Search Results for author:
Found 134 papers, 42 papers with code
LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care.
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
To tackle these challenges, deep learning frameworks have been created to match patients to trials.
SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization
Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes.
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking
To overcome this challenge, we introduce a clean-label backdoor watermarking framework that uses imperceptible perturbations to replace mislabeled samples.
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction.
Data-centric Artificial Intelligence: A Survey
Artificial Intelligence (AI) is making a profound impact in almost every domain.
Does Synthetic Data Generation of LLMs Help Clinical Text Mining?
Our method has resulted in significant improvements in the performance of downstream tasks, improving the F1-score from 23. 37% to 63. 99% for the named entity recognition task and from 75. 86% to 83. 59% for the relation extraction task.
Weight Perturbation Can Help Fairness under Distribution Shift
In this paper, we first theoretically demonstrate the inherent connection between distribution shift, data perturbation, and weight perturbation.
CoRTX: Contrastive Framework for Real-time Explanation
In this work, we propose a COntrastive Real-Time eXplanation (CoRTX) framework to learn the explanation-oriented representation and relieve the intensive dependence of explainer training on explanation labels.
Towards Personalized Preprocessing Pipeline Search
However, the existing systems often have a very small search space for feature preprocessing with the same preprocessing pipeline applied to all the numerical features.
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
Efficient XAI Techniques: A Taxonomic Survey
Finally, we summarize the challenges of deploying XAI acceleration methods to real-world scenarios, overcoming the trade-off between faithfulness and efficiency, and the selection of different acceleration methods.
The Science of Detecting LLM-Generated Texts
The emergence of large language models (LLMs) has resulted in the production of LLM-generated texts that is highly sophisticated and almost indistinguishable from texts written by humans.
Retiring $Δ$DP: New Distribution-Level Metrics for Demographic Parity
Unfortunately, in this paper, we reveal that the fairness metric $\Delta DP$ can not precisely measure the violation of demographic parity, because it inherently has the following drawbacks: \textit{i)} zero-value $\Delta DP$ does not guarantee zero violation of demographic parity, \textit{ii)} $\Delta DP$ values can vary with different classification thresholds.
Data-centric AI: Perspectives and Challenges
The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability.
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP.
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Recently, the training paradigm of "pre-train, fine-tune" has been leveraged to improve the generalization capabilities of GNNs.
TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems
We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices.
Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising
In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process.
Mitigating Relational Bias on Knowledge Graphs
Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning.
QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks
Quantum neural networks (QNNs), an interdisciplinary field of quantum computing and machine learning, have attracted tremendous research interests due to the specific quantum advantages.
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
To this end, we propose Randomized Sparse Computation, which for the first time demonstrate the potential of training GNNs with approximated operations.
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs).
Ranked #2 on
Node Property Prediction
on ogbn-products
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Although prior work has explored learning-based approaches for the device placement of computational graphs, embedding table placement remains to be a challenging problem because of 1) the operation fusion of embedding tables, and 2) the generalizability requirement on unseen placement tasks with different numbers of tables and/or devices.
MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
Training graph neural networks (GNNs) on large graphs is complex and extremely time consuming.
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey
We first introduce the concepts and robustness challenge of LLMs.
AutoShard: Automated Embedding Table Sharding for Recommender Systems
This is a significant design challenge of distributed systems named embedding table sharding, i. e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard.
DIVISION: Memory Efficient Training via Dual Activation Precision
Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs).
Differentially Private Counterfactuals via Functional Mechanism
Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia.
Mitigating Algorithmic Bias with Limited Annotations
Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information.
Fair Machine Learning in Healthcare: A Review
Benefiting from the digitization of healthcare data and the development of computing power, machine learning methods are increasingly used in the healthcare domain.
Accelerating Shapley Explanation via Contributive Cooperator Selection
Even though Shapley value provides an effective explanation for a DNN model prediction, the computation relies on the enumeration of all possible input feature coalitions, which leads to the exponentially growing complexity.
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation.
G-Mixup: Graph Data Augmentation for Graph Classification
To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.
BED: A Real-Time Object Detection System for Edge Devices
DNNs have been an effective tool for data processing and analysis.
Geometric Graph Representation Learning via Maximizing Rate Reduction
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification.
FMP: Toward Fair Graph Message Passing against Topology Bias
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), the working mechanism (i. e., message passing) behind GNNs inducing unfairness issue remains unknown.
Deconfounding to Explanation Evaluation in Graph Neural Networks
In this work, we propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data.
Towards Similarity-Aware Time-Series Classification
Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner.
Defense Against Explanation Manipulation
Explainable machine learning attracts increasing attention as it improves transparency of models, which is helpful for machine learning to be trusted in real applications.
CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks
In this paper, we investigate GNNs from the lens of weight and feature loss landscapes, i. e., the loss changes with respect to model weights and node features, respectively.
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks.
Generalized Demographic Parity for Group Fairness
We show the understanding of GDP from the probability perspective and theoretically reveal the connection between GDP regularizer and adversarial debiasing.
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks
The main idea is to insert a watermark which is only known to defender into the protected model and the watermark will then be transferred into all stolen models.
An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Instance-dependent label noise (IDN) widely exists in real-world datasets and usually misleads the training of deep neural networks.
G-Mixup: Graph Augmentation for Graph Classification
To this end, we propose $\mathcal{G}$-Mixup to augment graphs for graph classification by interpolating the generator (i. e., graphon) of different classes of graphs.
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Based on the implementation, we propose a memory-efficient framework called ``EXACT'', which for the first time demonstrate the potential and evaluate the feasibility of training GNNs with compressed activations.
DEGREE: Decomposition Based Explanation for Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data.
AutoCoG: A Unified Data-Modal Co-Search Framework for Graph Neural Networks
Despite the preliminary success, we argue that for GNNs, NAS has to be customized further, due to the topological complicacy of GNN input data (graph) as well as the notorious training instability.
Orthogonal Graph Neural Networks
Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations.
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by recursively aggregating information from its neighbors, have become a predominant computational tool in many domains.
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study
In view of those, we present the first fair and reproducible benchmark dedicated to assessing the "tricks" of training deep GNNs.
AutoVideo: An Automated Video Action Recognition System
Action recognition is an important task for video understanding with broad applications.
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs.
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning
Our experiments demonstrate a significant improvement in the realism of the generated graph structures, typically by 1-2 orders of magnitude of graph structure metrics, compared to leading graph VAEand GAN models.
Fairness via Representation Neutralization
This process not only requires a lot of instance-level annotations for sensitive attributes, it also does not guarantee that all fairness sensitive information has been removed from the encoder.
Model-Based Counterfactual Synthesizer for Interpretation
To address these limitations, we propose a Model-based Counterfactual Synthesizer (MCS) framework for interpreting machine learning models.
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
Simplifying Deep Reinforcement Learning via Self-Supervision
Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks.
A General Taylor Framework for Unifying and Revisiting Attribution Methods
However, the attribution problem has not been well-defined, which lacks a unified guideline to the contribution assignment process.
Learning Disentangled Representations for Time Series
Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input and the source signal.
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization
We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity.
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models
These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample.
Model Complexity of Deep Learning: A Survey
Model complexity is a fundamental problem in deep learning.
Sparse-Interest Network for Sequential Recommendation
Our sparse-interest module can adaptively infer a sparse set of concepts for each user from the large concept pool and output multiple embeddings accordingly.
Dynamic Memory based Attention Network for Sequential Recommendation
It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
Unfortunately, methods based on intrinsic rewards often fall short in procedurally-generated environments, where a different environment is generated in each episode so that the agent is not likely to visit the same state more than once.
Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios.
Efficient Differentiable Neural Architecture Search with Model Parallelism
Differentiable NAS with supernets that encompass all potential architectures in a large graph cuts down search overhead to few GPU days or less.
Detecting Interactions from Neural Networks via Topological Analysis
Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks.
Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection
In this paper, we introduce DSN (Deep Serial Number), a new watermarking approach that can prevent the stolen model from being deployed by unauthorized parties.
Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection
In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort of geodesic distance metric should be used to discriminate the data samples.
Towards Interaction Detection Using Topological Analysis on Neural Networks
Detecting statistical interactions between input features is a crucial and challenging task.
TODS: An Automated Time Series Outlier Detection System
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications.
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability
Recent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models.
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
Specifically, existing strategies have been focused on making the top instances more likely to be anomalous based on the feedback.
Explainable Recommender Systems via Resolving Learning Representations
Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge.
A Unified Taylor Framework for Revisiting Attribution Methods
Attribution methods have been developed to understand the decision-making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features.
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
Combating fake news and misinformation propagation is a challenging task in the post-truth era.
Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction
Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers.
Policy-GNN: Aggregation Optimization for Graph Neural Networks
It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features.
AutoRec: An Automated Recommender System
To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models.
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.
Measuring Model Complexity of Neural Networks with Curve Activation Functions
Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.
Mitigating Gender Bias in Captioning Systems
Image captioning has made substantial progress with huge supporting image collections sourced from the web.
An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks
In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers.
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.
Dual Policy Distillation
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.
iCapsNets: Towards Interpretable Capsule Networks for Text Classification
On the other hand, iCapsNets explore a novel way to explain the model's general behavior, achieving global interpretability.
Adversarial Attacks and Defenses: An Interpretation Perspective
In this paper, we review recent work on adversarial attacks and defenses, particularly from the perspective of machine learning interpretation.
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
Outlier detection is an important task for various data mining applications.
Learning to Hash with Graph Neural Networks for Recommender Systems
In this work, we investigate the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
Multi-Channel Graph Convolutional Networks
To further improve the graph representation learning ability, hierarchical GNN has been explored.
XDeep: An Interpretation Tool for Deep Neural Networks
XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers.
RLCard: A Toolkit for Reinforcement Learning in Card Games
The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.
PyODDS: An End-to-End Outlier Detection System
PyODDS is an end-to end Python system for outlier detection with database support.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
Contextual Local Explanation for Black Box Classifiers
We introduce a new model-agnostic explanation technique which explains the prediction of any classifier called CLE.
Sub-Architecture Ensemble Pruning in Neural Architecture Search
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.
Distribution-Guided Local Explanation for Black-Box Classifiers
Existing local explanation methods provide an explanation for each decision of black-box classifiers, in the form of relevance scores of features according to their contributions.
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder
The analysis further shows that LAE outperforms the state-of-the-arts by 6. 52%, 12. 03%, and 3. 08% respectively on three deepfake detection tasks in terms of generalization accuracy on previously unseen manipulations.
Auto-GNN: Neural Architecture Search of Graph Neural Networks
First, the search space of GNN is different from the ones in existing NAS work.
Ranked #35 on
Node Classification
on Cora
Fairness in Deep Learning: A Computational Perspective
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives.
Learning Credible Deep Neural Networks with Rationale Regularization
Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions.
Deep Structured Cross-Modal Anomaly Detection
To this end, we propose a novel deep structured anomaly detection framework to identify the cross-modal anomalies embedded in the data.
Techniques for Automated Machine Learning
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem.
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning
Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.
Experience Replay Optimization
Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand.
Exact and Consistent Interpretation of Piecewise Linear Models Hidden behind APIs: A Closed Form Solution
More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs.
Coupled Variational Recurrent Collaborative Filtering
To bridge the gap, in this paper, we propose a Coupled Variational Recurrent Collaborative Filtering (CVRCF) framework based on the idea of Deep Bayesian Learning to handle the streaming recommendation problem.
Deep Bayesian Optimization on Attributed Graphs
Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications.
Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
Network embedding models are powerful tools in mapping nodes in a network into continuous vector-space representations in order to facilitate subsequent tasks such as classification and link prediction.
Deep Representation Learning for Social Network Analysis
First, we introduce the basic models for learning node representations in homogeneous networks.
Open Issues in Combating Fake News: Interpretability as an Opportunity
Combating fake news needs a variety of defense methods.
On Attribution of Recurrent Neural Network Predictions via Additive Decomposition
REAT decomposes the final prediction of a RNN into additive contribution of each word in the input text.
Multi-Label Adversarial Perturbations
Experiments on real-world multi-label image classification and ranking problems demonstrate the effectiveness of our proposed frameworks and provide insights of the vulnerability of multi-label deep learning models under diverse targeted attacking strategies.
Discriminative Graph Autoencoder
Moreover, the learned vector representations are not in a smooth space since the values can only be integers.
Ranked #9 on
Graph Classification
on PTC
Techniques for Interpretable Machine Learning
Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision.
Auto-Keras: An Efficient Neural Architecture Search System
In this paper, we propose a novel framework enabling Bayesian optimization to guide the network morphism for efficient neural architecture search.
r-Instance Learning for Missing People Tweets Identification
The number of missing people (i. e., people who get lost) greatly increases in recent years.
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics.
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical.
Tensor Completion Algorithms in Big Data Analytics
Tensor completion is a problem of filling the missing or unobserved entries of partially observed tensors.
Contextual Outlier Interpretation
Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority.
Deep Style Match for Complementary Recommendation
In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.
Neural Collaborative Filtering
When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.
Attributed Network Embedding for Learning in a Dynamic Environment
To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly.
Stacked Approximated Regression Machine: A Simple Deep Learning Approach
With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach".
