11383 papers
This paper introduces RefQuery, a lightweight and scalable transfer learning framework for Non-Intrusive Load Monitoring that utilizes a frozen shared model with learnable per-appliance embeddings to achieve accurate, real-time load disaggregation across diverse homes and resource-constrained edge devices.
This paper introduces a multi-operator reinforcement learning framework that integrates discrete choice theory to model competitive dynamics in Autonomous Mobility-on-Demand systems, demonstrating that competition fundamentally alters learned pricing and fleet rebalancing strategies compared to monopolistic settings while maintaining robust convergence.
This paper extends the Edge of Stability phenomenon to non-Euclidean gradient descent by introducing a generalized sharpness measure based on directional smoothness, demonstrating that diverse optimizers exhibit similar stability thresholds and oscillatory behaviors across arbitrary geometric norms.
This paper introduces BA-Logic, a novel framework for clean-label backdoor attacks on Graph Neural Networks that overcomes the limitations of existing methods by poisoning the model's inner prediction logic through a coordinated poisoned node selector and trigger generator, thereby achieving high attack success rates without modifying training labels.
This paper introduces the Credibility Index via Explanation Stability (CIES), a mathematically grounded metric that quantifies the robustness of AI explanations under realistic business data perturbations, demonstrating its superior ability to assess model reliability and guide decision-making across various high-stakes scenarios compared to existing baselines.
The paper introduces RepoLaunch, an agent that automates the build and test pipeline for code repositories across any language and platform, thereby enabling fully automated creation of software engineering datasets for benchmarking and training coding agents.
GELO is a lightweight protocol that preserves prompt privacy for LLMs on untrusted accelerators by applying fresh, per-batch invertible mixing to hidden states, effectively thwarting statistical attacks while maintaining exact output accuracy with only 20–30% latency overhead.
This paper proposes Margin-Based Cross-Entropy Loss (MCEL), a novel, efficient training objective that explicitly maximizes output logit margins to significantly enhance bit error tolerance in quantized neural networks, offering a scalable alternative to computationally expensive error-injection training methods.
This paper provides a precise asymptotic analysis of multi-task learning in misspecified perceptron models, demonstrating that combining related tasks is equivalent to adding implicit regularization that improves generalization and mitigates the double descent phenomenon.
This paper proposes a deep learning-driven framework for secure multicarrier ISAC systems that utilizes radar echo feedback and a novel nonparametric Fisher Information Matrix estimator to optimize directional friendly jamming and beamforming under channel uncertainty and unknown eavesdropper locations, while employing a quantized tensor train encoder for efficient implementation.
This paper introduces Reward-Conditioned Reinforcement Learning (RCRL), a framework that trains a single agent to optimize a family of reward specifications from a shared off-policy dataset, enabling robust and efficient adaptation to changing task preferences without sacrificing the simplicity of single-task training.
This paper introduces a novel clustering algorithm for unit hypersphere data based on the -dimensional generalized Kuramoto model, demonstrating its effectiveness and superior or comparable accuracy against traditional methods through experiments on both synthetic and real-world datasets.
This paper introduces Aura, a universal framework that enhances aviation time series forecasting by explicitly encoding and integrating three distinct types of multi-dimensional exogenous factors through a tailored tripartite mechanism, achieving state-of-the-art performance on large-scale industrial datasets.
This paper proposes a novel Axiomatic On-Manifold Shapley framework that utilizes optimal generative flows and Wasserstein-2 geodesics to eliminate off-manifold artifacts, ensuring geometric efficiency, reparameterization invariance, and superior semantic alignment in model attribution.
This paper introduces ARC-TGI, an open-source framework featuring human-validated Python generators that produce diverse, rule-consistent ARC-AGI tasks with natural language reasoning chains and code, addressing dataset overfitting by ensuring training examples collectively reveal underlying rules for scalable benchmarking.
This paper introduces BLINK, a trajectory-based recurrent state-space model that learns latent interaction dynamics from time-lapse microscopy to accurately predict and forecast NK cell cytotoxic outcomes while providing an interpretable representation of cellular behavioral modes.
This paper proposes a two-stage reward curriculum that decouples task-specific objectives from behavioral terms to improve exploration and training stability in robotic reinforcement learning, demonstrating superior performance and robustness across multiple environments compared to direct full-reward training.
This paper proposes FedBCGD and its accelerated variant FedBCGD+, novel federated learning algorithms that split model parameters into blocks to enable selective client uploads, thereby significantly reducing communication overhead and achieving faster convergence for large-scale deep models compared to existing methods.
The paper proposes SRasP, a novel method for Cross-Domain Few-Shot Learning that mitigates domain shift and improves generalization by using global semantic guidance to reorient and aggregate style gradients, thereby stabilizing training and encouraging convergence to flatter, more transferable solutions.
This paper demonstrates that a particle-guided diffusion model trained on advection-reaction-diffusion solutions can effectively generate physically consistent concentration fields and accurately predict outlet concentrations for gas-phase chemical reactions, even under unseen parameter conditions.