146 citations · 173 across the 6 of their papers we have counts for
14 papers
On the Importance of Calibration in Semi-supervised Learning
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj +5
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency…
Targeted Neural Dynamical Modeling
Cole Hurwitz, Akash Srivastava, Kai Xu +4
Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously mea…
Security Assessment Rating Framework for Enterprises using MITRE ATT&CK Matrix
Hardik Manocha, Akash Srivastava, Chetan Verma +2
Threats targeting cyberspace are becoming more prominent and intelligent day by day. This inherently leads to a dire demand for continuous security validation and testing. Using th…
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
Seungwook Han, Akash Srivastava, Cole Hurwitz +2
State-of-the-art models for high-resolution image generation, such as BigGAN and VQVAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train,…
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
Robin Hirt, Akash Srivastava, Carlos Berg +1
In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the…
SimVAE: Simulator-Assisted Training forInterpretable Generative Models
Akash Srivastava, Jessie Rosenberg, Dan Gutfreund +1
This paper presents a simulator-assisted training method (SimVAE) for variational autoencoders (VAE) that leads to a disentangled and interpretable latent space. Training SimVAE is…