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Associative Content-Addressable Memory with Exponentially Many Robust Stable ...
Associative content-addressable memory with exponentially many robust stable states and robust error correction. -
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming
Recent years have witnessed the dramatic growth of Internet video traffic, where the video bitstreams are often compressed and delivered in low quality to fit the streamer’s... -
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
The dataset used in the paper is a multiresolution hash encoding for neural graphics primitives. -
Counting Digits Dataset
The dataset used in this paper is a synthetic dataset with a nonlinear response, where the response is learned by means of a neural network trained to count numbers in synthetic... -
Neural Variational Inference for Text Processing
Neural variational inference for text processing. -
ImageNet-Sketch
ImageNet-Sketch is used as target dataset for domain adaptation. -
SVHN, MNIST, and MNIST-M
SVHN, MNIST, and MNIST-M are used as source datasets for domain adaptation. -
CIFAR-10-C, CIFAR-100-C, and ImageNet-C
CIFAR-10-C, CIFAR-100-C, and ImageNet-C are used as target datasets for corruption robustness evaluation. -
Common corruptions and perturbations for evaluating robustness
Common corruptions and perturbations are used to evaluate the robustness of neural networks. -
Benchmarking neural network robustness to common corruptions and perturbations
Benchmarking neural network robustness to common corruptions and perturbations. -
VGG-16 and ResNet-50 DNNs
The VGG-16 and ResNet-50 DNNs are used as victim DNNs in the attack. -
VGG and ResNet DNNs
The VGG and ResNet DNNs are used as victim DNNs in the attack. -
Tensor Regression Networks with various Low-Rank Tensor Approximations
Tensor regression networks achieve high compression rate of neural networks while having slight impact on performances. They do so by imposing low tensor rank structure on the... -
Deep Neural Networks
Deep Neural Networks (DNNs) are universal function approximators providing state-of-the-art solutions on wide range of applications. Common perceptual tasks such as speech... -
Lookahead Pruning
The dataset used in this paper is a neural network, and the authors used it to test the performance of their lookahead pruning method. -
MobileNetV2
The dataset used in this paper is a MobileNetV2 model, which is a type of deep neural network. The dataset is used to evaluate the performance of the proposed heterogeneous system. -
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis -
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and...
The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used a range of datasets, including CIFAR-10, GTSRB, Google Speech Command,... -
Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networks
The bipartite Sherrington-Kirkpatrick model is a mathematically tractable spin glass model, which is formally similar to a Restricted Boltzmann Machine (RBM) neural network.
