Adaptative Inference Cost With Convolutional Neural Mixture Models | Paper Citation Record

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This paper cites Maskconnect: Con- nectivity learning by gradient descent.

Adaptative Inference Cost With Convolutional Neural Mixture Models Maskconnect: Con- nectivity learning by gradient descent

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This paper cites Understanding dropout.

Adaptative Inference Cost With Convolutional Neural Mixture Models Understanding dropout

Reference 2

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This paper cites Bagging predictors.

Adaptative Inference Cost With Convolutional Neural Mixture Models Bagging predictors

Reference 3

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This paper cites Learning efﬁcient object detection mod- els with knowledge distillation.

Adaptative Inference Cost With Convolutional Neural Mixture Models Learning efﬁcient object detection mod- els with knowledge distillation

Reference 4

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs.

Adaptative Inference Cost With Convolutional Neural Mixture Models Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs

Reference 5

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This paper cites The cityscapes dataset for semantic urban scene understanding.

Adaptative Inference Cost With Convolutional Neural Mixture Models The cityscapes dataset for semantic urban scene understanding

Reference 6

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This paper cites Gradient descent provably optimizes over-parameterized neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Gradient descent provably optimizes over-parameterized neural networks

Reference 7

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This paper cites Cou- pled ensembles of neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Cou- pled ensembles of neural networks

Reference 8

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This paper cites The lottery ticket hy- pothesis: Training pruned neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models The lottery ticket hy- pothesis: Training pruned neural networks

Reference 9

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This paper cites Concrete dropout.

Adaptative Inference Cost With Convolutional Neural Mixture Models Concrete dropout

Reference 10

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This paper cites Struc- tured variational learning of bayesian neural networks with horseshoe priors.

Adaptative Inference Cost With Convolutional Neural Mixture Models Struc- tured variational learning of bayesian neural networks with horseshoe priors

Reference 11

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This paper cites Branchout: Regularization for online ensemble tracking with convolu- tional neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Branchout: Regularization for online ensemble tracking with convolu- tional neural networks

Reference 12

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This paper cites Identity mappings in deep residual networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Identity mappings in deep residual networks

Reference 13

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This paper cites AMC: AutoML for model compression and ac- celeration on mobile devices.

Adaptative Inference Cost With Convolutional Neural Mixture Models AMC: AutoML for model compression and ac- celeration on mobile devices

Reference 14

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This paper cites Distilling the knowledge in a neural network.

Adaptative Inference Cost With Convolutional Neural Mixture Models Distilling the knowledge in a neural network

Reference 15

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Adaptative Inference Cost With Convolutional Neural Mixture Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 16

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This paper cites Huang, D.

Adaptative Inference Cost With Convolutional Neural Mixture Models Huang, D

Reference 17

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This paper cites Snapshot ensembles: Train 1, get m for free.

Adaptative Inference Cost With Convolutional Neural Mixture Models Snapshot ensembles: Train 1, get m for free

Reference 18

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This paper cites Huang, S.

Adaptative Inference Cost With Convolutional Neural Mixture Models Huang, S

Reference 19

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This paper cites Huang, Z.

Adaptative Inference Cost With Convolutional Neural Mixture Models Huang, Z

Reference 20

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This paper cites Deep networks with stochastic depth.

Adaptative Inference Cost With Convolutional Neural Mixture Models Deep networks with stochastic depth

Reference 21

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This paper cites Data-driven sparse struc- ture selection for deep neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Data-driven sparse struc- ture selection for deep neural networks

Reference 22

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This paper cites SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size.

Adaptative Inference Cost With Convolutional Neural Mixture Models SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

Reference 23

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This paper cites Uncertainty es- timates and multi-hypotheses networks for optical ﬂow.

Adaptative Inference Cost With Convolutional Neural Mixture Models Uncertainty es- timates and multi-hypotheses networks for optical ﬂow

Reference 24

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This paper cites Quantization and training of neural networks for efﬁcient integer-arithmetic-only inference.

Adaptative Inference Cost With Convolutional Neural Mixture Models Quantization and training of neural networks for efﬁcient integer-arithmetic-only inference

Reference 25

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This paper cites Speeding up convolutional neural networks with low rank expansions.

Adaptative Inference Cost With Convolutional Neural Mixture Models Speeding up convolutional neural networks with low rank expansions

Reference 26

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This paper cites Kingma and M.

Adaptative Inference Cost With Convolutional Neural Mixture Models Kingma and M

Reference 27

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This paper cites Vari- ational dropout and the local reparameterization trick.

Adaptative Inference Cost With Convolutional Neural Mixture Models Vari- ational dropout and the local reparameterization trick

Reference 28

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This paper cites Learning multiple layers of features from tiny images.

Adaptative Inference Cost With Convolutional Neural Mixture Models Learning multiple layers of features from tiny images

Reference 29

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This paper cites Neural network ensem- bles, cross validation, and active learning.

Adaptative Inference Cost With Convolutional Neural Mixture Models Neural network ensem- bles, cross validation, and active learning

Reference 30

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This paper cites Simple and scalable predictive uncertainty esti- mation using deep ensembles.

Adaptative Inference Cost With Convolutional Neural Mixture Models Simple and scalable predictive uncertainty esti- mation using deep ensembles

Reference 31

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This paper cites Fractalnet: Ultra-deep neural networks without residuals.

Adaptative Inference Cost With Convolutional Neural Mixture Models Fractalnet: Ultra-deep neural networks without residuals

Reference 32

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Observation 50b307f9-d4f5-41ac-94fb-0ae8e472961a · outbound

This paper cites LeCun, J.

Adaptative Inference Cost With Convolutional Neural Mixture Models LeCun, J

Reference 33

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This paper cites Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

Reference 34

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This paper cites Pruning ﬁlters for efﬁcient convnets.

Adaptative Inference Cost With Convolutional Neural Mixture Models Pruning ﬁlters for efﬁcient convnets

Reference 35

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This paper cites Fixed point quantization of deep convolutional networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Fixed point quantization of deep convolutional networks

Reference 36

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This paper cites Darts: Differentiable architecture search.

Adaptative Inference Cost With Convolutional Neural Mixture Models Darts: Differentiable architecture search

Reference 37

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This paper cites Learning efﬁcient convolutional networks through network slimming.

Adaptative Inference Cost With Convolutional Neural Mixture Models Learning efﬁcient convolutional networks through network slimming

Reference 38

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This paper cites Sgdr: Stochastic gradient descent with warm restarts.

Adaptative Inference Cost With Convolutional Neural Mixture Models Sgdr: Stochastic gradient descent with warm restarts

Reference 39

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This paper cites Multiplicative normaliz- ing ﬂows for variational bayesian neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Multiplicative normaliz- ing ﬂows for variational bayesian neural networks

Reference 40

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Observation 4b5db635-b2c1-4cb5-a8b8-90a6dfd49545 · outbound

This paper cites Learning sparse neural networks through l 0 regularization.

Adaptative Inference Cost With Convolutional Neural Mixture Models Learning sparse neural networks through l 0 regularization

Reference 41

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This paper cites The concrete distribution: A continuous relaxation of discrete random variables.

Adaptative Inference Cost With Convolutional Neural Mixture Models The concrete distribution: A continuous relaxation of discrete random variables

Reference 42

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Observation dcd8f3cf-3dfe-4a46-8b6e-671573e186a8 · outbound

This paper cites Domain-adaptive deep net- work compression.

Adaptative Inference Cost With Convolutional Neural Mixture Models Domain-adaptive deep net- work compression

Reference 43

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This paper cites Espnet: Efﬁcient spatial pyramid of dilated convolutions for semantic segmentation.

Adaptative Inference Cost With Convolutional Neural Mixture Models Espnet: Efﬁcient spatial pyramid of dilated convolutions for semantic segmentation

Reference 44

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This paper cites Espnetv2: A light-weight, power ef- ﬁcient, and general purpose convolutional neural network.

Adaptative Inference Cost With Convolutional Neural Mixture Models Espnetv2: A light-weight, power ef- ﬁcient, and general purpose convolutional neural network

Reference 45

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This paper cites Optimal en- semble averaging of neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Optimal en- semble averaging of neural networks

Reference 46

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This paper cites Structured bayesian pruning via log-normal multiplicative noise.

Adaptative Inference Cost With Convolutional Neural Mixture Models Structured bayesian pruning via log-normal multiplicative noise

Reference 47

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Observation 5db5e81a-55c7-4701-884d-d7490c2a5d63 · outbound

This paper cites Rezende, S.

Adaptative Inference Cost With Convolutional Neural Mixture Models Rezende, S

Reference 48

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Observation 89084203-7db4-47d0-aa75-871358f6f972 · outbound

This paper cites The boosting approach to machine learn- ing: An overview.

Adaptative Inference Cost With Convolutional Neural Mixture Models The boosting approach to machine learn- ing: An overview

Reference 49

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Observation ad9400c5-303d-450d-b963-15d4dca9b2ab · outbound

This paper cites Swapout: Learning an ensemble of deep architectures.

Adaptative Inference Cost With Convolutional Neural Mixture Models Swapout: Learning an ensemble of deep architectures

Reference 50

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This paper cites Data-free parameter pruning for deep neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Data-free parameter pruning for deep neural networks

Reference 51

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Observation efccfa97-eb61-4687-99a6-4eb464de0e21 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overﬁtting.

Adaptative Inference Cost With Convolutional Neural Mixture Models Dropout: a simple way to prevent neural networks from overﬁtting

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.381808Z

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Observation 1b0dd2da-986f-449e-95f0-e6b415e3583a · outbound

This paper cites Rethinking the inception ar- chitecture for computer vision.

Adaptative Inference Cost With Convolutional Neural Mixture Models Rethinking the inception ar- chitecture for computer vision

Reference 53

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This paper cites Convolutional neural networks with low-rank regularization.

Adaptative Inference Cost With Convolutional Neural Mixture Models Convolutional neural networks with low-rank regularization

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.355637Z

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Observation ac534109-22bc-4f52-868f-62e2e495f52a · outbound

This paper cites Resid- ual networks behave like ensembles of relatively shallow net- works.

Adaptative Inference Cost With Convolutional Neural Mixture Models Resid- ual networks behave like ensembles of relatively shallow net- works

Reference 55

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Observation 0748f865-6910-4544-a183-0f64d33066b7 · outbound

This paper cites Learning time/memory- efﬁcient deep architectures with budgeted super networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Learning time/memory- efﬁcient deep architectures with budgeted super networks

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.328801Z

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Observation 2c48c0d7-a972-4568-8072-c19f1e5dd4d0 · outbound

This paper cites Slimmable neural networks.

Adaptative Inference Cost With Convolutional Neural Mixture Models Slimmable neural networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.316795Z

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Observation e61d5170-b442-4818-8888-5e05db97cfc6 · outbound

This paper cites Shufﬂenet: An extremely efﬁcient convolutional neural net- work for mobile devices.

Adaptative Inference Cost With Convolutional Neural Mixture Models Shufﬂenet: An extremely efﬁcient convolutional neural net- work for mobile devices

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.305476Z

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Observation 5c9cb884-bbb3-4712-acd7-1229767256ec · outbound

This paper cites Pyramid scene parsing network.

Adaptative Inference Cost With Convolutional Neural Mixture Models Pyramid scene parsing network

Reference 59

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Observation 80531f32-f6c3-454a-8703-051f9480f027 · outbound

This paper cites Re- training: A simple way to improve the ensemble accuracy of deep neural networks for image classiﬁcation.

Adaptative Inference Cost With Convolutional Neural Mixture Models Re- training: A simple way to improve the ensemble accuracy of deep neural networks for image classiﬁcation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.285673Z

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Observation 6df25eff-950a-4a00-86d1-26098b8dbd63 · outbound

This paper cites Ensembling neu- ral networks: many could be better than all.

Adaptative Inference Cost With Convolutional Neural Mixture Models Ensembling neu- ral networks: many could be better than all

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.272404Z

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Observation a9906c45-3e9a-498e-bf07-d5bf100e89ae · outbound

This paper cites Binary ensemble neural network: More bits per network or more networks per bit? CVPR, 2019.

Adaptative Inference Cost With Convolutional Neural Mixture Models Binary ensemble neural network: More bits per network or more networks per bit? CVPR, 2019

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.258809Z

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Observation a672844f-68bd-4eb9-b8c5-a2c970bb5810 · outbound

This paper cites Training with expectations.

Adaptative Inference Cost With Convolutional Neural Mixture Models Training with expectations

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:41:42.245188Z

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