Mathematical analysis of the gradients in deep learning | Paper Citation Record

Observation c4197251-d85e-4647-b255-13d763e15d4b · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Mathematical analysis of the gradients in deep learning TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.075137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2cd1384d-7819-49a9-8206-f4fb72d35904 · outbound

This paper cites Learning Theory from First Principles.

Mathematical analysis of the gradients in deep learning Learning Theory from First Principles

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.079600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07e570e5-81b0-4bba-81a4-327baf331d8f · outbound

This paper cites J., and Zhang, Y.

Mathematical analysis of the gradients in deep learning J., and Zhang, Y

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.236616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0bd24e4a-9259-4986-82f1-e79ec3902d01 · outbound

This paper cites On the complexity of nonsmooth automatic differentiation.

Mathematical analysis of the gradients in deep learning On the complexity of nonsmooth automatic differentiation

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.897472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 386b18a8-919b-4bbd-98d3-137ca1f53ccf · outbound

This paper cites The /suppress lojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems.

Mathematical analysis of the gradients in deep learning The /suppress lojasiewicz inequality for nonsmooth subanalytic functions with applications to subgradient dynamical systems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.224193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38756945-2ca7-4bfe-a275-52d5d8f00612 · outbound

This paper cites A mathematical model for automatic differentiation in machine learning.

Mathematical analysis of the gradients in deep learning A mathematical model for automatic differentiation in machine learning

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.881349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b8e90943-66f2-42a2-9cd8-2fee265ddf93 · outbound

This paper cites Conservative set valued ﬁelds, automatic diﬀerentiation, stochastic gradient methods and deep learning.

Mathematical analysis of the gradients in deep learning Conservative set valued ﬁelds, automatic diﬀerentiation, stochastic gradient methods and deep learning

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.212131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eec2a3c5-b9d9-47db-bffd-d581aebd50e8 · outbound

This paper cites Diﬀerentiating nonsmooth solutions to parametric monotone inclusion problems.

Mathematical analysis of the gradients in deep learning Diﬀerentiating nonsmooth solutions to parametric monotone inclusion problems

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.199136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7de81cdc-eaaf-45fa-adcf-190e8cac1a6e · outbound

This paper cites Automatic differentiation of nonsmooth iterative algorithms.

Mathematical analysis of the gradients in deep learning Automatic differentiation of nonsmooth iterative algorithms

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.865319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 981b5c36-5e7a-45cf-9ba0-d2ec49606f72 · outbound

This paper cites A proof of conver- gence for gradient descent in the training of artiﬁcial neur al networks for constant target functions.

Mathematical analysis of the gradients in deep learning A proof of conver- gence for gradient descent in the training of artiﬁcial neur al networks for constant target functions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.186067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67f90682-0a80-4db7-96cc-8f883e6cd23d · outbound

This paper cites Non-convergence of stochastic gradient descent in the training of deep neural networks.

Mathematical analysis of the gradients in deep learning Non-convergence of stochastic gradient descent in the training of deep neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.173592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 96174e1b-66a5-43e1-9f10-caec25de07ed · outbound

This paper cites Landscape analysis for shallow neural networks: complete classiﬁcation of critical point s for aﬃne target functions.

Mathematical analysis of the gradients in deep learning Landscape analysis for shallow neural networks: complete classiﬁcation of critical point s for aﬃne target functions

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.161634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 298ebf6c-13f7-4d44-af12-ecf75480c4d6 · outbound

This paper cites On the mathematical foundations of learning.

Mathematical analysis of the gradients in deep learning On the mathematical foundations of learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.149980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c4d9f116-ace4-4096-9460-39d1965ca551 · outbound

This paper cites Conservative and semismooth derivatives are equiv- alent for semialgebraic maps.

Mathematical analysis of the gradients in deep learning Conservative and semismooth derivatives are equiv- alent for semialgebraic maps

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.137872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4dbe2bae-0527-4de1-a815-73e8aa838dae · outbound

This paper cites an unresolved cited work.

Mathematical analysis of the gradients in deep learning Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:10:56.125431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f9aaa7f2-7500-4db6-b94c-683116f86386 · outbound

This paper cites Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates.

Mathematical analysis of the gradients in deep learning Non-convergence of Adam and other adaptive stochastic gradient descent optimization methods for non-vanishing learning rates

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.137201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4232203-5f03-46fa-aed2-d4310676e10d · outbound

This paper cites Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes.

Mathematical analysis of the gradients in deep learning Convergence of stochastic gradient descent schemes for Lojasiewicz-landscapes

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.140914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a76bc57-5b4e-49be-acea-7ad9090d1fc4 · outbound

This paper cites Adam-family Methods with Decoupled Weight Decay in Deep Learning.

Mathematical analysis of the gradients in deep learning Adam-family Methods with Decoupled Weight Decay in Deep Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.144839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45dc87df-2ace-4e74-80f8-32e5a802df13 · outbound

This paper cites Sub-Optimal Local Minima Exist for Neural Networks with Almost All Non-Linear Activations.

Mathematical analysis of the gradients in deep learning Sub-Optimal Local Minima Exist for Neural Networks with Almost All Non-Linear Activations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.149307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 237b81ec-86ee-4ab1-a3c2-a32df0fd1f55 · outbound

This paper cites Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't.

Mathematical analysis of the gradients in deep learning Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.153180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b27e058-fa9f-4ea2-a1b0-eacf244f4589 · outbound

This paper cites an unresolved cited work.

Mathematical analysis of the gradients in deep learning Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:10:56.112630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d3d9e9cc-cec9-4211-a353-d84650cddd89 · outbound

This paper cites Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks.

Mathematical analysis of the gradients in deep learning Blow up phenomena for gradient descent optimization methods in the training of artificial neural networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.160595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01304cc6-e5b7-404b-a619-5801727b4eb9 · outbound

This paper cites Handbook of Convergence Theorems for (Stochastic) Gradient Methods.

Mathematical analysis of the gradients in deep learning Handbook of Convergence Theorems for (Stochastic) Gradient Methods

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.164695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34356b5b-423a-401e-be69-5d66b7e03ae3 · outbound

This paper cites Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$.

Mathematical analysis of the gradients in deep learning Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.769225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7d58f89a-2b2c-4fde-92c0-aca6e4701d44 · outbound

This paper cites Non-convergence to global minimizers in data driven supervised deep learning: Adam and stochastic gradient descent optimization provably fail to converge to global minimizers in the training of deep neural networks with ReLU activation.

Mathematical analysis of the gradients in deep learning Non-convergence to global minimizers in data driven supervised deep learning: Adam and stochastic gradient descent optimization provably fail to converge to global minimizers in the training of deep neural networks with ReLU activation

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.752584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 65143bab-0e87-4d47-b350-06063f650409 · outbound

This paper cites Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions.

Mathematical analysis of the gradients in deep learning Convergence proof for stochastic gradient descent in the training of deep neural networks with ReLU activation for constant target functions

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.737848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation af440117-6d22-4089-a733-751858c2023f · outbound

This paper cites Convergence to good non-optimal critical points in the training of neural networks: Gradient descent optimization with one random initialization overcomes all bad non-global local minima with high probability.

Mathematical analysis of the gradients in deep learning Convergence to good non-optimal critical points in the training of neural networks: Gradient descent optimization with one random initialization overcomes all bad non-global local minima with high probability

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.721846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23c3f78c-f589-4871-bc95-8213a9807e5f · outbound

This paper cites Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory.

Mathematical analysis of the gradients in deep learning Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.184143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56865051-e276-4cac-8445-25a6fb17bf8d · outbound

This paper cites On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks.

Mathematical analysis of the gradients in deep learning On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.693711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 77ca3d51-68d6-42d2-9cb2-b1b4cb6e0829 · outbound

This paper cites On the existence of global minima and convergence analyses for gradient descent methods in the training of dee p neural networks.

Mathematical analysis of the gradients in deep learning On the existence of global minima and convergence analyses for gradient descent methods in the training of dee p neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.101057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 730c4d9f-1152-4a86-943e-3dc45a151caa · outbound

This paper cites A proof of convergence for stochastic gradient descent in the training of artiﬁcial neural networks with ReLU activation for constant target functions.

Mathematical analysis of the gradients in deep learning A proof of convergence for stochastic gradient descent in the training of artiﬁcial neural networks with ReLU activation for constant target functions

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.089558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4766fd9e-0563-4699-8fc0-cedf26672fda · outbound

This paper cites Convergence analysis for gradient ﬂows in the training of artiﬁcial neural networks with ReLU activation.

Mathematical analysis of the gradients in deep learning Convergence analysis for gradient ﬂows in the training of artiﬁcial neural networks with ReLU activation

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.076929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4eedb1c9-c93b-4322-95cf-0880eaeeb427 · outbound

This paper cites Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks.

Mathematical analysis of the gradients in deep learning Non-convergence to global minimizers for Adam and stochastic gradient descent optimization and constructions of local minimizers in the training of artificial neural networks

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.203554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69f09e38-d5c9-4b34-a18e-2ce0ddbe999e · outbound

This paper cites M., and Lee, J.

Mathematical analysis of the gradients in deep learning M., and Lee, J

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.064425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 34629051-9f5a-49aa-8680-06075f1cd961 · outbound

This paper cites Does a sparse ReLU network training problem always admit an optimum?.

Mathematical analysis of the gradients in deep learning Does a sparse ReLU network training problem always admit an optimum?

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T14:10:55.664692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fee10728-e53e-4f3a-9771-574812d91ad8 · outbound

This paper cites On correctness of automatic diﬀerentiation for non-diﬀerentiable functions.

Mathematical analysis of the gradients in deep learning On correctness of automatic diﬀerentiation for non-diﬀerentiable functions

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.052008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 59c9386c-576d-46db-b028-18257b59b67d · outbound

This paper cites S., and Tian, T.

Mathematical analysis of the gradients in deep learning S., and Tian, T

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.040243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a2a99617-256c-44a6-ac47-bcdbedb122a8 · outbound

This paper cites an unresolved cited work.

Mathematical analysis of the gradients in deep learning Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:10:56.028119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d9636a0d-b071-4e95-835d-0f5b0a7940b7 · outbound

This paper cites Introductory lectures on convex optimization , vol.

Mathematical analysis of the gradients in deep learning Introductory lectures on convex optimization , vol

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.015946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e73bedc5-766f-4a3e-9074-5399620c1a26 · outbound

This paper cites Automatic diﬀerentiation in PyTorch.

Mathematical analysis of the gradients in deep learning Automatic diﬀerentiation in PyTorch

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:56.003282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18600bc8-8a6f-42d7-acfd-8744b14e2c17 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Mathematical analysis of the gradients in deep learning PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.234656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3d61f3a-0a6d-45ac-8add-0c11fabcb32f · outbound

This paper cites Conservative parametric optimality and the ridge method for tame min-max problems.

Mathematical analysis of the gradients in deep learning Conservative parametric optimality and the ridge method for tame min-max problems

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:55.991827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c5e849ba-d9cf-4a7e-81b3-1e073f726c5d · outbound

This paper cites Topological properties of the set of functions generated by neural networks of ﬁxed size.

Mathematical analysis of the gradients in deep learning Topological properties of the set of functions generated by neural networks of ﬁxed size

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:55.980143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d8bd2e87-a613-4e51-8aa5-9ae1eee4fbcc · outbound

This paper cites Mathematical theory of deep learning.

Mathematical analysis of the gradients in deep learning Mathematical theory of deep learning

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.246396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04fab9aa-cc6d-4de7-928d-caf7c839d88a · outbound

This paper cites On the Convergence of Adam and Beyond.

Mathematical analysis of the gradients in deep learning On the Convergence of Adam and Beyond

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.250440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6596ba1a-4111-4321-a318-7bb990f3c059 · outbound

This paper cites T., and Wets, R.

Mathematical analysis of the gradients in deep learning T., and Wets, R

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:55.967280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 587b5c32-04c1-459a-b2e3-85e6a28f2179 · outbound

This paper cites An overview of gradient descent optimization algorithms.

Mathematical analysis of the gradients in deep learning An overview of gradient descent optimization algorithms

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.258087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e73331b-66c2-4d4e-a9b9-b4224f0c7db5 · outbound

This paper cites Spurious Local Minima are Common in Two-Layer ReLU Neural Networks.

Mathematical analysis of the gradients in deep learning Spurious Local Minima are Common in Two-Layer ReLU Neural Networks

Reference 48

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.559559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa99968d-afbc-48fa-8857-030d520112f9 · outbound

This paper cites The gradient’s limit of a deﬁnable family of functions is a co nservative set-valued ﬁeld.

Mathematical analysis of the gradients in deep learning The gradient’s limit of a deﬁnable family of functions is a co nservative set-valued ﬁeld

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.265908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72b69994-0986-4f7c-8459-8d7434480bc8 · outbound

This paper cites an unresolved cited work.

Mathematical analysis of the gradients in deep learning Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:10:55.956109Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 26fff830-1fc2-4193-a377-d46ca9e8f0c2 · outbound

This paper cites Optimization for deep learning: theory and algorithms.

Mathematical analysis of the gradients in deep learning Optimization for deep learning: theory and algorithms

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.368199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f61b343a-98b8-488c-98a2-f0f5034bd062 · outbound

This paper cites Local minima in training of neural networks.

Mathematical analysis of the gradients in deep learning Local minima in training of neural networks

Reference 52

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.470243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2e04d224-a0a5-4b26-8625-9e01e5213426 · outbound

This paper cites S., and Bruna, J.

Mathematical analysis of the gradients in deep learning S., and Bruna, J

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:55.944838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c50e8cdc-96f1-4cd6-b8f4-348c3b7ac367 · outbound

This paper cites Approximation and Gradient Descent Training with Neural Networks.

Mathematical analysis of the gradients in deep learning Approximation and Gradient Descent Training with Neural Networks

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-10T14:10:55.453704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b09235ce-e803-4d66-828b-d312a10535e8 · outbound

This paper cites Approximation results for gradient ﬂow trained neural netw orks.

Mathematical analysis of the gradients in deep learning Approximation results for gradient ﬂow trained neural netw orks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:10:55.933568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 18d67b15-0b65-4b23-84f4-71b7548b57a3 · outbound

This paper cites Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees.

Mathematical analysis of the gradients in deep learning Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.387153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 409d5b73-5735-41b6-b555-24d8cf6ce3d2 · outbound

This paper cites Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization.

Mathematical analysis of the gradients in deep learning Stochastic Subgradient Methods with Guaranteed Global Stability in Nonsmooth Nonconvex Optimization

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.390854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02b81597-8b7b-4ff3-98c1-b4846b707d05 · outbound

This paper cites an unresolved cited work.

Mathematical analysis of the gradients in deep learning Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:10:55.922227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers