11 citations · 18 across the 3 of their papers we have counts for
6 papers
PruneNet: Channel Pruning via Global Importance
Ashish Khetan, Zohar Karnin
Channel pruning is one of the predominant approaches for accelerating deep neural networks. Most existing pruning methods either train from scratch with a sparsity inducing term su…
schuBERT: Optimizing Elements of BERT
Ashish Khetan, Zohar Karnin
Transformers \citep{vaswani2017attention} have gradually become a key component for many state-of-the-art natural language representation models. A recent Transformer based model-…
Robust conditional GANs under missing or uncertain labels
Kiran Koshy Thekumparampil, Sewoong Oh, Ashish Khetan
Matching the performance of conditional Generative Adversarial Networks with little supervision is an important task, especially in venturing into new domains. We design a new trai…
DARC: Differentiable ARchitecture Compression
Shashank Singh, Ashish Khetan, Zohar Karnin
In many learning situations, resources at inference time are significantly more constrained than resources at training time. This paper studies a general paradigm, called Different…
Number of Connected Components in a Graph: Estimation via Counting Patterns
Ashish Khetan, Harshay Shah, Sewoong Oh
Due to the limited resources and the scale of the graphs in modern datasets, we often get to observe a sampled subgraph of a larger original graph of interest, whether it is the wo…
Robustness of Conditional GANs to Noisy Labels
Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin +1
We study the problem of learning conditional generators from noisy labeled samples, where the labels are corrupted by random noise. A standard training of conditional GANs will not…