15 citations · 21 across the 8 of their papers we have counts for
9 papers
Maelstrom Networks
Matthew Evanusa, Cornelia Fermüller, Yiannis Aloimonos
Artificial Neural Networks has struggled to devise a way to incorporate working memory into neural networks. While the ``long term'' memory can be seen as the learned weights, the…
ProtoVAE: Prototypical Networks for Unsupervised Disentanglement
Vaishnavi Patil, Matthew Evanusa, Joseph JaJa
Generative modeling and self-supervised learning have in recent years made great strides towards learning from data in a completely unsupervised way. There is still however an open…
DOT-VAE: Disentangling One Factor at a Time
Vaishnavi Patil, Matthew Evanusa, Joseph JaJa
As we enter the era of machine learning characterized by an overabundance of data, discovery, organization, and interpretation of the data in an unsupervised manner becomes a criti…
SpikeMS: Deep Spiking Neural Network for Motion Segmentation
Chethan M. Parameshwara, Simin Li, Cornelia Fermüller +3
Spiking Neural Networks (SNN) are the so-called third generation of neural networks which attempt to more closely match the functioning of the biological brain. They inherently enc…
Hybrid Backpropagation Parallel Reservoir Networks
Matthew Evanusa, Snehesh Shrestha, Michelle Girvan +2
In many real-world applications, fully-differentiable RNNs such as LSTMs and GRUs have been widely deployed to solve time series learning tasks. These networks train via Backpropag…
Deep Reservoir Networks with Learned Hidden Reservoir Weights using Direct Feedback Alignment
Matthew Evanusa, Cornelia Fermüller, Yiannis Aloimonos
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 wi…
A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP
Matthew Evanusa, Cornelia Fermuller, Yiannis Aloimonos
The brain is known to be a highly complex, asynchronous dynamical system that is highly tailored to encode temporal information. However, recent deep learning approaches to not tak…
Event-based attention and tracking on neuromorphic hardware
Alpha Renner, Matthew Evanusa, Yulia Sandamirskaya
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 V…
Network Deconvolution
Chengxi Ye, Matthew Evanusa, Hua He +5
Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image. However, because of the strong c…