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Deep Sets

10 Mar 2017NeurIPS 2017 12arXiv:1703.06114archive 2025-07-28

Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander Smola

We study the problem of designing models for machine learning tasks defined on \emph{sets}. In contrast to traditional approach of operating on fixed dimensional vectors, we consider objective functions defined on sets that are invariant to permutations. Such problems are widespread, ranging from estimation of population statistics \cite{poczos13aistats}, to anomaly detection in piezometer data of embankment dams \cite{Jung15Exploration}, to cosmology \cite{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem characterizes the permutation invariant functions and provides a family of functions to which any permutation invariant objective function must belong. This family of functions has a special structure which enables us to design a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks. We also derive the necessary and sufficient conditions for permutation equivariance in deep models. We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and outlier detection.

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MathieuCarriere/perslay mentioned on GitHubtf report
acciorocketships/setautoencoder mentioned on GitHubpytorch report
frgsimpson/kitt mentioned on GitHubtf report
lwtnn/lwtnn mentioned on GitHubMIT report
pluskal-lab/massspecgym mentioned on GitHubpytorchMIT report
aai-institute/pyDVL pytorchLGPL-3.0 report

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1ran · honoured contract
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BatchNorm acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) ran · metamorphic tier: deterministic no licence file found · pointer only · report
Deep_Sets jmmartyn/neural-network-quantum-field-states/modules/deep_sets.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted no licence file found · pointer only · report
Encoder acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) ran · metamorphic tier: deterministic no licence file found · pointer only · report
MLP acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) ran · metamorphic tier: deterministic fingerprinted no licence file found · pointer only · report
PositionalEncoding acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) ran · metamorphic tier: deterministic no licence file found · pointer only · report
build_mlp acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · report
layergen acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) ran · honoured contract fingerprinted no licence file found · pointer only · report
permutation_equivariant_layer MathieuCarriere/perslay/perslay/perslay.py community (archive-listed) ran · fixture could not drive it MIT (permissive) · report
scatter acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · report
broadcast acciorocketships/setautoencoder/sae/sae_model.py community (archive-listed) unverified no licence file found · pointer only · report
build_nodes lwtnn/lwtnn/converters/sequential2graph.py community (archive-listed) unverified MIT (permissive) · report
funcify_inputs lwtnn/lwtnn/converters/sequential2graph.py community (archive-listed) unverified MIT (permissive) · report

Tasks

Anomaly DetectionOutlier DetectionPoint Cloud Classification

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Methods

Introduced by this paper: Deep Sets

Deep Sets

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