Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency | Paper Citation Record

Observation 2295d298-cf58-4b07-b4d7-78c5400c7be1 · outbound

This paper cites Uncertainty, neuromodulation, and attention.Neuron, 46(4):681–692, 2005.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Uncertainty, neuromodulation, and attention.Neuron, 46(4):681–692, 2005

Reference 1

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Observation 111a84dc-8a78-4f66-917c-30e50739dbf0 · outbound

This paper cites Decoupled greedy learning of cnns.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Decoupled greedy learning of cnns

Reference 2

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This paper cites Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets

Reference 3

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Observation d5a93c3e-ccc3-4457-9e32-ba4757fb331a · outbound

This paper cites A non-asymptotic analysis of non-parametric temporal-difference learning.Advances in Neural Information Processing Systems, 35:7599–7613, 2022.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency A non-asymptotic analysis of non-parametric temporal-difference learning.Advances in Neural Information Processing Systems, 35:7599–7613, 2022

Reference 4

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Observation ec23af91-6141-4ca4-af30-2a4fdcc6edc8 · outbound

This paper cites A finite time analysis of temporal difference learning with linear function approximation.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency A finite time analysis of temporal difference learning with linear function approximation

Reference 5

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Observation e48c7263-8c38-41b5-9aa0-aa0e1e8422fa · outbound

This paper cites A finite time analysis of temporal difference learning with linear function approximation.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency A finite time analysis of temporal difference learning with linear function approximation

Reference 6

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Observation 224fd486-6f8d-4012-8d14-e502c06bde83 · outbound

This paper cites Neural temporal-difference learning converges to global optima.Advances in Neural Information Processing Systems, 32, 2019.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Neural temporal-difference learning converges to global optima.Advances in Neural Information Processing Systems, 32, 2019

Reference 7

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Observation 4e666946-017d-4aa9-9fae-6d91cb052e49 · outbound

This paper cites Finite-time analysis of natural actor-critic for pomdps.SIAM Journal on Mathematics of Data Science, 6(4):869–896, 2024.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Finite-time analysis of natural actor-critic for pomdps.SIAM Journal on Mathematics of Data Science, 6(4):869–896, 2024

Reference 8

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Observation 9931cf42-4d03-4e54-9cfa-a5e3afdca4e1 · outbound

This paper cites On the statistical benefits of temporal difference learning.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency On the statistical benefits of temporal difference learning

Reference 9

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Observation 1fb09a15-45b7-4546-9818-1a8d434710b8 · outbound

This paper cites Exploring the use of synthetic gradients for distributed deep learning across cloud and edge resources.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Exploring the use of synthetic gradients for distributed deep learning across cloud and edge resources

Reference 10

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Observation f7b35fdb-5994-4763-9d5d-50df6f2be04e · outbound

This paper cites The surprising efficiency of temporal difference learning for rare event prediction.Advances in Neural Information Processing Systems, 37:81257–81286, 2024.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency The surprising efficiency of temporal difference learning for rare event prediction.Advances in Neural Information Processing Systems, 37:81257–81286, 2024

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 8b7880dc-d119-4a97-acb9-d861d1fa3887 · outbound

This paper cites Learning phrase representations using rnn encoder–decoder for statistical machine translation.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Learning phrase representations using rnn encoder–decoder for statistical machine translation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation afe1443e-695b-405d-9242-39e0056c173f · outbound

This paper cites Sobolev training for neural networks.Advances in neural information processing systems, 30, 2017.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Sobolev training for neural networks.Advances in neural information processing systems, 30, 2017

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation f56a4204-7da2-49c1-ab75-9c4426512cf8 · outbound

This paper cites Understanding synthetic gradients and decoupled neural interfaces.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Understanding synthetic gradients and decoupled neural interfaces

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation d7478585-a2d9-4298-9ede-015c1326e09f · outbound

This paper cites MIT press, 2005.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency MIT press, 2005

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 7169e20a-e175-4cd3-90a5-64e7adce1fbb · outbound

This paper cites Vectorized instructive signals in cortical dendrites.Nature, 652(8112):1254–1263, 2026.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Vectorized instructive signals in cortical dendrites.Nature, 652(8112):1254–1263, 2026

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation f0e1280f-8018-458e-ab89-789ea681b09b · outbound

This paper cites Optimality of lstd and its relation to mc.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Optimality of lstd and its relation to mc

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 3526aca3-e792-44f9-b3bf-6353b1bd83df · outbound

This paper cites Deep residual learning for image recognition.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Deep residual learning for image recognition

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 0c2302f2-dc68-4ade-849d-12d2aa0979b9 · outbound

This paper cites The Forward-Forward Algorithm: Some Preliminary Investigations.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency The Forward-Forward Algorithm: Some Preliminary Investigations

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-29T10:23:18.205839Z

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Observation e189f67a-dc0e-4d41-b83f-2ac89e1477e1 · outbound

This paper cites Empirical bayes transductive meta-learning with synthetic gradients.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Empirical bayes transductive meta-learning with synthetic gradients

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 12f4cd14-ce90-47b9-9600-807d630b9437 · outbound

This paper cites Decoupled parallel backpropagation with convergence guarantee.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Decoupled parallel backpropagation with convergence guarantee

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation daa78dc1-c1df-4430-be02-fe3650e10223 · outbound

This paper cites Decoupled neural interfaces using synthetic gradients.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Decoupled neural interfaces using synthetic gradients

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 87896304-72a0-4135-a546-9e9689d16775 · outbound

This paper cites Ada-gp: Accelerating dnn training by adaptive gradient prediction.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Ada-gp: Accelerating dnn training by adaptive gradient prediction

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation ef9d59d0-897e-4caa-939a-bfb7409732ae · outbound

This paper cites Synaptic plasticity dynamics for deep continuous local learning (decolle).Frontiers in Neuroscience, 14:424, 2020.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Synaptic plasticity dynamics for deep continuous local learning (decolle).Frontiers in Neuroscience, 14:424, 2020

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation edbda0ca-cc08-4cb3-82ff-60d4df9f78f5 · outbound

This paper cites The fusiform face area: a module in human extrastriate cortex specialized for face perception.Journal of neuroscience, 17(11):4302–4311, 1997.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency The fusiform face area: a module in human extrastriate cortex specialized for face perception.Journal of neuroscience, 17(11):4302–4311, 1997

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 9dc2d7b2-e829-4a1a-bbc7-1c5fd78cd982 · outbound

This paper cites Transformers are rnns: Fast autoregressive transformers with linear attention.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Transformers are rnns: Fast autoregressive transformers with linear attention

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation c80e0679-a23a-4bfc-8dc0-ecc226b5cb3e · outbound

This paper cites Bias-variance error bounds for temporal difference updates.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Bias-variance error bounds for temporal difference updates

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation b8fb8abf-30b4-4773-8677-bd7bbb35cd9f · outbound

This paper cites Is temporal difference learning optimal? an instance-dependent analysis.SIAM Journal on Mathematics of Data Science, 3(4):1013–1040, 2021.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Is temporal difference learning optimal? an instance-dependent analysis.SIAM Journal on Mathematics of Data Science, 3(4):1013–1040, 2021

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation b253d734-17c4-4038-bc90-95d487991696 · outbound

This paper cites Presynaptic release probability influences the locus of long-term potentiation.Nature, 360(6399):70–73, 1992.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Presynaptic release probability influences the locus of long-term potentiation.Nature, 360(6399):70–73, 1992

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 4d0972ee-8e2f-49c3-8ef5-a66ec171886c · outbound

This paper cites Finite-sample analysis of lstd.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Finite-sample analysis of lstd

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 52eac269-ad1f-4950-b949-2574cd652bff · outbound

This paper cites Deep learning.nature, 521(7553):436–444, 2015.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Deep learning.nature, 521(7553):436–444, 2015

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 69075038-675e-4eca-a601-f7a80e6edbd8 · outbound

This paper cites Backpropagation applied to handwritten zip code recognition.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Backpropagation applied to handwritten zip code recognition

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 56d4ebe0-5c53-4561-b1ab-e294359d4cd9 · outbound

This paper cites Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 2002.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Gradient-based learning applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 2002

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 02d24d12-e622-4765-b2ac-44ee208aafe6 · outbound

This paper cites Difference target propagation.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Difference target propagation

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

Source-reported events for the cited work

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Observation a6ceb875-20b3-4e71-876e-dbc730ed0832 · outbound

This paper cites High-probability sample complexities for policy evaluation with linear function approximation.IEEE transactions on information theory, 70(8):5969–5999, 2024.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency High-probability sample complexities for policy evaluation with linear function approximation.IEEE transactions on information theory, 70(8):5969–5999, 2024

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 6004b075-cadb-4e14-8e30-033595e970f1 · outbound

This paper cites Accelerated and instance-optimal policy evalua- tion with linear function approximation.SIAM Journal on Mathematics of Data Science, 5(1):174– 200, 2023.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Accelerated and instance-optimal policy evalua- tion with linear function approximation.SIAM Journal on Mathematics of Data Science, 5(1):174– 200, 2023

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 5c37916d-e5c9-466e-9f6d-e76fd837018f · outbound

This paper cites Random synap- tic feedback weights support error backpropagation for deep learning.Nature communications, 7(1):13276, 2016.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Random synap- tic feedback weights support error backpropagation for deep learning.Nature communications, 7(1):13276, 2016

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 722611e9-8ed6-4555-b2a6-0ae618aad423 · outbound

This paper cites Taylor expansion of the accumulated rounding error.BIT, 16(2):146–160, June 1976.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Taylor expansion of the accumulated rounding error.BIT, 16(2):146–160, June 1976

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 853482b6-d20a-4aba-877a-eadb237a6e37 · outbound

This paper cites Context-dependent computation by recurrent dynamics in prefrontal cortex.nature, 503(7474):78–84, 2013.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Context-dependent computation by recurrent dynamics in prefrontal cortex.nature, 503(7474):78–84, 2013

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 9459f545-5977-40c9-94ed-1cafb34fff53 · outbound

This paper cites Synthetic gradient methods with virtual forward-backward networks.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Synthetic gradient methods with virtual forward-backward networks

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 9b2acd08-8afb-46b0-95d8-4d2789d0d7b6 · outbound

This paper cites Popgym: Benchmarking partially observable reinforcement learning.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Popgym: Benchmarking partially observable reinforcement learning

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation d73e9a98-a8c6-4c33-aa6a-0804916849c4 · outbound

This paper cites Feed-forward on-edge fine- tuning using static synthetic gradient modules.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Feed-forward on-edge fine- tuning using static synthetic gradient modules

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 70a96700-f120-4854-9431-7c0f985269d4 · outbound

This paper cites A mesoscale connectome of the mouse brain.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency A mesoscale connectome of the mouse brain

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 5449f1a3-2199-4d0a-a756-30fd8fa60571 · outbound

This paper cites Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice.Nature neuroscience, 18(12):1798–1803, 2015.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice.Nature neuroscience, 18(12):1798–1803, 2015

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 6370902e-987a-4e60-be1c-ea5753e0de1d · outbound

This paper cites Neural variability and sampling-based probabilistic representations in the visual cortex.Neuron, 92(2):530–543, 2016.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Neural variability and sampling-based probabilistic representations in the visual cortex.Neuron, 92(2):530–543, 2016

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation e651badd-0d44-476f-baf9-4362a2067b95 · outbound

This paper cites Cortico-cerebellar networks as decoupling neural interfaces.Advances in neural information processing systems, 34:7745–7759, 2021.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Cortico-cerebellar networks as decoupling neural interfaces.Advances in neural information processing systems, 34:7745–7759, 2021

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 2ee06a01-1032-4b44-80b5-4c507934a407 · outbound

This paper cites BP($\mathbf{\lambda}$): Online learning via synthetic gradients.Transactions on Machine Learning Research, 2024.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency BP($\mathbf{\lambda}$): Online learning via synthetic gradients.Transactions on Machine Learning Research, 2024

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation aba9870a-a945-4f08-99c5-e4e03fd81389 · outbound

This paper cites Learning representations by back-propagating errors.nature, 323(6088):533–536, 1986.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Learning representations by back-propagating errors.nature, 323(6088):533–536, 1986

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 98ac0273-18cb-4856-b22a-6457b5459ce5 · outbound

This paper cites Equilibrium propagation: Bridging the gap between energy- based models and backpropagation.Frontiers in computational neuroscience, 11:24, 2017.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Equilibrium propagation: Bridging the gap between energy- based models and backpropagation.Frontiers in computational neuroscience, 11:24, 2017

Reference 49

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 5b76064f-4f8c-4625-976f-609ab8d4b25a · outbound

This paper cites Long short-term memory.Neural Comput, 9(8):1735– 1780, 1997.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Long short-term memory.Neural Comput, 9(8):1735– 1780, 1997

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation c72bddaa-30c5-43d1-8b03-252badc0cd69 · outbound

This paper cites A neural substrate of prediction and reward.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency A neural substrate of prediction and reward

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 742dfcd1-0ec7-4756-bafc-3ef9cbbad596 · outbound

This paper cites Learning by directional gradient descent.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Learning by directional gradient descent

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 0724d9bf-eaa2-4bfd-adbd-a51af32e3e5a · outbound

This paper cites The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps.Journal of neuroscience, 13(1):334–350, 1993.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps.Journal of neuroscience, 13(1):334–350, 1993

Reference 53

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 846cf88a-d6c0-4d56-a913-8e0054862d07 · outbound

This paper cites Highly nonrandom features of synaptic connectivity in local cortical circuits.PLoS biology, 3(3):e68, 2005.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Highly nonrandom features of synaptic connectivity in local cortical circuits.PLoS biology, 3(3):e68, 2005

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 139a603c-b0d7-4871-a156-6907f983b5f1 · outbound

This paper cites Finite-time analysis of adaptive temporal difference learning with deep neural networks.Advances in Neural Information Processing Systems, 35:19592–19604, 2022.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Finite-time analysis of adaptive temporal difference learning with deep neural networks.Advances in Neural Information Processing Systems, 35:19592–19604, 2022

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 575304b0-58fb-4ef5-a30d-56f7c1907b7a · outbound

This paper cites Learning to predict by the methods of temporal differences.Machine learning, 3(1):9–44, 1988.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Learning to predict by the methods of temporal differences.Machine learning, 3(1):9–44, 1988

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation cc734b3d-f2a3-46f4-ba53-86ec0ba387e7 · outbound

This paper cites Policy gradient methods for reinforcement learning with function approximation.Advances in neural information processing systems, 12, 1999.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Policy gradient methods for reinforcement learning with function approximation.Advances in neural information processing systems, 12, 1999

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 6ecd5956-17b1-483d-a05a-d4a3bbd0008f · outbound

This paper cites Shared and distinct transcriptomic cell types across neocortical areas.Nature, 563(7729):72–78, 2018.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Shared and distinct transcriptomic cell types across neocortical areas.Nature, 563(7729):72–78, 2018

Reference 58

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation b380d6c8-d7ab-462c-bc39-67df179d3d1d · outbound

This paper cites CRC press, 2025.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency CRC press, 2025

Reference 59

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation 20770f34-6605-4b5a-af52-17a0077fcb5f · outbound

This paper cites Applications of advances in nonlinear sensitivity analysis.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Applications of advances in nonlinear sensitivity analysis

Reference 60

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation f8bc2b22-f279-445e-9c10-d5d3b1d330bc · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforcement learning.Machine learning, 8(3):229–256, 1992.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Simple statistical gradient-following algorithms for connectionist reinforcement learning.Machine learning, 8(3):229–256, 1992

Reference 61

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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Observation f0d67276-82ef-4543-9fcc-336ff94c2a83 · outbound

This paper cites an unresolved cited work.

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency Unresolved cited work

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-29T10:18:54.610927Z

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