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Found 14 papers, 7 papers with code
DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction
The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses.
Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks
Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios.
A Simple Graph Contrastive Learning Framework for Short Text Classification
Subsequently, we directly apply contrastive learning on these embeddings.
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
We propose a novel model named MI-DELIGHT for short text classification in this work.
Information Entropy Invariance: Enhancing Length Extrapolation in Attention Mechanisms
Since the emergence of research on improving the length extrapolation capabilities of large language models in 2021, some studies have made modifications to the scaling factor in the scaled dot-product attention mechanism as part of their proposed methods without rigorous theoretical justifications.
Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
Graph few-shot learning has garnered significant attention for its ability to rapidly adapt to downstream tasks with limited labeled data, sparking considerable interest among researchers.
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Node classification is an essential problem in graph learning.
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference
By incorporating relevant visual information and leveraging linguistic knowledge, our approach bridges the gap between language and vision, leading to improved understanding and inference capabilities in NLI tasks.
AMPCliff: quantitative definition and benchmarking of activity cliffs in antimicrobial peptides
This study introduces a quantitative definition and benchmarking framework AMPCliff for the AC phenomenon in antimicrobial peptides (AMPs) composed by canonical amino acids.
A method for incremental discovery of financial event types based on anomaly detection
Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints; at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios.
A deep learning method for the long-term prediction of plant electrical signals under salt stress to identify salt tolerance
Furthermore, we developed a quantitative model, named the NaCl stress concentration discrimination model (SCDM), to investigate the relationship between the electrical signals, NaCl stress concentration, and time dependence, and used a salt tolerance classification model (STCM) to discover the most appropriate NaCl stress concentration for distinguishing the salt tolerance of wheat.
Using a one-dimensional convolutional neural network with a conditional generative adversarial network to classify plant electrical signals
This paper proposes a model, based on a one-dimensional convolutional neural network (1D-CNN) with a conditional generative adversarial network (CGAN), which can quickly and effectively identify the salt tolerance of the seedlings using plant electrical signals at the early seedling stage.
Towards Increased Reliability, Transparency and Accessibility in Crosslinking Mass Spectrometry
Crosslinking mass spectrometry (Crosslinking MS) has substantially matured as a method over the last two decades through parallel development in multiple labs, demonstrating its applicability for protein structure determination, conformation analysis and mapping protein interactions in complex mixtures.
e-Distance Weighted Support Vector Regression
We propose a novel support vector regression approach called e-Distance Weighted Support Vector Regression (e-DWSVR). e-DWSVR specifically addresses two challenging issues in support vector regression: first, the process of noisy data; second, how to deal with the situation when the distribution of boundary data is different from that of the overall data.
