Paper Citation Record · LEDGER

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss

As of 10 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2502.01342.

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

pith.paper-citation-record.v1
2502.01342 v2

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T15:43:34.601190Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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External citation measurements

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Outbound references

Observation bda03c52-fe66-46c1-8425-969c8cd55039 · outbound

This paper cites write newline.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss write newline

Reference 1

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Unresolved cited work

Reference 2

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This paper cites Resetting the optimizer in deep rl: An empirical study.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Resetting the optimizer in deep rl: An empirical study

Reference 3

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This paper cites and Adams, R.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss and Adams, R

Reference 4

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Layer Normalization

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss G., Naddaf, Y., Veness, J., and Bowling, M

Reference 6

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This paper cites A study on the plasticity of neural networks.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss A study on the plasticity of neural networks

Reference 7

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This paper cites Adaptive rational activations to boost deep reinforcement learning.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Adaptive rational activations to boost deep reinforcement learning

Reference 8

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This paper cites Continual Backprop: Stochastic Gradient Descent with Persistent Randomness.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Continual Backprop: Stochastic Gradient Descent with Persistent Randomness

Reference 9

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This paper cites Maintaining Plasticity in Deep Continual Learning.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Maintaining Plasticity in Deep Continual Learning

Reference 10

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This paper cites F., Lan, Q., Rahman, P., Mahmood, A.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss F., Lan, Q., Rahman, P., Mahmood, A

Reference 11

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Observation 719ab7ff-f470-4e6b-9fe1-589468fb6350 · outbound

This paper cites Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Addressing Loss of Plasticity and Catastrophic Forgetting in Continual Learning

Reference 12

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This paper cites Weight Clipping for Deep Continual and Reinforcement Learning.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Weight Clipping for Deep Continual and Reinforcement Learning

Reference 13

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This paper cites C., Clopath, C., Busoniu, L., and Pascanu, R.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss C., Clopath, C., Busoniu, L., and Pascanu, R

Reference 14

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This paper cites Consistent Dropout for Policy Gradient Reinforcement Learning.

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Consistent Dropout for Policy Gradient Reinforcement Learning

Reference 15

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

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 16

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Deep residual learning for image recognition

Reference 17

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Unresolved cited work

Reference 18

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Unresolved cited work

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Adam: A Method for Stochastic Optimization

Reference 20

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al

Reference 21

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Learning multiple layers of features from tiny images

Reference 22

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss and Hertz, J

Reference 23

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 24

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Maintaining Plasticity in Continual Learning via Regenerative Regularization

Reference 25

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss and Yang, X

Reference 26

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Plastic: Improving input and label plasticity for sample efficient reinforcement learning

Reference 27

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Slow and steady wins the race: Maintaining plasticity with hare and tortoise networks

Reference 28

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Unresolved cited work

Reference 29

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Observation 92a6d55c-6656-4f48-a72a-2d752f79babb · outbound

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Plastic Learning with Deep Fourier Features

Reference 30

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Drop-activation: implicit parameter reduction and harmonious regularization

Reference 31

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Understanding and Preventing Capacity Loss in Reinforcement Learning

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss A., Pascanu, R., and Dabney, W

Reference 33

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Disentangling the Causes of Plasticity Loss in Neural Networks

Reference 34

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss I., Farajtabar, M., and Ghasemzadeh, H

Reference 36

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss A., Veness, J., Bellemare, M

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss The primacy bias in deep reinforcement learning

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Deep reinforcement learning with plasticity injection

Reference 39

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Step Out and Seek Around: On Warm-Start Training with Incremental Data

Reference 40

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Dash: Warm-starting neural network training in stationary settings without loss of plasticity

Reference 41

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Very Deep Convolutional Networks for Large-Scale Image Recognition

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss S., and Evci, U

Reference 43

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Dropout: a simple way to prevent neural networks from overfitting

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Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss Empirical Evaluation of Rectified Activations in Convolutional Network

Reference 45

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