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Found 51 papers, 16 papers with code
Deep Streaming Label Learning
In order to fill in these research gaps, we propose a novel deep neural network (DNN) based framework, Deep Streaming Label Learning (DSLL), to classify instances with newly emerged labels effectively.
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI.
N15News: A New Dataset for Multimodal News Classification
Current news datasets merely focus on text features on the news and rarely leverage the feature of images, excluding numerous essential features for news classification.
Ranked #1 on
News Classification
on N15News
Synchronization of Power Systems under Stochastic Disturbances
The synchronization of power generators is an important condition for the proper functioning of a power system, in which the fluctuations in frequency and the phase angle differences between the generators are sufficiently small when subjected to stochastic disturbances.
MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection
This paper describes the system submitted to SemEval 2021 Task 5: Toxic Spans Detection.
Overcoming Difficulty in Obtaining Dark-skinned Subjects for Remote-PPG by Synthetic Augmentation
Camera-based remote photoplethysmography (rPPG) provides a non-contact way to measure physiological signals (e. g., heart rate) using facial videos.
Chinese Sentences Similarity via Cross-Attention Based Siamese Network
Measuring sentence similarity is a key research area nowadays as it allows machines to better understand human languages.
Fuzzy Discriminant Clustering with Fuzzy Pairwise Constraints
In semi-supervised fuzzy clustering, this paper extends the traditional pairwise constraint (i. e., must-link or cannot-link) to fuzzy pairwise constraint.
Equilibrium and Socially optimal of a double-sided queueing system with two-mass point matching time
The numerical scenarios illustrate the influence of parameters on the equilibrium strategy and socially optimal strategy under two information levels.
Probability 60K25, 91B50, 91A35
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery
In recent years, hyperspectral anomaly detection (HAD) has become an active topic and plays a significant role in military and civilian fields.
Semantic Inference Network for Few-shot Streaming Label Learning
In this work, we formulate and study few-shot streaming label learning (FSLL), which models emerging new labels with only a few annotated examples by utilizing the knowledge learned from past labels.
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries
This work proposes a generic evaluation metric ROBY, a novel attack-independent robustness measure based on the model's decision boundaries.
EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation
As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment.
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling
However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression.
Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention
We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.
Exercise Hierarchical Feature Enhanced Knowledge Tracing
Knowledge tracing is a fundamental task in the computer-aid educational system.
Exploring Common and Individual Characteristics of Students via Matrix Recovering
We assume that the characteristics matrix of students' is composed of two parts: one is a low-rank matrix representing the common characteristics of students; the other is a sparse matrix representing individual characteristics of students.
Unseen Target Stance Detection with Adversarial Domain Generalization
Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets.
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary
Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence.
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data
Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph.
HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent hierarchical relations between exercises.
Training Noise-Robust Deep Neural Networks via Meta-Learning
Label noise may significantly degrade the performance of Deep Neural Networks (DNNs).
Rationalizing Medical Relation Prediction from Corpus-level Statistics
Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain.
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.
Tree++: Truncated Tree Based Graph Kernels
At the heart of Tree++ is a graph kernel called the path-pattern graph kernel.
Multiple Flat Projections for Cross-manifold Clustering
In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems.
Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD).
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences.
A Distributed Fair Machine Learning Framework with Private Demographic Data Protection
In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data.
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data
To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i. e., the surface form information, and the global context information for synonym discovery.
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
Fisher-Bures Adversary Graph Convolutional Networks
We try to minimize the loss wrt the perturbed $G+\Delta{G}$ while making $\Delta{G}$ to be effective in terms of the Fisher information of the neural network.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Characterization of migrated seismic volumes using texture attributes: a comparative study
It is our hope that this comparative study will help acquaint the seismic interpretation community with the many available powerful image texture analysis techniques, providing more alternative attributes for their seismic exploration.
A general model for plane-based clustering with loss function
In this paper, we propose a general model for plane-based clustering.
A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i. e., seismic volume labeling.
Ramp-based Twin Support Vector Clustering
Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost.
Robust Bhattacharyya bound linear discriminant analysis through adaptive algorithm
In this paper, we propose a novel linear discriminant analysis criterion via the Bhattacharyya error bound estimation based on a novel L1-norm (L1BLDA) and L2-norm (L2BLDA).
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension
While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates.
A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning
This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).
Generalized two-dimensional linear discriminant analysis with regularization
However, 2DLDA may encounter the singularity issue theoretically and the sensitivity to outliers.
On the Effectiveness of Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss for both the discriminator and the generator.
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
In this paper, stochastic gradient descent algorithm is investigated to twin support vector machines for classification.
Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator.