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科技热榜

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CSDN · 网络热榜

21分钟前更新
今日全部37条查看完整榜单
今日全部20条查看完整榜单
  • 01
    Crunchbase Data: Nvidia’s Startup Funding Deals Hit A Record. What’s Next After Huang?
    Before it was thrust into crisis this week, Nvidia was steadily continuing its pace as one of the busiest startup investors around, including backing many of the biggest venture deals of 2026. We take a fresh look at where Nvidia and its venture arm NVentures have been investing and what the future could hold.Marlize van Romburgh
  • 02
    The Week’s 10 Biggest Funding Rounds: Agentic AI And Infrastructure Are Still On A Roll
    It was another big week for AI-related startup financings. Mirendil, a developer of agentic AI for R&D, led the pack with a $1 billion series A, followed by Mercor, which secured $750 million to connect AI developers with human experts.Joanna Glasner
  • 03
    Protein Startups Are Selling Lots Of Snacks And Drinks. Our Sample Tasting Found Most Neither Great Nor Horrible
    As we chronicled earlier this week, both startups and established food brands are making it simpler than ever to up protein intake. From sodas to snack bars to ice cream, it’s now possible to get a fix of this coveted macronutrient with foods and drinks more commonly associated with sugar, starch and fat. But, do they taste good? To find out, we hit up a local Target for a selection of protein foods sold by venture-backed startups and subjected them to a taste test. Here are our findings.Joanna Glasner
  • 04
    How Founders Can Reduce Platform Risk Before It Threatens Growth
    Founders should treat dependence on app stores as infrastructure risk, since a sudden removal can disrupt customer access, subscriptions and revenue with no reliable path to reinstatement. Sharing his experience, Yakov Filippenko, founder and CEO of Intch, suggests that building and testing alternative access and payment channels before a crisis can help keep the business operating.Guest Author
  • 05
    VCs Are Still Chasing Protein, Even As Fake Meat Falls Out Of Favor
    Recent startup investment has shifted away from meat substitutes and toward stuffing more protein into other packaged foods and drinks. It’s a fad that’s obvious on supermarket shelves, where startups and established brands alike relentlessly tout the added protein in their noodles, pancakes, cereals, energy bars, popcorn and so on. We take a look.Joanna Glasner
  • 06
    Exclusive: Nubank Alum Raises $14M For Colombia’s Palomma To Tackle Rent Payments, Landlord Financing
    Colombia-based startup, Palomma, has raised $10 million in Series A funding led by Mexican venture firm Cometa to expand its financial services business for landlords, tenants and property managers, Crunchbase News has exclusively learned.Judy Rider
  • 07
    Anthropic Seeks Record $88.5B Tied To Public Market Debut
    Anthropic is seeking to secure up to $88.5 billion in its planned IPO and a concurrent private placement, potentially making it the largest-ever fundraise tied to a public market debut. On Tuesday, the AI giant submitted an updated S-1 filing for its upcoming public offering, unveiling new details including a proposed share price range offering size.Joanna Glasner
  • 08
    The Crunchbase Unicorn Board Tops 1,900 Companies In October, With 34 Companies Joining — Half Under 4 Years Old
    A total of 34 companies joined The Crunchbase Unicorn Board in October, adding more than $96 billion in value and $14.8 billion in total funding to the board. The board also reached a new record of 1,900 private companies last month, along with a new record for the U.S. with more than 1,000 private unicorn companies.Gené Teare
  • 09
    Startup Founders Are Using AI To File Patents. Here’s Where DIY Becomes A Liability.
    AI can help startups reduce patent preparation costs, but DIY applications may overlook critical issues. In this guest commentary, attorneys Michael Drapkin and Nathan Mutter of AI-Native law firm Landfall IP, argue that combining AI with legal expertise can produce more efficient, commercially valuable patent protection that withstands investor scrutiny and is harder for competitors to circumvent.Guest Author
  • 10
    Series A In 2026: Capital Is Flowing Faster, But Fewer Startups Are Making The Cut
    More money is pouring into Series A rounds, but it isn’t opening the door to more startups. U.S. companies have raised $42 billion at that stage this year through October, up roughly 60% year over year, while deal counts remain on pace with the past few years, Crunchbase data shows.Gené Teare
今日全部12条查看完整榜单

GitHub · Blade / 周榜

3小时前更新
  • 018.5万stars
    laravel
    Laravel is a web application framework with expressive, elegant syntax. We’ve already laid the foundation for your next big idea — freeing you to create without sweating the small things.laravel
  • 029677stars
    grocy
    ERP beyond your fridge - Grocy is a web-based self-hosted groceries & household management solution for your homegrocy
共 2 条查看完整榜单
  • 01466
    Llama-Prompt-Guard-2-22M
    2 个仓库：Llama-Prompt-Guard-2-22M、Llama-Prompt-Guard-2-86M（text-classification）meta-llama
  • 02140
    Llama-Guard-4-12B
    1 个仓库：Llama-Guard-4-12B（image-text-to-text）meta-llama
  • 0377
    Llama-4-Maverick-17B-128E-Instruct-Original
    2 个仓库：Llama-4-Maverick-17B-128E-Instruct-Original、Llama-4-Maverick-17B-128E-Instruct-FP8-Originalmeta-llama
  • 041786
    Llama-4-Scout-17B-16E-Instruct
    4 个仓库：Llama-4-Scout-17B-16E-Instruct、Llama-4-Scout-17B-16E、Llama-4-Scout-17B-16E-Original、Llama-4-Scout-17B-16E-Instruct-Original（image-text-to-text）meta-llama
  • 05901
    Llama-4-Maverick-17B-128E-Instruct
    4 个仓库：Llama-4-Maverick-17B-128E-Instruct-FP8、Llama-4-Maverick-17B-128E-Instruct、Llama-4-Maverick-17B-128E、Llama-4-Maverick-17B-128E-Original（image-text-to-text）meta-llama
  • 063204
    Llama-3.3-70B-Instruct
    1 个仓库：Llama-3.3-70B-Instruct（text-generation）meta-llama
  • 07210
    Llama-3.2-1B-Instruct-QLORA_INT4_EO8
    4 个仓库：Llama-3.2-1B-Instruct-QLORA_INT4_EO8、Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8、Llama-3.2-3B-Instruct-QLORA_INT4_EO8、Llama-3.2-3B-Instruct-SpinQuant_INT4_EO8（text-generation）meta-llama
  • 08251
    Llama-Guard-3-11B-Vision
    3 个仓库：Llama-Guard-3-11B-Vision、Llama-Guard-3-1B、Llama-Guard-3-1B-INT4（image-text-to-text）meta-llama
  • 091.2万
    Llama-3.2-1B
    8 个仓库：Llama-3.2-1B、Llama-3.2-1B-Instruct、Llama-3.2-3B-Instruct、Llama-3.2-3B、Llama-3.2-11B-Vision、Llama-3.2-11B-Vision-Instruct等（text-generation）meta-llama
  • 10396
    Llama-Guard-3-8B
    2 个仓库：Llama-Guard-3-8B-INT8、Llama-Guard-3-8B（text-generation）meta-llama
  • 11435
    Prompt-Guard-86M
    1 个仓库：Prompt-Guard-86M（text-classification）meta-llama
  • 129228
    Llama-3.1-8B-Instruct
    3 个仓库：Llama-3.1-8B-Instruct、Llama-3.1-405B-Instruct-FP8、Llama-3.1-405B-FP8（text-generation）meta-llama
  • 135883
    Llama-3.1-8B
    5 个仓库：Llama-3.1-8B、Llama-3.1-70B、Llama-3.1-405B、Llama-3.1-70B-Instruct、Llama-3.1-405B-Instruct（text-generation）meta-llama
  • 14319
    Meta-Llama-Guard-2-8B
    1 个仓库：Meta-Llama-Guard-2-8B（text-generation）meta-llama
  • 151.5万
    Meta-Llama-3-70B
    4 个仓库：Meta-Llama-3-70B、Meta-Llama-3-70B-Instruct、Meta-Llama-3-8B-Instruct、Meta-Llama-3-8B（text-generation）meta-llama
  • 16398
    CodeLlama-7b-hf
    12 个仓库：CodeLlama-7b-hf、CodeLlama-7b-Python-hf、CodeLlama-7b-Instruct-hf、CodeLlama-13b-hf、CodeLlama-13b-Python-hf、CodeLlama-13b-Instruct-hf等（text-generation）meta-llama
  • 17250
    LlamaGuard-7b
    1 个仓库：LlamaGuard-7b（text-generation）meta-llama
  • 181.2万
    Llama-2-13b-chat-hf
    6 个仓库：Llama-2-13b-chat-hf、Llama-2-13b-hf、Llama-2-7b-hf、Llama-2-7b-chat-hf、Llama-2-70b-chat、Llama-2-70b-chat-hf（text-generation）meta-llama
  • 19858
    Llama-2-70b-hf
    1 个仓库：Llama-2-70b-hf（text-generation）meta-llama
  • 206400
    Llama-2-7b
    5 个仓库：Llama-2-7b、Llama-2-7b-chat、Llama-2-13b、Llama-2-13b-chat、Llama-2-70b（text-generation）meta-llama
共 20 条查看完整榜单
今日全部30条查看完整榜单
  • 01
    Semidefinite programming relaxations and debiasing for MAXCUT-based clustering
    In this paper, we consider the problem of partitioning a small data sample of size $n$ drawn from a mixture of $2$ subgaussian distributions in $\mathbb{R}^p$. We consider semidefinite programming relaxations of an integer quadratic program that is formulated essentially as finding the maximum cut on a graph, where edge weights in the cut represent dissimilarity scores between two nodes based on their $p$ features. We define the signal-to-noise ratio (SNR) as $s^2 := \min\{n p \gamma^2, \Delta^2Shuheng Zhou
  • 02
    Sample-Efficient Regret-Minimizing Double Oracle in Two-Player Zero-Sum Extensive-Form Games
    Extensive-Form Game (EFG) represents a fundamental model for analyzing sequential interactions among multiple agents and the primary challenge to solve it lies in mitigating sample complexity. Existing research indicates that Double Oracle (DO) can reduce the sample complexity dependence on the information set number $|\mathcal{S}|$ to a restricted game size $X$ in solving two-player zero-sum EFGs. This is attributed to the early convergence of full-game Nash Equilibrium (NE) through iterativelyXiaohang Tang, Chiyuan Wang, Chengdong Ma, Ilija Bogunovic, Stephen McAleer, Yaodong Yang
  • 03
    CLASSIX: A High-Performance Library for Explainable~Clustering
    We present CLASSIX, an open-source Python library for fast and explainable clustering. The library provides a unified interface to three metric-specific implementations, compiled acceleration with portable Python fallbacks, and extensive user and developer documentation. CLASSIX accepts NumPy arrays and Pandas DataFrames and is available under the MIT license at https://github.com/nla-group/classix.Xinye Chen, Stefan Güttel, Kaustubh Roy
  • 04
    Bayesian Predictive Inference Beyond Martingales
    There is growing interest in the so-called Bayesian Predictive Inference approach, which enables Bayesian inference without specifying a likelihood function or prior distribution and without resorting to MCMC methods. Instead, only a sequence of predictive distributions for the observations is required, and inference on the unknown estimand can be performed, cheaply in parallel, using bootstrap-type schemes. Understanding which classes of predictive distributions can be used within this frameworMarco Battiston, Lorenzo Cappello
  • 05
    A New Family of Poisson Non-negative Matrix Factorization Methods Using the Shifted Log Link
    Poisson non-negative matrix factorization (NMF) is a widely used method to find interpretable "parts-based" decompositions of count data. While many variants of Poisson NMF exist, they all assume that the "parts" in the decomposition combine additively. This assumption may be natural in some settings, but not in others. Here, we introduce Poisson NMF with the shifted-log link function to relax this assumption. The shifted-log link function has a single tuning parameter, and as this parameter varEric Weine, Peter Carbonetto, Rafael A. Irizarry, Matthew Stephens
  • 06
    Anderson Mixing in Bures–Wasserstein Space of Gaussian Measures
    Various statistical tasks, including sampling or computing Wasserstein barycenters, can be reformulated as fixed-point problems for operators on probability distributions. Accelerating standard fixed-point iteration schemes provides a promising novel approach to the design of efficient numerical methods for these problems. The Wasserstein geometry on the space of probability measures, although not precisely Riemannian, allows us to define various useful Riemannian notions, such as tangent spacesVitalii Aksenov, Martin Eigel, Mathias Oster
  • 07
    Stochastic Bilevel Optimization with Heavy-Tailed Noise
    This paper considers the smooth bilevel optimization in which the lower-level problem is strongly convex and the upper-level problem is possibly nonconvex. We focus on the stochastic setting where the algorithm can access the unbiased stochastic gradient evaluation with heavy-tailed noise, which is prevalent in many machine learning applications, such as training large language models and reinforcement learning. We propose a nested-loop normalized stochastic bilevel approximation (N$^2$SBA) forZhuanghua Liu, Luo Luo
  • 08
    Provable Hierarchical Martingale-based Testing for Visual Anomaly Detection
    Existing visual anomaly detection approaches primarily focus on designing robust score functions but often neglect investigating the decision-making process, which plays a key role in reliable and safety-critical applications. Currently, the decision-making process suffers from two key challenges: distribution shift and theoretical guarantee. To address these issues, this paper proposes a novel approach called Hierarchical Martingale-based Testing framework (HiMT). Specifically, our approach levJie Wu, Xinsong Ma, Xin Zou, Xiuwen Gong, Kaizhu Huang, Weiwei Liu
  • 09
    Flexible Modeling of Information Diffusion on Networks with Statistical Guarantees
    Modeling information spread through a network is one of the key problems of network analysis. Most approaches assume the spread is governed by some probabilistic diffusion model, often parameterized by the strength of connections between network members (edge weights), highlighting the need for methods that can accurately estimate them. Multiple prior works suggest estimators for particular diffusion models, but lack a rigorous statistical analysis establishing their asymptotic properties and alAlexander Kagan, Elizaveta Levina, Ji Zhu
  • 10
    Alternating Regret for Online Convex Optimization
    Motivated by alternating learning dynamics in two-player games, a recent work by Cevher et al. (2023) shows that $o(\sqrt{T})$ alternating regret is possible for any $T$-round adversarial Online Linear Optimization (OLO) problem, and left as an open question whether the same is true for general Online Convex Optimization (OCO). We answer this question in the affirmative by showing that the continuous Hedge algorithm achieves $\tilde{\mathcal{O}}(d^{\frac{2}{3}}T^{\frac{1}{3}})$ alternating regreSoumita Hait, Ping Li, Haipeng Luo, Mengxiao Zhang
  • 11
    Discovering and Reasoning of Causality in the Hidden World with Large Language Models
    Revealing hidden causal variables alongside the underlying causal mechanisms is essential to the development of science. Despite the progress in the past decades, existing practice in causal discovery (CD) heavily relies on high-quality measured variables, which are usually given by human experts. In fact, the lack of well-defined high-level variables behind unstructured data has been a longstanding roadblock to a broader real-world application of CD. This procedure can naturally benefit from anChenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang
  • 12
    Learning with Importance-weighted Variational Inference
    Several variational bounds involving importance weighting ideas generalize the Evidence Lower BOund (ELBO) for marginal likelihood optimization, such as the Importance-weighted Auto-Encoder (IWAE), Variational Rényi (VR) and VR-IWAE bounds. Yet, it remains unclear how the joint choice of bound and gradient estimator impacts the behavior of the resulting variational inference (VI) algorithms. This paper provides a unified theoretical comparison of reparameterized (REP) and doubly-reparameterizedKamélia Daudel, François Roueff
  • 13
    Sharp Generalization for Nonparametric Regression in Interpolation Space by Over-Parameterized Neural Networks Trained with Preconditioned Gradient Descent and Early Stopping
    We study nonparametric regression using an over-parameterized two-layer neural network trained with algorithmic guarantees in this paper. We consider the setting where the training features are drawn uniformly from the unit sphere in $\mathbb R^d$, and the target function lies in an interpolation space commonly studied in statistical learning theory. We demonstrate that training the neural network with a novel Preconditioned Gradient Descent (PGD) algorithm, equipped with early stopping, achieveYingzhen Yang, Ping Li
  • 14
    SVarM: Linear Support Varifold Machines for Classification and Regression on Geometric Data
    Despite progress in the rapidly developing field of geometric deep learning, performing statistical analysis on geometric data—where each observation is a shape such as a curve, graph, or surface—remains challenging due to the non-Euclidean nature of shape spaces, which are defined as equivalence classes under invariance groups. Building machine learning frameworks that incorporate such invariances, notably to shape parametrization, is often crucial to ensure generalizability of the trained modeEmmanuel Hartman, Nicolas Charon
  • 15
    Reluctant Interaction Inference after Additive Modeling
    Additive models enjoy the flexibility of nonlinear models while still being readily understandable to humans. By contrast, other nonlinear models, which involve interactions between features, are not only harder to fit but also substantially more complicated to explain. Guided by the principle of parsimony, a data analyst therefore may naturally be reluctant to move beyond an additive model unless it is truly warranted. To put this principle of interaction reluctance into practice, we formulateYiling Huang, Snigdha Panigrahi, Guo Yu, Jacob Bien
  • 16
    Efficient Estimation of Average Treatment Effect on the Treated under Endogenous Treatment Assignment
    In this paper, we consider estimation of the average treatment effect on the treated (ATT), an interpretable and relevant causal estimand to policymakers when treatment assignment is endogenous. By considering shadow variables that are unrelated to the treatment assignment but related to the outcomes of interest, we establish identification of the ATT. Then we focus on efficient estimation of the ATT by characterizing the geometric structure of the likelihood, deriving the semiparametric efficieTrinetri Ghosh, Jiawei Shan, Menggang Yu, Jiwei Zhao
  • 17
    Weisfeiler-Lehman Meets Optimal Transport: Theory, Algorithms, and Implications
    We introduce the Weisfeiler–Lehman (WL) distance, a pseudometric defined on labeled measure Markov chains (LMMCs)—a general class of structures encompassing labeled graphs. This distance is computable in polynomial time and is closely aligned with the classical WL graph isomorphism test: it assigns a positive value if and only if the WL test distinguishes the two input graphs. The WL distance captures fine structural differences in the underlying LMMCs and is, consequently, more discriminative tSamantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang
  • 18
    Residual connections provably mitigate oversmoothing in graph neural networks
    Graph neural networks (GNNs) have achieved remarkable empirical success in processing and representing graph-structured data across various domains. However, a significant challenge known as "oversmoothing" persists, where vertex features become nearly indistinguishable in deep GNNs, severely restricting their expressive power and practical utility. In this work, we analyze the asymptotic oversmoothing behavior of deep GNNs with and without residual connections by deriving explicit convergence rZiang Chen, Zhengjiang Lin, Shi Chen, Yury Polyanskiy, Philippe Rigollet
  • 19
    Atlas Gaussian processes on restricted domains and point clouds
    In real-world applications, data often reside in restricted domains with unknown boundaries, or as high-dimensional point clouds lying on a lower-dimensional, nontrivial, unknown manifold. Traditional Gaussian Processes (GPs) struggle to capture the underlying geometry in such settings. Some existing methods assume a flat space embedded in a point cloud, which can be represented by a single latent chart (latent space), while others exhibit weak performance when the point cloud is sparse or irregMu Niu, Yue Zhang, Ke Ye, Pokman Cheung, Yizhu Wang, Xiaochen Yang
  • 20
    Likelihood-based Inference in Fully and Partially Observed Exponential Family Graphical Models with Intractable Normalizing Constants
    Probabilistic graphical models that encode an underlying Markov random field are fundamental building blocks of generative modeling to learn latent representations in modern multivariate data sets with complex dependency structures. Among these, the exponential family graphical models are especially popular, given their fairly well-understood statistical properties and computational scalability to high-dimensional data based on pseudo-likelihood methods. These models have been successfully appliYujie Chen, Anindya Bhadra, Antik Chakraborty
  • 21
    Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
    Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets, and image generation. However, learning the underlying SDE from data is a challenging task, especially if individual trajectories are not observable. Motivated by burgeoning research in single-cell datasets, we present the first comprehensive approach for jointly identifying the drift and diffusion of an SDE from itsVincent Guan, Joseph Janssen, Hossein Rahmani, Andrew Warren, Stephen Y. Zhang, Elina Robeva, Geoffrey Schiebinger
  • 22
    Benign Overfitting and the Geometry of the Ridge Regression Solution in Binary Classification
    In this work, we investigate the behavior of ridge regression in an overparameterized binary classification task. We assume examples are drawn from (anisotropic) class-conditional cluster distributions with opposing means and we allow for the training labels to have a constant level of label-flipping noise. We characterize the classification error achieved by ridge regression under the assumption that the covariance matrix of the cluster distribution has a high effective rank in the tail. We shoAlexander Tsigler, Luiz F. O. Chamon, Spencer Frei, Peter L. Bartlett
  • 23
    Deep Neural Network for Estimating General Risk Scores with Right Censored Data
    In this article, we propose a methodology for learning a risk score based on a general loss function, in which the score function is parameterized by a rectified linear unit feedforward deep neural network. The non-asymptotic error bound of the loss function is established and three examples are provided to illustrate the proposed method. The finite sample behavior of the proposed method is evaluated through simulation studies and an application to a SEER colon cancer data is provided.Dongxiao Han, Zan Li, Xinyuan Song, Donglin Zeng, Liuquan Sun
  • 24
    Regret of exploratory policy improvement and q-learning
    We study the convergence of q-learning and related algorithms introduced by Jia and Zhou (J. Mach. Learn. Res., 24 (2023), 161) for controlled diffusion processes. For exploratory policy improvement, we establish exponential convergence under growth and regularity assumptions on the model parameters. For q-learning, we derive quantitative error and regret bounds under additional assumptions on the function approximation and the associated stochastic approximation dynamics.Wenpin Tang, Xun Yu Zhou
  • 25
    Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
    Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper, we explore the neural scaling laws for deep operator networks, which involve learning mappings between function spaces, with a focus on the Chen and Chen style architecture. These approaches, which include the popular Deep Operator Network (DeepONet)Hao Liu, Zecheng Zhang, Wenjing Liao, Hayden Schaeffer
  • 26
    A Negative-Control-Outcome Perspective on the Synthetic Control Method
    The synthetic controls method is a popular approach for analyzing panel data to estimate the causal effect of major policy interventions. The method is known to be justified when the data-generating process can be described by a linear factor model. This paper provides an alternative justification for the synthetic control method, which leads to a generalization of the algorithm that can incorporate non-linear factor models. Our justification centers around negative control outcomes, outcome varYixin Wang, Aaron Schein, Joshua Shou, David M. Blei
  • 27
    Exploring Inference Patterns of LLMs as Symbolic Interactions
    In this paper, we examine whether a set of sparse symbolic interactions can faithfully explain the complex inference patterns of large language models (LLMs), thereby using interactions to diagnose the detailed representations of LLMs. Since previous studies lack a comprehensive proof for interaction-based explanations, we first experimentally verify the sparsity of interactions in LLMs, i.e., we observe that 50--100 interactions are usually powerful enough to accurately mimic all subtle changesLei Cheng, Wen Shen, Quanshi Zhang, Yuchen Huang, Yiming Zhang, Yuxiao Yang, Mingjie Li
  • 28
    Integral Probability Metrics on Submanifolds: Interpolation Inequalities and Optimal Inference
    We study interpolation inequalities between Hölder Integral Probability Metrics (IPMs) in the case where the measures have densities on closed submanifolds. Precisely, it is shown that if two probability measures $\mu$ and $\mu^\star$ have $\beta$-smooth densities with respect to the volume measure of some submanifolds $\mathcal{M}$ and $\mathcal{M}^\star$ respectively, then the Hölder IPMs $d_{\mathcal{H}^\gamma_1}$ of smoothness $\gamma\geq 1$ and $d_{\mathcal{H}^\eta_1}$ of smoothness $\eta>\Arthur Stéphanovitch
  • 29
    PhiBE: A PDE-Based Bellman Equation for Continuous-Time Policy Evaluation
    In this paper, we study policy evaluation in continuous-time reinforcement learning (RL), where the state follows an unknown stochastic differential equation (SDE) but only discrete-time data are available. We first highlight that the discrete-time Bellman equation (BE) is not always a reliable approximation to the true value function because it ignores the underlying continuous-time structure. We then introduce a new Bellman equation, PhiBE, which integrates the discrete-time information into aYuhua Zhu
  • 30
    Probabilistic Truly Unordered Rule Sets
    Rule set learning has long been studied because individual rules can be directly read and understood by domain experts, making them particularly valuable in high-stakes decision-making settings. Existing methods have several shortcomings though. First, most existing methods impose an order among rules, either explicitly or implicitly, which makes the comprehension of any rule's condition depend on the conditions of preceding rules. Second, due to the difficulty of handling conflicts caused by ovLincen Yang, Matthijs van Leeuwen
  • 31
    Causal Inference under Unmeasured Confounding with Negative Controls: A Minimax Learning Approach
    We study the estimation of causal parameters when not all confounders are observed and instead negative controls are available. Recent work has shown how these can enable identification and efficient estimation via two so-called bridge functions. In this paper, we tackle the primary challenges in causal inference with negative controls: identifying and estimating these bridge functions. Previous work has typically relied on completeness conditions for identification, imposed uniqueness assumptioNathan Kallus, Xiaojie Mao, Masatoshi Uehara, Zhiheng Zhang
  • 32
    Convergence of gradient descent for deep neural networks
    We give a simple local Polyak--Lojasiewicz (PL) criterion that guarantees linear (exponential) convergence of gradient flow and gradient descent to a zero-loss solution of a nonnegative objective. We then verify this criterion for the squared training loss of a feedforward neural network with smooth, strictly increasing activation functions, in a regime that is complementary to the usual over-parameterized analyses: the network width and depth are fixed, while the input data vectors are assumedSourav Chatterjee
  • 33
    Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions
    We consider the problem of recovering the causal structure underlying observations from different experimental conditions when the targets of the interventions in each experiment are unknown. We assume a linear structural causal model with additive Gaussian noise and consider interventions that perturb their targets while maintaining the causal relationships in the system. Different models may entail the same distributions, offering competing causal explanations for the given observations. We fuJuan L. Gamella, Armeen Taeb, Christina Heinze-Deml, Peter Bühlmann
  • 34
    An Efficient HPR Algorithm for the Wasserstein Barycenter Problem with O(Dim(P)/ε) Computational Complexity
    In this paper, we study and implement an efficient Halpern--Peaceman--Rachford (HPR) algorithm for solving the Wasserstein barycenter problem (WBP) with fixed supports. While the Peaceman--Rachford (PR) splitting method itself may not converge for the WBP, applying the Halpern iteration to the PR operator yields an $O(1/\varepsilon)$ non-ergodic iteration complexity with respect to the Karush--Kuhn--Tucker (KKT) residual. The structured linear solve in the HPR subproblem corresponds to an affineGuojun Zhang, Yancheng Yuan, Defeng Sun
  • 35
    A Library for Learning Neural Operators
    We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a discretization convergence properties. Part of the official PyTorch Ecosystem, NeuralOperator provides all the tools for training and deploying neural operator models, as well as develJean Kossaifi, Nikola Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert J. George, Boris Bonev, Kamyar Azizzadenesheli, Julius Berner, Valentin Duruisseaux, Anima Anandkumar
  • 36
    MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models
    We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framework for streamlined watermarking algorithm integration and user-friendly interfaces; a mechanism visualization suite that intuitively presents embedded and extracted watermark patterns to aid public understanding; and a comprehensive evaluation module offering standard implementations of 24 tools for assessing detectabLeyi Pan, Sheng Guan, Zheyu Fu, Luyang Si, Huan Wang, Zian Wang, Hanqian Li, Xuming Hu, Irwin King, Philip S. Yu, Aiwei Liu, Lijie Wen
  • 37
    OptunaHub: A Platform for Black-Box Optimization
    Black-box optimization (BBO) underpins advances in domains such as AutoML and Materials Informatics, yet implementations of algorithms and benchmarks remain fragmented across research communities. We introduce OptunaHub (https://hub.optuna.org/), a community-oriented, decentralized platform for distributing BBO components under a unified Optuna-compatible interface. OptunaHub enables independent publication, discovery, and reuse of optimization algorithms and benchmark problems through a lightweYoshihiko Ozaki, Shuhei Watanabe, Toshihiko Yanase
  • 38
    Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
    Chain-of-Thought (CoT) prompting and its variants have gained significant attention as effective methods for solving multi-step reasoning tasks with pretrained large language models (LLMs). However, their theoretical underpinnings remain insufficiently explored. We analyze CoT prompting from a statistical perspective, offering insights into why “pretrained LLMs + CoT prompting” performs well. Additionally, we examine the role of the transformer architecture and the inclusion of intermediate reasXinyang Hu, Fengzhuo Zhang, Siyu Chen, Zhuoran Yang
  • 39
    Prob-GParareal: A Probabilistic Numerical Parallel-in-Time Solver for Differential Equations
    We introduce Prob-GParareal, a probabilistic extension of the GParareal algorithm designed to provide uncertainty quantification for the Parallel-in-Time (PinT) solution of (ordinary and partial) differential equations (ODEs, PDEs). The method employs Gaussian processes (GPs) to model the Parareal correction function, in line with GParareal, further enabling the propagation of numerical uncertainty across time and yielding probabilistic forecasts of the system's evolution. Furthermore, Prob-GParGuglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino
  • 40
    From learnable objects to learnable random objects
    We consider the relationship between learnability of a "base class" of functions on a set $X$, and learnability of a class of statistical functions derived from the base class. For example, we refine results showing that learnability of a family $h_p: p \in \Theta$ of functions implies learnability of the family of functions $h_\mu(p) = \mathbb{E}_\mu[h_p]$, where $\mathbb{E}_\mu$ is the expectation with respect to $\mu$, and $\mu$ ranges over probability distributions on $X$. We will look at boAaron Anderson, Michael Benedikt
  • 41
    A Theoretical Framework for Masked Pretraining (MPT)
    Recently, Masked Pretraining (MPT) based on reconstruction pretraining tasks has risen to a promising self-supervised learning paradigm across various domains and achieves remarkable performance in multiple downstream tasks. However, the theoretical understanding of the working mechanism behind MPT is still limited. In this paper, we introduce a new theoretical framework to analyze MPT and understand the crucial role of masking in extracting meaningful representations. We establish theoretical cQi Zhang, Runyu Zhou, Yifei Wang, Yisen Wang
  • 42
    Robustness Against Weak or Invalid Instruments: Exploring Nonlinear Treatment Models with Machine Learning
    We discuss causal inference for observational studies with possibly invalid instrumental variables. We propose a novel methodology called two-stage curvature identification (\texttt{TSCI}) by exploring the nonlinear treatment model with machine learning. The first-stage machine learning enables improving the instrumental variable's strength and adjusting for different forms of violating the instrumental variable assumptions. The success of \texttt{TSCI} requires the instrumental variable's effecZijian Guo, Mengchu Zheng, Peter Bühlmann
  • 43
    Optimising Utility Functions in Multi-Objective Markov Decision Processes
    Multi-Objective Markov Decision Processes (MOMDPs) are among the most prevalent formal frameworks for addressing sequential decision-making problems involving multiple, potentially conflicting objectives. In most MOMDP approaches, a utility function is employed to aggregate these objectives into a single scalar criterion that encodes user preferences. Despite its widespread adoption, the theoretical foundations of MOMDPs remain incomplete in two main respects: first, there is no general characteManel Rodriguez-Soto
  • 44
    Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach
    Building artificially intelligent geospatial systems requires rapid delivery of spatial data analysis on massive scales with minimal human intervention. Depending on their intended use, learning about underlying spatial processes can also involve model assessment and uncertainty quantification. We devise transfer learning frameworks for deployment in artificially intelligent systems, where a massive data set is split into smaller data sets that stream into the analytical framework to propagate lLuca Presicce, Sudipto Banerjee
  • 45
    Symmetric Rank-k Methods
    This paper proposes a novel class of block quasi-Newton methods for convex optimization which we call symmetric rank-$k$ (SR-$k$) methods. Each iteration of SR-$k$ incorporates the curvature information with $k$ Hessian-vector products achieved from the greedy or random strategy. We prove that SR-$k$ methods have the local superlinear convergence rate of $\mathcal{O}\big((1-k/d)^{t(t-1)/2}\big)$ for minimizing smooth and strongly convex functions, where $d$ is the problem dimension and $t$ is thChengchang Liu, Cheng chen, Luo Luo
  • 46
    From Zipf's Law to Neural Scaling through Heaps' Law and Hilberg's Hypothesis
    We inspect the deductive connection between the neural scaling law and Zipf's law--two statements discussed in machine learning and quantitative linguistics. The neural scaling law describes how the cross entropy rate of a foundation model--such as a large language model--changes with respect to the amount of training tokens, parameters, and compute. By contrast, Zipf's law posits that the distribution of tokens exhibits a power law tail. Whereas similar claims have been made in more specific seŁukasz Dębowski
  • 47
    Efficient Inference under Label Shift in Unsupervised Domain Adaptation
    In many real-world applications, researchers aim to deploy models trained in a source domain to a target domain, where obtaining labeled data is often expensive, time-consuming, or even infeasible. While most existing literature assumes that the source and target data follow the same joint distribution, distribution shifts are common in practice. This paper considers a particular type of distribution shift, label shift, and develops an efficient inference procedure for general parameters charactSeong-ho Lee, Yanyuan Ma, Jiwei Zhao
  • 48
    Adaptive Algorithms for Infinitely Many-Armed Bandits: A Unified Framework
    We consider a bandit problem where the budget is smaller than the number of arms, which may be infinite. In this regime, the usual objective in the literature is to minimize simple regret. To analyze broad classes of distributions with potentially unbounded support, where simple regret may not be well-defined, we take a slightly different approach and seek to maximize the expected simple reward of the recommended arm, providing anytime guarantees. To that end, we introduce a distribution-free alEmmanuel Pilliat
  • 49
    Gradient Estimation for Mixture Variational Inference
    Mixture distributions are expressive variational families for black-box VI, but their discrete component choices complicate gradient estimation. We systematize reparameterization-based estimators for mixtures in a common notation, giving self-contained derivations and extending several to new settings. In particular, we provide an elementary derivation of a single-sample post-stratified estimator---previously derived via transport equations---and prove a variance reduction relative to simple ranJavier Burroni, Daniel Sheldon
  • 50
    torchsom: The Reference PyTorch Library for Self-Organizing Maps
    This paper introduces torchsom, an open-source Python library that provides a reference implementation of the Self-Organizing Map (SOM) in PyTorch. This package offers three main features: (i) dimensionality reduction, (ii) clustering, and (iii) friendly data visualization. It relies on a PyTorch backend, enabling (i) fast and efficient training of SOMs through GPU acceleration, and (ii) easy and scalable integration with the PyTorch ecosystem. torchsom also follows the scikit-learn API for easeLouis Berthier, Ahmed Shokry, Maxime Moreaud, Guillaume Ramelet, Eric Moulines
  • 51
    Pointwise Confidence Estimation in the Non-linear $\ell^2$-regularized Least Squares
    We consider a high-probability non-asymptotic confidence estimation in the $\ell^2$-regularized non-linear least-squares setting with fixed design. In particular, we study confidence estimation for local minimizers of the regularized training loss. We show a pointwise confidence bound, meaning that it holds for the prediction on any given fixed test input $x$. Importantly, the proposed confidence bound scales with similarity of the test input to the training data in the implicit feature space ofIlja Kuzborskij, Yasin Abbasi Yadkori
今日全部239条查看完整榜单

V2EX · 节点 · 拼车

14分钟前更新
  • 011
    claude 提交护照 3 天后的今天封了，然后发现这么多人封号，可以出租 claude 号吗
    然后想到认识人在印尼开公司和美国做生意，如果让他们注册账号，验证的时候提供身份证人脸，是不是可以出租号？ 1 远程桌面到家里的电脑是不是最安全的？ 听说也有用树莓派的。 2 远程桌面到云端电脑是不是也安全？ 有人说美国人很多也用机房代理 ip 3 把 claude code 安装在 美国 aws 远程 linux 上面，需要人多不多 4 还有直接买号的 一个 kyc 号多少钱，容易死 所以经常要号， 大家倾向于哪种呢？amzzoo
  • 02
    寻一个 infuse pro 的车
    国区，美区均可nosay
  • 03
    A/杀封了
    不知道还能坚持多久，快五个月了，一直是共用，好在这个印尼朋友在学习中文知识，提问中文问题，不然不可能能用这么久pengtx
  • 04
    # Claude 澳洲 东 8 区 本地企业 team 碰碰车队
    注意事项：不防封！不防封！不防封！ 现阶段不存在任何百分百防封的手段，只能降低概率，封号很正常，一定要有封号的预期，要做好 2-4 倍成本使用 Claude 的预期。但 海外当地企业+当地银行卡支付+当地人肉身支付 是目前最稳的方案。 （澳洲企业在佩斯 东 8 区） ps：封号后我们会申诉，如果 A\给退，我们就也会退钱给你 没有动手能力的不建议上车！因为涉及到代理配置，路由配置，本地指纹清理，浏览器指纹清理，整个链路需要自己动手。 原生直连 ， 不是中转站！不是中转站！不是中转站！ team 独立席位，独立空间，独立额度，不会用于蒸馏。 逆向，黑灰，滥用，一号多人使用，cpa 反代的不能上车！一旦发现直接踢出车队！ 整体上车流程： 付钱 -> 等待发车 -> 本机环境准备 准备上车 -> 客服环境校验 -> 环境检查通过 -> 收到邮箱账号密码 -> 打开 terminal /login 通过企业邮箱完成鉴权，正常使用 claude 车位方案 方案 价格 方案 1：澳洲本地企业 Team 席位 + premium 席位 + 企业邮箱登陆 > 我会提供企业邮箱登陆，在你电脑本地 打开hongch
  • 051
    美区家庭组 Apple One 超大杯 21 号有一个空位置，季度付，已经稳定开了有好几年了 联系 vx： IHdvd2d1b3Fpbmc=
    NeoWu
  • 06
    Microsoft 365 家庭版 拼车
    长期自用 Microsoft 365 家庭版 50/年/人，寻拼车的朋友 VX: echo bXlSZXNwb25zZVdyYXBwZXIK | base64 -dShukeShuke
  • 07
    百度网盘 SVIP9 拼车
    百度网盘 SVIP9 拼车，18T 空间平分 三人车缺两人，年付 58 拉微信群，希望来长期的车友 张小龙：dmFjY2Vyvaccer
  • 08
    自用 Netflix 港区家庭套餐拼车
    4K （超高清）+ HDR 空间音频 电视、电脑、手机、平板电脑 40 HKD$ /位 按汇率给人民币就行 每个人单独一个位置 目前位置: 2/4 还有 2 个位置 WX: TGVvWmhhbmd4eA== (base64)sealingpp
  • 091
    Claude 被封的有福了, 征求种子用户
    openwises.com 是一个撮合海外有 claude 账户, 国内急切需要 claude 的用户平台, 使用了自己研发的客户端避免 Claude 官方客户端埋点封号. 目前征求种子用户进行测试, 平台目前只有一台节点, 3 个席位, 欢迎报名留下联系方式(邮件), 先到先得, 我会稍后发送开通邮件. 本平台通过技术手段, 避免超卖 同时也无比欢迎海外华人, 自己有 claude 套餐账号, 想分享给国内用户的, 欢迎注册试用, 目前只有 Linux 节点分发, 正在进行 Mac/Windows 平台开发, 也就这几天上线. 对于做托管 Mac/Linux 机器在国外相对国内售卖的, 也可以使用本平台进行免费分发. 有任何疑问欢迎, 欢迎提问.Openwises
  • 10
    vx 联系 bHBfMTc4MTE2MDYw 出 11.3 开的 88vip 权益 网易云音乐 39 优酷芒果 34
    lazyfighter
  • 115
    土区 Gemini Ultra 5X 拼车有人要吗
    Gemini Ultra 20X 的价格错过了，只能搞 Gemini Ultra 5X ，1999 按照当前汇率折算大概 260 人民币， 有两个土区 Apple ，现在想法是 3 人车，实际是 2 人车，方案如下： 80 元的只用 Google Flow, 主要是视频创作 120 元只用 antigravity 主要是编程 80 元 google colab ， 主要是硬件资源，这个好像可以通过 主账号 授权 cli 实现，所以说实际是 2 人车 我自己主要用 antigravity ， 其他可能会尝试下，这样的好处就是每个人只用一个服务，不会存在分配不均的问题 价格可能会调整，现在就是一个想法，有需要或者有其他想法的，可以留言x007xyzabc
  • 121
    ios surge 车位空 2 个，可用 surge 6
    11 月 8 号开通 27 年 11 月 8 日到期 80 一位 +v et123kkbtc888r
  • 132
    Claude 老是封号，我想到的解决方案
    因为我肉身在美国，所以可以提供 mac, windows,linux 电脑供人跑项目，需要远程操作。 甚至也可以帮助解决支付问题。 可能主要有两个因素需要考虑： 一是网络延迟。虽然家里用的是高速网络，但毕竟物理距离比较远，不知道是否可以舒适地使用。 电费问题。美国电费比较贵，24 小时跑多台电脑的话估计花费不少。 其它都好解决。电脑我有很多，如果放心，你甚至可以买了放我家里。 这个解决方案应该是靠谱的吧。哈哈Gomoku2024
  • 14
    [拼车] 会员拼车了， Apple 美区 apple one（1 人季付 140）、尼区 Netflix（2 人季付 40）、尼区 YouTube（1 人年付 53），都是 3 年以上的老车了。
    会员拼车了，Apple 美区 apple one （ 1 人季付 140 ）、尼区 Netflix （ 2 人季付 40 ）、尼区 YouTube （ 1 人年付 53 ），都是 3 年以上的老车了。微信：paralysesyingjun666
  • 155
    office365 寻一起拼车
    长期自用 office365 家庭版 50/年/人，寻车友wuxin5
  • 161
    今天刚开的 88vip， 38 出云音乐
    38 出今天刚开的网易云音乐 （优酷和夸克自用了） vx(base64)：bGlhbmdnZV8xMTY=hl8664
  • 17
    求稳定的 Google ultra 的车
    有车的朋友欢迎联系我，自用不反代：dmluY2tpdA==vincentjie
  • 18
    请问有没有 Claude team 的车，长期使用，想上个车
    请问有没有大佬开 team 的车，长期使用，求拉一个，我自己解决 ip 等问题ghmum
  • 19
    问问 v2 有没有用企业席位的 claude 的，今天还存活么
    在 v2 看到好几个类似 ffsb 这种企业席位的，今天剩余的号全 g 了 不得不另寻出路了 看新规收紧主要针对企业 有没有上过企业车的 现在还存活着么zzNaLOGIC
  • 20
    出 11 月 09 号的 88vip 会员权益
    vx 联系 bGlhbmd4dzEyMw== 出刚开的 88vip 权益 网易云音乐 38 优酷芒果 32 夸克高德飞猪等其它 5 需要请联系ihhhkz
  • 21
    iCloud+ 2T 5 人车，招一名乘客
    国区 iCloud+，2T 空间，空出 1 个车位，按照当年订阅价格平摊收取费用，今年是 11.57 元/月（约等于），每个车位平均可以使用 400GB 空间，每半年付费（今年费用为 138.8 元/年，半年是 69.4 元），中途下车请提前告知。 车上附加设施（无保证可用性）： Infuse 美区，乘客订阅 付费号是国区，加入的账号不限区。车上有国区和美区的乘客。iCloud+ 是共享 2T 的总容量，车上的人看不到其他人的数据。 绿色软件：dGhlbHluYW4=lynan
  • 221
    季付 33 2T iCloud 国区合租缺一人
    5b6u5L+h77yaeGxpbmdqcw==xiaolingjs
  • 23
    出 11.11 开的山姆副卡
    100 出，联系 aW5zaXN0NjI0fantasyqqq
  • 24
    88vip 11.12 开的 权益
    网易云 40 优酷/芒果 35 VX：MTY2MDIxNTE4MTk=Listen0828
  • 25
    出 11 月 3 日开的 88vip 视频权益
    视频 35, 网易云 40 ，MTMzNjY3MDc4NzI=lazyfighter
  • 26
    出山姆副卡， 11.11 号刚开的
    100 出，联系 aW5zaXN0NjI0fantasyqqq
  • 273
    有需要使用境外服务器访问 claude 的朋友吗
    我可以给大家提供一个独享的 claude 或者 codex 账号。通过境外的虚拟机以及民用的出口 IP 来规避账号封禁的问题， 如果遇到 kyc 我可以想办法帮大家解决这个问题。 我自己是通过远程使用的 claude ，目前比较安全，没有遇到过账号封禁的问题。 因为能够提供的虚拟机有限，我更加希望找一些自用的小伙伴长期合作。 有需要的可以联系绿泡泡 bWF4Nzg3NA==thehorizon
  • 281
    Codex Pro20X，美区 2 人车
    准备开美区 Codex pro20X 2 人车，sub2api 交付 周额度二人平分，暂定周限 700 刀，跟随官方重置，拼车价格 660r/月，翻车按比例退，看官方退多少剩余价值，人满发车 V:base64= SGFja1ll3004
  • 29
    Netflix 老车补一位长期车友 年 300
    禁电视端和 ps 等xxxteddy
  • 30
    [老车找人]Codex 200 刀 四人老车补人
    车队情况 8 月 21 日发的车，11 月 21 日有 2 人跳车，现在补 2 个位置，11 月 22 日 0 点起才能用。 网络 用的是美国 Verizon Business 家宽，IP 很干净。 模型质量 糖果测试没问题,会自动拨测降智,最近 48 小时没发现降智 额度怎么分 账号周额度四个人平分，比如账号每周 $2400 ，每人就是 $600 系统自动分配，没有日限额 OAI 主动重置的话，车队额度跟着一起重置 OAI 发重置卡的话，大家投票决定用不用 速率限制 车队总共 平均每人 并发 20 5 左右 RPM 60 15 左右 延迟 连接方式 首字响应（ TTFT ） HTTP SSE 1 – 5 秒 WebSocket 稳定 0.3 – 0.5 秒 TPS 也正常。 怎么用 走 Cloudflare ，API 形式交付，平台是自己搭的（不是 sub2 / CPA ） 只支持 /v1/responses 端点 支持 WebSocket 、生图、search 端点 没有 Web 端 售后说明 炸车 ：按 OAI 的退款政策来，OAI 退多少我就退多少，OAI 不退我也没法退 降智htlz2000
  • 31
    慢蹲一张山姆副卡
    最好最近开的，留联系方式。Jimson
  • 32
    出 GPT tea,m 订阅，每月 85R 带 codex
    Codex 额度跟 GPT Plus 一样 ，网页端比 plus 多 GPT-6 Pro 、xhigh 两档模型。 总体而已，性价比高于 gpt plus 。且同样是官方正规订阅。 📩费用是 85/月； 可以先上车试用两个小时，觉得合适再付款。 全程质保，翻车了我给你拉新的，不怕封号。 绿泡泡(Base64)：eHVmYWNlMTk4OA==（备注：V 友）zgdselina26
  • 332
    [拼车]bibigpt 双一十半价拼团，满五人年付每人多得 6 个月会员， plus 480r
    https://bibigpt.co/g/8CXHRLJM 目前进度 2/5kevins96
  • 341
    codex Pro20x 车找人
    我昨天下午开通的 GPT Pro ，希望找 1 个使用 codex ，不反代账号，不走 sub2api ，不滥用的，需要联系。 我的号还有 3 万多 积分额度网页，一张重置卡,自己的号 预估价格： codex 一半额度（可以用重置卡） 550wyh1717
  • 35
    尼日利亚区 YouTube 家庭会员招两人
    自用两年老车，需自备未锁区账号或者改区到车主同区上车 价格 75 元/年 联系 tg:kimigaokimigaooo
  • 361
    有没有 office365 的车
    求一辆 office365 的家庭车drw2w
  • 37
    出 11 月 11 日 刚开的 88vip 视频权益
    价格：35 vx：a3Vtb2N1virpanda
  • 382
    在韩国放了一批 Mac mini 出租，另外有纯海外环境本地人实体卡开的 Claude Max / Ultra 号
    各位大佬好，先声明这是推广，不绕弯子，主打一个真实和省心。 我们目前提供两款独立服务（不捆绑，按需选择）： 海外原生 Claude 独享号（ Max / Ultra ） 吃过被封号、KYC 卡死、付款失败苦头的朋友懂的都懂。 真·海外老外实名开卡：当地人用实体信用卡订阅的，绝非虚拟卡，风控级别完全不同。 省心到家：你不需要做任何 KYC ，也不用折腾海外支付。 纯正独享：一号一人，绝不拼车，隐私和体验都有保障。 价格：Max 档 $199 / Ultra 档 $389 (Pro 暂无货) 韩国本土物理 Mac mini （真机挂载） 这不是虚拟机！是一台实打实的 Mac mini 放在韩国当地。 硬件配置：M6 / 16G / 256G ，一人一物理机。 真·家宽 IP：韩国 KT 原生家庭宽带，固定 IP 。官网上放了第三方 IP 检测截图，随时可验。 流畅远控：默认支持网易 UU ，也支持 RustDesk 或 AnyDesk ，操作顺滑。 价格：$199 / 期（一期 30 天，这个可以续费）。 👉 缺点也先说清楚： 非秒开交付：付款后按顺序人工架设，7 天内交付。 非大带宽服务AIMacMini
  • 39
    出一自用 ChatGPT Business 席位外加每月 40 $API 额度
    之前的领的 48 个月 Business 优惠，目前的队友失业不续费了。包含一个车位和每月 40$api 。计费周期每月 11 号 规则如下： 1 ，账户和 api 必须个人使用，禁止转让，严禁违反 安全政策，如有违规马上踢人不退款 2 ，要求长期，所以两个月一付，一个车位+40 美金 api 230 元/月 3 ，如果 后续取消每月 api 额度，这部分将不收费，只收取每月 90 元一个席位 由于我本人不用 api ，所以额度可以你一个人用，仅限当月有效。能用 usdt 付款也可以francie
  • 40
    腾讯云服务器拼团有人吗
    base64 链接：aHR0cHM6Ly9hZHMucWNsb3VkLmNvbS9VUGFqZjVOZg==xzylzz
  • 41
    DMIT CORONA 美西节点拼车
    回程三网 CMIN2 + 联通 9929 优化线路，去程电信联通走 CN2 ，移动走 CMIN2 。 NS 同 ID 也在找人拼，有需要可以 tg 联系~ 机型详情： LAX.AN4.EB.CORONA • 协议配置：VLESS + Reality 拼车方案： 配置：500G/月（双向计费）。 • 价格：100 年付 补充信息 关于退款：中途跳车不退款，车主问题按剩余退款，上车代表同意退款政策！希望车友稳定！ 关于人数：仅限单人使用，请勿二次分享节点给他人！一旦发现，直接清退且不退款。 关于带宽：共享宽带，请勿长时间跑满，影响他人体验！jonty
  • 42
    出今天新办的山姆副卡 100r
    需要的留下绿泡泡。gurachin
  • 436
    美国 华盛顿 Verizon 静态住宅 ip
    2G2C 、25R / 200G / 月 / 人 双向 Shadowsocks 节点（ SS ），自备中转 带宽：峰值 1024 Mbps ，家宽共享带宽，请勿长期跑满 Telegram：@wooooong 如果你的 Telegram 被双向限制，请先添加我，在站内私信留下你的 Telegram 用户名，我会第一时间加你好友。witness998
  • 441
    印度区 YouTube Premium 拼车（剩一个位置）
    印度区自用车主，老车已经开了两年多，年付 108 元 剩一个位置 中途跳车不退，翻车按比例退。 上车需未绑定区域的 google 账号或印度区账号。联系 tg: @ funnybaohaofereida
  • 451
    求 claude team 6.25x 的渠道
    我想要不要太贵的 有没有 我很需要！！！！！simonzym0505
  • 461
    出 2026.11.9 开通的 88VIP 权益
    1.网易云 35r 2.优酷/芒果年卡 35r 3.夸克网盘 10r 有需要联系 v：bHVva2VjaGVuZw==roocky
  • 471
    美区 iCloud 找乘客，四年车，缺两个， 2T， 6 人车年付 156，人均 333G
    套餐：美区 iCloud+ 2TB 车型：6 人，已开四年，主要家人用，现在空两个。 人均容量：约 333GB：1 车主+ 5 乘客 费用：156 元/年 支付方式：年付，一次性付清 有意欢迎 wx 联系我 emhhbmdfeWFuZ3NodQ==cusuanan
  • 48
    🎮 出 GTA 6 认证号（自用账号）
    🎮 出 GTA 6 认证号（自用账号） 个人自用 PSN 账号， 仅出 1 个认证名额，不是商家批量号 ，希望找一位靠谱的个人玩家长期使用。价格 190 元。 📦 账号游戏 GTA 6 黑神话：悟空（黑猴） 巫师 3：狂猎 地平线：零之曙光 GTA 5 刺客信条：黑旗 COD 3 📋 交易说明 使用期限： 1 年 主机要求： 需要外服机器 认证方式： 扫码认证 名额数量： 仅 1 位 🤝 交易要求 仅面向个人玩家， 商家勿扰 优先交易给 V2EX 信用记录良好的老用户 具体细节可私聊沟通 📮 联系方式 请用 base64 解码： eGl3ZWlqa2Zk 个人自用账号，只想找个靠谱玩家共享游戏，有意请联系，感谢！eoeapp
  • 49
    [车找人]GPT PRO20X 菲区三人车
    🚗 车况 价格：360 元/月 额度： 每周 400 刀 额度, 倍率 1x, 在使用 6 系列模型时相当于开倍率的 600-800 刀 已设置 6.1sol 模型的阶梯计费起始点为 500k(sub2api 默认 272k) 重置： 跟随官方自动重置 日限额：150 刀 并发:5 生图: 未开生图 交付方式：sub2api 线路：megabox pro 美西三网优化 网络：aaitr Verizon 加州家宽落地,自己开的家宽非拼车 挑的权重较高的老号,菲区付款,目前自用期间未遇到降智情况 📌 使用规则 仅限正常个人使用，禁止逆向、NSFW 、批量滥用以及其他违规操作； 发现明显违规行为会直接踢下车，不予退款； 如果后续需要提前跳车，请至少 提前一周说明，方便重新安排车位； 如有需要，可以提供打码后的 支付账单 / 订阅凭证； 如果因为 OpenAI 官方侧退款、订阅异常等不可控情况导致翻车，会根据官方实际退款情况给车友进行相应退款 / 补偿。 📌 上车方式 绿泡泡 c3lzdXl6dw==Zhepro
  • 50
    team 车找人 月付 80
    需要可以联系我crazy2haiouhun
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