3932 papers
This paper introduces "Sample-and-Search," a learning-augmented algorithm for high-dimensional -median clustering that utilizes a predictor to preprocess data, thereby significantly reducing both computational complexity and exponential dimensionality dependency while achieving lower clustering costs compared to state-of-the-art methods.
CacheSolidarity is a lightweight system that secures multi-tenant LLM serving against Automatic Prefix Caching side-channel attacks by selectively isolating suspicious cache reuse, thereby achieving significantly higher cache efficiency and lower latency compared to existing all-or-nothing isolation defenses.
This paper compares Monte Carlo Dropout and Conformal Prediction for uncertainty estimation in CNNs trained on Fashion-MNIST, revealing that while H-CNN VGG16 achieves higher accuracy, GoogLeNet offers better calibration and Conformal Prediction provides statistically guaranteed reliability for high-stakes applications.
This paper proposes a structural remedy for data leakage in machine learning by introducing a grammar of seven kernel primitives connected by a typed directed acyclic graph with four hard constraints, specifically a runtime-enforced terminal assess constraint that prevents selection and memorization leakage, a claim validated across 2,047 experimental instances and implemented in Python, R, and Julia.
CUPID is a novel, plug-in framework that enables joint estimation of aleatoric and epistemic uncertainty in pretrained deep learning models without requiring retraining or architectural modifications, thereby enhancing interpretability and trust in high-stakes AI applications.
This paper proposes a deep learning-based autoencoder architecture for the Randomized Distributed Function Computation (RDFC) framework that minimizes the total variation distance to an unknown target distribution using only data samples, demonstrating superior communication efficiency compared to traditional data compression methods, particularly under limited common randomness.
This paper proposes a method to protect intellectual property in neural network models by binding their weights to unique hardware properties using Physically Unclonable Functions (PUFs), thereby preventing accurate execution on cloned hardware.
This paper proposes Sign-Prioritized FL (SP-FL), a novel wireless federated learning framework that enhances model training reliability under resource constraints by prioritizing the transmission of gradient signs through a hierarchical resource allocation scheme, achieving up to 9.96% higher accuracy than existing methods on the CIFAR-10 dataset.
This paper proposes a fully data-driven framework that combines adaptively computed, mechanism-aware precursors based on Optimal Time-Dependent (OTD) modes with a Transformer model to significantly extend the prediction horizons for extreme events in high-dimensional chaotic systems like Kolmogorov flow without requiring knowledge of the underlying governing equations.
This paper introduces "Super Neurons," a training-free method that leverages scalar activations from the first generated token to create highly accurate classifiers for categorical VQA, enabling extreme early exiting from the first layer and achieving up to a 5.10x speedup while improving performance over the original network.
This paper proposes an AI-driven framework that improves 5G/6G traffic demand prediction accuracy and spatial generalization by employing a context-aware two-stage splitting strategy and residual error correction to mitigate neighborhood leakage, as validated by experiments across five Canadian cities.
This paper proposes a hierarchical Graph Attention Network (HR-GAT) model that leverages public deployment records to accurately estimate fine-grained spectrum demand across multiple cities, significantly outperforming existing baselines and providing regulators with actionable insights for efficient spectrum sharing and allocation.
This paper introduces MCCOP, a framework that leverages a pretrained diffusion model within a joint sequence-structure latent space to generate minimal, biologically plausible protein mutations that flip predictive model outcomes to desired states, thereby bridging the gap between deep learning predictions and actionable protein engineering.
This paper proposes and evaluates randomized smoothing as an effective, simple, and computationally efficient defense mechanism that significantly enhances the robustness of trajectory prediction models against adversarial attacks without compromising their accuracy in standard settings.
ReTabSyn is a reinforcement learning-based pipeline for realistic tabular data synthesis that prioritizes learning conditional distributions to better preserve feature correlations and improve downstream model utility in low-data, imbalanced, and shifted settings.
The paper introduces EvoKernel, a self-evolving agentic framework that leverages value-driven memory and reinforcement learning to overcome data scarcity in NPU kernel synthesis, significantly improving model correctness and achieving substantial speedups through automated drafting and iterative refinement.
The paper proposes , a novel method that dynamically fuses a Generalist Value Model's prior with sparse RL rollouts via real-time statistical testing to minimize baseline estimation error, thereby achieving faster convergence and over 10% performance gains on mathematical reasoning benchmarks compared to GRPO and DAPO.
This paper introduces 6ABOS, an open-source Python framework that leverages the 6S radiative transfer model and Google Earth Engine to automate the atmospheric correction of EnMAP hyperspectral imagery, successfully validating its accuracy in retrieving water-leaving reflectance over diverse Mediterranean reservoirs.
SNPgen is a two-stage conditional latent diffusion framework that generates privacy-preserving, phenotype-aligned synthetic genotype data, enabling machine learning models trained on synthetic samples to achieve predictive performance comparable to those trained on real data while maintaining strict privacy guarantees and preserving key genetic structures.
The paper proposes LAtte, a novel cross-subject EEG classification framework that combines an InceptionTime-based encoder with a Lorentz Attention Module to learn shared baseline patterns and subject-specific embeddings, thereby achieving robust generalization and superior performance over state-of-the-art methods on multiple datasets.