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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/fsalv/ClusterWay

공식 코드

Waypoint Generation in Row-based Crops with Deep Learning and Contrastive Clustering

https://github.com/fsoft-aic/waver

공식 코드

WAVER: Writing-style Agnostic Text-Video Retrieval via Distilling Vision-Language Models Through Open-Vocabulary Knowledge

https://github.com/fstrnad/pyDRLinWESM

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Deep reinforcement learning in World-Earth system models to discover sustainable management strategies

https://github.com/gaetanserre/l2rpn-2022_ppo-baseline

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Reinforcement learning for Energies of the future and carbon neutrality: a Challenge Design

https://github.com/gagawjbytw/empirical-vix

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Closed-form solutions for VIX derivatives in a Legendre empirical model

https://github.com/gbrltv/process_meta_learning

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Using Meta-learning to Recommend Process Discovery Methods

https://github.com/gcvcg/voltex

공식 코드

VolTex: Food Volume Estimation using Text-Guided Segmentation and Neural Surface Reconstruction

https://github.com/geirstorvik/smc.covid

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A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the Covid-19 case

https://github.com/genglongling/SagaLLM

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SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning

https://github.com/getao/icae

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In-context Autoencoder for Context Compression in a Large Language Model

https://github.com/gfz/weedsgalore

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WeedsGalore: A Multispectral and Multitemporal UAV-based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields

https://github.com/ghost-in-moss/ghostos

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MOSS: Enabling Code-Driven Evolution and Context Management for AI Agents

https://github.com/giezijo/cvpr23-earthvision-cnn-lstm-inundation

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Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping

https://github.com/gipplab/preprint_generator

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CiteAssist: A System for Automated Preprint Citation and BibTeX Generation

https://github.com/gist-ailab/Food-Instance-Segmentation

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Deep Learning based Food Instance Segmentation using Synthetic Data

https://github.com/gitOmmix/gitOmmix

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Enhancing Clinical Data Warehouses with Provenance and Large File Management: The gitOmmix Approach for Clinical Omics Data

https://github.com/gmberton/EarthMatch

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EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut Photography

https://github.com/golmschenk/sr-gan

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Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling

https://github.com/google-research-datasets/global_streamflow_model_paper

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AI Increases Global Access to Reliable Flood Forecasts

https://github.com/google-research/google-research/tree/master/simulation_research/traffic

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Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements through Case Studies

https://github.com/google/dopamine

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Deep Reinforcement Learning

https://github.com/gourangc/Vision_Aided_Blockage_prediction

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Computer Vision Aided Blockage Prediction in Real-World Millimeter Wave Deployments

https://github.com/grill-lab/oat

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GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants

https://github.com/grill-lab/wote

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GRILLBot In Practice: Lessons and Tradeoffs Deploying Large Language Models for Adaptable Conversational Task Assistants

https://github.com/guanyuezhen/ar-cdnet

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Hard Region Aware Network for Remote Sensing Change Detection

https://github.com/gusseppe/scanflow

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Scanflow: A multi-graph framework for Machine Learning workflow management, supervision, and debugging

https://github.com/gvisen/credit-rbm-model

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Universal approximation of credit portfolio losses using Restricted Boltzmann Machines

https://github.com/gyeongmin47/kochet

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KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks

https://github.com/hadi-ghnd/accidentdetection

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Real-Time Accident Detection in Traffic Surveillance Using Deep Learning

https://github.com/haoyu-r/how-to-manage-tinyml-at-scale

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How to Manage Tiny Machine Learning at Scale: An Industrial Perspective

https://github.com/happy2git/unlearning_inference_iam

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Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness

https://github.com/hasibzunair/synthetic-covid-cxr-dataset

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Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis

https://github.com/hatton613/bearllm

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BearLLM: A Prior Knowledge-Enhanced Bearing Health Management Framework with Unified Vibration Signal Representation

https://github.com/hayder-hussein/Satellite-Traffic-Simulator

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Traffic Simulator for Multibeam Satellite Communication Systems

https://github.com/hazyresearch/eclair-agents

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Automating the Enterprise with Foundation Models

https://github.com/hazyresearch/fm_data_tasks

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Can Foundation Models Wrangle Your Data?

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