Efficiently updates KRR for big streams with minimal redundant computation.
problem Redundant computation in incremental KRR for big data streams.
method Supports incremental/decremental processing for single and multiple samples, dividing data into batches.
result Significantly reduced computational time without sacrificing accuracy.
A new method for fast incremental/decremental analysis without recursion.
problem Efficiently updating support-vector models with new data.
method Ridge Support Vector Models with Weight-Error Curves (WECs) for recursion-free computation.
result All new Lagrangian multipliers can be computed simultaneously, relaxing previous constraints.
Edge computing tackles dynamic data in IIoT with incremental learning.
problem Latency and bandwidth limitations in IoT devices.
method Incremental learning applied to edge-computing systems for continual learning.
result Reduces catastrophic forgetting and provides efficient real-time quality control.
Efficiently processes dynamic inputs in AI writing assistants with incremental computation.
problem Efficiently updating AI models in real-time with dynamic inputs.
method Incremental computing using vector quantization to filter and reuse intermediate values in neural networks.
result Comparable accuracy with 12.1X fewer operations for processing dynamic inputs.
hi-RF method improves large-scale multi-class data classification with less computational time.
problem Dynamic growth of data and classes poses challenges to traditional classification methods.
method hi-RF is an incremental learning method that replaces or updates trees in random forest adaptively.
result hi-RF achieves comparable precision with significantly reduced computational time.
SaMbaTen efficiently maintains tensor decompositions for growing datasets.
problem Maintaining tensor decompositions for dynamic, growing datasets.
method Sampling-based batch incremental tensor decomposition algorithm.
result SaMbaTen achieves comparable accuracy to state-of-the-art techniques but is significantly faster and scalable.
A new method reduces CV computation time for big data.
problem Efficiently estimating model performance and tuning parameters.
method A general approach for reducing CV computation time in incremental learning.
result Running time scales logarithmically with the number of CV folds.
New approach AR1 improves performance in class-incremental learning.
problem Training deep models sequentially on a single incremental task without forgetting.
method Combining architectural and regularization strategies, AR1 is specifically designed for incremental task scenarios.
result AR1 outperformed existing regularization strategies on CORe50 and iCIFAR-100.
Incremental method for graph Laplacian eigenpairs improves clustering efficiency.
problem Determining the number of clusters in spectral clustering.
method Incremental computation of graph Laplacian eigenpairs.
result Efficiently computes the K-th smallest eigenpair. GraphSAIL updates GNN-based recommender models incrementally to reduce computation time and improve frequent updates.
problem Incremental updates in GNN-based recommender systems are computationally expensive and prone to forgetting.
method GraphSAIL uses a graph structure preservation strategy to update GNN models incrementally, preserving long-term preferences and properties.
result GraphSAIL reduces computation time and improves frequent updates compared to other incremental learning techniques.
A new framework for quickly updating classifiers with minimal re-computation.
problem Efficiently updating classifiers with incremental data changes.
method Proposes a novel sensitivity analysis framework that provides bounds on the updated classifier without full re-optimization.
result The proposed framework can quickly provide tight bounds on the updated classifier with minimal computational cost.
DID solves NMF for large distributed data.
problem Large distributed nonnegative matrix factorization.
method Distributed incremental block coordinate descent.
result Only one communication step per iteration.
A new model learns preferences incrementally without personal data.
problem Incremental session-based recommendation without personal data.
method Memory Augmented Neural model (MAN) that combines a neural recommender with a nonparametric memory.
result MAN consistently outperforms existing methods in incremental session-based recommendation.
Paper proposes a method to train neural networks incrementally using cloud computing despite disconnections and resource outages.
problem Frequent disconnections and resource outages in cloud computing and local machines hinder deep learning model training.
method Introduces an incremental learning framework that allows continuous training of neural networks even with interruptions.
result Demonstrates that incremental learning can maintain progress and train neural networks effectively despite interruptions.
CNAS optimizes neural architectures for class-incremental learning.
problem Capacity saturation in static neural architectures for class-incremental learning.
method CNAS uses reinforcement learning and network transformations to adaptively select architectures.
result CNAS outperforms static architectures and is more efficient.
We consider a situation in which we see samples in Rd drawn i.i.d. from some distribution with mean zero and unknown covariance A. We wish to compute the top eigenvector of A in an incremental fashion - with an algorithm that maintains an estimate of the top eigenvector in O(d) space, and incrementally adju…
For classification of the high frequency trading quantities, waiting times, price increments within and between sessions are referred to as the a-, b-, and c-increments. Statistics of the a-b-c-increments are computed for the Time & Sales records posted by the Chicago Mercantile Exchange Group for the futures traded on…
Proposes MEDIC to improve incremental learning by preventing forgetting and intransigence.
problem Challenges of forgetting old knowledge and intransigence on new knowledge in incremental learning.
method Maximum Entropy Regularizer (MER) and DropOut Sampling (DOS) to penalize uncertain knowledge and reduce class imbalance.
result Proposed method 'MEDIC' outperforms state-of-the-art algorithms in accuracy, forgetting, and intransigence.
An incremental method for spectral clustering of unknown clusters.
problem Determining the number of clusters in spectral clustering when it is unknown.
method Incremental method leveraging eigenstructure of graph Laplacian matrix.
result Effective eigenpair computation and determining the number of clusters.
New method to decompose portfolio performance ratios.
problem Understanding the drivers of portfolio performance ratios.
method Using Euler's theorem, decomposes performance ratios into modified ratios.
result Derives condition for new asset to improve portfolio performance.
AdaAnn optimizes annealing for efficient probability density approximation.
problem Efficiently approximating complex probability distributions with multiple modes.
method AdaAnn is an adaptive annealing scheduler that adjusts temperature increments based on KL divergence.
result AdaAnn improves computational efficiency in variational inference and parameter estimation.
Unified multilinear model for causal factor disentanglement.
problem Disentangling causal factors from complex data without direct manipulation.
method Hierarchical block multilinear factorization (M-mode Block SVD) and incremental approach.
result Interpretable object representation robust to occlusion and reduced training data.
Develops an efficient method for real-time data analysis and visualization.
problem Challenges of analyzing high-dimensional data.
method Incremental non-linear manifold approximation using GMRA framework.
result Accurately represents non-linear manifolds with small initial samples.
FINGER computes von Neumann graph entropy efficiently for online graph sequence analysis.
problem Efficiently compute von Neumann graph entropy for online graph sequence analysis.
method Fast Incremental von Neumann Graph Entropy (FINGER) framework.
result FINGER reduces VNGE computation complexity from cubic to linear.
Improved BLS by reducing pseudoinverse complexity for added inputs.
problem High computational complexity in pseudoinverse for incremental learning.
method Used inverse of a sum of matrices to reduce matrix inversion size.
result Significant reduction in computational complexity (1.24 - 1.30 speedups).
Efficiently samples sequences without replacement for machine learning models.
problem Generating diverse outputs from sequential models without duplicates.
method Incremental sampling procedure for randomized programs, including neural models.
result Efficacy and flexibility of incremental sampling for large output spaces.
Incremental machine learning models predict COVID-19 cases more efficiently than traditional methods.
problem Predicting the spread of COVID-19 cases in real-time across multiple countries.
method Comparison of online incremental machine learning algorithms against traditional LSTM models.
result Incremental machine learning models are more efficient and computationally cheaper than traditional methods.
FISHDBC clusters arbitrary data with flexible, scalable, and hierarchical features.
problem Clustering arbitrary data with arbitrary distance functions efficiently.
method Flexible, incremental, scalable, hierarchical density-based clustering algorithm.
result Flexible clustering of arbitrary data without feature extraction.
iCVI-ARTMAP accelerates clustering with adaptive resonance theory and validity indices.
problem Improving clustering efficiency and accuracy using adaptive resonance theory.
method Integrates adaptive resonance theory (ARTMAP) with incremental cluster validity indices (iCVIs) for clustering.
result Significantly reduces clustering time and outperforms other methods on synthetic and real-world data.
Best-choice edge grafting speeds up MRF structure learning.
problem Efficiently learning the structure of Markov random fields (MRFs) in a scalable manner.
method Incremental, structured approach that activates edges in groups of features.
result Significant speedup in structure learning with a controllable trade-off between speed and quality.
Paper introduces a new method for Gaussian Processes that improves prediction and hyper-parameter optimization.
problem Efficiently predicting unknown functions and optimizing hyper-parameters in Gaussian Processes.
method Sequential randomized low-rank matrix factorization for incremental predictions and hyper-parameter optimization.
result The proposed method outperforms existing approaches in terms of accuracy and computational efficiency.
Optimal Volt/VAR control rules are designed using deep neural networks.
problem Designing optimal Volt/VAR control rules for distributed energy resources (DERs).
method Formulate optimal rule design as a bilevel program, then reformulate it as training a deep neural network (DNN). Use proximal gradient descent (PGD) iterations to emulate Volt/VAR dynamics.
result The proposed solution can be adapted to single/multi-phase feeders and achieves enhanced steady-state voltage profiles.
CIAG method accelerates optimization for high-dimensional problems.
problem Training classifiers for high-dimensional problems with large datasets.
method CIAG method uses curvature information to accelerate IAG methods.
result Improved linear convergence rate over state-of-the-art IAG methods.
Algorithm estimates bounds of updated classifier coefficients efficiently.
problem Determining sensitivity of updated classifiers without retraining.
method Proposes an algorithm to estimate upper and lower bounds of updated classifier coefficients.
result Estimates bounds with low computational complexity and tightness.
PEC improves class-incremental learning by measuring prediction error.
problem Challenges in class-incremental learning, particularly forgetting and class imbalance.
method Prediction Error-based Classification (PEC) measures prediction error of a model trained on data from a class.
result PEC outperforms other methods in class-incremental learning across multiple benchmarks.
Lambda Learner improves model freshness in data streams.
problem Balancing model freshness and computational costs in data streams.
method Incremental updates in response to mini-batches from data streams.
result Lambda Learner outperforms offline models in time-sensitive updates.
Two new inverse-free ELM algorithms for incremental and decremental learning are proposed.
problem Efficiently updating and removing multiple hidden nodes in ELM.
method Improved inverse-free recursive algorithms for Tikhonov regularization.
result Inverse-free algorithms for ELM with multiple hidden nodes and redundant nodes.
Optimizes subset selection in sparse learning problems.
problem Sparse learning problems, particularly best subset selection.
method Developed an efficient primal-dual algorithm leveraging dual range estimation and incremental strategy.
result Improves solutions of best subset selection with reduced redundant computation.
Incremental variational inference speeds up LDA processing.
problem Efficiently processing large document collections in LDA.
method Inspired by incremental EM, introduces incremental variational inference for LDA.
result Incremental LDA converges faster and monotonically improves variational bound.
Paper develops efficient methods for covariance updates and belief space planning.
problem Efficiently updating covariance and evaluating belief space planning in high-dimensional state spaces.
method Novel incremental covariance update technique and factor-graph action tree approach.
result State-of-the-art methods for covariance updates and belief space planning are improved.
New algorithm extends Greville's method for partitioned matrices efficiently and stably.
problem Efficiently compute pseudoinverse of partitioned matrices without retraining.
method Incorporates inverse Cholesky factorization to reduce computational complexity and improve stability.
result 1 iteration to compute pseudoinverse of whole matrix from first part, addressing all cases.
New process from fractional BM and OU process yields simpler variance.
problem Simpler model for autocovariance structure.
method Construct new process using fractional BM and OU process, analyze increments.
result Variance of new process easier to compute than FARIMA.
FILDNE learns dynamic graph embeddings efficiently.
problem Learning node embeddings on evolving graphs.
method Integrates static graph learning methods into dynamic graphs using convex combination and alignment.
result FILDNE reduces memory and computational costs while improving downstream task performance.
A new method for asynchronous eigenspace computation on the Grassmannian.
problem Asynchronous optimization for finite-sum eigenspace computation in distributed systems.
method Grassmannian incremental aggregation method that refreshes only arriving components and reuses cached gradients.
result Two-phase linear convergence with constants controlled by component spectral spreads.
Real-time personalization for HAR models learns from new users without prior data.
problem Poor performance of HAR models on new users without labeled data.
method Incremental online domain adaptation using batch normalization.
result Personalized HAR models adapt to new users in real-time.
ActiveHARNet improves resource efficiency in deep learning for HAR and fall detection.
problem Resource efficiency and real-time learning for HAR models.
method Deep ensembled model with incremental learning and active learning.
result Significant efficiency boost during inference and reduction in acquired pool points.
A new algorithm for faster incremental kernel PCA and Nyström approximation.
problem Efficiency in handling streaming data and large datasets.
method Incremental kernel PCA and Nyström approximation using rank one updates.
result More computationally and memory efficient than existing methods.
The paper extends cluster validity indices for incremental analysis.
problem Providing incremental alternatives for cluster validation.
method Extending iCVI family to include 6 incremental indices and examining their behavior under under- and over-partitioning.
result Over-partitioning is more challenging to detect than under-partitioning.