Search Results for author:
Found 135 papers, 82 papers with code
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese
We investigate the usage of entity linking (EL)in downstream tasks and present the first modularized EL toolkit for easy task adaptation.
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks
Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training.
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation
We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge.
Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit
Name disambiguation -- a fundamental problem in online academic systems -- is now facing greater challenges with the increasing growth of research papers.
Scaling laws for single-agent reinforcement learning
Recent work has shown that, in generative modeling, cross-entropy loss improves smoothly with model size and training compute, following a power law plus constant scaling law.
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution
In the fine area, we use an Intra-Patch Self-Attention (IPSA) module to model long-range pixel dependencies in a local patch, and then a $3\times3$ convolution is applied to process the finest details.
AICOM-MP: an AI-based Monkeypox Detector for Resource-Constrained Environments
Also, through the AICOM-MP project, we have generalized a methodology of developing health AI technologies for AMCs to allow universal access even in resource-constrained environments.
Parameter-Efficient Tuning Makes a Good Classification Head
In this paper, we find that parameter-efficient tuning makes a good classification head, with which we can simply replace the randomly initialized heads for a stable performance gain.
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on
Language Modelling
on CLUE (CMRC2018)
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
In this work, we present the Knowledge Graph Transformer (kgTransformer) with masked pre-training and fine-tuning strategies.
Towards a General Pre-training Framework for Adaptive Learning in MOOCs
Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations.
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
By updating only 0. 1% of the model parameters, the prompt tuning strategy can help retrieval models achieve better generalization performance than traditional methods in which all parameters are updated.
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities.
GACT: Activation Compressed Training for Generic Network Architectures
Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint.
Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge
In this paper, we apply Masked Autoencoders to improve algorithm performance on the GEBD tasks.
CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers
Large-scale pretrained transformers have created milestones in text (GPT-3) and text-to-image (DALL-E and CogView) generation.
Ranked #7 on
Video Generation
on UCF-101
Rethinking the Setting of Semi-supervised Learning on Graphs
graph benchmark (IGB) consisting of 4 datasets.
GraphMAE: Self-Supervised Masked Graph Autoencoders
Despite this, contrastive learning-which heavily relies on structural data augmentation and complicated training strategies-has been the dominant approach in graph SSL, while the progress of generative SSL on graphs, especially graph autoencoders (GAEs), has thus far not reached the potential as promised in other fields.
Ranked #1 on
Node Classification
on Cora: fixed 20 node per class
DeepStruct: Pretraining of Language Models for Structure Prediction
We introduce a method for improving the structural understanding abilities of language models.
Ranked #1 on
Relation Extraction
on TACRED
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers
The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images.
Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
Besides, FMEN-S achieves the lowest memory consumption and the second shortest runtime in NTIRE 2022 challenge on efficient super-resolution.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models
The results show that WuDaoMM can be applied as an efficient dataset for VLPMs, especially for the model in text-to-image generation task.
EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training
Large-scale pre-training has shown remarkable performance in building open-domain dialogue systems.
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper.
GRAND+: Scalable Graph Random Neural Networks
In this work, we present a scalable and high-performance GNN framework GRAND+ for semi-supervised graph learning.
Ranked #1 on
Node Classification
on MAG-scholar-C
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks
Specifically, we first formulate the concept recommendation in MOOCs as a reinforcement learning problem to better model the dynamic interaction among users and knowledge concepts.
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs
We present SelfKG with efficient strategies to optimize this objective for aligning entities without label supervision.
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering
Recent works on knowledge base question answering (KBQA) retrieve subgraphs for easier reasoning.
Training Free Graph Neural Networks for Graph Matching
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution without training (training-free).
BodyGAN: General-Purpose Controllable Neural Human Body Generation
This is because current methods mainly rely on a single pose/appearance model, which is limited in disentangling various poses and appearance in human images.
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understanding of their advancements.
SCR: Training Graph Neural Networks with Consistency Regularization
However, it is unclear how to best design the generalization strategies in GNNs, as it works in a semi-supervised setting for graph data.
Ranked #3 on
Node Property Prediction
on ogbn-papers100M
A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems
To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further.
Adaptive Diffusion in Graph Neural Networks
Notably, message passing based GNNs, e. g., graph convolutional networks, leverage the immediate neighbors of each node during the aggregation process, and recently, graph diffusion convolution (GDC) is proposed to expand the propagation neighborhood by leveraging generalized graph diffusion.
AdaDM: Enabling Normalization for Image Super-Resolution
We found that the standard deviation of the residual feature shrinks a lot after normalization layers, which causes the performance degradation in SR networks.
Network representation learning: A macro and micro view
Representation learning can facilitate the design of new algorithms on the graph data.
Calculating Question Similarity is Enough: A New Method for KBQA Tasks
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base.
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.
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training.
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
The few-shot natural language understanding (NLU) task has attracted much recent attention.
Zero-Shot Information Extraction as a Unified Text-to-Triple Translation
We cast a suite of information extraction tasks into a text-to-triple translation framework.
Ranked #1 on
Open Information Extraction
on OIE2016
(using extra training data)
Graph Contrastive Learning for Anomaly Detection
To achieve the contrastive objective, we design a graph neural network encoder that can infer and further remove suspicious links during message passing, as well as learn the global context of the input graph.
Modeling Protein Using Large-scale Pretrain Language Model
The emergence of deep learning models makes modeling data patterns in large quantities of data possible.
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning
Most previous methods for text data augmentation are limited to simple tasks and weak baselines.
EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training
Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.
Evaluating Large Language Models Trained on Code
We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities.
Ranked #1 on
Multi-task Language Understanding
on BBH-alg
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks.
Turing Award elites revisited: patterns of productivity, collaboration, authorship and impact
The Turing Award is recognized as the most influential and prestigious award in the field of computer science(CS).
Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions
Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications.
A Self-supervised Method for Entity Alignment
We present SelfKG by leveraging this discovery to design a contrastive learning strategy across two KGs.
Pre-Trained Models: Past, Present and Future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
TDGIA:Effective Injection Attacks on Graph Neural Networks
In the GIA scenario, the adversary is not able to modify the existing link structure and node attributes of the input graph, instead the attack is performed by injecting adversarial nodes into it.
A Generalizable Approach to Learning Optimizers
A core issue with learning to optimize neural networks has been the lack of generalization to real world problems.
M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
CogView: Mastering Text-to-Image Generation via Transformers
Text-to-Image generation in the general domain has long been an open problem, which requires both a powerful generative model and cross-modal understanding.
Ranked #51 on
Text-to-Image Generation
on COCO
(using extra training data)
UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis
Conditional image synthesis aims to create an image according to some multi-modal guidance in the forms of textual descriptions, reference images, and image blocks to preserve, as well as their combinations.
Anchor-based Plain Net for Mobile Image Super-Resolution
Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward.
FastMoE: A Fast Mixture-of-Expert Training System
However, training trillion-scale MoE requires algorithm and system co-design for a well-tuned high performance distributed training system.
Controllable Generation from Pre-trained Language Models via Inverse Prompting
To tackle this challenge, we propose an innovative method, inverse prompting, to better control text generation.
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1. 25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
Ranked #2 on
Document Summarization
on CNN / Daily Mail
GPT Understands, Too
On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning.
Understanding WeChat User Preferences and "Wow" Diffusion
"Top Stories" is a novel friend-enhanced recommendation engine in WeChat, in which users can read articles based on preferences of both their own and their friends.
Graph Representation Learning
Social and Information Networks
OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services
Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools.
CogDL: A Toolkit for Deep Learning on Graphs
Deep learning on graphs has attracted tremendous attention from the graph learning community in recent years.
M6: A Chinese Multimodal Pretrainer
In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1. 9TB images and 292GB texts that cover a wide range of domains.
Local Clustering Graph Neural Networks
Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.
Generalizing Graph Convolutional Networks
Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking
In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.
Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines
We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe.
Self-Driving Cars
Hardware Architecture
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs
Within the prosperity of Massive Open Online Courses (MOOCs), the education applications that automatically provide extracurricular knowledge for MOOC users become rising research topics.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
CPM: A Large-scale Generative Chinese Pre-trained Language Model
However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
CogLTX: Applying BERT to Long Texts
BERTs are incapable of processing long texts due to its quadratically increasing memory and time consumption.
Residual Feature Distillation Network for Lightweight Image Super-Resolution
Thanks to FDC, we can rethink the information multi-distillation network (IMDN) and propose a lightweight and accurate SISR model called residual feature distillation network (RFDN).
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
A Survey of FPGA-Based Robotic Computing
On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios.
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices
Our result gives the first bound on the convergence rate of the co-occurrence matrix and the first sample complexity analysis in graph representation learning.
MOOCCube: A Large-scale Data Repository for NLP Applications in MOOCs
The prosperity of Massive Open Online Courses (MOOCs) provides fodder for many NLP and AI research for education applications, e. g., course concept extraction, prerequisite relation discovery, etc.
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
To address this issue, we leverage both content information and context information to learn the representation of entities via graph convolution network.
Spectral-Energy Efficiency Trade-off-based Beamforming Design for MISO Non-Orthogonal Multiple Access Systems
In particular, we formulate a joint SE-EE based design as a multi-objective optimization (MOO) problem to achieve a good tradeoff between the two performance metrics.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
Self-supervised Learning: Generative or Contrastive
As an alternative, self-supervised learning attracts many researchers for its soaring performance on representation learning in the last several years.
Attention: to Better Stand on the Shoulders of Giants
In the progress of science, the previously discovered knowledge principally inspires new scientific ideas, and citation is a reasonably good reflection of this cumulative nature of scientific research.
Graph Random Neural Network for Semi-Supervised Learning on Graphs
We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.
Understanding Negative Sampling in Graph Representation Learning
To the best of our knowledge, we are the first to derive the theory and quantify that the negative sampling distribution should be positively but sub-linearly correlated to their positive sampling distribution.
Controllable Multi-Interest Framework for Recommendation
Recent works usually give an overall embedding from a user's behavior sequence.
Modelling High-Order Social Relations for Item Recommendation
The prevalence of online social network makes it compulsory to study how social relations affect user choice.
A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation
Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors.
Dota 2 with Large Scale Deep Reinforcement Learning
On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game.
Simple and Lightweight Human Pose Estimation
Specifically, our LPN-50 can achieve 68. 7 in AP score on the COCO test-dev set, with only 2. 7M parameters and 1. 0 GFLOPs, while the inference speed is 17 FPS on an Intel i7-8700K CPU machine.
Blockwise Self-Attention for Long Document Understanding
We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies.
Diagonal Graph Convolutional Networks with Adaptive Neighborhood Aggregation
In this paper, we revisit the mathematical foundation of GCNs and study how to extend their representation capacity.
Dimensional Reweighting Graph Convolution Networks
In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs.
Course Concept Expansion in MOOCs with External Knowledge and Interactive Game
As Massive Open Online Courses (MOOCs) become increasingly popular, it is promising to automatically provide extracurricular knowledge for MOOC users.
Towards Knowledge-Based Recommender Dialog System
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System.
Ranked #5 on
Text Generation
on ReDial
Learning Guided Convolutional Network for Depth Completion
It is thus necessary to complete the sparse LiDAR data, where a synchronized guidance RGB image is often used to facilitate this completion.
Infer Implicit Contexts in Real-time Online-to-Offline Recommendation
Understanding users' context is essential for successful recommendations, especially for Online-to-Offline (O2O) recommendation, such as Yelp, Groupon, and Koubei.
Dimensional Reweighting Graph Convolutional Networks
Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs.
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization
Previous research shows that 1) popular network embedding benchmarks, such as DeepWalk, are in essence implicitly factorizing a matrix with a closed form, and 2)the explicit factorization of such matrix generates more powerful embeddings than existing methods.
Gift Contagion in Online Groups: Evidence From WeChat Red Packets
Using data on 36 million online red packet gifts on China's social site WeChat, we leverage a natural experimental design to identify the social contagion of gift giving in online groups.
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.
Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
Inferring new facts from existing knowledge graphs (KG) with explainable reasoning processes is a significant problem and has received much attention recently.
Sequential Scenario-Specific Meta Learner for Online Recommendation
Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks.
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
We propose a new CogQA framework for multi-hop question answering in web-scale documents.
Ranked #54 on
Question Answering
on HotpotQA
Multi-hop Question Answering
Multi-Hop Reading Comprehension
+1
Representation Learning for Attributed Multiplex Heterogeneous Network
Network embedding (or graph embedding) has been widely used in many real-world applications.
Ranked #1 on
Link Prediction
on Amazon
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc.
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization.
Ranked #3 on
Node Classification
on NELL
Bandit Learning with Implicit Feedback
Implicit feedback, such as user clicks, although abundant in online information service systems, does not provide substantial evidence on users' evaluation of system's output.
Modeling and Predicting Citation Count via Recurrent Neural Network with Long Short-Term Memory
The rapid evolution of scientific research has been creating a huge volume of publications every year.
Digital Libraries Physics and Society
Modeling and Predicting Popularity Dynamics via Deep Learning Attention Mechanism
Here we propose a deep learning attention mechanism to model the process through which individual items gain their popularity.
Fast Randomized PCA for Sparse Data
The algorithm has similar accuracy to the basic randomized SVD (rPCA) algorithm (Halko et al., 2011), but is largely optimized for sparse data.
Semi-supervised Learning on Graphs with Generative Adversarial Nets
We first provide insights on working principles of adversarial learning over graphs and then present GraphSGAN, a novel approach to semi-supervised learning on graphs.
DeepInf: Social Influence Prediction with Deep Learning
Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence.
Spectral Network Embedding: A Fast and Scalable Method via Sparsity
In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis.
Expert Finding in Community Question Answering: A Review
Moreover, we use innovative diagrams to clarify several important concepts of ensemble learning, and find that ensemble models with several specific single models can further boosting the performance.
Teaching Autonomous Driving Using a Modular and Integrated Approach
Then, once the students have understood these modules, the experimental platforms for integration we have developed allow the students to fully understand how the modules interact with each other.
Revisiting Knowledge Base Embedding as Tensor Decomposition
We study the problem of knowledge base (KB) embedding, which is usually addressed through two frameworks---neural KB embedding and tensor decomposition.
Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation
Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education.
Fast Top-k Area Topics Extraction with Knowledge Base
We leverage a large-scale knowledge base (Wikipedia) to generate topic embeddings using neural networks and use this kind of representations to help capture the representativeness of topics for given areas.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
Prerequisite Relation Learning for Concepts in MOOCs
What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares?
Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm
The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data.
A Probabilistic Framework for Location Inference from Social Media
In this paper, we formalize the problem of inferring location from social media into a semi-supervised factor graph model (SSFGM).
Weakly Learning to Match Experts in Online Community
The challenge is how to trade off the matching degree between users' expertise and the question topic, and the likelihood of positive response from the invited users.
An Empirical Study on Academic Commentary and Its Implications on Reading and Writing
The relationship between reading and writing (RRW) is one of the major themes in learning science.
Word Embedding based Correlation Model for Question/Answer Matching
With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web.
Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs
GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities---i. e., social network users and knowledge concepts---in a shared latent topic space.
Panther: Fast Top-k Similarity Search in Large Networks
The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.
Social and Information Networks
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Our model can predict social status of individuals with 93% accuracy.
