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Found 13 papers, 2 papers with code
Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment
In this survey, we examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection
Out-of-distribution (OOD) inputs can compromise the performance and safety of real world machine learning systems.
A Robust Backpropagation-Free Framework for Images
While current deep learning algorithms have been successful for a wide variety of artificial intelligence (AI) tasks, including those involving structured image data, they present deep neurophysiological conceptual issues due to their reliance on the gradients that are computed by backpropagation of errors (backprop).
Continual Competitive Memory: A Neural System for Online Task-Free Lifelong Learning
In this article, we propose a novel form of unsupervised learning, continual competitive memory (CCM), as well as a computational framework to unify related neural models that operate under the principles of competition.
Improving Label Quality by Jointly Modeling Items and Annotators
We propose a fully Bayesian framework for learning ground truth labels from noisy annotators.
Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication
For lossy image compression, we develop a neural-based system which learns a nonlinear estimator for decoding from quantized representations.
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks
Hand-crafting effective and efficient structures for recurrent neural networks (RNNs) is a difficult, expensive, and time-consuming process.
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution
EXAMM, in this study, was used to train over 10. 56 million RNNs in 5, 280 repeated experiments with varying components.
A Neural Temporal Model for Human Motion Prediction
We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation.
Like a Baby: Visually Situated Neural Language Acquisition
We examine the benefits of visual context in training neural language models to perform next-word prediction.
Biologically Motivated Algorithms for Propagating Local Target Representations
Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research.
Learned Neural Iterative Decoding for Lossy Image Compression Systems
For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques.
Conducting Credit Assignment by Aligning Local Representations
Using back-propagation and its variants to train deep networks is often problematic for new users.
