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Found 52 papers, 22 papers with code
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.
Rethinking the Setting of Semi-supervised Learning on Graphs
graph benchmark (IGB) consisting of 4 datasets.
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.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Vertical Federated Learning: Challenges, Methodologies and Experiments
As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different clients.
Negative-ResNet: Noisy Ambulatory Electrocardiogram Signal Classification Scheme
With recently successful applications of deep learning in computer vision and general signal processing, deep learning has shown many unique advantages in medical signal processing.
A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection
Cardiovascular disease has become one of the most significant threats endangering human life and health.
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.
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.
Federated Learning for Smart Healthcare: A Survey
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI).
Federated Learning for COVID-19 Detection with Generative Adversarial Networks in Edge Cloud Computing
COVID-19 has spread rapidly across the globe and become a deadly pandemic.
Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning
The convergence of mobile edge computing (MEC) and blockchain is transforming the current computing services in mobile networks, by offering task offloading solutions with security enhancement empowered by blockchain mining.
BEdgeHealth: A Decentralized Architecture for Edge-based IoMT Networks Using Blockchain
The healthcare industry has witnessed significant transformations in e-health services by using mobile edge computing (MEC) and blockchain to facilitate healthcare operations.
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.
6G Internet of Things: A Comprehensive Survey
The sixth generation (6G) wireless communication networks are envisioned to revolutionize customer services and applications via the Internet of Things (IoT) towards a future of fully intelligent and autonomous systems.
Low-Latency Federated Learning over Wireless Channels with Differential Privacy
Then, we convert the MAMAB to a max-min bipartite matching problem at each communication round, by estimating rewards with the upper confidence bound (UCB) approach.
Federated Learning for Industrial Internet of Things in Future Industries
The Industrial Internet of Things (IIoT) offers promising opportunities to transform the operation of industrial systems and becomes a key enabler for future industries.
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 #22 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.
Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design
Owing to the low communication costs and privacy-promoting capabilities, Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.
Federated Learning for Internet of Things: A Comprehensive Survey
The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).
Realistic Differentially-Private Transmission Power Flow Data Release
For the modeling, design and planning of future energy transmission networks, it is vital for stakeholders to access faithful and useful power flow data, while provably maintaining the privacy of business confidentiality of service providers.
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.
GPT Understands, Too
On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning.
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
Language Modelling
on WikiText-103
DP-Image: Differential Privacy for Image Data in Feature Space
The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public.
IdentityDP: Differential Private Identification Protection for Face Images
We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy (DP) mechanism.
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.
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization
An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP).
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation
Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
Generalizing Graph Convolutional Networks
Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs.
F-manifold color algebras
In this paper, we introduce the notion of F-manifold color algebras and study their properties which extend some results for F-manifold algebras.
Rings and Algebras
Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients
The proposed BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning.
CogLTX: Applying BERT to Long Texts
BERTs are incapable of processing long texts due to its quadratically increasing memory and time consumption.
When Machine Learning Meets Privacy: A Survey and Outlook
The newly emerged machine learning (e. g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial technology, and surveillance systems.
When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm
Motivated by the explosive computing capabilities at end user equipments, as well as the growing privacy concerns over sharing sensitive raw data, a new machine learning paradigm, named federated learning (FL) has emerged.
Networking and Internet Architecture
RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network
Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
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.
User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization
According to our analysis, the UDP framework can realize $(\epsilon_{i}, \delta_{i})$-LDP for the $i$-th MT with adjustable privacy protection levels by varying the variances of the artificial noise processes.
Federated Learning with Differential Privacy: Algorithms and Performance Analysis
Specifically, the theoretical bound reveals the following three key properties: 1) There is a tradeoff between the convergence performance and privacy protection levels, i. e., a better convergence performance leads to a lower protection level; 2) Given a fixed privacy protection level, increasing the number $N$ of overall clients participating in FL can improve the convergence performance; 3) There is an optimal number of maximum aggregation times (communication rounds) in terms of convergence performance for a given protection level.
On Safeguarding Privacy and Security in the Framework of Federated Learning
Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL).
Networking and Internet Architecture
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.
Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning
Blockchain technology with its secure, transparent and decentralized nature has been recently employed in many mobile applications.
Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning
How to implement offloading to alleviate computation burdens at MDs while guaranteeing high security in mobile edge cloud is a challenging problem.
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.
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 #53 on
Question Answering
on HotpotQA
Multi-hop Question Answering
Multi-Hop Reading Comprehension
+1
Privacy Preserving Location Data Publishing: A Machine Learning Approach
By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use $k$-means algorithm for this purpose.
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.
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.
Privacy Preservation in Location-Based Services: A Novel Metric and Attack Model
In this paper, we incorporate a new type of side information based on consecutive location changes of users and propose a new metric called transition-entropy to investigate the location privacy preservation, followed by two algorithms to improve the transition-entropy for a given dummy generation algorithm.
