24 citations · 33 across the 7 of their papers we have counts for
7 papers
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
Muthu Chidambaram, Rong Ge
Data augmentation has been pivotal in successfully training deep learning models on classification tasks over the past decade. An important subclass of data augmentation techniques…
On the Limitations of Temperature Scaling for Distributions with Overlaps
Muthu Chidambaram, Rong Ge
Despite the impressive generalization capabilities of deep neural networks, they have been repeatedly shown to be overconfident when they are wrong. Fixing this issue is known as m…
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Muthu Chidambaram, Chenwei Wu, Yu Cheng +1
Sparse coding, which refers to modeling a signal as sparse linear combinations of the elements of a learned dictionary, has proven to be a successful (and interpretable) approach i…
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Muthu Chidambaram, Xiang Wang, Chenwei Wu +1
Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels. In recent years, Mixup has become a standard primit…
Towards Understanding the Data Dependency of Mixup-style Training
Muthu Chidambaram, Xiang Wang, Yuzheng Hu +2
In the Mixup training paradigm, a model is trained using convex combinations of data points and their associated labels. Despite seeing very few true data points during training, m…
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
Muthuraman Chidambaram, Yinfei Yang, Daniel Cer +4
A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data. One potential method for overcoming this issue is learning cross-lingual t…
Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently
Muthuraman Chidambaram, Yanjun Qi
The idea of style transfer has largely only been explored in image-based tasks, which we attribute in part to the specific nature of loss functions used for style transfer. We prop…