https://github.com/mingjiewang0606/fedcl_pubic
공식 코드FedCL: Federated Multi-Phase Curriculum Learning to Synchronously Correlate User Heterogeneity
Papers with Code 아카이브
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mit-wu-lab/l-rho
공식 코드Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling
https://github.com/mit-wu-lab/mapf-autonomous-driving
공식 코드https://github.com/mkuzma96/customprod
공식 코드https://github.com/mlindauer/GenericWrapper4AC
공식 코드https://github.com/moe-inf/awesome-moe-inference
공식 코드A Survey on Inference Optimization Techniques for Mixture of Experts Models
https://github.com/mohammadzahd93/when2measurekf
공식 코드https://github.com/moonbow721/dposer
공식 코드https://github.com/mossr/CrossEntropyVariants.jl
공식 코드https://github.com/msramada/robust-less-dist-shifts
공식 코드Dampening parameter distributional shifts under robust control and gain scheduling
https://github.com/mushfiqur11/ClassSchedule
공식 코드https://github.com/mvandermerwe/BP-GPU-Message-Scheduling
공식 코드Message Scheduling for Performant, Many-Core Belief Propagation
https://github.com/nathangrinsztajn/DAG-scheduling
공식 코드Geometric Deep Reinforcement Learning for Dynamic DAG Scheduling
https://github.com/naver-ai/RapFlow-TTS
공식 코드RapFlow-TTS: Rapid and High-Fidelity Text-to-Speech with Improved Consistency Flow Matching
https://github.com/naver/goal-co
공식 코드GOAL: A Generalist Combinatorial Optimization Agent Learning
https://github.com/navid-naderi/LinkSchedulingGNNs_SupervisionStudy
공식 코드https://github.com/netease-youdao/confucius3-math
공식 코드Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning
https://github.com/netx-lab/ayo
공식 코드Teola: Towards End-to-End Optimization of LLM-based Applications
https://github.com/nikskiks/learning-reward-function-demand-response
공식 코드Learning a Reward Function for User-Preferred Appliance Scheduling
https://github.com/niveck/LLMafia
공식 코드Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
https://github.com/nmarzz/clip
공식 코드To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions
https://github.com/noamelata/nesteddiffusion
공식 코드https://github.com/nsidn98/NICE
공식 코드NICE: Robust Scheduling through Reinforcement Learning-Guided Integer Programming
https://github.com/ntnghia1908/master_thesis
공식 코드Reinforcement Learning -based Adaptation and Scheduling Methods for Multi-source DASH
https://github.com/nums-project/nums
공식 코드https://github.com/obaghirli/syn10-diffusion
공식 코드https://github.com/olly-styles/workbench
공식 코드WorkBench: a Benchmark Dataset for Agents in a Realistic Workplace Setting
https://github.com/olympiquemarcel/rasda
공식 코드https://github.com/opengvlab/controlllm
공식 코드ControlLLM: Augment Language Models with Tools by Searching on Graphs
https://github.com/openrlhf/openrlhf
공식 코드OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
https://github.com/optimization-and-machine-learning-lab/job-shop
공식 코드Learning to generalize Dispatching rules on the Job Shop Scheduling
https://github.com/orionstarai/orion
공식 코드https://github.com/osayamenja/aristos
공식 코드https://github.com/osayamenja/kleos
공식 코드https://github.com/pabloo22/job_shop_lib
공식 코드https://github.com/paoxiaode/DF-GNN
공식 코드DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.