Popular GitHub Repositories for Strict Constraints

AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.

An open-source data logging library for machine learning models and data pipelines. ? Provides visibility into data quality & model performance over time. ?️ Supports privacy-preserving data collection, ensuring safety & robustness. ?

BionicGPT is an on-premise replacement for ChatGPT, offering the advantages of Generative AI while maintaining strict data confidentiality

Code implementation of "Learning Efficient Online 3D Bin Packing on Packing Configuration Trees". We propose to enhance the practical applicability of online 3D Bin Packing Problem (BPP) via learning on a hierarchical packing configuration tree which makes the deep reinforcement learning (DRL) model easy to deal with practical constraints and well-performing even with continuous solution space.

Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.

A toolkit for non-parallel voice conversion based on vector-quantized variational autoencoder

Extreme Angular is a strict & opinionated starter template with pre-configured ESLint, Prettier, Stylelint, CSpell, Git hooks, CI/CD, and VS Code settings.

[CVPRW 2024]Official PyTorch Implementation of "LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints"

Controlled text generation with programmable constraints.

[ACL 2024] FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models

[CVPR'24] Validation-free few-shot adaptation of CLIP, using a well-initialized Linear Probe (ZSLP) and class-adaptive constraints (CLAP).

[ICLR 2022] The implementation for the paper "Equivariant Graph Mechanics Networks with Constraints".

Leveraging Structural Priors and Constraints for Cryo-EM Heterogeneous Reconstruction

Constrained deep learning is an advanced approach to training deep neural networks by incorporating domain-specific constraints into the learning process.

:dart: Accumulated Gradients for TensorFlow 2

NuCS is a Python constraint programming library for solving Constraint Satisfaction and Optimization Problems over finite domains

β-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Verification

EMNLP BlackBox NLP 2020: Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples

mi-optimize is a versatile tool designed for the quantization and evaluation of large language models (LLMs). The library's seamless integration of various quantization methods and evaluation techniques empowers users to customize their approaches according to specific requirements and constraints, providing a high level of flexibility.

Code for the paper : "Weakly supervised segmentation with cross-modality equivariant constraints", available at https://arxiv.org/pdf/2104.02488.pdf