Paper Citation Record · LEDGER

Mish: A Self Regularized Non-Monotonic Activation Function

As of 16 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 66 inbound Pith citation observations for arXiv:1908.08681.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.08681 v3

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

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measured 118 of 118 standing notices

One-hop event checks from named stored sources.

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measured 66 of 66 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:17:20.973338Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-08T11:24:54.882129Z

Reference resolution

52 of 52 outbound references displayed

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External citation measurements

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Outbound references

Observation af01463f-2504-40e9-b731-0922f3bacfa2 · outbound

This paper cites On the rate of convergence of the preconditioned conjugate gradient method.

Mish: A Self Regularized Non-Monotonic Activation Function On the rate of convergence of the preconditioned conjugate gradient method

Reference 1

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Observation 35e873f5-c9d3-4dd5-89d4-5b2fc74055c4 · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Mish: A Self Regularized Non-Monotonic Activation Function YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 2

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Observation e8178b7f-7f68-4d75-9838-5cec886337af · outbound

This paper cites Large-scale machine learning with stochastic gradient descent.

Mish: A Self Regularized Non-Monotonic Activation Function Large-scale machine learning with stochastic gradient descent

Reference 3

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Observation db9b90c0-c373-4749-b950-9a2c3a3a4969 · outbound

This paper cites Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs).

Mish: A Self Regularized Non-Monotonic Activation Function Improving Deep Learning by Inverse Square Root Linear Units (ISRLUs)

Reference 4

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Observation dbed67c1-db55-4531-9f3c-a5fada3398fd · outbound

This paper cites Xception: Deep learning with depthwise separable convolutions.

Mish: A Self Regularized Non-Monotonic Activation Function Xception: Deep learning with depthwise separable convolutions

Reference 5

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Observation e043e858-d9ed-4af0-9d2b-1219bcfea453 · outbound

This paper cites Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs).

Mish: A Self Regularized Non-Monotonic Activation Function Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)

Reference 6

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Observation af85f19b-64c8-49ff-a52a-beaab788331c · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Mish: A Self Regularized Non-Monotonic Activation Function Imagenet: A large-scale hierarchical image database

Reference 7

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Observation 71bac3c7-865b-4299-bf94-e851f9a285e4 · outbound

This paper cites SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization.

Mish: A Self Regularized Non-Monotonic Activation Function SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

Reference 8

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Observation 0564dea9-6bd8-4898-b556-f2f6e1ce026b · outbound

This paper cites Dropblock: A regularization method for convolutional networks.

Mish: A Self Regularized Non-Monotonic Activation Function Dropblock: A regularization method for convolutional networks

Reference 9

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Observation 1d6683f8-3109-4979-9337-646b707afdc3 · outbound

This paper cites Rich feature hierar- chies for accurate object detection and semantic segmentation.

Mish: A Self Regularized Non-Monotonic Activation Function Rich feature hierar- chies for accurate object detection and semantic segmentation

Reference 10

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Observation 4b53931b-bdd5-4466-92be-c0caf59b34aa · outbound

This paper cites Understanding the difﬁculty of training deep feed- forward neural networks.

Mish: A Self Regularized Non-Monotonic Activation Function Understanding the difﬁculty of training deep feed- forward neural networks

Reference 11

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Observation 860a4f32-414a-4fef-93d6-5f9b3bc3c7ce · outbound

This paper cites Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures.

Mish: A Self Regularized Non-Monotonic Activation Function Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures

Reference 12

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Observation bb68356b-3ad1-4ecb-8a0f-8b9781bb61e6 · outbound

This paper cites Delving deep into rectiﬁers: Surpassing human-level performance on imagenet classiﬁcation.

Mish: A Self Regularized Non-Monotonic Activation Function Delving deep into rectiﬁers: Surpassing human-level performance on imagenet classiﬁcation

Reference 13

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Observation abfb1859-136d-4ad6-8393-a3dbbb60d398 · outbound

This paper cites Spatial pyramid pooling in deep convolutional networks for visual recognition.

Mish: A Self Regularized Non-Monotonic Activation Function Spatial pyramid pooling in deep convolutional networks for visual recognition

Reference 14

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Observation 76bc8521-0081-48c1-8676-6bc9fcd66f1c · outbound

This paper cites Deep residual learning for image recognition.

Mish: A Self Regularized Non-Monotonic Activation Function Deep residual learning for image recognition

Reference 15

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Observation 9c1371db-1344-4274-9291-127c18ad0fdf · outbound

This paper cites Gaussian Error Linear Units (GELUs).

Mish: A Self Regularized Non-Monotonic Activation Function Gaussian Error Linear Units (GELUs)

Reference 16

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Observation f019212d-ea60-468b-8c47-fe0ee19e91f6 · outbound

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

Mish: A Self Regularized Non-Monotonic Activation Function MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 17

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Observation 9954a7ad-53dd-41ae-bd9f-fc98017fd7b3 · outbound

This paper cites Squeeze-and-excitation networks.

Mish: A Self Regularized Non-Monotonic Activation Function Squeeze-and-excitation networks

Reference 18

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Observation 7eebbf6d-947b-421a-9e50-46ac993aff69 · outbound

This paper cites Densely connected convolutional networks.

Mish: A Self Regularized Non-Monotonic Activation Function Densely connected convolutional networks

Reference 19

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Observation 1037566e-4a47-4111-bb53-1135714af536 · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

Mish: A Self Regularized Non-Monotonic Activation Function Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 20

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Observation 93d2fc00-9917-45fc-ba30-49d0ac730ae1 · outbound

This paper cites Deep learning with s-shaped rectiﬁed linear activation units.

Mish: A Self Regularized Non-Monotonic Activation Function Deep learning with s-shaped rectiﬁed linear activation units

Reference 21

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Observation 2e7c0d0e-53ed-4a98-878f-4374d018248a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Mish: A Self Regularized Non-Monotonic Activation Function Adam: A Method for Stochastic Optimization

Reference 22

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Observation 9e9825f4-d35e-401e-ad27-55241a844ee7 · outbound

This paper cites Self- normalizing neural networks.

Mish: A Self Regularized Non-Monotonic Activation Function Self- normalizing neural networks

Reference 23

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Observation 0ceb62f2-3634-41c2-b188-5532466e42f3 · outbound

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

Mish: A Self Regularized Non-Monotonic Activation Function Learning multiple layers of features from tiny images

Reference 24

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Observation 67d43d1b-8160-4220-aed7-8af2f27d186f · outbound

This paper cites Imagenet classiﬁcation with deep convolutional neural networks.

Mish: A Self Regularized Non-Monotonic Activation Function Imagenet classiﬁcation with deep convolutional neural networks

Reference 25

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Observation ff669ad0-e3e7-48a7-a6b0-17777941c211 · outbound

This paper cites Mnist handwritten digit database.

Mish: A Self Regularized Non-Monotonic Activation Function Mnist handwritten digit database

Reference 26

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Observation 1e0d9cbf-0b6a-4983-a311-a13ac630d35c · outbound

This paper cites Efﬁcient backprop.

Mish: A Self Regularized Non-Monotonic Activation Function Efﬁcient backprop

Reference 27

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Observation 0b5517f8-54cc-4dbd-ac87-b56bb83fe29b · outbound

This paper cites Visualizing the loss landscape of neural nets.

Mish: A Self Regularized Non-Monotonic Activation Function Visualizing the loss landscape of neural nets

Reference 28

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Observation 02fc863c-cd1e-4f9c-bb0c-9baab478e880 · outbound

This paper cites Preconditioned stochastic gradient descent.

Mish: A Self Regularized Non-Monotonic Activation Function Preconditioned stochastic gradient descent

Reference 29

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Observation 77c5c087-fbe4-413e-bccb-660c1a5949a6 · outbound

This paper cites Microsoft coco: Common objects in context.

Mish: A Self Regularized Non-Monotonic Activation Function Microsoft coco: Common objects in context

Reference 30

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Observation 0368a446-a818-4714-bbf3-24bb7f05b3ab · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Mish: A Self Regularized Non-Monotonic Activation Function SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 31

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Observation 193b01cf-12dd-4f6d-b74a-4e2bf4ac6554 · outbound

This paper cites Rectiﬁer nonlinearities improve neural network acoustic models.

Mish: A Self Regularized Non-Monotonic Activation Function Rectiﬁer nonlinearities improve neural network acoustic models

Reference 32

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Observation a48fb977-f099-46bd-978d-6665500b004c · outbound

This paper cites When does label smoothing help? In Advances in Neural Information Processing Systems, pages 4696–4705, 2019.

Mish: A Self Regularized Non-Monotonic Activation Function When does label smoothing help? In Advances in Neural Information Processing Systems, pages 4696–4705, 2019

Reference 33

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Observation 20523d45-cac7-42c1-9ce5-4f40676061fa · outbound

This paper cites Rectiﬁed linear units improve restricted boltzmann machines.

Mish: A Self Regularized Non-Monotonic Activation Function Rectiﬁed linear units improve restricted boltzmann machines

Reference 34

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Observation 56356c7c-1243-42a6-99ae-1e53fccacf5c · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Mish: A Self Regularized Non-Monotonic Activation Function Pytorch: An imperative style, high-performance deep learning library

Reference 35

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This paper cites Language models are unsupervised multitask learners.

Mish: A Self Regularized Non-Monotonic Activation Function Language models are unsupervised multitask learners

Reference 36

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Observation 76c12c13-e757-4973-93f7-07615c4c5232 · outbound

This paper cites Searching for Activation Functions.

Mish: A Self Regularized Non-Monotonic Activation Function Searching for Activation Functions

Reference 37

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Observation cb3606bf-c54e-4dc4-8dae-4c8626c60b85 · outbound

This paper cites Darknet: Open source neural networks in c.

Mish: A Self Regularized Non-Monotonic Activation Function Darknet: Open source neural networks in c

Reference 38

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Observation 1edd17c8-acfc-422b-af7a-e311f3ea346d · outbound

This paper cites YOLOv3: An Incremental Improvement.

Mish: A Self Regularized Non-Monotonic Activation Function YOLOv3: An Incremental Improvement

Reference 39

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Observation 3ab40688-a04c-4c12-a7c3-19bb2c6d884f · outbound

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Mish: A Self Regularized Non-Monotonic Activation Function Dynamic routing between cap- sules

Reference 40

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Observation 8994b42a-9847-42d2-8eaa-035e09fae65e · outbound

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

Mish: A Self Regularized Non-Monotonic Activation Function Dropout: a simple way to prevent neural networks from overﬁtting

Reference 41

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This paper cites Going deeper with convolutions.

Mish: A Self Regularized Non-Monotonic Activation Function Going deeper with convolutions

Reference 42

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Observation ac2c568e-1140-4e4b-9224-60653d967a00 · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Mish: A Self Regularized Non-Monotonic Activation Function EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 43

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Observation 5bc78505-99f7-4212-b373-710b02e354c8 · outbound

This paper cites EfficientDet: Scalable and Efficient Object Detection.

Mish: A Self Regularized Non-Monotonic Activation Function EfficientDet: Scalable and Efficient Object Detection

Reference 44

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Observation 9cd43a9e-0824-4486-95a7-0fc17975b13f · outbound

This paper cites CSPNet: A New Backbone that can Enhance Learning Capability of CNN.

Mish: A Self Regularized Non-Monotonic Activation Function CSPNet: A New Backbone that can Enhance Learning Capability of CNN

Reference 45

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This paper cites Pelee: A real-time object detection system on mobile devices.

Mish: A Self Regularized Non-Monotonic Activation Function Pelee: A real-time object detection system on mobile devices

Reference 46

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Observation 4eed6dd0-1b82-43ae-a551-1dcd5e28655b · outbound

This paper cites Aggregated residual transformations for deep neural networks.

Mish: A Self Regularized Non-Monotonic Activation Function Aggregated residual transformations for deep neural networks

Reference 47

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Observation 2d6cd1a1-c530-4701-bde3-b2a4cd20d430 · outbound

This paper cites Empirical Evaluation of Rectified Activations in Convolutional Network.

Mish: A Self Regularized Non-Monotonic Activation Function Empirical Evaluation of Rectified Activations in Convolutional Network

Reference 48

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Observation a87cd76b-29ab-4775-bbe4-6e506417dd7e · outbound

This paper cites Cutmix: Regularization strategy to train strong classiﬁers with lo- calizable features.

Mish: A Self Regularized Non-Monotonic Activation Function Cutmix: Regularization strategy to train strong classiﬁers with lo- calizable features

Reference 49

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Observation 2c34b8ce-0399-41cc-8dd2-00bf9ab030d0 · outbound

This paper cites Wide Residual Networks.

Mish: A Self Regularized Non-Monotonic Activation Function Wide Residual Networks

Reference 50

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Observation efe84c70-cb7f-457b-b50e-51ad4dc0c27b · outbound

This paper cites Shufﬂenet: An extremely efﬁcient convolutional neural network for mobile devices.

Mish: A Self Regularized Non-Monotonic Activation Function Shufﬂenet: An extremely efﬁcient convolutional neural network for mobile devices

Reference 51

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Observation fcc4f2e9-3fdf-4425-963b-b7f305359188 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Mish: A Self Regularized Non-Monotonic Activation Function Neural Architecture Search with Reinforcement Learning

Reference 52

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Pith citing papers

Observation 1c2b5420-62fc-4cd0-86aa-c97b1d5d5ec4 · inbound

YOLOv4: Optimal Speed and Accuracy of Object Detection cites this paper.

YOLOv4: Optimal Speed and Accuracy of Object Detection Mish: A Self Regularized Non-Monotonic Activation Function

Reference 55

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Observation 1ab868de-60e3-4385-97e3-e95cddc466a7 · inbound

TD-MPC2: Scalable, Robust World Models for Continuous Control cites this paper.

TD-MPC2: Scalable, Robust World Models for Continuous Control Mish: A Self Regularized Non-Monotonic Activation Function

Reference 176

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Observation 22966b41-bb6e-440d-a4f2-398901d361b3 · inbound

Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet cites this paper.

Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet Mish: A Self Regularized Non-Monotonic Activation Function

Reference 30

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Parseval Regularization for Continual Reinforcement Learning cites this paper.

Parseval Regularization for Continual Reinforcement Learning Mish: A Self Regularized Non-Monotonic Activation Function

Reference 40

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Observation 8e20f70d-8d4d-4aca-a82d-fb82c1e4ada3 · inbound

Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis cites this paper.

Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis Mish: A Self Regularized Non-Monotonic Activation Function

Reference 15

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Observation 22401449-ba7b-4b90-b2e5-6756d56417fa · inbound

Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026) cites this paper.

Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026) Mish: A Self Regularized Non-Monotonic Activation Function

Reference 89

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Observation 499905e2-63d4-4333-9138-fff193ae79d8 · inbound

Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning cites this paper.

Reconstructing Time-of-Flight Detector Values of Angular Streaking Using Machine Learning Mish: A Self Regularized Non-Monotonic Activation Function

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T20:18:25.575154Z

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Observation 17db94a0-97fe-47aa-8792-72e643bab166 · inbound

A Hands-free Spatial Selection and Interaction Technique using Gaze and Blink Input with Blink Prediction for Extended Reality cites this paper.

A Hands-free Spatial Selection and Interaction Technique using Gaze and Blink Input with Blink Prediction for Extended Reality Mish: A Self Regularized Non-Monotonic Activation Function

Reference 45

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Observation 53c1ade8-1970-4297-8ee3-ef44a3de66a1 · inbound

Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models cites this paper.

Softplus Attention with Re-weighting Boosts Length Extrapolation in Large Language Models Mish: A Self Regularized Non-Monotonic Activation Function

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T16:15:39.481878Z

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Observation 1f3f4444-892e-4ca8-91e1-949e6653f562 · inbound

Local Control Networks (LCNs): Optimizing Flexibility in Neural Network Data Pattern Capture cites this paper.

Local Control Networks (LCNs): Optimizing Flexibility in Neural Network Data Pattern Capture Mish: A Self Regularized Non-Monotonic Activation Function

Reference 15

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Unavailable: canonical work link unavailable.

Observation 253a7c72-92bf-460e-8071-8503e3ad9007 · inbound

Neural Networks Learn Distance Metrics cites this paper.

Neural Networks Learn Distance Metrics Mish: A Self Regularized Non-Monotonic Activation Function

Reference 26

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Observation 1bfc6acc-d79a-4a41-8a2b-caba8a8910a2 · inbound

Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics cites this paper.

Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning Dynamics Mish: A Self Regularized Non-Monotonic Activation Function

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T04:17:04.778491Z

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Observation 485639cf-2548-45c7-bd80-25388ee87c7d · inbound

PAGNet: Pluggable Adaptive Generative Networks for Information Completion in Multi-Agent Communication cites this paper.

PAGNet: Pluggable Adaptive Generative Networks for Information Completion in Multi-Agent Communication Mish: A Self Regularized Non-Monotonic Activation Function

Reference 50

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Unavailable: canonical work link unavailable.

Observation 2106b4ee-9ce9-4e78-b0b9-515d453b673e · inbound

CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus cites this paper.

CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic Feature Trust Modulus Mish: A Self Regularized Non-Monotonic Activation Function

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-08T10:14:31.675916Z

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Observation cfe4bc50-daeb-4a50-ab88-a0b5466b851c · inbound

Estimating Probabilities of Causation with Machine Learning Models cites this paper.

Estimating Probabilities of Causation with Machine Learning Models Mish: A Self Regularized Non-Monotonic Activation Function

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T23:32:55.658655Z

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Unavailable: canonical work link unavailable.

Observation 188a5cb1-133c-49b8-aa3a-1dd1cb97dcc6 · inbound

Hadamard product in deep learning: Introduction, Advances and Challenges cites this paper.

Hadamard product in deep learning: Introduction, Advances and Challenges Mish: A Self Regularized Non-Monotonic Activation Function

Reference 279

Resolution: unresolved; no resolver link, observed 2026-08-16T12:17:20.973338Z

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Observation 1318bce5-077b-44a3-9270-d4be664f95ac · inbound

Active RIS-Empowered Covert Satellite-Terrestrial Communications cites this paper.

Active RIS-Empowered Covert Satellite-Terrestrial Communications Mish: A Self Regularized Non-Monotonic Activation Function

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-16T11:24:23.128686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe12f69c-07e6-4705-83aa-8c4cc74f3d9d · inbound

Dual-Branch Residual Network for Cross-Domain Few-Shot Hyperspectral Image Classification with Refined Prototype cites this paper.

Dual-Branch Residual Network for Cross-Domain Few-Shot Hyperspectral Image Classification with Refined Prototype Mish: A Self Regularized Non-Monotonic Activation Function

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T06:06:08.868618Z

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Unavailable: canonical work link unavailable.

Observation 58ed05f9-02c9-4536-a56c-1e9991973389 · inbound

Neural Stereo Video Compression with Hybrid Disparity Compensation cites this paper.

Neural Stereo Video Compression with Hybrid Disparity Compensation Mish: A Self Regularized Non-Monotonic Activation Function

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-16T05:37:11.772540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffc06570-4494-4429-8e4f-45d03ec796a8 · inbound

Preserving Plasticity in Continual Learning with Adaptive Linearity Injection cites this paper.

Preserving Plasticity in Continual Learning with Adaptive Linearity Injection Mish: A Self Regularized Non-Monotonic Activation Function

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T21:33:59.049816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ccf2fe4e-d7b2-4ccc-9501-c01f458b10c6 · inbound

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning cites this paper.

FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning Mish: A Self Regularized Non-Monotonic Activation Function

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:50.478554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e9c9ffe-095d-489f-8dd0-3db0f473a863 · inbound

Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction cites this paper.

Revisiting Feature Interactions from the Perspective of Quadratic Neural Networks for Click-through Rate Prediction Mish: A Self Regularized Non-Monotonic Activation Function

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:31.214961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e33387b-a5cd-4fe0-8ccc-6ff22f0ce5a7 · inbound

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations cites this paper.

SG-Blend: Learning an Interpolation Between Improved Swish and GELU for Robust Neural Representations Mish: A Self Regularized Non-Monotonic Activation Function

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:03.531466Z

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Unavailable: canonical work link unavailable.

Observation 4bcfc627-8130-47e1-9fa1-c0cc91ef5ad8 · inbound

A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement cites this paper.

A Composite Predictive-Generative Approach to Monaural Universal Speech Enhancement Mish: A Self Regularized Non-Monotonic Activation Function

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T12:24:12.307546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfb46c1d-85a6-4889-b1b2-37873f04faca · inbound

Transformers Learn Faster with Semantic Focus cites this paper.

Transformers Learn Faster with Semantic Focus Mish: A Self Regularized Non-Monotonic Activation Function

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T00:26:31.146060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0aedb8d0-db2f-4d98-a80f-e8afb80d309d · inbound

Distributional Soft Actor-Critic with Diffusion Policy cites this paper.

Distributional Soft Actor-Critic with Diffusion Policy Mish: A Self Regularized Non-Monotonic Activation Function

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:05.887630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14dc7594-b590-424b-9a4a-e7106257ff0c · inbound

Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos cites this paper.

Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos Mish: A Self Regularized Non-Monotonic Activation Function

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T20:17:52.339417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68d0dc3c-55b4-4864-950f-1327c3810744 · inbound

SoftReMish: A Novel Activation Function for Enhanced Convolutional Neural Networks for Visual Recognition Performance cites this paper.

SoftReMish: A Novel Activation Function for Enhanced Convolutional Neural Networks for Visual Recognition Performance Mish: A Self Regularized Non-Monotonic Activation Function

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-06T19:14:18.599866Z

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Unavailable: canonical work link unavailable.

Observation acb26513-50c1-491a-ae3d-b0f233393eb6 · inbound

EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning cites this paper.

EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning Mish: A Self Regularized Non-Monotonic Activation Function

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:47:42.043659Z

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Unavailable: canonical work link unavailable.

Observation 8f11c8db-8397-4935-80fa-c20a6690caac · inbound

Tangma: A Tanh-Guided Activation Function with Learnable Parameters cites this paper.

Tangma: A Tanh-Guided Activation Function with Learnable Parameters Mish: A Self Regularized Non-Monotonic Activation Function

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T20:42:01.089597Z

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Unavailable: canonical work link unavailable.

Observation 35a28022-ebac-45cd-9897-fcba2d1f11f3 · inbound

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime cites this paper.

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime Mish: A Self Regularized Non-Monotonic Activation Function

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T15:48:43.299681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7089f6de-0c18-4d17-ac42-1806484665e1 · inbound

Compress-Align-Detect: onboard change detection from unregistered images cites this paper.

Compress-Align-Detect: onboard change detection from unregistered images Mish: A Self Regularized Non-Monotonic Activation Function

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T15:33:58.353123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 038f740b-befb-4042-9b68-ac2b6bb27fe6 · inbound

Joint Inference of Trajectory and Obstacle in Mean-Field Games via Bilevel Optimization cites this paper.

Joint Inference of Trajectory and Obstacle in Mean-Field Games via Bilevel Optimization Mish: A Self Regularized Non-Monotonic Activation Function

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T18:01:27.327171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1af972da-eb98-4630-9b01-52330caba868 · inbound

Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization cites this paper.

Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization Mish: A Self Regularized Non-Monotonic Activation Function

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T12:39:10.624184Z

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Unavailable: canonical work link unavailable.

Observation 1a0940ec-32ec-4a72-96d8-6c8e82422356 · inbound

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process cites this paper.

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process Mish: A Self Regularized Non-Monotonic Activation Function

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T11:44:50.366251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab171fc6-9365-4f75-95ab-8ab66b1d14c5 · inbound

Silent Impact: Tracking Tennis Shots from the Passive Arm cites this paper.

Silent Impact: Tracking Tennis Shots from the Passive Arm Mish: A Self Regularized Non-Monotonic Activation Function

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T11:02:23.551319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac335f87-ce03-490d-91c3-3691910635d5 · inbound

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges cites this paper.

YOLOv1 to YOLOv11: A Comprehensive Survey of Real-Time Object Detection Innovations and Challenges Mish: A Self Regularized Non-Monotonic Activation Function

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T05:15:42.396390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a697ca39-cd0f-43c1-a309-f3df1768b290 · inbound

FLUX-Makeup: High-Fidelity, Identity-Consistent, and Robust Makeup Transfer via Diffusion Transformer cites this paper.

FLUX-Makeup: High-Fidelity, Identity-Consistent, and Robust Makeup Transfer via Diffusion Transformer Mish: A Self Regularized Non-Monotonic Activation Function

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T23:35:29.658524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00451267-5599-441a-ac36-a5a36edd6172 · inbound

Improving the Accuracy of Amortized Model Comparison with Self-Consistency cites this paper.

Improving the Accuracy of Amortized Model Comparison with Self-Consistency Mish: A Self Regularized Non-Monotonic Activation Function

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T21:16:51.076712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2c3804c1-d3de-4228-876b-4e2d9f0d22d5 · inbound

The Algorithm Is Not the Behavior: Learned Priors Override Look-Ahead in a Chess-Playing Neural Network cites this paper.

The Algorithm Is Not the Behavior: Learned Priors Override Look-Ahead in a Chess-Playing Neural Network Mish: A Self Regularized Non-Monotonic Activation Function

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T14:24:15.822040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be8d892e-fc03-476e-913d-6662aa919404 · inbound

Empowering Multi-Robot Cooperation via Sequential World Models cites this paper.

Empowering Multi-Robot Cooperation via Sequential World Models Mish: A Self Regularized Non-Monotonic Activation Function

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:31:37.405447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3c2b4ae7-6690-47b1-a932-130221a83ab8 · inbound

DiFlowDubber: Discrete Flow Matching for Automated Video Dubbing via Cross-Modal Alignment and Synchronization cites this paper.

DiFlowDubber: Discrete Flow Matching for Automated Video Dubbing via Cross-Modal Alignment and Synchronization Mish: A Self Regularized Non-Monotonic Activation Function

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-15T11:59:59.601393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 37c45461-28c3-4851-a987-984b78ff0629 · inbound

VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale cites this paper.

VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale Mish: A Self Regularized Non-Monotonic Activation Function

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:11:02.509102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 061ff3c9-73b2-457d-84cd-5e61601a0502 · inbound

Geometric Monomial (GEM): a family of rational 2N-differentiable activation functions cites this paper.

Geometric Monomial (GEM): a family of rational 2N-differentiable activation functions Mish: A Self Regularized Non-Monotonic Activation Function

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:39:14.453194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation acaf4d63-40bb-4636-b839-4fbf4f5d8743 · inbound

Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients cites this paper.

Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients Mish: A Self Regularized Non-Monotonic Activation Function

Reference 34

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T05:45:21.262768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 108a5e27-0d11-40b0-b0e0-4b118d0b14c6 · inbound

Universal Smoothness via Bernstein Polynomials: A Constructive Approximation Approach for Activation Functions cites this paper.

Universal Smoothness via Bernstein Polynomials: A Constructive Approximation Approach for Activation Functions Mish: A Self Regularized Non-Monotonic Activation Function

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:45:44.257871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7f14eea4-159a-46ad-8f93-9d3d432043fa · inbound

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks cites this paper.

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Mish: A Self Regularized Non-Monotonic Activation Function

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:45:56.882993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 07b2662e-dd87-4a40-8274-e0786247e634 · inbound

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks cites this paper.

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Mish: A Self Regularized Non-Monotonic Activation Function

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T06:29:49.747303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e9284b4f-bc0d-47e4-bc8c-7689d0fffb28 · inbound

Voice Biomarkers for Depression and Anxiety cites this paper.

Voice Biomarkers for Depression and Anxiety Mish: A Self Regularized Non-Monotonic Activation Function

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:06:27.810670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0ad67021-018e-4fd7-805f-fed21592dd47 · inbound

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization cites this paper.

How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization Mish: A Self Regularized Non-Monotonic Activation Function

Reference 267

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:49:44.687054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6fbf1165-24bd-4839-8bcf-bf1e5660107d · inbound

Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance cites this paper.

Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance Mish: A Self Regularized Non-Monotonic Activation Function

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T01:29:37.421261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1be5e133-319d-4316-a55e-c8029e9424fc · inbound

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations cites this paper.

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations Mish: A Self Regularized Non-Monotonic Activation Function

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.851852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 963a3e83-292b-45ff-ab37-30f78eea67bf · inbound

Expressive Power of Floating-Point Neural Networks with Arbitrary Reduction Orders and Inexact Activation Implementations cites this paper.

Expressive Power of Floating-Point Neural Networks with Arbitrary Reduction Orders and Inexact Activation Implementations Mish: A Self Regularized Non-Monotonic Activation Function

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-29T13:43:29.151721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 218bf73c-1ba1-403b-b01b-db7d158ae348 · inbound

Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models cites this paper.

Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models Mish: A Self Regularized Non-Monotonic Activation Function

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:17:29.658521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 77d7a6ef-afb0-4e23-b558-913808f2080d · inbound

Performance of the Eos detector with water cites this paper.

Performance of the Eos detector with water Mish: A Self Regularized Non-Monotonic Activation Function

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T03:57:38.964692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4595382c-c9a9-4d7e-8c13-710b1b1cc71d · inbound

DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy cites this paper.

DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy Mish: A Self Regularized Non-Monotonic Activation Function

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:19:13.757876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 31b8f230-4f2d-4900-8ff6-733b12f531da · inbound

Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network cites this paper.

Bayesian three-dimensional seismic travel-time tomography for active- and passive-source seismic data using physics-informed neural network Mish: A Self Regularized Non-Monotonic Activation Function

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:09:40.843940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c52b5bbe-8036-4dd4-b1b0-3117424f4b0e · inbound

Rethinking Neural Nonlinearity as Gating cites this paper.

Rethinking Neural Nonlinearity as Gating Mish: A Self Regularized Non-Monotonic Activation Function

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T04:33:35.163697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5e2538f-ab4d-412c-aa52-a881edf270a6 · inbound

Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting cites this paper.

Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting Mish: A Self Regularized Non-Monotonic Activation Function

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-08T11:24:54.884730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ec98a2f6-0353-4e26-ac15-6e1eb717126f · inbound

Path optimization method for the sign problem: Insights from random matrix models cites this paper.

Path optimization method for the sign problem: Insights from random matrix models Mish: A Self Regularized Non-Monotonic Activation Function

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T22:29:29.740549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0292d3f-72a3-4336-92c3-8739f9bf83a6 · inbound

Measurement of the branching ratio of the $K^{+}\rightarrow\pi^{+}\nu\bar{\nu}$ decay cites this paper.

Measurement of the branching ratio of the $K^{+}\rightarrow\pi^{+}\nu\bar{\nu}$ decay Mish: A Self Regularized Non-Monotonic Activation Function

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T21:05:37.659769Z

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Observation 0f6ecba2-37ca-4b7a-ac23-4dd9191fb78d · inbound

Reinforcement Learning: From Algorithms To Foundation Models cites this paper.

Reinforcement Learning: From Algorithms To Foundation Models Mish: A Self Regularized Non-Monotonic Activation Function

Reference 264

Resolution: unresolved; no resolver link, observed 2026-08-01T17:45:22.256131Z

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Unavailable: canonical work link unavailable.

Observation 313093d6-dbc1-4fd6-a195-b394b0a43ccc · inbound

UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction cites this paper.

UniRank: Benchmarking Ranking Models for Unified Sequential Modeling and Feature Interaction Mish: A Self Regularized Non-Monotonic Activation Function

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-01T11:07:58.437825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69e7ea55-825c-44ec-94bc-4b07c8d6d11b · inbound

Relative Value Learning cites this paper.

Relative Value Learning Mish: A Self Regularized Non-Monotonic Activation Function

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-01T08:32:00.094258Z

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Observation a1362a05-cff5-40bd-a2b7-dd34ca43d2c4 · inbound

Meteosat Third Generation imagery improves CNN-based SSI retrieval cites this paper.

Meteosat Third Generation imagery improves CNN-based SSI retrieval Mish: A Self Regularized Non-Monotonic Activation Function

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T18:02:34.664093Z

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Unavailable: canonical work link unavailable.

Observation 30e82690-5ac2-428b-adb0-34e66b4b4fe8 · inbound

Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control? cites this paper.

Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control? Mish: A Self Regularized Non-Monotonic Activation Function

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T05:05:10.518816Z

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Unavailable: canonical work link unavailable.