The authors identify a Golden Partition Zone based on an intra-class variance shift in entropy bounds that enables intrinsic model inversion resistance when partitioning neural networks for collaborative inference.
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Deep Learning for Classical Japanese Literature
20 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Dataset available at https://github.com/rois-codh/kmnist
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Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
E-PMQ improves 4-bit quantization accuracy on merged models by 8-42 points across CLIP and GLUE tasks through expert-guided calibration and merged-weight anchoring.
Bayesian Model Merging introduces a bi-level optimization framework that merges task-specific models via closed-form Bayesian regression with an anchor prior and global hyperparameter search, outperforming baselines and nearly matching expert averages on up to 20-task vision and 5-task language Merg
ACE-Merging estimates task input covariances from parameter differences to enable closed-form data-free merging that reduces interference and outperforms prior baselines on vision and language tasks.
New MDW benchmarks demonstrate that isolated digit classifiers struggle with multi-digit numbers from the same writer, necessitating task-specific metrics and advanced methods.
Hardness-Based Resampling reduces class recall gaps in balanced datasets by up to 32% on CIFAR-10 and 16% on CIFAR-100 by prioritizing harder samples over random or frequency-based selection.
New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
DAPPr introduces a possibilistic framework that projects parameter posteriors to predictions via supremum and approximates them with Dirichlet possibility functions to yield efficient, closed-form epistemic uncertainty estimates.
New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.
A passive steering method for quantum state preparation improves adversarial accuracy in QML models by up to 40% across tested cases.
Task alignment serves as an efficient proxy for hyperparameter selection in model merging, accelerating the process by orders of magnitude while preserving performance in vision models with heterogeneous decoders.
SE2D stabilizes continual distillation across heterogeneous teachers by preserving logits on external unlabeled data to mitigate unseen knowledge forgetting.
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.
Stimulus symmetries render many neural representations functionally equivalent yet produce qualitatively different RSMs, including drifting ones from SGD or regularization in image-encoding networks.
The paper introduces risk-consistent multiclass learning from random label-subset queries by deriving an unbiased risk estimator under ERM, plus non-negative and absolute-value corrections, with generalization bounds and consistency results.
Dendritic EP matches standard EP on simple tasks but significantly outperforms it on KMNIST and FMNIST, and in deeper models, approaching the performance of backpropagation-trained dendritic networks.
CAMNet uses data-dependent routing across parallel tensors in a multi-path network to outperform equivalent single-path, multi-path, and deeper networks on classification and pixel-labeling tasks for individual, sequential, and combined datasets.
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The authors identify a Golden Partition Zone based on an intra-class variance shift in entropy bounds that enables intrinsic model inversion resistance when partitioning neural networks for collaborative inference.
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Quantum Interval Bound Propagation for Certified Training of Quantum Neural Networks
QIBP adapts interval bound propagation to quantum neural networks for certified adversarial robustness via interval and affine arithmetic implementations.
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Grokking of Diffusion Models: Case Study on Modular Addition
Diffusion models show grokking on modular addition by composing periodic operand representations in simple data regimes or by separating arithmetic computation from visual denoising across timesteps in varied regimes.
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E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring
E-PMQ improves 4-bit quantization accuracy on merged models by 8-42 points across CLIP and GLUE tasks through expert-guided calibration and merged-weight anchoring.
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Bayesian Model Merging
Bayesian Model Merging introduces a bi-level optimization framework that merges task-specific models via closed-form Bayesian regression with an anchor prior and global hyperparameter search, outperforming baselines and nearly matching expert averages on up to 20-task vision and 5-task language Merg
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ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation
ACE-Merging estimates task input covariances from parameter differences to enable closed-form data-free merging that reduces interference and outperforms prior baselines on vision and language tasks.
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Realistic Handwritten Multi-Digit Writer (MDW) Number Recognition Challenges
New MDW benchmarks demonstrate that isolated digit classifiers struggle with multi-digit numbers from the same writer, necessitating task-specific metrics and advanced methods.
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Reducing Class Bias In Data-Balanced Datasets Through Hardness-Based Resampling
Hardness-Based Resampling reduces class recall gaps in balanced datasets by up to 32% on CIFAR-10 and 16% on CIFAR-100 by prioritizing harder samples over random or frequency-based selection.
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Model Merging: Foundations and Algorithms
New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
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Possibilistic Predictive Uncertainty for Deep Learning
DAPPr introduces a possibilistic framework that projects parameter posteriors to predictions via supremum and approximates them with Dirichlet possibility functions to yield efficient, closed-form epistemic uncertainty estimates.
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Efficient Mutation Testing of Quantum Machine Learning Models
New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.
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Controlled Steering-Based State Preparation for Adversarial-Robust Quantum Machine Learning
A passive steering method for quantum state preparation improves adversarial accuracy in QML models by up to 40% across tested cases.
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Task Alignment: A simple and effective proxy for model merging in computer vision
Task alignment serves as an efficient proxy for hyperparameter selection in model merging, accelerating the process by orders of magnitude while preserving performance in vision models with heterogeneous decoders.
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Continual Distillation of Teachers from Different Domains
SE2D stabilizes continual distillation across heterogeneous teachers by preserving logits on external unlabeled data to mitigate unseen knowledge forgetting.
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Algebraic Machine Learning for Small-to-Medium Datasets Is Competitive against Strong Standard Baselines
AML outperforms cross-validated baselines including CNNs on 50-2000 example image datasets and is comparable to XGBoost/LightGBM on tabular data using only training data and no task-dependent hyperparameters.
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Stimulus symmetries can confound representational similarity analyses
Stimulus symmetries render many neural representations functionally equivalent yet produce qualitatively different RSMs, including drifting ones from SGD or regularization in image-encoding networks.
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Risk-Consistent Multiclass Learning from Random Label-Subset Membership Queries
The paper introduces risk-consistent multiclass learning from random label-subset queries by deriving an unbiased risk estimator under ERM, plus non-negative and absolute-value corrections, with generalization bounds and consistency results.
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Dendritic Neural Networks with Equilibrium Propagation
Dendritic EP matches standard EP on simple tasks but significantly outperforms it on KMNIST and FMNIST, and in deeper models, approaching the performance of backpropagation-trained dendritic networks.
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Context-Aware Multipath Networks
CAMNet uses data-dependent routing across parallel tensors in a multi-path network to outperform equivalent single-path, multi-path, and deeper networks on classification and pixel-labeling tasks for individual, sequential, and combined datasets.
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