Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation | Paper Citation Record

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This paper cites Springer, 2018.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Springer, 2018

Reference 1

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This paper cites Generalization In Quantum Machine Learning From Few Training Data.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Generalization In Quantum Machine Learning From Few Training Data

Reference 2

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This paper cites LORA: Low-Rank Adaptation of Large Language Models.International Conference on Learning Representation, 1(2):3, 2022.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation LORA: Low-Rank Adaptation of Large Language Models.International Conference on Learning Representation, 1(2):3, 2022

Reference 3

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This paper cites The Expressive Power of Low-Rank Adaptation.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation The Expressive Power of Low-Rank Adaptation

Reference 4

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This paper cites DORA: Weight-Decomposed Low-Rank Adaptation.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation DORA: Weight-Decomposed Low-Rank Adaptation

Reference 5

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This paper cites Tensor-Train Decomposition.SIAM Journal on Scientific Computing, 33(5):2295–2317, 2011.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Tensor-Train Decomposition.SIAM Journal on Scientific Computing, 33(5):2295–2317, 2011

Reference 6

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This paper cites Bitfit: Simple Parameter-Efficient Fine-Tuning for Transformer-Based Masked Language Models.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Bitfit: Simple Parameter-Efficient Fine-Tuning for Transformer-Based Masked Language Models

Reference 7

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This paper cites Neural Tangent Kernel: Convergence and Generalization in Neural Networks.Advances in Neural Information Processing Systems, 31, 2018.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Neural Tangent Kernel: Convergence and Generalization in Neural Networks.Advances in Neural Information Processing Systems, 31, 2018

Reference 8

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This paper cites On The Inductive Bias of Neural Tangent Kernels.Advances in Neural Information Processing Systems, 32, 2019.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation On The Inductive Bias of Neural Tangent Kernels.Advances in Neural Information Processing Systems, 32, 2019

Reference 9

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This paper cites Parameter-Efficient Fine-Tuning of Large-Scale Pre-Trained Language Models.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Parameter-Efficient Fine-Tuning of Large-Scale Pre-Trained Language Models

Reference 10

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This paper cites Few-Shot Parameter-Efficient Fine-Tuning Is Better and Cheaper Than In-Context Learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Few-Shot Parameter-Efficient Fine-Tuning Is Better and Cheaper Than In-Context Learning.Advances in Neural Information Processing Systems, 35:1950–1965, 2022

Reference 11

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This paper cites V oice2series: Reprogramming Acoustic Models for Time Series Classification.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation V oice2series: Reprogramming Acoustic Models for Time Series Classification

Reference 12

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This paper cites Language Models Are Unsupervised Multitask Learners.OpenAI blog, 1(8):9, 2019.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Language Models Are Unsupervised Multitask Learners.OpenAI blog, 1(8):9, 2019

Reference 13

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This paper cites Language Models Are Few-Shot Learners.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Language Models Are Few-Shot Learners

Reference 14

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Observation f7d24bb5-916a-4b4a-9652-5b69fdab3e1f · outbound

This paper cites An Image Is Worth 16x16 Words: Transformers for Image Recognition At Scale.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation An Image Is Worth 16x16 Words: Transformers for Image Recognition At Scale

Reference 15

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Observation ea8652c9-2f55-4db1-91f5-7289567ff35f · outbound

This paper cites A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States.Annals of Physics, 349:117–158, 2014.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States.Annals of Physics, 349:117–158, 2014

Reference 16

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This paper cites Simulation of Quantum Many-Body Systems with Strings of Operators and Monte Carlo Tensor Contractions.Physical Review Letters, 100(4):040501, 2008.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Simulation of Quantum Many-Body Systems with Strings of Operators and Monte Carlo Tensor Contractions.Physical Review Letters, 100(4):040501, 2008

Reference 17

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Observation b9bf231c-2756-4b43-bc3b-76c6f8252e8f · outbound

This paper cites Tensorizing Neural Networks.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Tensorizing Neural Networks

Reference 18

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This paper cites Tensor-Train Recurrent Neural Networks for Video Classification.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Tensor-Train Recurrent Neural Networks for Video Classification

Reference 19

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Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 20

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This paper cites Convolutional Tensor-Train LSTM for Spatio-Temporal Learning.Advances in Neural Information Processing Systems, 33:13714–13726, 2020.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Convolutional Tensor-Train LSTM for Spatio-Temporal Learning.Advances in Neural Information Processing Systems, 33:13714–13726, 2020

Reference 21

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Observation 381f9f81-6a7b-4a1d-8be8-9acd0acb24e8 · outbound

This paper cites Gradient Descent Finds Global Minima of Deep Neural Networks.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Gradient Descent Finds Global Minima of Deep Neural Networks

Reference 22

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Observation 96196844-a361-4f89-83c1-d6a523456940 · outbound

This paper cites On Exact Computation with An Infinitely Wide Neural Net.Advances in Neural Information Processing Systems, 32, 2019.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation On Exact Computation with An Infinitely Wide Neural Net.Advances in Neural Information Processing Systems, 32, 2019

Reference 23

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Observation ffc837a9-b200-4e2f-9099-ff34ba5e7beb · outbound

This paper cites Universal Approximation Bounds for Superpositions of A Sigmoidal Function.IEEE Transactions on Information Theory, 39(3):930–945, 1993.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Universal Approximation Bounds for Superpositions of A Sigmoidal Function.IEEE Transactions on Information Theory, 39(3):930–945, 1993

Reference 24

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Observation 1f1af0e5-fa5d-460e-91e6-6f32af758a89 · outbound

This paper cites Approximation by Superpositions of A Sigmoidal Function.Mathematics of Control, Signals and Systems, 2(4):303–314, 1989.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Approximation by Superpositions of A Sigmoidal Function.Mathematics of Control, Signals and Systems, 2(4):303–314, 1989

Reference 25

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This paper cites An Analysis of The Rayleigh–Ritz Method for Approximating Eigenspaces.Mathematics of Computation, 70(234):637–647, 2001.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation An Analysis of The Rayleigh–Ritz Method for Approximating Eigenspaces.Mathematics of Computation, 70(234):637–647, 2001

Reference 26

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This paper cites Springer Science & Business Media, 2011.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Springer Science & Business Media, 2011

Reference 27

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Observation 447910b1-b467-4577-a9a4-d3ad79e71aab · outbound

This paper cites Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach.Operations Research, 70(1):454–471, 2022.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Data-Driven Optimization: A Reproducing Kernel Hilbert Space Approach.Operations Research, 70(1):454–471, 2022

Reference 28

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Observation 48af958b-1c45-45e6-9882-b05256194160 · outbound

This paper cites Storage of Multiple Single-photon Pulses Emitted from A Quantum Dot in A Solid-state Quantum Memory.Nature Communications, 6(1):8652, 2015.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Storage of Multiple Single-photon Pulses Emitted from A Quantum Dot in A Solid-state Quantum Memory.Nature Communications, 6(1):8652, 2015

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.934712Z

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Observation 456dbdcb-bd70-458c-a49f-54b63bdd9f5c · outbound

This paper cites Tuning Arrays with Rays: Physics-informed Tuning of Quantum Dot Charge States.Physical Review Applied, 20(3):034067, 2023.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Tuning Arrays with Rays: Physics-informed Tuning of Quantum Dot Charge States.Physical Review Applied, 20(3):034067, 2023

Reference 30

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Observation e57b8560-332d-4ea3-abb1-fa46c2f9143d · outbound

This paper cites Intrinsic Exciton-exciton Coupling in GaN-based Quantum Dots: Applica- tion to Solid-state Quantum Computing.Physical Review B, 65(8):081309, 2002.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Intrinsic Exciton-exciton Coupling in GaN-based Quantum Dots: Applica- tion to Solid-state Quantum Computing.Physical Review B, 65(8):081309, 2002

Reference 31

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Observation 8f233b0d-41e6-48a9-945a-f09335d21de3 · outbound

This paper cites Machine Learning Techniques for State Recognition and Auto-tuning in Quantum Dots.npj Quantum Information, 5(1):6, 2019.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Machine Learning Techniques for State Recognition and Auto-tuning in Quantum Dots.npj Quantum Information, 5(1):6, 2019

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.885820Z

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Observation 96263315-5a7c-4b94-9c19-acdd69b16bbc · outbound

This paper cites Theoretical Error Perfor- mance Analysis for Variational Quantum Circuit Based Functional Regression.npj Quantum Information, 9(1):4, 2023.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Theoretical Error Perfor- mance Analysis for Variational Quantum Circuit Based Functional Regression.npj Quantum Information, 9(1):4, 2023

Reference 33

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Observation d6d97d30-d352-4b4d-a897-a6ae99fe6764 · outbound

This paper cites Resnet in Resnet: Generalizing Residual Architectures.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Resnet in Resnet: Generalizing Residual Architectures

Reference 34

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Observation 027c5272-ec5d-48ed-ab9f-9d23265c9054 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Deep Residual Learning for Image Recognition

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This paper cites Imagenet: A Large-scale Hierarchical Image Database.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Imagenet: A Large-scale Hierarchical Image Database

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.826407Z

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Observation 95504134-de69-4633-92fc-708771480f03 · outbound

This paper cites Improving Language Understanding by Generative Pre-Training.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Improving Language Understanding by Generative Pre-Training

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This paper cites Wiki-40B: Multilingual Language Model Dataset.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Wiki-40B: Multilingual Language Model Dataset

Reference 38

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Observation 53fb43d8-7ae8-4c39-9536-7efda468fe58 · outbound

This paper cites Lower Perplexity Is Not Always Human-Like.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Lower Perplexity Is Not Always Human-Like

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.782561Z

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Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 40

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Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 41

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Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 42

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Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 43

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Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 44

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Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 45

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This paper cites The procedure begins by initializing the TT cores{Gk}K k , which serve as the compact parameterization of the adaptation matrices ˆW1 and ˆW2.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation The procedure begins by initializing the TT cores{Gk}K k , which serve as the compact parameterization of the adaptation matrices ˆW1 and ˆW2

Reference 46

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This paper cites 13 Let D and Q denote the input and output dimensions of a weight matrix in the original model (e.g., W0∈R D×Q), and letr denote the intrinsic low-rank dimension used in LoRA.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation 13 Let D and Q denote the input and output dimensions of a weight matrix in the original model (e.g., W0∈R D×Q), and letr denote the intrinsic low-rank dimension used in LoRA

Reference 47

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Observation 46bec753-ba03-4496-9930-6e7345bce7b1 · outbound

This paper cites While LoRA is efficient for small r, the lack of joint structure acrossW 1 andW 2 limits expressivity and parameter reuse.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation While LoRA is efficient for small r, the lack of joint structure acrossW 1 andW 2 limits expressivity and parameter reuse

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.641955Z

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Observation ea3ec1a1-1eed-4592-b5c3-0edc2327e9c6 · outbound

This paper cites Assume each matrix is reshaped as an order-K tensor with mode sizeH and TT-rankrtt.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Assume each matrix is reshaped as an order-K tensor with mode sizeH and TT-rankrtt

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.624405Z

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Observation 13f4664a-33fa-496f-b8fe-8511bea1eec5 · outbound

This paper cites Let the TT output have shaper(D+Q) , factorized intoK cores.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Let the TT output have shaper(D+Q) , factorized intoK cores

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.607033Z

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Observation 3f1424f7-4f3f-433f-8340-b0e1346bb4a0 · outbound

This paper cites LetHT be the RKHS associated with the NTKTtg(x,x′), and∥fθ∥HT its RKHS norm.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation LetHT be the RKHS associated with the NTKTtg(x,x′), and∥fθ∥HT its RKHS norm

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.589361Z

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Observation f0ebdb6c-2077-4130-b3e3-a12a043953e8 · outbound

This paper cites The corresponding test loss and accuracy curves over training epochs are illustrated in Figure 5.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation The corresponding test loss and accuracy curves over training epochs are illustrated in Figure 5

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.571786Z

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Observation 02b3fbd2-296a-40de-bb29-8cb38f1b0f55 · outbound

This paper cites an unresolved cited work.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:34:26.554511Z

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Observation 9318159c-ba58-41c0-9c47-e88263e0b399 · outbound

This paper cites DoRA dynamically allocates low-rank dimensions across layers, enhancing parameter efficiency and flexibility.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation DoRA dynamically allocates low-rank dimensions across layers, enhancing parameter efficiency and flexibility

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:26.538490Z

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Observation c8a80d54-2b52-4ebf-9d35-64988059a5ed · outbound

This paper cites an unresolved cited work.

Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:34:26.520989Z

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