Model spider: Learning to rank pre-trained models efficiently
Abstract Figuring out which Pre-Trained Model (PTM) from a model zoo fits the target task is
essential to take advantage of plentiful model resources. With the availability of numerous …
essential to take advantage of plentiful model resources. With the availability of numerous …
Leveraging small-scale datasets for additive manufacturing process modeling and part certification: Current practice and remaining gaps
Additive manufacturing (AM) provides a data-rich environment for collecting a variety of
process data. These crucial data can be used to develop effective machine learning (ML) …
process data. These crucial data can be used to develop effective machine learning (ML) …
Beimingwu: A learnware dock system
The learnware paradigm proposed by Zhou (2016) aims to enable users to leverage
numerous existing high-performing models instead of building machine learning models …
numerous existing high-performing models instead of building machine learning models …
[PDF][PDF] Handling Learnwares Developed from Heterogeneous Feature Spaces without Auxiliary Data.
The learnware paradigm proposed by Zhou [2016] devotes to constructing a market of
numerous wellperformed models, enabling users to solve problems by reusing existing …
numerous wellperformed models, enabling users to solve problems by reusing existing …
Identifying helpful learnwares without examining the whole market
The learnware paradigm aims to construct a market of numerous well-performing machine
learning models, which enables users to leverage these models to accomplish specific tasks …
learning models, which enables users to leverage these models to accomplish specific tasks …
Learning Only When It Matters: Cost-Aware Long-Tailed Classification
Most current long-tailed classification approaches assume the cost-agnostic scenario, where
the training distribution of classes is long-tailed while the testing distribution of classes is …
the training distribution of classes is long-tailed while the testing distribution of classes is …
Which Model to Transfer? A Survey on Transferability Estimation
Transfer learning methods endeavor to leverage relevant knowledge from existing source
pre-trained models or datasets to solve downstream target tasks. With the increase in the …
pre-trained models or datasets to solve downstream target tasks. With the increase in the …
[PDF][PDF] Working With What You've Got: Leveraging Mislabeled Datasets And Improving Imperfect Pretrained Models
W Gerych - 2023 - digital.wpi.edu
Resources such as OpenML and HuggingFace have made large datasets and powerful
pretrained models more accessible than ever for deep learning practitioners and …
pretrained models more accessible than ever for deep learning practitioners and …
Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation
The learnware paradigm aims to help users leverage numerous existing high-performing
models instead of starting from scratch, where a learnware consists of a well-trained model …
models instead of starting from scratch, where a learnware consists of a well-trained model …
LLM4GCL: CAN LARGE LANGUAGE MODEL EM-POWER GRAPH CONTRASTIVE LEARNING?
Graph contrastive learning (GCL) has made significant strides in pre-training graph neural
networks (GNNs) without requiring human annotations. Previous GCL efforts have primarily …
networks (GNNs) without requiring human annotations. Previous GCL efforts have primarily …