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Found 220 papers, 63 papers with code
A Progressive Framework for Role-Aware Rumor Resolution
Existing works on rumor resolution have shown great potential in recognizing word appearance and user participation.
SPDB Innovation Lab at SemEval-2022 Task 10: A Novel End-to-End Structured Sentiment Analysis Model based on the ERNIE-M
In this paper, we propose a method which can predict structured sentiment information on multiple languages with limited data.
SPDB Innovation Lab at SemEval-2022 Task 3: Recognize Appropriate Taxonomic Relations Between Two Nominal Arguments with ERNIE-M Model
Synonym and antonym practice are the most common practices in our early childhood.
VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum
Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.
Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning
In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation.
Holistic risk assessment of inference attacks in machine learning
As far as concerned, researchers have studied and analyzed in depth several types of inference attacks, albeit in isolation, but there is still a lack of a holistic rick assessment of inference attacks against machine learning models, such as their application in different scenarios, the common factors affecting the performance of these attacks and the relationship among the attacks.
X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection
Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios.
Multi-Label Classification
Natural Language Understanding
+1
FakeEdge: Alleviate Dataset Shift in Link Prediction
In this paper, we first identify the dataset shift problem in the link prediction task and provide theoretical analyses on how existing link prediction methods are vulnerable to it.
Self-attention based high order sequence feature reconstruction of dynamic functional connectivity networks with rs-fMRI for brain disease classification
Dynamic functional connectivity networks (dFCN) based on rs-fMRI have demonstrated tremendous potential for brain function analysis and brain disease classification.
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection
Based on the embedding tangent space, featMAP enables the interpretability by locally demonstrating the source features and feature importance.
WiserVR: Semantic Communication Enabled Wireless Virtual Reality Delivery
Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks.
Generating Accurate and Faithful Discharge Instructions: Task, Dataset, and Model
We build a benchmark clinical dataset and propose the Re3Writer, which imitates the working patterns of physicians to first retrieve related working experience from historical PIs written by physicians, then reason related medical knowledge.
MMGA: Multimodal Learning with Graph Alignment
In this paper, we propose MMGA (Multimodal learning with Graph Alignment), a novel multimodal pre-training framework to incorporate information from graph (social network), image and text modalities on social media to enhance user representation learning.
SA-DNet: A on-demand semantic object registration network adapting to non-rigid deformation
After utilizing TPS to transform infrared and visible images based on the corresponding feature points in sROI, the registered images are fused using image fusion module (IFM) to achieve a fully functional registration and fusion network.
InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power.
Prompt Tuning for Graph Neural Networks
In this paper, we propose Graph Prompt as an efficient and effective alternative to full fine-tuning for adapting the pre-trianed GNN models to downstream tasks.
Real-Time Cattle Interaction Recognition via Triple-stream Network
Based on this, we tackle the challenging task of real-time recognizing interactions between cattle in a single frame in this paper.
Deep Joint Source-Channel Coding Based on Semantics of Pixels
The semantic information of the image for intelligent tasks is hidden behind the pixels, and slight changes in the pixels will affect the performance of intelligent tasks.
Rain Removal from Light Field Images with 4D Convolution and Multi-scale Gaussian Process
However, making full use of the abundant information available from LFIs, such as 2D array of sub-views and the disparity map of each sub-view, for effective rain removal is still a challenging problem.
Continual Unsupervised Domain Adaptation for Semantic Segmentation using a Class-Specific Transfer
In this work, we focus on UDA and additionally address the case of adapting not only to a single domain, but to a sequence of target domains.
OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms
In this paper, we present OpenMedIA, an open-source toolbox library containing a rich set of deep learning methods for medical image analysis under heterogeneous Artificial Intelligence (AI) computing platforms.
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
Realizing the potential of neural video codecs on mobile devices is a big technological challenge due to the computational complexity of deep networks and the power-constrained mobile hardware.
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have shown that learning based approaches can outperform heuristic methods in terms of speed and performance.
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition
Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification.
DGraph: A Large-Scale Financial Dataset for Graph Anomaly Detection
Since GAD emphasizes the application and the rarity of anomalous samples, enriching the varieties of its datasets is a fundamental work.
Towards KAB2S: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem
As "a new frontier in evolutionary computation research", evolutionary transfer optimization(ETO) will overcome the traditional paradigm of zero reuse of related experience and knowledge from solved past problems in researches of evolutionary computation.
Multi-View Clustering for Open Knowledge Base Canonicalization
In this paper, we propose CMVC, a novel unsupervised framework that leverages these two views of knowledge jointly for canonicalizing OKBs without the need of manually annotated labels.
AutoDisc: Automatic Distillation Schedule for Large Language Model Compression
As a connection, the scale and the performance of the teacher assistant is crucial for transferring the knowledge from the teacher to the student.
Measuring Perceptual Color Differences of Smartphone Photography
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography.
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.
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.
A Bottom-Up End-User Intelligent Assistant Approach to Empower Gig Workers against AI Inequality
The AI inequality is caused by (1) the technology divide in who has access to AI technologies in gig work; and (2) the data divide in who owns the data in gig work leads to unfair working conditions, growing pay gap, neglect of workers' diverse preferences, and workers' lack of trust in the platforms.
Continual Learning with Bayesian Model based on a Fixed Pre-trained Feature Extractor
In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases.
DropMessage: Unifying Random Dropping for Graph Neural Networks
First, it is challenging to find a universal method that are suitable for all cases considering the divergence of different datasets and models.
Invertible Mask Network for Face Privacy-Preserving
Then, put the "Mask" face onto the protected face and generate the masked face, in which the masked face is indistinguishable from "Mask" face.
Adaptable Semantic Compression and Resource Allocation for Task-Oriented Communications
To solve the problem, both compression ratio and resource allocation are optimized for the task-oriented communication system to maximize the success probability of tasks.
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval
To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.
Positioning Using Visible Light Communications: A Perspective Arcs Approach
In this paper, the arc feature of the circular luminaire and the coordinate information obtained via visible light communication (VLC) are jointly used for VLC-enabled indoor positioning, and a novel perspective arcs approach is proposed.
Firebolt: Weak Supervision Under Weaker Assumptions
In particular, Firebolt learns the class balance and class-specific accuracy of LFs jointly from unlabeled data.
FORCE: A Framework of Rule-Based Conversational Recommender System
The conversational recommender systems (CRSs) have received extensive attention in recent years.
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
We propose in this paper an end-to-end network, named CS-Net, to complete the point clouds contaminated by noises or containing outliers.
Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
Underwater visual perception is essentially important for underwater exploration, archeology, ecosystem and so on.
Adaptive Information Bottleneck Guided Joint Source-Channel Coding
Joint source channel coding (JSCC) has attracted increasing attentions due to its robustness and high efficiency.
Region-of-Interest Based Neural Video Compression
To the best of our knowledge, our proposals are the first solutions that integrate ROI-based capabilities into neural video compression models.
A Coarse-to-fine Morphological Approach With Knowledge-based Rules and Self-adapting Correction for Lung Nodules Segmentation
The segmentation module which precisely outlines the nodules is a crucial step in a computer-aided diagnosis(CAD) system.
An Exploratory Study of Stock Price Movements from Earnings Calls
Third, the semantic features of transcripts are more predictive of stock price movements than sales and earnings per share, i. e., traditional hard data in most of the cases.
MHSnet: Multi-head and Spatial Attention Network with False-Positive Reduction for Pulmonary Nodules Detection
The false positive reduction module significantly decreases the average number of candidates generated per scan by 68. 11% and the false discovery rate by 13. 48%, which is promising to reduce distracted proposals for the downstream tasks based on the detection results.
Semantic-assisted image compression
Experimental results show that the proposed SAIC method can retain more semantic-level information and achieve better performance of downstream AI tasks compared to the traditional deep learning-based method and the advanced perceptual method at the same compression ratio.
Bandwidth and Power Allocation for Task-Oriented SemanticCommunication
The first subproblem is a compression ratio optimization problem with a given resource allocation scheme, which is solved by a enumeration algorithm.
Privacy-aware Early Detection of COVID-19 through Adversarial Training
One of our main contributions is that we specifically target the development of effective COVID-19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks.
Real-time Rail Recognition Based on 3D Point Clouds
Accurate rail location is a crucial part in the railway support driving system for safety monitoring.
Attention-based Dual Supervised Decoder for RGBD Semantic Segmentation
Encoder-decoder models have been widely used in RGBD semantic segmentation, and most of them are designed via a two-stream network.
Ranked #7 on
Semantic Segmentation
on SUN-RGBD
Self-Augmented Unpaired Image Dehazing via Density and Depth Decomposition
With estimated scene depth, our method is capable of re-rendering hazy images with different thicknesses which further benefits the training of the dehazing network.
Conditional Generative Data-free Knowledge Distillation
In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data.
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration.
VIRT: Improving Representation-based Models for Text Matching through Virtual Interaction
With the booming of pre-trained transformers, representation-based models based on Siamese transformer encoders have become mainstream techniques for efficient text matching.
ACNet: Approaching-and-Centralizing Network for Zero-Shot Sketch-Based Image Retrieval
The huge domain gap between sketches and photos and the highly abstract sketch representations pose challenges for sketch-based image retrieval (\underline{SBIR}).
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
To bridge this gap, we present the Graph Robustness Benchmark (GRB) with the goal of providing a scalable, unified, modular, and reproducible evaluation for the adversarial robustness of GML models.
Neural Embeddings of Urban Big Data Reveal Emergent Structures in Cities
In this study, we propose using a neural embedding model-graph neural network (GNN)- that leverages the heterogeneous features of urban areas and their interactions captured by human mobility network to obtain vector representations of these areas.
Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning
In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency.
Region Semantically Aligned Network for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes.
Semantic Communications With AI Tasks
A radical paradigm shift of wireless networks from ``connected things'' to ``connected intelligence'' undergoes, which coincides with the Shanno and Weaver's envisions: Communications will transform from the technical level to the semantic level.
Few-Shot Classification with Task-Adaptive Semantic Feature Learning
Such approach could lead to information imbalance between support and query samples, which confounds model generalization from support to query samples.
Transformer-based Transform Coding
Neural data compression based on nonlinear transform coding has made great progress over the last few years, mainly due to improvements in prior models, quantization methods and nonlinear transforms.
Extended Successive Convex Approximation for Phase Retrieval with Dictionary Learning
The second algorithm, referred to as SCAphase, uses auxiliary variables and is favorable in the case of highly diverse mixture models.
Boosting Graph Search with Attention Network for Solving the General Orienteering Problem
We propose a novel combination of a variant beam search algorithm and a learned heuristic for solving the general orienteering problem.
Cohort Characteristics and Factors Associated with Cannabis Use among Adolescents in Canada Using Pattern Discovery and Disentanglement Method
COMPASS is a longitudinal, prospective cohort study collecting data annually from students attending high school in jurisdictions across Canada.
Prior-Guided Deep Interference Mitigation for FMCW Radars
The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals.
Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems
First, a MU-MC-VLC system model is established, and then a sum-rate maximization problem under dimming level and illumination uniformity constraints is formulated.
Rethinking the Misalignment Problem in Dense Object Detection
On the basis of SALT and SDR loss, we propose SALT-Net, which explicitly exploits task-aligned point-set features for accurate detection results.
Crypto Wash Trading
We introduce systematic tests exploiting robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges.
RGB Image Classification with Quantum Convolutional Ansaetze
With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest.
PSD: Principled Synthetic-to-Real Dehazing Guided by Physical Priors
Deep learning-based methods have achieved remarkable performance for image dehazing.
Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos
Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation.
Language Scaling for Universal Suggested Replies Model
Faced with increased compute requirements and low resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system.
Objects as Extreme Points
We also propose a new metric to measure the similarity between two groups of extreme points, namely, Extreme Intersection over Union (EIoU), and incorporate this EIoU as a new regression loss.
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport
Moreover, we introduce the extrinsic unlabeled multi-modal multi-instance data, and propose the M3DNS, which considers the instance-level auto-encoder for single modality and modified bag-level optimal transport to strengthen the consistency among modalities.
Attribute-Based Robotic Grasping with One-Grasp Adaptation
In this work, we introduce an end-to-end learning method of attribute-based robotic grasping with one-grasp adaptation capability.
Collision-Aware Target-Driven Object Grasping in Constrained Environments
Grasping a novel target object in constrained environments (e. g., walls, bins, and shelves) requires intensive reasoning about grasp pose reachability to avoid collisions with the surrounding structures.
Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering
To leverage the multi-view complementary information, we concatenate all views' embedded features to form the global features, which can overcome the negative impact of some views' unclear clustering structures.
Toward Tweet Entity Linking with Heterogeneous Information Networks
The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets.
HAMIL: Hierarchical Aggregation-Based Multi-Instance Learning for Microscopy Image Classification
Multi-instance learning is common for computer vision tasks, especially in biomedical image processing.
ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction
Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities.
Optimization of User Selection and Bandwidth Allocation for Federated Learning in VLC/RF Systems
Then, the problem of user selection and bandwidth allocation is studied for FL implemented over a hybrid VLC/RF system aiming to optimize the FL performance.
Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning
Despite tremendous efforts, it is very challenging to generate a robust model to assist in the accurate quantification assessment of COVID-19 on chest CT images.
CogDL: A Toolkit for Deep Learning on Graphs
Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years.
Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces
Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format.
Integrating Pre-trained Model into Rule-based Dialogue Management
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility.
A proof by foliation that Lawson's cones are $A_Φ$-minimizing
We also analyze the behavior at infinity of the leaves in the foliations.
Analysis of PDEs Differential Geometry
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
To further employ the power of quantization, the mixed precision technique is incorporated in our framework by approximating the inter-layer and intra-layer sensitivity.
Progressive Neural Image Compression with Nested Quantization and Latent Ordering
We present PLONQ, a progressive neural image compression scheme which pushes the boundary of variable bitrate compression by allowing quality scalable coding with a single bitstream.
A Unified Light Framework for Real-time Fault Detection of Freight Train Images
Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements.
The hybrid dimensional representation of permeability tensor: a reinterpretation of the discrete fracture model and its extension on nonconforming meshes
The discrete fracture model (DFM) has been widely used in the simulation of fluid flow in fractured porous media.
Numerical Analysis Numerical Analysis
The Hybrid-dimensional Darcy's Law: A Reinterpreted Discrete Fracture Model for Fracture and Barrier Networks on Non-conforming Meshes
In this paper, we extend the reinterpreted discrete fracture model for flow simulation of fractured porous media containing flow blocking barriers on non-conforming meshes.
Numerical Analysis Numerical Analysis
Upper Limits on the Isotropic Gravitational-Wave Background from Advanced LIGO's and Advanced Virgo's Third Observing Run
Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB.
General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics
What social media told about us in the time of COVID-19: a scoping review
With the onset of COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption.
Misinformation
Social and Information Networks
Federated Unlearning
In this paper, we take the first step to fill this gap by presenting FedEraser, the first federated unlearning methodology that can eliminate the influence of a federated client's data on the global FL model while significantly reducing the time used for constructing the unlearned FL model. The basic idea of FedEraser is to trade the central server's storage for unlearned model's construction time, where FedEraser reconstructs the unlearned model by leveraging the historical parameter updates of federated clients that have been retained at the central server during the training process of FL.
PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols
Many proximity-based tracing (PCT) protocols have been proposed and deployed to combat the spreading of COVID-19.
Computers and Society C.3; H.4; J.3; J.7; K.4; K.6.5
Learning to Check Contract Inconsistencies
This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract.
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
To bridge the gap between theoretical graph attacks and real-world scenarios, in this work, we propose a novel and more realistic setting: strict black-box graph attack, in which the attacker has no knowledge about the victim model at all and is not allowed to send any queries.
Denoising-based Turbo Message Passing for Compressed Video Background Subtraction
In this paper, we consider the compressed video background subtraction problem that separates the background and foreground of a video from its compressed measurements.
Improving Knowledge Tracing via Pre-training Question Embeddings
Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response.
Ranked #3 on
Knowledge Tracing
on EdNet
Unsupervised Adversarially-Robust Representation Learning on Graphs
Additionally, we explore a provable connection between the robustness of the unsupervised graph encoder and that of models on downstream tasks.
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task.
Query-aware Tip Generation for Vertical Search
Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios.
Text-Embedded Bilinear Model for Fine-Grained Visual Recognition
Specially, we first conduct a text-embedded network to embed text feature into the discriminative image feature learning to get a embedded feature.
Fine-Grained Image Recognition
Fine-Grained Visual Recognition
+1
Universal Weighting Metric Learning for Cross-Modal Matching
Furthermore, we introduce a new polynomial loss under the universal weighting framework, which defines a weight function for the positive and negative informative pairs respectively.
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
Ranked #1 on
3D Face Reconstruction
on Florence
(Mean NME metric)
AAG: Self-Supervised Representation Learning by Auxiliary Augmentation with GNT-Xent Loss
Here we present a self-supervised representation learning method, namely AAG, which is featured by an auxiliary augmentation strategy and GNT-Xent loss.
GIKT: A Graph-based Interaction Model for Knowledge Tracing
With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.
Ranked #7 on
Knowledge Tracing
on EdNet
Learning Adaptive Embedding Considering Incremental Class
To this end, we propose a Class-Incremental Learning without Forgetting (CILF) framework, which aims to learn adaptive embedding for processing novel class detection and model update in a unified framework.
S2OSC: A Holistic Semi-Supervised Approach for Open Set Classification
Open set classification (OSC) tackles the problem of determining whether the data are in-class or out-of-class during inference, when only provided with a set of in-class examples at training time.