Disentangled Feature Importance
arXiv:2507.00260v3 Announce Type: replace-cross Abstract: When predictors are statistically dependent, the appropriate definition of feature importance depends on the operational goal. Conditional-inc
Knowledge catalogue
arXiv:2507.00260v3 Announce Type: replace-cross Abstract: When predictors are statistically dependent, the appropriate definition of feature importance depends on the operational goal. Conditional-inc
arXiv:2606.07677v1 Announce Type: cross Abstract: Electronic health records (EHR) pose large-scale multi-disease modeling problems in which many outcomes are rare and strongly influenced by shared ris
arXiv:2507.19700v2 Announce Type: replace Abstract: We propose a new framework for generating tabular synthetic datasets via disjoint generative models. In this paradigm, a dataset is partitioned into
arXiv:2606.09646v1 Announce Type: cross Abstract: We study whether pretrained video foundation models encode intuitive-physics information in their frozen representations, and how this information var
arXiv:2606.09043v1 Announce Type: new Abstract: Reward models trained from pairwise preferences often exploit superficial shortcut cues rather than learning true response quality. We propose DynaCF, a
arXiv:2606.09803v1 Announce Type: new Abstract: We present extbf{Echo-Memory}, a controlled study of memory mechanisms in action-conditioned world models. These models generate multi-segment videos fr
arXiv:2606.09081v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) object detection requires compact detectors that retain small-object details under onboard computation and memory constrai
arXiv:2510.10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environ
arXiv:2606.08495v1 Announce Type: cross Abstract: Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified traje
arXiv:2606.08565v1 Announce Type: cross Abstract: Tensor networks provide efficient representations for compressing large neural networks. By carefully designing shapes and topologies, they can signif
arXiv:2406.07318v3 Announce Type: replace Abstract: The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems
arXiv:2606.07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite r
arXiv:2606.07571v1 Announce Type: cross Abstract: Key-value (KV) caching for shared prefixes is essential for high-throughput large language model (LLM) serving, but it faces critical challenges in em
arXiv:2606.07902v1 Announce Type: new Abstract: Powered prostheses conventionally rely on impedance controllers that require extensive manual tuning and explicit mode classification. In this work, we
arXiv:2606.09792v1 Announce Type: new Abstract: End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formal
arXiv:2505.20137v5 Announce Type: replace-cross Abstract: Predictive Coding (PC) offers a brain-inspired alternative to backpropagation for neural network training, described as a physical system mini
arXiv:2606.08980v1 Announce Type: new Abstract: This paper introduces EPS3D, a new end-to-end feed-forward framework for open-vocabulary 3D panoptic segmentation. Unlike existing methods relying on ad
arXiv:2606.08941v1 Announce Type: cross Abstract: This paper proposes a collapsible method for estimating causal effects that maintains the estimator's consistency before and after marginalization ove
arXiv:2606.09718v1 Announce Type: cross Abstract: Diffusion models have demonstrated remarkable generative capabilities and have also emerged as powerful self-supervised representation learners, yet t
arXiv:2606.07702v1 Announce Type: cross Abstract: The heterogeneity of client data and systems makes it difficult to achieve satisfactory convergence speed and robustness in federated learning with ra
arXiv:2606.09360v1 Announce Type: new Abstract: Open-domain open-vocabulary detection (ODOVD) requires detectors to generalize to both novel categories and unseen domains, making it more challenging t
arXiv:2606.08658v1 Announce Type: new Abstract: LLMs have revolutionized knowledge representation and retrieval, but lack the explicit modeling that knowledge ontologies possess. This paper surveys th
arXiv:2606.08908v1 Announce Type: cross Abstract: Semiconductor lithography inspection requires reliable detection of small pattern defects such as bridge, burr, pinch, and contamination. In this stud
arXiv:2507.00322v2 Announce Type: replace-cross Abstract: Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanc
arXiv:2606.07949v1 Announce Type: cross Abstract: This study analyses the Ako tidal flat in the Seto Inland Sea, Japan, where nearly all Zostera marina disappeared within a single year in 2025. Using
arXiv:2606.08898v1 Announce Type: cross Abstract: In the task of few-shot class-incremental audio classification, the number of classes is assumed to always increase without considering the possibilit
arXiv:2606.05556v2 Announce Type: replace Abstract: This study presents a comprehensive field validation of a multi-resolution Convolutional Long Short-Term Memory (ConvLSTM) framework for predicting
arXiv:2606.08476v1 Announce Type: cross Abstract: Context parallelism (CP) is essential for training large-scale, long-context language models, as it partitions sequences to reduce memory overhead. Ho
arXiv:2606.07572v1 Announce Type: cross Abstract: Despite Japan being one of the world's largest advanced democracies, the development of election forecasting models for its national elections remains
arXiv:2506.10341v2 Announce Type: replace Abstract: Interactively learning from observation and language feedback is an increasingly studied area driven by the emergence of large language model (LLM)
arXiv:2606.07575v1 Announce Type: cross Abstract: Regulatory stress testing frameworks, including the Comprehensive Capital Analysis and Review (CCAR) and the Internal Capital Adequacy Assessment Proc
arXiv:2602.08733v2 Announce Type: replace Abstract: Ordinary differential equations (ODEs) are central to scientific modelling, but inferring their vector fields from noisy trajectories remains challe
arXiv:2606.08871v1 Announce Type: cross Abstract: The Fourier neural operator (FNO) is a neural network architecture that learns mappings between function spaces. Its efficient implementation is based
arXiv:2606.09666v1 Announce Type: new Abstract: Output space pattern sampling is a powerful alternative to exhaustive pattern mining for exploring large pattern spaces, as it enables users to focus on
arXiv:2606.09029v1 Announce Type: new Abstract: Methods based on implicit neural representations have demonstrated superior performance in Screen Content Image Super-Resolution (SCISR) . However, they
arXiv:2606.07791v1 Announce Type: cross Abstract: Classical spectral descriptors such as the Heat Kernel Signature and Wave Kernel Signature are widely used for non-rigid 3D shape retrieval, yet their
arXiv:2606.09134v1 Announce Type: cross Abstract: Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to form
arXiv:2606.08282v1 Announce Type: new Abstract: We consider a problem arising in proof-of-stake blockchain environments, where agents called nominators select validators - entities responsible for mai
arXiv:2602.02431v2 Announce Type: replace-cross Abstract: It is folklore that reusing training data more than once can improve the statistical efficiency of gradient-based learning. While this phenome
arXiv:2603.27493v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs), characterized by their event-driven computation and low power consumption, have shown great potential for energy-eff
arXiv:2606.07560v1 Announce Type: cross Abstract: Function-vector (FV) heads (Todd et al., 2024) are typically identified by the magnitude of their causal contribution to in-context rule tasks, under
arXiv:2606.08866v1 Announce Type: new Abstract: CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. These hea
arXiv:2509.21925v2 Announce Type: replace-cross Abstract: This paper investigates the theoretical behavior of generative models under finite training populations. Within the stochastic interpolation g
arXiv:2606.08360v1 Announce Type: cross Abstract: Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infec
arXiv:2606.08303v1 Announce Type: new Abstract: This paper investigates a novel concept of time series geolocalization, where the goal is to infer the geographic origin of each raw time series. Succes
arXiv:2509.11485v3 Announce Type: replace-cross Abstract: Labyrinthine stripe patterns are common in many physical systems, yet their lack of long-range order makes quantitative characterization chall
arXiv:2602.16015v2 Announce Type: replace Abstract: Conformal prediction gives finite-sample coverage guarantees for regression, but most standard constructions are designed for Euclidean output space
arXiv:2606.08404v1 Announce Type: new Abstract: Cortical folding reflects coordinated neurodevelopmental processes and is increasingly recognized as a sensitive marker of neurological disease. However
arXiv:2606.07725v1 Announce Type: cross Abstract: Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic
arXiv:2606.09476v1 Announce Type: new Abstract: Hindsight relabeling usually turns achieved future states into exact goals, which can overconstrain offline robot learning when task success depends onl
arXiv:2603.10395v2 Announce Type: replace Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery. Recently, discrete flow matching-based graph generation, aka
arXiv:2606.09432v1 Announce Type: new Abstract: Modeling interacting dynamical systems requires capturing spatial interactions alongside long-range temporal dependencies. Graph neural networks (GNNs)
arXiv:2606.08440v1 Announce Type: cross Abstract: Robotic grasping is a fundamental capability in robotic manipulation. Yet grasping remains challenging under partial observations. Reliable grasping d
arXiv:2606.08133v1 Announce Type: new Abstract: Ground contact forces acting on the human body, are crucial for biomechanics studies or sport performance analysis. Prior methods rely on force plates o
arXiv:2606.08302v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models adopt a next-scale prediction paradigm, offering high-quality generation with substantially fewer decoding steps. How
arXiv:2505.07833v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) improves the reliability of large language models by integrating external knowledge, but serving RAG pipe
arXiv:2606.08403v1 Announce Type: cross Abstract: Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views. We study a reproducible LLM01-
arXiv:2601.06599v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often encode whether a statement is true as a vector in their residual stream activations. These vectors, also kn
arXiv:2606.08218v1 Announce Type: cross Abstract: Compositional priors describe the generic properties of layered functions in deep Bayesian models, where deep neural networks with random weights are
arXiv:2606.08777v1 Announce Type: cross Abstract: Visual Language Models (VLMs) are known to produce hallucinated predictions that are not grounded in visual evidence, yet existing approaches lack a p