An alternative presentation of the Oxford purification protocol is obtained by using dynamical variables. I suggest to introduce the degree of separability as a purification parameter, where the purified state has a smaller degree of separability t...
a parameter server for distributed machine learning applications (⭐ 105)
The bidirectional steerability between different-size subsystems is discussed for a single parameter accelerated qubit-qutrit system. The decoherence due to the mixing and acceleration parameters is investigated, where for the total system and the qu...
We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-size LMs like BERT and R...
An analytical expression is derived for the thermal response observed during spontaneous imbibition of water into a dry core of zeolitic tuff. Sample tortuosity, thermal conductivity, and thermal source strength are estimated from fitting an analytic...
Anytime inference is inference performed incrementally, with the accuracy of the inference being controlled by a tunable parameter, usually time. Such anytime inference algorithms are also usually interruptible, gradually converging to the exact infe...
On November 25, 2025, Black Forest Labs launched FLUX.2 dev, a powerful 32-billion-parameter open-weight text-to-image model now available on Hugging Face. **Key features:** - Professional-grade imag...
Setting the value of the filter query-string parameter to a string using those delimiters creates a list of name/value pairs which can be parsed easily on the server-side and utilized to enhance database …
In recent years, Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) have significantly enhanced the adaptability of large-scale pre-trained models. Weight-Decomposed Low-Rank Adaptation (DoRA) improves upon LoRA by separat...
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). It approximates the update of a pretrained weight matrix $W\in\mathbb{R}^{m\times n}$ by the product of two low-rank matrices,...
This paper studies a notion of parameterized flatness in the enriched context: p-flatness where the parameter p stands for a class of presheaves. One obtains a completion of a category A by considering the category F_p(A) of p-flat presheaves over...
For a language-agnostic consideration of the design decision, see What is the advantage of having this/self pointer mandatory explicit?. To close debugging questions where OP omitted a self …
In \cite{ CLEVACKTHI, CLEVACK} an attempt is made to find a comprehensive mathematical framework in which to investigate the problems of well-posedness, asymptotic analysis and parameter estimation for fully nonlinear evolutionary game models. A theo...
Tools for creating tuning parameter values (⭐ 116)
A simple asperity model using random process theory is developed in the presence of adhesion. Using the DMT model for each individual asperity, and asymptotic results at large separations, a new adhesion parameter is found, on which the model depends...
Deep learning models trained on large data sets have been widely successful in both vision and language domains. As state-of-the-art deep learning architectures have continued to grow in parameter count so have the compute budgets and times required...
Feb 5, 2018 · So parameter bindings used in normal DB:insert doesn't give protection to SQL injection ? In other words it doesn't implement prepared statements internally ? I tried to use DB::statement for …
Activation steering provides parameter-efficient control over large language models (LLMs) at inference time, but many methods rely on off-distribution supervision and discrete masking, leading to brittle interventions. We propose ROAST (Rollout-base...
Lasso is a seminal contribution to high-dimensional statistics, but it hinges on a tuning parameter that is difficult to calibrate in practice. A partial remedy for this problem is Square-Root Lasso, because it inherently calibrates to the noise vari...
Heterogenous data is prevalent in real-world federated learning. We propose a parameter-efficient framework, Federated Low-Rank Adaptive Learning (FLoRAL), that allows clients to personalize in groups by mixing between low-rank adaptors, where the mi...