially now, due to the massive digital footprints of an individual.
Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events
Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.
Deep Learning for Short-Term Voltage Stability Assessment of Power Systems
Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics.
NRTSI: Non-Recurrent Time Series Imputation
Time series imputation is a fundamental task for understanding time series with missing data.
Partially Observed Exchangeable Modeling
Modeling dependencies among features is fundamental for many machine learning tasks.
Spacewalker: Rapid UI Design Exploration Using Lightweight Markup Enhancement and Crowd Genetic Programming
Based on our experiments, Spacewalker allows designers to effectively search a large design space of a UI, using the language they are familiar with, and improve their design rapidly at a minimal cost.
TapNet: The Design, Training, Implementation, and Applications of a Multi-Task Learning CNN for Off-Screen Mobile Input
To make off-screen interaction without specialized hardware practical, we investigate using deep learning methods to process the common built-in IMU sensor (accelerometers and gyroscopes) on mobile phones into a useful set of one-handed interaction events.
Distinct thermodynamic and dynamic effects produced by scale factors in conformally related Einstein-power-Yang-Mills black holes
We find that the thermodynamics of this class of conformally related black holes is independent of scale factors.
General Relativity and Quantum Cosmology High Energy Physics - Theory
Relaxed phase-matching constraints in zero-index waveguides
However, the utility of all such parametric nonlinear optical processes is hampered by phase-matching requirements.
Optics Applied Physics
Robust Kalman filter-based dynamic state estimation of natural gas pipeline networks
To obtain the accurate transient states of the big scale natural gas pipeline networks under the bad data and non-zero mean noises conditions, a robust Kalman filter-based dynamic state estimation method is proposed using the linearized gas pipeline transient flow equations in this paper.
Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link
Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets.
Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach
In order to balance the interests of integrated energy operator (IEO) and users, a novel Stackelberg game-based optimization framework is proposed for the optimal scheduling of integrated demand response (IDR)-enabled integrated energy systems with uncertain renewable generations, where the IEO acts as the leader who pursues the maximization of his profits by setting energy prices, while the users are the follower who adjusts energy consumption plans to minimize their energy costs.
OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations
Specifically, we use optimal transport to estimate domain difference and the optimal coupling between source and target distributions, which is then used to derive the conditional entropy of the target task (task difference).
Joint Optimization for SSIM-Based CTU-Level Bit Allocation and Rate Distortion Optimization
However, the MSE-based rate distortion optimization (RDO) was still used in these studies.
GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention
This task is challenging as models need not only to capture spatial dependency and temporal dependency within the data, but also to leverage useful auxiliary information for accurate predictions.
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-based tasks.
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Moreover, modeling and inferring complex relations of one-to-many (1-N), many-to-one (N-1), and many-to-many (N-N) by previous knowledge graph completion approaches requires high model complexity and a large amount of training instances.
BLM-17m: A Large-Scale Dataset for Black Lives Matter Topic Detection on Twitter
In this paper, our aim is to provide a dataset which covers one of the most significant human rights contradiction in recent months affected the whole world, George Floyd incident.
4DComplete: Non-Rigid Motion Estimation Beyond the Observable Surface
Tracking non-rigidly deforming scenes using range sensors has numerous applications including computer vision, AR/VR, and robotics.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
Joint Text and Label Generation for Spoken Language Understanding
In this work, we propose to leverage the prior information embedded in pretrained language models (LM) to improve generalization for intent classification and slot labeling tasks with limited training data.
RtFPS: An Interactive Map that Visualizes and Predicts Wildfires in the US
Climate change has largely impacted our daily lives.
Enhance Multimodal Model Performance with Data Augmentation: Facebook Hateful Meme Challenge Solution
In this paper, we utilize multi-modal, pre-trained models VilBERT and Visual BERT.
BackEISNN: A Deep Spiking Neural Network with Adaptive Self-Feedback and Balanced Excitatory-Inhibitory Neurons
With the combination of the two mechanisms, we propose a deep spiking neural network with adaptive self-feedback and balanced excitatory and inhibitory neurons (BackEISNN).
BSNN: Towards Faster and Better Conversion of Artificial Neural Networks to Spiking Neural Networks with Bistable Neurons
Also, when ResNet structure-based ANNs are converted, the information of output neurons is incomplete due to the rapid transmission of the shortcut path.
OpenBox: A Generalized Black-box Optimization Service
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
IID-GAN: an IID Sampling Perspective for Regularizing Mode Collapse
Based on this observation, considering a necessary condition of IID generation that the inverse samples from target data should also be IID in the source distribution, we propose a new loss to encourage the closeness between inverse samples of real data and the Gaussian source in latent space to regularize the generation to be IID from the target distribution.
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding
Attentional mechanisms are order-invariant.
Rethinking Training from Scratch for Object Detection
The ImageNet pre-training initialization is the de-facto standard for object detection.
Practical Transferability Estimation for Image Classification Tasks
Transferability estimation is an essential problem in transfer learning to predict how good the performance is when transferring a source model (or source task) to a target task.
Spatial Assembly Networks for Image Representation Learning
It has been long recognized that deep neural networks are sensitive to changes in spatial configurations or scene structures.
Relative Order Analysis and Optimization for Unsupervised Deep Metric Learning
During training, this relative order prediction network and the feature embedding network are tightly coupled, providing mutual constraints to each other to improve overall metric learning performance in a cooperative manner.
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading.
Graph Routing between Capsules
Therefore, in this paper, we introduce a new capsule network with graph routing to learn both relationships, where capsules in each layer are treated as the nodes of a graph.
Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image
Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor.
Dep-$L_0$: Improving $L_0$-based Network Sparsification via Dependency Modeling
We term our algorithm Dep-$L_0$ as it prunes networks via a dependency-enabled $L_0$ regularization.
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation
Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation.
Context-Aware Attention-Based Data Augmentation for POI Recommendation
It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications.
Attribute-aware Explainable Complementary Clothing Recommendation
As a result, this creates a severe bottleneck when we are trying to advance the recommendation accuracy and generating fine-grained explanations since the explicit attributes have only loose connections to the actual recommendation process.
Virtual synchronous generator of PV generation without energy storage for frequency support in autonomous microgrid
PV generation reserve a part of the active power in accordance with the pre-defined power versus voltage curve.
Towards Robust Active Feature Acquisition
Our framework can easily handle a large number of features using a hierarchical acquisition policy and is more robust to OOD inputs with the help of an OOD detector for partially observed data.
Dynamic State Estimation for Integrated Natural Gas and Electric Power Systems
Boundary conditions of pipeline networks are used as supplementary constraints in the system model.
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.
Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach
Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables.
Markov Blanket Discovery using Minimum Message Length
Causal discovery automates the learning of causal Bayesian networks from data and has been of active interest from their beginning.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies
Designing neural architectures requires immense manual efforts.
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.
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).
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
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).
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.
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.
DSBERT:Unsupervised Dialogue Structure learning with BERT
Unsupervised dialogue structure learning is an important and meaningful task in natural language processing.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
