arXiv:2602.12305v1 Announce Type: new Abstract: Generating high-performance CUDA kernels remains challenging due to the need to navigate a combinatorial space of low-level transformations under noisy and expensive hardware feedback. Although large language models can synthesize functionally correct CUDA code, achieving competitive performance requires systematic exploration and verification of optimization choices. We present OptiML, an end-to-end framework that maps either natural-language intent or input CUDA code to performance-optimized CUDA kernels by formulating kernel optimization as search under verification. OptiML […]

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arXiv:2602.07146v1 Announce Type: new Abstract: While superconductors are highly attractive for energy-efficient computing, fundamental limitations in their logic circuit integration have hindered scaling and led to increased energy consumption. We therefore propose and experimentally demonstrate a novel superconducting switching device utilizing the proximity magnetization from a spin-orbit torque-switched magnet to control the resistivity of a superconductor. We further propose a complete logic family comprised solely of these devices. This novel implementation has the potential to drastically outperform existing […]

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arXiv:2602.13298v1 Announce Type: new Abstract: Increasing convolutional depth has been central to advances in image recognition, yet deeper networks do not uniformly yield higher accuracy, stable optimization, or efficient computation. We present a controlled comparative study of three canonical convolutional neural network architectures – VGG, ResNet, and GoogLeNet – to isolate how depth influences classification performance, convergence behavior, and computational efficiency. By standardizing training protocols and explicitly distinguishing between nominal and effective depth, we show that the benefits […]

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In this paper we consider a regularization approach to variable selection when the regression function depends nonlinearly on a few input variables. The proposed method is based on a regularized least square estimator penalizing large values of the partial derivatives. An efficient iterative procedure is proposed to solve the underlying variational problem, and its convergence is proved. The empirical properties of the obtained estimator are tested both for prediction and variable selection. The algorithm compares favorably to more […]

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arXiv:2602.00978v1 Announce Type: new Abstract: Evolutionary success depends on the capacity to adapt: organisms must respond to environmental challenges through both genetic innovation and lifetime learning. The gene-centric paradigm attributes evolutionary causality exclusively to genes, while Denis Noble’s phenotype-first framework argues that organisms are active agents capable of interpreting genetic resources, learning from experience, and shaping their own development. However, this framework has remained philosophically intuitive but algorithmically opaque. We show for the first time that organismal agency […]

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arXiv:2602.04019v1 Announce Type: new Abstract: As large language models (LLMs) continue to grow, the cost of full-parameter fine-tuning has made parameter-efficient fine-tuning (PEFT) the default strategy for downstream adaptation. Constraints from inference latency in scalable serving and fine-tuning cost in edge or rapid-deployment settings make the choice of which layers to fine-tune unavoidable. Yet current practice typically applies PEFT uniformly across all layers, with limited understanding or leverage of layer selection. This paper develops a unified projected residual […]

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In this blog post, I am (briefly) reviewing Christoph Molnar’s *Interpretable Machine Learning Book*. Then, I am writing about two classic generalized linear models, linear and logistic regression. Mainly, this blog post explains the relationship between feature weights and predictions and demonstrates how to construct confidence intervals via Python.

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arXiv:2601.15505v1 Announce Type: new Abstract: The quantum hashing bound guarantees that rates up to $1-H(p_I, p_X, p_Y, p_Z)$ are achievable for memoryless Pauli channels, but it is not generally tight. A known way to improve achievable rates for certain asymmetric Pauli channels is to apply a small inner stabilizer code to a few channel uses, decode, and treat the resulting logical noise as an induced Pauli channel; reapplying the hashing argument to this induced channel can beat the […]

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RL/ML papers love equations before intuition. This post attempts to flip it: each idea appears only when the previous approach breaks, and every concept shows up exactly when it’s needed to fix what just broke. Reinforcement Learning for LLMs “made easy” submitted by /u/zephyr770 [link] [comments]

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