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Found 78 papers, 31 papers with code
Ruleformer: Context-aware Rule Mining over Knowledge Graph
Based on this idea, we propose a transformer-based rule mining approach, Ruleformer.
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?
Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages.
RISs and Sidelink Communications in Smart Cities: The Key to Seamless Localization and Sensing
A smart city involves, among other elements, intelligent transportation, crowd monitoring, and digital twins, each of which requires information exchange via wireless communication links and localization of connected devices and passive objects (including people).
Modeling and Analysis of 6G Joint Localization and Communication under Hardware Impairments
Complementary to the localization analysis, we also investigate the effect of individual and overall HWIs on communication in terms of symbol error rate (SER).
RIS-Aided Radar Sensing and Object Detection with Single and Double Bounce Multipath
We propose a framework for monostatic sensing by a user equipment (UE), aided by a reconfigurable intelligent surface (RIS) in environments with single- and double-bounce signal propagation.
RIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mapping
In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces~(RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation.
Joint RIS Calibration and Multi-User Positioning
Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform.
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition
Modeling the speaker variability is a key challenge for automatic speech recognition (ASR) systems.
Misspecified Cramér-Rao Bound of RIS-aided Localization under Geometry Mismatch
In 5G/6G wireless systems, reconfigurable intelligent surfaces (RIS) can play a role as a passive anchor to enable and enhance localization in various scenarios.
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection
To pick up the ground plane prior for M3OD, we propose a Ground Plane Enhanced Network (GPENet) which resolves both issues at one go.
Positioning and Sensing in 6G: Gaps, Challenges, and Opportunities
Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio based positioning and sensing.
MM-Align: Learning Optimal Transport-based Alignment Dynamics for Fast and Accurate Inference on Missing Modality Sequences
Existing multimodal tasks mostly target at the complete input modality setting, i. e., each modality is either complete or completely missing in both training and test sets.
Generative Knowledge Graph Construction: A Review
Our contributions are threefold: (1) We present a detailed, complete taxonomy for the generative KGC methods; (2) We provide a theoretical and empirical analysis of the generative KGC methods; (3) We propose several research directions that can be developed in the future.
SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Training
Self-training methods have been explored in recent years and have exhibited great performance in improving semi-supervised learning.
Joint User Localization and Location Calibration of A Hybrid Reconfigurable Intelligent Surface
The recent research in the emerging technology of reconfigurable intelligent surfaces (RISs) has identified its high potential for localization and sensing.
Localization Coverage Analysis of THz Communication Systems with a 3D Array
This paper considers the problem of estimating the position and orientation of a user equipped with a three-dimensional (3D) array receiving downlink far-field THz signals from multiple base stations with known positions and orientations.
Neural-Symbolic Entangled Framework for Complex Query Answering
In this work, we propose a Neural and Symbolic Entangled framework (ENeSy) for complex query answering, which enables the neural and symbolic reasoning to enhance each other to alleviate the cascading error and KG incompleteness.
Detecting Network-based Internet Censorship via Latent Feature Representation Learning
To estimate the probability of censorship events from the inferred latent features, we rely on a densely connected multi-layer neural network model.
SANCL: Multimodal Review Helpfulness Prediction with Selective Attention and Natural Contrastive Learning
With the boom of e-commerce, Multimodal Review Helpfulness Prediction (MRHP), which aims to sort product reviews according to the predicted helpfulness scores has become a research hotspot.
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification
This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification.
You Only Search Once: On Lightweight Differentiable Architecture Search for Resource-Constrained Embedded Platforms
Benefiting from the search efficiency, differentiable neural architecture search (NAS) has evolved as the most dominant alternative to automatically design competitive deep neural networks (DNNs).
Doppler Exploitation in Bistatic mmWave Radio SLAM
Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as simultaneous localization and mapping (SLAM).
Bi-Static Sensing for Near-Field RIS Localization
Due to the deployment of RISs with a large dimension, near-field (NF) scenarios are likely to occur, especially for indoor applications, and are the focus of this work.
Channel Model Mismatch Analysis for XL-MIMO Systems from a Localization Perspective
Radio localization is applied in high-frequency (e. g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure.
Doppler-Enabled Single-Antenna Localization and Mapping Without Synchronization
With the help of multipath components (MPCs), localization and mapping tasks can be done with a single base station (BS) and single unsynchronized user equipment (UE) if both of them are equipped with an antenna array.
6G Radio Requirements to Support Integrated Communication, Localization, and Sensing
6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing.
Antenna Selection in Switch-Based MIMO Arrays via DOA threshold region Approximation
We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set.
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce
In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation.
Bridging the Gap between Reality and Ideality of Entity Matching: A Revisiting and Benchmark Re-Construction
Experimental results demonstrate that the assumptions made in the previous benchmark construction process are not coincidental with the open environment, which conceal the main challenges of the task and therefore significantly overestimate the current progress of entity matching.
MCRB-based Performance Analysis of 6G Localization under Hardware Impairments
Location information is expected to be the key to meeting the needs of communication and context-aware services in 6G systems.
Quantity over Quality: Training an AV Motion Planner with Large Scale Commodity Vision Data
This motivates the use of commodity sensors like cameras for data collection, which are an order of magnitude cheaper than HD sensor suites, but offer lower fidelity.
From Discrimination to Generation: Knowledge Graph Completion with Generative Transformer
Knowledge graph completion aims to address the problem of extending a KG with missing triples.
Ranked #43 on
Link Prediction
on FB15k-237
Ontology-enhanced Prompt-tuning for Few-shot Learning
Specifically, we develop the ontology transformation based on the external knowledge graph to address the knowledge missing issue, which fulfills and converts structure knowledge to text.
FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks
Quantization is applied to reduce the latency and storage cost of CNNs.
Constrained Wrapped Least Squares: A Tool for High Accuracy GNSS Attitude Determination
Attitude determination is a popular application of Global Navigation Satellite Systems (GNSS).
Ghost Imaging Based on Recurrent Neural Network
The testing results show that the proposed method can achieve image reconstruction at a very low sampling rate (0. 38$\%$).
A Tutorial on Terahertz-Band Localization for 6G Communication Systems
Terahertz (THz) communications are celebrated as key enablers for converged localization and sensing in future sixth-generation (6G) wireless communication systems and beyond.
Multi-trends Enhanced Dynamic Micro-video Recommendation
In this paper, we propose the DMR framework to explicitly model dynamic multi-trends of users' current preference and make predictions based on both the history and future potential trends.
Learning to Ask for Data-Efficient Event Argument Extraction
Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles.
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
In this work, we propose a framework named MultiModal InfoMax (MMIM), which hierarchically maximizes the Mutual Information (MI) in unimodal input pairs (inter-modality) and between multimodal fusion result and unimodal input in order to maintain task-related information through multimodal fusion.
Ranked #4 on
Multimodal Sentiment Analysis
on CMU-MOSI
Knowledge Perceived Multi-modal Pretraining in E-commerce
In this paper, we address multi-modal pretraining of product data in the field of E-commerce.
0.8% Nyquist computational ghost imaging via non-experimental deep learning
We present a framework for computational ghost imaging based on deep learning and customized pink noise speckle patterns.
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment Analysis
Multimodal sentiment analysis aims to extract and integrate semantic information collected from multiple modalities to recognize the expressed emotions and sentiment in multimodal data.
A Game-Theoretic Approach to Multi-Agent Trust Region Optimization
Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration.
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an essential role in semantic search.
OntoED: Low-resource Event Detection with Ontology Embedding
Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types.
Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph
We first construct a billion-scale conversation knowledge graph (CKG) from information about users, items and conversations, and then pretrain CKG by introducing knowledge graph embedding method and graph convolution network to encode semantic and structural information respectively. To make the CTR prediction model sensible of current state of users and the relationship between dialogues and items, we introduce user-state and dialogue-interaction representations based on pre-trained CKG and propose K-DCN. In K-DCN, we fuse the user-state representation, dialogue-interaction representation and other normal feature representations via deep cross network, which will give the rank of candidate items to be recommended. We experimentally prove that our proposal significantly outperforms baselines and show it's real application in Alime.
Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging
Ghost imaging (GI) has been paid attention gradually because of its lens-less imaging capability, turbulence-free imaging and high detection sensitivity.
Text-guided Legal Knowledge Graph Reasoning
In this paper, we propose a novel legal application of legal provision prediction (LPP), which aims to predict the related legal provisions of affairs.
Generative-Adversarial-Networks-based Ghost Recognition
The proposed method can improve the problems caused by conventional recognition methods that based on target image information, and provide a certain turbulence-free ability.
Learning to Shape Rewards using a Game of Two Partners
Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards.
Deep Structural Estimation: With an Application to Option Pricing
We propose a novel structural estimation framework in which we train a surrogate of an economic model with deep neural networks.
DOA Estimation with Non-Uniform Linear Arrays: A Phase-Difference Projection Approach
The concept of \emph{wrapped phased-difference pattern} (WPDP) is introduced, which allows the proposed algorithm to compute most of the parameters required for DOA estimation in an offline manner, hence resulting in a superior computational speed in realtime.
Multi-Agent Trust Region Learning
We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.
Contrastive Coding for Active Learning Under Class Distribution Mismatch
However, its performance deteriorates under class distribution mismatch, wherein the unlabeled data contain many samples out of the class distribution of labeled data.
Physical rendering of synthetic spaces for topological sound transport
Synthetic dimensions can be rendered in the physical space and this has been achieved with photonics and cold atomic gases, however, little to no work has been succeeded in acoustics because acoustic wave-guides cannot be weakly coupled in a continuous fashion.
Mesoscale and Nanoscale Physics Classical Physics
Order of magnitude increase in laser-target coupling at near-relativistic intensities using compound parabolic concentrators
Achieving a high conversion efficiency into relativistic electrons is central to short-pulse laser application and fundamentally relies on creating interaction regions with intensities ${\gg}10^{18}$~W/cm$^2$.
Plasma Physics
Persuasive Dialogue Understanding: the Baselines and Negative Results
Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies.
Learning to Infer Unseen Attribute-Object Compositions
The composition recognition of unseen attribute-object is critical to make machines learn to decompose and compose complex concepts like people.
Decision-Aware Conditional GANs for Time Series Data
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation.
DualDE: Dually Distilling Knowledge Graph Embedding for Faster and Cheaper Reasoning
In DualDE, we propose a soft label evaluation mechanism to adaptively assign different soft label and hard label weights to different triples, and a two-stage distillation approach to improve the student's acceptance of the teacher.
Dialogue Relation Extraction with Document-level Heterogeneous Graph Attention Networks
This graph is fed to a graph attention network for context propagation among relevant nodes, which effectively captures the dialogue context.
Ranked #7 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
FLFE: A Communication-Efficient and Privacy-Preserving Federated Feature Engineering Framework
Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques and is a key step to improve the performance of machine learning algorithms.
A Derivation of Identifiable Condition for Non-Uniform Linear Array DOA Estimation
Phase ambiguity happens in uniform linear arrays (ULAs) when the sensor distance is greater than $\lambda/2$.
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 Natural Language Processing Pipeline of Chinese Free-text Radiology Reports for Liver Cancer Diagnosis
Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports.
IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Existing methods leverage the attention mechanism to explore such correspondence in a fine-grained manner.
Ranked #14 on
Cross-Modal Retrieval
on Flickr30k
BEYOND SUPERVISED LEARNING: RECOGNIZING UNSEEN ATTRIBUTE-OBJECT PAIRS WITH VISION-LANGUAGE FUSION AND ATTRACTOR NETWORKS
This paper handles a challenging problem, unseen attribute-object pair recognition, which asks a model to simultaneously recognize the attribute type and the object type of a given image while this attribute-object pair is not included in the training set.
An Iterative Polishing Framework based on Quality Aware Masked Language Model for Chinese Poetry Generation
In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation.
Enhanced Meta-Learning for Cross-lingual Named Entity Recognition with Minimal Resources
For languages with no annotated resources, transferring knowledge from rich-resource languages is an effective solution for named entity recognition (NER).
Ranked #1 on
Cross-Lingual NER
on MSRA
UER: An Open-Source Toolkit for Pre-training Models
Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks.
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance.
GRN: Gated Relation Network to Enhance Convolutional Neural Network for Named Entity Recognition
The dominant approaches for named entity recognition (NER) mostly adopt complex recurrent neural networks (RNN), e. g., long-short-term-memory (LSTM).
Ranked #22 on
Named Entity Recognition
on Ontonotes v5 (English)
A new Majorana platform in an Fe-As bilayer superconductor
On the other hand, some iron-pnictide (Fe-As) superconductors, such as LiFeAs, have been demonstrated to have a similar topological band structure, yet no MZM has been observed in its vortex cores, raising a question of universality of MZM presence in iron-based superconductors.
Superconductivity Strongly Correlated Electrons
A Variational Approach for Learning from Positive and Unlabeled Data
Learning binary classifiers only from positive and unlabeled (PU) data is an important and challenging task in many real-world applications, including web text classification, disease gene identification and fraud detection, where negative samples are difficult to verify experimentally.
Computer-aided diagnosis of lung carcinoma using deep learning - a pilot study
Aim: Early detection and correct diagnosis of lung cancer are the most important steps in improving patient outcome.
Geometry-Contrastive GAN for Facial Expression Transfer
Geometry information is introduced into cGANs as continuous conditions to guide the generation of facial expressions.
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.
