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Paper Citation Record · LEDGER

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases

As of 13 August 2026, this Paper Citation Record lists 100 of 300 outbound references and 0 inbound Pith citation observations for arXiv:2411.09827.

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

pith.paper-citation-record.v1
2411.09827 v1

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measured 100 of 300 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

100 of 300 outbound references displayed

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Outbound references

Observation e11e364b-36a9-4b7c-b3ec-1817e17a14b5 · outbound

This paper cites Convolutional neural networks for speech recognition.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Convolutional neural networks for speech recognition

Reference 1

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This paper cites End-to-end en- vironmental sound classification using a 1d convolutional neural network.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases End-to-end en- vironmental sound classification using a 1d convolutional neural network

Reference 2

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Applications of the generalized fourier transform in numerical linear algebra

Reference 3

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This paper cites Noether networks: meta-learning useful conserved quantities.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Noether networks: meta-learning useful conserved quantities

Reference 4

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This paper cites Statistical applications for equivariant matrices.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Statistical applications for equivariant matrices

Reference 5

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Deep scattering spectrum

Reference 6

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This paper cites Unitary evolution recurrent neural networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Unitary evolution recurrent neural networks

Reference 7

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This paper cites Layer Normalization.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Layer Normalization

Reference 8

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This paper cites The UEA multivariate time series classification archive, 2018.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases The UEA multivariate time series classification archive, 2018

Reference 9

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This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Neural Machine Translation by Jointly Learning to Align and Translate

Reference 10

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This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 11

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This paper cites Trellis Networks for Sequence Modeling.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Trellis Networks for Sequence Modeling

Reference 12

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This paper cites Mad Max: Affine Spline Insights into Deep Learning.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Mad Max: Affine Spline Insights into Deep Learning

Reference 13

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases The quickhull algo- rithm for convex hulls

Reference 14

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Ai in healthcare: Ethical and privacy challenges

Reference 15

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This paper cites Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Reference 16

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases B-spline {cnn}s on lie groups

Reference 17

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Roto-translation covariant convolutional networks for medical image analysis

Reference 18

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space

Reference 19

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Attention Augmented Convolutional Networks

Reference 20

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Understanding and simplifying one-shot architecture search

Reference 21

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Learning long-term depen- BIBLIOGRAPHY 193 dencies with gradient descent is difficult

Reference 22

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 23

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases A Comprehensive Survey on Hardware-Aware Neural Architecture Search

Reference 24

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Learning invariances in neural networks from training data

Reference 25

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Stabilizing DARTS with Amended Gradient Estimation on Architectural Parameters

Reference 26

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Spectrotemporal resolution tradeoff in auditory processing as revealed by human auditory brainstem re- sponses and psychophysical indices

Reference 27

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Recognition-by-components: a theory of human image under- standing

Reference 28

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Experiment tracking with weights and biases, 2020

Reference 29

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases The role of temporal structure in human vision

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Lorentz group equivariant neural network for particle physics

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This paper cites SMASH: One-Shot Model Architecture Search through HyperNetworks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases SMASH: One-Shot Model Architecture Search through HyperNetworks

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Recognition by children of inverted photos of faces

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Language models are few-shot learners

Reference 34

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Recognizing objects and faces

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Invariant scattering convolution networks

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Observation fc04c7e3-e568-4c48-a9d7-6fe1ad5d1903 · outbound

This paper cites ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

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Observation f95f3091-a08d-45c5-85ae-a4541cf293f9 · outbound

This paper cites Once-for-All: Train One Network and Specialize it for Efficient Deployment.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Once-for-All: Train One Network and Specialize it for Efficient Deployment

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Observation b9b928a1-4036-4af0-ba1c-c2cfd9299c01 · outbound

This paper cites GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

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Observation b9dc201d-6484-4033-87a1-85bb802ac868 · outbound

This paper cites The concept of group and the theory of perception.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases The concept of group and the theory of perception

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Observation f90e7d69-3b53-4e81-90e6-8de36655b92c · outbound

This paper cites A program to build e (n)- equivariant steerable cnns.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases A program to build e (n)- equivariant steerable cnns

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Observation 7faac380-5a83-43b0-896c-265d1f182091 · outbound

This paper cites AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

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Observation 6f8fa819-2e6c-472f-b64e-0cf68ca3aa56 · outbound

This paper cites Principled weight initialization for hypernetworks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Principled weight initialization for hypernetworks

Reference 43

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Observation f8b5fd7e-62f3-4f50-b884-a7eedf4e92c0 · outbound

This paper cites Di- lated recurrent neural networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Di- lated recurrent neural networks

Reference 44

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Observation ca080954-e784-4a7f-8749-7a6becb1bca2 · outbound

This paper cites Learning Augmentation Distributions using Transformed Risk Minimization.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Learning Augmentation Distributions using Transformed Risk Minimization

Reference 45

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Observation c9d08436-faf7-4d7c-a020-320735762b0d · outbound

This paper cites Recurrent neural networks for multivariate time series with missing values.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Recurrent neural networks for multivariate time series with missing values

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source=pdf_text observed=2026-08-12T20:23:10.306477Z digest=sha256:0efef77036cbe5ceafa016984757424b4b67d24e1256f531d7725030d4d47966

Observation 03d36327-8a89-43c2-8a7e-83c3d937ee74 · outbound

This paper cites Linear system theory and design.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Linear system theory and design

Reference 47

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source=pdf_text observed=2026-08-12T20:23:10.310027Z digest=sha256:92718f3de0c6a28f668c38798f1fa804fbadfff6f24379b808e0ff07afc37120

Observation 946d7d2f-108b-48ff-925f-86b2934ea256 · outbound

This paper cites A group-theoretic framework for data augmentation.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases A group-theoretic framework for data augmentation

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source=pdf_text observed=2026-08-12T20:23:10.314337Z digest=sha256:3f346cbfd7052921f01102981e2b56eafb016d2a9f8904a3ee8f0ae0ffdca391

Observation b10bd0ad-467a-4600-be74-fca6317d0699 · outbound

This paper cites Stabilizing differentiable architecture search via perturbation-based regularization.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Stabilizing differentiable architecture search via perturbation-based regularization

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Observation e6d06fa6-1a48-4091-b45e-1a6732d267e7 · outbound

This paper cites Progressive darts: Bridging the op- timization gap for nas in the wild.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Progressive darts: Bridging the op- timization gap for nas in the wild

Reference 50

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source=pdf_text observed=2026-08-12T20:23:10.322648Z digest=sha256:44d56107eceab4944212b706e47906e71a6aac626a91eb2046f8eead88df8a47

Observation e4c3c6e6-541d-408d-8c1e-7082deea643e · outbound

This paper cites Graph-based global reasoning networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Graph-based global reasoning networks

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Observation 0472f6a8-3af2-487e-97ac-98bae4a8e198 · outbound

This paper cites Long Short-Term Memory-Networks for Machine Reading.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Long Short-Term Memory-Networks for Machine Reading

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Observation 99c56b69-4f1e-4edf-b30a-17205c7d28fd · outbound

This paper cites RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

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Observation 0b164171-9515-43a4-aedf-4000b7240ff0 · outbound

This paper cites Some experiments on the recognition of speech, with one and with two ears.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Some experiments on the recognition of speech, with one and with two ears

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Observation 61108a06-873a-4511-bc9b-273a2f1ec6b5 · outbound

This paper cites Parallelizing Legendre Memory Unit Training.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Parallelizing Legendre Memory Unit Training

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source=pdf_text observed=2026-08-12T20:23:10.343496Z digest=sha256:e2829a5e3dcdc1e7712afbab0423f01e61911ebaa9f3dba8ad05185b55e0bd21

Observation 2c180617-0208-4fad-9993-e06e4e12776c · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

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Observation 3ee003e8-7265-4653-a6ad-85376ba81c18 · outbound

This paper cites Automatic tagging using deep convolutional neural networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Automatic tagging using deep convolutional neural networks

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Observation a1cc09c7-763d-41a6-9b77-e5b766b7a5fe · outbound

This paper cites Xception: Deep learning with depthwise separable convolu- tions.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Xception: Deep learning with depthwise separable convolu- tions

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Observation b07b342c-1cb2-4ae3-bffc-a7f7a180558b · outbound

This paper cites Rethinking Attention with Performers.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Rethinking Attention with Performers

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source=pdf_text observed=2026-08-12T20:23:10.360056Z digest=sha256:a0ba29e09aa8e7328466494b56ca4eb933d567cd92578c859d62e6a47f6b3b91

Observation ffb39d39-ca2c-4139-b428-0fc591d52337 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases PaLM: Scaling Language Modeling with Pathways

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Observation 5c68012d-9973-4249-8a6c-4edcfadbd853 · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

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source=pdf_text observed=2026-08-12T20:23:10.369321Z digest=sha256:a4d4c6df55c3158b202a252fa358d342325ba778d8df614ef5f3853221bda110

Observation 7ba3c284-c5ba-4b6a-a70d-6edc7ac1caaa · outbound

This paper cites DARTS-: Robustly Stepping out of Performance Collapse Without Indicators.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases DARTS-: Robustly Stepping out of Performance Collapse Without Indicators

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source=pdf_text observed=2026-08-12T20:23:10.373217Z digest=sha256:c7a7f4c0a43ea5be6ea0d69f19f4d6cb25f0b2f34a149eb160a5ad894d3ce0c4

Observation 2a479731-90e5-45b3-8a88-aca88b77e431 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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source=pdf_text observed=2026-08-12T20:23:10.377326Z digest=sha256:d36b8ee8016ddcc1fbc205af7bfc509beceebf5b7e57e68653a7e3f37dfe1357

Observation ae880d73-27cc-4c8a-85da-15417e609983 · outbound

This paper cites An analysis of single-layer net- works in unsupervised feature learning.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases An analysis of single-layer net- works in unsupervised feature learning

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source=pdf_text observed=2026-08-12T20:23:10.381782Z digest=sha256:8847e43540aa5e7a4a3804a481402704c6fe087a2d838a93f8b3d046bdd63f09

Observation 30430f16-40b0-41a0-b80c-89218409347d · outbound

This paper cites Group equivariant convolutional networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Group equivariant convolutional networks

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source=pdf_text observed=2026-08-12T20:23:10.385638Z digest=sha256:528c38828dcef4d5676aaf9287f57a47480cd209649609999c67c9aafcc78437

Observation c4c49952-7e89-49b5-b3d7-c36a7183fb27 · outbound

This paper cites Steerable CNNs.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Steerable CNNs

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source=pdf_text observed=2026-08-12T20:23:10.389534Z digest=sha256:6de7162db64d3ce3450cc606441b4d1c65e9cb19546eb6d72ccc3ff59a8ab13f

Observation 6771c2d1-9075-4bd8-8d39-4c8f8ba57742 · outbound

This paper cites Spherical cnns.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Spherical cnns

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Observation 720a63ed-8347-4639-b377-8a6e610996a1 · outbound

This paper cites A general theory of equivariant cnns on homogeneous spaces.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases A general theory of equivariant cnns on homogeneous spaces

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source=pdf_text observed=2026-08-12T20:23:10.398182Z digest=sha256:9accfe716cb249c2e73a117513ab73a91cb10777dbc401d46b5280c1cc91c42c

Observation 93ddeaa5-588a-4f53-9048-85818da78118 · outbound

This paper cites Gauge Equivariant Convolutional Networks and the Icosahedral CNN.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Gauge Equivariant Convolutional Networks and the Icosahedral CNN

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source=pdf_text observed=2026-08-12T20:23:10.402833Z digest=sha256:b3ecc13f64fefc156e28212adac3a9cce17d7d2341a82f74d5b85fd2b7eb3a9e

Observation 23355ce8-3027-4611-b0ef-11041b49178f · outbound

This paper cites Very Deep Convolutional Networks for Text Classification.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Very Deep Convolutional Networks for Text Classification

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source=pdf_text observed=2026-08-12T20:23:10.407162Z digest=sha256:438e73508380d9dc57f7c247898b9d2974aee7926f8e8528aa208dbc64cb001b

Observation 1c766b1e-b57b-4394-8ea1-c6cb6b51f954 · outbound

This paper cites Fast construction of k-nearest neighbor graphs for point clouds.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Fast construction of k-nearest neighbor graphs for point clouds

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source=pdf_text observed=2026-08-12T20:23:10.411545Z digest=sha256:64e74e4ba4464f995b5f8e387ddcf5f23feb17f9997ee0e56f2db20ec36a2c77

Observation 9359e152-c650-4dbf-8c21-35e804b5cab8 · outbound

This paper cites On the relation- ship between self-attention and convolutional layers.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases On the relation- ship between self-attention and convolutional layers

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source=pdf_text observed=2026-08-12T20:23:10.415118Z digest=sha256:9d3fe59dd7655f7c2dbcc32834c5a8313ba99b3fe06bff3855fd475a747ab677

Observation d31bdbf7-7048-4743-840a-e7bc28a75578 · outbound

This paper cites A volumetric method for building complex models from range images.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases A volumetric method for building complex models from range images

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This paper cites Deformable convolutional networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Deformable convolutional networks

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Observation ac1ba052-1ce1-4b6e-a720-3d2ebf6b18d1 · outbound

This paper cites Very deep convo- lutional neural networks for raw waveforms.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Very deep convo- lutional neural networks for raw waveforms

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Observation 3abc047d-f911-48c6-897d-90126e95b2b9 · outbound

This paper cites Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

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This paper cites Picture memory experiments.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Picture memory experiments

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Observation eecd0cd1-8b8d-4901-818d-59902b077f26 · outbound

This paper cites Fundamental papers in wavelet theory.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Fundamental papers in wavelet theory

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The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Unresolved cited work

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Observation b51120c2-2955-4c92-8a4c-b7d616aeafe4 · outbound

This paper cites Language mod- eling with gated convolutional networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Language mod- eling with gated convolutional networks

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Observation 1b09c368-dff0-4a98-8133-098dfddf6a9b · outbound

This paper cites Gru-ode-bayes: Continuous modeling of sporadically-observed time series.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Gru-ode-bayes: Continuous modeling of sporadically-observed time series

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Observation 8da4abb2-dc82-4105-816c-922da1baf066 · outbound

This paper cites DeepSphere: a graph-based spherical CNN.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases DeepSphere: a graph-based spherical CNN

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Observation 48e77e55-3963-4cb4-a99d-5ba90d4e4443 · outbound

This paper cites Au- tomatic symmetry discovery with lie algebra convolutional network.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Au- tomatic symmetry discovery with lie algebra convolutional network

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This paper cites Insect cyborgs: Bio-mimetic feature gen- erators improve ml accuracy on limited data.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Insect cyborgs: Bio-mimetic feature gen- erators improve ml accuracy on limited data

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This paper cites Diffusion models beat gans on image synthesis.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Diffusion models beat gans on image synthesis

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Observation d68eaf9b-05ec-4ad8-b4a8-13c5b2f612fe · outbound

This paper cites Affine Self Convolution.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Affine Self Convolution

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This paper cites Learning to convolve: A generalized weight-tying approach.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Learning to convolve: A generalized weight-tying approach

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This paper cites End-to-end learning for music au- dio.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases End-to-end learning for music au- dio

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This paper cites Exploiting cyclic symmetry in convolutional neural networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Exploiting cyclic symmetry in convolutional neural networks

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This paper cites Searching for a robust neural architecture in four gpu hours.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Searching for a robust neural architecture in four gpu hours

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

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Observation 8d03f604-bdce-4680-92e4-0a16c3f6d6a4 · outbound

This paper cites Brp-nas: Prediction-based nas using gcns.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Brp-nas: Prediction-based nas using gcns

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Observation 463314c0-6771-4624-848b-e026f4c00473 · outbound

This paper cites Abstract algebra, volume 3.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Abstract algebra, volume 3

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This paper cites Generative Models as Distributions of Functions.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Generative Models as Distributions of Functions

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Observation da51decc-f16c-4b1e-9dc2-8288ef4dd2e0 · outbound

This paper cites Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution

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Observation 97399485-0ec5-41ff-956d-8b4179cc6aed · outbound

This paper cites Neural architecture search: A survey.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Neural architecture search: A survey

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Observation bbd51b07-25c0-43b9-b285-cf99fe083ef7 · outbound

This paper cites Lipschitz Recurrent Neural Networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Lipschitz Recurrent Neural Networks

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Observation e5fb6591-b3fe-4634-b3ea-b936ad20b8d7 · outbound

This paper cites Cross-domain 3d equivariant image embeddings.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Cross-domain 3d equivariant image embeddings

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Observation f01c220b-ad08-466c-bc09-4e9cc622f375 · outbound

This paper cites Equivariant multi-view networks.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Equivariant multi-view networks

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Observation dd9c69d1-9696-4ce9-af49-7d65ef1535aa · outbound

This paper cites Spin-weighted spherical cnns.

The Good, The Efficient and the Inductive Biases: Exploring Efficiency in Deep Learning Through the Use of Inductive Biases Spin-weighted spherical cnns

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