Search Results for author:
Found 13 papers, 3 papers with code
Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning
Active vision theories postulate that the learning of visual relations is grounded in actions that we take to fixate objects and their parts by moving our eyes.
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
In this work, we pair L2L with in-memory computing NMHW based on phase-change memory devices to build efficient AI models that can rapidly adapt to new tasks.
Intrinsic Biologically Plausible Adversarial Robustness
Artificial Neural Networks (ANNs) trained with Backpropagation (BP) excel in different daily tasks but have a dangerous vulnerability: inputs with small targeted perturbations, also known as adversarial samples, can drastically disrupt their performance.
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.
Neuromorphic Optical Flow and Real-time Implementation with Event Cameras
Optical flow provides information on relative motion that is an important component in many computer vision pipelines.
Online Spatio-Temporal Learning with Target Projection
Recurrent neural networks trained with the backpropagation through time (BPTT) algorithm have led to astounding successes in various temporal tasks.
Dynamic Event-based Optical Identification and Communication
Temporal pattern recognition, depending on the technology, involves a trade-off between communication frequency, range and accurate tracking.
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.
Towards efficient end-to-end speech recognition with biologically-inspired neural networks
Automatic speech recognition (ASR) is a capability which enables a program to process human speech into a written form.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learning in Deep Neural Networks Using a Biologically Inspired Optimizer
Here, we propose a novel biologically inspired optimizer for artificial (ANNs) and spiking neural networks (SNNs) that incorporates key principles of synaptic integration observed in dendrites of cortical neurons: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals).
Online Spatio-Temporal Learning in Deep Neural Networks
For shallow networks, OSTL is gradient-equivalent to BPTT enabling for the first time online training of SNNs with BPTT-equivalent gradients.
Deep learning incorporating biologically-inspired neural dynamics
Neural networks have become the key technology of artificial intelligence and have contributed to breakthroughs in several machine learning tasks, primarily owing to advances in deep learning applied to Artificial Neural Networks (ANNs).
