7 citations · 7 across the 2 of their papers we have counts for
5 papers
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification
Sharath Girish, Kamal Gupta, Saurabh Singh +1
We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off. Prior works approach th…
PatchGame: Learning to Signal Mid-level Patches in Referential Games
Kamal Gupta, Gowthami Somepalli, Anubhav Gupta +3
We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal. In our referential game, the…
The Lottery Ticket Hypothesis for Object Recognition
Sharath Girish, Shishira R. Maiya, Kamal Gupta +3
Recognition tasks, such as object recognition and keypoint estimation, have seen widespread adoption in recent years. Most state-of-the-art methods for these tasks use deep network…
Improved Modeling of 3D Shapes with Multi-view Depth Maps
Kamal Gupta, Susmija Jabbireddy, Ketul Shah +2
We present a simple yet effective general-purpose framework for modeling 3D shapes by leveraging recent advances in 2D image generation using CNNs. Using just a single depth image…
PatchVAE: Learning Local Latent Codes for Recognition
Kamal Gupta, Saurabh Singh, Abhinav Shrivastava
Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations. A promising technique for unsupervised learni…