9495 papers
This paper presents a framework for the certified and accurate computation of deep neural network function space norms (including , , and ) by combining interval arithmetic, adaptive refinement, and quadrature to derive guaranteed global bounds from local certificates, thereby enabling reliable error control for PDE applications like PINNs.
This paper introduces a Correlation-Complexity Map, comprising Quantum Correlation-Likeness and Classical Correlation-Complexity indicators, to identify real-world data distributions suitable for IQP-based quantum generative models, demonstrating that such models can achieve competitive performance with fewer resources on complex turbulence data when guided by this diagnostic framework.
This paper demonstrates that in quantum Gaussian dynamics, reversing a noising process using a fixed-diffusion Wigner-score drift generally violates complete positivity unless specific conditions are met, thereby necessitating the injection of additional diffusion to ensure physical validity and incurring a fundamental cost in fidelity.
The paper introduces NOBLE, a pretraining architecture that permanently augments transformer linear layers with learnable nonlinear low-rank branches (specifically using CosNet activation), achieving significant training efficiency and speedups across various models with minimal parameter and time overhead, though its benefits may be hindered by certain stochastic data augmentations.
COLD-Steer is a training-free framework that steers large language models by approximating the representational changes of gradient descent on in-context examples, achieving up to 95% steering effectiveness with 50 times fewer samples than existing methods.
This paper introduces a semantics-aware caching approach that significantly reduces the runtime of concept learning by efficiently managing instance retrieval across diverse symbolic and neuro-symbolic reasoners, achieving speedups of up to an order of magnitude.
This paper challenges the assumption that multimodal backdoor attacks are synergistic by revealing a "winner-takes-all" phenomenon called Backdoor Modality Collapse, where attacks degenerate to rely predominantly on a single modality, and introduces new metrics to quantify this vulnerability.
This paper presents an artificial intelligence system trained on over 45,000 ultrasound images that achieves diagnostic accuracy comparable to senior radiologists for fetal orofacial clefts, significantly enhances junior radiologists' performance when used as a copilot, and accelerates clinical expertise development for rare conditions.
This paper demonstrates how transformer models can serve as computationally efficient surrogates to accurately predict atomistic transition pathways in nano-clusters, enabling the generation of diverse microstates while overcoming the high costs of conventional simulation techniques.
This paper introduces a novel interpretability method for large hierarchical probabilistic time-series forecasting that addresses structural and uncertainty challenges, successfully explaining state-of-the-art industrial models to enhance stakeholder trust and decision-making through real-world case studies and semi-synthetic evaluations.
This paper introduces CODEC, a method that utilizes sparse autoencoders to decompose neural network computations into sparse, causal motifs of hidden-neuron contributions, thereby revealing how nonlinear processes evolve across layers and enabling greater interpretability and control of both artificial and biological neural systems.
This paper proposes Hybrid Hierarchical RL (H^2RL), a two-stage framework that leverages logical option-based pretraining to inject symbolic structure into deep reinforcement learning agents, effectively mitigating reward misalignment and improving long-horizon decision-making while outperforming existing neural, symbolic, and neuro-symbolic baselines.
This paper introduces AllScAIP, a scalable, attention-based machine-learning interatomic potential that leverages all-to-all node attention to effectively capture long-range interactions and achieve state-of-the-art accuracy across diverse molecular and material systems without relying on explicit physics-based terms.
SCOPE introduces a plug-and-play framework for incremental few-shot 3D segmentation that enriches novel class prototypes by retrieving and fusing high-confidence pseudo-instances from unlabelled background regions, thereby achieving state-of-the-art performance on ScanNet and S3DIS while mitigating catastrophic forgetting without retraining the backbone.
The paper proposes BEVLM, a framework that bridges the gap between spatially consistent Bird's-Eye View representations and Large Language Models by distilling semantic knowledge, thereby significantly enhancing both cross-view reasoning accuracy and safety-critical end-to-end driving performance.
This paper proposes a Neyman-orthogonal estimator for linear multidimensional panel data with interactive fixed-effects that combines factor model methods with a weighted-within transformation to achieve parametric consistency and asymptotic normality, demonstrated through an application to beer demand elasticity.
This paper proposes two novel single-loop zeroth-order primal-dual algorithms, ZO-PDAPG and ZO-RMPDPG, that achieve state-of-the-art iteration complexity guarantees for solving nonconvex-(strongly) concave minimax problems with coupled linear constraints under both deterministic and stochastic settings.
This paper introduces Orthonormal Data Collaboration (ODC), a method that enforces orthonormal bases to transform the alignment challenge into a closed-form Orthogonal Procrustes problem, thereby achieving orthogonal concordance, significantly reducing computational complexity, and improving accuracy without compromising privacy or communication efficiency.
This paper proposes a novel multi-task subset selection framework that achieves localized distributional robustness by introducing a relative-entropy regularization term, which is proven equivalent to maximizing a monotone composition of submodular functions and can be efficiently solved via greedy algorithms, as validated by experiments on satellite sensor selection and image summarization.
This paper proposes a novel two-phase learning framework that distills privileged information from LKH-generated expert trajectories to enable a non-holonomic vehicle to solve Dubins Traveling Salesman Problems with Neighborhoods approximately 50 times faster than traditional methods while ensuring all task points are visited.