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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/columbia/alibaba-dp-workload
공식 코드https://github.com/columbia/dpack
공식 코드https://github.com/columbia/privatekube
공식 코드https://github.com/core-robotics-lab/personalized_neural_trees
공식 코드https://github.com/crystal-zwz/HTS_RRM_Joint-Load-and-Capacity-Scheduling
공식 코드https://github.com/ct-wei/who-is-the-spy
공식 코드Exploring Large Language Models for Word Games:Who is the Spy?
https://github.com/danielfperez/castillo
공식 코드CASTILLO: Characterizing Response Length Distributions of Large Language Models
https://github.com/dawoz/jsp-deeprl-seq2seq
공식 코드Job Shop Scheduling via Deep Reinforcement Learning: a Sequence to Sequence approach
https://github.com/dazz993/mist
공식 코드Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization
https://github.com/decargroup/matrix_scheduling_qsr_dissipative_systems
공식 코드https://github.com/decargroup/matrix_scheduling_vsp_controllers
공식 코드Passivity-Based Gain-Scheduled Control with Scheduling Matrices
https://github.com/decisionintelligence/adformer
공식 코드ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting
https://github.com/deep-cognition-lab/sauce
공식 코드SAUCE: Synchronous and Asynchronous User-Customizable Environment for Multi-Agent LLM Interaction
https://github.com/deitsp/deitsp
공식 코드An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem
https://github.com/dfki-asr/xengine
공식 코드XEngine: Optimal Tensor Rematerialization for Neural Networks in Heterogeneous Environments
https://github.com/diegorossit/anor-s-24-01950
공식 코드https://github.com/dilsonpereira/MWSRPDT
공식 코드A Multiperiod Workforce Scheduling and Routing Problem with Dependent Tasks
https://github.com/donghyeonk/nesa
공식 코드Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
https://github.com/dos-group/fedzero
공식 코드FedZero: Leveraging Renewable Excess Energy in Federated Learning
https://github.com/dselsam/neurocore-public
공식 코드Guiding High-Performance SAT Solvers with Unsat-Core Predictions
https://github.com/dts-ait/seamless
공식 코드https://github.com/dytroshut/OMT-with-Flux-rate-Constraint
공식 코드Monge-Kantorovich Optimal Transport Through Constrictions and Flow-rate Constraints
https://github.com/eboursier/making_most_of_your_time
공식 코드Making the most of your day: online learning for optimal allocation of time
https://github.com/eddiegaoo/apt-serve
공식 코드Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving
https://github.com/efeslab/consumerbench
공식 코드ConsumerBench: Benchmarking Generative AI Applications on End-User Devices
https://github.com/ehw-fit/timeloop-with-quantization
공식 코드Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network Accelerators
https://github.com/emadalamoudi/lookahead_study
공식 코드A Wall-time Minimizing Parallelization Strategy for Approximate Bayesian Computation
https://github.com/ergoucao/erkg
공식 코드From Dense to Sparse: Event Response for Enhanced Residential Load Forecasting
https://github.com/erzhuoshao/psrnet-cikm
공식 코드https://github.com/fabian-sp/lr-scheduling
공식 코드https://github.com/facebookresearch/adaptive_scheduling
공식 코드Optimal Linear Decay Learning Rate Schedules and Further Refinements
https://github.com/facebookresearch/neural-rewriter
공식 코드Learning to Perform Local Rewriting for Combinatorial Optimization
https://github.com/facebookresearch/schedule_free
공식 코드https://github.com/fl-har/graph-federated-learning-for-ciot-devices
공식 코드Graph Federated Learning for CIoT Devices in Smart Home Applications
https://github.com/flashinfer-ai/flashinfer
공식 코드FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.