The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular value decomposition (SVD) provides a principled approach for model reduction, but its exact computation is expensive for large weight matrices. Randomized alternatives such as randomized SVD (RSVD) improve efficiency, yet they can suffer from poor approximation quality when the singular value spectrum decays slowly, a regime commonly observed in modern pretrained models. In this work, we address this limitation from both theoretical and empirical perspectives. First, we establish a connection between low-rank approximation error and predictive performance by analyzing softmax perturbations, showing that deviations in class probabilities are controlled by the spectral error of the compressed weights. Second, we demonstrate that RSVD is inadequate, and we propose randomized subspace iteration (RSI) as a more effective alternative. By incorporating multiple power iterations, RSI improves spectral separation and provides a controllable mechanism for enhancing approximation quality. We evaluate our approach on both convolutional networks and transformer-based architectures. Our results show that RSI achieves near-optimal approximation quality while outperforming RSVD in predictive accuracy under aggressive compression, enabling efficient model compression.

Existing machine learning frameworks for compliance monitoring -- Markov Logic Networks, Probabilistic Soft Logic, supervised models -- share a fundamental paradigm: they treat observed data as ground truth and attempt to approximate rules from it. This assumption breaks down in rule-governed domains such as taxation or regulatory compliance, where authoritative rules are known a priori and the true challenge is to infer the latent state of rule activation, compliance, and parametric drift from partial and noisy observations. We propose Rule-State Inference (RSI), a Bayesian framework that inverts this paradigm by encoding regulatory rules as structured priors and casting compliance monitoring as posterior inference over a latent rule-state space S = {(a_i, c_i, delta_i)}, where a_i captures rule activation, c_i models the compliance rate, and delta_i quantifies parametric drift. We prove three theoretical guarantees: (T1) RSI absorbs regulatory changes in O(1) time via a prior ratio correction, independently of dataset size; (T2) the posterior is Bernstein-von Mises consistent, converging to the true rule state as observations accumulate; (T3) mean-field variational inference monotonically maximizes the Evidence Lower BOund (ELBO). We instantiate RSI on the Togolese fiscal system and introduce RSI-Togo-Fiscal-Synthetic v1.0, a benchmark of 2,000 synthetic enterprises grounded in real OTR regulatory rules (2022-2025). Without any labeled training data, RSI achieves F1=0.519 and AUC=0.599, while absorbing regulatory changes in under 1ms versus 683-1082ms for full model retraining -- at least a 600x speedup.

Let X,Y,Z be random variables. Let g: X R be a measurable function, and let Ex Q[expg(x)] .Then DKL(P||Q)=sup Their work has built a connection between PACBayes meta-learning and Hierarchical Variational Bayes. In Appendix A.3 of [1], they give thegenerativegraph model formeta learning whereU W S (their notation usedψ instead of U). The proof technique is analogous to Theorem 5.1. LetΦ = (U,W1:n) be a collection of random variables whereΦ U Wn such thatΦandS1:n follow the joint distributionPΦ,S1;n. Based on Theorem 5.2, for the Meta-SGLD that satisfies Assumption 1, if we set Infact, the algorithm has anest-loop structure, we just list the abovesimple sub-structures for the firststepoftheproof.

Do the main claims made in the abstract and introduction accurately reflect the paper's Did you describe the limitations of your work? Did you discuss any potential negative societal impacts of your work? Did you state the full set of assumptions of all theoretical results? Did you include complete proofs of all theoretical results? Appendix C. 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] The PEP Similarly, our experience in Appendix A is trivially reproduceable.

Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remains a significant challenge. The volatile nature of the cryptocurrency market makes it even harder for traders and investors to make decisions. This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market, i.e., whether prices will increase or decrease. The model is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. We illustrate our approach with an empirical study of the closing price of Bitcoin. Several simulations, including a confusion matrix and Receiver Operating Characteristic curve, are used to assess the model's performance, and the results show a buy/sell signal accuracy of over 92%. These findings demonstrate how machine learning models can assist investors and traders of cryptocurrencies in making wise/informed decisions in a very volatile market.

The present document delineates the analysis, design, implementation, and benchmarking of various neural network architectures within a short-term frequency prediction system for the foreign exchange market (FOREX). Our aim is to simulate the judgment of the human expert (technical analyst) using a system that responds promptly to changes in market conditions, thus enabling the optimization of short-term trading strategies. We designed and implemented a series of LSTM neural network architectures which are taken as input the exchange rate values and generate the short-term market trend forecasting signal and an ANN custom architecture based on technical analysis indicator simulators We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost.

The high motivations for building the real superintelligence (RSI) Technology Platform are plain and clear: 1. To have the most powerful human-machine superintelligent technology platform for solving the most complex global problems humanity has ever faced, environmental, geopolitical, social, economic, humanitarian, and technological. The RSI as a digital synergy of human and machine is emerging as the summit of all human knowledge: Mythology Religion Philosophy Science & Technology Computing Machines the Internet/WWW Emerging Technologies NAI/ML/DL BCI Human Intelligence Digital Superintelligence Global Human-AI Superintelligence (RSI). The only way to reach the point of Technological Singularity is via real superintelligence (RSI), relying on the the comprehensive and consistent world model machine, integrating causal, mathematical, scientific, conceptual, statistic and probabilistic models, algorithms and techniques. It is all supported by exponential emerging technologies.