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Found 13 papers, 1 papers with code
Towards Robust Real-Time Hardware-based Mobile Malware Detection using Multiple Instance Learning Formulation
This study introduces RT-HMD, a Hardware-based Malware Detector (HMD) for mobile devices, that refines malware representation in segmented time-series through a Multiple Instance Learning (MIL) approach.
Learning Locally Interacting Discrete Dynamical Systems: Towards Data-Efficient and Scalable Prediction
Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively simple, and often stochastic interactions between dynamic elements.
Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks
Recent work by \cite{barannikov2021representation} has introduced a novel method to compare topological mappings of learned representations called Representation Topology Divergence (RTD).
Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN
We show that the LNP can leverage diversity in neuronal timescales to design a sparse Heterogeneous RSNN (HRSNN).
Has the Deep Neural Network learned the Stochastic Process? A Wildfire Perspective
This paper presents the first systematic study of evalution of Deep Neural Network (DNN) designed and trained to predict the evolution of a stochastic dynamical system, using wildfire prediction as a case study.
Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction
Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data.
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning.
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity
We present an unsupervised deep learning model for 3D object classification.
Heterogeneous Recurrent Spiking Neural Network for Spatio-Temporal Classification
Spiking Neural Networks are often touted as brain-inspired learning models for the third wave of Artificial Intelligence.
$μ$DARTS: Model Uncertainty-Aware Differentiable Architecture Search
We present a Model Uncertainty-aware Differentiable ARchiTecture Search ($\mu$DARTS) that optimizes neural networks to simultaneously achieve high accuracy and low uncertainty.
Ranked #12 on
Neural Architecture Search
on CIFAR-100
Characterization of Generalizability of Spike Timing Dependent Plasticity trained Spiking Neural Networks
A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications.
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection
This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms.
Cost-aware Feature Selection for IoT Device Classification
In this work, we take a more realistic approach, and argue that feature extraction has a cost, and the costs are different for different features.
