Popular GitHub Repositories for Cardinality 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. ?

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.

This methodology provides a structured approach for collaborating with AI systems on software development projects. It addresses common issues like code bloat, architectural drift, and context dilution through systematic constraints and validation checkpoints.

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

Ovis-Image is a 7B text-to-image model specifically optimized for high-quality text rendering, designed to operate efficiently under stringent computational constraints.

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

PilotScope is a middleware to bridge the gaps of deploying AI4DB (Artificial Intelligence for Databases) algorithms into actual database systems.

Controlled text generation with programmable constraints.

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

Query language for blending SQL and LLMs across structured + unstructured data, with type constraints.

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

AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.

Neural Relation Understanding: neural cardinality estimators for tabular data

State-of-the-art neural cardinality estimators for join queries

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

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

Leveraging Structural Priors and Constraints for Cryo-EM Heterogeneous Reconstruction

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

:dart: Accumulated Gradients for TensorFlow 2