Search Results for author:
Found 6 papers, 4 papers with code
A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge
We present CBNet, a low-latency and energy-efficient DNN inference framework tailored for edge devices.
Event-based Shape from Polarization with Spiking Neural Networks
Recent advances in event-based shape determination from polarization offer a transformative approach that tackles the trade-off between speed and accuracy in capturing surface geometries.
Boost Event-Driven Tactile Learning with Location Spiking Neurons
Moreover, to demonstrate the representation effectiveness of our proposed neurons and capture the complex spatio-temporal dependencies in the event-driven tactile data, we exploit the location spiking neurons to propose two hybrid models for event-driven tactile learning.
Event-Driven Tactile Learning with Location Spiking Neurons
In this paper, to improve the representative capabilities of existing spiking neurons, we propose a novel neuron model called "location spiking neuron", which enables us to extract features of event-based data in a novel way.
Hierarchical Gating Networks for Sequential Recommendation
However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed.
Ranked #1 on
Recommendation Systems
on Amazon-CDs
(Recall@10 metric)
Gated Attentive-Autoencoder for Content-Aware Recommendation
In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder.
