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Found 7 papers, 2 papers with code
ProtoVAE: Prototypical Networks for Unsupervised Disentanglement
Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely unsupervised way.
DOT-VAE: Disentangling One Factor at a Time
One promising approach to this endeavour is the problem of Disentanglement, which aims at learning the underlying generative latent factors, called the factors of variation, of the data and encoding them in disjoint latent representations.
Hybrid Backpropagation Parallel Reservoir Networks
In many real-world applications, fully-differentiable RNNs such as LSTMs and GRUs have been widely deployed to solve time series learning tasks.
Deep Reservoir Networks with Learned Hidden Reservoir Weights using Direct Feedback Alignment
Deep Reservoir Computing has emerged as a new paradigm for deep learning, which is based around the reservoir computing principle of maintaining random pools of neurons combined with hierarchical deep learning.
A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP
Here we show that a large, deep layered SNN with dynamical, chaotic activity mimicking the mammalian cortex with biologically-inspired learning rules, such as STDP, is capable of encoding information from temporal data.
Event-based attention and tracking on neuromorphic hardware
We present a fully event-driven vision and processing system for selective attention and tracking, realized on a neuromorphic processor Loihi interfaced to an event-based Dynamic Vision Sensor DAVIS.
Network Deconvolution
Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image.
