2 citations · 2 across the 6 of their papers we have counts for
6 papers
Interval-Memoized Backtracking on ZDDs for Fast Enumeration of All Lower Cost Solutions
Shin-ichi Minato, Mutsunori Banbara, Takashi Horiyama +3
In this paper, we propose a fast method for exactly enumerating a very large number of all lower cost solutions for various combinatorial problems. Our method is based on backtrack…
Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102,400 nodes
Yuya Sugie, Yuki Yoshida, Normann Mertig +7
Minor embedding heuristics have become an indispensable tool for compiling problems in quadratically unconstrained binary optimization (QUBO) into the hardware graphs of quantum an…
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki +4
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature ex…
Jointly learning relevant subgraph patterns and nonlinear models of their indicators
Ryo Shirakawa, Yusei Yokoyama, Fumiya Okazaki +1
Classification and regression in which the inputs are graphs of arbitrary size and shape have been paid attention in various fields such as computational chemistry and bioinformati…
Machine learning reveals orbital interaction in crystalline materials
Tien Lam Pham, Hiori Kino, Kiyoyuki Terakura +4
We propose a novel representation of crystalline materials named orbital-field matrix (OFM) based on the distribution of valence shell electrons. We demonstrate that this new repre…
Sparse Learning over Infinite Subgraph Features
Ichigaku Takigawa, Hiroshi Mamitsuka
We present a supervised-learning algorithm from graph data (a set of graphs) for arbitrary twice-differentiable loss functions and sparse linear models over all possible subgraph f…