Deep Ensembles: A Loss Landscape Perspective | Paper Citation Record

Observation f9ff5b4a-b3b0-4ca7-911d-4b180819afb4 · inbound

Editing Models with Task Arithmetic cites this paper.

Editing Models with Task Arithmetic Deep Ensembles: A Loss Landscape Perspective

Reference 25

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Deep Loss Convexification for Learning Iterative Models cites this paper.

Deep Loss Convexification for Learning Iterative Models Deep Ensembles: A Loss Landscape Perspective

Reference 23

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Why you don't overfit, and don't need Bayes if you only train for one epoch cites this paper.

Why you don't overfit, and don't need Bayes if you only train for one epoch Deep Ensembles: A Loss Landscape Perspective

Reference 2020

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Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems cites this paper.

Ex Uno Pluria: Insights on Ensembling in Low Precision Number Systems Deep Ensembles: A Loss Landscape Perspective

Reference 2021

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Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout cites this paper.

Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout Deep Ensembles: A Loss Landscape Perspective

Reference 39

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Observation 3795009c-91b8-405e-93d9-74bc2f753b1e · inbound

AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble cites this paper.

AI-powered Digital Twin of the Ocean: Reliable Uncertainty Quantification for Real-time Wave Height Prediction with Deep Ensemble Deep Ensembles: A Loss Landscape Perspective

Reference 53

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Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling cites this paper.

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling Deep Ensembles: A Loss Landscape Perspective

Reference 12

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A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation cites this paper.

A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation Deep Ensembles: A Loss Landscape Perspective

Reference 23

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Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification cites this paper.

Early Failure Detection in Autonomous Surgical Soft-Tissue Manipulation via Uncertainty Quantification Deep Ensembles: A Loss Landscape Perspective

Reference 13

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Observation 7360fbcf-18d8-46a8-9b60-7ecb94eb1e0f · inbound

Ensembles of Low-Rank Expert Adapters cites this paper.

Ensembles of Low-Rank Expert Adapters Deep Ensembles: A Loss Landscape Perspective

Reference 20

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UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV Satellite-Thermal Geo-localization cites this paper.

UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV Satellite-Thermal Geo-localization Deep Ensembles: A Loss Landscape Perspective

Reference 32

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Observation 2f64d21f-ebc5-48b6-acc8-a6b76331b8a2 · inbound

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing cites this paper.

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing Deep Ensembles: A Loss Landscape Perspective

Reference 14

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Observation 635f33ac-0bf5-4e80-9df6-fa8552a1e172 · inbound

Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation cites this paper.

Learning from Stochastic Teacher Representations Using Student-Guided Knowledge Distillation Deep Ensembles: A Loss Landscape Perspective

Reference 9

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The effect of the number of parameters and the number of local feature patches on loss landscapes in distributed quantum neural networks cites this paper.

The effect of the number of parameters and the number of local feature patches on loss landscapes in distributed quantum neural networks Deep Ensembles: A Loss Landscape Perspective

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-22T18:55:03.250527Z

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Observation 6b7221ef-c268-4d1b-bd1c-cff1a09d73e8 · 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 Deep Ensembles: A Loss Landscape Perspective

Reference 7

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Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks cites this paper.

Uncertainty quantification with approximate variational learning for wearable photoplethysmography prediction tasks Deep Ensembles: A Loss Landscape Perspective

Reference 40

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Observation ab55122c-64ce-4a6a-9fc6-57d0a554e119 · inbound

SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty cites this paper.

SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty Deep Ensembles: A Loss Landscape Perspective

Reference 28

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Observation 441716cf-2151-4685-a6b6-9933a8a788d0 · inbound

Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images cites this paper.

Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images Deep Ensembles: A Loss Landscape Perspective

Reference 7

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Observation c10780f4-52c4-481a-b710-b08327abbb3a · inbound

NAN: A Training-Free Solution to Coefficient Estimation in Model Merging cites this paper.

NAN: A Training-Free Solution to Coefficient Estimation in Model Merging Deep Ensembles: A Loss Landscape Perspective

Reference 12

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Observation a5ca5e0a-a4d4-4818-b051-271d96a6202d · inbound

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling cites this paper.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Deep Ensembles: A Loss Landscape Perspective

Reference 17

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Observation 62f62517-646a-42d8-8c4b-04dbf953a21a · inbound

Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift cites this paper.

Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift Deep Ensembles: A Loss Landscape Perspective

Reference 3

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CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray cites this paper.

CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray Deep Ensembles: A Loss Landscape Perspective

Reference 51

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Observation 0cf1255b-44f2-4e4b-a0e2-83970c4f9ab6 · inbound

Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble cites this paper.

Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble Deep Ensembles: A Loss Landscape Perspective

Reference 12

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Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs cites this paper.

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for time-dependent PDEs Deep Ensembles: A Loss Landscape Perspective

Reference 28

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Observation bae07758-fc9d-4d00-a3cf-32931d685c12 · inbound

Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators cites this paper.

Accelerating Hamiltonian Monte Carlo for Bayesian Inference in Neural Networks and Neural Operators Deep Ensembles: A Loss Landscape Perspective

Reference 16

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LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process cites this paper.

LVM-GP: Uncertainty-Aware PDE Solver via coupling latent variable model and Gaussian process Deep Ensembles: A Loss Landscape Perspective

Reference 30

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Observation 0e984262-de88-4d80-b691-f7cd603485f0 · inbound

ART: Adaptive Relation Tuning for Generalized Relation Prediction cites this paper.

ART: Adaptive Relation Tuning for Generalized Relation Prediction Deep Ensembles: A Loss Landscape Perspective

Reference 7

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Observation bafb494b-ca48-4dec-96c6-069d7883c20a · inbound

Structured Basis Function Networks: Loss-Centric Multi-Hypothesis Ensembles with Controllable Diversity cites this paper.

Structured Basis Function Networks: Loss-Centric Multi-Hypothesis Ensembles with Controllable Diversity Deep Ensembles: A Loss Landscape Perspective

Reference 15

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Observation aa2a93e4-5636-46ef-ae4f-96e5503c0a48 · inbound

Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning cites this paper.

Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning Deep Ensembles: A Loss Landscape Perspective

Reference 2012

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Observation 1591e577-4fab-4aed-bcba-88f4e02df218 · inbound

Exploring the Rashomon Set for Concept-Based Models cites this paper.

Exploring the Rashomon Set for Concept-Based Models Deep Ensembles: A Loss Landscape Perspective

Reference 15

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Observation 9a0685f0-527c-4ddf-91ca-62e07420a21d · inbound

Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity cites this paper.

Expectation and Acoustic Neural Network Representations Enhance Music Identification from Brain Activity Deep Ensembles: A Loss Landscape Perspective

Reference 45

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Observation fe138a92-b651-4d54-8eab-56c334d54e61 · inbound

FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation cites this paper.

FLAME: Condensing Ensemble Diversity into a Single Network for Efficient Sequential Recommendation Deep Ensembles: A Loss Landscape Perspective

Reference 13

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Observation f7499393-2050-47bd-84a6-cc33d660643c · inbound

Rethinking Data Mixing from the Perspective of Large Language Models cites this paper.

Rethinking Data Mixing from the Perspective of Large Language Models Deep Ensembles: A Loss Landscape Perspective

Reference 2

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Observation 8f8f5ce4-e471-4707-877c-b33c09d80de1 · inbound

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification cites this paper.

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification Deep Ensembles: A Loss Landscape Perspective

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:05:29.150474Z

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Observation ac5e7557-7b9b-4f1c-905e-b8dd7ba53d18 · inbound

Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models cites this paper.

Scalable Hyperparameter-Divergent Ensemble Training with Automatic Learning Rate Exploration for Large Models Deep Ensembles: A Loss Landscape Perspective

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T21:51:20.547192Z

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Observation 7705e54b-00c7-4c5b-b5b6-66beb1943856 · inbound

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models cites this paper.

Experience Sharing in Mutual Reinforcement Learning for Heterogeneous Language Models Deep Ensembles: A Loss Landscape Perspective

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:00:55.087360Z

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Observation 33db8a8a-6d39-47fe-ac1e-fd393bd07e41 · inbound

Flowing with Confidence cites this paper.

Flowing with Confidence Deep Ensembles: A Loss Landscape Perspective

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:23:08.636545Z

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Observation c5f336b9-caf7-47f3-82f5-e996135714db · inbound

Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions cites this paper.

Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions Deep Ensembles: A Loss Landscape Perspective

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:29:53.119593Z

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Observation f3452912-5c5f-4f08-83b6-881e631a30a4 · inbound

A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting cites this paper.

A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting Deep Ensembles: A Loss Landscape Perspective

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:46:15.053524Z

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Observation e9d26d6d-cad4-4bd0-8790-cb443d187b63 · inbound

Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks? cites this paper.

Do Deep Ensembles Actually Capture Uncertainty in Graph Neural Networks? Deep Ensembles: A Loss Landscape Perspective

Reference 16

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Observation 1d0c996c-2ecd-434c-b836-5bf348793e51 · inbound

Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning cites this paper.

Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning Deep Ensembles: A Loss Landscape Perspective

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:00:21.151018Z

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Observation eebe9cdf-b1a5-484d-b50b-02f5f8184962 · inbound

ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks cites this paper.

ChainzRule: Sample-Efficient, Robust Deep Learning Across Tabular, NLP, and Vision Tasks Deep Ensembles: A Loss Landscape Perspective

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-06-30T15:14:46.770746Z

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Observation a59a3d25-6aa2-4fa0-b581-be20e248fb46 · inbound

Soft Specialists: $\alpha$-R\'enyi Ensembles for Uncertainty-Aware LLM Post-Training cites this paper.

Soft Specialists: $\alpha$-R\'enyi Ensembles for Uncertainty-Aware LLM Post-Training Deep Ensembles: A Loss Landscape Perspective

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-29T15:23:32.971694Z

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Observation 27447cfa-63b2-45ef-b7d4-b15991a88960 · inbound

DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning cites this paper.

DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning Deep Ensembles: A Loss Landscape Perspective

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:43:15.054598Z

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Observation 9f60fd2c-3402-47c3-a733-a2c449d7590e · inbound

q0: Primitives for Hyper-Epoch Pretraining cites this paper.

q0: Primitives for Hyper-Epoch Pretraining Deep Ensembles: A Loss Landscape Perspective

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:16:26.588106Z

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Observation 11a24d47-9570-4d5e-afc6-989e127fc4ba · inbound

Recoverable but Not Stationary:Local Linear Structures in Weights and Activations cites this paper.

Recoverable but Not Stationary:Local Linear Structures in Weights and Activations Deep Ensembles: A Loss Landscape Perspective

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T04:27:37.348400Z

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Observation 4ebe8da0-c827-4faa-aa34-bf55fc3fb08c · inbound

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation cites this paper.

The FID Lottery: Quantifying Hidden Randomness in Generative-Model Evaluation Deep Ensembles: A Loss Landscape Perspective

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-04T03:49:29.508159Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4af57d2d-cf0a-47dc-9e82-36f5e02bce6b · inbound

Beyond Modality Fusion: Deep Ensembles for Multimodal Classification cites this paper.

Beyond Modality Fusion: Deep Ensembles for Multimodal Classification Deep Ensembles: A Loss Landscape Perspective

Reference 15

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Observation 86bd2ff4-7461-4401-9d45-61e65f63e851 · inbound

TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning cites this paper.

TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning Deep Ensembles: A Loss Landscape Perspective

Reference 136

Resolution: verified exact; local_arxiv, observed 2026-07-07T14:03:48.736431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1d80db88-90ed-4715-ae00-ebe4a3aef3a4 · inbound

Cross-Trajectory Chimera Interventions Reveal Dissociable Roles of Weight Magnitude and Direction in Grokking cites this paper.

Cross-Trajectory Chimera Interventions Reveal Dissociable Roles of Weight Magnitude and Direction in Grokking Deep Ensembles: A Loss Landscape Perspective

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-07-11T01:17:45.505491Z

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Observation ae2d4724-e143-455b-bf83-ee07f3130d52 · inbound

Efficient Bayesian Deep Ensembles via Analytic Predictive Inference cites this paper.

Efficient Bayesian Deep Ensembles via Analytic Predictive Inference Deep Ensembles: A Loss Landscape Perspective

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-10T21:37:42.004110Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8b4fca59-ebd8-4286-ba97-062aa212d2cc · inbound

Ensemble Diversity Optimization for Subjective Supervision cites this paper.

Ensemble Diversity Optimization for Subjective Supervision Deep Ensembles: A Loss Landscape Perspective

Reference 74

Resolution: verified exact; local_arxiv, observed 2026-07-10T06:36:52.472892Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8527b905-7922-492f-b9e8-a0c1e2e1645c · inbound

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification cites this paper.

Vertical Fusion: Condensing Internal Representations for Robust ViT Classification Deep Ensembles: A Loss Landscape Perspective

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-14T12:06:22.561885Z

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Observation 0eb23339-4846-4a63-aced-295ff1932b12 · inbound

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers cites this paper.

Condition-Stratified Robustness Analysis of Post-Hoc Calibration Methods for Probabilistic Classifiers Deep Ensembles: A Loss Landscape Perspective

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T04:52:20.985671Z

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Observation 09c20fd9-1990-4904-b047-94badefd30b1 · inbound

A Step Forward Towards Trustworthy Risk-Aware Facial Retrieval (RA-FR) cites this paper.

A Step Forward Towards Trustworthy Risk-Aware Facial Retrieval (RA-FR) Deep Ensembles: A Loss Landscape Perspective

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T07:53:44.247942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a208d961-81df-435a-a7ee-b4fd9b9d63b6 · inbound

First-Order Predictable but Pairwise Fragile: Local Task Adaptation in Trained Transformers cites this paper.

First-Order Predictable but Pairwise Fragile: Local Task Adaptation in Trained Transformers Deep Ensembles: A Loss Landscape Perspective

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T19:55:49.217375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ed1613b-8745-4b57-befb-6c541f13a9f6 · inbound

Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark cites this paper.

Uncertainty Quantification for AI-Driven Crash Simulation Surrogates: A Comparative Study of Monte Carlo Dropout and Deep Ensemble on Open-Source Bumper Beam Benchmark Deep Ensembles: A Loss Landscape Perspective

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T09:18:45.469539Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f511372-101c-4e3b-89dc-c5b7aeed645c · inbound

Search for new scalars via $X \rightarrow SH \rightarrow b\bar{b}b\bar{b}$ in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector cites this paper.

Search for new scalars via $X \rightarrow SH \rightarrow b\bar{b}b\bar{b}$ in proton-proton collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector Deep Ensembles: A Loss Landscape Perspective

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-01T15:25:09.428140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93e117e7-9fff-4377-b667-baee1ddb69cc · inbound

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era cites this paper.

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era Deep Ensembles: A Loss Landscape Perspective

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T05:59:04.483142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6e3e9e6-243e-4bce-b7ac-86596792d8b8 · inbound

Controllable Diversity in Normalization-Based Implicit Ensembles via Softmax-Temperature Modulation cites this paper.

Controllable Diversity in Normalization-Based Implicit Ensembles via Softmax-Temperature Modulation Deep Ensembles: A Loss Landscape Perspective

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-07-30T14:07:56.689567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f457107-9d67-4f3b-b4a7-bf21834bc871 · inbound

Uncertainty quantification for trustworthy deep learning: Methods and measures cites this paper.

Uncertainty quantification for trustworthy deep learning: Methods and measures Deep Ensembles: A Loss Landscape Perspective

Reference 1914

Resolution: unresolved; no resolver link, observed 2026-07-31T13:23:56.762521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7cc8097-465b-4035-88b7-a1fd8ba23983 · inbound

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields cites this paper.

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields Deep Ensembles: A Loss Landscape Perspective

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-05T00:38:51.189524Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b622313-ff38-424b-ad85-fb1172826940 · inbound

Using Diffusion Models to Estimate Uncertainties in Analytic Continuation cites this paper.

Using Diffusion Models to Estimate Uncertainties in Analytic Continuation Deep Ensembles: A Loss Landscape Perspective

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T16:23:16.644817Z

Source-reported events for the cited work

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