Create adversarial attacks against machine learning Windows malware detectors
- adversarial-machine-learning
- infosec
- python
- attack
65,264 visible repositories in the index
913–936 of 1,032 · Most stars
Create adversarial attacks against machine learning Windows malware detectors
ROS package for the Perception (Sensor Processing, Detection, Tracking and Evaluation) of the KITTI Vision Benchmark Suite
INGInious is a secure and automated exercises assessment platform using your own tests, also providing a pluggable interface with your existing LMS.
Python-based implementations of algorithms for learning on imbalanced data.
A library for augmenting annotated audio data
Relevance Vector Machine implementation using the scikit-learn API.
Automated 3D cell detection in very large images
A fast xgboost feature selection algorithm
Deep and Machine Learning for Microscopy
Stacked Generalization (Ensemble Learning)
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Companion repository for “In-Context Learning for Robots: Methods and Applications” — methods, datasets, and benchmarks organized by taxonomy and release date.
Deep Reinforcement Learning for Financial Trading using Price Trailing @ ICASSP 2019
TranslatedBuild GPT-2 from scratch in PyTorch, step by step
A high-performance, general-purpose toolkit for versatile robotic tasks.
TranslatedAn agent development practice room based on LangGraph subgraphs: requirement clarification, user notes, document generation, and human review.
BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.
The repository host gives no description.
Meta Self-learning for Multi-Source Domain Adaptation: A Benchmark
DynaFlow: Learning Free-Form Multi-Agent Orchestration
PyTorch Connectomics: segmentation toolbox for EM connectomics
Whole-body motion tracking for the Unitree G1 in under 3 minutes on average on one GPU: return 0.82 with 0/40 clips failed, held from 2.74 min (mean of 15 seeds, range 2.21-3.61, fine checkpoints), 11.87 it/s (world model + Muon + overlapped CUDA streams, mjlab)
A micro neural network multilayer perceptron for MicroPython (used on ESP32 and Pycom modules)
A simple, flexible, and extensible template for PyTorch. It's beautiful.