Backpropagation is implemented in deep learning frameworks like Tensorflow, Torch, Theano, etc., by using computational graphs. More significantly, understanding back propagation on computational graphs combines several different algorithms and its variations such as backprop through time and backprop with ...
Beyond its use in deep learning, backpropagation is a powerful computational tool in many other areas, ranging from weather forecasting to analyzing numerical stability - it just goes by different names. In fact, the algorithm has been reinvented at least dozens of times in differen...
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires m
Biologically inspired graphs to explore massive genetic datasets A recent study proposes a data structure that addresses crucial challenges related to storage and computation of large genome databases. Ryan M. Layer News & Views31 Jan 2025 Efficient large language model with analog in-memory computing...
We hope that there will be more modular parts in the future, so system building can be fun and rewarding.LinksMXNet is moving to NNVM as its intermediate representation layer for symbolic graphs.About Intermediate Computational Graph Representation for Deep Learning Systems Resources Readme License...
Our library allows automatic bound derivation and computation for general computational graphs, in a similar manner that gradients are obtained in modern deep learning frameworks -- users only define the computation in a forward pass, andauto_LiRPAtraverses through the computational graph and derives ...
● Optimization in dynamic and/or noisy environments ● Optimization on graphs. ● Large-Scale optimization, in parallel and distributed computational environments. ● Meta-heuristics for optimization, nature-inspired approaches and any other derivative-free methods. ...
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters. Although some work has explored training on large-scale graphs, we pioneer efficient trai...
where we use Einstein notation, i.e., the right-hand side is summed overa, b ∈ {1, 2, 3}. Specific full tensor contractions are defined by using generating graphs75. In a practical implementation, we compute all GMs at once and reduce the number of invariant features based...
Chapter 6 The Advancement of Knowledge Graphs in Cybersecurity: A Comprehensive OverviewYuke Ma; Yonggang Chen; Yanjun Wang; Jun Yu; Yanting Li; Jinyu Lu; Yong Wang2024 Chapter 7 Active Disturbance Rejection Control of Hypersonic Vehicle Based on Q-Learning AlgorithmJie Yan; Liang Zhang2024 ...