New method for online influence maximization in social networks.
problem Identifying influential nodes in social networks.
method Factorization of activation probabilities into latent factors on nodes, using upper confidence bound online learning.
result Significant reduction in regret with proposed algorithm.
New algorithm for online training of Spiking Neural Networks (SNNs).
problem Training Spiking Neural Networks (SNNs) online with BPTT-equivalent gradients.
method Clear separation of spatial and temporal gradient components, derived from biological insights.
result Online training of SNNs with BPTT-equivalent gradients and low time complexity.
Predicts student performance in interactive online question pools using GNNs.
problem Predicting student performance in interactive online question pools with evolving knowledge.
method Proposes R^2GCN, a GNN model for heterogeneous networks to predict student performance.
result Achieves higher accuracy in student performance prediction than traditional methods.
Efficient RNN algorithm guarantees convergence in online learning.
problem Online nonlinear regression with RNNs.
method First-order training algorithm with convergence guarantee.
result The algorithm converges to optimum network parameters.
Deep neural networks improve online learning by ensuring convergence to best strategies.
problem Challenges in online learning due to dependencies between observations.
method Lipschitz regularized deep neural networks for online learning.
result Guaranteed convergence to the best prediction strategy.
GLCB uses Gated Linear Networks for online contextual bandits.
problem Online learning in contextual bandits with uncertainty estimation.
method Gated Linear Networks (GLNs) for prediction and uncertainty estimation.
result GLCB outperforms state-of-the-art methods in online contextual bandits.
Online Normalization normalizes neural network activations without batching for better accuracy.
problem Theoretical limitations of Batch Normalization and its inapplicability to certain network types.
method Introduces an unbiased gradient computation technique for normalized activations without using batches.
result Equivalent accuracy to Batch Normalization without batch usage.
Online algorithm detects community structure in dynamic event streams.
problem Community detection in networks with temporal event streams.
method Continuous-time point process latent network models with fast online variational inference.
result Online inference achieves comparable community recovery to non-online methods but with computational gains.
FAKEDETECTOR detects fake news from social networks.
problem Identifying fake news in online social networks.
method Deep diffusive neural network model.
result Demonstrated effectiveness of FAKEDETECTOR on real-world fake news dataset.
Paper explores offline and online image recognition using neural networks.
problem Challenges in evolving image recognition through different settings.
method Used Convolutional Neural Networks and Multi-layer Perceptrons.
result Encouraging preliminary results in offline and online image classification.
POLA adapts learning rates for online time series prediction.
problem Adapting to changing data distributions in dynamic environments.
method Adaptive learning rate regulation for recurrent neural networks.
result POLA outperforms other online prediction methods in real-world datasets.
Robots learn quickly from few interactions using mental replay and intrinsic motivation.
problem Continuous online adaptation for robots in changing environments.
method Bio-inspired stochastic recurrent neural network with learning signals and mental replay.
result Robots can adapt to novel environments in seconds from few interactions.
A new algorithm learns optimal source placement in large networks.
problem Optimizing source placement in large scale networks with unknown processes.
method Graph-Kernel Multi-Armed Bandit (Grab-UCB) algorithm with adaptive graph dictionary model.
result Online learning algorithm outperforms offline methods in terms of cumulative regret, sample efficiency, and computational complexity.
This paper proposes an online knowledge distillation method that transfers feature map information in addition to class probabilities.
problem Previous online knowledge distillation methods only utilized class probabilities, missing feature map information.
method Adversarial training framework to transfer feature map information; multiple networks trained simultaneously with discriminators.
result Our method performs better than direct alignment methods and is more suitable for online distillation.
Proposes online learning for Hawkes processes with network structure and event interaction.
problem Modeling complex interactions and latent structures in network events.
method Online learning approach for mixture of multivariate Hawkes processes.
result Efficacy demonstrated on synthetic and real-world data.
SnAp approximates RTRL for online training of sparse recurrent networks.
problem Training large sparse recurrent networks online is computationally expensive.
method Sparse n-step Approximation (SnAp) of the RTRL influence matrix.
result SnAp with n=2 remains tractable for highly sparse networks and outperforms backpropagation through time.
Wide and Deep GNN learns from distributed graphs and retrain online.
problem Decentralized graph support changes over time, causing mismatch between training and testing graphs.
method Wide and Deep GNN architecture with distributed online learning.
result Convergence guarantees for online retraining of the wide part of the GNN.
The paper explores how to learn from incomplete online social networks.
problem Learning from partially observed networks via node querying.
method Developed algorithms NOL* for sequential node querying to maximize network observability.
result It is possible to sequentially learn which nodes to query for maximal network observability.
First online structure learning for SPNs with Gaussian leaves.
problem Specifying a valid sum-product network by hand is difficult.
method Online structure learning technique for continuous SPNs with Gaussian leaves.
result First online structure learning technique for SPNs with Gaussian leaves.
Kolmogorov-Arnold Networks enable ultrafast online learning with fixed-point quantization.
problem Efficient online learning for high-frequency systems with strict memory constraints.
method Fixed-point online training on FPGAs exploiting B-spline locality in KANs.
result Kolmogorov-Arnold Networks are more efficient and expressive than MLPs for low-latency tasks.
Revisits online Laplace methods for neural networks, showing they are sound under certain conditions.
problem Online Laplace methods violate the Laplace approximation's critical assumption.
method Re-derives online Laplace methods, showing they target a variational bound on a mode-corrected variant of the Laplace evidence.
result Online Laplace and its mode-corrected counterpart share stationary points that satisfy the Laplace method's assumption.
Paper proposes a method for efficient online classification using siamese networks and active learning.
problem Online learning challenges with limited, nonstationary, and imbalanced data.
method Synergistic combination of siamese neural networks and active learning with multi-sliding window approach.
result Significantly outperforms baselines and state-of-the-art algorithms in terms of learning speed and performance.
Bayesian online learning method improves neural network performance.
problem Overcoming catastrophic forgetting in neural networks.
method Kronecker factored online Laplace approximation for Bayesian online learning.
result Achieves over 90% test accuracy across 50 MNIST tasks.
Study how communication and feedback graphs affect learning outcomes.
problem Understanding the impact of feedback graphs on cooperative online learning.
method Analyzed network regret in terms of the independence number of the strong product of communication and feedback graphs.
result Proved bounds for network regret and demonstrated the non-improvable nature of positive results in pathological cases.
LMNs enhance neural networks with memory to adapt online.
problem Online adaptation of neural networks for domain-relevant data.
method LMNs use label-based memory replacement and write to memory only for instances with non-zero loss.
result Significant accuracy gains on various tasks including word-modelling and few-shot learning.
This work improves online regression and contextual bandits using neural networks.
problem Improving online regression and contextual bandits using neural networks.
method Investigates neural networks for online regression, showing O ( log T ) \mathcal{O}(\log T) O ( log T ) regret for almost convex losses and KL loss. result Shows i l d e O ( K L ∗ + K ) ilde{\mathcal{O}}(\sqrt{KL^*} + K) i l d e O ( K L ∗ + K ) regret for NeuCB, outperforming existing algorithms. Self-calibrating neural networks adaptively determine dimensionality reduction.
problem Adaptive determination of dimensionality reduction magnitude.
method Derive online algorithms from similarity matching principle, self-calibrate threshold based on singular values.
result Effectiveness demonstrated in various settings via mathematical and simulation.
Online learning algorithm for RBM adapts network architecture to streaming data.
problem Adapting RBM architecture to streaming data for better feature representation and classification.
method Two-phase training: online generative phase for unsupervised feature learning, discriminative phase for classification.
result Online RBM converges to a stable, concise network architecture with inherently discriminative neurons.
Paper proposes online learning for estimating AC network admittance matrix.
problem Missing or outdated information on power grid topology and parameters.
method Recursive identification algorithm using phasor measurements, enhanced with DOE for optimal data excitation.
result Improves on existing techniques and substantiated by numerical studies.
Study online and offline social interactions using game theory.
problem Effects of online social networking on offline interactions and collective well-being.
method Evolutionary game theory approach to model socialization strategies.
result Self-protective behaviors can lead to non-socially optimal outcomes.
New algorithms improve online learning performance for convex and neural network settings.
problem Improving online learning performance for streaming data.
method Introducing a new performance metric (rolling window regret) and proposing algorithms based on gradient descent with constant or adaptive learning rates.
result Established regret bounds for algorithms in both convex and neural network settings.
New algorithm optimizes online network resource allocation with long-term constraints.
problem Optimal resource reservation in communication networks with job transfers and budget limits.
method Randomized exponentially weighted method for long-term constraints.
result Upper bound for regret and cumulative constraint violations established.
Gated Linear Networks bypass feature learning for fast online learning.
problem Fast online learning and feature learning trade-offs in neural networks.
method Distributed and local credit assignment mechanism, data-dependent gating, online convex optimization.
result GLNs achieve universal learning capabilities and resilience to catastrophic forgetting.
Unified framework for efficient online training of RNNs.
problem Efficient and biologically plausible online training of recurrent neural networks.
method Organizes algorithms based on criteria like past vs. future facing, tensor structure, stochastic vs. deterministic, and closed form vs. numerical.
result Algorithms cluster according to criteria, revealing conceptual connections.
SVOCD detects changepoints in complex systems efficiently.
problem Detecting changepoints in complex systems efficiently.
method Stein variational online changepoint detection integrating SVN method and BOCPD.
result Successfully applied to Hawkes processes and LSTM neural networks.
Online PaLD extends PaLD for semi-supervised online applications.
problem Scalability of unsupervised clustering algorithms for large datasets.
method Adapted partitioned local depth algorithm for online semi-supervised prediction.
result Online PaLD extends cohesion network to new data points efficiently.
OMF algorithm adapts to Markovian data streams for efficient feature extraction.
problem Convergence of OMF algorithms for dependent data streams.
method Non-convex generalization of OMF for Markovian data, convergence to critical points.
result OMF converges to critical points of expected loss function for Markovian data.
Paper proposes online learning for MIMO channel estimation using neural networks.
problem Challenges in MIMO channel estimation due to physical model simplifications and system configuration requirements.
method Unfold a channel estimation algorithm as a neural network, allowing online learning and correction of imperfect models.
result Achieves channel estimation error close to that of a perfectly calibrated system.
Residual Networks are shown to be equivalent to boosting feature representation.
problem Improving feature representation in deep learning models.
method Proved ResNet's equivalence to Online Gradient Boosting and proposed decision tree residual modules.
result ResNet can achieve Online Gradient Boosting regret bounds through architectural changes.
Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have bee…
Converts GBDT trees to neural networks for online updates.
problem Performance loss in converting GBDT trees to neural networks.
method Converts existing GBDT implementations to neural network architectures, allowing online updates of decision splits.
result Learning bounds for neural network architecture with updated splits.
New algorithm detects online communities in evolving networks.
problem Detecting communities in time-varying networks.
method Uses MCMC Louvain method to maximize modularity.
result Algorithm dynamically maintains community structure.
Paper presents a neural network for estimating wavefronts in direction of arrival scenarios.
problem Estimating the number of wavefronts in direction of arrival scenarios.
method Cross-entropy trained multilayer neural network for online adaptation of antenna array imperfections.
result The method outperforms classical model order selection schemes in accuracy, especially at low signal-to-noise-ratios.
Paper proposes federated learning for SNNs to enable low-power, online training.
problem Limited data at each device for on-device SNN training.
method Federated Learning (FL) for cooperative SNN training, leveraging local and global feedback.
result FL-SNN achieves significant advantages over separate training and offers a flexible trade-off between accuracy and communication load.
Study on online learning with networked agents, showing how network structure affects performance.
problem Understanding how network structure impacts performance in online learning settings.
method Characterized the effect of network structure on regret in both stochastic and adversarial settings.
result Optimal regret bound of order α T \sqrt{αT} α T when activations are stochastic and network structure is known. Deep learning detects radical content on social media.
problem Detecting extremist content on social media platforms.
method Employed an LSTM based feed forward neural network to classify radical content.
result Achieved a precision of 85.9% in detecting radical content.
New algorithms for fast online decision making using neural networks and martingale posteriors.
problem Online sequential decision making under uncertainty.
method Martingale posterior neural networks for fast online learning and decision making.
result Achieves competitive performance-speed trade-offs in non-stationary contextual bandits and Bayesian optimization.
Develops a framework to control risk in online learning models.
problem Rigorous uncertainty quantification for online learning models.
method A framework for constructing uncertainty sets that provably control risk.
result Guarantees risk control at any user-specified level even with distribution shifts.