50 citations · 389 across the 39 of their papers we have counts for
43 papers
Repair Before Reinforce: Context-Augmented Knowledge Graph Reasoning for Multi-Hop Question Answering
Tharaka D. Fonseka, Niraj K. Jha
Question-answering often requires reasoning across multiple connected facts rather than retrieving a single isolated relation. Knowledge graphs (KGs) provide a structured way to re…
SweetDeep: A Wearable AI Solution for Real-Time Non-Invasive Diabetes Screening
Ian Henriques, Lynda Elhassar, Sarvesh Relekar +11
The global rise in type 2 diabetes underscores the need for scalable and cost-effective screening methods. Current diagnosis requires biochemical assays, which are invasive and cos…
GraphMERT: Efficient and Scalable Distillation of Reliable Knowledge Graphs from Unstructured Data
Margarita Belova, Jiaxin Xiao, Shikhar Tuli +1
Researchers have pursued neurosymbolic artificial intelligence (AI) applications for nearly three decades. A marriage of the neural and symbolic components can lead to rapid advanc…
LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity
Hongjie Wang, Chih-Yao Ma, Yen-Cheng Liu +10
Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the numb…
DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling
Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu +3
Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we…
BREATHE: Second-Order Gradients and Heteroscedastic Emulation based Design Space Exploration
Shikhar Tuli, Niraj K. Jha
Researchers constantly strive to explore larger and more complex search spaces in various scientific studies and physical experiments. However, such investigations often involve so…
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Hongjie Wang, Bhishma Dedhia, Niraj K. Jha
Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of t…
Im-Promptu: In-Context Composition from Image Prompts
Bhishma Dedhia, Michael Chang, Jake C. Snell +2
Large language models are few-shot learners that can solve diverse tasks from a handful of demonstrations. This implicit understanding of tasks suggests that the attention mechanis…
DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors
Chia-Hao Li, Niraj K. Jha
Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven m…
TransCODE: Co-design of Transformers and Accelerators for Efficient Training and Inference
Shikhar Tuli, Niraj K. Jha
Automated co-design of machine learning models and evaluation hardware is critical for efficiently deploying such models at scale. Despite the state-of-the-art performance of trans…
EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge Platforms
Shikhar Tuli, Niraj K. Jha
Automated design of efficient transformer models has recently attracted significant attention from industry and academia. However, most works only focus on certain metrics while se…
AccelTran: A Sparsity-Aware Accelerator for Dynamic Inference with Transformers
Shikhar Tuli, Niraj K. Jha
Self-attention-based transformer models have achieved tremendous success in the domain of natural language processing. Despite their efficacy, accelerating the transformer is chall…
ML-FEED: Machine Learning Framework for Efficient Exploit Detection
Tanujay Saha, Tamjid Al-Rahat, Najwa Aaraj +2
Machine learning (ML)-based methods have recently become attractive for detecting security vulnerability exploits. Unfortunately, state-of-the-art ML models like long short-term me…
CODEBench: A Neural Architecture and Hardware Accelerator Co-Design Framework
Shikhar Tuli, Chia-Hao Li, Ritvik Sharma +1
Recently, automated co-design of machine learning (ML) models and accelerator architectures has attracted significant attention from both the industry and academia. However, most c…
CTRL: Clustering Training Losses for Label Error Detection
Chang Yue, Niraj K. Jha
In supervised machine learning, use of correct labels is extremely important to ensure high accuracy. Unfortunately, most datasets contain corrupted labels. Machine learning models…
SCouT: Synthetic Counterfactuals via Spatiotemporal Transformers for Actionable Healthcare
Bhishma Dedhia, Roshini Balasubramanian, Niraj K. Jha
The Synthetic Control method has pioneered a class of powerful data-driven techniques to estimate the counterfactual reality of a unit from donor units. At its core, the technique…
FlexiBERT: Are Current Transformer Architectures too Homogeneous and Rigid?
Shikhar Tuli, Bhishma Dedhia, Shreshth Tuli +1
The existence of a plethora of language models makes the problem of selecting the best one for a custom task challenging. Most state-of-the-art methods leverage transformer-based m…
Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems
Tanujay Saha, Najwa Aaraj, Niraj K. Jha
The core network architecture of telecommunication systems has undergone a paradigm shift in the fifth-generation (5G)networks. 5G networks have transitioned to software-defined in…
GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security
Jacob Brown, Tanujay Saha, Niraj K. Jha
Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist of thousands of devices connected in a complex network topology. The diversity and complexity of these compon…
Fast Design Space Exploration of Nonlinear Systems: Part II
Prerit Terway, Kenza Hamidouche, Niraj K. Jha
Nonlinear system design is often a multi-objective optimization problem involving search for a design that satisfies a number of predefined constraints. The design space is typical…
Fast Design Space Exploration of Nonlinear Systems: Part I
Sanjai Narain, Emily Mak, Dana Chee +4
System design tools are often only available as input-output blackboxes: for a given design as input they compute an output representing system behavior. Blackboxes are intended to…
HW/SW Framework for Improving the Safety of Implantable and Wearable Medical Devices
Malin Prematilake, Younghyun Kim, Vijay Raghunathan +2
Implantable and wearable medical devices (IWMDs) are widely used for the monitoring and therapy of an increasing range of medical conditions. Improvements in medical devices, enabl…
MHDeep: Mental Health Disorder Detection System based on Body-Area and Deep Neural Networks
Shayan Hassantabar, Joe Zhang, Hongxu Yin +1
Mental health problems impact quality of life of millions of people around the world. However, diagnosis of mental health disorders is a challenging problem that often relies on se…
SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning
Tanujay Saha, Najwa Aaraj, Neel Ajjarapu +1
Cyber-physical systems (CPS) and Internet-of-Things (IoT) devices are increasingly being deployed across multiple functionalities, ranging from healthcare devices and wearables to…