Search Results for author:
Found 16 papers, 4 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.
Inductive Link Prediction in Knowledge Graphs using Path-based Neural Networks
Embedding-based models usually need fine-tuning on new entity embeddings, and hence are difficult to be directly applied to inductive link prediction tasks.
Nonlinear Correct and Smooth for Semi-Supervised Learning
However, utilizing labels and features jointly in higher-order graphs has not been explored.
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.
Understanding the Spectral Bias of Coordinate Based MLPs Via Training Dynamics
However, in low dimensional settings, a severe spectral bias occurs that obstructs convergence to high frequency components entirely.
Too Much in Common: Shifting of Embeddings in Transformer Language Models and its Implications
The success of language models based on the Transformer architecture appears to be inconsistent with observed anisotropic properties of representations learned by such models.
ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structure
The prediction is very fast compared with other protein sequence prediction servers - it takes only a few minutes for a query protein on average.
Dense Embeddings Preserving the Semantic Relationships in WordNet
In order to create suitable labels for the training of sense spectra, we designed a new similarity measurement for noun and verb synsets in WordNet.
An Analysis on the Learning Rules of the Skip-Gram Model
To improve the generalization of the representations for natural language processing tasks, words are commonly represented using vectors, where distances among the vectors are related to the similarity of the words.
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.
High-resolution home location prediction from tweets using deep learning with dynamic structure
We obtained over 90% accuracy for large subsets on a commonly used dataset.
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.
Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks
Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets were derived from a pixel-based single-date multi-spectral remotely sensed image with low accuracy.
Transformed Residual Quantization for Approximate Nearest Neighbor Search
The success of product quantization (PQ) for fast nearest neighbor search depends on the exponentially reduced complexities of both storage and computation with respect to the codebook size.
