https://github.com/YuemingJin/TMRNet
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
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/agiresearch/aios.server
https://github.com/agongee/prema_sim
PREMA: A Predictive Multi-task Scheduling Algorithm For Preemptible Neural Processing Units
https://github.com/ai4co/camp
Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
https://github.com/ajauhri/mobility-modeling
https://github.com/alexandervnikitin/tsgm
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations
https://github.com/andreas-abel/difftune-revisited
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates
https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws
Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources
https://github.com/balajiselvaraj1601/Step_Dropout
https://github.com/bravegroup/sheetcopilot
SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
https://github.com/caltech-netlab/acnportal-experiments
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
https://github.com/camma-public/ssg-qa
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
https://github.com/camma-public/ssg-vqa
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
https://github.com/camma-public/tf-cholec80
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
https://github.com/cmu-enyac/LeGR
Layer-compensated Pruning for Resource-constrained Convolutional Neural Networks
https://github.com/cpp-taskflow/cpp-taskflow
Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System
https://github.com/daskol/paper-reviews
Learning Gradient Descent: Better Generalization and Longer Horizons
https://github.com/dmksjfl/Job_Shop_Scheduling_Problem_with_Reinforcement_Learning
A Reinforcement Learning Environment For Job-Shop Scheduling
https://github.com/dwjshift/IL_ADS
Imitation Learning from Observation with Automatic Discount Scheduling
https://github.com/eeci/bsim23-voi-examples
https://github.com/facebookresearch/taser-tgnn
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
https://github.com/garygsw/Nav-NNDial
https://github.com/google-research/pix2seq
https://github.com/guicho271828/dsama
Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS
https://github.com/harvard-acc/RecPipe
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
https://github.com/hpaulkeeler/DetPoisson_MATLAB
Determinantal thinning of point processes with network learning applications
https://github.com/huaweicloud/vm-placement-dataset
https://github.com/instadeepai/poppy
Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization
https://github.com/intelligent-machine-learning/glake
LayerKV: Optimizing Large Language Model Serving with Layer-wise KV Cache Management
https://github.com/jilljenn/ktm
https://github.com/jonathandumas/generative-models-power-systems
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
https://github.com/karthikRavichandran/LR_exploration_tf
Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification
https://github.com/karthikv792/gpt-plan-benchmark
https://github.com/karthikv792/llms-planning
https://github.com/kondrasso/DQN
Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks
https://github.com/kuleuven-micas/acco
ACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.