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.
Transformer improves sequence generation with insertion and deletion phases.
problem Sequence generation challenges in machine translation.
method Insertion-Deletion Transformer with iterative insertion and deletion phases.
result Significant BLEU score improvement over insertion-only models.
Insertion Transformer generates sequences with flexible insertion orders.
problem Generating sequences with arbitrary orderings.
method Iterative, partially autoregressive model with insertion operations.
result Outperforms non-autoregressive approaches in translation tasks.
ID-ExpO fine-tunes neural networks for more faithful explanations.
problem Improving the faithfulness of explanations for complex machine learning models.
method Differentiable insertion/deletion metric-aware regularizers for optimization.
result Fine-tuned predictors produce more faithful explanations.
XL-Editor improves sentence post-editing using XLNet's variable-length insertion probability.
problem Post-editing sentences to refine generated text.
method XL-Editor trains XLNet to estimate variable-length insertion probabilities and apply post-editing operations.
result XL-Editor outperforms XLNet on text insertion and deletion tasks, and achieves significant style transfer improvements.
New framework for consistent submodular maximization with insertions and deletions.
problem Maintaining near-optimal solutions in a dynamic setting with insertions and deletions.
method Developed a general framework for fully dynamic submodular maximization, instantiated for cardinality and rank-k matroid constraints.
result First constant-factor approximations with sublinear consistency for both cardinality and rank-k matroid constraints.
New attacks improve privacy audits by analyzing model updates.
problem Improving privacy audits of AI models through sequence analysis.
method Developed SeMI attacks to identify target insertions in model sequences.
result SeMI attacks achieve higher power and tighter privacy audits.
The paper learns compact implicit surface maps from streaming data using an ensemble of sparse Gaussian processes.
problem Creating compact and accurate implicit surface maps from streaming range data.
method An ensemble of sparse Gaussian process experts, incrementally adjusted, trades-off between model complexity and prediction error.
result The approach learns compact and accurate implicit surface models comparable to or better than exact GP regression with subsampled data.
The paper compares inserting and stretching points for grid refinement near critical points.
problem Decreased accuracy of finite difference methods near discontinuities.
method Comparison of inserting and stretching points for grid refinement near critical points.
result Proposes a new fast and simple stretching function.
CMT efficiently manages memory by inserting and querying memories in logarithmic time.
problem Managing large memory stores efficiently for quick access and updates.
method Designing a Contextual Memory Tree (CMT) that inserts and retrieves memories in logarithmic time.
result CMT improves classification algorithms and image-captioning tasks, demonstrating better computational efficiency.
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
problem Interim decisions based on current dose data may overlook signals from neighboring doses.
method Bayesian model-assisted design using Beta kernel process with kernel-weighted pseudo-counts.
result Significant improvements in identifying maximum tolerated dose and safety.
XGES improves GES by favoring early edge deletion, outperforming GES in finite data settings.
problem Learning directed acyclic graphs from finite data.
method Extremely Greedy Equivalent Search (XGES) improves GES by favoring early edge deletion.
result XGES consistently outperforms GES in recovering the correct graphs, and is 10 times faster.
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 …
New formulas for feature importance tests in regression models.
problem Identifying important features in regression models.
method Established formulas for AUC criteria and proposed alternative metrics.
result Integrated Gradients (IG) performs nearly as well as Kernel SHAP (KS) but is faster.
IFH models graph generation with adjustable sequentiality.
problem Designing flexible graph generation models between one-shot and sequential approaches.
method Based on DDPM, IFH uses a node removal process to generate graphs with adjustable sequentiality.
result IFH models improve graph generation quality and efficiency compared to current methods.
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.
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.
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.
Time and Sales of corn futures traded electronically on the CME Group Globex are studied. Theories of continuous prices turn upside down reality of intra-day trading. Prices and their increments are discrete and obey lattice probability distributions. A function for systematic evolution of futures trading volume is pro…
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.
Study curvature and torsion from cross-ratios in discrete curves.
problem Define curvature and torsion for discrete curves using cross-ratios.
method Use Möbius invariant point-insertion-rule to construct circles and express torsion using cross-ratio.
result Discrete curvature and torsion defined using cross-ratios converge to smooth curvature and torsion as sampling density increases.
New algorithms update dynamic graph regression faster than existing methods.
problem Efficiently updating linear regression solutions for dynamic graphs.
method Subsampled randomized Hadamard transform and CountSketch.
result First sublinear update time randomized algorithms for dynamic graph regression.
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.
Inserts proximal mapping into deep networks for better regularization.
problem Effective regularization of deep learning models to handle adversarial perturbations and correlations between modalities.
method Proposes a new layer that directly produces regularized hidden layer outputs using proximal mapping.
result Outperforms state-of-the-art methods in robust temporal learning and multiview modeling.
Deep Incremental Boosting speeds up Deep Learning training with improved generalization.
problem Reducing training time and improving generalization in Deep Learning.
method Inspired by AdaBoost and Transfer Learning, Deep Incremental Boosting adapts Ensemble methods to Deep Learning.
result Reduces training time and improves generalization on Deep Learning datasets.
We give a proof of Ilmanen's lemma, which asserts that between a locally semi-convex and a locally semi-concave function it is possible to find a C1,1 function.
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 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.
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.
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical …
Data is continuously generated by modern data sources, and a recent challenge in machine learning has been to develop techniques that perform well in an incremental (streaming) setting. In this paper, we investigate the problem of private machine learning, where as common in practice, the data is not given at once, but…
We analyze the question whether sliding window time averages applied to stationary increment processes converge to a limit in probability. The question centers on averages, correlations, and densities constructed via time averages of the increment x(t,T)=x(t+T)-x(t)and the assumption is that the increment is distribute…
Paper detects backdoors in neural networks using activation clustering.
problem Inserting backdoors into neural networks trained on untrusted data.
method Activation clustering to identify poisoned data.
result Demonstrates effectiveness in text and image classification.
Paper aims to improve AI's ability to learn new tasks incrementally.
problem Catastrophic Forgetting in AI systems learning multiple tasks simultaneously.
method Analyzed iCaRL method, identified knowledge distillation as key, proposed dynamic threshold moving algorithm.
result Demonstrated effectiveness of dynamic threshold moving algorithm on CIFAR100 and MNIST datasets.
New algorithm improves on EM for streaming data, outperforming existing methods.
problem Processing high-volume, streaming data efficiently.
method Incremental stochastic Majorization-Minimization (MM) algorithm.
result The algorithm converges to a stationary point with vanishing gradient.
Incremental clustering approaches have been proposed for handling large data when given data set is too large to be stored. The key idea of these approaches is to find representatives to represent each cluster in each data chunk and final data analysis is carried out based on those identified representatives from all t…
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.
Backdrop uses dropout-like masking in backpropagation for multi-scale data.
problem Improving generalization in multi-scale data.
method Inserting masking layers after convolutional layers to mask backward gradients.
result Backdrop leads to significant improvements in generalization.
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.
Stochastic Gradient Trees learn decision trees incrementally.
problem Learning decision trees using stochastic gradient information.
method Incremental learning setting, soft splits not used, new tree not constructed per update.
result Performs similarly to standard incremental classification trees, outperforms state of the art incremental regression trees, comparable to batch multi-instance learning methods.
Incremental training method for deep neural networks.
problem Training deep neural networks efficiently and with incremental growth.
method Partitioning the network into sub-networks, gradually incorporating them, and using look-ahead initialization.
result Incremental approach reaches baseline accuracy and identifies smaller network partitions.
Paper evaluates incremental learning from non-stationary data.
problem Lack of effective evaluation metrics for non-stationary incremental learning.
method Synthetic datasets and testbed for algorithms evaluation.
result Proposed methodology effectively characterizes strengths and weaknesses of algorithms.
A new generative classification strategy outperforms existing methods in class-incremental learning.
problem Incrementally training deep neural networks to recognize new classes is challenging.
method Proposes learning the joint distribution p(x,y) and performing classification using Bayes' rule, implemented with variational autoencoders and importance sampling.
result Performs very well on continual learning benchmarks, outperforming existing baselines.
The statistical properties of the increments x(t+T) - x(t) of a financial time series depend on the time resolution T on which the increments are considered. A non-parametric approach is used to study the scale dependence of the empirical distribution of the price increments x(t+T) - x(t) of S&P Index futures, for time…
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…
Inserting label noise can improve model accuracy and fairness.
problem Improving model accuracy and fairness with noisy labels.
method Increasing label noise rates to balance and detect noisy instances.
result Inserting label noise can lead to more accurate and fair models.