Search Results for author:
Found 38 papers, 15 papers with code
InfuserKI: Enhancing Large Language Models with Knowledge Graphs via Infuser-Guided Knowledge Integration
Though Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains, they struggle with knowledge-intensive tasks.
Uncertainty Quantification for In-Context Learning of Large Language Models
Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.
Open-ended Commonsense Reasoning with Unrestricted Answer Scope
In this work, we leverage pre-trained language models to iteratively retrieve reasoning paths on the external knowledge base, which does not require task-specific supervision.
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning
However, existing research primarily addresses only one type of noise, thereby limiting the effectiveness of noise reduction.
Large Language Models Can Be Good Privacy Protection Learners
To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.
Interpretable Imitation Learning with Dynamic Causal Relations
After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it.
GLAD: Content-aware Dynamic Graphs For Log Anomaly Detection
Subsequently, GLAD utilizes a temporal-attentive graph edge anomaly detection model for identifying anomalous relations in these dynamic log graphs.
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty
Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text, typically in the form of (subject, relation, object) triples.
Ranked #6 on
Open Information Extraction
on OIE2016
Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations
Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations.
Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey
In this article, we present a comprehensive survey on domain specification techniques for large language models, an emerging direction critical for large language model applications.
Disentangled Causal Graph Learning for Online Unsupervised Root Cause Analysis
In this paper, we propose CORAL, a novel online RCA framework that can automatically trigger the RCA process and incrementally update the RCA model.
Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation
Sequential recommendation (SR) aims to model user preferences by capturing behavior patterns from their item historical interaction data.
Uncertainty-Aware Bootstrap Learning for Joint Extraction on Distantly-Supervised Data
To mitigate such impact, we propose uncertainty-aware bootstrap learning, which is motivated by the intuition that the higher uncertainty of an instance, the more likely the model confidence is inconsistent with the ground truths.
Personalized Federated Learning under Mixture of Distributions
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy.
Time Series Contrastive Learning with Information-Aware Augmentations
A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences
It is critical and important to detect anomalies in event sequences, which becomes widely available in many application domains. In-deed, various efforts have been made to capture abnormal patterns from event sequences through sequential pattern analysis or event representation learning. However, existing approaches usually ignore the semantic information of event content. To this end, in this paper, we propose a self-attentive encoder-decoder transformer framework, Content-Aware Transformer(CAT), for anomaly detection in event sequences. In CAT, the encoder learns preamble event sequence representations with content awareness, and the decoder embeds sequences under detection into a latent space, where anomalies are distinguishable. Specifically, the event content is first fed to a content-awareness layer, generating representations of each event. The encoder accepts preamble event representation sequence, generating feature maps. In the decoder, an additional token is added at the beginning of the sequence under detection, denoting the sequence status. A one-class objective together with sequence reconstruction loss is collectively applied to train our framework under the label efficiency scheme. Furthermore, CAT is optimized under a scalable and efficient setting. Finally, extensive experiments on three real-world datasets demonstrate the superiority of CAT.
Contrastive Learning with Bidirectional Transformers for Sequential Recommendation
Contrastive learning with Transformer-based sequence encoder has gained predominance for sequential recommendation.
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation
To this end, in this paper, we propose a Learnable Model Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).
Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification
To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users.
Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification
Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users' dynamic preferences.
Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?
During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstream tasks.
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence.
Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment
Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.
Quaternion-Based Graph Convolution Network for Recommendation
Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings.
Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation
In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning.
Information-Aware Time Series Meta-Contrastive Learning
How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.
Unsupervised Concept Representation Learning for Length-Varying Text Similarity
Measuring document similarity plays an important role in natural language processing tasks.
Unsupervised Document Embedding via Contrastive Augmentation
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications.
T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce
Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.
Inductive and Unsupervised Representation Learning on Graph Structured Objects
Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.
Exploiting Interpretable Patterns for Flow Prediction in Dockless Bike Sharing Systems
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility.
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.
Semi-Supervised Hierarchical Recurrent Graph Neural Network for City-Wide Parking Availability Prediction
However, it is a non-trivial task for predicting citywide parking availability because of three major challenges: 1) the non-Euclidean spatial autocorrelation among parking lots, 2) the dynamic temporal autocorrelation inside of and between parking lots, and 3) the scarcity of information about real-time parking availability obtained from real-time sensors (e. g., camera, ultrasonic sensor, and GPS).
Deep Cross Networks with Aesthetic Preference for Cross-domain Recommendation
Then, we integrate the aesthetic features into a cross-domain network to transfer users' domain independent aesthetic preferences.
Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation
Furthermore, to reduce the number of parameters and improve efficiency, we further integrate coupled input and forget gates with our proposed model.
