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Found 143 papers, 42 papers with code
Learn to Cross-lingual Transfer with Meta Graph Learning Across Heterogeneous Languages
Recent emergence of multilingual pre-training language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks.
Transferring SLU Models in Novel Domains
When building models for novel natural language domains, a major challenge is the lack of data in the new domains, no matter whether the data is annotated or not.
FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom
To address these issues, we propose a Federated Evaluation framework of Large Language Models, named FedEval-LLM, that provides reliable performance measurements of LLMs on downstream tasks without the reliance on labeled test sets and external tools, thus ensuring strong privacy-preserving capability.
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
This vulnerability may lead the current heuristic hyperparameter configuration of SecureBoost to a suboptimal trade-off between utility, privacy, and efficiency, which are pivotal elements toward a trustworthy federated learning system.
Evaluating Membership Inference Attacks and Defenses in Federated Learning
Firstly, combining model information from multiple communication rounds (Multi-temporal) enhances the overall effectiveness of MIAs compared to utilizing model information from a single epoch.
Decentralized Federated Learning: A Survey on Security and Privacy
Federated learning has been rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages.
A Survey on Cross-Domain Sequential Recommendation
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain).
A Theoretical Analysis of Efficiency Constrained Utility-Privacy Bi-Objective Optimization in Federated Learning
Concerns about utility, privacy, and training efficiency in FL have garnered significant research attention.
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
A Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting
Specifically, the proposed LIWUDA method constructs a weight network to assign weights to each instance based on its probability of belonging to common classes, and designs Weighted Optimal Transport (WOT) for domain alignment by leveraging instance weights.
Grounding Foundation Models through Federated Transfer Learning: A General Framework
Motivated by the strong growth in FTL-FM research and the potential impact of FTL-FM on industrial applications, we propose an FTL-FM framework that formulates problems of grounding FMs in the federated learning setting, construct a detailed taxonomy based on the FTL-FM framework to categorize state-of-the-art FTL-FM works, and comprehensively overview FTL-FM works based on the proposed taxonomy.
Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark
In this survey, we provide a systematic overview of the important and recent developments of research on federated learning.
A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models
The springing up of Large Language Models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-task learning paradigm.
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models
FATE-LLM (1) facilitates federated learning for large language models (coined FedLLM); (2) promotes efficient training of FedLLM using parameter-efficient fine-tuning methods; (3) protects the intellectual property of LLMs; (4) preserves data privacy during training and inference through privacy-preserving mechanisms.
A Survey of Heterogeneous Transfer Learning
These methods typically presuppose identical feature spaces and label spaces in both domains, known as homogeneous transfer learning, which, however, is not always a practical assumption.
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning
When personalized federated learning (FL) meets large foundation models, new challenges arise from various limitations in resources.
A Survey for Federated Learning Evaluations: Goals and Measures
Evaluation is a systematic approach to assessing how well a system achieves its intended purpose.
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization
We propose DIVERSIFY, a general framework, for OOD detection and generalization on dynamic distributions of time series.
Transferable Graph Structure Learning for Graph-based Traffic Forecasting Across Cities
To address the problem, we propose TransGTR, a transferable structure learning framework for traffic forecasting that jointly learns and transfers the graph structures and forecasting models across cities.
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning
To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage.
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
A Meta-learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning
Motivated by this requirement, we propose a framework that (1) formulates TFL as a problem of finding a protection mechanism to optimize the tradeoff between privacy leakage, utility loss, and efficiency reduction and (2) formally defines bounded measurements of the three factors.
Theoretically Principled Federated Learning for Balancing Privacy and Utility
We propose a general learning framework for the protection mechanisms that protects privacy via distorting model parameters, which facilitates the trade-off between privacy and utility.
A Topic-aware Summarization Framework with Different Modal Side Information
To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information.
FedSOV: Federated Model Secure Ownership Verification with Unforgeable Signature
Federated learning allows multiple parties to collaborate in learning a global model without revealing private data.
FedZKP: Federated Model Ownership Verification with Zero-knowledge Proof
Federated learning (FL) allows multiple parties to cooperatively learn a federated model without sharing private data with each other.
Towards Achieving Near-optimal Utility for Privacy-Preserving Federated Learning via Data Generation and Parameter Distortion
The nature of the widely-adopted protection mechanisms including \textit{Randomization Mechanism} and \textit{Compression Mechanism} is to protect privacy via distorting model parameter.
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning
Different from existing CMOFL works focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system.
Learn to Cluster Faces with Better Subgraphs
Face clustering can provide pseudo-labels to the massive unlabeled face data and improve the performance of different face recognition models.
A Game-theoretic Framework for Privacy-preserving Federated Learning
To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks.
Probably Approximately Correct Federated Learning
However, existing multi-objective optimization frameworks are very time-consuming, and do not guarantee the existence of the Pareto frontier, this motivates us to seek a solution to transform the multi-objective problem into a single-objective problem because it is more efficient and easier to be solved.
Federated Learning without Full Labels: A Survey
Therefore, the problem of federated learning without full labels is important in real-world FL applications.
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Vertical federated learning (VFL) allows an active party with labeled feature to leverage auxiliary features from the passive parties to improve model performance.
FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders
Extensive experimental results under a variety of settings justify the superiority of FedCut, which demonstrates extremely robust model performance (MP) under various attacks.
Vertical Federated Learning: Concepts, Advances and Challenges
Motivated by the rapid growth in VFL research and real-world applications, we provide a comprehensive review of the concept and algorithms of VFL, as well as current advances and challenges in various aspects, including effectiveness, efficiency, and privacy.
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants.
A Survey on Heterogeneous Federated Learning
Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL.
A Framework for Evaluating Privacy-Utility Trade-off in Vertical Federated Learning
We then use this framework as a guide to comprehensively evaluate a broad range of protection mechanisms against most of the state-of-the-art privacy attacks for three widely-deployed VFL algorithms.
Trading Off Privacy, Utility and Efficiency in Federated Learning
In addition, it is a mandate for a federated learning system to achieve high \textit{efficiency} in order to enable large-scale model training and deployment.
Cross-domain Cross-architecture Black-box Attacks on Fine-tuned Models with Transferred Evolutionary Strategies
To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example.
A Hybrid Self-Supervised Learning Framework for Vertical Federated Learning
In this work, we propose a Federated Hybrid Self-Supervised Learning framework, named FedHSSL, that utilizes cross-party views (i. e., dispersed features) of samples aligned among parties and local views (i. e., augmentation) of unaligned samples within each party to improve the representation learning capability of the VFL joint model.
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology.
WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning
The experimental results demonstrate that WrapperFL can be successfully applied to a wide range of applications under practical settings and improves the local model with federated learning at a low cost.
FadMan: Federated Anomaly Detection across Multiple Attributed Networks
The proposed algorithm FadMan is a vertical federated learning framework for public node aligned with many private nodes of different features, and is validated on two tasks correlated anomaly detection on multiple attributed networks and anomaly detection on an attributeless network using five real-world datasets.
Multi-core fiber enabled fading noise suppression in φ-OFDR based quantitative distributed vibration sensing
Coherent fading has been regarded as a critical issue in phase-sensitive optical frequency domain reflectometry ({\phi}-OFDR) based distributed fiber-optic sensing.
Hybrid Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks
The sensor data detection model based on Gaussian and Bayesian algorithms can detect the anomalous sensor data in real-time and upload them to the cloud for further analysis, filtering the normal sensor data and reducing traffic load.
No Free Lunch Theorem for Security and Utility in Federated Learning
In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms.
LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization
Federated optimization (FedOpt), which targets at collaboratively training a learning model across a large number of distributed clients, is vital for federated learning.
Batch Label Inference and Replacement Attacks in Black-Boxed Vertical Federated Learning
An immediate defense strategy is to protect sample-level messages communicated with Homomorphic Encryption (HE), and in this way only the batch-averaged local gradients are exposed to each party (termed black-boxed VFL).
Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability
We present a novel privacy-preserving federated adversarial domain adaptation approach ($\textbf{PrADA}$) to address an under-studied but practical cross-silo federated domain adaptation problem, in which the party of the target domain is insufficient in both samples and features.
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data.
SecureBoost+ : A High Performance Gradient Boosting Tree Framework for Large Scale Vertical Federated Learning
Gradient boosting decision tree (GBDT) is a widely used ensemble algorithm in the industry.
SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary
Sports game summarization aims to generate news articles from live text commentaries.
Personalized Heterogeneous Federated Learning with Gradient Similarity
In SPFL, the server uses the Softmax Normalized Gradient Similarity (SNGS) to weight the relationship between clients, and sends the personalized global model to each client.
Federated Deep Learning with Bayesian Privacy
To address the aforementioned perplexity, we propose a novel Bayesian Privacy (BP) framework which enables Bayesian restoration attacks to be formulated as the probability of reconstructing private data from observed public information.
FedIPR: Ownership Verification for Federated Deep Neural Network Models
To address these risks, the ownership verification of federated learning models is a prerequisite that protects federated learning model intellectual property rights (IPR) i. e., FedIPR.
Multi-Task Learning in Natural Language Processing: An Overview
Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP).
Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies
The increasing demand for privacy-preserving collaborative learning has given rise to a new computing paradigm called federated learning (FL), in which clients collaboratively train a machine learning (ML) model without revealing their private training data.
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.
Practical and Secure Federated Recommendation with Personalized Masks
In this paper, we proposed federated masked matrix factorization (FedMMF) to protect the data privacy in federated recommender systems without sacrificing efficiency and effectiveness.
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing
Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives.
Transferring Knowledge Distillation for Multilingual Social Event Detection
Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks
Ever since Machine Learning as a Service emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.
Frustratingly Easy Transferability Estimation
Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer.
Ternary Hashing
This paper proposes a novel ternary hash encoding for learning to hash methods, which provides a principled more efficient coding scheme with performances better than those of the state-of-the-art binary hashing counterparts.
Towards Personalized Federated Learning
In parallel with the rapid adoption of Artificial Intelligence (AI) empowered by advances in AI research, there have been growing awareness and concerns of data privacy.
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity Attack
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties.
Self-supervised Cross-silo Federated Neural Architecture Search
Federated Learning (FL) provides both model performance and data privacy for machine learning tasks where samples or features are distributed among different parties.
TrNews: Heterogeneous User-Interest Transfer Learning for News Recommendation
To take advantage of the existing corpus, we propose a transfer learning model (dubbed as TrNews) for news recommendation to transfer the knowledge from a source corpus to a target corpus.
Privacy and Robustness in Federated Learning: Attacks and Defenses
Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.
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.
Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Deep neural networks (DNNs) are known to be prone to adversarial attacks, for which many remedies are proposed.
FedEval: A Holistic Evaluation Framework for Federated Learning
In this paper, we propose a holistic evaluation framework for FL called FedEval, and present a benchmarking study on seven state-of-the-art FL algorithms.
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation
Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain.
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model.
Protect, Show, Attend and Tell: Empowering Image Captioning Models with Ownership Protection
By and large, existing Intellectual Property (IP) protection on deep neural networks typically i) focus on image classification task only, and ii) follow a standard digital watermarking framework that was conventionally used to protect the ownership of multimedia and video content.
FedML: A Research Library and Benchmark for Federated Machine Learning
Federated learning (FL) is a rapidly growing research field in machine learning.
Data science and AI in FinTech: An overview
Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas.
Privacy Threats Against Federated Matrix Factorization
Matrix Factorization has been very successful in practical recommendation applications and e-commerce.
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks.
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories.
Incentive Mechanism Design for Resource Sharing in Collaborative Edge Learning
In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous.
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.
Network On Network for Tabular Data Classification in Real-world Applications
As a result, the intra-field information and the non-linear interactions between those operations (e. g. neural network and factorization machines) are ignored.
Fisher Deep Domain Adaptation
Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance.
Threats to Federated Learning: A Survey
It is thus of paramount importance to make FL system designers to be aware of the implications of future FL algorithm design on privacy-preservation.
Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective
Federated Learning (FL) proposed in recent years has received significant attention from researchers in that it can bring separate data sources together and build machine learning models in a collaborative but private manner.
RPN: A Residual Pooling Network for Efficient Federated Learning
Federated learning is a distributed machine learning framework which enables different parties to collaboratively train a model while protecting data privacy and security.
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Federated learning (FL) is a promising approach to resolve this challenge.
A Communication Efficient Collaborative Learning Framework for Distributed Features
We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression
Our approach can considerably reduce the number of communication rounds with a little additional communication cost per round.
Prototypical Networks for Multi-Label Learning
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech
With this demonstration, the audience can experience the effect of LMA in an interactive and real-time fashion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning
Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition
Modern Automatic Speech Recognition (ASR) systems primarily rely on scores from an Acoustic Model (AM) and a Language Model (LM) to rescore the N-best lists.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Real-World Image Datasets for Federated Learning
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private.
Federated Transfer Reinforcement Learning for Autonomous Driving
Reinforcement learning (RL) is widely used in autonomous driving tasks and training RL models typically involves in a multi-step process: pre-training RL models on simulators, uploading the pre-trained model to real-life robots, and fine-tuning the weight parameters on robot vehicles.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
Transfer Learning with Dynamic Distribution Adaptation
Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions.
Ranked #7 on
Domain Adaptation
on ImageCLEF-DA
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices.
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Secure Federated Matrix Factorization
The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data.
Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content.
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.
AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications
Feature crossing captures interactions among categorical features and is useful to enhance learning from tabular data in real-world businesses.
Easy Transfer Learning By Exploiting Intra-domain Structures
In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.
Ranked #4 on
Transfer Learning
on Office-Home
Learning to Transfer Examples for Partial Domain Adaptation
Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer.
Ranked #4 on
Partial Domain Adaptation
on ImageNet-Caltech
AutoML @ NeurIPS 2018 challenge: Design and Results
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018.
Federated Machine Learning: Concept and Applications
We propose a possible solution to these challenges: secure federated learning.
SecureBoost: A Lossless Federated Learning Framework
This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned data set.
Federated Deep Reinforcement Learning
In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited.
Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text
Another thread is to transfer knowledge from other source domains such as improving the movie recommendation with the knowledge from the book domain, leading to transfer learning methods.
Secure Federated Transfer Learning
A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation.
Building Ethics into Artificial Intelligence
As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination.
Differential Private Stack Generalization with an Application to Diabetes Prediction
To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms.
Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification
Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).
Smart City Development with Urban Transfer Learning
To address this problem, transfer learning can be leveraged to accelerate the smart city development, which we term the urban transfer learning paradigm.
Bike Flow Prediction with Multi-Graph Convolutional Networks
We propose a new multi-graph convolutional neural network model to predict the bike flow at station-level, where the key novelty is viewing the bike sharing system from the graph perspective.
Transfer Learning via Learning to Transfer
In transfer learning, what and how to transfer are two primary issues to be addressed, as different transfer learning algorithms applied between a source and a target domain result in different knowledge transferred and thereby the performance improvement in the target domain.
Learning to Multitask
Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation.
Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification
Existing cross-domain sentiment classification meth- ods cannot automatically capture non-pivots, i. e., the domain- specific sentiment words, and pivots, i. e., the domain-shared sentiment words, simultaneously.
Parameter Transfer Unit for Deep Neural Networks
Unfortunately, the transferability is usually defined as discrete states and it differs with domains and network architectures.
Cross-domain Dialogue Policy Transfer via Simultaneous Speech-act and Slot Alignment
Also, they depend on either common slots or slot entropy, which are not available when the source and target slots are totally disjoint and no database is available to calculate the slot entropy.
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa.
LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
By modeling content information as local memories, LCMR attentively learns what to exploit with the guidance of user-item interaction.
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city.
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient.
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Then the built user model is used as a leverage to train the agent model by deep reinforcement learning.
Flexible End-to-End Dialogue System for Knowledge Grounded Conversation
Although existing generative question answering (QA) systems can be applied to knowledge grounded conversation, they either have at most one entity in a response or cannot deal with out-of-vocabulary entities.
Learning to Transfer
We establish the L2T framework in two stages: 1) we first learn a reflection function encrypting transfer learning skills from experiences; and 2) we infer what and how to transfer for a newly arrived pair of domains by optimizing the reflection function.
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks.
Ridesourcing Car Detection by Transfer Learning
Finally, we use the resulting ensemble of RF and CNN to identify the ridesourcing cars in the candidate pool.
Online Hashing
Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space.
Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks
We compare cosine normalization with batch, weight and layer normalization in fully-connected neural networks as well as convolutional networks on the data sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN.
Personalizing a Dialogue System with Transfer Reinforcement Learning
One way to solve this problem is to consider a collection of multiple users' data as a source domain and an individual user's data as a target domain, and to perform a transfer learning from the source to the target domain.
Partially Observable Markov Decision Process for Recommender Systems
We report the "Recurrent Deterioration" (RD) phenomenon observed in online recommender systems.
Transitive Hashing Network for Heterogeneous Multimedia Retrieval
Hashing has been widely applied to large-scale multimedia retrieval due to the storage and retrieval efficiency.
Collaborative Receptive Field Learning
However, measuring pairwise distance of RF's for building the similarity graph is a nontrivial problem.
Learning Mid-Level Features and Modeling Neuron Selectivity for Image Classification
We now know that mid-level features can greatly enhance the performance of image learning, but how to automatically learn the image features efficiently and in an unsupervised manner is still an open question.
Action-Model Based Multi-agent Plan Recognition
Previous MAPR approaches required a library of team activity sequences (team plans) be given as input.
