Paper Citation Record · LEDGER

The State of Sparsity in Deep Neural Networks

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 50 inbound Pith citation observations for arXiv:1902.09574.

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

pith.paper-citation-record.v1
1902.09574 v1

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measured 50 of 50 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 50 of 50 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:31:46.676503Z

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Source: pith, observed 2026-07-04T20:50:11.582245Z

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Pith citing papers

Observation 3344081b-58ba-47be-ae60-86557f84ba0b · inbound

PaLM: Scaling Language Modeling with Pathways cites this paper.

PaLM: Scaling Language Modeling with Pathways The State of Sparsity in Deep Neural Networks

Reference 48

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Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs cites this paper.

Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs The State of Sparsity in Deep Neural Networks

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-05-17T11:11:21.691961Z

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Observation b57b6c71-058f-41b5-b3a1-0b380274211a · inbound

Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs cites this paper.

Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs The State of Sparsity in Deep Neural Networks

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-24T06:34:01.026726Z

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Observation ec2d74e3-4027-4fe6-86f4-e944ffb3bd23 · inbound

EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty cites this paper.

EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty The State of Sparsity in Deep Neural Networks

Reference 52

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Observation bec35519-5992-43eb-b82b-9e4046cb22d9 · inbound

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts cites this paper.

Ghost-Connect Net: A Generalization-Enhanced Guidance For Sparse Deep Networks Under Distribution Shifts The State of Sparsity in Deep Neural Networks

Reference 12

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Observation 2fe0309e-92e5-4137-9114-a3e13428ce06 · inbound

Is Oracle Pruning the True Oracle? cites this paper.

Is Oracle Pruning the True Oracle? The State of Sparsity in Deep Neural Networks

Reference 18

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Observation 1e5c65e8-4b9b-4a79-82e0-55aa1be8502f · inbound

Efficient Model Compression Techniques with FishLeg cites this paper.

Efficient Model Compression Techniques with FishLeg The State of Sparsity in Deep Neural Networks

Reference 6

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Observation f4da5441-9fe4-48ab-861a-5f827b4d5304 · inbound

PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models cites this paper.

PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models The State of Sparsity in Deep Neural Networks

Reference 8

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On the Compression of Language Models for Code: An Empirical Study on CodeBERT cites this paper.

On the Compression of Language Models for Code: An Empirical Study on CodeBERT The State of Sparsity in Deep Neural Networks

Reference 27

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Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference cites this paper.

Dovetail: A CPU/GPU Heterogeneous Speculative Decoding for LLM inference The State of Sparsity in Deep Neural Networks

Reference 9

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Observation 547f3d11-6f6f-4aba-9a98-949f18c5abdb · inbound

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning cites this paper.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning The State of Sparsity in Deep Neural Networks

Reference 35

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Observation 274b0cc3-b844-434c-a24f-7eea21be9ee4 · inbound

Compact Bayesian Neural Networks via pruned MCMC sampling cites this paper.

Compact Bayesian Neural Networks via pruned MCMC sampling The State of Sparsity in Deep Neural Networks

Reference 31

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Observation fafb59b2-8aec-422c-b692-88e2459022ff · inbound

Foundations of Large Language Models cites this paper.

Foundations of Large Language Models The State of Sparsity in Deep Neural Networks

Reference 78

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Observation ddcde12d-6cfd-4def-975b-995732b15bdb · inbound

Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning cites this paper.

Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning The State of Sparsity in Deep Neural Networks

Reference 29

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Observation 4eca3aca-c646-42d9-ae2c-0888e1a9bcaf · inbound

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws cites this paper.

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws The State of Sparsity in Deep Neural Networks

Reference 12

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Observation e37a2963-511b-4c3a-aa57-4ebeeeac58fb · inbound

CoNNect: Connectivity-Based Regularization for Structural Pruning cites this paper.

CoNNect: Connectivity-Based Regularization for Structural Pruning The State of Sparsity in Deep Neural Networks

Reference 11

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Observation 217ec8cf-c51d-4f92-830b-974e84dbe68c · inbound

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries cites this paper.

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries The State of Sparsity in Deep Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T12:04:04.889372Z

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Observation 3745d38d-55a3-4f90-94c4-7cb8d8c3b6aa · inbound

Advancing Weight and Channel Sparsification with Enhanced Saliency cites this paper.

Advancing Weight and Channel Sparsification with Enhanced Saliency The State of Sparsity in Deep Neural Networks

Reference 14

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Observation b8b55980-8ed0-4bd2-9495-955ee0c8f340 · inbound

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment cites this paper.

Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment The State of Sparsity in Deep Neural Networks

Reference 43

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Observation 020a4ca2-4b30-4a4b-883a-4a57cc08dc4f · inbound

SCALE-Sim v3: A modular cycle-accurate systolic accelerator simulator for end-to-end system analysis cites this paper.

SCALE-Sim v3: A modular cycle-accurate systolic accelerator simulator for end-to-end system analysis The State of Sparsity in Deep Neural Networks

Reference 40

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Precision Neural Network Quantization via Learnable Adaptive Modules cites this paper.

Precision Neural Network Quantization via Learnable Adaptive Modules The State of Sparsity in Deep Neural Networks

Reference 7

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TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks cites this paper.

TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks The State of Sparsity in Deep Neural Networks

Reference 30

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Observation d71fe424-281d-4abc-a363-ec4955f00644 · inbound

Efficient Shapley Value-based Non-Uniform Pruning of Large Language Models cites this paper.

Efficient Shapley Value-based Non-Uniform Pruning of Large Language Models The State of Sparsity in Deep Neural Networks

Reference 42

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Observation 5f9eed83-fd55-494d-96cc-045bc4885d23 · inbound

Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry cites this paper.

Sparse Training from Random Initialization: Aligning Lottery Ticket Masks using Weight Symmetry The State of Sparsity in Deep Neural Networks

Reference 10

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Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption cites this paper.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption The State of Sparsity in Deep Neural Networks

Reference 29

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Sparsified State-Space Models are Efficient Highway Networks cites this paper.

Sparsified State-Space Models are Efficient Highway Networks The State of Sparsity in Deep Neural Networks

Reference 7

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Observation a346b8cd-a253-4c5c-9b7b-aa2e131af6ce · inbound

ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation cites this paper.

ALTER: All-in-One Layer Pruning and Temporal Expert Routing for Efficient Diffusion Generation The State of Sparsity in Deep Neural Networks

Reference 38

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Observation cab59ff9-327e-4ea6-ad2b-3bc796fc25d7 · inbound

The Resurrection of the ReLU cites this paper.

The Resurrection of the ReLU The State of Sparsity in Deep Neural Networks

Reference 6

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Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum cites this paper.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum The State of Sparsity in Deep Neural Networks

Reference 12

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A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs cites this paper.

A Novel Compiler Transformation for Fast Sparse Matrix Multiplication in GPUs The State of Sparsity in Deep Neural Networks

Reference 11

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Observation 336b8e6b-bf68-4b5b-97b7-15a8f6d89d31 · inbound

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity cites this paper.

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity The State of Sparsity in Deep Neural Networks

Reference 15

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Observation 80b1897e-5c54-4d09-a326-0788600fe6f8 · inbound

Projected Compression: Trainable Projection for Efficient Transformer Compression cites this paper.

Projected Compression: Trainable Projection for Efficient Transformer Compression The State of Sparsity in Deep Neural Networks

Reference 9

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Observation 0e0005c1-dc2b-4da3-9ff5-7f4654567325 · inbound

Efficient Column-Wise N:M Pruning on RISC-V CPU cites this paper.

Efficient Column-Wise N:M Pruning on RISC-V CPU The State of Sparsity in Deep Neural Networks

Reference 15

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Improving Neural Network Training using Dynamic Learning Rate Schedule for PINNs and Image Classification cites this paper.

Improving Neural Network Training using Dynamic Learning Rate Schedule for PINNs and Image Classification The State of Sparsity in Deep Neural Networks

Reference 14

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Observation 4a2d1fa6-d4b6-4958-bcc5-cef03a417f75 · inbound

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits cites this paper.

Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits The State of Sparsity in Deep Neural Networks

Reference 15

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Observation 8ea73934-14ce-4d52-a0ea-5294e691de32 · inbound

Effective Model Pruning: Measure The Redundancy of Model Components cites this paper.

Effective Model Pruning: Measure The Redundancy of Model Components The State of Sparsity in Deep Neural Networks

Reference 5

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Observation b6cdc5bc-0fcf-446f-a42e-0202591167c9 · inbound

Optimized Architectures for Kolmogorov-Arnold Networks cites this paper.

Optimized Architectures for Kolmogorov-Arnold Networks The State of Sparsity in Deep Neural Networks

Reference 27

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Observation e07455ce-cc3a-48a9-aee3-4b39dd424d61 · inbound

Probabilistic Computers for Neural Quantum States cites this paper.

Probabilistic Computers for Neural Quantum States The State of Sparsity in Deep Neural Networks

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-05-16T19:38:20.788131Z

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Observation 7aa30c56-b1eb-450a-8417-78b80f3c2b94 · inbound

Performance and Complexity Trade-off Optimization of Speech Models During Training cites this paper.

Performance and Complexity Trade-off Optimization of Speech Models During Training The State of Sparsity in Deep Neural Networks

Reference 20

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Observation ceb099b4-009b-467c-929b-c8f508f30b2b · inbound

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria cites this paper.

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria The State of Sparsity in Deep Neural Networks

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:16:01.310870Z

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Observation 6c52a29d-b8df-4663-ac6e-3034b45965de · inbound

Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment cites this paper.

Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment The State of Sparsity in Deep Neural Networks

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:05:58.980517Z

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Observation c5813177-f0c6-49cf-9712-228d2550e0b3 · inbound

SparseForge: Efficient Semi-Structured LLM Sparsification via Annealing of Hessian-Guided Soft-Mask cites this paper.

SparseForge: Efficient Semi-Structured LLM Sparsification via Annealing of Hessian-Guided Soft-Mask The State of Sparsity in Deep Neural Networks

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:06:08.437067Z

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Observation e6578f56-6cd0-43bf-88de-62c358764aab · inbound

HASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces cites this paper.

HASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces The State of Sparsity in Deep Neural Networks

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T21:06:13.176571Z

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Observation fcc6d792-040f-4b74-a321-74f3a612f1c4 · inbound

Pruning Deep Neural Networks via the Marchenko--Pastur Distribution cites this paper.

Pruning Deep Neural Networks via the Marchenko--Pastur Distribution The State of Sparsity in Deep Neural Networks

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-06-30T14:34:45.611254Z

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Observation 78dd79c0-2f79-42c6-b1d7-4ae1614d0e17 · inbound

Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning cites this paper.

Finding Sparse Subnetworks in One Training Cycle via Progressive Magnitude-Based Pruning The State of Sparsity in Deep Neural Networks

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-03T10:17:57.372651Z

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Observation fc315de0-7473-4746-b838-a718887e4f3e · inbound

Complementary Attention Head Pruning for Efficient Transformers cites this paper.

Complementary Attention Head Pruning for Efficient Transformers The State of Sparsity in Deep Neural Networks

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-04T00:49:18.745134Z

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Observation 9731e3c2-3dd3-4bc9-bc68-226bab5c2399 · inbound

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization cites this paper.

Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization The State of Sparsity in Deep Neural Networks

Reference 9

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T20:50:11.586242Z

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Observation a1d96ac6-3ecc-4878-ae7c-46e5d24722a8 · inbound

Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs cites this paper.

Weight-Adjusted Gradients Reveal Parameter Importance and Failure Modes in LLMs The State of Sparsity in Deep Neural Networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-14T09:09:29.470093Z

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Observation 5d872f14-e0d5-46b4-95f6-ed36c179fccd · inbound

The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores cites this paper.

The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores The State of Sparsity in Deep Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T05:19:32.695733Z

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Observation 0ac30512-8df6-4b48-ae00-442f9538e303 · inbound

MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning cites this paper.

MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning The State of Sparsity in Deep Neural Networks

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-16T00:36:02.282506Z

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