Papers Biologically-plausible Training
“Biologically-plausible Training” 태그가 달린 논문 10편 · 필터 해제
Blending Optimal Control and Biologically Plausible Learning for Noise-Robust Physical Neural Networks
The rapidly increasing computational demands for artificial intelligence (AI) have spurred the exploration of computing principles beyond conventional digital computers. Physical neural networks (PNNs) offer efficient ne…
Biologically-plausible TrainingConnectivity-Inspired Network for Context-Aware Recognition
The aim of this paper is threefold. We inform the AI practitioner about the human visual system with an extensive literature review; we propose a novel biologically motivated neural network for image classification; and,…
Biologically-plausible TrainingCausal DiscoveryFunctional Connectivityimage-classification+1Towards Biologically Plausible Computing: A Comprehensive Comparison
Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the discrepancy between actual and desired outp…
Biologically-plausible TrainingFeed-Forward Optimization With Delayed Feedback for Neural Networks
Backpropagation has long been criticized for being biologically implausible, relying on concepts that are not viable in natural learning processes. This paper proposes an alternative approach to solve two core issues, i.…
Biologically-plausible TrainingComputational EfficiencyBiologically Plausible Training of Deep Neural Networks Using a Top-down Credit Assignment Network
Despite the widespread adoption of Backpropagation algorithm-based Deep Neural Networks, the biological infeasibility of the BP algorithm could potentially limit the evolution of new DNN models. To find a biologically pl…
Biologically-plausible Trainingreinforcement-learningPhysical Deep Learning with Biologically Plausible Training Method
The ever-growing demand for further advances in artificial intelligence motivated research on unconventional computation based on analog physical devices. While such computation devices mimic brain-inspired analog inform…
Biologically-plausible TrainingDeep LearningBiologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks
We develop biologically plausible training mechanisms for self-supervised learning (SSL) in deep networks. Specifically, by biological plausible training we mean (i) All updates of weights are based on current activities…
Biologically-plausible TrainingSelf-Supervised LearningTransfer LearningLong Distance Relationships without Time Travel: Boosting the Performance of a Sparse Predictive Autoencoder in Sequence Modeling
In sequence learning tasks such as language modelling, Recurrent Neural Networks must learn relationships between input features separated by time. State of the art models such as LSTM and Transformer are trained by back…
Biologically-plausible TrainingLanguage ModellingBiologically-plausible learning algorithms can scale to large datasets
The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address …
Biologically-plausible TrainingVariational Probability Flow for Biologically Plausible Training of Deep Neural Networks
The quest for biologically plausible deep learning is driven, not just by the desire to explain experimentally-observed properties of biological neural networks, but also by the hope of discovering more efficient methods…
Biologically-plausible Training