Paper Citation Record · LEDGER

The Computational Limits of Deep Learning

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 38 inbound Pith citation observations for arXiv:2007.05558.

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

pith.paper-citation-record.v1
2007.05558 v2

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measured 38 of 38 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 38 of 38 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T20:17:23.290766Z

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Source: arxiv_reference, observed 2026-07-03T15:28:33.717389Z

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Pith citing papers

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Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models cites this paper.

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models The Computational Limits of Deep Learning

Reference 216

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A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties cites this paper.

A Self-Supervised Robotic System for Autonomous Contact-Based Spatial Mapping of Semiconductor Properties The Computational Limits of Deep Learning

Reference 22

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LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models cites this paper.

LP Data Pipeline: Lightweight, Purpose-driven Data Pipeline for Large Language Models The Computational Limits of Deep Learning

Reference 44

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Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy cites this paper.

Language Models in Software Development Tasks: An Experimental Analysis of Energy and Accuracy The Computational Limits of Deep Learning

Reference 28

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Bayesian Data Augmentation and Training for Perception DNN in Autonomous Aerial Vehicles cites this paper.

Bayesian Data Augmentation and Training for Perception DNN in Autonomous Aerial Vehicles The Computational Limits of Deep Learning

Reference 10

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YOLOv11 Optimization for Efficient Resource Utilization cites this paper.

YOLOv11 Optimization for Efficient Resource Utilization The Computational Limits of Deep Learning

Reference 30

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Observation 29f0f6e7-406e-4840-8187-797290de0b93 · inbound

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI cites this paper.

tuGEMM: Area-Power-Efficient Temporal Unary GEMM Architecture for Low-Precision Edge AI The Computational Limits of Deep Learning

Reference 19

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TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering cites this paper.

TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering The Computational Limits of Deep Learning

Reference 18

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Attention Is All You Need For Mixture-of-Depths Routing cites this paper.

Attention Is All You Need For Mixture-of-Depths Routing The Computational Limits of Deep Learning

Reference 16

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Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices cites this paper.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices The Computational Limits of Deep Learning

Reference 129

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Observation 79bba974-e0e5-431f-b7fd-71a4374c9c40 · inbound

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs cites this paper.

PC-MoE: Memory-Efficient and Privacy-Preserving Collaborative Training for Mixture-of-Experts LLMs The Computational Limits of Deep Learning

Reference 33

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A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge cites this paper.

A Layered Self-Supervised Knowledge Distillation Framework for Efficient Multimodal Learning on the Edge The Computational Limits of Deep Learning

Reference 10

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Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation cites this paper.

Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation The Computational Limits of Deep Learning

Reference 1

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What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness cites this paper.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness The Computational Limits of Deep Learning

Reference 143

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The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis cites this paper.

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis The Computational Limits of Deep Learning

Reference 96

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From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems cites this paper.

From Propagator to Oscillator: The Dual Role of Symmetric Differential Equations in Neural Systems The Computational Limits of Deep Learning

Reference 5

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Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures cites this paper.

Real-Time Analysis of Unstructured Data with Machine Learning on Heterogeneous Architectures The Computational Limits of Deep Learning

Reference 98

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Observation 69aba07d-1ebd-47ff-bc73-3ab3df3f82e4 · inbound

Progressive Depth Up-scaling via Optimal Transport cites this paper.

Progressive Depth Up-scaling via Optimal Transport The Computational Limits of Deep Learning

Reference 2024

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From Membership-Privacy Leakage to Quantum Machine Unlearning cites this paper.

From Membership-Privacy Leakage to Quantum Machine Unlearning The Computational Limits of Deep Learning

Reference 32

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Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity cites this paper.

Quantum optical neural networks using atom-cavity interactions to provide all-optical nonlinearity The Computational Limits of Deep Learning

Reference 2

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A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models cites this paper.

A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models The Computational Limits of Deep Learning

Reference 13

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Koopman Model Dimension Reduction via Variational Bayesian Inference and Graph Search cites this paper.

Koopman Model Dimension Reduction via Variational Bayesian Inference and Graph Search The Computational Limits of Deep Learning

Reference 12

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Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units cites this paper.

Physical Analogue Kolmogorov-Arnold Networks based on Reconfigurable Nonlinear-Processing Units The Computational Limits of Deep Learning

Reference 14

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Neural Networks With Dense Weights Are Not Universal Approximators cites this paper.

Neural Networks With Dense Weights Are Not Universal Approximators The Computational Limits of Deep Learning

Reference 11

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Neural Networks With Dense Weights Are Not Universal Approximators cites this paper.

Neural Networks With Dense Weights Are Not Universal Approximators The Computational Limits of Deep Learning

Reference 11

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STRIDe: Cross-Coupled STT-MRAM Enabling Robust In-Memory-Computing for Deep Neural Network Accelerators cites this paper.

STRIDe: Cross-Coupled STT-MRAM Enabling Robust In-Memory-Computing for Deep Neural Network Accelerators The Computational Limits of Deep Learning

Reference 8

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Blockchain and AI: Securing Intelligent Networks for the Future cites this paper.

Blockchain and AI: Securing Intelligent Networks for the Future The Computational Limits of Deep Learning

Reference 131

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Rates of forgetting for the sequentially Markov coalescent cites this paper.

Rates of forgetting for the sequentially Markov coalescent The Computational Limits of Deep Learning

Reference 152

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Mixture of Heterogeneous Grouped Experts for Language Modeling cites this paper.

Mixture of Heterogeneous Grouped Experts for Language Modeling The Computational Limits of Deep Learning

Reference 23

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Auto-Relational Reasoning cites this paper.

Auto-Relational Reasoning The Computational Limits of Deep Learning

Reference 6

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OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators cites this paper.

OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators The Computational Limits of Deep Learning

Reference 2

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OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators cites this paper.

OptiLookUp: An Optical ROM-Based Lookup Table Engine for Photonic Accelerators The Computational Limits of Deep Learning

Reference 2

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Position: LLM Inference Should Be Evaluated as Energy-to-Token Production cites this paper.

Position: LLM Inference Should Be Evaluated as Energy-to-Token Production The Computational Limits of Deep Learning

Reference 72

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General-Purpose Photonic Computing Primitive for Contemporary Artificial Intelligence cites this paper.

General-Purpose Photonic Computing Primitive for Contemporary Artificial Intelligence The Computational Limits of Deep Learning

Reference 8

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Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models cites this paper.

Recursive Block-Diagonal Coupling for Resource-Efficient Training of Vision Models The Computational Limits of Deep Learning

Reference 29

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A Functional Data Framework For Analyzing Shapes and Textures in Images cites this paper.

A Functional Data Framework For Analyzing Shapes and Textures in Images The Computational Limits of Deep Learning

Reference 20

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Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs cites this paper.

Efficient PEFT Methods with Adaptive Checkpointing for Vision Models and VLMs on Resource Constrained Consumer-GPUs The Computational Limits of Deep Learning

Reference 35

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MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics cites this paper.

MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics The Computational Limits of Deep Learning

Reference 233

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