704 research outputs found
Collaboration based Multi-Label Learning
It is well-known that exploiting label correlations is crucially important to
multi-label learning. Most of the existing approaches take label correlations
as prior knowledge, which may not correctly characterize the real relationships
among labels. Besides, label correlations are normally used to regularize the
hypothesis space, while the final predictions are not explicitly correlated. In
this paper, we suggest that for each individual label, the final prediction
involves the collaboration between its own prediction and the predictions of
other labels. Based on this assumption, we first propose a novel method to
learn the label correlations via sparse reconstruction in the label space.
Then, by seamlessly integrating the learned label correlations into model
training, we propose a novel multi-label learning approach that aims to
explicitly account for the correlated predictions of labels while training the
desired model simultaneously. Extensive experimental results show that our
approach outperforms the state-of-the-art counterparts.Comment: Accepted by AAAI-1
Keyword Search on RDF Graphs - A Query Graph Assembly Approach
Keyword search provides ordinary users an easy-to-use interface for querying
RDF data. Given the input keywords, in this paper, we study how to assemble a
query graph that is to represent user's query intention accurately and
efficiently. Based on the input keywords, we first obtain the elementary query
graph building blocks, such as entity/class vertices and predicate edges. Then,
we formally define the query graph assembly (QGA) problem. Unfortunately, we
prove theoretically that QGA is a NP-complete problem. In order to solve that,
we design some heuristic lower bounds and propose a bipartite graph
matching-based best-first search algorithm. The algorithm's time complexity is
, where is the number of the keywords and is a
tunable parameter, i.e., the maximum number of candidate entity/class vertices
and predicate edges allowed to match each keyword. Although QGA is intractable,
both and are small in practice. Furthermore, the algorithm's time
complexity does not depend on the RDF graph size, which guarantees the good
scalability of our system in large RDF graphs. Experiments on DBpedia and
Freebase confirm the superiority of our system on both effectiveness and
efficiency
Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction
Biological functions of RNAs are determined by their three-dimensional (3D)
structures. Thus, given the limited number of experimentally determined RNA
structures, the prediction of RNA structures will facilitate elucidating RNA
functions and RNA-targeted drug discovery, but remains a challenging task. In
this work, we propose a Graph Neural Network (GNN)-based scoring function
trained only with the atomic types and coordinates on limited solved RNA 3D
structures for distinguishing accurate structural models. The proposed
Physics-aware Multiplex Graph Neural Network (PaxNet) separately models the
local and non-local interactions inspired by molecular mechanics. Furthermore,
PaxNet contains an attention-based fusion module that learns the individual
contribution of each interaction type for the final prediction. We rigorously
evaluate the performance of PaxNet on two benchmarks and compare it with
several state-of-the-art baselines. The results show that PaxNet significantly
outperforms all the baselines overall, and demonstrate the potential of PaxNet
for improving the 3D structure modeling of RNA and other macromolecules. Our
code is available at https://github.com/zetayue/Physics-aware-Multiplex-GNN.Comment: Accepted by the Machine Learning for Structural Biology Workshop
(MLSB) at the 36th Conference on Neural Information Processing Systems
(NeurIPS 2022
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