Search Results for "black box"

...Instrumentation via compiler hooks or hardware/perf counters guides mutations toward previously unseen edges, while persistent mode keeps the target process alive to amortize startup costs. The tool integrates tightly with sanitizers and can attach to already running processes, making it convenient for both white-box and black-box fuzzing. When it finds a crash, honggfuzz captures detailed context, minimizes the input, and can pin reproducibility by controlling CPU affinity and resource limits. Its small footprint and straightforward CLI make it easy to drop into CI or long-running campaigns across many cores.

BayesianOptimization is a Python library that helps find the maximum (or minimum) of expensive or unknown objective functions using Bayesian optimization. This technique is especially useful for hyperparameter tuning in machine learning, where evaluating the objective function is costly. The library provides an easy-to-use API for defining bounds and optimizing over parameter spaces using probabilistic models like Gaussian Processes.

...While not required, for optimal performance(especially for the detector) it is highly recommended to run the code using a CUDA-enabled GPU. While here the work is presented as a black box, if you want to know more about the intrisecs of the method please check the original paper either on arxiv or my webpage.

Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and then experiment with multiple strategies—evolutionary algorithms, Bayesian optimization, bandit methods, genetic algorithms, etc. ...

...One of its defining characteristics is its copy-and-paste philosophy, allowing developers to directly own and modify component code instead of relying on rigid packages or black-box abstractions. The library includes a growing collection of reusable components such as buttons, forms, modals, tabs, and layout elements, all crafted with attention to design quality and usability. It also integrates animation capabilities through Framer Motion, enabling smooth transitions and micro-interactions that enhance user experience without requiring complex implementation.

Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual prediction). The concept of model interpretability in the field of machine learning is still new, largely subjective, and, at times, controversial. Model interpretation is the ability to explain and validate the decisions of a predictive model to enable fairness, accountability, and transparency in algorithmic decision-making. ...

EasyCP is a modern, user-friendly C++ library that lets you use constraint programming in a very natural way, taking advantage of its expressive power. Just model and let the CSP (Constraint Satisfaction Problem) solver do the job !

Raken, web service controller, is based on JSON as a data definition language. it utilizes simple protocol, optional asynchronous interaction, recovery, localization, security, caching, batch, and multi-part messaging.

wpcObj - the C++ wrappers over the libpcap/winpcap library. webEngine - engine for black-box web site analyzing.