Scheduling
366 papers with code • 0 benchmarks • 0 datasets
Project or Job Scheduling
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Libraries
Use these libraries to find Scheduling models and implementationsMost implemented papers
MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inference
yuanmu97/mlink
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The cost efficiency of model inference is critical to real-world machine learning (ML) applications, especially for delay-sensitive tasks and resource-limited devices.
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recent technical innovations.
Learning Gradient Descent: Better Generalization and Longer Horizons
vfleaking/rnnprop
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ICML 2017
Training deep neural networks is a highly nontrivial task, involving carefully selecting appropriate training algorithms, scheduling step sizes and tuning other hyperparameters.
Curriculum Dropout
pmorerio/curriculum-dropout
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ICCV 2017
This induces an adaptive regularization scheme that smoothly increases the difficulty of the optimization problem.
Pitfalls and Best Practices in Algorithm Configuration
Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning).
Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification
BIGBALLON/HTD
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Learning rate scheduler has been a critical issue in the deep neural network training.
Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling
Many optimization techniques evaluate solutions consecutively, where the next candidate for evaluation is determined by the results of previous evaluations.
Learning Scheduling Algorithms for Data Processing Clusters
Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI.
Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing
haofuml/cyclical_annealing
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NAACL 2019
Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks.
