Results for “mfe”

Benchmarks automatic post-editing (APE) models on WMT'18 SMT, SubEdits, and MLQE-PE datasets, reporting BLEU, ChrF, and TER scores computed with SacreBLEU and TERCOM.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.

Provides guidance for training Mixture-of-Experts models with long context windows, covering context parallelism sizing, selective recomputation, dispatcher choices, and practical patterns from recent experiments.

Generate a complete PHP Model Context Protocol server project with tools, resources, prompts, and tests using the official PHP SDK.

Govern Power Automate flows and Power Apps at scale by classifying business impact, detecting orphaned resources, auditing connectors, enforcing compliance, and computing archive scores — all without Dataverse or the CoE Starter Kit.

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrupt-driven I/O, and peripheral drivers.

Configure and optimize .faf files, MCP servers, and bi-directional sync for AI context across multiple platforms, with championship scoring to achieve 85%+ AI-readiness.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks, including model serving, feature engineering, A/B testing, and monitoring.

Integrates on-device and cloud machine learning into Flutter apps with TensorFlow Lite and Firebase ML Kit, covering image classification, object detection, OCR, face detection, and barcode scanning.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning using 100+ featurizers, including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa.

Refactor messy Pharo code safely through the genie MCP tools, working from the live image (no files). Use for renames, method extraction, moving behavior, or general cleanup of existing code.

Scan Model Context Protocol servers and tool metadata for poisoning, SSRF, and unauthenticated exposure.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring. Use PROACTIVELY for ML model deployment, inference optimization, or production ML infrastructure.

Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

User-Mode Driver Framework v2 (UMDF). User-mode driver model that uses the same WDF object model as KMDF but runs in a host process (WUDFHost.exe) protected by the reflector. Required for some categories (Indirect Display Drivers, many sensor and camera drivers) and recommended for any driver that doesn't strictly need kernel mode. USE WHEN: user mentions "UMDF", "WUDFHost", "user-mode driver", "reflector", "IDD", "ISensor", "WDFHOST", "UMDF v2", "FX2" DO NOT USE FOR: KMDF (use `wdf-kmdf`), classic UMDF v1 (deprecated, COM-based)