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NITI: INTEGER TRAINING
The dataset used in this paper is MNIST, CIFAR10, and ImageNet. -
MNIST and ResNet50
The MNIST and ResNet50 datasets are used to test the onnx-mlir compiler. -
MNIST handwritten data set
MNIST handwritten data set used for training and testing the proposed method, containing 60,000 examples for training and 10,000 samples for testing. -
Low-Latency CryptoNets (LoLa) for Private Inference
The CalTech-101 dataset is used to evaluate the performance of the proposed Low-Latency CryptoNets (LoLa) solution for private inference. -
MNIST and celebA
MNIST and celebA datasets were used to train and evaluate the proposed QDCGAN architecture. -
A Deep Hashing Learning Network
The proposed method uses two benchmark datasets with different kinds of images, MNIST and CIFAR-10. -
MNIST and ImageNet datasets
The MNIST dataset is a large dataset of handwritten digits, and the ImageNet dataset is a large dataset of images. -
Support Vector Machine on MNIST
Support Vector Machine on MNIST -
MNIST, FMNIST, and CIFAR10 datasets
The MNIST, FMNIST, and CIFAR10 datasets are used to evaluate the proposed methods of spiking-MaxPooling. -
MNIST-parity experiment
The MNIST-parity experiment uses the MNIST dataset to test the performance of a ReLU network and various linear models on the parity of a single MNIST image and the parity of... -
MNIST dataset for handwritten digits
The MNIST dataset is a collection of images of handwritten digits, with size n = 70,000 and D = 784. -
Parallelizing Autoregressive Generation with Variational State Space Models
The MNIST and CIFAR datasets are used to evaluate the proposed Variational State Space Model (VSSM) for autoregressive generation. -
MNIST and CelebA datasets
The authors used MNIST and CelebA datasets for their experiments. -
MNIST, CIFAR10, and CelebA datasets
The dataset used in the paper is a MNIST dataset, a CIFAR10 dataset, and a CelebA dataset. -
CIFAR-10, CIFAR-100, SVHN, MNIST, KMNIST, FashionMNIST
CIFAR-10, CIFAR-100, SVHN, MNIST, KMNIST, FashionMNIST -
Visual Domain Adaptation
The MNIST, MNIST-M, Street View House Numbers (SVHN), Synthetic Digits (SYN DIGITS), CIFAR-10 and STL-10 datasets are used for visual domain adaptation experiments. -
MNIST and OMNIGLOT datasets
The MNIST dataset is a large dataset of handwritten digits, and the OMNIGLOT dataset is a large dataset of handwritten characters. -
Colored MNIST dataset
The dataset used in the paper is a binary classification task in a 300-dimensional space. The procedure for generating the training dataset is as follows: Each label y ∈ {−1, 1}... -
MNIST and CIFAR-10
The MNIST dataset is a large dataset of handwritten digits, and the CIFAR-10 dataset is a dataset of images from 10 different classes.
