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Found 56 papers, 15 papers with code
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition
Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network.
A Unified Collaborative Representation Learning for Neural-Network based Recommender Systems
On the other hand, ME models directly employ inner products as a default loss function metric that cannot project users and items into a proper latent space, which is a methodological disadvantage.
Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis
Respiratory sound classification is an important tool for remote screening of respiratory-related diseases such as pneumonia, asthma, and COVID-19.
Unsupervised Image Deraining: Optimization Model Driven Deep CNN
Consequently, we design an optimization model-driven deep CNN in which the unsupervised loss function of the optimization model is enforced on the proposed network for better generalization.
Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet
We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects.
Robust Federated Learning Against Adversarial Attacks for Speech Emotion Recognition
The proposed framework consists of i) federated learning for data privacy, and ii) adversarial training at the training stage and randomisation at the testing stage for model robustness.
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron.
IMBENS: Ensemble Class-imbalanced Learning in Python
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for quick implementing and deploying ensemble learning algorithms on class-imbalanced data.
Towards Inter-class and Intra-class Imbalance in Class-imbalanced Learning
We also present a detailed discussion and analysis about the pros and cons of different inter/intra-class balancing strategies based on DUBE .
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.
Orthogonal Graph Neural Networks
Graph neural networks (GNNs) have received tremendous attention due to their superiority in learning node representations.
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction
Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence.
Part2Word: Learning Joint Embedding of Point Clouds and Text by Matching Parts to Words
To resolve this issue, we propose a method to learn joint embedding of point clouds and text by matching parts from shapes to words from sentences in a common space.
Image Restoration for Remote Sensing: Overview and Toolbox
Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community.
Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery
Recently, transform-based tensor nuclear norm minimization methods are considered to capture low-rank tensor structures to recover third-order tensors in multi-dimensional image processing applications.
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation
Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.
Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation
Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy counterparts.
Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks
We study the problem of incorporating prior knowledge into a deep Transformer-based model, i. e., Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks.
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection
AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data.
Robust early-learning: Hindering the memorization of noisy labels
The \textit{early stopping} method therefore can be exploited for learning with noisy labels.
Ranked #26 on
Image Classification
on mini WebVision 1.0
(ImageNet Top-1 Accuracy metric)
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction
Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem.
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.
Unsupervised Hyperspectral Mixed Noise Removal Via Spatial-Spectral Constrained Deep Image Prior
Recently, convolutional neural network (CNN)-based methods are proposed for hyperspectral images (HSIs) denoising.
Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion
In experiments, we achieve state-of-the-art performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a textual encoding method.
Ranked #2 on
Link Prediction
on UMLS
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks
In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method.
Self-paced Ensemble for Highly Imbalanced Massive Data Classification
To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction.
Ranked #5 on
Relation Extraction
on NYT10-HRL
Classical Chinese Sentence Segmentation for Tomb Biographies of Tang Dynasty
Chinese characters that are and are not followed by a punctuation mark are classified into two categories.
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction
In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards.
JIM: Joint Influence Modeling for Collective Search Behavior
This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would pose a joint influence on the user search behavior rather than posing influence independently.
Gradient-Coherent Strong Regularization for Deep Neural Networks
However, we observe that imposing strong L1 or L2 regularization with stochastic gradient descent on deep neural networks easily fails, which limits the generalization ability of the underlying neural networks.
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback.
Sequenced-Replacement Sampling for Deep Learning
We propose sequenced-replacement sampling (SRS) for training deep neural networks.
Zero-shot User Intent Detection via Capsule Neural Networks
User intent detection plays a critical role in question-answering and dialog systems.
Rain Streak Removal for Single Image via Kernel Guided CNN
Then, our framework starts with learning the motion blur kernel, which is determined by two factors including angle and length, by a plain neural network, denoted as parameter net, from a patch of the texture component.
Document Modeling with External Attention for Sentence Extraction
Document modeling is essential to a variety of natural language understanding tasks.
Target-Sensitive Memory Networks for Aspect Sentiment Classification
In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target.
Abstract Meaning Representation for Paraphrase Detection
Abstract Meaning Representation (AMR) parsing aims at abstracting away from the syntactic realization of a sentence, and denote only its meaning in a canonical form.
Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning
Stage one extracts/groups the target-related words (call t-words) for a given target.
Contextual and Position-Aware Factorization Machines for Sentiment Classification
While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which can lead to poor results for fine-grained analysis at the snippet level (a phrase or sentence).
Achieving Strong Regularization for Deep Neural Networks
L1 and L2 regularizers are critical tools in machine learning due to their ability to simplify solutions.
Transformed Low-Rank Model for Line Pattern Noise Removal
This paper addresses the problem of line pattern noise removal from a single image, such as rain streak, hyperspectral stripe and so on.
Weighted Low-rank Tensor Recovery for Hyperspectral Image Restoration
To overcome these limitations, in this work, we propose a unified low-rank tensor recovery model for comprehensive HSI restoration tasks, in which non-local similarity between spectral-spatial cubic and spectral correlation are simultaneously captured by 3-order tensors.
Hyper-Laplacian Regularized Unidirectional Low-Rank Tensor Recovery for Multispectral Image Denoising
Recent low-rank based matrix/tensor recovery methods have been widely explored in multispectral images (MSI) denoising.
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.
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
However, sophisticated learning models are computationally unfeasible for data with millions of features.
Streaming Recommender Systems
The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios.
Hierarchical Attention Network for Action Recognition in Videos
Understanding human actions in wild videos is an important task with a broad range of applications.
Scaling Submodular Maximization via Pruned Submodularity Graphs
We propose a new random pruning method (called "submodular sparsification (SS)") to reduce the cost of submodular maximization.
A Survey of Signed Network Mining in Social Media
Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines.
Convex Factorization Machine for Regression
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
Consistent Collective Matrix Completion under Joint Low Rank Structure
We address the collective matrix completion problem of jointly recovering a collection of matrices with shared structure from partial (and potentially noisy) observations.
N$^3$LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way.
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
We consider stochastic strongly convex optimization with a complex inequality constraint.
