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Found 12 papers, 4 papers with code
Heteroskedasticity as a Signature of Association for Age-Related Genes
To our knowledge, along with identifying age-associated genes, this is the first work to propose a framework for computational causal inference on age-related genes, using a dataset of human dermal fibroblast gene expression data.
Cardiovascular Disease Risk Prediction via Social Media
Results demonstrated that analyzing tweets' emotions surpassed the predictive power of demographic data alone, enabling the identification of individuals at potential risk of developing CVD.
ChatGPT in the Age of Generative AI and Large Language Models: A Concise Survey
We also lay out essential foundational literature on LLMs and GAI in general and their connection with ChatGPT.
More Synergy, Less Redundancy: Exploiting Joint Mutual Information for Self-Supervised Learning
This paper is a fundamental work where, we investigate role of mutual information in SSL, and reformulate the problem of SSL in the context of a new perspective on mutual information.
A Robust Likelihood Model for Novelty Detection
While this is a known problem that has been studied in recent years for the case of supervised learning, the case of novelty detection has received very limited attention.
FUSSL: Fuzzy Uncertain Self Supervised Learning
This double supervision approach is captured in two key steps: 1) invariance enforcement to data augmentation, and 2) fuzzy pseudo labeling (both hard and soft annotation).
Deep Active Ensemble Sampling For Image Classification
We show empirical results of our framework, and comparative performance with the state-of-the-art on four datasets, namely, MNIST, CIFAR10, CIFAR100 and ImageNet to establish a new baseline in two different settings.
Human Age Estimation from Gene Expression Data using Artificial Neural Networks
In this line, we propose a new framework for human age estimation using information from human dermal fibroblast gene expression data.
Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention
Plant species identification in the wild is a difficult problem in part due to the high variability of the input data, but also because of complications induced by the long-tail effects of the datasets distribution.
Generative Probabilistic Novelty Detection with Adversarial Autoencoders
We assume that training data is available to describe only the inlier distribution.
Unified Deep Supervised Domain Adaptation and Generalization
This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models.
Ranked #87 on
Domain Generalization
on PACS
Information Bottleneck Learning Using Privileged Information for Visual Recognition
The problem is challenging because of the intrinsic asymmetry caused by the missing auxiliary view during testing.
