arXiv:2511.20888v2 Announce Type: replace Abstract: This paper argues that DNNs implement a computational Occam’s razor — finding the `simplest’ algorithm that fits the data — and that this could explain their incredible and wide-ranging success over more traditional statistical methods. We start with the discovery that the set of real-valued function $f$ that can be $epsilon$-approximated with a binary circuit of size at most $cepsilon^{-gamma}$ becomes convex in the `Harder than Monte Carlo’ (HTMC) regime, when $gamma>2$, allowing […]

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arXiv:2604.06227v1 Announce Type: new Abstract: Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developing economies, yet machine-learning-ready datasets for this purpose remain scarce in South Asia. This paper makes two contributions. First, we introduce AgriPriceBD, a benchmark dataset of 1,779 daily retail mid-prices for five Bangladeshi commodities – garlic, chickpea, green chilli, cucumber, and sweet pumpkin – spanning July 2020 to June 2025, extracted from government reports via […]

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arXiv:2505.17204v2 Announce Type: replace Abstract: The sliced Wasserstein flow (SWF), a nonparametric and implicit generative gradient flow, is transformed to a Liouville partial differential equation (PDE)-based formalism. First, the stochastic diffusive term from the Fokker-Planck equation-based Monte Carlo is reformulated to Liouville PDE-based transport without the diffusive term, and the involved density estimation is handled by normalizing flows of neural ODE. Next, the computation of the Wasserstein barycenter is approximated by the Liouville PDE-based SWF barycenter with the […]

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I have implemented dreamer but cannot get it to solve the walker2d environment. I copied and pasted much of the code from public repositories, but wrote the loss computation part myself. I’ve spent several days trying to debug the code and would really appreciate your help.. I’ve put a github link to the code. I’m suspecting the indexing might be wrong in the computation of lambda returns, but there could many other mistakes. I usually don’t post anything […]

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arXiv:2603.28971v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) improves sample efficiency by leveraging learned dynamics models for policy optimization. However, the effectiveness of methods such as actor-critic is often limited by compounding model errors, which degrade long-horizon value estimation. Existing approaches, such as Model-Based Value Expansion (MVE), partially mitigate this issue through multi-step rollouts, but remain sensitive to rollout horizon selection and residual model bias. Motivated by the Pontryagin Maximum Principle (PMP), we propose Hamiltonian Actor-Critic (HAC), […]

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arXiv:2601.08070v1 Announce Type: new Abstract: Negative constraints (instructions of the form “do not use word X”) represent a fundamental test of instruction-following capability in large language models. Despite their apparent simplicity, these constraints fail with striking regularity, and the conditions governing failure have remained poorly understood. This paper presents the first comprehensive mechanistic investigation of negative instruction failure. We introduce semantic pressure, a quantitative measure of the model’s intrinsic probability of generating the forbidden token, and demonstrate that […]

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One-shot federated learning (OSFL) addresses the communication overhead of federated learning by limiting training to a single round, but doing so without sacrificing model quality is non-trivial, particularly when client data distributions diverge. Recent work has addressed this challenge by aggregating client knowledge on the server through the construction of transferable synthetic datasets or distillates. However, most of these methods lack formal privacy guarantees, leaving a gap in jointly achieving low communication, robustness to heterogeneity, and rigorous privacy. […]

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:::info Astounding Stories of Super-Science March, 1932, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Affair of the Brains – Chapter I: Off to the Rendezvous Astounding Stories of Super-Science March 1932: The Affair of the Brains – Chapter I Off to the Rendezvous By Anthony Gilmore ::: :::tip Hawk Carse himself goes to keep Judd the Kite’s rendezvous with the sinister genius Ku Sui. ::: […]

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arXiv:2603.10050v1 Announce Type: new Abstract: This paper presents a geometrically explicit formulation for Cosserat rods that unifies configuration-space and strain-based representations within a single modeling framework. The proposed method uses nodal configurations on the Lie group SE(3) as generalized coordinates, while internal strains are reconstructed via a piecewise-linear parameterization. This hybrid design preserves the geometric rigor of Lie-group formulations and retains the locality, simplicity, and computational efficiency characteristic of strain-parameterized rod models. The formulation naturally avoids shear and […]

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arXiv:2603.12305v1 Announce Type: new Abstract: The ability to understand and reason about cause and effect — encompassing interventions, counterfactuals, and underlying mechanisms — is a cornerstone of robust artificial intelligence. While deep learning excels at pattern recognition, it fundamentally lacks a model of causality, making systems brittle under distribution shifts and unable to answer “what-if” questions. This paper introduces the emph{Hierarchical Causal Primitive Dynamic Composition Network (HCP-DCNet)}, a unified framework that bridges continuous physical dynamics with discrete symbolic […]

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