1 citations · 1 across the 17 of their papers we have counts for
18 papers
Contextual Flow Matching: Adaptive Step Selection in Flow Models for Efficient Visual Generation
Divya Jyoti Bajpai, Arun Verma, Manjesh Kumar Hanawal
Flow Matching enables high-quality visual generation via continuous-time dynamics, but inference remains costly due to multiple sequential function evaluations. Existing accelerati…
SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference
Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar Hanawal
Large Language Models (LLMs) have achieved remarkable success in natural language understanding and generation, but their deployment is constrained by high computational demands. D…
FastFlow: Accelerating The Generative Flow Matching Models with Bandit Inference
Divya Jyoti Bajpai, Dhruv Bhardwaj, Soumya Roy +4
Flow-matching models deliver state-of-the-art fidelity in image and video generation, but the inherent sequential denoising process renders them slower. Existing acceleration metho…
FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching
Divya Jyoti Bajpai, Shubham Agarwal, Apoorv Saxena +3
Flow Matching (FM) has recently emerged as a powerful approach for high-quality visual generation. However, their prohibitively slow inference due to a large number of denoising st…
FastVLM: Self-Speculative Decoding for Fast Vision-Language Model Inference
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Vision-language Models (VLMs) have made significant strides in visual understanding and query response generation, but often face challenges of high computational cost and inferenc…
Beyond Greedy Exits: Improved Early Exit Decisions for Risk Control and Reliability
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Early-Exit Deep Neural Networks enable adaptive inference by allowing prediction at intermediary layers, significantly reducing computational costs and latency. Most of the early e…
Know What You Don't Know: Selective Prediction for Early Exit DNNs
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Inference latency and trustworthiness of Deep Neural Networks (DNNs) are the bottlenecks in deploying them in critical applications like sensitive tasks. Early Exit (EE) DNNs overc…
FREE: Fast and Robust Vision Language Models with Early Exits
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
In recent years, Vision-Language Models (VLMs) have shown remarkable performance improvements in Vision-Language tasks. However, their large size poses challenges for real-world ap…
BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Early Exit (EE) techniques have emerged as a means to reduce inference latency in Deep Neural Networks (DNNs). The latency improvement and accuracy in these techniques crucially de…
A Survey of Early Exit Deep Neural Networks in NLP
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Deep Neural Networks (DNNs) have grown increasingly large in size to achieve state of the art performance across a wide range of tasks. However, their high computational requiremen…
Distributed Inference on Mobile Edge and Cloud: A Data-Cartography based Clustering Approach
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
The large size of DNNs poses a significant challenge for deployment on devices with limited resources, such as mobile, edge, and IoT platforms. To address this issue, a distributed…
Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approach
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Recent advances in Deep Neural Networks (DNNs) have demonstrated outstanding performance across various domains. However, their large size is a challenge for deployment on resource…
CAPEEN: Image Captioning with Early Exits and Knowledge Distillation
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Deep neural networks (DNNs) have made significant progress in recognizing visual elements and generating descriptive text in image-captioning tasks. However, their improved perform…
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Pre-trained Language Models (PLMs) exhibit good accuracy and generalization ability across various tasks using self-supervision, but their large size results in high inference late…
CEEBERT: Cross-Domain Inference in Early Exit BERT
Divya Jyoti Bajpai, Manjesh Kumar Hanawal
Pre-trained Language Models (PLMs), like BERT, with self-supervision objectives exhibit remarkable performance and generalization across various tasks. However, they suffer in infe…
FAIR: Filtering of Automatically Induced Rules
Divya Jyoti Bajpai, Ayush Maheshwari, Manjesh Kumar Hanawal +1
The availability of large annotated data can be a critical bottleneck in training machine learning algorithms successfully, especially when applied to diverse domains. Weak supervi…
I-SplitEE: Image classification in Split Computing DNNs with Early Exits
Divya Jyoti Bajpai, Aastha Jaiswal, Manjesh Kumar Hanawal
The recent advances in Deep Neural Networks (DNNs) stem from their exceptional performance across various domains. However, their inherent large size hinders deploying these networ…
SplitEE: Early Exit in Deep Neural Networks with Split Computing
Divya J. Bajpai, Vivek K. Trivedi, Sohan L. Yadav +1
Deep Neural Networks (DNNs) have drawn attention because of their outstanding performance on various tasks. However, deploying full-fledged DNNs in resource-constrained devices (ed…