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Found 43 papers, 17 papers with code
Robust Graph Representation Learning via Neural Sparsification
Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
Time Series Contrastive Learning with Information-Aware Augmentations
A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.
Dynamic Prompting: A Unified Framework for Prompt Tuning
In particular, the inserted prompt position, length, and the representations of prompts for diversified instances through different tasks could all affect the prompt tuning performance.
Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization
Text summarization has been a crucial problem in natural language processing (NLP) for several decades.
Personalized Federated Learning via Heterogeneous Modular Networks
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.
Deep Federated Anomaly Detection for Multivariate Time Series Data
Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device.
Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis
Specifically, we compress the light fields for novel view human rendering as conditional implicit neural radiance fields from both geometry and appearance aspects.
SEED: Sound Event Early Detection via Evidential Uncertainty
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.
Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?
During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstream tasks.
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence.
InfoGCL: Information-Aware Graph Contrastive Learning
The key point of this framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized.
Information-Aware Time Series Meta-Contrastive Learning
How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.
Code Editing from Few Exemplars by Adaptive Multi-Extent Composition
To achieve this, we propose a machine learning approach to adapt the editorial style derived from few exemplars to a query code snippet.
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings
The dilemma necessitates the adaptation of a "coarsely" pretrained model to new tasks with a few unseen "finer-grained" training labels.
Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction
Compliments and concerns in reviews are valuable for understanding users' shopping interests and their opinions with respect to specific aspects of certain items.
Insight from NLP Analysis: COVID-19 Vaccines Sentiments on Social Media
We found residents in the two countries are willing to share their views and feelings concerning the vaccine.
Unsupervised Concept Representation Learning for Length-Varying Text Similarity
Measuring document similarity plays an important role in natural language processing tasks.
FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks.
Unsupervised Document Embedding via Contrastive Augmentation
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.
Local strict singular characteristics: Cauchy problem with smooth initial data
Especially, we obtain an existence result of smooth strict singular characteristic from and to non-conjugate singular initial point based on the structure of the superdifferential of the solution, which is even new in the classical time-dependent case.
Analysis of PDEs
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series
Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications.
Cumulant Expansion of Mutual Information for Quantifying Leakage of a Protected Secret
The information leakage of a cryptographic implementation with a given degree of protection is evaluated in a typical situation when the signal-to-noise ratio is small.
Information Theory Information Theory
Aspect-based Sentiment Classification via Reinforcement Learning
As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step.
SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression
The emergence of large-scale data sets provides great opportunities for better understanding of genomics, especially gene regulation.
Learning to Drop: Robust Graph Neural Network via Topological Denoising
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics.
Parameterized Explainer for Graph Neural Network
The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.
T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.
Generalizing Variational Autoencoders with Hierarchical Empirical Bayes
Recent methods have mitigated this issue by deterministically moment-matching an aggregated posterior distribution to an aggregate prior.
Inductive and Unsupervised Representation Learning on Graph Structured Objects
Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.
Interpretable Click-Through Rate Prediction through Hierarchical Attention
For this problem, existing approaches, with shallow or deep architectures, have three major drawbacks.
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations.
Learning Robust Representations with Graph Denoising Policy Network
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.
Time Series Anomaly Detection
Unsupervised Anomaly Detection
Target Transfer Q-Learning and Its Convergence Analysis
In this paper, we propose to transfer the Q-function learned in the source task to the target of the Q-learning in the new task when certain safe conditions are satisfied.
Learning Deep Network Representations with Adversarially Regularized Autoencoders
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.
FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification
Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e. g., covariance matrix), and the "linear inseparability" of EHR data.
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.
FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras
We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera.
Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations
Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis.
Particle Swarm Optimized Power Consumption of Trilateration
Trilateration-based localization (TBL) has become a corner stone of modern technology.
Flexible and Robust Co-Regularized Multi-Domain Graph Clustering.
Moreover, relationships between instances in different domains may be associated with weights based on prior (partial) knowledge.
