technocracy

arXiv:2501.01696v2 Announce Type: replace Abstract: Tensors, which give a faithful and effective representation to deliver the intrinsic structure of multi-dimensional data, play a crucial role in an increasing number of signal processing and machine learning problems. However, tensor data are often accompanied by arbitrary signal corruptions, including missing entries and sparse noise. A fundamental challenge is to reliably extract the meaningful information from corrupted tensor data in a statistically and computationally efficient manner. This paper develops a scaled […]

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Let’s learn about Ai Top Story via these 100 free blog posts. They are ordered by HackerNoon reader engagement data. Visit the /Learn or LearnRepo.com to find the most read blog posts about any technology. AI Top Story refers to significant news or developments within the field of artificial intelligence that gain widespread attention. These stories highlight pivotal advancements, ethical debates, or major applications of AI, shaping public understanding and future directions of the technology. 1. Imagic: AI […]

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Three weeks ago, we were preparing for a launch and running extensive tests. We utilized multiple AIs for the process: GPT, Cohere, Mistral, Perplexity… At first, I looked at the information provided and made adjustments to improve it. By the third day, however, I was just passing information through. Needless to say, it took more than twice as many days to recover and regain my independent thinking. What happened to me？ Generative AI offers clear advantages. It saves […]

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Reinforcement Learning (RL) has become essential for eliciting complex reasoning capabilities in Large Language Models (LLMs). However, the substantial memory overhead of storing Key-Value (KV) caches during long-horizon rollouts acts as a critical bottleneck, often prohibiting efficient training on limited hardware. While existing KV compression techniques offer a remedy for inference, directly applying them to RL training induces a severe policy mismatch, leading to catastrophic performance collapse. To address this, we introduce Sparse-RL empowers stable RL training under […]

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We develop a semi-parametric state-space model for time-series data with latent regime transitions. Classical Markov-switching models use fixed parametric transition functions, such as logistic or probit links, which restrict flexibility when transitions depend on nonlinear and context-dependent effects. We replace this assumption with learned functions $f_0, f_1 in calH$, where $calH$ is either a reproducing kernel Hilbert space or a spline approximation space, and define transition probabilities as $p_{jk,t} = sigmoid(f(bx_{t-1}))$. The transition functions are estimated jointly with […]

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Trajectory inference investigates how to interpolate paths between observed timepoints of dynamical systems, such as temporally resolved population distributions, with the goal of inferring trajectories at unseen times and better understanding system dynamics. Previous work has focused on continuous geometric priors, utilizing data-dependent spatial features to define a Riemannian metric. In many applications, there exists discrete, directed prior knowledge over admissible transitions (e.g. lineage trees in developmental biology). We introduce a Finsler metric that combines geometry with classification […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Create a Website Without Code: How Fabricate Turns Conversations Into Full-Stack Apps By @boostlegends1 [ 9 Min read ] Build full-stack web apps without coding. Fabricate uses AI to generate frontend, backend, databases, and payments from simple prompts in minutes. Read More. How to […]

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Table of Links Abstract and 1. Introduction Some recent trends in theoretical ML 2.1 Deep Learning via continuous-time controlled dynamical system 2.2 Probabilistic modeling and inference in DL 2.3 Deep Learning in non-Euclidean spaces 2.4 Physics Informed ML Kuramoto model 3.1 Kuramoto models from the geometric point of view 3.2 Hyperbolic geometry of Kuramoto ensembles 3.3 Kuramoto models with several globally coupled sub-ensembles Kuramoto models on higher-dimensional manifolds 4.1 Non-Abelian Kuramoto models on Lie groups 4.2 Kuramoto models […]

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