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Found 24 papers, 0 papers with code
Online Saddle Point Problem and Online Convex-Concave Optimization
Centered around solving the Online Saddle Point problem, this paper introduces the Online Convex-Concave Optimization (OCCO) framework, which involves a sequence of two-player time-varying convex-concave games.
Online Continual Learning via the Knowledge Invariant and Spread-out Properties
However, a key challenge in this continual learning paradigm is catastrophic forgetting, namely adapting a model to new tasks often leads to severe performance degradation on prior tasks.
Online Continual Learning via the Meta-learning Update with Multi-scale Knowledge Distillation and Data Augmentation
In this paper, we overcome these challenges by proposing a novel framework called Meta-learning update via Multi-scale Knowledge Distillation and Data Augmentation (MMKDDA).
$β$-CapsNet: Learning Disentangled Representation for CapsNet by Information Bottleneck
We present a framework for learning disentangled representation of CapsNet by information bottleneck constraint that distills information into a compact form and motivates to learn an interpretable factorized capsule.
Self-supervised Learning for Heterogeneous Graph via Structure Information based on Metapath
Empirical results validate the performance of SESIM method and demonstrate that this method can improve the representation ability of traditional neural networks on link prediction task and node classification task.
Selecting Related Knowledge via Efficient Channel Attention for Online Continual Learning
Based on this fact, in this paper we propose a new framework, named Selecting Related Knowledge for Online Continual Learning (SRKOCL), which incorporates an additional efficient channel attention mechanism to pick the particular related knowledge for every task.
Optimistic Online Convex Optimization in Dynamic Environments
Existing works have shown that Ader enjoys an $O\left(\sqrt{\left(1+P_T\right)T}\right)$ dynamic regret upper bound, where $T$ is the number of rounds, and $P_T$ is the path length of the reference strategy sequence.
Bilevel Online Deep Learning in Non-stationary Environment
In this paper, we proposed a new Bilevel Online Deep Learning (BODL) framework, which combine bilevel optimization strategy and online ensemble classifier.
GMM Discriminant Analysis with Noisy Label for Each Class
Real world datasets often contain noisy labels, and learning from such datasets using standard classification approaches may not produce the desired performance.
Multi-Scale Iterative Refinement Network for RGB-D Salient Object Detection
The extensive research leveraging RGB-D information has been exploited in salient object detection.
Online Deep Learning based on Auto-Encoder
And so, with this in minds, the online deep learning model we need to design should have a variable underlying structure; (3) moreover, it is of utmost importance to fusion these abstract hierarchical latent representations to achieve better classification performance, and we should give different weights to different levels of implicit representation information when dealing with the data streaming where the data distribution changes.
Multi-View representation learning in Multi-Task Scene
By this way, the original multi-task multi-view data has degenerated into multi-task data, and exploring the correlations among multiple tasks enables to make an improvement on the performance of learning algorithm.
Multi-granularity Relabeled Under-sampling Algorithm for Imbalanced Data
The Tomek-Link sampling algorithm can effectively reduce the class overlap on data, remove the majority instances that are difficult to distinguish, and improve the algorithm classification accuracy.
Auto-Encoder based Co-Training Multi-View Representation Learning
As we known, auto-encoder is a method of deep learning, which can learn the latent feature of raw data by reconstructing the input, and based on this, we propose a novel algorithm called Auto-encoder based Co-training Multi-View Learning (ACMVL), which utilizes both complementarity and consistency and finds a joint latent feature representation of multiple views.
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss
But not all of this information is useful for classification tasks.
Adaptive Online Incremental Learning for Evolving Data Streams
The last one we often ignore is the learning of the latent representation.
Partially latent factors based multi-view subspace learning
To this end, a two stage fusion strategy is proposed to embed representation learning into the process of multi-view subspace clustering.
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity
Multi-view learning attempts to generate a model with a better performance by exploiting the consensus and/or complementarity among multi-view data.
Multi-view Subspace Adaptive Learning via Autoencoder and Attention
Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention.
Universal Transformer Hawkes Process with Adaptive Recursive Iteration
Enlighten by transformer model, which can learning sequence data efficiently without recurrent and convolutional structure, transformer Hawkes process is come out, and achieves state-of-the-art performance.
Temporal Attention Augmented Transformer Hawkes Process
With this in mind, we come up with a new kind of Transformer-based Hawkes process model, Temporal Attention Augmented Transformer Hawkes Process (TAA-THP), we modify the traditional dot-product attention structure, and introduce the temporal encoding into attention structure.
Attentive Multi-View Deep Subspace Clustering Net
In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view's dynamic contribution obtained by attention mechanism.
A Unified Analysis Method for Online Optimization in Normed Vector Space
This paper studies online optimization from a high-level unified theoretical perspective.
ENHANCE THE DYNAMIC REGRET VIA OPTIMISM
In particular, if $\widehat{x}_t^*\in\partial\widehat{\varphi}_t$, where $\widehat{\varphi}_t$ represents the estimated convex loss function and $\partial\widehat{\varphi}_t$ is Lipschitz continuous, then the dynamic regret upper bound of OMD has a subgradient variation type.
