4253 papers
This paper introduces a novel in-context learning approach using Time-Series Foundation Models (TSFMs) to classify bearing health status from vibration data without fine-tuning, enabling scalable, zero-shot maintenance solutions across varying operational conditions.
This paper proposes "Periodic Asynchrony," a framework that accelerates LLM reinforcement learning by decoupling inference and training into a provably on-policy asynchronous pipeline, achieving a 3- to 5-fold throughput improvement on NPU platforms without sacrificing accuracy.
This paper introduces Decoder-DeepONet (DDON), a novel interpretable operator-learning model that significantly outperforms previous neural network and classical methods in reconstructing electric field profiles from EFISH signals by offering superior accuracy, generalizability, and robustness to incomplete data while identifying optimal sampling windows.
This paper introduces ELERAG, an enhanced Retrieval-Augmented Generation system that integrates Wikidata-based Entity Linking and a hybrid re-ranking strategy to significantly improve factual accuracy in Italian educational question-answering, particularly outperforming standard methods in domain-specific contexts while demonstrating the importance of domain-adapted strategies.
This paper introduces SAGFM, a robust Graph Foundation Model framework that enhances domain-adaptive representations and generalization by integrating structure-aware semantic augmentation, an information bottleneck mechanism, and expert adaptive routing to effectively mitigate domain noise, structural perturbations, and adversarial attacks.
ADHint is a novel reinforcement learning framework that enhances reasoning capabilities and generalization by integrating sample difficulty priors to adaptively schedule hint ratios and employing consistency-based gradient modulation with rollout difficulty posteriors to stabilize learning and prevent destructive imitation.
This paper introduces Directional Textual Inversion (DTI), a method that improves personalized text-to-image generation by constraining learned token embeddings to a fixed magnitude and optimizing only their direction on a hypersphere, thereby preventing norm inflation, enhancing text fidelity, and enabling smooth semantic interpolation between concepts.
This study demonstrates that while the multichannel linear descriptors-based block field method (MLD-BFM) achieves the highest accuracy in decoding five finger-joint degrees of freedom from HD sEMG, its performance is not statistically superior to conventional time-domain features, though it significantly outperforms dimensionality reduction methods, highlighting the critical importance of preserving spatial resolution in high-density recordings.
This paper introduces EMFusion, a conditional multivariate diffusion-based framework that leverages a residual U-Net with cross-attention and imputation-based sampling to provide accurate, uncertainty-quantified, frequency-selective electromagnetic field forecasts for wireless network planning, significantly outperforming existing baseline models.
This study introduces the enhanced WT-RDF+ framework, which leverages machine learning-assisted parameter tuning to overcome amplitude accuracy limitations in reconstructing Radial Distribution Functions for amorphous Ge-Se and Ag-Ge-Se systems, thereby outperforming standard ML benchmarks even with limited training data.
This paper identifies a systematic mismatch in activation updates during gradient descent, proposing that correcting this issue through first-principles derivation not only reinterprets the role of normalization but also yields a new, empirically superior alternative called "PatchNorm" that challenges conventional affine-based approaches.
This paper rigorously proves that incorporating local updates into the distributed DIGing algorithm can provably accelerate convergence for a broad class of objective functions, demonstrating that two local updates are sufficient to achieve maximal improvement without requiring reduced step sizes.
This paper introduces Bayesian Generative Modeling (BGM), a unified framework that leverages a stochastic iterative Bayesian updating algorithm to learn a single generative model capable of performing arbitrary conditional inference with principled uncertainty quantification, without requiring retraining for different conditioning structures.
This paper presents an automated system using LLM-powered web research agents to generate and resolve diverse, real-world forecasting questions at scale, demonstrating high-quality question creation and resolution rates that surpass human-curated platforms while effectively evaluating and improving AI forecasting performance.
The paper introduces "Infusion," a framework that leverages scalable influence-function approximations to compute subtle perturbations in training data, demonstrating that modifying as little as 0.2% of a dataset can effectively and transferably shape model behavior across vision and language domains.
This paper proposes a robust, data-driven framework for assortment optimization that maximizes worst-case expected revenue under potential customer preference shifts, establishing computational tractability and deriving statistically optimal algorithms with tight sample complexity bounds to ensure reliable generalization.
This paper proposes a novel bottleneck transformer architecture that integrates convolutional blocks for frame-level feature extraction and multi-head self-attention for information aggregation to achieve improved non-intrusive prediction of the Short-Time Objective Intelligibility (STOI) metric, outperforming state-of-the-art self-supervised learning models in both seen and unseen scenarios.
The paper proposes B-DENSE, a novel distillation framework that leverages multi-branch trajectory alignment to enforce dense intermediate supervision, thereby overcoming the structural information loss and discretization errors of existing methods to achieve superior image generation quality with reduced inference latency.
The paper introduces Missing-by-Design (MBD), a unified framework for revocable multimodal sentiment analysis that combines structured representation learning with a certifiable parameter-modification pipeline to enable the machine-verifiable deletion of specific data modalities while maintaining predictive performance and privacy compliance.
This paper proposes a curriculum-based continual learning framework that decomposes complex robust control problems with multiple uncertainties into sequential tasks, combining a model-based controller with deep reinforcement learning to achieve efficient, non-forgetting policy updates and successful sim-to-real transfer for automotive powertrain vibration control.