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Found 11 papers, 3 papers with code
Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks
This work proposes a novel parameter-efficient kernel modulation (KM) method that adapts all parameters of a base network instead of a subset of layers.
Exploiting Spatial Sparsity for Event Cameras with Visual Transformers
The input to the ViT consists of events that are accumulated into time bins and spatially separated into non-overlapping sub-regions called patches.
T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events
This work proposes a self-supervised method called Temporal Network Grafting Algorithm (T-NGA), which grafts a recurrent network pretrained on spectrogram features so that the network works with the cochlea event features.
v2e: From Video Frames to Realistic DVS Events
The first experiment is object recognition with N-Caltech 101 dataset.
DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames.
Character-Level Translation with Self-attention
We explore the suitability of self-attention models for character-level neural machine translation.
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a pretrained deep network that processes intensity frames.
Ranked #4 on
Event-based Object Segmentation
on RGBE-SEG
Character-level Chinese-English Translation through ASCII Encoding
Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs.
Overcoming the vanishing gradient problem in plain recurrent networks
Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deliver promising results in many sequence learning tasks through sophisticated network designs.
Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge.
Classify Images with Conceptor Network
This article demonstrates a new conceptor network based classifier in classifying images.
