Claude AI has developed into one of the most reliable business-oriented AI systems that operate in the current market. Claude serves as the perfect AI solution for enterprises because Anthropic designed it to maintain safety, dependability, and deliver optimal performance. The transition from Claude 3 to Claude 4 represents a substantial improvement in reasoning, scalability, and real-world application. Enterprises now prefer Claude because it has developed capabilities to process long contexts and generate structured outputs. Why Claude AI? […]

Ver mais

arXiv:2504.16585v2 Announce Type: replace-cross Abstract: In large-scale supervised learning, penalized logistic regression (PLR) effectively mitigates overfitting through regularization, yet its performance critically depends on robust variable selection. This paper demonstrates that label noise introduced during manual annotation, often dismissed as a mere artifact, can serve as a valuable source of information to enhance variable selection in PLR. We theoretically show that such noise, intrinsically linked to classification difficulty, helps refine the estimation of non-zero coefficients compared to using […]

Ver mais

Multimodal affective computing aims to predict humans’ sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. However, current models often learn spurious correlations that harm generalization under distribution shifts or noisy modalities. To address this, we propose a causal modality-invariant representation (CmIR) learning framework for robust multimodal learning. At its core, we introduce a theoretically grounded disentanglement method that separates each modality into `causal invariant representation’ and `environment-specific spurious representation’ from a causal inference perspective. CmIR […]

Ver mais

arXiv:2601.07979v1 Announce Type: cross Abstract: Although extensive research exists in spatial modeling, few studies have addressed finite mixture model-based clustering methods for spatial data. Finite mixture models, especially Gaussian mixture models, particularly suffer from high dimensionality due to the number of free covariance parameters. This study introduces a spatial covariance constraint for Gaussian mixture models that requires only four free parameters for each component, independent of dimensionality. Using a coordinate system, the spatially constrained Gaussian mixture model enables […]

Ver mais

This study examines whether the monthly inflation rate of the energy-related consumer price index component in Turkey can be nowcast more accurately with mixed-frequency indicators. An expanding-window pseudo-out-of-sample design is used to compare a seasonal naive benchmark with Elastic Net, XGBoost, and LightGBM. The predictor set combines monthly macroeconomic indicators with features derived from daily Brent oil prices, daily USD/TRY exchange rates, and hourly EPİAŞ day-ahead electricity market data for 2012–2025. Forecast performance is evaluated with root mean […]

Ver mais

arXiv:2604.18791v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models fail systematically on long-horizon manipulation tasks despite strong short-horizon performance. We show that this failure is not resolved by extending context length alone in the current reactive execution setting; instead, it stems from three recurring execution-loop deficiencies: the memory gap, the verification gap, and the recovery gap. We present HELM, a model-agnostic framework that addresses these deficiencies with three components: an Episodic Memory Module (EMM) that retrieves key task history […]

Ver mais