Count-ception predicts object counts in images with reduced errors.
problem Counting objects in images is time-consuming and error-prone.
method Fully convolutional redundant counting approach using a Count-ception network.
result 20% relative improvement in accuracy over state-of-the-art methods.
This paper introduces a new measure to identify model redundancy in compressed CNNs.
problem Identifying remaining model redundancy in compressed CNNs.
method Developed a statistical formulation of CNNs and compressed CNNs via tensor decomposition, revealing discrepancies in sample complexity and model redundancy.
result Introduced a new model redundancy measure, the K/R ratio, for compressed CNNs. A new complexity measure for neural networks improves upon classical methods.
problem Lack of a refined complexity measure for comparing different neural network architectures, especially permutation-invariant ones.
method Introduced an equivalence relation among linear functions and counted them relative to this relation.
result The new complexity measure clearly distinguishes between different models and increases exponentially with depth.
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.
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.
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.
Reduces redundant words in neural summarization models.
problem Redundant repeating generation in RNN-based models.
method Jointly estimates vocabulary frequency and controls output based on estimation.
result Significant improvement over RNN baseline, best results on benchmark.
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.
New method avoids redundancy in spectral embeddings.
problem Redundant coordinates in spectral dimensionality reduction.
method Introduces unpredictability constraints to avoid redundancy.
result Significantly more informative and compact representations.
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.
This paper explores how optimizing data access and reducing redundancy can improve machine learning algorithm performance.
problem Performance issues in machine learning algorithms due to data locality and redundancy.
method Analysis of data access patterns and computational redundancy in machine learning algorithms, identifying opportunities for reuse and experimentation.
result Initial indicative results show potential for improving performance through data access optimization and reuse of computation results.
Optimized GPRNN reduces model complexity and overfitting, improving performance.
problem Overfitting in neural networks and high model complexity.
method Gaussian Process Regression - Neural Network hybrid with optimized redundant coordinates.
result Optimized GPRNN achieves lower test set error with fewer terms/neurons.
One of Powell's generators is not necessary.
problem Unresolved conjecture about generating Goeritz group.
method Short argument showing redundancy of one generator.
result One of Powell's generators is a consequence of others.
The study analyzes optimization trajectories in neural networks to reveal redundancy and redundancy-reducing strategies.
problem Understanding the directional structure and redundancy in neural network optimization.
method Introducing natural notions of complexity for optimization trajectories and analyzing their directional nature.
result Training only scalar batchnorm parameters can match the performance of training the entire network, indicating potential for hybrid optimization schemes.
This work introduces RISE to explain LLMs more reliably by distinguishing essential context.
problem Identifying which context elements influence LLM outputs reliably.
method RISE (Redundancy-Insensitive Scoring of Explanation) method.
result RISE provides more robust explanations than traditional methods.
This work identifies redundant tests in conditional-independence-based discovery that can improve graphical model accuracy.
problem Reliability and sensitivity of conditional-independence-based discovery algorithms.
method Analysis of redundant tests and their impact on error detection and correction.
result Redundant tests can improve graphical model accuracy but not all are beneficial.
Contrastive learning works well with redundant data views.
problem Improving unsupervised learning with redundant data views.
method Theoretical analysis of contrastive learning in a multi-view setting.
result Linear functions of learned representations are nearly optimal on prediction tasks when views are redundant.
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.
This paper explains adversarial examples as feature redundancy abuse.
problem Understanding and mitigating adversarial examples in machine learning.
method Information-theoretic model to explain adversarial attacks.
result Feature redundancy is necessary for adversarial examples.
AVE measures redundancy in ligand-based benchmarks, revealing overfitting.
problem Overfitting in ligand-based classification benchmarks.
method AVE (Training-Validation Redundancy Measure) for ligand-based classification problems.
result Performance of ligand-based methods correlates with AVE bias, not generalization.
Study on how deterministic dependencies affect information synergy and redundancy.
problem Understanding how deterministic dependencies impact information synergy and redundancy.
method Systematic analysis of deterministic dependencies on information decomposition.
result Identifies how negative terms can originate from deterministic dependencies and discusses implications for neural coding.
Estimates the number of connected components in a graph from a sampled subgraph.
problem Inferring the number of connected components in a larger graph from a sampled subgraph.
method A highly redundant and large-dimensional representation of the subgraph using counts of network motifs, leading to a novel estimator for the number of connected components.
result Improves upon competing algorithms for graphs with spectral gaps bounded away from zero.
New method distinguishes feature relevance in non-linear contexts.
problem Finding relevant features with preserved redundancies.
method Random forest models and statistical methods.
result Distinguishes strong from weak feature relevance in non-linear problems.
Paper compresses deep neural networks by eliminating redundant neurons.
problem Challenges in deploying deep learning models due to high parameter count.
method Exploits non-linear redundancy to compress neural networks without loss.
result Reduces network size by up to 99% with minimal performance loss.
FactorMiner discovers financial alpha factors with low redundancy.
problem Finding novel financial alpha factors in a vast search space.
method Modular Skill Architecture and Experience Memory to distill and guide exploration.
result FactorMiner constructs a diverse library of high-quality factors with competitive performance.
Wide neural networks' last hidden layers split into groups of redundant neurons.
problem Understanding why wide neural networks generalize well despite overfitting.
method Analyzed the last hidden layer representations of various convolutional neural networks.
result Wide hidden layers split into groups of redundant neurons, which help generalize.
BNs extract sparse, non-redundant features from climate data networks.
problem Redundant information in correlation networks limits physical feature extraction.
method Construct data-driven complex networks using Bayesian Networks.
result Sparse, non-redundant features reveal generalizable physical features.
mRMR method selects relevant features for marketing models without redundancy.
problem Selecting relevant features from large feature spaces in machine learning.
method Extends mRMR framework with non-linear redundancy and model-based relevance measures.
result Implemented mRMR method in production for Uber's marketing machine learning platform.
This paper enhances ML algorithms by improving data locality and reducing redundancy.
problem Improving performance of machine learning algorithms with complex data.
method Exploiting data locality and reuse in memory hierarchies of modern processors.
result Efficient implementation of machine learning algorithms can be achieved by reusing computation results.
HAGs eliminate redundant computations in GNNs, improving training efficiency.
problem Redundant computations in GNNs leading to inefficiencies.
method Hierarchically Aggregated computation Graphs (HAGs) to manage and eliminate redundant computations.
result Significant improvement in training efficiency (up to 2.8x) with HAGs.
Collage inference uses redundancy to reduce cloud image classification latency variance.
problem Reducing latency variance in cloud image classification.
method Integrates collage-cnn for low-cost redundancy in multi-image classification.
result Significant reduction in 99th percentile tail latency and inference latency variation.
Collage-CNN reduces cloud inference latency by 1.47X with 9X reduced latency variation.
problem Reducing latency variance in cloud machine learning inference.
method Proposes a novel Collage-CNN model that combines multiple images for classification, providing redundancy and cost efficiency.
result Significant reduction in 99th percentile tail latency and variation in inference latency.
Redundancy helps speed up slow nodes in distributed learning.
problem Slow nodes (stragglers) bottleneck distributed optimization and learning performance.
method Encode data with redundancy, dynamically exclude stragglers, and compensate losses.
result Optimization algorithms converge to solutions even with straggling nodes.
Counting tripods on a flat torus using lattice point counting.
problem Counting finite BPS webs in flat torus geometry.
method Lattice point counting techniques in C2. result Asymptotic counting result for tripods on the torus.
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.
Cell-based NAS search spaces are redundant and lack novelty.
problem Redundancy and lack of novelty in cell-based neural architecture search.
method Empirical analysis of architectures from popular cell-based search spaces.
result Randomly sampled architectures can match or outperform state-of-the-art results.
Redundancy in AI perception systems doesn't guarantee independent error occurrences.
problem Lack of direct statistical evidence of super-human automated driving performance.
method Investigated the effectiveness of redundancy in neural networks for independent error occurrences.
result Errors in neural networks for computer vision tasks are correlated, not independent.
Estimates interactions between modalities for multimodal data.
problem Accurately quantifying interactions between different data types.
method Developed Lightweight Sample-wise Multimodal Interaction (LSMI) estimator using pointwise information theory.
result LSMI reveals fine-grained dynamics in multimodal data.
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.
Signed pairwise interactions conflate uniqueness, redundancy, and synergy
problem Signed pairwise interactions conflate uniqueness, redundancy, and synergy
method Stochastic Hi-Fi
result Stochastic Hi-Fi recovers structure missed by scalar baselines
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.