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Found 130 papers, 67 papers with code
Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training
Most existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
Ruleformer: Context-aware Rule Mining over Knowledge Graph
Based on this idea, we propose a transformer-based rule mining approach, Ruleformer.
The Xiaomi Text-to-Text Simultaneous Speech Translation System for IWSLT 2022
Our system is built based on the Transformer model with novel techniques borrowed from our recent research work.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Tencent Translation System for the WMT21 News Translation Task
Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.
Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation
Recent studies on end-to-end speech translation(ST) have facilitated the exploration of multilingual end-to-end ST and end-to-end simultaneous ST.
Cross-Layer-Optimized Link Selection for Hologram Video Streaming over Millimeter Wave Networks
However, the existing link selection schemes in MC-based mmWave networks neglect the source content characteristics of hologram videos and the coordination among the parameters of different protocol layers in each link, leading to sub-optimal streaming performance.
Human Decision-making is Susceptible to AI-driven Manipulation
Through a randomized controlled trial with 233 participants, we examined human susceptibility to such manipulation in financial (e. g., purchases) and emotional (e. g., conflict resolution) decision-making contexts.
K-ON: Stacking Knowledge On the Head Layer of Large Language Model
Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks.
OntoTune: Ontology-Driven Self-training for Aligning Large Language Models
Compared to existing domain LLMs based on newly collected large-scale domain-specific corpora, our OntoTune, which relies on the existing, long-term developed ontology and LLM itself, significantly reduces data maintenance costs and offers improved generalization ability.
Have We Designed Generalizable Structural Knowledge Promptings? Systematic Evaluation and Rethinking
Large language models (LLMs) have demonstrated exceptional performance in text generation within current NLP research.
Is Large Language Model Good at Triple Set Prediction? An Empirical Study
Thus in this paper we proposed a new framework to explore the strengths and limitations of LLM in the TSP task.
UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction
Experimental results across 7 datasets from 3 types of KGs demonstrate that our UniHR outperforms baselines designed for one specific kind of KG, indicating strong generalization capability of HiDR form and the effectiveness of HiSL module.
A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction
Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance.
MKGL: Mastery of a Three-Word Language
Large language models (LLMs) have significantly advanced performance across a spectrum of natural language processing (NLP) tasks.
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications.
Open-World Electrocardiogram Classification via Domain Knowledge-Driven Contrastive Learning
Overall, the investigated problem (i. e., open-world ECG classification) helps to draw attention to the reliability of automatic ECG diagnosis, and the proposed method is proven effective in tackling the challenges.
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network
In SF-GNN, we define two representations for each node, one is the node representation that represents the feature of the node itself, and the other is the message representation specifically for propagating messages to neighbor nodes.
Croppable Knowledge Graph Embedding
Knowledge Graph Embedding (KGE) is a common method for Knowledge Graphs (KGs) to serve various artificial intelligence tasks.
ZeroDDI: A Zero-Shot Drug-Drug Interaction Event Prediction Method with Semantic Enhanced Learning and Dual-Modal Uniform Alignment
Drug-drug interactions (DDIs) can result in various pharmacological changes, which can be categorized into different classes known as DDI events (DDIEs).
Heterogeneous Causal Metapath Graph Neural Network for Gene-Microbe-Disease Association Prediction
HCMGNN constructs a heterogeneous graph linking genes, microbes, and diseases through their pairwise associations, and utilizes six predefined causal metapaths to extract directed causal subgraphs, which facilitate the multi-view analysis of causal relations among three entity types.
TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
Further more, we have demonstrated the potential of our method for more general QA tasks, QA over mixed structured data and QA across structured data.
Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion
In this paper, we propose a novel graph-level automatic KG completion task called Triple Set Prediction (TSP) which assumes none of the elements in the missing triples is given.
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs
LLMs fine-tuned with this data have improved planning capabilities, better equipping them to handle complex QA tasks that involve retrieval.
Liberal Entity Matching as a Compound AI Toolchain
Entity matching (EM), the task of identifying whether two descriptions refer to the same entity, is essential in data management.
Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation
Additionally, the ablation studies and visualizations demonstrate the superiority of MIPE owing to the better representations acquired through multi-modal contrastive learning and the interaction patterns comprehended by the interaction informativeness estimation.
Ranked #2 on
Antibody-antigen binding prediction
on MIPE
Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning
Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC).
Multi-domain Knowledge Graph Collaborative Pre-training and Prompt Tuning for Diverse Downstream Tasks
In response to the lack of open-source benchmarks, we constructed a new multi-domain KGP benchmark called KPI with two large-scale KGs and six different sub-domain tasks to evaluate our method and open-sourced it for subsequent research.
Coefficient Decomposition for Spectral Graph Convolution
Then, we show that the convolution block in existing SGCNs can be derived by performing a certain coefficient decomposition operation on the coefficient tensor.
Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation
To further augment the multi-modal representations, MyGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations.
InBox: Recommendation with Knowledge Graph using Interest Box Embedding
This homogenization limits the precise exploitation of knowledge graph data and interest connectivity.
Prompt-fused framework for Inductive Logical Query Answering
Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning.
Noise-powered Multi-modal Knowledge Graph Representation Framework
In this work, we explore the efficacy of models in accurately embedding entities within MMKGs through two pivotal tasks: Multi-modal Knowledge Graph Completion (MKGC) and Multi-modal Entity Alignment (MMEA).
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
To address the mentioned problems, we propose Adaptive Multi-modal Fusion and Modality Adversarial Training (AdaMF-MAT) to unleash the power of imbalanced modality information for MMKGC.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm.
Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs
To stimulate the LLMs' reasoning ability, the chain-of-thought (CoT) prompting method is widely used, but existing methods still have some limitations in our scenario: (1) Previous methods either use simple "Let's think step by step" spells or provide fixed examples in demonstrations without considering compatibility between prompts and concrete questions, making LLMs ineffective when the marketers' demands are abstract and diverse.
Improving PTM Site Prediction by Coupling of Multi-Granularity Structure and Multi-Scale Sequence Representation
Specifically, multigranularity structure-aware representation learning is designed to learn neighborhood structure representations at the amino acid, atom, and whole protein granularity from AlphaFold predicted structures, followed by utilizing contrastive learning to optimize the structure representations. Additionally, multi-scale sequence representation learning is used to extract context sequence information, and motif generated by aligning all context sequences of PTM sites assists the prediction.
A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generation
Therapeutic peptides represent a unique class of pharmaceutical agents crucial for the treatment of human diseases.
Prompting Disentangled Embeddings for Knowledge Graph Completion with Pre-trained Language Model
Accordingly, we propose a new KGC method named PDKGC with two prompts -- a hard task prompt which is to adapt the KGC task to the PLM pre-training task of token prediction, and a disentangled structure prompt which learns disentangled graph representation so as to enable the PLM to combine more relevant structure knowledge with the text information.
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especially in ensuring that responses are both accommodating to user requirements and appropriately leveraging domain-specific knowledge bases.
Sound field reconstruction using neural processes with dynamic kernels
Accurately representing the sound field with the high spatial resolution is critical for immersive and interactive sound field reproduction technology.
CBSiMT: Mitigating Hallucination in Simultaneous Machine Translation with Weighted Prefix-to-Prefix Training
Simultaneous machine translation (SiMT) is a challenging task that requires starting translation before the full source sentence is available.
Reliable Academic Conference Question Answering: A Study Based on Large Language Model
Specifically, for each conference, we first organize academic conference data in a tree-structured format through a semi-automated method.
Negative Sampling with Adaptive Denoising Mixup for Knowledge Graph Embedding
Most existing negative sampling methods assume that non-existent triples with high scores are high-quality negative triples.
Making Large Language Models Perform Better in Knowledge Graph Completion
In this paper, we explore methods to incorporate structural information into the LLMs, with the overarching goal of facilitating structure-aware reasoning.
A Comprehensive Study on Knowledge Graph Embedding over Relational Patterns Based on Rule Learning
Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern.
MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion
Nevertheless, existing methods emphasize the design of elegant KGC models to facilitate modality interaction, neglecting the real-life problem of missing modalities in KGs.
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm.
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to identify identical entities across disparate knowledge graphs (KGs) by exploiting associated visual information.
Ranked #1 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v2
(using extra training data)
CausE: Towards Causal Knowledge Graph Embedding
Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC).
Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).
Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development.
Towards Better Entity Linking with Multi-View Enhanced Distillation
Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders.
Exploring Better Text Image Translation with Multimodal Codebook
Text image translation (TIT) aims to translate the source texts embedded in the image to target translations, which has a wide range of applications and thus has important research value.
Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i. e., generating new entities).
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations.
NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning
Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph representation learning (KGRL) works have been proposed in recent years, focusing on enabling prediction over new entities.
Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding
Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training.
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.
Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation
Simultaneous machine translation (SimulMT) models start translation before the end of the source sentence, making the translation monotonically aligned with the source sentence.
Entity-Agnostic Representation Learning for Parameter-Efficient Knowledge Graph Embedding
In our proposed model, Entity-Agnostic Representation Learning (EARL), we only learn the embeddings for a small set of entities and refer to them as reserved entities.
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
In this paper, we use a set of general terminologies to unify these methods and refer to them collectively as Knowledge Extrapolation.
Analogical Inference Enhanced Knowledge Graph Embedding
And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.
Ranked #1 on
Entity Alignment
on FBYG15k
(using extra training data)
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
Relational Message Passing for Fully Inductive Knowledge Graph Completion
Subgraph reasoning with message passing is a promising and popular solution.
Chatbots for Mental Health Support: Exploring the Impact of Emohaa on Reducing Mental Distress in China
In this study, we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.
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.
Knowledge Graph Completion with Pre-trained Multimodal Transformer and Twins Negative Sampling
Twins negative sampling is suitable for multimodal scenarios and could align different embeddings for entities.
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge.
Ranked #22 on
Visual Question Answering (VQA)
on OK-VQA
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.
Ranked #2 on
Zero-Shot Learning
on CUB-200-2011
Disentangled Ontology Embedding for Zero-shot Learning
In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
We study the knowledge extrapolation problem to embed new components (i. e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting.
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinity
The identification of drug-target binding affinity (DTA) has attracted increasing attention in the drug discovery process due to the more specific interpretation than binary interaction prediction.
PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application
On the one hand, it could provide item knowledge services in a uniform way with service vectors for embedding-based and item-knowledge-related task models without accessing triple data.
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs.
Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective
Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry.
Joint-optimization of Node placement and UAV's Trajectory for Self-sustaining Air-Ground IoT system
Due to the sustainable power supply and environment-friendly features, self-powered IoT devices have been increasingly employed in various fields such as providing observation data in remote areas, especially in rural areas or post-disaster scenarios.
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
How Good Is Aesthetic Ability of a Fashion Model?
To date, it is the first work to address the AI model's aesthetic ability with detailed characterization based on the professional fashion domain knowledge.
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
HampDTI: a heterogeneous graph automatic meta-path learning method for drug-target interaction prediction
More importantly, HampDTI identifies the important meta-paths for DTI prediction, which could explain how drugs connect with targets in HNs.
Knowledge Graph Embedding in E-commerce Applications: Attentive Reasoning, Explanations, and Transferable Rules
We first identity three important desiderata for e-commerce KG systems: 1) attentive reasoning, reasoning over a few target relations of more concerns instead of all; 2) explanation, providing explanations for a prediction to help both users and business operators understand why the prediction is made; 3) transferable rules, generating reusable rules to accelerate the deployment of a KG to new systems.
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs.
Molecular Contrastive Learning with Chemical Element Knowledge Graph
To address these issues, we construct a Chemical Element Knowledge Graph (KG) to summarize microscopic associations between elements and propose a novel Knowledge-enhanced Contrastive Learning (KCL) framework for molecular representation learning.
Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding
In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce entity embeddings.
Explaining Knowledge Graph Embedding via Latent Rule Learning
KGExplainer is a multi-hop reasoner learning latent rules for link prediction and is encouraged to behave similarly to KGEs during prediction through knowledge distillation.
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.
Billion-scale Pre-trained E-commerce Product Knowledge Graph Model
As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation.
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.
Click-Through Rate Prediction
Conversational Recommendation
+3
EPIHC: Improving Enhancer-Promoter Interaction Prediction by using Hybrid features and Communicative learning
The computational experiments show that EPIHC outperforms the existing state-of-the-art EPI prediction methods on the benchmark datasets and chromosome-split datasets, and the study reveal that the communicative learning module can bring explicit information about EPIs, which is ignore by CNN.
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings
Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner.
Towards Playing Full MOBA Games with Deep Reinforcement Learning
However, existing work falls short in handling the raw game complexity caused by the explosion of agent combinations, i. e., lineups, when expanding the hero pool in case that OpenAI's Dota AI limits the play to a pool of only 17 heroes.
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
To recognize unseen relations at test time, we explore the problem of zero-shot relation classification.
FedE: Embedding Knowledge Graphs in Federated Setting
Knowledge graphs (KGs) consisting of triples are always incomplete, so it's important to do Knowledge Graph Completion (KGC) by predicting missing triples.
Distributed Representations of Entities in Open-World Knowledge Graphs
DAN leverages neighbor context as the query vector to score the neighbors of an entity, thereby distributing the entity semantics only among its neighbor embeddings.
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.
A Survey on Negative Transfer
Transfer learning (TL) utilizes data or knowledge from one or more source domains to facilitate the learning in a target domain.
Deep Representation Learning For Multimodal Brain Networks
The higher-order network mappings from brain structural networks to functional networks are learned in the node domain.
Neural Entity Summarization with Joint Encoding and Weak Supervision
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.
Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation
Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the conditional distribution discrepancy; however, a more natural metric may be their joint probability distribution discrepancy.
Modeling Fluency and Faithfulness for Diverse Neural Machine Translation
Neural machine translation models usually adopt the teacher forcing strategy for training which requires the predicted sequence matches ground truth word by word and forces the probability of each prediction to approach a 0-1 distribution.
Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations
To this end, we innovatively represent miRNA-disease-type triplets as a tensor and introduce Tensor Decomposition methods to solve the prediction task.
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produces an unbalanced translation.
ItLnc-BXE: a Bagging-XGBoost-ensemble method with multiple features for identification of plant lncRNAs
However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identification.
Redistribution Mechanism on Networks
Redistribution mechanisms have been proposed for more efficient resource allocation but not for profit.
Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces
Experiments on four EEG datasets from two different BCI paradigms demonstrated that MEKT outperformed several state-of-the-art transfer learning approaches, and DTE can reduce more than half of the computational cost when the number of source subjects is large, with little sacrifice of classification accuracy.
Geometric Brain Surface Network For Brain Cortical Parcellation
Our model is a two-stage deep network which contains a coarse parcellation network with a U-shape structure and a refinement network to fine-tune the coarse results.
Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples.
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
Bridging the Gap between Training and Inference for Neural Machine Translation
Neural Machine Translation (NMT) generates target words sequentially in the way of predicting the next word conditioned on the context words.
Neural Learning of Online Consumer Credit Risk
This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase.
Fixed-price Diffusion Mechanism Design
We consider a fixed-price mechanism design setting where a seller sells one item via a social network, but the seller can only directly communicate with her neighbours initially.
Collaborative Data Acquisition
We consider a requester who acquires a set of data (e. g. images) that is not owned by one party.
Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning
We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently.
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for entities and relations, and is proven to be effective in many applications.
Graph Neural Networks for User Identity Linkage
In particular, we provide a principled approach to jointly capture local and global information in the user-user social graph and propose the framework {\m}, which jointly learning user representations for user identity linkage.
End-to-End Model for Speech Enhancement by Consistent Spectrogram Masking
The experiments comparing ourCSM based end-to-end model with other methods are conductedto confirm that the CSM accelerate the model training andhave significant improvements in speech quality.
Regularized Wasserstein Means for Aligning Distributional Data
We propose to align distributional data from the perspective of Wasserstein means.
Label-Free Distant Supervision for Relation Extraction via Knowledge Graph Embedding
This paper proposes a label-free distant supervision method, which makes no use of the relation labels under this inadequate assumption, but only uses the prior knowledge derived from the KG to supervise the learning of the classifier directly and softly.
Speeding Up Neural Machine Translation Decoding by Cube Pruning
Although neural machine translation has achieved promising results, it suffers from slow translation speed.
Variational Wasserstein Clustering
We propose a new clustering method based on optimal transportation.
Refining Source Representations with Relation Networks for Neural Machine Translation
Although neural machine translation with the encoder-decoder framework has achieved great success recently, it still suffers drawbacks of forgetting distant information, which is an inherent disadvantage of recurrent neural network structure, and disregarding relationship between source words during encoding step.
An Optimal Transportation Based Univariate Neuroimaging Index
We compute the OT from each image to a template and measure the Wasserstein distance between them.
Refining Source Representations with Relation Networks for Neural Machine Translation
Although neural machine translation (NMT) with the encoder-decoder framework has achieved great success in recent times, it still suffers from some drawbacks: RNNs tend to forget old information which is often useful and the encoder only operates through words without considering word relationship.
Information-Propogation-Enhanced Neural Machine Translation by Relation Model
Even though sequence-to-sequence neural machine translation (NMT) model have achieved state-of-art performance in the recent fewer years, but it is widely concerned that the recurrent neural network (RNN) units are very hard to capture the long-distance state information, which means RNN can hardly find the feature with long term dependency as the sequence becomes longer.
Towards Evidence-Based Ontology for Supporting Systematic Literature Review
[Conclusions]: The approach of using ontology could effectively and efficiently support the conducting of systematic literature review.
Intrinsic Light Field Images
We present a method to automatically decompose a light field into its intrinsic shading and albedo components.
Shape Analysis With Hyperbolic Wasserstein Distance
Experimental results demonstrate that our method may be used as an effective shape index, which outperforms some other standard shape measures in our AD versus healthy control classification study.
