Adjoint sharding for very long context training of state space models | Paper Citation Record

Observation e99b3f86-32a1-4820-8131-f8ff9c1e4fd6 · outbound

This paper cites BlackMamba: Mixture of Experts for State-Space Models.

Adjoint sharding for very long context training of state space models BlackMamba: Mixture of Experts for State-Space Models

Reference 1

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Observation bf8d5a77-5e96-4a43-ab94-2b34547d5a1f · outbound

This paper cites Fast Jacobian-Vector Product for Deep Networks.

Adjoint sharding for very long context training of state space models Fast Jacobian-Vector Product for Deep Networks

Reference 2

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This paper cites Automatic differentiation in machine learning: a survey.

Adjoint sharding for very long context training of state space models Automatic differentiation in machine learning: a survey

Reference 4

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Observation f895bba9-0913-44b8-9667-381d1a53c7a4 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Adjoint sharding for very long context training of state space models xLSTM: Extended Long Short-Term Memory

Reference 5

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Observation 85553efb-415d-49f1-b461-85f0f64bcfff · outbound

This paper cites Longformer: The Long-Document Transformer.

Adjoint sharding for very long context training of state space models Longformer: The Long-Document Transformer

Reference 6

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This paper cites Internlm2 technical report,.

Adjoint sharding for very long context training of state space models Internlm2 technical report,

Reference 7

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Observation 0baf7150-8e83-4678-818c-fd34b341cff6 · outbound

This paper cites Adjoint sensitivity analysis for differential-algebraic equations: algorithms and software.

Adjoint sharding for very long context training of state space models Adjoint sensitivity analysis for differential-algebraic equations: algorithms and software

Reference 8

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Observation aecbb979-4b57-4028-99f2-0f5bb7231634 · outbound

This paper cites Neural Ordinary Differential Equations.

Adjoint sharding for very long context training of state space models Neural Ordinary Differential Equations

Reference 9

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Observation edbbc815-26f6-4d04-a078-fd8f06a5d57f · outbound

This paper cites Extending Context Window of Large Language Models via Positional Interpolation.

Adjoint sharding for very long context training of state space models Extending Context Window of Large Language Models via Positional Interpolation

Reference 10

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Observation 624eaeba-c5b0-4f3a-8862-1ecbeae640df · outbound

This paper cites LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models.

Adjoint sharding for very long context training of state space models LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

Reference 11

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Observation c5668d3e-01c5-47dd-b180-8403a30e76b6 · outbound

This paper cites The Backpropagation algorithm for a math student.

Adjoint sharding for very long context training of state space models The Backpropagation algorithm for a math student

Reference 12

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Observation ef15a127-c9d4-4244-b772-366c06a7db7d · outbound

This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Adjoint sharding for very long context training of state space models FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 13

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Observation 1094675c-c2f8-4d8b-a53c-f348e20cbd90 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Adjoint sharding for very long context training of state space models Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 15

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Observation 8a49de87-00d2-443a-b115-f71f4beb563f · outbound

This paper cites FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness.

Adjoint sharding for very long context training of state space models FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Reference 16

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This paper cites an unresolved cited work.

Adjoint sharding for very long context training of state space models Unresolved cited work

Reference 17

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Observation 4277793a-c7d5-4b8b-a87b-e385df3b5f10 · outbound

This paper cites LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens.

Adjoint sharding for very long context training of state space models LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Reference 19

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Observation 4ef388c6-e212-4c3e-961a-2c65891d49d6 · outbound

This paper cites Augmented Neural ODEs.

Adjoint sharding for very long context training of state space models Augmented Neural ODEs

Reference 20

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This paper cites Fu, Tri Dao, Khaled K.

Adjoint sharding for very long context training of state space models Fu, Tri Dao, Khaled K

Reference 21

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Observation 42dda1f6-ddd6-4dd3-83db-7ee9bf0e7251 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Adjoint sharding for very long context training of state space models Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 23

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Observation 283b746e-b320-4c1b-bf46-a6f3ebf6f618 · outbound

This paper cites Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers.

Adjoint sharding for very long context training of state space models Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers

Reference 24

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Observation dd6a2829-5bc1-4f5a-897c-8eee0cf5a9c5 · outbound

This paper cites How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections.

Adjoint sharding for very long context training of state space models How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections

Reference 25

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This paper cites Attention mechanisms in computer vision: A survey.

Adjoint sharding for very long context training of state space models Attention mechanisms in computer vision: A survey

Reference 26

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Observation 22e3801c-b225-49bc-8bc2-f8d06774a95d · outbound

This paper cites Simplifying and Understanding State Space Models with Diagonal Linear RNNs.

Adjoint sharding for very long context training of state space models Simplifying and Understanding State Space Models with Diagonal Linear RNNs

Reference 27

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Observation 2bfc6337-d651-443b-aba5-7c2030a42c8b · outbound

This paper cites Deep Residual Learning for Image Recognition.

Adjoint sharding for very long context training of state space models Deep Residual Learning for Image Recognition

Reference 28

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Observation 1d7e40de-c191-407a-bcb0-97e1b28d4108 · outbound

This paper cites Deep residual learning for image recognition.

Adjoint sharding for very long context training of state space models Deep residual learning for image recognition

Reference 29

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Observation 4e9c8b16-e551-43a8-8a21-be6f31b3a379 · outbound

This paper cites Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory, 2019.

Adjoint sharding for very long context training of state space models Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory, 2019

Reference 30

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Observation 6cf268e0-d752-4456-8bdd-b6f75035f474 · outbound

This paper cites A tutorial on training recurrent neural networks , covering bppt , rtrl , ekf and the ” echo state network ” approach - semantic scholar.

Adjoint sharding for very long context training of state space models A tutorial on training recurrent neural networks , covering bppt , rtrl , ekf and the ” echo state network ” approach - semantic scholar

Reference 31

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This paper cites Adjoint methods and sensitivity analysis for recurrence, 01 2007.

Adjoint sharding for very long context training of state space models Adjoint methods and sensitivity analysis for recurrence, 01 2007

Reference 32

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Observation 5bcfbff1-cf11-448a-81fb-c1315c223a02 · outbound

This paper cites Linear dynamical systems as a core computational primitive.

Adjoint sharding for very long context training of state space models Linear dynamical systems as a core computational primitive

Reference 33

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Observation 55b31091-3be3-4176-ad29-9abff81abd8c · outbound

This paper cites Segment anything.

Adjoint sharding for very long context training of state space models Segment anything

Reference 34

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Observation b45875e8-6c89-4120-814a-ba7e78919f9e · outbound

This paper cites Gonzalez, Ion Stoica, Xuezhe Ma, and Hao Zhang.

Adjoint sharding for very long context training of state space models Gonzalez, Ion Stoica, Xuezhe Ma, and Hao Zhang

Reference 35

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Observation 94d34a26-4942-442e-8c0f-5424ce90dda9 · outbound

This paper cites Long-context LLMs Struggle with Long In-context Learning.

Adjoint sharding for very long context training of state space models Long-context LLMs Struggle with Long In-context Learning

Reference 36

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Observation e761159d-6db4-4d7b-8acb-01dfb22f5555 · outbound

This paper cites Jamba: A Hybrid Transformer-Mamba Language Model.

Adjoint sharding for very long context training of state space models Jamba: A Hybrid Transformer-Mamba Language Model

Reference 37

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This paper cites Ring attention with blockwise transformers for near-infinite context,.

Adjoint sharding for very long context training of state space models Ring attention with blockwise transformers for near-infinite context,

Reference 38

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Observation 3d1ec21e-95f6-47a5-9577-58695bad7677 · outbound

This paper cites World Model on Million-Length Video And Language With Blockwise RingAttention.

Adjoint sharding for very long context training of state space models World Model on Million-Length Video And Language With Blockwise RingAttention

Reference 39

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This paper cites The Llama 3 Herd of Models.

Adjoint sharding for very long context training of state space models The Llama 3 Herd of Models

Reference 40

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This paper cites Mixed Precision Training.

Adjoint sharding for very long context training of state space models Mixed Precision Training

Reference 41

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Observation 0f8e9868-8353-4e7d-9dd9-99b5e5303970 · outbound

This paper cites Fast Finite Width Neural Tangent Kernel.

Adjoint sharding for very long context training of state space models Fast Finite Width Neural Tangent Kernel

Reference 42

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This paper cites Matrix multiplication background user’s guide, 2024.

Adjoint sharding for very long context training of state space models Matrix multiplication background user’s guide, 2024

Reference 43

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Observation a9f0488d-6a2a-473b-854b-f8504d364620 · outbound

This paper cites GPT-4 Technical Report.

Adjoint sharding for very long context training of state space models GPT-4 Technical Report

Reference 44

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This paper cites Resurrecting recurrent neural networks for long sequences, 2023.

Adjoint sharding for very long context training of state space models Resurrecting recurrent neural networks for long sequences, 2023

Reference 45

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Observation 231e8049-bb44-49ad-a252-7d3d0182f369 · outbound

This paper cites On the difficulty of training recurrent neural networks,.

Adjoint sharding for very long context training of state space models On the difficulty of training recurrent neural networks,

Reference 46

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

Adjoint sharding for very long context training of state space models PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 47

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This paper cites Scalable diffusion models with transformers.

Adjoint sharding for very long context training of state space models Scalable diffusion models with transformers

Reference 48

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This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Adjoint sharding for very long context training of state space models RWKV: Reinventing RNNs for the Transformer Era

Reference 49

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This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

Adjoint sharding for very long context training of state space models YaRN: Efficient Context Window Extension of Large Language Models

Reference 50

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This paper cites MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts.

Adjoint sharding for very long context training of state space models MoE-Mamba: Efficient Selective State Space Models with Mixture of Experts

Reference 51

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This paper cites ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.

Adjoint sharding for very long context training of state space models ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Reference 53

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This paper cites ZeRO-Offload: Democratizing Billion-Scale Model Training.

Adjoint sharding for very long context training of state space models ZeRO-Offload: Democratizing Billion-Scale Model Training

Reference 54

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This paper cites Flashattention-3: Fast and accurate attention with asynchrony and low-precision, 2024.

Adjoint sharding for very long context training of state space models Flashattention-3: Fast and accurate attention with asynchrony and low-precision, 2024

Reference 55

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Observation c968568b-da2d-4c22-8a6d-6eb31d3dd92b · outbound

This paper cites Low-Memory Neural Network Training: A Technical Report.

Adjoint sharding for very long context training of state space models Low-Memory Neural Network Training: A Technical Report

Reference 56

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This paper cites Unbiasing Truncated Backpropagation Through Time.

Adjoint sharding for very long context training of state space models Unbiasing Truncated Backpropagation Through Time

Reference 57

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This paper cites Focused transformer: Contrastive training for context scaling, 2023.

Adjoint sharding for very long context training of state space models Focused transformer: Contrastive training for context scaling, 2023

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:29.733806Z

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This paper cites Ntk-aware scaled rope, 2023.

Adjoint sharding for very long context training of state space models Ntk-aware scaled rope, 2023

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:50:29.719374Z

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Observation b8ba6447-40e7-4c8f-8362-e8d8c5f2d1d7 · outbound

This paper cites Attention Is All You Need.

Adjoint sharding for very long context training of state space models Attention Is All You Need

Reference 60

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This paper cites Rellermeyer.

Adjoint sharding for very long context training of state space models Rellermeyer

Reference 61

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This paper cites An Empirical Study of Mamba-based Language Models.

Adjoint sharding for very long context training of state space models An Empirical Study of Mamba-based Language Models

Reference 62

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This paper cites State-space Models with Layer-wise Nonlinearity are Universal Approximators with Exponential Decaying Memory.

Adjoint sharding for very long context training of state space models State-space Models with Layer-wise Nonlinearity are Universal Approximators with Exponential Decaying Memory

Reference 63

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This paper cites an unresolved cited work.

Adjoint sharding for very long context training of state space models Unresolved cited work

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:28.814054Z

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This paper cites Efficient Streaming Language Models with Attention Sinks.

Adjoint sharding for very long context training of state space models Efficient Streaming Language Models with Attention Sinks

Reference 65

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This paper cites Characteristic Neural Ordinary Differential Equations.

Adjoint sharding for very long context training of state space models Characteristic Neural Ordinary Differential Equations

Reference 66

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This paper cites Focal self- attention for local-global interactions in vision transformers, 2021.

Adjoint sharding for very long context training of state space models Focal self- attention for local-global interactions in vision transformers, 2021

Reference 67

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This paper cites Long Context Compression with Activation Beacon.

Adjoint sharding for very long context training of state space models Long Context Compression with Activation Beacon

Reference 68

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This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

Adjoint sharding for very long context training of state space models PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 69

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Observation 51b3fa41-2ff7-4646-b78c-2eb3f411c157 · outbound

This paper cites On the difficulty of training Recurrent Neural Networks.

Adjoint sharding for very long context training of state space models On the difficulty of training Recurrent Neural Networks

Reference 2013

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This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

Adjoint sharding for very long context training of state space models Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 2023

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This paper cites InternLM2 Technical Report.

Adjoint sharding for very long context training of state space models InternLM2 Technical Report

Reference 2024

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