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Found 6 papers, 0 papers with code
Intriguing Differences Between Zero-Shot and Systematic Evaluations of Vision-Language Transformer Models
Transformer-based models have dominated natural language processing and other areas in the last few years due to their superior (zero-shot) performance on benchmark datasets.
Intriguing Equivalence Structures of the Embedding Space of Vision Transformers
Pre-trained large foundation models play a central role in the recent surge of artificial intelligence, resulting in fine-tuned models with remarkable abilities when measured on benchmark datasets, standard exams, and applications.
Improved Stock Price Movement Classification Using News Articles Based on Embeddings and Label Smoothing
We further incorporate weight decay, batch normalization, dropout, and label smoothing to improve the generalization of the trained models.
Towards Quantifying Intrinsic Generalization of Deep ReLU Networks
We show that the generalization intervals of a ReLU network behave similarly along pairwise directions between samples of the same label in both real and random cases on the MNIST and CIFAR-10 datasets.
Consensus-based Interpretable Deep Neural Networks with Application to Mortality Prediction
In particular, while the proposed method maintains similar interpretability as conventional shallow models such as logistic regression, it improves the prediction accuracy significantly.
Overfitting Mechanism and Avoidance in Deep Neural Networks
Assisted by the availability of data and high performance computing, deep learning techniques have achieved breakthroughs and surpassed human performance empirically in difficult tasks, including object recognition, speech recognition, and natural language processing.
