Prof. Mausam, IIT Delhi
- We show why and how Pooling (and attention) based BiLSTMs demonstrate improved learning ability and positional invariance over standard BiLSTMs.
- Further, we find that Pooling helps improve sample efficiency (low-resource settings) and is particularly beneficial when important words lie towards the middle of the sentence
- Our proposed pooling technique max-attention (MaxAtt) helps improve upon past approaches on standard accuracy metrics, and is more robust to distribution shift
- Analyses done on multiple Text Classification tasks.
Adversarial Robustness against the Union of Multiple Perturbation Models
Carnegie Mellon University, Pittsburgh, USA
Prof. Zico Kolter, 2019
- Designed an algorithm for robustness against the union of multiple perturbations types (L1, L2, Linf).
- Showed improved robustness exceeding previous benchmarks on MNIST and CIFAR-10 datasets.
- Accepted at ICML 2020.
The code and trained models for this work can be found on the Github link.
A manuscript for the same is available on ArXiv
Image Dehazing for Low Computation Devices
Samsung Research and Development Headquarters, Suwon, South Korea
- Worked on the task of image dehazing for low computation scenarios like mobile devices and refrigerators.
- Designed a novel loss function to train a Generative Adversarial Network (GAN) for low resource settings.
- Model was 1500 times faster & 200 times lighter than the state-of-the-art with acceptable loss in quality.
- Received Pre-Placement Offer for exceptional performance. Work done is in preparation for submission.
École Polytechnique Fédérale de Lausanne, Switzerland
Prof. James Larus, Summer 2018
- Worked on a TensorFlow based Cluster-scale, high-throughput bioinformatics framework, PERSONA.
- Implemented Striped Smith-Waterman Algorithm to make gene sequencing computationally feasible.
- Succeeded in speeding-up the sub-process of protein alignment by over 6 times. [Presentation]