Generative Adversarial Networks create realistic brain MRI images.
problem Lack of sufficient training data for medical research.
method Experimented three GAN architectures with different loss functions and hyperparameter tuning.
result Achieved high-quality, realistic synthetic MRIs with gradient penalty and mini-batch similarity layer.
Online data augmentation improves forecasting performance in deep learning.
problem Insufficient training data for forecasting tasks.
method An online data augmentation framework that generates synthetic samples during training.
result Online data augmentation leads to better forecasting performance.
With the large rising of complex data, the nonconvex models such as nonconvex loss function and nonconvex regularizer are widely used in machine learning and pattern recognition. In this paper, we propose a class of mini-batch stochastic ADMMs (alternating direction method of multipliers) for solving large-scale noncon…
SRS improves deep learning accuracy on small image datasets.
problem Training deep neural networks with limited images per class.
method Fixed sequence index sampling with batch replacement.
result Significantly improved classification accuracy on CIFAR-100.
FSGD uses latent factors to scale SGD for high-dimensional learning.
problem Scalable optimization in high-dimensional machine learning.
method Factor-Augmented SGD (FSGD) that operates on streaming data.
result Established theoretical framework for latent factor estimation error in SGD.
Self-augmentation improves deep networks for few-shot learning with minimal training data.
problem Improving deep networks' generalization to unseen classes with limited training examples.
method Self-augmentation using self-mix and self-distillation techniques, combined with regional dropout and local representation learning.
result The method outperforms state-of-the-art few-shot learning methods on prevalent benchmarks.
Data augmentation is an effective technique for improving the accuracy of modern image classifiers. However, current data augmentation implementations are manually designed. In this paper, we describe a simple procedure called AutoAugment to automatically search for improved data augmentation policies. In our implement…
Paper analyzes gradient descent with noisy data copies for linear regression, showing regularization and acceleration effects.
problem Improving generalization in machine learning through data augmentation with noise.
method Gradient descent with on-line noisy copies for linear regression analysis.
result Training with on-line noisy copies is equivalent to ridge regularization with a specific regularization parameter.
A new memory replay mechanism improves reinforcement learning stability and speed.
problem Forgetting in reinforcement learning with continuous control.
method Augmented Memory Replay (AMR) that optimizes the replay of past experiences.
result AMR enhances stability and convergence speed of learning algorithms.
A new algorithm is proposed which accelerates the mini-batch k-means algorithm of Sculley (2010) by using the distance bounding approach of Elkan (2003). We argue that, when incorporating distance bounds into a mini-batch algorithm, already used data should preferentially be reused. To this end we propose using nested …
Mini-batch EM algorithm speeds up convergence for large datasets.
problem Efficiently processing large datasets in latent variable models.
method Proposes mini-batch version of Stochastic Approximation EM algorithm for exponential models.
result Converges under classical conditions with mini-batch sampling.
Proposes m-POT to improve m-OT's misspecified mappings issue.
problem Misspecified mappings in mini-batch optimal transport.
method Partial optimal transport (POT) between mini-batch empirical measures.
result m-POT alleviates incorrect mappings compared to current methods.
New streaming methods improve convergence rates for optimization problems.
problem Optimizing large-scale, sequential data problems.
method Time-varying mini-batches and Polyak-Ruppert averaging for gradient-based algorithms.
result Time-varying mini-batches and averaging achieve optimal convergence and variance reduction.
Modern deep neural network training is typically based on mini-batch stochastic gradient optimization. While the use of large mini-batches increases the available computational parallelism, small batch training has been shown to provide improved generalization performance and allows a significantly smaller memory footp…
A new method for mini-batch optimal transport improves scalability and accuracy.
problem Desired estimation and proper metric approximation in m-OT.
method BoMb-OT: Finds optimal coupling between mini-batches.
result BoMb-OT approximates a proper metric and improves m-OT's performance.
We study a mini-batch diversification scheme for stochastic gradient descent (SGD). While classical SGD relies on uniformly sampling data points to form a mini-batch, we propose a non-uniform sampling scheme based on the Determinantal Point Process (DPP). The DPP relies on a similarity measure between data points and g…
In this paper, we develop a new accelerated stochastic gradient method for efficiently solving the convex regularized empirical risk minimization problem in mini-batch settings. The use of mini-batches is becoming a golden standard in the machine learning community, because mini-batch settings stabilize the gradient es…
Proposes a method to learn conditional VAEs from datasets with missing covariates.
problem Learning conditional VAEs from datasets with missing covariates.
method Augments conditional VAEs with a prior distribution for missing covariates and estimates their posterior using amortised variational inference.
result The proposed method outperforms previous methods in learning conditional VAEs from non-temporal, temporal, and longitudinal datasets.
Federated multi-mini-batch improves efficiency in non-IID environments.
problem Performance and communication efficiency challenges in federated learning with non-IID data.
method Introduces federated multi-mini-batch approach to balance performance and communication.
result Federated multi-mini-batch outperforms federated averaging in non-IID settings.
Paper finds optimal mini-batch size for SGD to speed up learning.
problem Optimizing mini-batch size for faster SGD convergence.
method Empirical inverse law and theoretical bound on mini-batch SGD training.
result An accurate model for predicting training time and identifying implications for algorithm and hardware.
Study shows how mini-batch GD with random reshuffling affects least squares regression dynamics.
problem Analyzing the error dynamics of mini-batch GD with random reshuffling for least squares regression.
method Represented training and generalization errors through a sample cross-covariance matrix Z, compared with sample covariance matrix of original features X, and used linear scaling rule for analysis.
result Mini-batch GD with random reshuffling exhibits subtle step-size dependence not detectable by gradient flow analysis, converging to a limit dependent on the step size.
Faster convergence and handling larger mini-batches for deep neural networks.
problem Generalization gap in large-scale distributed training of deep neural networks.
method Second-order optimization using Kronecker-factored approximate curvature.
result Achieved 75% Top-1 validation accuracy with mini-batch size of 131,072 in 978 iterations.
Generalizes SGD convergence analysis and optimizes stepsize.
problem Analyzes convergence of stochastic gradient descent (SGD) under arbitrary sampling.
method Introduces expected smoothness and derives convergence rates for various mini-batching strategies.
result Derives optimal mini-batch size and stepsize-switching rules.
New method selects diverse mini-batches for active learning.
problem Reduce labeled data for deep learning models.
method Sequential selection of diverse mini-batches using K-means clustering.
result Achieves comparable or better performance than previous methods.
We present a generic framework for trading off fidelity and cost in computing stochastic gradients when the costs of acquiring stochastic gradients of different quality are not known a priori. We consider a mini-batch oracle that distributes a limited query budget over a number of stochastic gradients and aggregates th…
We propose regularizing the empirical loss for semi-supervised learning by acting on both the input (data) space, and the weight (parameter) space. We show that the two are not equivalent, and in fact are complementary, one affecting the minimality of the resulting representation, the other insensitivity to nuisance va…
Ensembling DNNs improves minority group performance, leading to fairness.
problem Improving subgroup performances in DNN classifiers.
method Simple homogeneous ensembling of DNNs.
result Fairness naturally emerges from ensembling, improving minority group performance.
Improves deep learning training by matching mini-batch distributions.
problem Overfitting and noise in mini-batch training.
method ITDM, which matches the moments of mini-batch distributions to reduce overfitting.
result ITDM reduces overfitting and improves DNN training.
This study addresses the challenges of dynamic mini-batch sub-sampling in neural network training.
problem Challenges in training neural networks due to dynamic mini-batch sub-sampling.
method Distinguishes between static and dynamic sub-sampling, recasting optimization to find SNN-GPPs.
result SNN-GPPs are less susceptible to sub-sampling-induced discontinuities and better approximate true optima.
Improved robustness in optimization methods using second-order information.
problem Scalability and sensitivity to mini-batch size in optimization methods.
method Mini-Batch Stochastic Variance-Reduced Newton (extttMb−SVRN) algorithm incorporating partial second-order information. result Achieves a fast linear convergence rate independent of mini-batch size for large data sizes.
SP-NGD improves deep learning models' generalization with large mini-batch sizes.
problem Worse generalization performance with large mini-batch sizes in deep learning.
method SP-NGD, a natural gradient descent approach for large-scale deep learning.
result SP-NGD achieves similar generalization performance to first-order methods with accelerated convergence and negligible overhead.
In this work we develop a new algorithm for regularized empirical risk minimization. Our method extends recent techniques of Shalev-Shwartz [02/2015], which enable a dual-free analysis of SDCA, to arbitrary mini-batching schemes. Moreover, our method is able to better utilize the information in the data defining the ER…
In this paper we aim to formally explain the phenomenon of fast convergence of SGD observed in modern machine learning. The key observation is that most modern learning architectures are over-parametrized and are trained to interpolate the data by driving the empirical loss (classification and regression) close to zero…
New optimal step sizes and mini-batch sizes for SAGA.
problem Finding optimal step sizes and mini-batch sizes for SAGA.
method Provided closed-form expressions for expected smoothness constant and suggested new step sizes and mini-batch sizes.
result Total complexity of SAGA decreases linearly with mini-batch size up to an optimal value.
In statistical learning for real-world large-scale data problems, one must often resort to "streaming" algorithms which operate sequentially on small batches of data. In this work, we present an analysis of the information-theoretic limits of mini-batch inference in the context of generalized linear models and low-rank…
New findings show mini-batch SGD operates in a 'Edge of Stochastic Stability' regime.
problem Understanding the stability and convergence of mini-batch SGD.
method Analyzing the mini-batch Hessian and its directional curvature.
result Mini-batch SGD operates in a different stability regime (Edge of Stochastic Stability) compared to full-batch GD.
CBN improves batch normalization for small mini-batch sizes.
problem Reduced effectiveness of Batch Normalization in small mini-batch sizes.
method CBN uses statistics from multiple recent iterations, compensating for network weight changes via Taylor polynomials.
result CBN outperforms original batch normalization and direct iteration statistics in object detection and image classification.
New statistical properties for mini-batch Cox-NN optimization.
problem Optimizing deep Cox neural networks using mini-batches.
method Developed mini-batch maximum partial-likelihood estimator (mb-MPLE) for Cox-NN.
result mb-MPLE is consistent and achieves optimal convergence rate.
Stochastic dual coordinate ascent (SDCA) is an effective technique for solving regularized loss minimization problems in machine learning. This paper considers an extension of SDCA under the mini-batch setting that is often used in practice. Our main contribution is to introduce an accelerated mini-batch version of SDC…
We present Optimal Transport GAN (OT-GAN), a variant of generative adversarial nets minimizing a new metric measuring the distance between the generator distribution and the data distribution. This metric, which we call mini-batch energy distance, combines optimal transport in primal form with an energy distance define…
We propose a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent applied to the problem of minimizing a strongly convex composite function represented as the sum of an average of a large number of smooth convex functions, and simple nonsmooth conve…
We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We consider the problem of minimizing a strongly convex function represented as the sum of an average of a large number of smooth convex function…
FRN layer eliminates batch dependence in deep learning, improving performance across various tasks.
problem Batch Normalization's dependency on mini-batch elements can degrade performance for small batches.
method Filter Response Normalization (FRN) operates independently on each activation channel of each batch element.
result FRN layer outperforms BN and other alternatives in various settings for all batch sizes.
The convergence speed of stochastic gradient descent (SGD) can be improved by actively selecting mini-batches. We explore sampling schemes where similar data points are less likely to be selected in the same mini-batch. In particular, we prove that such repulsive sampling schemes lowers the variance of the gradient est…
New method reduces variance in complex probabilistic model optimization.
problem High variance in stochastic optimisation of complex models.
method Use recognition network to approximate optimal control variate for each mini-batch.
result Sub-optimal variance reduction is improved with new approach.
The paper analyzes time-dependent streaming data with biased gradient estimates and proposes improved stochastic optimization methods.
problem Stochastic optimization in a streaming setting with time-dependent and biased gradient estimates.
method Analysis of several first-order methods including SGD, mini-batch SGD, and time-varying mini-batch SGD, along with their Polyak-Ruppert averages.
result Time-varying mini-batch SGD methods can break long- and short-range dependence structures, and biased SGD methods can achieve comparable performance to their unbiased counterparts.
Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limi…
HydaLearn dynamically adjusts task weights for better MTL performance.
problem Constant loss weights in MTL lead to poor results due to drifting relevance and varying mini-batch composition.
method HydaLearn uses mini-batch gradients to dynamically adjust task weights.
result HydaLearn improves performance on synthetic and real-world data.