112,887 research outputs found
Structural Optimization of Factor Graphs for Symbol Detection via Continuous Clustering and Machine Learning
We propose a novel method to optimize the structure of factor graphs for
graph-based inference. As an example inference task, we consider symbol
detection on linear inter-symbol interference channels. The factor graph
framework has the potential to yield low-complexity symbol detectors. However,
the sum-product algorithm on cyclic factor graphs is suboptimal and its
performance is highly sensitive to the underlying graph. Therefore, we optimize
the structure of the underlying factor graphs in an end-to-end manner using
machine learning. For that purpose, we transform the structural optimization
into a clustering problem of low-degree factor nodes that incorporates the
known channel model into the optimization. Furthermore, we study the
combination of this approach with neural belief propagation, yielding
near-maximum a posteriori symbol detection performance for specific channels.Comment: Submitted to ICASSP 202
Neural Topological Ordering for Computation Graphs
Recent works on machine learning for combinatorial optimization have shown
that learning based approaches can outperform heuristic methods in terms of
speed and performance. In this paper, we consider the problem of finding an
optimal topological order on a directed acyclic graph with focus on the memory
minimization problem which arises in compilers. We propose an end-to-end
machine learning based approach for topological ordering using an
encoder-decoder framework. Our encoder is a novel attention based graph neural
network architecture called \emph{Topoformer} which uses different topological
transforms of a DAG for message passing. The node embeddings produced by the
encoder are converted into node priorities which are used by the decoder to
generate a probability distribution over topological orders. We train our model
on a dataset of synthetically generated graphs called layered graphs. We show
that our model outperforms, or is on-par, with several topological ordering
baselines while being significantly faster on synthetic graphs with up to 2k
nodes. We also train and test our model on a set of real-world computation
graphs, showing performance improvements.Comment: To appear in NeurIPS 202
Learnable Graph Matching: A Practical Paradigm for Data Association
Data association is at the core of many computer vision tasks, e.g., multiple
object tracking, image matching, and point cloud registration. Existing methods
usually solve the data association problem by network flow optimization,
bipartite matching, or end-to-end learning directly. Despite their popularity,
we find some defects of the current solutions: they mostly ignore the
intra-view context information; besides, they either train deep association
models in an end-to-end way and hardly utilize the advantage of
optimization-based assignment methods, or only use an off-the-shelf neural
network to extract features. In this paper, we propose a general learnable
graph matching method to address these issues. Especially, we model the
intra-view relationships as an undirected graph. Then data association turns
into a general graph matching problem between graphs. Furthermore, to make
optimization end-to-end differentiable, we relax the original graph matching
problem into continuous quadratic programming and then incorporate training
into a deep graph neural network with KKT conditions and implicit function
theorem. In MOT task, our method achieves state-of-the-art performance on
several MOT datasets. For image matching, our method outperforms
state-of-the-art methods with half training data and iterations on a popular
indoor dataset, ScanNet. Code will be available at
https://github.com/jiaweihe1996/GMTracker.Comment: Submitted to TPAMI on Mar 21, 2022. arXiv admin note: substantial
text overlap with arXiv:2103.1617
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