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Arijit Mukherjee
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2020 – today
- 2024
- [c60]Swarnava Dey
, Arijit Mukherjee
Towards a Task-agnostic Distillation Methodology for Creating Edge Foundation Models. EdgeFM@MobiSys 2024: 10-15 - [c59]Shalini Mukhopadhyay, Swarnava Dey, Arijit Mukherjee, Arpan Pal, Ashwin S:
Time Series Classification on Edge with Lightweight Attention Networks. PerCom Workshops 2024: 487-492 - [i4]Sohini Roychowdhury, Marko Krema, Anvar Mahammad, Brian Moore, Arijit Mukherjee, Punit Prakashchandra:
ERATTA: Extreme RAG for Table To Answers with Large Language Models. CoRR abs/2405.03963 (2024) - 2023
- [j14]Arijit Mukherjee
The Connection between Imported Inputs and Exports: The Importance of Strategic Interdependence. Games 14(1): 6 (2023) - [j13]Stefano Colombo
Location of Firms and Outsourcing. Games 14(6): 70 (2023) - [j12]Arun M. George, Sounak Dey, Dighanchal Banerjee, Arijit Mukherjee
Online time-series forecasting using spiking reservoir. Neurocomputing 518: 82-94 (2023) - [j11]Jin Li
What Makes Agility Fragile? A Dynamic Theory of Organizational Rigidity. Manag. Sci. 69(6): 3578-3601 (2023) - [j10]Tien-Der Han, Arijit Mukherjee
Mergers of complements, endogenous product differentiation and welfare. Math. Soc. Sci. 126: 30-41 (2023) - [c58]Jayeeta Mondal
Binary Convolutional Neural Network for Efficient Gesture Recognition at Edge. AIMLSystems 2023: 18:1-18:10 - [c57]Syed Mujibul Islam
TinyML Demonstration of Time-series Prediction and Vision-based Gesture Recognition. AIMLSystems 2023: 60:1-60:3 - [c56]Sohini Roychowdhury, Andres Alvarez, Brian Moore, Marko Krema, Maria Paz Gelpi, Punit Agrawal, Federico Martin Rodriguez, Angel Rodriguez, Jose Ramon Cabrejas, Pablo Martinez Serrano, Arijit Mukherjee:
Hallucination-minimized Data-to-answer Framework for Financial Decision-makers. IEEE Big Data 2023: 4693-4702 - [c55]Chetan Kadway, Sounak Dey, Arijit Mukherjee, Arpan Pal, Gilles Bézard:
Low Power & Low Latency Cloud Cover Detection in Small Satellites Using On-board Neuromorphic Processors. IJCNN 2023: 1-8 - [c54]Sayan Kahali, Sounak Dey, Chetan Kadway, Arijit Mukherjee, Arpan Pal, Manan Suri:
Low-Power Lossless Image Compression on Small Satellite Edge using Spiking Neural Network. IJCNN 2023: 1-8 - [i3]Jayeeta Mondal, Swarnava Dey, Arijit Mukherjee:
Automated Heterogeneous Low-Bit Quantization of Multi-Model Deep Learning Inference Pipeline. CoRR abs/2311.05870 (2023) - [i2]Sohini Roychowdhury, Andres Alvarez, Brian Moore, Marko Krema, Maria Paz Gelpi, Federico Martin Rodriguez, Angel Rodriguez, Jose Ramon Cabrejas, Pablo Martinez Serrano, Punit Agrawal, Arijit Mukherjee:
Hallucination-minimized Data-to-answer Framework for Financial Decision-makers. CoRR abs/2311.07592 (2023) - 2022
- [j9]Ku-Chu Tsao
Competition and Innovation in Markets with Technology Leaders. Games 13(1): 9 (2022) - [j8]Arijit Mukherjee, Chenhang Zeng:
Social desirability of entry in a bilateral oligopoly - The implications of (non) sunk costs. Math. Soc. Sci. 118: 12-19 (2022) - [j7]Arijit Mukherjee
Accelerated Fire Detection and Localization at Edge. ACM Trans. Embed. Comput. Syst. 21(6): 70:1-70:27 (2022) - [c53]Jeet Dutta
Acceleration-aware, Retraining-free Evolutionary Pruning for Automated Fitment of Deep Learning Models on Edge Devices. AIMLSystems 2022: 10:1-10:10 - [c52]Kavya Borra
Performance improvement of reinforcement learning algorithms for online 3D bin packing using FPGA. AIMLSystems 2022: 18:1-18:7 - [c51]Arijit Mukherjee
Automated Deep Learning Model Partitioning for Heterogeneous Edge Devices. AIMLSystems 2022: 19:1-19:8 - [c50]Arijit Mukherjee
TinyML Techniques for running Machine Learning models on Edge Devices. AIMLSystems 2022: 27:1-27:2 - [c49]Sounak Dey, Dighanchal Banerjee, Arun M. George, Arijit Mukherjee
Efficient Time Series Classification using Spiking Reservoir. IJCNN 2022: 1-8 - [c48]Arijit Mukherjee, Shubham Bansal, Sandeepkumar Satpal, Rupesh K. Mehta:
Text aware Emotional Text-to-speech with BERT. INTERSPEECH 2022: 4601-4605 - [c47]Dighanchal Banerjee, Sounak Dey, Arun M. George, Arijit Mukherjee
Efficient Optimized Spike Encoding of Multivariate Time-series. NICE 2022: 22-28 - 2020
- [j6]Arijit Mukherjee
Information Disclosure through Technology Licensing. Games 11(3): 37 (2020) - [c46]Dighanchal Banerjee, Smriti Rani, Arun M. George
Application of Spiking Neural Networks for Action Recognition from Radar Data. IJCNN 2020: 1-10 - [c45]Arun M. George
A Reservoir-based Convolutional Spiking Neural Network for Gesture Recognition from DVS Input. IJCNN 2020: 1-9 - [c44]Shubham Bansal, Arijit Mukherjee, Sandeepkumar Satpal, Rupesh Kumar Mehta:
On Improving Code Mixed Speech Synthesis with Mixlingual Grapheme-to-Phoneme Model. INTERSPEECH 2020: 2957-2961
2010 – 2019
- 2019
- [c43]Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi:
Cross-Lingual Training for Automatic Question Generation. ACL (1) 2019: 4863-4872 - [c42]Jayeeta Mondal, Swarnava Dey
Edge Acceleration of Deep Neural Networks. MobiSys 2019: 691-692 - [c41]Soumyadeep Choudhury, Sounak Dey, Arijit Mukherjee
A Distributed Semantic Knowledge Framework for Collaborative Robotics. PerCom Workshops 2019: 651-657 - [c40]Swarnava Dey
Offloaded Execution of Deep Learning Inference at Edge: Challenges and Insights. PerCom Workshops 2019: 855-861 - [c39]Swarnava Dey
Embedded Deep Inference in Practice: Case for Model Partitioning. SenSys-ML 2019: 25-30 - [i1]Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi:
Cross-Lingual Training for Automatic Question Generation. CoRR abs/1906.02525 (2019) - 2018
- [j5]Sourav Bhattacharya
On the optimality of diverse expert panels in persuasion games. Games Econ. Behav. 107: 345-363 (2018) - [j4]Ajay Kattepur, Hemant Kumar Rath, Arijit Mukherjee, Anantha Simha:
Distributed Optimization Framework for Industry 4.0 Automated Warehouses. EAI Endorsed Trans. Ind. Networks Intell. Syst. 5(15): e2 (2018) - [c38]Swagata Biswas, Swarnava Dey
(WIP) At Most M - A Flexible Redundancy Model for Cloud Robotics. IEEE CLOUD 2018: 577-581 - [c37]Kaustab Pal, Sakyajit Bhattacharya, Sounak Dey, Arijit Mukherjee
Modelling HTM Learning and Prediction for Robotic Path-Learning. BioRob 2018: 839-844 - [c36]Ajay Kattepur, Arijit Mukherjee
Verification and Timing Analysis of Industry 4.0 Warehouse Automation Workflows. ETFA 2018: 1297-1304 - [c35]Swarnava Dey
Implementing Deep Learning and Inferencing on Fog and Edge Computing Systems. PerCom Workshops 2018: 818-823 - [c34]Ajay Kattepur, Hemant Kumar Rath, Anantha Simha, Arijit Mukherjee
Distributed optimization in multi-agent robotics for industry 4.0 warehouses. SAC 2018: 808-815 - [c33]Swarnava Dey
Partitioning of CNN Models for Execution on Fog Devices. CitiFog@SenSys 2018: 19-24 - 2017
- [c32]Sounak Dey, Abhijan Bhattacharyya, Arijit Mukherjee
Semantic data exchange between collaborative robots in fog environment: Can CoAP be a choice? GIoTS 2017: 1-6 - [c31]Tapan Chowdhury
An Efficient MapReduce-Based Adaptive K-Means Clustering for Large Dataset. iNIS 2017: 157-162 - [c30]Swagata Biswas, Swarnava Dey, Rimita Lahiri, Arijit Mukherjee:
A Distributed and Fault Tolerant Robotic Localisation and Mapping in Network Edge. ARMS-CC@PODC 2017: 7-16 - 2016
- [j3]Sujoy Mistry, Arijit Mukherjee, Nandini Mukherjee, Dibyanshu Jaiswal:
P2P Based Service Provisioning on Distributed Resources. Int. J. Next Gener. Comput. 7(3) (2016) - [c29]Swarnava Dey
CAD Patient Classification Using MIMIC-II. eHealth 360° 2016: 370-375 - [c28]Swarnava Dey
Robotic SLAM: a Review from Fog Computing and Mobile Edge Computing Perspective. MobiQuitous (Adjunct Proceedings) 2016: 153-158 - [c27]Ansuman Banerjee, Himadri Sekhar Paul, Arijit Mukherjee
I2oT: Inexactness in IoT. ARMS-CC@PODC 2016: 40-45 - 2015
- [c26]Sujoy Mistry, Dibyanshu Jaiswal
P2P-Based Service Distribution over Distributed Resources. AINA 2015: 515-520 - [c25]Sounak Dey, Dibyanshu Jaiswal
Organization and management of Semantic Sensor information using SSN ontology: An energy meter use case. ICST 2015: 468-473 - [c24]Tanushyam Chattopadhyay, Avik Ghose, Arijit Mukherjee
Automated Workflow Formation for IoT Analytics: A Case Study. IoT 360 (2) 2015: 36-43 - [c23]Sounak Dey, Dibyanshu Jaiswal
A Semantic Algorithm Repository and Workflow Designer Tool: Signal Processing Use Case. IoT 360 (2) 2015: 53-61 - [c22]Himadri Sekhar Paul, Pubali Datta, Ansuman Banerjee, Arijit Mukherjee
Compute on the Go: A Case of Mobile-Cloud Collaborative Computing Under Mobility. ARMS-CC@PODC 2015: 78-90 - [c21]Dibyanshu Jaiswal
Demo: A Smart Framework for IoT Analytic Workflow Development. SenSys 2015: 455-456 - 2014
- [c20]Ansuman Banerjee, Himadri Sekhar Paul, Arijit Mukherjee
An access point to device association technique for optimized data transfer in mobile grids. ICPADS 2014: 774-779 - [c19]Arpan Pal
Model-Driven Development for Internet of Things: Towards Easing the Concerns of Application Developers. IoT360 (1) 2014: 339-346 - [c18]Arijit Mukherjee
To Run or Not to Run: Predicting Resource Usage Pattern in a Smartphone. IoT360 (1) 2014: 367-375 - [c17]Santa Maiti, Arpan Pal
Historical data based real time prediction of vehicle arrival time. ITSC 2014: 1837-1842 - [c16]Swarnava Dey, Arijit Mukherjee
Dual scheme phone: a user assisted mechanism to effectively run sensor analytics applications on smartphones. MobiQuitous 2014: 347-349 - [c15]Ansuman Banerjee, Himadri Sekhar Paul, Arijit Mukherjee
A Framework for Speculative Scheduling and Device Selection for Task Execution on a Mobile Cloud. ARMS-CC@PODC 2014: 36-51 - [c14]Swarnava Dey, Pubali Datta, Arijit Mukherjee
Facilitating continued run of sensor data analytics services using user driven proactive memory reclamation scheme. SenSys 2014: 338-339 - [c13]Arijit Mukherjee
ANGELS for distributed analytics in IoT. WF-IoT 2014: 565-570 - 2013
- [c12]Sujoy Mistry, Dibyanshu Jaiswal, Sagar Virani, Arijit Mukherjee, Nandini Mukherjee:
An Architecture for Dynamic Web Service Provisioning Using Peer-to-Peer Networks. ICDCIT 2013: 290-301 - [c11]Swarnava Dey
Challenges of Using Edge Devices in IoT Computation Grids. ICPADS 2013: 564-569 - [c10]Ansuman Banerjee, Arijit Mukherjee
Offloading work to mobile devices: an availability-aware data partitioning approach. MCS@Middleware 2013: 4:1-4:6 - [c9]Dibyanshu Jaiswal
Efficient Dynamic Service Provisioning over Distributed Resources Using Chord. SITIS 2013: 257-264 - [c8]Arijit Mukherjee
Utilising condor for data parallel analytics in an IoT context - An experience report. WiMob 2013: 325-331 - 2012
- [j2]Arijit Mukherjee
Case for dynamic deployment in a grid-based distributed query processor. Future Gener. Comput. Syst. 28(1): 171-183 (2012) - [c7]Arijit Mukherjee
Data Analytics in Ubiquitous Sensor-Based Health Information Systems. NGMAST 2012: 193-198
2000 – 2009
- 2009
- [j1]Steven J. Lynden
The design and implementation of OGSA-DQP: A service-based distributed query processor. Future Gener. Comput. Syst. 25(3): 224-236 (2009) - 2008
- [b1]Arijit Mukherjee:
Exploiting dynamic deployment in a distributed query processor for the grid. University of Newcastle Upon Tyne, UK, 2008 - 2006
- [c6]Arijit Mukherjee, Paul Watson:
Adding Dynamism to OGSA-DQP: Incorporating the DynaSOAr Framework in Distributed Query Processing. Euro-Par Workshops 2006: 22-33 - [c5]Paul Watson, Chris Fowler, Charles Kubicek, Arijit Mukherjee, John Colquhoun, Mark Hewitt, Savas Parastatidis:
Dynamically Deploying Web Services on a Grid using Dynasoar. ISORC 2006: 151-158 - 2005
- [c4]Mahmut Nedim Alpdemir
Contextualised Workflow Execution in MyGrid. EGC 2005: 444-453 - 2004
- [c3]Mahmut Nedim Alpdemir
OGSA-DQP: A Service for Distributed Querying on the Grid. EDBT 2004: 858-861 - [c2]Mahmut Nedim Alpdemir
Using OGSA-DQP to Support Scientific Applications for the Grid. SAG 2004: 13-24 - 2003
- [c1]Mahmut Nedim Alpdemir
Service-Based Distributed Querying on the Grid. ICSOC 2003: 467-482
Coauthor Index
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