This work prunes CNN filters based on their functionality, not just size.
problem Redundant filters in CNNs waste computation resources.
method Functionality-oriented filter pruning method.
result Pruning based on functionality optimizes computation and interprets filter importance.
Evolutionary method reduces CNN complexity for mobile devices.
problem Efficiently compressing CNNs for mobile devices.
method Evolutionary algorithm to identify and remove redundant convolution filters.
result Generates an extremely compact CNN with improved compression and speed-up ratios.
Centripetal SGD prunes deep CNNs by making filters collapse.
problem Pruning deep CNNs with complex structures.
method Centripetal SGD, a novel optimization method.
result Pruning deep CNNs without performance loss.
Prunes CNNs by removing redundant filters with provable guarantees.
problem Redundant filters in over-parameterized neural networks.
method Sampling-based approach using saliency scores and importance sampling.
result Consistently generates sparser and more efficient models.
Max-plus operators improve neural network filter selection and pruning.
problem Improving neural network efficiency and reducing redundancy.
method Exploiting Max-plus operators in neural network layers for filter selection and model pruning.
result Max-plus layers enhance filter selection and reduce redundancy without performance loss.
The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.
problem Flaws in existing pruning criteria for CNNs.
method Empirical experiments and Convolutional Weight Distribution Assumption.
result The Convolutional Weight Distribution Assumption improves filter selection in pruning.
CPOT prunes deep networks by identifying redundant filters using optimal transport.
problem Redundant filters in deep neural networks make models hard to deploy on resource-limited platforms.
method CPOT uses optimal transport to find the mean of channel distributions, pruning redundant information.
result CPOT outperforms state-of-the-art methods in pruning ResNet models and image-to-image translation tasks.
Novel approach reduces DNN redundancy and enhances computational efficiency.
problem Redundant parameters in large-scale DNNs pose hardware deployment challenges.
method WGSEF regularization technique for structured sparsity.
result Reduces redundancy and enhances computational efficiency.
DETOX improves distributed training resilience with redundancy and robust aggregation.
problem Byzantine node failures in distributed training.
method Combines redundancy and robust aggregation methods.
result Substantial increase in robustness with nearly linear runtime.
BELIEF method efficiently selects features from Big Data sets.
problem Feature selection for large datasets in Big Data.
method Distributed feature weighting algorithm using redundancy elimination.
result BELIEF method ranks millions of features in parallel efficiently.
Network deconvolution removes redundant data to improve neural network performance.
problem Redundant data in neural networks makes training challenging.
method Network deconvolution optimally removes pixel-wise and channel-wise correlations before each layer.
result Network deconvolution improves performance across various datasets.
We compare in this paper several feature selection methods for the Naive Bayes Classifier (NBC) when the data under study are described by a large number of redundant binary indicators. Wrapper approaches guided by the NBC estimation of the classification error probability out-perform filter approaches while retaining …
New filter bank sparsifying transforms outperform patch-based methods for image denoising.
problem Improving image denoising performance using data-adaptive sparsifying transforms.
method Proposes a new transform learning framework using undecimated perfect reconstruction filter banks, allowing independent filter length choice.
result Filter bank sparsifying transforms outperform existing patch-based methods for image denoising.
Enhances financial time series forecasting with a multi-period learning framework.
problem Accurate financial time series forecasting requires considering both short-term and long-term trends.
method Proposes a Multi-period Learning Framework (MLF) with three modules: Inter-period Redundancy Filtering, Learnable Weighted-average Integration, and Multi-period self-Adaptive Patching.
result Improves financial time series forecasting accuracy and efficiency.
Pruning filters in CNNs improves interpretability, showing shape-selective filters are crucial for object recognition.
problem Interpreting the complex decision-making process of CNNs is challenging due to their large number of parameters.
method We developed a greedy structural compression scheme that prunes filters based on the classification accuracy reduction (CAR) index.
result Pruned filters in CNNs, especially those in the first and second layers, are more likely to be shape-selective, indicating their importance in object recognition.
Proposes a new feature selection method integrating feature relationships.
problem Feature selection in machine learning models.
method Integrates feature-feature and feature-target relationships via penalized mRMR.
result Correctly identifies inactive features, reducing false discoveries.
We revisit the problem of estimating the parameters of a partially observed diffusion process, consisting of a hidden state process and an observed process, with a continuous time parameter. The estimation is to be done online, i.e. the parameter estimate should be updated recursively based on the observation filtratio…
Inf-FS selects features by graph paths, ranking them for infinite feature sets.
problem Feature selection in large datasets with relevance and redundancy.
method Graph-based feature selection with infinite paths, evaluating feature subsets using matrix power series and Markov chains.
result Inf-FS outperforms other methods in various feature selection scenarios.
Funnel-Transformer reduces computation by compressing sequence data.
problem Redundant token-level representations in language processing.
method Gradually compresses sequence of hidden states to a shorter one.
result Funnel-Transformer outperforms standard Transformer with fewer FLOPs.
This work compares data reduction criteria for online Gaussian Processes.
problem The computational complexity of Gaussian Processes limits their applicability to small datasets and streaming scenarios.
method Unified comparison of several data reduction criteria, analyzing computational complexity and reduction behavior.
result Practical guidelines for choosing a suitable data reduction criterion for online Gaussian Processes.
Data acquisition, storage and management have been improved, while the key factors of many phenomena are not well known. Consequently, irrelevant and redundant features artificially increase the size of datasets, which complicates learning tasks, such as regression. To address this problem, feature selection methods ha…
This paper explores how balancing and filtering techniques affect predictive multiplicity in machine learning models.
problem Predictive multiplicity due to Rashomon effect in high-stakes environments.
method Investigates the impact of balancing and filtering techniques on predictive multiplicity using 21 real-world datasets.
result Data-centric AI strategies can mitigate predictive multiplicity, but preprocessing methods may introduce it.
The paper deals with the adaptation of a new measure for the unsupervised feature selection problems. The proposed measure is based on space filling concept and is called the coverage measure. This measure was used for judging the quality of an experimental space filling design. In the present work, the coverage measur…
Holistic Filter Pruning reduces DNN complexity efficiently.
problem Redundant parameters in deep neural networks.
method Holistic Filter Pruning (HFP) for efficient DNN training.
result Achieves state-of-the-art performance with 60% reduction in multiplications on ImageNet.
Review and benchmark 58 feature selection methods for ML applications.
problem Selecting appropriate features for predictive modeling in ML applications.
method Synthesizes literature on feature selection benchmarking and evaluates 58 methods.
result Well-performing candidate methods identified: random forest, DISR, JIM.
Improved change point detection using matched filters for non-parametric tests.
problem False positives and localization ambiguity in non-parametric two-sample tests.
method Derived and applied matched filters for various two-sample tests.
result Matched filters reduce false positives and improve test precision.
The introduction of automated flight control and management systems have made possible aircraft designs that sacrifice arodynamic stability in order to incorporate stealth technology intro their shape, operate more efficiently, and are highly maneuverable. Therefore, modern flight management systems are reliant on mult…
This paper deals with a new filter algorithm for selecting the smallest subset of features carrying all the information content of a data set (i.e. for removing redundant features). It is an advanced version of the fractal dimension reduction technique, and it relies on the recently introduced Morisita estimator of Int…
A new method to compress CNNs without changing architectures.
problem Redundancy in CNN filter weights.
method Replacing filters with a fixed set and a learnable 1D layer.
result Significant reduction in parameters and operations with minimal performance loss.
RGCF improves collaborative filtering by refining graph convolution embeddings.
problem GCN-based recommendation models introduce noise and redundancy, limiting high-order connectivity capture.
method Developed RGCF, a new GCN-based Collaborative Filtering model with redesigned embeddings.
result RGCF significantly outperforms state-of-the-art models on public datasets.
SAE-FiRE extracts key financial info from long documents, improving earnings surprise predictions.
problem Predicting earnings surprises from long, redundant financial documents.
method Sparse Autoencoder feature selection to filter out noise and identify key dimensions.
result SAE-FiRE significantly outperforms baseline approaches in financial datasets.
Novel framework improves graph learning for out-of-distribution generalization.
problem Graph out-of-distribution generalization challenges in neural networks.
method Invariant Graph Learning based on Information bottleneck theory (InfoIGL).
result Achieves state-of-the-art performance in graph classification tasks under OOD generalization.
New method quantifies multivariate redundancy using maximum entropy decompositions.
problem Elusive multivariate measures of redundancy that comply with nonnegativity and axioms.
method Maximum entropy framework, rooted tree-based decompositions of mutual information.
result Quantifies different multivariate redundancy contributions.
This work enhances collaborative inference privacy by minimizing conditional entropy and boosting robustness against model inversion attacks.
problem Privacy leakage in collaborative inference systems via model inversion attacks.
method Theoretical proof and derivation of a differentiable measure for bounding conditional entropy, followed by a CEM algorithm to maximize it.
result Theoretical proof and experimental validation show that CEM consistently boosts inversion robustness without compromising feature utility or efficiency.
The paper introduces a method to explain redundancy in deep CNNs using unit impulse response.
problem Redundancy in deep CNNs leads to unnecessary computations and increased cost.
method Empirical demonstration and unit impulse response analysis to identify and quantify redundancy across layers and depth.
result Identifies and quantifies redundancy in deep CNNs, providing better insights into their internal dynamics.
Redundancy improves learning stability and generalization in structured systems.
problem Understanding redundancy in structured systems for learning and generalization.
method Developed a theoretical framework that redefines redundancy as a geometric principle unifying various measures.
result Redundancy balances structure and coupling, leading to optimal stability and generalization.
New method quantifies redundant information using information bottleneck.
problem Quantifying redundant information among multiple sources.
method Formulated as an information bottleneck problem, termed redundancy bottleneck.
result Extracts information that best predicts the target without revealing source identity.
Transformers reduce redundancy by focusing on invariant relational quantities.
problem Substantial internal redundancy in Transformer models due to coordinate-dependent representations and continuous symmetries.
method Reformulate representations, attention mechanisms, and optimization dynamics in terms of invariant relational quantities, eliminating redundant degrees of freedom by construction.
result Architectures that operate directly on relational structures, providing a principled geometric framework for reducing parameter redundancy and analyzing optimization.
Study shows DNNs often extract redundant features, influenced by network size and activation function.
problem Redundancy in deep neural network features.
method Hierarchical clustering of features based on cosine distances, varying network sizes and activation functions.
result Network size and activation function are key factors in DNN redundancy.
Paper proposes redundancy-free features for zero-shot object recognition.
problem Redundant visual features degrade zero-shot object recognition.
method Project original features into a new, statistically independent space.
result RFF-GZSL achieves competitive results on benchmark datasets.
This work explains scaling laws as redundancy laws in deep learning.
problem The mathematical origins of scaling laws in deep learning models remain unclear.
method Kernel regression and analysis of data covariance spectra.
result Scaling laws can be explained as redundancy laws, revealing the learning curve's slope depends on data redundancy.
Simplifies GCNs to improve efficiency and interpretability.
problem Excessive complexity and redundant computation in GCNs.
method Successively removing nonlinearities and collapsing weight matrices.
result Simplified model maintains accuracy and scales to larger datasets.
Study on neural networks to identify redundancy issues in safe machine learning.
problem Identifying redundancy in neural network architectures for safe machine learning.
method Experiments with MNIST database using neural network classifiers.
result Underlines difficulties in using neural network classifiers for safe systems.
Study finds 10% redundant images in image classification datasets.
problem Redundancy in large image classification datasets.
method Analysis of CIFAR-10 and ImageNet datasets to identify redundant images.
result 10% of images are redundant and can be removed without significant loss of performance.
Clustering helps reduce spatial data size by identifying redundant points.
problem Too much spatial data makes it hard to answer research questions.
method Density-based clustering to compress data into representative features.
result Reduces spatial data set size by identifying and removing redundant points.
We simplify SSL by approximating redundant structural components with low-rank factorization.
problem Improving self-supervised learning performance with limited labeled data.
method Low-rank approximation of structural redundancy, introducing ε_s to measure approximation quality.
result The proposed method enhances SSL performance, as shown by theoretical and experimental validations.
New theoretical insights improve feature selection using mutual information.
problem Improving feature selection methods with theoretical guarantees.
method Proposed novel stopping condition for greedy feature selection methods.
result Ideal prediction error remains bounded by a threshold.
This work reduces computation cost for on-device CNN training.
problem High computation cost during on-device CNN training.
method Self-supervised instance filtering and error map pruning.
result Substantial computation saving without significant accuracy loss.