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Found 25 papers, 5 papers with code
Contrast R-CNN for Continual Learning in Object Detection
In our paper, we propose a new scheme for continual learning of object detection, namely Contrast R-CNN, an approach strikes a balance between retaining the old knowledge and learning the new knowledge.
Privacy Threats Analysis to Secure Federated Learning
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties.
Weakly-Supervised Open-Retrieval Conversational Question Answering
Our method is more flexible as it can handle both span answers and freeform answers.
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation
We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations
More specifically, we proposed a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT based response ranker to rank the PRF-enhanced responses.
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
Federated learning (FL) has attracted increasing attention in recent years.
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.
A Theoretical Perspective on Differentially Private Federated Multi-task Learning
In the era of big data, the need to expand the amount of data through data sharing to improve model performance has become increasingly compelling.
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy
The success of machine learning algorithms often relies on a large amount of high-quality data to train well-performed models.
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue.
A Hierarchical Deep Convolutional Neural Network and Gated Recurrent Unit Framework for Structural Damage Detection
Specifically, CNN is utilized to model the spatial relations and the short-term temporal dependencies among sensors, while the output features of CNN are fed into the GRU to learn the long-term temporal dependencies jointly.
Open-Retrieval Conversational Question Answering
We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.
End-to-end Semantics-based Summary Quality Assessment for Single-document Summarization
Canonical automatic summary evaluation metrics, such as ROUGE, suffer from two drawbacks.
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance
The insurance industry has been creating innovative products around the emerging online shopping activities.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
Attentive History Selection for Conversational Question Answering
First, we propose a positional history answer embedding method to encode conversation history with position information using BERT in a natural way.
TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business.
A Deep Framework for Bone Age Assessment based on Finger Joint Localization
In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.
Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation
Segmentation stands at the forefront of many high-level vision tasks.
Review Helpfulness Prediction with Embedding-Gated CNN
Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions.
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators
With the growing amount of reviews in e-commerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingly to consumers.
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
We present a deep learning framework for computer-aided lung cancer diagnosis.
Aspect Extraction from Product Reviews Using Category Hierarchy Information
Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories.