Paper proposes faster incremental subclass discriminant analysis.
problem Efficiently classify subclasses in incremental data.
method Exact and approximate linear and kernelized solutions.
result Superior training time and accuracy compared to existing methods.
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.
Paper proposes CNE-net to tackle incremental learning in (T)ACSA tasks.
problem Catastrophic forgetting in multi-task incremental learning for (T)ACSA.
method Category Name Embedding network (CNE-net) with shared encoder and decoder.
result State-of-the-art performance on (T)ACSA benchmark datasets.
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.
New method for clustering large multi-view data.
problem Handling large multi-view data efficiently.
method Incremental minimax optimization based fuzzy clustering (IminimaxFCM).
result IminimaxFCM outperforms related methods in clustering accuracy.
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.
Dex improves reinforcement learning by solving complex environments incrementally.
problem Training reinforcement learning agents for complex, ever-changing environments.
method Incremental learning approach, using optimal weights from simpler environments.
result Incremental learning yields superior performance across multiple Dex environments.
Paper describes an on-line PCA algorithm for real-time data analysis.
problem Continuity of principal components during real-time analysis.
method Incremental PCA algorithm that updates coefficients on-line without keeping all samples.
result Algorithm maintains the same transformation coefficients as the batch version.
Lyapunov's second theorem is an essential tool for stability analysis of differential equations. The paper provides an analog theorem for incremental stability analysis by lifting the Lyapunov function to the tangent bundle. The Lyapunov function endows the state-space with a Finsler structure. Incremental stability is…
GD learns matrix solutions incrementally, revealing insights into generalization.
problem Matrix sensing problem of recovering low-rank matrices from linear measurements.
method Fine-grained analysis of GD dynamics for matrix sensing.
result GD follows an incremental learning procedure, solving matrices of increasing ranks.
Improved metric indexes support malware analysis by handling insertions and queries in real-time.
problem Handling insertions and queries in real-time for malware analysis.
method Modified and improved three algorithms to support incremental insertion and querying with arbitrary metrics.
result Improved Vantage-Point tree of Minimum-Variance performs best for this scenario.
The method of cointegration in regression analysis is based on an assumption of stationary increments. Stationary increments with fixed time lag are called integration I(d). A class of regression models where cointegration works was identified by Granger and yields the ergodic behavior required for equilibrium expectat…
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.
New method combines deep learning and streaming learning for better incremental learning.
problem Catastrophic forgetting in deep neural networks when updated incrementally.
method Combining streaming linear discriminant analysis with deep learning.
result Outperforms incremental batch learning and streaming learning on ImageNet and CORe50.
Paper analyzes fast SAGA method for nonconvex optimization problems.
problem Optimizing nonconvex problems of the form min x ∑ i f i ( x ) \min_x \sum_i f_i(x) min x ∑ i f i ( x ) method Incremental aggregated gradient method (SAGA) within an Incremental First-order Oracle framework
result SAGA converges to a stationary point faster than gradient descent and stochastic gradient descent, and at a linear rate to the global optimum for a specific class of nonconvex problems.
Paper improves convergence rates and step sizes for gradient algorithms.
problem Limited convergence rates and step sizes in existing algorithms.
method General proximal incremental aggregated gradient algorithm.
result Better and novel convergence results under general scheme.
GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremental algorithm for identifying a subspace of Rn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis has shown that GROUSE converges locally at an expected linea…
New method combines population and completion tasks in knowledge graphs.
problem Insufficient external resources hinder statistical inference in knowledge graphs.
method Probabilistic factorisation method that uses path structure for both population and completion.
result Balanced exploitation-exploration helps incremental population and improves prediction of missing information.
Greedy selection works well in a toy model of independent increments.
problem Iterative selection of maximum-value processes from i.i.d. stochastic processes.
method Fixed greedy selection at each stage.
result Optimal strategy is greedy selection under independent increments.
We introduce a novel sensitivity analysis framework for large scale classification problems that can be used when a small number of instances are incrementally added or removed. For quickly updating the classifier in such a situation, incremental learning algorithms have been intensively studied in the literature. Alth…
New method improves blockchain analysis by handling temporal changes and scalability.
problem Limited focus on evolving nature and scalability of blockchain transaction networks.
method Incremental approach with Metropolis-Hastings random walks.
result Comparable performance in node classification tasks with reduced computational overhead.
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.
ARCH and GARCH models assume either i.i.d. or (what economists lable as) white noise as is usual in regression analysis while assuming memory in a conditional mean square fluctuation with stationary increments. We will show that ARCH/GARCH is inconsistent with uncorrelated increments, violating the i.i.d. and white ass…
Study finds Bitcoin volatility exhibits roughness, not constant over time.
problem Investigating roughness in Bitcoin volatility.
method Multifractal detrended fluctuation analysis and shuffled time series analysis.
result Bitcoin volatility exhibits roughness (generalized Hurst exponent < 1/2).
Anomalous scaling explained by Joseph, Noah, and Moses effects.
problem Understanding and quantifying anomalous scaling in stochastic processes.
method Defined and measured scaling exponents for Joseph, Noah, and Moses effects.
result Intraday financial data shows anomalous scaling due to Moses effect.
By applying the multifractal detrended fluctuation analysis to the high-frequency tick-by-tick data from Deutsche Börse both in the price and in the time domains, we investigate multifractal properties of the time series of logarithmic price increments and inter-trade intervals of time. We show that both quantities rev…
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.
Neural networks learn incrementally from orthogonal data, interpolating with minimal complexity.
problem Understanding the learning dynamics and implicit bias in ReLU networks with orthogonal data.
method Gradient flow analysis of two-layer ReLU networks from small initialization with orthogonal training data.
result The learned interpolator has a squared ℓ 2 \ell_2 ℓ 2 -norm scaling as n \sqrt{n} n , close to the minimal interpolator's complexity. A new method for efficient CNN compression by incrementally assigning regularization factors to weights.
problem Efficiently compressing ConvNets while maintaining performance.
method IncReg: Incremental Regularization for Parameter Pruning.
result IncReg achieves comparable or better results than state-of-the-art methods.
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.
The level crossing and inverse statistics analysis of DAX and oil price time series are given. We determine the average frequency of positive-slope crossings, ν α + ν_α^+ ν α + , where T α = 1 / ν α + T_α =1/ν_α^+ T α = 1/ ν α + is the average waiting time for observing the level α α α again. We estimate the probability P ( K , α ) P(K, α) P ( K , α ) , which provides us the probab…
Method predicts LFSM increments from past observations using codifference.
problem Forecasting LFSM increments from discrete-time observations.
method Uses codifference for serial dependence, with conditional expectation or projection for α > 1 α>1 α > 1 or α < 2 α<2 α < 2 . result Method shows promising performance in forecasting volatilities, capturing kurtosis and serial dependence.
Transformers learn to integrate information from past positions incrementally, specializing heads in distinct patterns.
problem How transformers learn to integrate information from multiple past positions with varying statistical significance.
method High-order Markov chain task, incremental learning, sparse attention patterns, simplified differential equations, stage-wise convergence, early stopping as regularizer.
result Transformers learn to specialize heads in distinct patterns, shifting from competitive to cooperative learning dynamics.
We introduce incremental variational inference and apply it to latent Dirichlet allocation (LDA). Incremental variational inference is inspired by incremental EM and provides an alternative to stochastic variational inference. Incremental LDA can process massive document collections, does not require to set a learning …
Growth rate of real GDP per capita is represented as a sum of two components -- a monotonically decreasing economic trend and fluctuations related to a specific age population change. The economic trend is modeled by an inverse function of real GDP per capita with a numerator potentially constant for the largest develo…
Develops online cluster validation indices for streaming data.
problem Validating clusters in streaming data.
method Two online versions of Xie-Beni and Davies-Bouldin indices, analyzed using sk-means and online ellipsoidal clustering.
result Incremental Xie-Beni index with forgetting factor superior to other indices.
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.
New framework simplifies analysis of asynchronous parallel optimization algorithms.
problem Difficulty in analyzing asynchronous parallel optimization algorithms due to delay and inconsistency.
method Revisit and clarify technical issue in convergence rate proofs, propose simplified 'perturbed iterate' framework.
result Proves linear speedup for ASAGA and KROMAGNON on multi-core systems without sparsity assumptions.
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.
Sabrina integrates financial data and domain knowledge for better visualization.
problem Scattered financial data across various sources makes it hard for analysts to understand the economy.
method Sabrina uses a pipeline to fuse firm-specific and macroeconomic data, visualizing it in a unified interface.
result Sabrina aids financial analysts in their analysis process, as shown in a user study.
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.
EILearn learns incrementally using previous classifier knowledge.
problem Incremental learning with previous data.
method Retains and uses previous classifier knowledge, monitors performance, eliminates poorly performing classifiers.
result Outperforms existing incremental learning approaches.
Depth helps neural networks learn simpler solutions incrementally.
problem Understanding why deep neural networks generalize well despite complex architectures.
method Formal definition of incremental learning dynamics, theoretical analysis of depth and initialization effects, experiments with various models.
result Incremental learning dynamics can arise in deeper models, but not in shallow ones, under specific conditions.
A new SOHP filter improves trend estimation in economic time series.
problem Improving trend estimation in nonlinear economic time series.
method Recursive application of one-sided HP filter on updated cyclical components, combined with an incremental HP filtering algorithm.
result Better performance of SOHP filter compared to other HP-type filters on real economic data.
New method rebalances evolving data streams incrementally.
problem Incremental rebalancing of evolving data streams.
method Proposes a new streaming approach for rebalancing data streams online.
result Outperforms existing approaches in rebalancing data streams.
DFI maps covariates to latent representations for feature importance.
problem Feature importance when predictors are statistically dependent.
method Disentangled Feature Importance (DFI) using entropic optimal transport.
result DFI yields stable, interpretable, uncertainty-quantified attributions of shared predictive signal.
Paper tackles few-shot class-incremental learning with a neural gas network.
problem Incrementally learn new classes from very few labelled samples without forgetting old classes.
method Proposes TOPIC framework using a neural gas network to preserve class topology and adapt to new samples.
result Significantly outperforms other methods on CIFAR100, miniImageNet, and CUB200 datasets.
The growth rate of real GDP per capita in the biggest OECD countries is represented as a sum of two components - a steadily decreasing trend and fluctuations related to the change in some specific age population. The long term trend in the growth rate is modelled by an inverse function of real GDP per capita with a con…