Online Training and Pruning of Deep Reinforcement Learning Networks | Paper Citation Record

Observation 5adbb0aa-ba8e-489f-bff9-5c7d3e1b8547 · outbound

This paper cites Imagenet classification with deep convolutional neural networks,.

Online Training and Pruning of Deep Reinforcement Learning Networks Imagenet classification with deep convolutional neural networks,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.124172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b2f03e3-8ac8-4b2d-abc3-d81959d4056f · outbound

This paper cites Rethinking Atrous Convolution for Semantic Image Segmentation.

Online Training and Pruning of Deep Reinforcement Learning Networks Rethinking Atrous Convolution for Semantic Image Segmentation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.127902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e184e0a3-f345-43d8-bd51-0961048e9ce2 · outbound

This paper cites Speech recognition with deep recurrent neural networks,.

Online Training and Pruning of Deep Reinforcement Learning Networks Speech recognition with deep recurrent neural networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.625187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7eaf585e-f1af-4656-9a3b-3c4bcb18aaf6 · outbound

This paper cites Attention is all you need,.

Online Training and Pruning of Deep Reinforcement Learning Networks Attention is all you need,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.136750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc7e3626-3bdf-4802-949e-90c33a83b5b5 · outbound

This paper cites Self-supervised learning: Generative or contrastive,.

Online Training and Pruning of Deep Reinforcement Learning Networks Self-supervised learning: Generative or contrastive,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.140459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a249cf3-76e0-4980-bce3-e15c2eeee26f · outbound

This paper cites Language models are few-shot learners,.

Online Training and Pruning of Deep Reinforcement Learning Networks Language models are few-shot learners,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.143221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e03599f9-a4a9-4fbd-b82d-6fb9dafc81f6 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Online Training and Pruning of Deep Reinforcement Learning Networks Playing Atari with Deep Reinforcement Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.145892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a969424-75c8-430b-bc87-d71d0e714705 · outbound

This paper cites Human-level control through deep reinforce- ment learning,.

Online Training and Pruning of Deep Reinforcement Learning Networks Human-level control through deep reinforce- ment learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.607540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 612ee690-754d-435e-a18e-f42fcfba9962 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,.

Online Training and Pruning of Deep Reinforcement Learning Networks Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.600250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 169a99c4-7aee-41fb-9ea1-f1ea79bfbafa · outbound

This paper cites Addressing function approximation error in actor-critic meth- ods,.

Online Training and Pruning of Deep Reinforcement Learning Networks Addressing function approximation error in actor-critic meth- ods,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.593517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a24931a3-2a2e-47a1-9fbd-13bf153d8d33 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Online Training and Pruning of Deep Reinforcement Learning Networks Proximal Policy Optimization Algorithms

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.157029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8699051-7f93-4e96-a5e3-5f164f97471c · outbound

This paper cites Deep Reinforcement Learning and the Deadly Triad.

Online Training and Pruning of Deep Reinforcement Learning Networks Deep Reinforcement Learning and the Deadly Triad

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.160121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bfbc8f4-465a-4d32-8509-a295fe290c3d · outbound

This paper cites D2RL: Deep Dense Architectures in Reinforcement Learning.

Online Training and Pruning of Deep Reinforcement Learning Networks D2RL: Deep Dense Architectures in Reinforcement Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.163294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0bd07b4-47d3-45fb-8dd6-c316ec3a8241 · outbound

This paper cites What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study.

Online Training and Pruning of Deep Reinforcement Learning Networks What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.167535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44dd1381-2aef-49d3-bb5f-3ef10a720172 · outbound

This paper cites Deterministic policy gradient algorithms,.

Online Training and Pruning of Deep Reinforcement Learning Networks Deterministic policy gradient algorithms,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.586314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a589ac17-4db4-4fb0-a82f-62b28de40bf0 · outbound

This paper cites Can increasing input dimensionality improve deep reinforcement learning?,.

Online Training and Pruning of Deep Reinforcement Learning Networks Can increasing input dimensionality improve deep reinforcement learning?,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.579523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 812ec8fd-83c2-421b-be56-8fb4a43951ac · outbound

This paper cites A framework for training larger networks for deep Reinforce- ment learning,.

Online Training and Pruning of Deep Reinforcement Learning Networks A framework for training larger networks for deep Reinforce- ment learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.571286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 656e0d66-ca70-4c26-85e2-e76f34780862 · outbound

This paper cites Bigger, better, faster: human-level atari with human-level efficiency,.

Online Training and Pruning of Deep Reinforcement Learning Networks Bigger, better, faster: human-level atari with human-level efficiency,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.563994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1aebfac9-46fb-4dfd-83b5-054efaf6b447 · outbound

This paper cites Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks.

Online Training and Pruning of Deep Reinforcement Learning Networks Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:16.358844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0723549-b88f-4ace-8b6c-b87d8c3c96ef · outbound

This paper cites Mastering Diverse Domains through World Models.

Online Training and Pruning of Deep Reinforcement Learning Networks Mastering Diverse Domains through World Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.183468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a57f3804-d4ca-44ef-85b4-83002c95ebb4 · outbound

This paper cites Learning both weights and connections for efficient neural networks,.

Online Training and Pruning of Deep Reinforcement Learning Networks Learning both weights and connections for efficient neural networks,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.557017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e9fbab70-f307-4599-8c82-e46478f8d94b · outbound

This paper cites What is the state of neural network pruning?,.

Online Training and Pruning of Deep Reinforcement Learning Networks What is the state of neural network pruning?,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.548863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5e4675f8-d82e-4607-86b8-8ba62b132588 · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Online Training and Pruning of Deep Reinforcement Learning Networks Pruning Filters for Efficient ConvNets

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.190846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 846c6d15-1b28-4694-9cb8-e14a7470b052 · outbound

This paper cites Lost in pruning: The effects of pruning neural networks beyond test accuracy,.

Online Training and Pruning of Deep Reinforcement Learning Networks Lost in pruning: The effects of pruning neural networks beyond test accuracy,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.539662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 743ab64d-1ee0-4e35-8d3a-75128e2fe48e · outbound

This paper cites SCOP: scientific control for reliable neural network pruning,.

Online Training and Pruning of Deep Reinforcement Learning Networks SCOP: scientific control for reliable neural network pruning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.531595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5ad395b9-e524-46f5-970b-980bbcbd9cbb · outbound

This paper cites Robust learning of parsimonious deep neural networks,.

Online Training and Pruning of Deep Reinforcement Learning Networks Robust learning of parsimonious deep neural networks,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.523851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20484b75-ce33-41d1-b2ef-1decc143ae03 · outbound

This paper cites Shallowing deep networks: Layer-wise pruning based on feature representa- tions,.

Online Training and Pruning of Deep Reinforcement Learning Networks Shallowing deep networks: Layer-wise pruning based on feature representa- tions,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.514958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a2602985-b3b3-4641-aaa7-c1f2459c45aa · outbound

This paper cites DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration.

Online Training and Pruning of Deep Reinforcement Learning Networks DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.204646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcbb66cd-9d04-47a9-9245-f88f6bc35c8c · outbound

This paper cites Concurrent Training and Layer Pruning of Deep Neural Networks.

Online Training and Pruning of Deep Reinforcement Learning Networks Concurrent Training and Layer Pruning of Deep Neural Networks

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.207626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 077b9460-62eb-4280-aa8b-21b325227f79 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

Online Training and Pruning of Deep Reinforcement Learning Networks The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.210505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52569f7f-427f-423a-bf02-edd2435c72e7 · outbound

This paper cites The state of sparse training in deep reinforcement learning,.

Online Training and Pruning of Deep Reinforcement Learning Networks The state of sparse training in deep reinforcement learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.500702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 990b365b-583c-44a0-ae0e-9d79efc4b6ce · outbound

This paper cites Automatic noise filtering with dynamic sparse training in deep reinforcement learning,.

Online Training and Pruning of Deep Reinforcement Learning Networks Automatic noise filtering with dynamic sparse training in deep reinforcement learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.492813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 13e34952-13ba-4ec9-bdad-9f44f194942a · outbound

This paper cites In value-based deep reinforcement learning, a pruned network is a good network,.

Online Training and Pruning of Deep Reinforcement Learning Networks In value-based deep reinforcement learning, a pruned network is a good network,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.484983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0cb0439-19f1-4dde-acae-aaa0a1c7e752 · outbound

This paper cites an unresolved cited work.

Online Training and Pruning of Deep Reinforcement Learning Networks Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-06T17:05:16.476928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2f612277-8f92-4ae1-9899-32960ad99a4f · outbound

This paper cites Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery.

Online Training and Pruning of Deep Reinforcement Learning Networks Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:05:16.327100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0db245f-0116-47fc-b940-4fb12dc81a83 · outbound

This paper cites Observational Overfitting in Reinforcement Learning.

Online Training and Pruning of Deep Reinforcement Learning Networks Observational Overfitting in Reinforcement Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.229659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e651a0b8-8fc1-4d14-ba87-a88158cbfd99 · outbound

This paper cites A Study on Overfitting in Deep Reinforcement Learning.

Online Training and Pruning of Deep Reinforcement Learning Networks A Study on Overfitting in Deep Reinforcement Learning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.233411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0457d13-9d9c-408e-9be9-3b69210cbcc2 · outbound

This paper cites Learning state representation for deep actor-critic control,.

Online Training and Pruning of Deep Reinforcement Learning Networks Learning state representation for deep actor-critic control,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.469538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bc746c52-0d47-4821-b9e9-c5e54baa1946 · outbound

This paper cites Densely connected convolutional net- works,.

Online Training and Pruning of Deep Reinforcement Learning Networks Densely connected convolutional net- works,

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.239992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1d234d2-e4bd-4001-9483-4e6ec4242098 · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Online Training and Pruning of Deep Reinforcement Learning Networks Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.242243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9a0a543-82b3-40ca-b780-9b93b0f1a161 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Online Training and Pruning of Deep Reinforcement Learning Networks Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.245637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 631b7a3f-3b58-4175-bc79-fa2e76fbbba4 · outbound

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

Online Training and Pruning of Deep Reinforcement Learning Networks Dropout: a simple way to prevent neural networks from overfitting,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.456950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d252ee97-7557-4d17-9d46-36dbc45db5c7 · outbound

This paper cites Batch normalization: Accelerating deep network training by reducing internal covariate shift,.

Online Training and Pruning of Deep Reinforcement Learning Networks Batch normalization: Accelerating deep network training by reducing internal covariate shift,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.448994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 31a16712-0d15-4fa5-9179-46ddf9c96242 · outbound

This paper cites How does batch normalization help optimization?,.

Online Training and Pruning of Deep Reinforcement Learning Networks How does batch normalization help optimization?,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.439794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2b458270-4b06-4c91-8151-8e7dd30a2443 · outbound

This paper cites Deep residual learning for image recognition,.

Online Training and Pruning of Deep Reinforcement Learning Networks Deep residual learning for image recognition,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.257106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2c10150-1c06-4875-8eba-8d718d61435e · outbound

This paper cites Deep reinforcement learning that matters,.

Online Training and Pruning of Deep Reinforcement Learning Networks Deep reinforcement learning that matters,

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.260023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c12f384d-4ab2-4c2c-a19b-4cf4650eab4f · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning,.

Online Training and Pruning of Deep Reinforcement Learning Networks Dropout as a bayesian approximation: Representing model uncertainty in deep learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.425769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5723c572-75dc-4b8d-9441-86fdeb180f28 · outbound

This paper cites Dropout q-functions for doubly efficient reinforcement learning,.

Online Training and Pruning of Deep Reinforcement Learning Networks Dropout q-functions for doubly efficient reinforcement learning,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.417015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4e3b9397-2f69-47f6-909b-7b8e43ca2c5e · outbound

This paper cites Regularization matters in policy optimization-an empirical study on continuous control,.

Online Training and Pruning of Deep Reinforcement Learning Networks Regularization matters in policy optimization-an empirical study on continuous control,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.409991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1a174c10-caf5-4b6a-96e8-963c2e1c2e73 · outbound

This paper cites Implicit under-parameterization inhibits data-efficient deep reinforcement learning,.

Online Training and Pruning of Deep Reinforcement Learning Networks Implicit under-parameterization inhibits data-efficient deep reinforcement learning,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:05:16.401286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9ea20eae-0311-4e67-a99a-7a6f0008e3b8 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Online Training and Pruning of Deep Reinforcement Learning Networks Adam: A Method for Stochastic Optimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T17:05:16.273950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers