Search Results for author:
Found 9 papers, 1 papers with code
High-performance deep spiking neural networks with 0.3 spikes per neuron
Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains.
Factorizers for Distributed Sparse Block Codes
We provide a methodology to flexibly integrate our factorizer in the classification layer of CNNs with a novel loss function.
An Exact Mapping From ReLU Networks to Spiking Neural Networks
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence.
On the visual analytic intelligence of neural networks
Visual oddity task was conceived as a universal ethnic-independent analytic intelligence test for humans.
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
We demonstrate for the first time how the EM unit can physically superpose multiple training examples, expand to accommodate unseen classes, and perform similarity search during inference, using operations on an IMC core based on phase-change memory (PCM).
Constrained Few-shot Class-incremental Learning
Moreover, it is imperative that such learning must respect certain memory and computational constraints such as (i) training samples are limited to only a few per class, (ii) the computational cost of learning a novel class remains constant, and (iii) the memory footprint of the model grows at most linearly with the number of classes observed.
Ranked #4 on
Few-Shot Class-Incremental Learning
on mini-Imagenet
continual few-shot learning
Few-Shot Class-Incremental Learning
+1
Robust High-dimensional Memory-augmented Neural Networks
Traditional neural networks require enormous amounts of data to build their complex mappings during a slow training procedure that hinders their abilities for relearning and adapting to new data.
Few-Shot Image Classification
Vocal Bursts Intensity Prediction
File Classification Based on Spiking Neural Networks
In this paper, we propose a system for file classification in large data sets based on spiking neural networks (SNNs).
In-memory hyperdimensional computing
Hyperdimensional computing (HDC) is an emerging computational framework that takes inspiration from attributes of neuronal circuits such as hyperdimensionality, fully distributed holographic representation, and (pseudo)randomness.
