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Found 16 papers, 5 papers with code
ReLU and Addition-based Gated RNN
It is demonstrated that the novel gating mechanism can capture long-term dependencies for a standard synthetic sequence learning task while significantly reducing computational costs such that execution time is reduced by half on CPU and by one-third under encryption.
Deep Perceptual Similarity is Adaptable to Ambiguous Contexts
The concept of image similarity is ambiguous, and images can be similar in one context and not in another.
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
Deep perceptual loss is a type of loss function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks.
A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons
With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition.
Integration of Neuromorphic AI in Event-Driven Distributed Digitized Systems: Concepts and Research Directions
Based on this analysis, we propose a microservice-based framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which provides virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction.
Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics
This work investigates the most common DPS metric, where deep features are compared by spatial position, along with metrics comparing the averaged and sorted deep features.
Technical Language Supervision for Intelligent Fault Diagnosis in Process Industry
In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety.
Spatiotemporal Pattern Recognition in Single Mixed-Signal VLSI Neurons with Heterogeneous Dynamic Synapses
Mixed-signal neuromorphic processors with brain-like organization and device physics offer an ultra-low-power alternative to the unsustainable developments of conventional deep learning and computing.
Pretraining Image Encoders without Reconstruction via Feature Prediction Loss
To evaluate this method we perform experiments on three standard publicly available datasets (LunarLander-v2, STL-10, and SVHN) and compare six different procedures for training image encoders (pixel-wise, perceptual similarity, and feature prediction losses; combined with two variations of image and feature encoding/decoding).
Synaptic Integration of Spatiotemporal Features with a Dynamic Neuromorphic Processor
Here, we investigate synaptic integration of spatiotemporal spike patterns with multiple dynamic synapses on point-neurons in the DYNAP-SE neuromorphic processor, which offers a complementary resource-efficient, albeit less flexible, approach to feature detection.
Improving Image Autoencoder Embeddings with Perceptual Loss
Autoencoders are trained to embed images from three different computer vision datasets using perceptual loss based on a pretrained model as well as pixel-wise loss.
Synaptic Delays for Temporal Feature Detection in Dynamic Neuromorphic Processors
Furthermore, we present a network that mimics the auditory feature detection circuit of crickets and demonstrate how varying synapse weights, input noise and processor temperature affects the circuit.
Interoperability and machine-to-machine translation model with mappings to machine learning tasks
Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators.
Dictionary learning approach to monitoring of wind turbine drivetrain bearings
In this study, dictionaries are learned from gearbox vibrations in six different turbines, and the dictionaries are subsequently propagated over a few years of monitoring data when faults are known to occur.
Dictionary Learning with Equiprobable Matching Pursuit
Sparse signal representations based on linear combinations of learned atoms have been used to obtain state-of-the-art results in several practical signal processing applications.
Towards zero-configuration condition monitoring based on dictionary learning
In this paper, we investigate the possibility to automate the condition monitoring process by continuously learning a dictionary of optimized shift-invariant feature vectors using a well-known sparse approximation method.
