5185 papers
This paper proposes CbLDM, a Condition-based Latent Diffusion Model that utilizes conditional priors and Laplacian matrices to effectively and stably recover the nanostructures of monometallic nanoparticles from their atomic pair distribution functions, addressing the highly ill-posed nature of the inverse problem.
This paper proposes a unified training framework that combines entropy-driven curriculum learning, which sequences training from simple to complex trajectories based on Lempel-Ziv compression, with multi-task learning to simultaneously optimize location, distance, and direction predictions, thereby achieving state-of-the-art performance and significantly faster convergence in human mobility prediction.
This paper benchmarks Multivariate Imputation by Chained Equations (MICE) against deep generative models like Variational Autoencoders and Conditional Tabular GANs for synthetic ratemaking data, finding that MICE offers a simpler yet high-fidelity alternative that effectively preserves statistical distributions and supports robust Generalized Linear Model training.
This paper proposes the F²SA- method, which utilizes -th order finite differences to achieve a nearly optimal complexity for finding -stationary points in stochastic bilevel optimization with highly smooth objectives, thereby improving upon previous first-order bounds and matching the fundamental lower limit.
Through large-scale experiments with over 1.5 million LLM-generated behavioral sequences, this paper reveals a fundamental asymmetry in behavioral inference where agent motivations are nearly perfectly recoverable while belief systems remain largely opaque due to inherent information-theoretic limits and architectural constraints, particularly within a "neutral zone" of behavioral ambiguity.
This paper introduces a modular, multimodal framework that leverages generative AI to synthesize realistic residential building data from public images and information, thereby overcoming data accessibility and privacy barriers to advance energy modeling and machine learning research.
The paper introduces PANO, a physics-aware neural operator that performs direct, single-pass inversion of raw sensor data into high-quality 3D photoacoustic images, outperforming traditional algorithms and enabling real-time reconstruction across diverse sparse acquisition settings to facilitate the clinical translation of 3D PACT.
This paper presents a fast, scalable method that combines deep learning with Dynamic Input Conductances (DICs) to reconstruct diverse, degenerate populations of conductance-based neuron models directly from spike times, effectively bridging experimental recordings and mechanistic biophysical parameters.
This paper introduces MICA, a privacy-preserving, speech-interactive multi-agent system that leverages Adaptive Step Fusion and a safety-audited coordination topology to deliver robust, real-time industrial assistance for assembly and maintenance tasks on resource-constrained hardware.
This paper introduces the ORIC framework and benchmark to evaluate and improve Large Vision-Language Models' object recognition capabilities under contextual incongruity, demonstrating that such scenarios significantly degrade performance and that targeted Visual Reinforcement Fine-Tuning can effectively mitigate these failures.
This paper proposes ORN-CBF, a hypernetwork-based learning framework that utilizes Hamilton-Jacobi reachability analysis to generate observation-conditioned neural control barrier functions, ensuring rigorous safety guarantees and improved generalization in partially observable environments through simulation and hardware experiments.
This paper introduces the first fully empirical PAC-Bayes bound for Markov chains by deriving a data-dependent estimate for the pseudo-spectral gap, thereby eliminating the need for unknown constants related to mixing properties that typically hinder practical generalization guarantees.
This paper demonstrates that linear probes used to detect harmful behaviors in language models are heavily reliant on explicit textual evidence, as their performance significantly degrades when such surface-level cues are filtered out or when models are trained to express behaviors without verbalization.
The paper introduces AEGIS, an edge-only augmentation framework that resamples existing training edges to enhance link prediction in edge-sparse bipartite knowledge graphs, demonstrating that authenticity-constrained resampling preserves data integrity while semantic KNN augmentation further boosts performance when node descriptions are available.
Aurora is a multimodal time series foundation model that leverages text and image modalities to guide temporal representation learning and prototype-based flow matching, achieving state-of-the-art zero-shot cross-domain generalization across diverse forecasting benchmarks.
This paper introduces GDR-learners, a flexible suite of generative models (including CNFs, CGANs, CVAEs, and CDMs) that achieve quasi-oracle efficiency and double robustness for estimating potential outcome distributions, thereby outperforming existing methods in both theoretical properties and empirical performance.
CLAD-Net is a continual learning framework for wearable human activity recognition that combines a self-supervised transformer for long-term memory and a supervised CNN with knowledge distillation to effectively mitigate catastrophic forgetting and handle label scarcity across diverse subjects.
The paper introduces Generative Evolutionary Meta-Solver (GEMS), a scalable, surrogate-free multi-agent reinforcement learning framework that replaces explicit policy populations with a compact generator and latent anchors to achieve significantly faster training, lower memory usage, and higher rewards than traditional methods like PSRO while maintaining game-theoretic guarantees.
This paper introduces FS-KAN, a principled framework that constructs permutation equivariant and invariant Kolmogorov-Arnold Networks via function sharing, offering superior data efficiency and interpretability while maintaining the expressive power of standard parameter-sharing networks.
This paper introduces Overlap-Adaptive Regularization (OAR), a novel method that enhances the performance of existing CATE meta-learners in low-overlap regions by proportionally increasing regularization based on overlap weights, while offering flexible, debiased variants that preserve Neyman-orthogonality for robust inference.