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Found 125 papers, 44 papers with code
Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi
In this paper, we develop Sindhi subjective lexicon using a merger of existing English resources: NRC lexicon, list of opinion words, SentiWordNet, Sindhi-English bilingual dictionary, and collection of Sindhi modifiers.
SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging
In this paper, we introduce the SiPOS dataset for part-of-speech tagging in the low-resource Sindhi language with quality baselines.
On the Necessity of Collaboration in Online Model Selection with Decentralized Data
We consider online model selection with decentralized data over $M$ clients, and study a fundamental problem: the necessity of collaboration.
MVEB: Self-Supervised Learning with Multi-View Entropy Bottleneck
One can learn this representation by maximizing the mutual information between the representation and the supervised view while eliminating superfluous information.
FedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning
Extensive experiments on real-world medical data demonstrate the effectiveness of FedPIT in improving federated few-shot performance while preserving privacy and robustness against data heterogeneity.
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling
To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information.
Enhancing Efficiency in Sparse Models with Sparser Selection
To address this issue, we present \tool, a novel MoE designed to enhance both the efficacy and efficiency of sparse MoE models.
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results.
Cumulative Distribution Function based General Temporal Point Processes
Our contributions encompass the introduction of a pioneering CDF-based TPP model, the development of a methodology for incorporating past event information into future event prediction, and empirical validation of CuFun's effectiveness through extensive experimentation on synthetic and real-world datasets.
Preparing Lessons for Progressive Training on Language Models
The rapid progress of Transformers in artificial intelligence has come at the cost of increased resource consumption and greenhouse gas emissions due to growing model sizes.
SecFormer: Towards Fast and Accurate Privacy-Preserving Inference for Large Language Models
However, the application of SMPC in Privacy-Preserving Inference (PPI) for large language models, particularly those based on the Transformer architecture, often leads to considerable slowdowns or declines in performance.
Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks
To address these challenges, we conduct a thorough theoretical convergence analysis for DFL and derive a convergence bound.
On Diversified Preferences of Large Language Model Alignment
Our analysis reveals a correlation between the calibration performance of reward models (RMs) and the alignment performance of LLMs.
Federated Knowledge Graph Completion via Latent Embedding Sharing and Tensor Factorization
To address these issues, we propose a novel method, i. e., Federated Latent Embedding Sharing Tensor factorization (FLEST), which is a novel approach using federated tensor factorization for KG completion.
Generalized Category Discovery with Clustering Assignment Consistency
To address the GCD without knowing the class number of unlabeled dataset, we propose a co-training-based framework that encourages clustering consistency.
Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels
In FEEL, both mobile devices transmitting model parameters over noisy channels and collecting data in diverse environments pose challenges to the generalization of trained models.
Advocating for the Silent: Enhancing Federated Generalization for Non-Participating Clients
Federated Learning (FL) has surged in prominence due to its capability of collaborative model training without direct data sharing.
Less is More: On the Feature Redundancy of Pretrained Models When Transferring to Few-shot Tasks
Transferring a pretrained model to a downstream task can be as easy as conducting linear probing with target data, that is, training a linear classifier upon frozen features extracted from the pretrained model.
Tackling Hybrid Heterogeneity on Federated Optimization via Gradient Diversity Maximization
The combined effects of statistical and system heterogeneity can significantly reduce the efficiency of federated optimization.
Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance
Central to this process is the technique of unbiased client sampling, which ensures a representative selection of clients.
Personalized Federated Learning via Amortized Bayesian Meta-Learning
Federated learning is a decentralized and privacy-preserving technique that enables multiple clients to collaborate with a server to learn a global model without exposing their private data.
Practical Privacy-Preserving Gaussian Process Regression via Secret Sharing
In contrast to existing studies that protect the data privacy of GPR via homomorphic encryption, differential privacy, or federated learning, our proposed method is more practical and can be used to preserve the data privacy of both the model inputs and outputs for various data-sharing scenarios (e. g., horizontally/vertically-partitioned data).
FedNoisy: Federated Noisy Label Learning Benchmark
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients.
Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
In this paper, we explore how to enable intelligent cost-effective interactions between the agent and an LLM.
Tensorized Hypergraph Neural Networks
THNN is a faithful hypergraph modeling framework through high-order outer product feature message passing and is a natural tensor extension of the adjacency-matrix-based graph neural networks.
Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping
In this paper, we thoroughly investigate the intrinsic effectiveness of recent approaches and make three key observations: 1) linear mapping is critical to prior long-term time series forecasting efforts; 2) RevIN (reversible normalization) and CI (Channel Independent) play a vital role in improving overall forecasting performance; and 3) linear mapping can effectively capture periodic features in time series and has robustness for different periods across channels when increasing the input horizon.
Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition
There are three types of NER tasks, including flat, nested and discontinuous entity recognition.
Stochastic Clustered Federated Learning
Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices.
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society.
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Learning with imbalanced data is a challenging problem in deep learning.
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
Specifically, we find that (1) attention is not necessary for capturing temporal dependencies, (2) the entanglement and redundancy in the capture of temporal and channel interaction affect the forecasting performance, and (3) it is important to model the mapping between the input and the prediction sequence.
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
Interestingly, although these two types of networks originate from different observations, they are inherently linked through the common multilinearity structure underlying both TNs and NNs, thereby motivating a significant number of intellectual developments regarding combinations of TNs and NNs.
Ti-MAE: Self-Supervised Masked Time Series Autoencoders
Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios.
Contrastive Learning
Multivariate Time Series Forecasting
+2
MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering
Multi-view hierarchical clustering (MCHC) plays a pivotal role in comprehending the structures within multi-view data, which hinges on the skillful interaction between hierarchical feature learning and comprehensive representation learning across multiple views.
When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning Methods
To facilitate the research of PETuning in FL, we also develop a federated tuning framework FedPETuning, which allows practitioners to exploit different PETuning methods under the FL training paradigm conveniently.
MultiCoder: Multi-Programming-Lingual Pre-Training for Low-Resource Code Completion
On the other hand, there are few studies exploring the effects of multi-programming-lingual (MultiPL) pre-training for the code completion, especially the impact on low-resource programming languages.
Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid
In this paper, we find that this problem usually occurs when the positions of support samples are in the vicinity of task centroid -- the mean of all class centroids in the task.
Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling
Transformer-based models have achieved great success on sentence pair modeling tasks, such as answer selection and natural language inference (NLI).
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities
We introduce ViLPAct, a novel vision-language benchmark for human activity planning.
Field-aware Variational Autoencoders for Billion-scale User Representation Learning
Moreover, we deploy the proposed method in real-world applications and conduct online A/B tests in a look-alike system.
Channel Importance Matters in Few-Shot Image Classification
When facing novel few-shot tasks in the test-time datasets, this transformation can greatly improve the generalization ability of learned image representations, while being agnostic to the choice of training algorithms and datasets.
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization
In detail, by studying the long-tailed distribution of node degrees in the graph, we propose a novel normalization method for GNNs, which is termed ResNorm (\textbf{Res}haping the long-tailed distribution into a normal-like distribution via \textbf{norm}alization).
A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks
To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs.
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
However, privacy information could be leaked from uploaded gradients and be exposed to malicious attackers or an honest-but-curious server.
Contrastive Multi-view Hyperbolic Hierarchical Clustering
Then, we embed the representations into a hyperbolic space and optimize the hyperbolic embeddings via a continuous relaxation of hierarchical clustering loss.
MarkBERT: Marking Word Boundaries Improves Chinese BERT
We present a Chinese BERT model dubbed MarkBERT that uses word information in this work.
Chinese Named Entity Recognition
named-entity-recognition
+7
Semantically Proportional Patchmix for Few-Shot Learning
Few-shot learning aims to classify unseen classes with only a limited number of labeled data.
Source Code Summarization with Structural Relative Position Guided Transformer
We further show that how the proposed SCRIPT captures the structural relative dependencies.
Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
In this paper, we propose FedGS, which is a hierarchical cloud-edge-end FL framework for 5G empowered industries, to improve industrial FL performance on non-i. i. d.
Heterogeneous Federated Learning via Grouped Sequential-to-Parallel Training
In this paper, we propose a data heterogeneity-robust FL approach, FedGSP, to address this challenge by leveraging on a novel concept of dynamic Sequential-to-Parallel (STP) collaborative training.
Exploring Category-correlated Feature for Few-shot Image Classification
To alleviate this problem, we present a simple yet effective feature rectification method by exploring the category correlation between novel and base classes as the prior knowledge.
Self-Paced Deep Regression Forests with Consideration of Ranking Fairness
To answer this question, this paper proposes a new SPL method: easy and underrepresented examples first, for learning DDMs.
Label-Aware Distribution Calibration for Long-tailed Classification
Real-world data usually present long-tailed distributions.
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs
Graph Convolutional Networks (GCNs) are powerful for processing graph-structured data and have achieved state-of-the-art performance in several tasks such as node classification, link prediction, and graph classification.
FedLab: A Flexible Federated Learning Framework
Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.
Boosting Few-Shot Classification with View-Learnable Contrastive Learning
The goal of few-shot classification is to classify new categories with few labeled examples within each class.
ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE
To address this issue, we propose Bayesian Pseudocoresets Exemplar VAE (ByPE-VAE), a new variant of VAE with a prior based on Bayesian pseudocoreset.
Stein Variational Gradient Descent with Multiple Kernel
In practice, we notice that the kernel used in SVGD-based methods has a decisive effect on the empirical performance.
Rectifying the Shortcut Learning of Background for Few-Shot Learning
The category gap between training and evaluation has been characterised as one of the main obstacles to the success of Few-Shot Learning (FSL).
Discrete Auto-regressive Variational Attention Models for Text Modeling
We further design discrete latent space for the variational attention and mathematically show that our model is free from posterior collapse.
AFINet: Attentive Feature Integration Networks for Image Classification
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification.
Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
The first mechanism is a selective domain adaptation (SDA) method, which transfers knowledge from the closest source domain.
TedNet: A Pytorch Toolkit for Tensor Decomposition Networks
Tensor Decomposition Networks (TDNs) prevail for their inherent compact architectures.
Pseudo-supervised Deep Subspace Clustering
To tackle these problems, we use pairwise similarity to weigh the reconstruction loss to capture local structure information, while a similarity is learned by the self-expression layer.
Partial Differential Equations is All You Need for Generating Neural Architectures -- A Theory for Physical Artificial Intelligence Systems
In this work, we generalize the reaction-diffusion equation in statistical physics, Schr\"odinger equation in quantum mechanics, Helmholtz equation in paraxial optics into the neural partial differential equations (NPDE), which can be considered as the fundamental equations in the field of artificial intelligence research.
Contrastive Disentanglement in Generative Adversarial Networks
Disentanglement is defined as the problem of learninga representation that can separate the distinct, informativefactors of variations of data.
A Survey on Deep Semi-supervised Learning
Deep semi-supervised learning is a fast-growing field with a range of practical applications.
Graph-based Semi-supervised Learning: A Comprehensive Review
An important class of SSL methods is to naturally represent data as graphs such that the label information of unlabelled samples can be inferred from the graphs, which corresponds to graph-based semi-supervised learning (GSSL) methods.
MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance
Deep Convolutional Neural Networks (DCNNs) and their variants have been widely used in large scale face recognition(FR) recently.
RegNet: Self-Regulated Network for Image Classification
The ResNet and its variants have achieved remarkable successes in various computer vision tasks.
Ranked #3 on
Medical Image Classification
on NCT-CRC-HE-100K
AFINets: Attentive Feature Integration Networks for Image Classification
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks, e. g., image classification.
Auto-view contrastive learning for few-shot image recognition
Few-shot learning aims to recognize new classes with few annotated instances within each category.
A Subword Guided Neural Word Segmentation Model for Sindhi
The word segmentation is a fundamental and inevitable prerequisite for many languages.
Block-term Tensor Neural Networks
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e. g., image classification, natural language processing, etc.
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Interestingly, we discover that part of the rank elements is sensitive and usually aggregate in a narrow region, namely an interest region.
Structured Graph Learning for Clustering and Semi-supervised Classification
Furthermore, most existing graph-based methods conduct clustering and semi-supervised classification on the graph learned from the original data matrix, which doesn't have explicit cluster structure, thus they might not achieve the optimal performance.
Deep Embedded Multi-view Clustering with Collaborative Training
Firstly, the embedded representations of multiple views are learned individually by deep autoencoders.
CoreGen: Contextualized Code Representation Learning for Commit Message Generation
In this paper, we propose a novel Contextualized code representation learning strategy for commit message Generation (CoreGen).
Relation-Guided Representation Learning
In this work, we propose a new representation learning method that explicitly models and leverages sample relations, which in turn is used as supervision to guide the representation learning.
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
Existing algorithms of MS-UDA either only exploit the shared features, i. e., the domain-invariant information, or based on some weak assumption in NLP, e. g., smoothness assumption.
Multi-Source Unsupervised Domain Adaptation
Sentiment Analysis
+2
Angular Triplet Loss-based Camera Network for ReID
Many complex network structures are proposed recently and many of them concentrate on multi-branch features to achieve high performance.
Mutual Information Gradient Estimation for Representation Learning
To this end, we propose the Mutual Information Gradient Estimator (MIGE) for representation learning based on the score estimation of implicit distributions.
SiNER: A Large Dataset for Sindhi Named Entity Recognition
We introduce the SiNER: a named entity recognition (NER) dataset for low-resourced Sindhi language with quality baselines.
Discrete Variational Attention Models for Language Generation
Variational autoencoders have been widely applied for natural language generation, however, there are two long-standing problems: information under-representation and posterior collapse.
Self-Paced Deep Regression Forests with Consideration on Underrepresented Examples
Deep discriminative models (e. g. deep regression forests, deep neural decision forests) have achieved remarkable success recently to solve problems such as facial age estimation and head pose estimation.
Structure Learning with Similarity Preserving
Leveraging on the underlying low-dimensional structure of data, low-rank and sparse modeling approaches have achieved great success in a wide range of applications.
Multi-view Subspace Clustering via Partition Fusion
Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way.
Word Embedding based New Corpus for Low-resourced Language: Sindhi
Our intrinsic evaluation results demonstrate the high quality of our generated Sindhi word embeddings using SG, CBoW, and GloVe as compare to SdfastText word representations.
Large-scale Multi-view Subspace Clustering in Linear Time
A plethora of multi-view subspace clustering (MVSC) methods have been proposed over the past few years.
Deep Minimax Probability Machine
In a worst-case scenario, MPM tries to minimize an upper bound of misclassification probabilities, considering the global information (i. e., mean and covariance information of each class).
On Model Robustness Against Adversarial Examples
We propose to exploit an energy function to describe the stability and prove that reducing such energy guarantees the robustness against adversarial examples.
Multi-graph Fusion for Multi-view Spectral Clustering
Most existing methods don't pay attention to the quality of the graphs and perform graph learning and spectral clustering separately.
Multiple Partitions Aligned Clustering
Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie.
Constructing Interpretive Spatio-Temporal Features for Multi-Turn Responses Selection
Response selection plays an important role in fully automated dialogue systems.
Structured Pruning of Recurrent Neural Networks through Neuron Selection
Recurrent neural networks (RNNs) have recently achieved remarkable successes in a number of applications.
Clustering with Similarity Preserving
A key step of graph-based approach is the similarity graph construction.
Additive Adversarial Learning for Unbiased Authentication
Authentication is a task aiming to confirm the truth between data instances and personal identities.
Learning to Search Efficient DenseNet with Layer-wise Pruning
Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources.
Low-rank Kernel Learning for Graph-based Clustering
By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other.
Similarity Learning via Kernel Preserving Embedding
Data similarity is a key concept in many data-driven applications.
Semi-supervised deep embedded clustering
Deep embedded clustering (DEC) is one of the state-of-the-art deep clustering methods.
DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification
In this paper, we propose a new unsupervised domain adaptation method named Domain-Adversarial Residual-Transfer (DART) learning of Deep Neural Networks to tackle cross-domain image classification tasks.
Latent Dirichlet Allocation in Generative Adversarial Networks
We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs).
Robust Graph Learning from Noisy Data
The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption, 2) improved graph construction by exploiting clean data recovered by robust PCA.
Deep Density-based Image Clustering
Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications.
Ranked #1 on
Image Clustering
on LetterA-J
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling.
Self-Paced Multi-Task Clustering
Despite the success of traditional MTC models, they are either easy to stuck into local optima, or sensitive to outliers and noisy data.
Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification
There are two possible reasons for the failure: (i) most existing MKL methods assume that the optimal kernel is a linear combination of base kernels, which may not hold true; and (ii) some kernel weights are inappropriately assigned due to noises and carelessly designed algorithms.
Adversarial Noise Layer: Regularize Neural Network By Adding Noise
In this paper, we introduce a novel regularization method called Adversarial Noise Layer (ANL) and its efficient version called Class Adversarial Noise Layer (CANL), which are able to significantly improve CNN's generalization ability by adding carefully crafted noise into the intermediate layer activations.
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Given the limited GPU DRAM, SuperNeurons not only provisions the necessary memory for the training, but also dynamically allocates the memory for convolution workspaces to achieve the high performance.
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition
Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification.
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
On three challenging tasks, including Action Recognition in Videos, Image Captioning and Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of both prediction accuracy and convergence rate.
Two Birds with One Stone: Transforming and Generating Facial Images with Iterative GAN
Generating high fidelity identity-preserving faces with different facial attributes has a wide range of applications.
Unified Spectral Clustering with Optimal Graph
Second, the discrete solution may deviate from the spectral solution since k-means method is well-known as sensitive to the initialization of cluster centers.
Stochastic Sequential Neural Networks with Structured Inference
Inheriting these advantages of stochastic neural sequential models, we propose a structured and stochastic sequential neural network, which models both the long-term dependencies via recurrent neural networks and the uncertainty in the segmentation and labels via discrete random variables.
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data.
Tensor Decomposition via Variational Auto-Encoder
Another assumption of these methods is that a predefined rank should be known.
Distributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multi-way data.
Exact and Stable Recovery of Pairwise Interaction Tensors
For the noisy cases, we also prove error bounds for a constrained convex program for recovering the tensors.
Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis
To unify these two tasks, we present a new sparse Bayesian approach for joint association study and disease diagnosis.
Robust Metric Learning by Smooth Optimization
Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints.
Heavy-Tailed Symmetric Stochastic Neighbor Embedding
Based on this finding, we present a parameterized subset of similarity functions for choosing the best tail-heaviness for HSSNE; (2) we present a fixed-point optimization algorithm that can be applied to all heavy-tailed functions and does not require the user to set any parameters; and (3) we present two empirical studies, one for unsupervised visualization showing that our optimization algorithm runs as fast and as good as the best known t-SNE implementation and the other for semi-supervised visualization showing quantitative superiority using the homogeneity measure as well as qualitative advantage in cluster separation over t-SNE.
Adaptive Regularization for Transductive Support Vector Machine
In this framework, SVM and TSVM can be regarded as a learning machine without regularization and one with full regularization from the unlabeled data, respectively.
An Extended Level Method for Efficient Multiple Kernel Learning
We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem.
Efficient Convex Relaxation for Transductive Support Vector Machine
We consider the problem of Support Vector Machine transduction, which involves a combinatorial problem with exponential computational complexity in the number of unlabeled examples.
