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Neural Network Training on In-memory-computing Hardware with Radix-4 Gradients
The dataset used in this paper is a neural network training dataset with radix-4 gradients. -
ReLU Neural Networks
The dataset used in the paper is a set of functions representable by ReLU neural networks with integer weights and arbitrary width. -
Neural Convolutional Surfaces
A neural representation of shapes that disentangles fine, local and possibly repeating geometry from global, coarse structures. -
Deep Neural Networks over Encrypted Data
The dataset used in this paper is not explicitly mentioned, but it is implied to be a large-scale dataset for machine learning. -
Training a Neural Network Model on Encrypted MNIST Data
The dataset used in this paper is not explicitly mentioned, but it is implied to be a large-scale dataset for machine learning. -
1D Self-Organized Operational Neural Networks
The proposed 1D Self-ONNs for patient-specific ECG classification and arrhythmia detection. -
Linear Frequency-Principle (LFP) model for two-layer neural networks
The dataset used in this paper is a collection of training data and target functions for two-layer neural networks. The dataset is used to test the performance of the Linear... -
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Sto...
NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems -
TensorQuant
TensorQuant toolbox is used to apply fixed point quantization to DNNs. The simulations are focused on popular CNN topologies, such as Inception V1, Inception V3, ResNet 50 and... -
TransparentFPGAAccelerationwithTensorFlow
The dataset used in this paper is a collection of neural network acceleration with TensorFlow and FPGA. -
Population Based Augmentation
A key challenge in leveraging data augmentation for neural network training is choosing an effective augmentation policy from a large search space of candidate operations. -
Anomalous diffusion dynamics of learning in deep neural networks
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used ResNet-14, ResNet-20, ResNet-56, and ResNet-110 networks, as well as... -
Transformations between deep neural networks
The dataset used in the paper is a collection of neural networks trained on different tasks, including scalar functions, two-dimensional vector fields, and images of a rotating... -
Domain-Adversarial Training of Neural Networks
The UPNA Synthetic dataset consists of 12 videos for each of 10 subjects; 120 videos in total with 38,800 frames. -
Neural Tangent Kernel
The neural tangent kernel Jacot et al. (2018) for fully-connected and convolutional networks describes the behavior and asymptotic performance of these networks under the... -
Deep Learning Models
The dataset used in this paper is a set of 20 well-known deep-learning models, including AlexNet, ResNet, VGG, DenseNet, etc. -
Progressive Feedforward Collapse of ResNet Training
The dataset used in the paper is a ResNet trained on various datasets, including MNIST, Fashion MNIST, CIFAR10, STL10, and CIFAR100. -
EntropicOTBenchmark
The dataset is used to test existing neural (continuous) solvers for the Entropic Optimal Transport and Schrödinger Bridge problems. -
Real-time Capable Modeling of Neutral Beam Injection on NSTX-U using Neural N...
A dataset of plasma dynamics in a tokamak, used to evaluate the performance of the Neural Dynamical Systems (NDS) method. -
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Pre...
A dataset of synthetic dynamical systems, including the Lorenz system and a generalized cartpole problem, used to evaluate the performance of the Neural Dynamical Systems (NDS)...
