https://github.com/locuslab/e2e-model-learning
공식 코드Task-based End-to-end Model Learning in Stochastic Optimization
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/longkangli/pfss-il
공식 코드Learning to Optimize Permutation Flow Shop Scheduling via Graph-based Imitation Learning
https://github.com/lscpku/temple
공식 코드TEMPLE:Temporal Preference Learning of Video LLMs via Difficulty Scheduling and Pre-SFT Alignment
https://github.com/lucaballotta/vrem-fl
공식 코드VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
https://github.com/lukebhan/neuraloperatorapproximatedgainscheduling
공식 코드Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation
https://github.com/lukebhan/neuraloperatorsforgainkernels
공식 코드Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping
https://github.com/lyan62/data-curation
공식 코드https://github.com/lzhangbv/dear_pytorch
공식 코드DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining
https://github.com/lzhengchun/BFTrainer-Log-Replayer
공식 코드BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes
https://github.com/lzk23/hpcschedule
공식 코드https://github.com/macrohuang1993/ESNet
공식 코드https://github.com/mad-stat/epicasting
공식 코드Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics
https://github.com/mahdihosseini/AdaS
공식 코드https://github.com/mail-ecnu/vmagent
공식 코드https://github.com/marcusklasson/replay_scheduling
공식 코드Learn the Time to Learn: Replay Scheduling in Continual Learning
https://github.com/marielouiselackner/ovenschedulingcli
공식 코드https://github.com/marl-smart-grids/energy-trading
공식 코드A Stochastic Game Framework for Efficient Energy Management in Microgrid Networks
https://github.com/marrlab/domainlab
공식 코드https://github.com/matenure/GNN_planner
공식 코드Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
https://github.com/matteo-tucat/delta-gradient-clipping
공식 코드Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks
https://github.com/mavens-lab/layered_3d_gaussian_splats
공식 코드L3GS: Layered 3D Gaussian Splats for Efficient 3D Scene Delivery
https://github.com/mazumder-lab/skinnytrees
공식 코드End-to-end Feature Selection Approach for Learning Skinny Trees
https://github.com/mengyuan-lee/graph_embedding_link_scheduling
공식 코드Graph Embedding based Wireless Link Scheduling with Few Training Samples
https://github.com/mertnakip/Recurrent-Trend-Predictive-Neural-Network
공식 코드https://github.com/mervekarakas/mamab_erasures
공식 코드Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels
https://github.com/messixiang/sr-flight-software
공식 코드SRFS: Parallel Processing Fault-tolerant ROS2-based Flight Software for the Space Ranger Cubesat
https://github.com/meta-diffub/meta-diffub
공식 코드Meta-DiffuB: A Contextualized Sequence-to-Sequence Text Diffusion Model with Meta-Exploration
https://github.com/mhdslh/Federated-Learning-in-Unreliable-and-Resource-Constrained-Cellular-Wireless-Networks
공식 코드Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networks
https://github.com/microsoft/FLAML
공식 코드https://github.com/microsoft/attentionengine
공식 코드https://github.com/microsoft/qlib/tree/main/examples/benchmarks/TCTS
공식 코드https://github.com/microsoft/sarathi-serve
공식 코드Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve
https://github.com/microsoft/statopt
공식 코드https://github.com/microsoft/vidur
공식 코드https://github.com/mikekalnt/bsvbs
공식 코드Energy-aware Scheduling of Virtualized Base Stations in O-RAN with Online Learning
https://github.com/milesywong/TIP-Search
공식 코드TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.