modularity
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Seyed Mohammad Javad Hosseini, Su-In Lee
Second, GRAB blocks (Figa priorioruseasequential fixed. Thefirsttwo terms,logdet ( ) trace (S ), in Eq (3) correspondtologP(X| ), thelog-likelihoodof GGM givenaparticularparameter (i.e., anestimateof 1), asdescribedin Section 2.1.
Professionals
The need to analyze graphs is ubiquitous across various fields, from social networks tobiological research and recommendation systems.
daea32adcae6abcb548134fa98f139f9-AuthorFeedback.pdf
Our paper differs from that work in at least 4ways.
Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
Deepak Pathak, Christopher Lu, Trevor Darrell, Phillip Isola, Alexei A. Efros
Contemporary sensorimotor learning approaches typically start with anexisting complexagent (e.g., arobotic arm), which theylearn tocontrol. Limbs pass messages to their neighbors in this graph in order to coordinate behavior.
b9432d0f94275f0571c6cc99cf8b1664-Paper-Conference.pdf
Parametric CAD modeling is a standard paradigm for mechanical CAD design nowadays.
Neural Sculpting: Uncovering hierarchically modular task structure in neural networks through pruning and network analysis
The high-level question in this work is: If we learn a task using a sufficiently deep NN, how can we uncover the underlying hierarchical organization of sub-functions in that task?
