February 2026

Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks. Despite its popularity, its theoretical foundations and behavior under distributional shift remain poorly understood. In this work, we provide a principled statistical interpretation by proving that EDL training corresponds to amortized variational inference in a hierarchical Bayesian model with a tempered pseudo-likelihood. This perspective reveals a major drawback: standard EDL conflates epistemic and aleatoric uncertainty, leading […]

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Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs) is a fundamental yet computationally challenging problem arising in domains such as diagnosis, planning, and structured prediction. In many practical settings, the graphical model remains fixed while inference must be performed repeatedly for varying evidence patterns. Stochastic Local Search (SLS) algorithms scale to large models but rely on myopic best-improvement rule that prioritizes immediate likelihood gains and often stagnate in poor local optima. Heuristics such as Guided Local […]

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Building AI features is straightforward until you need to integrate them into a production system. In production, a simple ‘call an LLM’ often becomes: wiring HTTP/JMS/Kafka/file inputs, enforcing timeouts, retries, fallbacks, stitching context obtained from internal data sources, protecting secrets, making it testable without burning API credits, and ensuring the system is observable when issues occur overnight. This is where Apache Camel excels. With Camel 4.5 and later, you can handle LLM calls as standard integration endpoints using […]

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This article explores a conservative looping strategy on Fluid using USDe/USDT on Arbitrum. With ~8.9% APY and a liquidation buffer of ~12%, the strategy prioritizes liquidity and risk-adjusted returns over maximum yield. Break-even is reached after ~6–8 weeks. Assets Use reliable stablecoins from trusted issuers, where the probability of a depeg is very low. The final choice is always yours. Personally, I’d start with: USDC USDT USDe Later, this list of reliable stablecoins can be expanded. Fluid strategy: […]

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According to a popular viewpoint, neural networks learn from data by first identifying low-dimensional representations, and subsequently fitting the best model in this space. Recent works provide a formalization of this phenomenon when learning multi-index models. In this setting, we are given $n$ i.i.d. pairs $({boldsymbol x}_i,y_i)$, where the covariate vectors ${boldsymbol x}_iinmathbb{R}^d$ are isotropic, and responses $y_i$ only depend on ${boldsymbol x}_i$ through a $k$-dimensional projection ${boldsymbol Θ}_*^{sf T}{boldsymbol x}_i$. Feature learning amounts to learning the latent […]

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Few-shot learning requires models to generalize under limited supervision while remaining robust to distribution shifts. Existing Sinkhorn Distributionally Robust Optimization (DRO) methods provide theoretical guarantees but rely on a fixed reference distribution, which limits their adaptability. We propose a Prototype-Guided Distributionally Robust Optimization (PG-DRO) framework that learns class-adaptive priors from abundant base data via hierarchical optimal transport and embeds them into the Sinkhorn DRO formulation. This design enables few-shot information to be organically integrated into producing class-specific robust […]

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High-level Computer-Aided Process Planning (CAPP) generates manufacturing process plans from part specifications. It suffers from limited dataset availability in industry, reducing model generalization. We propose a semi-supervised learning approach to improve transformer-based CAPP transformer models without manual labeling. An oracle, trained on available transformer behaviour data, filters correct predictions from unseen parts, which are then used for one-shot retraining. Experiments on small-scale datasets with simulated ground truth across the full data distribution show consistent accuracy gains over baselines, […]

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Deep Reinforcement Learning (DRL), a subset of machine learning focused on sequential decision-making, has emerged as a powerful approach for tackling financial trading problems. In finance, DRL is commonly used either to generate discrete trade signals or to determine continuous portfolio allocations. In this work, we propose a novel reinforcement learning framework for portfolio optimization that incorporates Physics-Informed Kolmogorov-Arnold Networks (PIKANs) into several DRL algorithms. The approach replaces conventional multilayer perceptrons with Kolmogorov-Arnold Networks (KANs) in both actor […]

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Machine learning (ML) offers transformative potential for computational fluid dynamics (CFD), promising to accelerate simulations, improve turbulence modelling, and enable real-time flow prediction and control-capabilities that could fundamentally change how engineers approach fluid dynamics problems. However, the exploration of ML in fluid dynamics is critically hampered by the scarcity of large, diverse, and high-fidelity datasets suitable for training robust models. This limitation is particularly acute for highly turbulent flows, which dominate practical engineering applications yet remain computationally prohibitive […]

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