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Found 11 papers, 3 papers with code
PETA: Evaluating the Impact of Protein Transfer Learning with Sub-word Tokenization on Downstream Applications
Moreover, despite the wealth of benchmarks and studies in the natural language community, there remains a lack of a comprehensive benchmark for systematically evaluating protein language model quality.
MedChatZH: a Better Medical Adviser Learns from Better Instructions
Generative large language models (LLMs) have shown great success in various applications, including question-answering (QA) and dialogue systems.
Multi-level Protein Representation Learning for Blind Mutational Effect Prediction
Directed evolution plays an indispensable role in protein engineering that revises existing protein sequences to attain new or enhanced functions.
A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era
Deep learning has been successfully applied to heart sound analysis in the past years.
Finding the Most Transferable Tasks for Brain Image Segmentation
Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance.
Transferability-Guided Cross-Domain Cross-Task Transfer Learning
We propose two novel transferability metrics F-OTCE (Fast Optimal Transport based Conditional Entropy) and JC-OTCE (Joint Correspondence OTCE) to evaluate how much the source model (task) can benefit the learning of the target task and to learn more transferable representations for cross-domain cross-task transfer learning.
Transferability Estimation for Semantic Segmentation Task
Recent analytical transferability metrics are mainly designed for image classification problem, and currently there is no specific investigation for the transferability estimation of semantic segmentation task, which is an essential problem in autonomous driving, medical image analysis, etc.
Practical Transferability Estimation for Image Classification Tasks
Transferability estimation is an essential problem in transfer learning to predict how good the performance is when transferring a source model (or source task) to a target task.
OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations
Specifically, we use optimal transport to estimate domain difference and the optimal coupling between source and target distributions, which is then used to derive the conditional entropy of the target task (task difference).
Justlookup: One Millisecond Deep Feature Extraction for Point Clouds By Lookup Tables
Deep models are capable of fitting complex high dimensional functions while usually yielding large computation load.
Face Recognition from Sequential Sparse 3D Data via Deep Registration
However, such devices only provide sparse(limited speckles in structured light system) and noisy 3D data which can not support face recognition directly.
