Model Optimization
87 papers with code • 0 benchmarks • 0 datasets
To Optimize already existing models in Training/Inferencing tasks.
Benchmarks
These leaderboards are used to track progress in Model Optimization
Most implemented papers
Multi-objective Asynchronous Successive Halving
Hyperparameter optimization (HPO) is increasingly used to automatically tune the predictive performance (e. g., accuracy) of machine learning models.
Improved Distribution Matching for Dataset Condensation
uitrbn/idm
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CVPR 2023
In this paper, we propose a novel dataset condensation method based on distribution matching, which is more efficient and promising.
Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner
This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data.
Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks
abhaskumarsinha/GRU-varients
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The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates.
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.
Differentiable Satisfiability and Differentiable Answer Set Programming for Sampling-Based Multi-Model Optimization
We propose Differentiable Satisfiability and Differentiable Answer Set Programming (Differentiable SAT/ASP) for multi-model optimization.
Dependency or Span, End-to-End Uniform Semantic Role Labeling
bcmi220/unisrl
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Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence.
Video Relationship Reasoning using Gated Spatio-Temporal Energy Graph
yaohungt/GSTEG_CVPR_2019
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CVPR 2019
Visual relationship reasoning is a crucial yet challenging task for understanding rich interactions across visual concepts.
Variational Inference for Sparse Gaussian Process Modulated Hawkes Process
We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection.
Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results
xxlya/Fed_ABIDE
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However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required.
