Credit assignment techniques in stochastic computation graphs
Stochastic computation graphs (SCGs) provide a formalism to represent structured
optimization problems arising in artificial intelligence, including supervised, unsupervised …
optimization problems arising in artificial intelligence, including supervised, unsupervised …
Storchastic: A framework for general stochastic automatic differentiation
Modelers use automatic differentiation (AD) of computation graphs to implement complex
Deep Learning models without defining gradient computations. Stochastic AD extends AD to …
Deep Learning models without defining gradient computations. Stochastic AD extends AD to …
Optimisation in Neurosymbolic Learning Systems
E van Krieken - arXiv preprint arXiv:2401.10819, 2024 - arxiv.org
Neurosymbolic AI aims to integrate deep learning with symbolic AI. This integration has
many promises, such as decreasing the amount of data required to train a neural network …
many promises, such as decreasing the amount of data required to train a neural network …
Hulu video recommendation: from relevance to reasoning
Online Video Streaming services such as Hulu hosts tens of millions of premium videos,
which requires an effective recommendation system to help viewers discover what they …
which requires an effective recommendation system to help viewers discover what they …
Front contribution instead of back propagation
S Mishra, A Arunkumar - arXiv preprint arXiv:2106.05569, 2021 - arxiv.org
Deep Learning's outstanding track record across several domains has stemmed from the
use of error backpropagation (BP). Several studies, however, have shown that it is …
use of error backpropagation (BP). Several studies, however, have shown that it is …