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Found 19 papers, 3 papers with code
Enhancing Bandwidth Efficiency for Video Motion Transfer Applications using Deep Learning Based Keypoint Prediction
Prediction of keypoints, to enable transmission using lower frames per second on the source device, is performed using a Variational Recurrent Neural Network (VRNN).
Binary Gaussian Copula Synthesis: A Novel Data Augmentation Technique to Advance ML-based Clinical Decision Support Systems for Early Prediction of Dialysis Among CKD Patients
Early prediction of dialysis is crucial as it can significantly improve patient outcomes and assist healthcare providers in making timely and informed decisions.
An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything
Foundation models are currently driving a paradigm shift in computer vision tasks for various fields including biology, astronomy, and robotics among others, leveraging user-generated prompts to enhance their performance.
Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization
The UCB-to-EI switching policy dictated guided through continuous monitoring of the model uncertainty during each step of sequential sampling results in navigating through the MDS more efficiently while ensuring rapid convergence.
Strategic Data Augmentation with CTGAN for Smart Manufacturing: Enhancing Machine Learning Predictions of Paper Breaks in Pulp-and-Paper Production
In this article, operational data is analyzed from a paper manufacturing machine in which paper breaks are relatively rare but have a high economic impact.
Automatic Task Parallelization of Dataflow Graphs in ML/DL models
Especially in the case of inference, when the batch size is 1 and execution is on CPUs or for power-constrained edge devices, current techniques can become costly, complicated or inapplicable.
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations
State-of-the-art quantization techniques are currently applied to both the weights and activations; however, pruning is most often applied to only the weights of the network.
Tuning Confidence Bound for Stochastic Bandits with Bandit Distance
We propose a novel modification of the standard upper confidence bound (UCB) method for the stochastic multi-armed bandit (MAB) problem which tunes the confidence bound of a given bandit based on its distance to others.
Kernel distance measures for time series, random fields and other structured data
Applications are demonstrated for clustering of synthetic and real-life time series and image data, and the performance of kdiff is compared to competing distance measures for clustering.
An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling
We analyze and compare the inference properties of convolution-based upsampling algorithms using a quantitative model of incurred time and energy costs and show that using deconvolution for inference at the edge improves both system latency and energy efficiency when compared to their sub-pixel or resize convolution counterparts.
Generative and Discriminative Deep Belief Network Classifiers: Comparisons Under an Approximate Computing Framework
The use of Deep Learning hardware algorithms for embedded applications is characterized by challenges such as constraints on device power consumption, availability of labeled data, and limited internet bandwidth for frequent training on cloud servers.
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems
Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, image occlusion removal, pattern completion, and motion synthesis.
AX-DBN: An Approximate Computing Framework for the Design of Low-Power Discriminative Deep Belief Networks
The power budget for embedded hardware implementations of Deep Learning algorithms can be extremely tight.
A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA
In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors.
ApproxDBN: Approximate Computing for Discriminative Deep Belief Networks
Probabilistic generative neural networks are useful for many applications, such as image classification, speech recognition and occlusion removal.
A Nonparametric Framework for Quantifying Generative Inference on Neuromorphic Systems
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in probabilistic generative model applications such as image occlusion removal, pattern completion and motion synthesis.
Mapping Generative Models onto a Network of Digital Spiking Neurons
For this, we first propose a method of producing the Gibbs sampler using bio-inspired digital noisy integrate-and-fire neurons.
Gibbs Sampling with Low-Power Spiking Digital Neurons
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition.
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
However the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate.
