113 citations · 149 across the 9 of their papers we have counts for
10 papers
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Karthik Garimella, Nandan Kumar Jha, Brandon Reagen
Privacy concerns in client-server machine learning have given rise to private inference (PI), where neural inference occurs directly on encrypted inputs. PI protects clients' perso…
Circa: Stochastic ReLUs for Private Deep Learning
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen +1
The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI). While recent work demonstrate…
DeepReDuce: ReLU Reduction for Fast Private Inference
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg +1
The recent rise of privacy concerns has led researchers to devise methods for private neural inference -- where inferences are made directly on encrypted data, never seeing inputs.…
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance
Nandan Kumar Jha, Sparsh Mittal
In recent years, researchers have focused on reducing the model size and number of computations (measured as "multiply-accumulate" or MAC operations) of DNNs. The energy consumptio…
DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs
Nandan Kumar Jha, Sparsh Mittal, Binod Kumar +1
The remarkable predictive performance of deep neural networks (DNNs) has led to their adoption in service domains of unprecedented scale and scope. However, the widespread adoption…
On the Demystification of Knowledge Distillation: A Residual Network Perspective
Nandan Kumar Jha, Rajat Saini, Sparsh Mittal
Knowledge distillation (KD) is generally considered as a technique for performing model compression and learned-label smoothing. However, in this paper, we study and investigate th…