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Found 19 papers, 8 papers with code
Debugging using Orthogonal Gradient Descent
In this report we consider the following problem: Given a trained model that is partially faulty, can we correct its behaviour without having to train the model from scratch?
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers
Over three orders of magnitude, we show that our new architecture attains the same accuracy as transformers with 10x fewer tokens.
A Spiking Neural Network for Image Segmentation
We explore the advantages of regularizing firing rates of Loihi neurons for converting ANN to SNN with minimum accuracy loss and optimized energy consumption.
Parallelizing Legendre Memory Unit Training
For instance, our LMU sets a new state-of-the-art result on psMNIST, and uses half the parameters while outperforming DistilBERT and LSTM models on IMDB sentiment analysis.
Ranked #6 on
Sequential Image Classification
on Sequential MNIST
Feedforward Legendre Memory Unit
Our models, despite their simplicity, achieve new state-of-the-art results for RNNs on psMNIST and QQP, and exhibit superior performance on the remaining three datasets while using up to 1000x fewer parameters.
Low-Power Low-Latency Keyword Spotting and Adaptive Control with a SpiNNaker 2 Prototype and Comparison with Loihi
We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control.
Hardware Aware Training for Efficient Keyword Spotting on General Purpose and Specialized Hardware
Keyword spotting (KWS) provides a critical user interface for many mobile and edge applications, including phones, wearables, and cars.
Nengo and low-power AI hardware for robust, embedded neurorobotics
In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware using familiar tools, such as Keras and Python.
A Spike in Performance: Training Hybrid-Spiking Neural Networks with Quantized Activation Functions
The machine learning community has become increasingly interested in the energy efficiency of neural networks.
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Backpropagation through the ODE solver allows each layer to adapt its internal time-step, enabling the network to learn task-relevant time-scales.
Ranked #12 on
Sequential Image Classification
on Sequential MNIST
Passive nonlinear dendritic interactions as a general computational resource in functional spiking neural networks
Nonlinear interactions in the dendritic tree play a key role in neural computation.
Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase.
Point Neurons with Conductance-Based Synapses in the Neural Engineering Framework
This, in particular, significantly affects the influence of inhibitory signals on the neuronal dynamics.
Methods for applying the Neural Engineering Framework to neuromorphic hardware
We review our current software tools and theoretical methods for applying the Neural Engineering Framework to state-of-the-art neuromorphic hardware.
Training Spiking Deep Networks for Neuromorphic Hardware
We describe a method to train spiking deep networks that can be run using leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012 benchmark.
BioSpaun: A large-scale behaving brain model with complex neurons
We describe a large-scale functional brain model that includes detailed, conductance-based, compartmental models of individual neurons.
Spiking Deep Networks with LIF Neurons
We train spiking deep networks using leaky integrate-and-fire (LIF) neurons, and achieve state-of-the-art results for spiking networks on the CIFAR-10 and MNIST datasets.
Hyperopt-Sklearn: Automatic Hyperparameter Configuration for Scikit-Learn
Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library.
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
The Kalman filter was trained to predict the arm’s velocity and mapped on to the SNN using the Neural Engineer- ing Framework (NEF).
