8247 papers
This paper proposes a simple algebraic rearrangement of the Yates covariance decomposition for the Brier score that decomposes forecast error into three non-negative terms—variance mismatch, correlation deficit, and calibration-in-the-large—thereby making the conditions for optimal probabilistic forecasting transparent.
The paper introduces IntSeqBERT, a dual-stream Transformer model that combines continuous log-scale magnitude embeddings with modulo-spectrum embeddings to effectively learn the arithmetic structure of OEIS integer sequences, significantly outperforming standard tokenized baselines in both sequence modeling accuracy and next-term prediction via a probabilistic Chinese Remainder Theorem solver.
This study employs a stochastic-process model to demonstrate that the optimal autocorrelation of time-series signals for solving multi-armed bandit problems depends on the reward environment, with negative autocorrelation being advantageous in reward-rich settings and positive autocorrelation in reward-poor ones, while performance remains independent of autocorrelation when the sum of winning probabilities equals one.
This paper demonstrates that the Continuous-Time Koopman Autoencoder (CT-KAE) serves as a lightweight, stable, and efficient surrogate model for long-horizon ocean state forecasting, outperforming autoregressive Transformer baselines by maintaining bounded errors and consistent large-scale statistics over 2083-day rollouts while enabling resolution-invariant predictions.
This paper presents a task-based model demonstrating that while generative AI homogenizes individual skills, it can simultaneously increase aggregate inequality by shifting economic value toward concentrated complementary assets, with the net outcome determined by the technology's structure and labor market institutions rather than a single universal verdict.
This paper proposes CDDS, a novel cross-modal alignment algorithm that utilizes a dual-path UNet for constrained decoupling of semantic and modality components and a distribution sampling method to bridge the modality gap, thereby achieving superior semantic consistency and outperforming state-of-the-art methods by 6.6% to 14.2%.
FuseDiff is a novel end-to-end diffusion model that jointly generates a single ligand and two pocket-specific binding poses for dual-target structure-based drug design by employing a symmetry-preserving message-passing backbone with Dual-target Local Context Fusion to overcome the limitations of existing staged pipelines.
This paper proposes a prior information-based distributionally robust individualized treatment rule (PDRO-ITR) that integrates multi-source data to address posterior shift by maximizing the worst-case policy value over a covariate-dependent uncertainty set, thereby ensuring robust performance and achieving superior results in simulations and real-world applications.
This study presents an end-to-end machine learning pipeline utilizing XGBoost and SHAP explainability to integrate bulk and single-cell transcriptomic data from multiple sclerosis patients, successfully identifying high-performance biomarkers and novel mechanistic pathways involving immune activation, non-canonical checkpoints, and Epstein-Barr virus-related processes.
This paper utilizes a Lie-algebraic control perspective to demonstrate that increasing the depth of parallelizable sequence models exponentially reduces approximation error by expanding their expressivity through a tower of Lie algebra extensions, a finding validated by experiments on symbolic and continuous state-tracking tasks.
This paper proposes a prediction-powered conditional inference method that leverages abundant unlabeled data and machine learning predictors to construct valid, variance-reduced confidence intervals for conditional functionals at fixed test points without requiring parametric models, while establishing rigorous nonasymptotic error bounds and asymptotic normality.
This paper introduces the Deep Koopman Speech Disentanglement Autoencoder (DKSD-AE), a scalable and efficient architecture that leverages Koopman operators and instance normalization to effectively disentangle speaker identity from linguistic content for robust speaker verification without relying on textual supervision or large pretrained models.
This paper proposes a novel Hybrid Heuristic-Reinforcement Learning (HHRL) framework that integrates railway-specific heuristics with Q-learning to efficiently solve complex railcar shunting problems involving both one-sided and two-sided classification tracks by decomposing multi-locomotive tasks into manageable subproblems.
This study proposes and validates a GeoAI hybrid framework integrating MGWR, Random Forest, and ST-GCN to effectively model the spatiotemporal heterogeneity of multimodal traffic flows and their interaction with land use, demonstrating superior predictive accuracy and revealing distinct urban traffic typologies that underscore the critical role of local morphological context in mobility planning.
This paper introduces Bias-Invariant Subnetwork Extraction (BISE), a method that identifies and isolates fair, bias-agnostic subnetworks within standard pre-trained models through pruning, enabling effective bias mitigation without retraining or additional unbiased data.
This paper demonstrates that pretraining tokenizers with an autoencoding objective before training dynamics models significantly enhances the computational efficiency and accuracy of physics foundation models, particularly when the pretraining data aligns with the downstream physical system.
This paper introduces the first coupled robustness verification framework for heatmap-based keypoint detectors that uses a mixed-integer linear program to jointly bound deviations across all keypoints, thereby providing sound and less conservative guarantees than prior decoupled methods.
This paper introduces behavior-decomposed linear dynamical systems (b-dLDS), a novel modeling approach that disentangles behavior-related neural dynamics from internal computations in large-scale brain recordings, demonstrating superior performance over existing supervised models and successfully scaling to tens of thousands of neurons in zebrafish hindbrain data.
The paper introduces RACAS, a robot-agnostic agentic system that uses natural language communication between LLM/VLM-based modules to control diverse robotic platforms without requiring code modifications or retraining, successfully demonstrating its effectiveness across wheeled, multi-jointed, and underwater robots.
This paper proposes a domain-agnostic framework to identify the most probable characteristics of an attacker from an observed data-manipulation attack, demonstrating that such identification enables more effective exogenous mitigation and improves the performance of learning-based defenses.