New handwritten digits dataset for Kannada script.
problem Lack of datasets for Kannada numeral digits.
method Developed Kannada-MNIST and Dig-MNIST datasets.
result Initial CNN accuracy is lower than MNIST, indicating a challenge in generalization.
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48 results for “MNIST Dataset”
MNIST-Nd offers synthetic datasets to benchmark clustering across dimensions.
problem Clustering performance degrades with high-dimensional data.
method Training mixture variational autoencoders on MNIST to create synthetic datasets with varying latent dimensions.
result Leiden clustering algorithm is most robust as dimensionality grows.
We present Fashion-MNIST, a new dataset comprising of 28x28 grayscale images of 70,000 fashion products from 10 categories, with 7,000 images per category. The training set has 60,000 images and the test set has 10,000 images. Fashion-MNIST is intended to serve as a direct drop-in replacement for the original MNIST dat…
This paper removes near-duplicates from Fashion-MNIST to improve testing accuracy.
problem Near-duplicate images in Fashion-MNIST increase testing accuracy, reducing dataset quality.
method Identified and removed near-duplicate images between training and testing sets.
result Improved dataset quality for better testing accuracy in machine learning models.
Reconstructs lost MNIST dataset to validate previous findings.
problem Lost precise derivation steps of MNIST dataset.
method Trace each MNIST digit to NIST source, reconstruct complete test set.
result Reconstructed MNIST dataset confirms previous findings on classifier performance.
Quantum-assisted GAN learns MNIST and LSUN datasets.
problem Learning latent variable generative models with adversarial networks.
method Generative adversarial learning with quantum annealing.
result Quantum-assisted GAN successfully learns MNIST and LSUN datasets.
The impact of the maximally possible batch size (for the better runtime) on performance of graphic processing units (GPU) and tensor processing units (TPU) during training and inference phases is investigated. The numerous runs of the selected deep neural network (DNN) were performed on the standard MNIST and Fashion-M…
Study shows dataset properties impact adversarial machine learning robustness.
problem Vulnerability of DNNs to adversarial attacks.
method Examined five datasets, analyzed input size and contrast effects.
result Input size and contrast significantly influence adversarial success.
The Adversarially Learned Mixture Model (AMM) is a generative model for unsupervised or semi-supervised data clustering. The AMM is the first adversarially optimized method to model the conditional dependence between inferred continuous and categorical latent variables. Experiments on the MNIST and SVHN datasets show t…
Bidirectional VAE reduces parameters and improves image tasks.
problem Improving image reconstruction, classification, interpolation, and generation.
method Uses a single neural network for both encoding and decoding in both forward and backward directions.
result Bidirectional VAEs reduce parameters by almost 50% and slightly outperform unidirectional VAEs.
Deep networks learn spurious features instead of actual object features, leading to poor generalization.
problem Deep networks' poor generalization on out-of-distribution samples due to learning spurious features.
method Evaluation of existing robust feature learning methods and regularization methods, comparison against a baseline designed to capture high correlation features.
result A baseline method trained on high correlation features outperforms existing methods in generalization.
Skip connections improve biologically-inspired learning rules.
problem Biologically-inspired learning rules often underperform compared to backpropagation.
method Introduced skip connections between intermediate layers in biologically-motivated learning rules.
result Skip connections can match the performance of backpropagation and are robust to hyper-parameters.
q-CNN learns data features through entangled states.
problem Classifying MNIST and Fashion MNIST datasets.
method Introduces q-CNN model, a tensor network description, and studies entanglement structure.
result q-CNN learns entanglement structure to perform classification tasks accurately.
AWDO trains neural networks for digit classification.
problem Training feedforward neural networks.
method Adaptive Wind Driven Optimization (AWDO).
result AWDO outperforms steepest descent method in digit classification.
Method discovers symmetries in data with neural networks.
problem Discovering symmetries in high-dimensional data.
method Fully connected neural networks trained with a loss function for symmetry.
result Generators for symmetries in latent space.
KIP meta-learning compresses datasets significantly.
problem Training data size and quality issues in machine learning.
method Kernel Inducing Points (KIP) for dataset compression.
result Significant reduction in dataset size with similar model performance.
A standard Variational Autoencoder, with a Euclidean latent space, is structurally incapable of capturing topological properties of certain datasets. To remove topological obstructions, we introduce Diffusion Variational Autoencoders with arbitrary manifolds as a latent space. A Diffusion Variational Autoencoder uses t…
New optimization algorithms for neural networks using operator splitting.
problem Training efficiency and convergence in neural networks.
method Sequential operator splitting technique applied to neural network training.
result Empirical rate of convergence towards local minimum of loss function validated.
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an o…
TDA classifies MNIST digits with reduced feature set.
problem Classifying MNIST digits using machine learning.
method Persistent homology for feature generation and classification.
result 5x reduction in feature set size with similar accuracy.
Variational inference is a powerful tool for approximate inference. However, it mainly focuses on the evidence lower bound as variational objective and the development of other measures for variational inference is a promising area of research. This paper proposes a robust modification of evidence and a lower bound for…
This paper addresses the issue of building a part-based representation of a dataset of images. More precisely, we look for a non-negative, sparse decomposition of the images on a reduced set of atoms, in order to unveil a morphological and interpretable structure of the data. Additionally, we want this decomposition to…
Paper proposes InfoAE for disentangled representation learning.
problem Learning disentangled representations from unlabeled data.
method InfoAE learns disentangled representation by maximizing mutual information.
result Achieved 98.9% test accuracy on MNIST with unsupervised training.
We propose a new type of hidden layer for a multilayer perceptron, and demonstrate that it obtains the best reported performance for an MLP on the MNIST dataset.
We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities. Each data distribution is modeled with a mixture of generator distributions. As the generators are partially shared between the modeling of different true data distributions, shared ones ca…
In this work we present Discrete Attend Infer Repeat (Discrete-AIR), a Recurrent Auto-Encoder with structured latent distributions containing discrete categorical distributions, continuous attribute distributions, and factorised spatial attention. While inspired by the original AIR model andretaining AIR model's capabi…
Quantum computing at room temperature achieves high accuracy in image classification.
problem Classifying images with single photons at room temperature.
method Optical transformation of quantum state to exploit interference and entanglement.
result Theoretical accuracy of 41.27% for MNIST and 36.14% for Fashion-MNIST.
Proposes a model for semi-supervised learning using both labeled and unlabeled data.
problem Semi-supervised learning with limited labeled data.
method Semi-conditional normalizing flow model with conditional coupling layer.
result Model outperforms variational auto-encoders on MNIST dataset.
Develops a VAE model for datasets with missing data.
problem Applying VAEs to datasets with missing data.
method A novel latent variable model of a corruption process generating missing data, with a tractable ELBO.
result Improved marginal log-likelihood and better missing data imputation compared to existing approaches.
Bayesian autoencoders improve OOD detection by addressing Bernoulli likelihood issues.
problem Out-of-distribution (OOD) detection fails with Bernoulli likelihood for certain datasets.
method Proposes Bayesian autoencoders and alternative likelihood models to fix the issue.
result Bayesian autoencoders and alternative likelihood models improve OOD detection accuracy.
Enhanced BCPNN learns hidden representations without labels.
problem Unsupervised learning of hierarchical representations in neural networks.
method Extended BCPNN architecture with local Hebbian learning mechanisms.
result Demonstrated capability for unsupervised learning of salient hidden representations.
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed Interpretationstat.ML
Develops a method to interpret deep learning models by identifying key features.
problem Revealing the decision-making process of blackbox models from raw data to predictions.
method Adversarial attacks to localize discriminative features with statistical guarantees.
result Locally identified features are both biologically plausible and statistically significant.
Paper tackles unsupervised learning of 3D shapes from single images.
problem Learning 3D shapes from single images without supervision.
method Generative models, variational auto-encoders, adversarial methods.
result Model learns 3D shapes and poses from single images, showing potential for various datasets.
Develops metrics to identify outliers in machine learning datasets.
problem Identifying outliers in machine learning datasets.
method Five methods to score examples based on well-representedness.
result All methods correlate highly, suggesting a quantifiable notion of well-representedness.
We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that has been corrupted by worst-case noise, namely a bounded number of coordinates of the signal have been corrupted arbitrarily. Our techniques generalize to a wide range of linear transformations that are…
Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. …
A dataset of 10 molecule types for machine learning studies.
problem Lack of suitable datasets for machine learning in molecular imaging.
method Generated 2D cross-sectional projections of 10 molecule types from Molecular Dynamics trajectories.
result Benchmark dataset for machine learning, deep learning, and image processing in scattering, imaging, and microscopy.
Convolutional neural networks (CNNs) are similar to "ordinary" neural networks in the sense that they are made up of hidden layers consisting of neurons with "learnable" parameters. These neurons receive inputs, performs a dot product, and then follows it with a non-linearity. The whole network expresses the mapping be…
We introduce a semiparametric approach to neighbor-based classification. We build off the recently proposed Boundary Trees algorithm by Mathy et al.(2015) which enables fast neighbor-based classification, regression and retrieval in large datasets. While boundary trees use an Euclidean measure of similarity, the Differ…
Deep neural networks reveal a low-dimensional manifold structure in data.
problem Understanding the structure of data for better model performance.
method Model-centric analysis of the data manifold using the local data matrix and Fisher information matrix.
result The dataset lies on a data leaf with a dimension bounded by the number of labels.
Synthetic dataset for deep learning with known Gaussian distribution.
problem Lack of datasets with known distribution for deep learning verification.
method Proposes a method to generate a synthetic dataset with Gaussian distribution.
result Synthetic dataset mimics MNIST and can be used with DNNs.
New method makes deep nets robust to large perturbations.
problem Making deep nets robust to large perturbations.
method Simple weight initialization and rigorous evaluation on MNIST and CIFAR10.
result Learning on higher levels of noise is feasible with new initialization.
We propose a stepsize adaptation scheme for stochastic gradient descent. It operates directly with the loss function and rescales the gradient in order to make fixed predicted progress on the loss. We demonstrate its capabilities by conclusively improving the performance of Adam and Momentum optimizers. The enhanced op…
Research proposes a test case generation system for deep learning models using dataset properties.
problem Automated generation of extensive test cases for deep learning models is challenging.
method Measures dataset quality and proposes a test case generation system guided by dataset properties.
result Systematic test case generation for deep learning models is effective.
The paper introduces a method to make neural networks more robust to adversarial attacks.
problem Vulnerability of deep neural networks to small, adversarially designed perturbations.
method A bottom-up strategy using a nonlinear front end that polarizes and quantizes data.
result The approach can completely eliminate adversarial perturbations on MNIST and Fashion MNIST datasets.
We present a variation of the Autoencoder (AE) that explicitly maximizes the mutual information between the input data and the hidden representation. The proposed model, the InfoMax Autoencoder (IMAE), by construction is able to learn a robust representation and good prototypes of the data. IMAE is compared both theore…
Study compares various optimization algorithms for deep learning.
problem Challenges in optimizing deep neural networks as they grow deeper and datasets increase.
method Implemented and compared adaptive gradient methods for supervised and unsupervised tasks on four image datasets.
result Differences in behavior and performance of optimization algorithms during training.
NeuPDE uses neural networks to model time-dependent data using differential equations.
problem Modeling time-dependent data from dynamic datasets.
method Neural network approach with both shallow multilayer perceptrons and nonlinear differential terms.
result Demonstrated on various dynamical systems, NeuPDE outperforms other methods.