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LAA Dataset
A dataset for estimating measurements received over a network, using a deep neural network based approximation architecture. -
Learning Trajectories are Generalization Indicators
This paper explores the connection between learning trajectories of Deep Neural Networks (DNNs) and their generalization capabilities when optimized using stochastic gradient... -
ILMPQ: An Intra-Layer Multi-Precision Deep Neural Network Quantization framew...
ILMPQ: An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA -
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function... -
FATE: Fast and Accurate Timing Error Prediction Framework for Low Power DNN A...
Deep neural networks (DNN) are increasingly being accelerated on application-specific hardware such as the Google TPU designed especially for deep learning. Timing speculation... -
FlowMur: A Stealthy and Practical Audio Backdoor Attack with Limited Knowledge
Speech recognition systems driven by Deep Neural Networks (DNNs) have revolutionized human-computer interaction through voice interfaces, which significantly facilitate our... -
News session-based recommendations using deep neural networks
News session-based recommendations using deep neural networks. -
DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks
The proposed approach is compared with state-of-the-art methods on the reprocessed Color Checker dataset and the NUS 8-camera dataset. -
Learning Generalized Reactive Policies using Deep Neural Networks
A new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. -
MIXED PRECISION TRAINING
The dataset used for training deep neural networks using half-precision floating point numbers. -
RML2016.10a
The dataset used in this paper for Automatic Modulation Classification (AMC) using deep learning. -
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used various neural networks, including AlexNet, VGG16, InceptionV4, ResNet50,... -
COCO object detection dataset
The dataset used in the paper is a 2D object detection dataset, where the authors investigate the issues of achieving sufficient rigor in the arguments for the safety of machine... -
Dataset for Energy-Efficient Deep Neural Networks
The dataset used in this paper is a collection of 25 state-of-the-art deep neural networks (DNNs) with different architectures and sizes.
