ResidualDroppath: Enhancing Feature Reuse over Residual Connections | Paper Citation Record

Observation 262943c6-9f7a-42b0-b2c8-7a7dcafc036f · outbound

This paper cites Layer by layer: Uncovering where multi-task learning happens in instruction-tuned large language mod- els.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Layer by layer: Uncovering where multi-task learning happens in instruction-tuned large language mod- els

Reference 1

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Observation b9b60667-fda0-4fd5-aaf9-5dc15d69d831 · outbound

This paper cites Understanding and improving features learned in deep functional maps.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Understanding and improving features learned in deep functional maps

Reference 2

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Observation 1e660ee1-838f-4ee1-8340-edec0c406baf · outbound

This paper cites Information complexity of stochastic convex optimization: Applications to generaliza- tion, memorization, and tracing.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Information complexity of stochastic convex optimization: Applications to generaliza- tion, memorization, and tracing

Reference 3

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Observation d40188de-692a-4af9-b839-bce6d5f4da11 · outbound

This paper cites Does roberta perform better than bert in continual learning: An attention sink perspective.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Does roberta perform better than bert in continual learning: An attention sink perspective

Reference 4

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Observation 3a43e384-f08c-4bd3-b7d2-8f1a5a6953fa · outbound

This paper cites Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models

Reference 5

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Observation 0081a7f8-2acc-4de4-a885-da8b63eac5e7 · outbound

This paper cites Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning

Reference 6

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Observation bfc4952f-f1d4-4bfc-a3ac-39d1c456aa2f · outbound

This paper cites Network dissection: Quantifying inter- pretability of deep visual representations.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Network dissection: Quantifying inter- pretability of deep visual representations

Reference 7

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Observation 672cbd99-3432-459d-9d08-e9753f17dec6 · outbound

This paper cites GAN Dissection: Visualizing and Understanding Generative Adversarial Networks.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Reference 8

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Observation b33995b7-9d20-4705-8d79-9976b506a789 · outbound

This paper cites The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models

Reference 9

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Observation 6131caba-7c09-42ee-a2b8-42ecb92c94d4 · outbound

This paper cites Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance

Reference 10

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Observation 9399ff0e-2079-4a67-959d-4b8119ecf00e · outbound

This paper cites Exploration by Random Network Distillation.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Exploration by Random Network Distillation

Reference 11

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Observation 114a3f33-5e2c-476a-85dc-deab160c6863 · outbound

This paper cites Adashift: Learning discriminative self-gated neural feature activation with an adaptive shift factor.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Adashift: Learning discriminative self-gated neural feature activation with an adaptive shift factor

Reference 12

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Observation 214e7ad1-b916-4b6a-b7d9-afb943486e8e · outbound

This paper cites SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models

Reference 13

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Observation 1404346b-e431-498d-9cf1-05439b9bf881 · outbound

This paper cites Dsg-kd: Knowledge distillation from domain-specific to general language models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Dsg-kd: Knowledge distillation from domain-specific to general language models

Reference 14

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Observation 2211fdf7-da9c-46e1-bfa4-1aaff186d18c · outbound

This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 15

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Observation 876ac165-6165-43d3-b245-d0baa8faae10 · outbound

This paper cites Vision Transformers Need Registers.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Vision Transformers Need Registers

Reference 16

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Observation f4ec108e-aa52-4fa3-9256-3bee2df9b2da · outbound

This paper cites Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models

Reference 17

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Observation 640b3bb0-558c-4323-9259-af7ebb366ce3 · outbound

This paper cites Why fine-tuning strug- gles with forgetting in machine unlearning? theoretical in- sights and a remedial approach, 2024.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Why fine-tuning strug- gles with forgetting in machine unlearning? theoretical in- sights and a remedial approach, 2024

Reference 18

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Observation 0ba789b3-e649-4170-a7f6-0a1075f88471 · outbound

This paper cites Decaf: A deep convolutional activation feature for generic visual recog- nition.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Decaf: A deep convolutional activation feature for generic visual recog- nition

Reference 19

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Observation e6b4e22b-710d-4091-a413-f1fa639e0274 · outbound

This paper cites Incorporating nesterov momentum into adam.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Incorporating nesterov momentum into adam

Reference 20

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Observation 30a82ba2-91a9-4670-80f9-a7eb7839230a · outbound

This paper cites Rosetta neurons: Mining the common units in a model zoo.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Rosetta neurons: Mining the common units in a model zoo

Reference 21

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Observation 977e4b22-dbfc-4947-ac07-2454d47c1019 · outbound

This paper cites Compositional Generative Modeling: A Single Model is Not All You Need.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Compositional Generative Modeling: A Single Model is Not All You Need

Reference 22

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Observation 1017ebf1-703f-4d46-a7ca-d741c6be78b1 · outbound

This paper cites Content-adaptive non-local convolution for remote sensing pansharpening.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Content-adaptive non-local convolution for remote sensing pansharpening

Reference 23

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Observation 42489058-24f5-4dd1-9fbe-ae9611a3c9ad · outbound

This paper cites Roma: Robust dense feature matching.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Roma: Robust dense feature matching

Reference 24

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Observation 1bdbedd3-840e-4b97-b031-0e9d3fcea307 · outbound

This paper cites Scaling rectified flow transformers for high-resolution image synthesis.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Scaling rectified flow transformers for high-resolution image synthesis

Reference 25

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Observation 6909d368-00b8-4182-ae49-e1862caf9052 · outbound

This paper cites KIF: Knowledge Identification and Fusion for Language Model Continual Learning.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections KIF: Knowledge Identification and Fusion for Language Model Continual Learning

Reference 26

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Observation 84b8f97c-0045-4bcb-83bc-d07c15c3d2d4 · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Sharpness-aware minimization for efficiently improving generalization

Reference 27

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Observation a4205148-7bcf-4574-92ce-4cd166cb1d26 · outbound

This paper cites Enhancing elusive clues in knowledge learning by contrasting attention of language models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Enhancing elusive clues in knowledge learning by contrasting attention of language models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-08-12T20:40:33.444470Z

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Observation 1fa23ffe-27bf-4d9e-87dc-2e07ef1b1a11 · outbound

This paper cites Building a Subspace of Policies for Scalable Continual Learning.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Building a Subspace of Policies for Scalable Continual Learning

Reference 29

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Observation c538e3c6-04d4-4d8a-9823-7cd63c719167 · outbound

This paper cites What do Vision Transformers Learn? A Visual Exploration.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections What do Vision Transformers Learn? A Visual Exploration

Reference 30

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Observation b56c4daf-f814-4e0d-93bf-81297f0dde9c · outbound

This paper cites Dropblock: A regularization method for convolutional networks.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Dropblock: A regularization method for convolutional networks

Reference 31

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Observation a703ce3c-4b81-4869-9753-5d6f8495083c · outbound

This paper cites Task-adaptive pretrained language models via clustered- importance sampling, 2024.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Task-adaptive pretrained language models via clustered- importance sampling, 2024

Reference 32

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Observation fb8498f5-918a-464b-b78a-1b286b76ed4e · outbound

This paper cites Uncovering Unique Concept Vectors through Latent Space Decomposition.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Uncovering Unique Concept Vectors through Latent Space Decomposition

Reference 33

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Observation 79cc3571-bd4d-4008-8dcd-f157ab9dcfec · outbound

This paper cites Preserving linear separability in continual learning by backward fea- ture projection.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Preserving linear separability in continual learning by backward fea- ture projection

Reference 34

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Observation e1f7c1d7-ca2f-48cc-bd73-b09df54eb9ef · outbound

This paper cites Cpp-net: Embracing multi- scale feature fusion into deep unfolding cp-ppa network for compressive sensing.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Cpp-net: Embracing multi- scale feature fusion into deep unfolding cp-ppa network for compressive sensing

Reference 35

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Observation ddfd57df-0f2a-40c5-84b0-6c70f5bba4e2 · outbound

This paper cites Slim: Let llm learn more and forget less with soft lora and identity mixture, 2024.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Slim: Let llm learn more and forget less with soft lora and identity mixture, 2024

Reference 36

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Observation 92a1ebf6-8334-49ed-b550-b0e00a877f62 · outbound

This paper cites Upcycling large language models into mixture of experts, 2024.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Upcycling large language models into mixture of experts, 2024

Reference 37

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Observation a4e5943f-20ed-4030-9c47-660ae2cae5e8 · outbound

This paper cites Deep residual learning for image recognition.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Deep residual learning for image recognition

Reference 38

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Observation 03f6b28c-ecc5-4799-8b2e-f750ca78d19a · outbound

This paper cites Identity mappings in deep residual networks.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Identity mappings in deep residual networks

Reference 39

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Observation 13a24bd6-254d-45ed-8572-62011876a6b1 · outbound

This paper cites Debiasing Text-to-Image Diffusion Models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Debiasing Text-to-Image Diffusion Models

Reference 40

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Observation f2f1685b-9721-42a7-b5e5-5ac80e22cdcc · outbound

This paper cites Bag of tricks for image classifica- tion with convolutional neural networks.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Bag of tricks for image classifica- tion with convolutional neural networks

Reference 41

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Observation e19c15ab-fc8c-4246-ac2c-0202dd385d0e · outbound

This paper cites Gaussian Error Linear Units (GELUs).

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Gaussian Error Linear Units (GELUs)

Reference 42

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Observation 2059835e-b2da-4721-8deb-d2ec6a12e5a7 · outbound

This paper cites AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Reference 43

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Observation a6b16bea-aca0-4275-aabf-eedcb00daa57 · outbound

This paper cites From Tinkering to Engineering: Measurements in Tensorflow Playground.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections From Tinkering to Engineering: Measurements in Tensorflow Playground

Reference 44

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Observation 0a966b85-62de-49a7-9723-e41f701799af · outbound

This paper cites Concept-centric transformers: Enhancing model inter- pretability through object-centric concept learning within a shared global workspace.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Concept-centric transformers: Enhancing model inter- pretability through object-centric concept learning within a shared global workspace

Reference 45

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Observation 6023e61a-039a-48f2-8c0e-4e68883d2d18 · outbound

This paper cites Deep networks with stochastic depth.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Deep networks with stochastic depth

Reference 46

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Observation fe44b1e7-18f2-4a2c-954a-40006be1113a · outbound

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Densely connected convolutional net- works

Reference 47

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Observation 672a06ae-1aa2-40fd-ade0-7ffdf494944b · outbound

This paper cites The Platonic Representation Hypothesis.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections The Platonic Representation Hypothesis

Reference 48

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Observation 345110cd-1880-4ea9-bbbb-7bae5f24a974 · outbound

This paper cites Comparing the decision-making mechanisms by transformers and cnns via explanation methods.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Comparing the decision-making mechanisms by transformers and cnns via explanation methods

Reference 49

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Observation 42937489-2d40-45fb-8fdc-bc303b5286fa · outbound

This paper cites No train no gain: Revisiting ef- ficient training algorithms for transformer-based language models.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections No train no gain: Revisiting ef- ficient training algorithms for transformer-based language models

Reference 50

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Observation 80aa70c4-59cb-45a8-8793-5c7c72042506 · outbound

This paper cites Localized semantic feature mixers for effi- cient pedestrian detection in autonomous driving.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Localized semantic feature mixers for effi- cient pedestrian detection in autonomous driving

Reference 51

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Observation 2fde43ec-adca-4ce4-9dc1-a7d900abe1ff · outbound

This paper cites SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling

Reference 52

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Observation 7aef824f-4607-4852-a0b5-1e70087911a9 · outbound

This paper cites DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections DenseNets Reloaded: Paradigm Shift Beyond ResNets and ViTs

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-08-12T20:40:33.309004Z

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Observation 144e9ef4-691c-4981-90de-42b5dc51cd3b · outbound

This paper cites Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference

Reference 54

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Observation 8d4c6157-1879-426a-8dfe-0e51fc3b81db · outbound

This paper cites Feature separation and recalibration for adversarial robustness.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Feature separation and recalibration for adversarial robustness

Reference 55

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Observation 7077094f-4935-4e81-aeff-35fc09ffa50a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Adam: A Method for Stochastic Optimization

Reference 56

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Observation c266adeb-e62f-4e31-bddf-bcacb2fff3ec · outbound

This paper cites Mean-shift feature transformer.

ResidualDroppath: Enhancing Feature Reuse over Residual Connections Mean-shift feature transformer

Reference 57

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Learning multiple layers of features from tiny images

Reference 58

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Gradient-based learning applied to document recognition

Reference 59

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Fix the noise: Disen- tangling source feature for controllable domain translation

Reference 60

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Automated progres- sive learning for efficient training of vision transformers

Reference 61

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Robustness preserving fine-tuning using neuron importance

Reference 62

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Formality is favored: Unraveling the learning preferences of large language models on data with conflicting knowl- edge, 2024

Reference 63

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Adver- sarial feature hallucination networks for few-shot learning

Reference 64

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Parameter-efficient fine- tuning in spectral domain for point cloud learning, 2024

Reference 65

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning

Reference 66

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Class Incremental Learning via Likelihood Ratio Based Task Prediction

Reference 67

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels

Reference 68

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Soft augmentation for image classification

Reference 69

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections PAT: Pruning-Aware Tuning for Large Language Models

Reference 70

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections UNLEARN Efficient Removal of Knowledge in Large Language Models

Reference 71

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Decoupled Weight Decay Regularization

Reference 72

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Mono-internvl: Pushing the boundaries of monolithic multimodal large language mod- els with endogenous visual pre-training, 2024

Reference 73

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Language Models "Grok" to Copy

Reference 74

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Curvature-balanced feature manifold learning for long-tailed classification

Reference 75

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Finite Scalar Quantization: VQ-VAE Made Simple

Reference 76

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Pace: marrying generalization in parameter-efficient fine-tuning with con- sistency regularization

Reference 77

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections House of Cards: Massive Weights in LLMs

Reference 78

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Zoom in: An in- troduction to circuits

Reference 79

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Unresolved cited work

Reference 80

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections DINOv2: Learning Robust Visual Features without Supervision

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Diverse Feature Learning by Self-distillation and Reset

Reference 82

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Learning More Generalized Experts by Merging Experts in Mixture-of-Experts

Reference 83

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

Reference 84

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Enhancing deformable local features by jointly learning to detect and describe keypoints

Reference 85

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Cafeboost: Causal feature boost to eliminate task-induced bias for class incremental learning

Reference 86

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Unlocking emergent modularity in large language models

Reference 87

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Berg, and Li Fei-Fei

Reference 88

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections LARE: Latent Augmentation using Regional Embedding with Vision-Language Model

Reference 89

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Scaling Smart: Accelerating Large Language Model Pre-training with Small Model Initialization

Reference 90

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Overcoming catastrophic forgetting with hard attention to the task

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Adaptive subspaces for few-shot learn- ing

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections SG-MIM: Structured Knowledge Guided Efficient Pre-training for Dense Prediction

Reference 93

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Out-of-distribution generalization via composition: a lens through induction heads in Transformers

Reference 94

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Striving for Simplicity: The All Convolutional Net

Reference 95

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Dropout: a simple way to prevent neural networks from overfitting

Reference 96

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Locat- ing information in large language models via random ma- trix theory

Reference 97

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections SVFit: Parameter-Efficient Fine-Tuning of Large Pre-Trained Models Using Singular Values

Reference 98

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models

Reference 99

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ResidualDroppath: Enhancing Feature Reuse over Residual Connections Scaling monosemanticity: Extracting in- terpretable features from claude 3 sonnet

Reference 100

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