494 citations · 623 across the 25 of their papers we have counts for
6 papers · 1 filter
Can Adversarial Weight Perturbations Inject Neural Backdoors?
Siddhant Garg, Adarsh Kumar, Vibhor Goel +1
Adversarial machine learning has exposed several security hazards of neural models and has become an important research topic in recent times. Thus far, the concept of an "adversar…
Functional Regularization for Representation Learning: A Unified Theoretical Perspective
Siddhant Garg, Yingyu Liang
Unsupervised and self-supervised learning approaches have become a crucial tool to learn representations for downstream prediction tasks. While these approaches are widely used in…
Advances in Quantum Deep Learning: An Overview
Siddhant Garg, Goutham Ramakrishnan
The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing…
Beyond Fine-tuning: Few-Sample Sentence Embedding Transfer
Siddhant Garg, Rohit Kumar Sharma, Yingyu Liang
Fine-tuning (FT) pre-trained sentence embedding models on small datasets has been shown to have limitations. In this paper we show that concatenating the embeddings from the pre-tr…
BAE: BERT-based Adversarial Examples for Text Classification
Siddhant Garg, Goutham Ramakrishnan
Modern text classification models are susceptible to adversarial examples, perturbed versions of the original text indiscernible by humans which get misclassified by the model. Rec…
Cross-Shape Attention for Part Segmentation of 3D Point Clouds
Marios Loizou, Siddhant Garg, Dmitry Petrov +2
We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-…