Search Results for author:
Found 34 papers, 6 papers with code
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems
In remedy, we use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data, and to prune the training dataset in a manner that maximizes distribution overlap.
Neuro-mimetic Task-free Unsupervised Online Learning with Continual Self-Organizing Maps
The major challenge that makes crafting such a system difficult is known as catastrophic forgetting - an agent, such as one based on artificial neural networks (ANNs), struggles to retain previously acquired knowledge when learning from new samples.
A Review of Neuroscience-Inspired Machine Learning
As a result, it accommodates hardware and scientific modeling, e. g. learning with physical systems and non-differentiable behavior.
Minimally Supervised Learning using Topological Projections in Self-Organizing Maps
In this work, we introduce a semi-supervised learning approach based on topological projections in self-organizing maps (SOMs), which significantly reduces the required number of labeled data points to perform parameter prediction, effectively exploiting information contained in large unlabeled datasets.
Mortal Computation: A Foundation for Biomimetic Intelligence
This review motivates and synthesizes research efforts in neuroscience-inspired artificial intelligence and biomimetic computing in terms of mortal computation.
A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization
Over the last few years, large neural generative models, capable of synthesizing semantically rich passages of text or producing complex images, have recently emerged as a popular representation of what has come to be known as ``generative artificial intelligence'' (generative AI).
On the Computational Complexity and Formal Hierarchy of Second Order Recurrent Neural Networks
In this work, we extend the theoretical foundation for the $2^{nd}$-order recurrent network ($2^{nd}$ RNN) and prove there exists a class of a $2^{nd}$ RNN that is Turing-complete with bounded time.
Brain-Inspired Computational Intelligence via Predictive Coding
Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century.
Disagreement Matters: Preserving Label Diversity by Jointly Modeling Item and Annotator Label Distributions with DisCo
Annotator disagreement is common whenever human judgment is needed for supervised learning.
Backpropagation-Free 4D Continuous Ant-Based Neural Topology Search
Continuous Ant-based Topology Search (CANTS) is a previously introduced novel nature-inspired neural architecture search (NAS) algorithm that is based on ant colony optimization (ACO).
Contrastive-Signal-Dependent Plasticity: Forward-Forward Learning of Spiking Neural Systems
We develop a neuro-mimetic architecture, composed of spiking neuronal units, where individual layers of neurons operate in parallel and adapt their synaptic efficacies without the use of feedback pathways.
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting
Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods, including online linear regression, fixed long short-term memory (LSTM) and gated recurrent unit (GRU) models trained online, as well as state-of-the-art, online ARIMA strategies.
The Predictive Forward-Forward Algorithm
We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems.
Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images
In this work, we develop convolutional neural generative coding (Conv-NGC), a generalization of predictive coding to the case of convolution/deconvolution-based computation.
A Neural Active Inference Model of Perceptual-Motor Learning
The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning.
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems
In this article, we propose a backpropagation-free approach to robotic control through the neuro-cognitive computational framework of neural generative coding (NGC), designing an agent built completely from powerful predictive coding/processing circuits that facilitate dynamic, online learning from sparse rewards, embodying the principles of planning-as-inference.
Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture
We present the COGnitive Neural GENerative system (CogNGen), a cognitive architecture that combines two neurobiologically-plausible, computational models: predictive processing and hyperdimensional/vector-symbolic models.
An Empirical Analysis of Recurrent Learning Algorithms In Neural Lossy Image Compression Systems
Recent advances in deep learning have resulted in image compression algorithms that outperform JPEG and JPEG 2000 on the standard Kodak benchmark.
Neural JPEG: End-to-End Image Compression Leveraging a Standard JPEG Encoder-Decoder
In light of this, we propose a system that learns to improve the encoding performance by enhancing its internal neural representations on both the encoder and decoder ends, an approach we call Neural JPEG.
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay
A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data.
FBERT: A Neural Transformer for Identifying Offensive Content
Transformer-based models such as BERT, XLNET, and XLM-R have achieved state-of-the-art performance across various NLP tasks including the identification of offensive language and hate speech, an important problem in social media.
Backprop-Free Reinforcement Learning with Active Neural Generative Coding
In humans, perceptual awareness facilitates the fast recognition and extraction of information from sensory input.
Towards a Predictive Processing Implementation of the Common Model of Cognition
In this article, we present a cognitive architecture that is built from powerful yet simple neural models.
WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans
In recent years, the widespread use of social media has led to an increase in the generation of toxic and offensive content on online platforms.
Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units
Two particular tasks that test this type of reasoning are (1) mathematical equation verification, which requires determining whether trigonometric and linear algebraic statements are valid identities or not, and (2) equation completion, which entails filling in a blank within an expression to make it true.
The Neural Coding Framework for Learning Generative Models
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates.
Continuous Ant-Based Neural Topology Search
This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), which utilizes synthetic ants that move over a continuous search space based on the density and distribution of pheromones, is strongly inspired by how ants move in the real world.
Recognizing Long Grammatical Sequences Using Recurrent Networks Augmented With An External Differentiable Stack
In this paper, we improve the memory-augmented RNN with important architectural and state updating mechanisms that ensure that the model learns to properly balance the use of its latent states with external memory.
Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment
Training deep neural networks on large-scale datasets requires significant hardware resources whose costs (even on cloud platforms) put them out of reach of smaller organizations, groups, and individuals.
The Neural State Pushdown Automata
The NSPDA is also compared to a classical analog stack neural network pushdown automaton (NNPDA) as well as a wide array of first and second-order RNNs with and without external memory, trained using different learning algorithms.
Spiking Neural Predictive Coding for Continual Learning from Data Streams
For energy-efficient computation in specialized neuromorphic hardware, we present spiking neural coding, an instantiation of a family of artificial neural models grounded in the theory of predictive coding.
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered.
Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution
This paper presents a new algorithm, Evolutionary eXploration of Augmenting Memory Models (EXAMM), which is capable of evolving recurrent neural networks (RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM, MGU and UGRNN cells.
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations
We compare our model and learning procedure to other back-propagation through time alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization.
