research
∙
10/18/2022
Uncertainty in Extreme Multi-label Classification
Uncertainty quantification is one of the most crucial tasks to obtain tr...
share
research
∙
10/13/2021
Adversarial Attack across Datasets
It has been observed that Deep Neural Networks (DNNs) are vulnerable to ...
research
∙
06/03/2021
DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Attention is sparse in vision transformers. We observe the final predict...
research
∙
03/11/2021
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification
Recent works in neural network verification show that cheap incomplete v...
research
∙
10/01/2019
Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Imbalanced Data
We propose a novel unsupervised generative model, Elastic-InfoGAN, that ...
research
∙
08/09/2019
VisualBERT: A Simple and Performant Baseline for Vision and Language
We propose VisualBERT, a simple and flexible framework for modeling a br...
research
∙
04/01/2019
Reducing BERT Pre-Training Time from 3 Days to 76 Minutes
Large-batch training is key to speeding up deep neural network training ...
research
∙
12/06/2017
Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning
Modern neural image captioning systems typically adopt the encoder-decod...
research
∙
09/14/2017
