Generalizes machine learning models using localization kernels and local means.
problem Understanding and unifying diverse machine learning models.
method Formal definition of localization method through localization kernels and local means.
result Unified theoretical lens and new methodological tools for designing flexible learning systems.
Paper introduces a new network learning method with local propagation.
problem Learning in complex, multi-path networks with multiple goals.
method Locally decoupled network parameter learning with local propagation.
result Advantages in learning time and network size compared to state-of-the-art methods.
Locally private reinforcement learning protects individual environments from reverse engineering.
problem Protecting private information in distributed reinforcement learning environments.
method Locally differentially private algorithms that protect local agents' models from adversarial reverse engineering.
result Demonstrated that the proposed algorithm performs well under local differential privacy (LDP).
In a physical neural system, where storage and processing are intimately intertwined, the rules for adjusting the synaptic weights can only depend on variables that are available locally, such as the activity of the pre- and post-synaptic neurons, resulting in local learning rules. A systematic framework for studying t…
Adaptive Siamese network improves local feature descriptor learning efficiency.
problem Estimating the size of neural networks for local feature descriptors.
method Adaptive pruning Siamese architecture based on neuron activation.
result Learned local feature descriptors outperform state-of-the-art methods in patch matching.
Improves local learning models for complex feature extraction.
problem Limited use of simple model families in local learning.
method Uses complex local model families to extract features.
result Demonstrates applications in various fields.
This paper explores estimating chaotic dynamics and parameters using local ensemble Kalman filters.
problem Estimating chaotic dynamics and parameters from observations.
method Local ensemble Kalman filters with covariance and local domain localisation.
result Rigorously updating global parameters using a local domain ensemble Kalman filter.
New method learns domain-invariant local feature patterns for unsupervised domain adaptation.
problem Performance degradation due to domain-shift in unsupervised domain adaptation.
method Jointly learns domain-invariant local feature patterns and holistic feature distributions.
result Superior performance on benchmark datasets compared to state-of-the-art methods.
Proposes ContSup to boost local learning by supplying context between isolated modules.
problem Local learning's performance degrades with more isolated modules.
method Theoretical analysis and ContSup scheme to supply context between modules.
result Significant performance improvement with minimal overhead.
Federated learning algorithm reduces global model size by combining local and global representations.
problem Scalability issues in training large models on private data distributed over multiple devices.
method Proposes a federated learning algorithm that jointly learns compact local representations and a global model.
result The global model can be smaller since it only operates on local representations, reducing the number of communicated parameters.
Two local learning rules are investigated to avoid weight transport in neural networks.
problem Local learning rules that avoid weight transport are unstable and require tuning.
method Investigated two non-local learning rules and a more robust local rule.
result Non-local learning rules match state-of-the-art performance and operate effectively in noisy updates.
The paper connects machine learning interpretability with learning theory.
problem Performance and explanation generalization in local machine learning models.
method Theoretical analysis and empirical validation of local approximation explanations.
result Theoretical bounds on test-time accuracy and explanation generalization.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
Paper presents machine learning for multi-target localization using various sensors.
problem Accurate multi-target localization for unmanned systems.
method Machine learning algorithms (K-means clustering, SVM) applied to sensor data.
result Different machine learning algorithms perform differently in data association and localization.
Reduces sound event localization error by 2.6x with hybrid parametric-deep learning.
problem Sound event localization and detection accuracy.
method Hybrid approach combining parametric spatial audio analysis and deep learning.
result Reduction of localization error by 2.6x compared to baseline.
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability tables (CPTs), that quantify these networks. This increases the spac…
LDReg addresses local dimensional collapse in self-supervised learning.
problem Local dimensional collapse in self-supervised learning representations.
method Local dimensionality regularization based on Fisher-Rao metric.
result LDReg improves representation quality and regularizes local and global dimensions.
This paper introduces a new method for semi-supervised learning on high dimensional nonlinear manifolds, which includes a phase of unsupervised basis learning and a phase of supervised function learning. The learned bases provide a set of anchor points to form a local coordinate system, such that each data point x on…
Active learning method improves local model validity estimation.
problem Ensuring local model validity in machine learning applications.
method Learning model error to estimate local validity using active learning.
result The proposed method can estimate local validity with a small amount of data.
Paper analyzes localized SVMs for robustness and consistency.
problem Handling large datasets efficiently and robustly.
method Localized support vector machines (SVMs) for non-parametric learning.
result Locally learnt kernel methods are universal consistent and robust.
Unified view of federated learning and distributed RL using local stochastic approximation.
problem Finding the root of an operator composed of local operators in a network of agents with dependent data.
method Local stochastic approximation over a network of agents with Markov process-dependent data.
result Convergence rates of local stochastic approximation for both constant and time-varying step sizes, within a logarithmic factor of independent data.
Improves domain classification across multiple locales with shared language.
problem Improves domain classification accuracy in Spoken Language Understanding across multiple locales with shared language.
method Selective multi-task learning to create a joint representation of utterances over locales with different sets of domains.
result The proposed approach outperforms other baselines models especially when classifying locale-specific domains and low-resourced domains.
Deep neural networks perform well on local tasks but struggle with global tasks.
problem Understanding the limitations of overparameterized deep neural networks in learning global functions.
method Introduced k-local and k-global functions to study the interplay between depth and function locality. result Depth is beneficial for learning local functions but detrimental to learning global functions.
Analyzes learning dynamics of RNNs under locality constraints.
problem Understanding learning dynamics in RNNs with locality constraints.
method Dynamical systems theory applied to data-aligned linear RNNs.
result RFLO solutions are restricted to low-rank perturbations of initial parameters.
Machine learning detects non-local correlations in complex scenarios.
problem Systematic derivation of non-local correlations becomes unfeasible in complex scenarios.
method Ensemble of multilayer perceptrons blended with genetic algorithms.
result High performance in relevant Bell scenarios.
The paper proposes a model to learn motion perception in V1 using vector and matrix representations.
problem Motion perception in primary visual cortex (V1).
method Coupling vector representations of local contents and matrix representations of local pixel displacements.
result The model can learn Gabor-like filter pairs and infer local motions.
LoCo learns local representations without end-to-end synchronization, improving performance on complex tasks.
problem Learning local representations without end-to-end synchronization constraints.
method Overlap local blocks to increase decoder depth and allow feedback from upper to lower layers.
result LoCo closes the performance gap between local learning and end-to-end contrastive learning.
A new online local metric learning framework reduces overfitting and scales with data dimensions.
problem Global metrics fail to capture the complexity of real-world datasets.
method Proposes an online multiple metric learning framework with a global and local component.
result Reduces overfitting and scales with data dimensions.
Deep learning improves single-molecule localization for super-resolution microscopy.
problem Accurate and efficient localization of single molecules for super-resolution microscopy.
method A novel deep learning network architecture that uses temporal context to simultaneously detect and localize molecules.
result Achieves state-of-the-art performance on the SMLM2016 challenge, excels at high densities.
Local EGOP learns functions varying along a few directions.
problem Efficient estimation of functions varying along a few directions in high-dimensional space.
method Local EGOP learning, a recursive algorithm using EGOP quadratic form as metric and inverse-covariance.
result Local EGOP learning achieves intrinsic dimensional learning rates under noisy manifold hypothesis.
Local regularization fails in transductive learning for some multiclass problems.
problem Whether local regularization can learn all transductive multiclass problems.
method Provided a negative answer by exhibiting a specific multiclass problem.
result Local regularization cannot learn all transductive multiclass problems.
ALIME proposes an autoencoder-based method for making deep learning models locally interpretable.
problem Making opaque deep learning models interpretable, especially in medical applications.
method Inspired by LIME, ALIME uses an autoencoder to generate a more stable and locally faithful explanation around a single instance.
result Our method improves both the stability and local fidelity of explanations compared to traditional LIME.
New method learns local structure for better data representation.
problem Global structure learning ignores local structure in nonnegative matrix factorization.
method Proposes a new nonnegative matrix factorization method that learns local similarity and clustering.
result The new representation reveals inherent geometric property of the data more effectively.
New optimization for federated learning with local models.
problem Training models with private data from multiple devices.
method Proposes a new optimization formulation and efficient SGD variants.
result Local steps can improve communication for heterogeneous data.
Locally adaptive federated learning improves convergence in distributed machine learning.
problem Balancing local updates in federated learning leads to slow convergence.
method Locally adaptive federated learning algorithms that use uncoordinated stepsizes based on local geometric information.
result Locally adaptive methods can be particularly efficient in overparameterized settings and outperform standard federated algorithms.
Local adaptive methods in FL can accelerate convergence but introduce bias, which is corrected.
problem The effect of using adaptive optimization methods for local updates in federated learning.
method Proposed correction techniques to overcome the bias introduced by local adaptive methods.
result Correction techniques can achieve faster convergence and higher test accuracy than baseline methods.
Local Gradient Descent with local steps converges to the centralized model in the interpolation regime.
problem Understanding the implicit bias of Local Gradient Descent in the interpolation regime.
method Analyzing the implicit bias of Local Gradient Descent for classification tasks with linearly separable data.
result The aggregated global model from Local-GD converges exactly to the centralized model in the interpolation regime.
Deep learning improves damage localization in ultrasonic waves under uncertainty.
problem Uncertainty in wave propagation due to environmental factors and noise.
method Deep learning model trained on simulated wave data with uncertainty.
result Deep learning model learns robust representations of wave data with uncertainty.
Personalized federated learning improves model accuracy and fairness by leveraging shared representations and local memorization.
problem Sub-optimal performance of federated learning when client data distributions are heterogeneous.
method Proposes a personalization mechanism based on local memorization of shared representations from a collectively trained global model.
result Significantly higher accuracy and fairness compared to state-of-the-art methods.
Local adaptation improves federated learning models.
problem Improving accuracy of federated learning models on non-iid data.
method Local adaptation techniques (fine-tuning, multi-task learning, knowledge distillation).
result Participants benefit from local adaptation, improving federated model accuracy.
New insights into optimizing Local SGD's outer optimizer for faster convergence.
problem Understanding the impact of outer optimizer and its hyperparameters in Local SGD.
method Analyzing convergence guarantees with new outer learning rates and momentum.
result Tuning the outer learning rate can improve convergence and handle inner learning rate ill-tuning.
New approach learns neural network parameters by balancing local objectives and data propagation constraints.
problem Learning neural network parameters with trade-offs between local objectives and data propagation.
method Introduces nonlinear transforms and local propagation constraints to balance learning objectives.
result Validated approach on image recognition tasks with improved learning time and network size.
New methods for federated learning reduce communication costs.
problem Efficiently solving optimization problems in a distributed setting.
method Developed two strategies for achieving consensus in federated learning: fixed number of local steps and randomized computations.
result Convergence analysis and experiments show benefits of the proposed methods.
A-MIL improves histopathology image classification and localization.
problem Improving diagnosis of breast cancer through better interpretation of histopathology images.
method Frame image classification as multiple instance learning, use attention-based learning for localization.
result A-MIL achieves better localization without compromising classification accuracy.
Unified federated learning via GTV minimization.
problem Training local models for decentralized datasets with network structure.
method Formulated federated learning as GTV minimization, developed a decentralized algorithm.
result Upper bound on local model parameters deviation, revealing conditions for pooling homogeneous datasets.
New method uses Gaussian processes to find local minima efficiently.
problem Finding all local minima of a black-box function with unknown derivatives.
method Sequentially selects input points to update GP derivatives' confidence intervals.
result Theoretical analysis and numerical experiments show the method's effectiveness.
Local unsupervised learning outperforms end-to-end training for image classification.
problem Local unsupervised learning is believed to be inferior to end-to-end training.
method Designing a local algorithm to learn convolutional filters at scale on large image datasets using local Hebbian learning.
result Convolutional networks with patch normalization significantly outperform standard networks on image classification tasks.
Proposes MC-AE for better unsupervised clustering of unlabeled data.
problem Lack of consideration for multi-local collaborative relationships in autoencoders.
method Integrates LSH for multi-local cross blocks, mcrRBM and mcrGRBM models.
result MC-AE improves unsupervised clustering performance.