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Found 14 papers, 2 papers with code
Temporally Layered Architecture for Efficient Continuous Control
We present a temporally layered architecture (TLA) for temporally adaptive control with minimal energy expenditure.
Meta-Analytic Operation of Threshold-independent Filtering (MOTiF) Reveals Sub-threshold Genomic Robustness in Trisomy
Trisomy, a form of aneuploidy wherein the cell possesses an additional copy of a specific chromosome, exhibits a high correlation with cancer.
Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment
As a beloved sport worldwide, dancing is getting integrated into traditional and virtual reality-based gaming platforms nowadays.
QuickNets: Saving Training and Preventing Overconfidence in Early-Exit Neural Architectures
However, early exits increase the training time of the neural networks.
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control
Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands.
Memory via Temporal Delays in weightless Spiking Neural Network
A common view in the neuroscience community is that memory is encoded in the connection strength between neurons.
Turing Completeness of Bounded-Precision Recurrent Neural Networks
Previous works have proved that recurrent neural networks (RNNs) are Turing-complete.
Adaptive Neural Connections for Sparsity Learning
We propose Adaptive Neural Connections (ANC), a method for explicitly parameterizing fine-grained neuron-to-neuron connections via adjacency matrices at each layer that are learned through backpropagation.
Ranked #1 on
Sparse Learning
on ImageNet32
Lattice Map Spiking Neural Networks (LM-SNNs) for Clustering and Classifying Image Data
Spiking neural networks (SNNs) with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps (SOMs).
Abstraction Mechanisms Predict Generalization in Deep Neural Networks
A longstanding problem for Deep Neural Networks (DNNs) is understanding their puzzling ability to generalize well.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to ATARI games
Previous studies in image classification domain demonstrated that standard NNs (with ReLU nonlinearity) trained using supervised learning can be converted to SNNs with negligible deterioration in performance.
Insulin Regimen ML-based control for T2DM patients
\begin{abstract} We model individual T2DM patient blood glucose level (BGL) by stochastic process with discrete number of states mainly but not solely governed by medication regimen (e. g. insulin injections).
Model-based Reinforcement Learning
Reinforcement Learning (RL)
A Support Vector Method for Clustering
Data points enclosed by each contour are defined as a cluster.
