16 citations · 37 across the 13 of their papers we have counts for
14 papers
Generative Kaleidoscopic Networks
Harsh Shrivastava
We discovered that the neural networks, especially the deep ReLU networks, demonstrate an `over-generalization' phenomenon. That is, the output values for the inputs that were not…
Federated Learning with Neural Graphical Models
Urszula Chajewska, Harsh Shrivastava
Federated Learning (FL) addresses the need to create models based on proprietary data in such a way that multiple clients retain exclusive control over their data, while all benefi…
Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska, Harsh Shrivastava
Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model (PGM) where the feature connections are modeled using an undirected graph and the edge weig…
DiversiGATE: A Comprehensive Framework for Reliable Large Language Models
Shima Imani, Ali Beyram, Harsh Shrivastava
In this paper, we introduce DiversiGATE, a unified framework that consolidates diverse methodologies for LLM verification. The proposed framework comprises two main components: Div…
MathPrompter: Mathematical Reasoning using Large Language Models
Shima Imani, Liang Du, Harsh Shrivastava
Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers. Unlike natural language understanding, math probl…
Are uGLAD? Time will tell!
Shima Imani, Harsh Shrivastava
We frequently encounter multiple series that are temporally correlated in our surroundings, such as EEG data to examine alterations in brain activity or sensors to monitor body mov…
Neural Graph Revealers
Harsh Shrivastava, Urszula Chajewska
Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries. This limits their adopt…
Methods for Recovering Conditional Independence Graphs: A Survey
Harsh Shrivastava, Urszula Chajewska
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships. Each edge represents the par…
Neural Graphical Models
Harsh Shrivastava, Urszula Chajewska
Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the underlying distribution. Theoretical…
uGLAD: Sparse graph recovery by optimizing deep unrolled networks
Harsh Shrivastava, Urszula Chajewska, Robin Abraham +1
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations…
Echo State Speech Recognition
Harsh Shrivastava, Ankush Garg, Yuan Cao +2
We propose automatic speech recognition (ASR) models inspired by echo state network (ESN), in which a subset of recurrent neural networks (RNN) layers in the models are randomly in…
AntMan: Sparse Low-Rank Compression to Accelerate RNN inference
Samyam Rajbhandari, Harsh Shrivastava, Yuxiong He
Wide adoption of complex RNN based models is hindered by their inference performance, cost and memory requirements. To address this issue, we develop AntMan, combining structured s…
Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
Harsh Shrivastava, Eugene Bart, Bob Price +3
We propose a new approach, called cooperative neural networks (CoNN), which uses a set of cooperatively trained neural networks to capture latent representations that exploit prior…
GLAD: Learning Sparse Graph Recovery
Harsh Shrivastava, Xinshi Chen, Binghong Chen +4
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an …