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Apurva Narayan
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2020 – today
- 2024
- [j6]Jose Alguindigue, Amandeep Singh
, Apurva Narayan, Siby Samuel:
Biosignals Monitoring for Driver Drowsiness Detection Using Deep Neural Networks. IEEE Access 12: 93075-93086 (2024) - [j5]Sneha Srinivasan, Amandeep Singh
Optimizing Intersection Design: Insights From Older Drivers' Physiological Responses and Gap Acceptance Behavior at Signalized Left Turns. IEEE Access 12: 154949-154962 (2024) - [c44]Gautham Anil, Vishnu Vinod, Apurva Narayan:
Generating Universal Adversarial Perturbations for Quantum Classifiers. AAAI 2024: 10891-10899 - [c43]Daya Kumar, Abhijith Sharma, Apurva Narayan:
Attacking CNNs in Histopathology with SNAP: Sporadic and Naturalistic Adversarial Patches (Student Abstract). AAAI 2024: 23550-23551 - [c42]Tanmay Khule, Rishabh Agrawal, Apurva Narayan:
PFA-ERC: Psuedo-Future Augmented Dynamic Emotion Recognition in Conversations. EMNLP (Findings) 2024: 16196-16207 - [c41]Mehar Prateek Kalra, Soniya, Apurva Narayan:
cf-TDFM: A Framework for Limiting Fault Infusion Attacks on Deep Neural Networks. QRS 2024: 418-424 - [c40]Abhijith Sharma, Phil Munz, Apurva Narayan:
Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction. WACV 2024: 3821-3830 - [i10]Gautham Anil, Vishnu Vinod, Apurva Narayan:
Generating Universal Adversarial Perturbations for Quantum Classifiers. CoRR abs/2402.08648 (2024) - 2023
- [j4]Debangsha Sarkar
Non data hungry smart composite manufacturing using active transfer learning with sigma point sampling (SPSATL). Comput. Ind. 151: 103989 (2023) - [c39]Abhijith Sharma
Vulnerability of CNNs against Multi-Patch Attacks. SAT-CPS@CODASPY 2023: 23-32 - [c38]Taif Anjum, Louis Chirade, Beiyu Lin, Apurva Narayan:
Learning Spatio-Temporal Features via 3D CNNs to Forecast Time-to-Accident. ICAART (3) 2023: 532-540 - [c37]Madhushan Buwaneswaran, Tehara Fonseka, Apurva Narayan, Katarina Grolinger:
Improving Adversarial Robustness of Few-Shot Learning with Contrastive Learning and Hypersphere Embedding. ICMLA 2023: 241-248 - [c36]Taif Anjum, Daya Kumar, Apurva Narayan:
Spatio-temporal Analysis of Dashboard Camera Videos for Time-To-Accident Forecasting. IJCNN 2023: 1-8 - [c35]Aviral Chharia, Runjhun Saran, Apurva Narayan:
cAPTured: Neural Reflex Arc-Inspired Fuzzy Continual Learning for Capturing in Silico Aptamer-Target Protein Interactions. IJCNN 2023: 1-9 - [c34]Abhijith Sharma, Phil Munz, Apurva Narayan:
NSA: Naturalistic Support Artifact to Boost Network Confidence. IJCNN 2023: 1-8 - [i9]Vipul Gupta, Apurva Narayan:
Do we need entire training data for adversarial training? CoRR abs/2303.06241 (2023) - [i8]Abhijith Sharma, Phil Munz, Apurva Narayan:
NSA: Naturalistic Support Artifact to Boost Network Confidence. CoRR abs/2307.14917 (2023) - [i7]Abhijith Sharma, Phil Munz, Apurva Narayan:
Assist Is Just as Important as the Goal: Image Resurfacing to Aid Model's Robust Prediction. CoRR abs/2311.01563 (2023) - 2022
- [c33]Arthur Grisel-Davy, Amrita Milan Bhogayata, Srijan Pabbi, Apurva Narayan, Sebastian Fischmeister:
Work-in-Progress: Boot Sequence Integrity Verification with Power Analysis. EMSOFT 2022: 3-4 - [c32]Taif Anjum, Beiyu Lin, Apurva Narayan:
Leveraging spatio-temporal features to forecast time-to-accident. SIGSPATIAL/GIS 2022: 112:1-112:2 - [c31]Abhijith Sharma
Soft Adversarial Training Can Retain Natural Accuracy. ICAART (3) 2022: 621-628 - [c30]Milad Ramezankhani, Amir Nazemi
A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study. ICPS 2022: 1-7 - [c29]Satyadwyoom Kumar, Apurva Narayan:
Introducing Diversity In Feature Scatter Adversarial Training Via Synthesis. ICPR 2022: 3069-3075 - [c28]Javier Perez Tobia
AGS: Attribution Guided Sharpening as a Defense Against Adversarial Attacks. IDA 2022: 225-236 - [c27]Ramashish Gaurav, Bryan P. Tripp, Apurva Narayan:
Spiking Approximations of the MaxPooling Operation in Deep SNNs. IJCNN 2022: 1-8 - [c26]Satyadwyoom Kumar, Apurva Narayan:
Towards Robust Certified Defense via Improved Randomized Smoothing. IJCNN 2022: 1-8 - [c25]Maulik Parmar, Apurva Narayan:
HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings. LREC 2022: 6069-6076 - [c24]Aniket Chakraborty, Anupama Vijaya Nadarja, Abbas S. Milani, Javier Perez Tobia, Apurva Narayan:
Improving imbalanced dataset classification using Conditional Classifier-Generator (cCGen). SMC 2022: 671-675 - [i6]Milad Ramezankhani, Amir Nazemi, Apurva Narayan, Heinz Voggenreiter, Mehrtash Harandi, Rudolf J. Seethaler, Abbas S. Milani:
A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study. CoRR abs/2202.06139 (2022) - [i5]Maulik Parmar, Apurva Narayan:
HyperBox: A Supervised Approach for Hypernym Discovery using Box Embeddings. CoRR abs/2204.02058 (2022) - [i4]Ramashish Gaurav, Bryan P. Tripp, Apurva Narayan:
Spiking Approximations of the MaxPooling Operation in Deep SNNs. CoRR abs/2205.07076 (2022) - [i3]Abhijith Sharma, Apurva Narayan:
Soft Adversarial Training Can Retain Natural Accuracy. CoRR abs/2206.01904 (2022) - [i2]Abhijith Sharma, Yijun Bian, Phil Munz, Apurva Narayan:
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey. CoRR abs/2206.08304 (2022) - 2021
- [c23]Ramashish Gaurav, Bryan P. Tripp, Apurva Narayan:
Driving Scene Understanding: How much temporal context and spatial resolution is necessary? Canadian AI 2021 - [c22]Aviral Chharia, Apurva Narayan:
A novel fuzzy approach towards in silico B-cell epitope identification inducing antigen-specific immune response for Vaccine Design. BIBE 2021: 1-6 - [c21]Sandeep Chinnareddy, Pranav Grandhi, Apurva Narayan:
Self-Attention Mechanism in GANs for Molecule Generation. ICMLA 2021: 57-60 - [c20]Rishab Sharma, Fatemeh H. Fard
Retrieval Enhanced Ensemble Model Framework For Rumor Detection On Micro-blogging Platforms. ICMLA 2021: 227-232 - [c19]Milad Ramezankhani, Apurva Narayan, Rudolf J. Seethaler, Abbas S. Milani:
An Active Transfer Learning (ATL) Framework for Smart Manufacturing with Limited Data: Case Study on Material Transfer in Composites Processing. ICPS 2021: 277-282 - [c18]Pradeep K. Mahato
MINTS: Unsupervised Temporal Specifications Miner. QRS 2021: 841-851 - [c17]Javier Perez Tobia, Apurva Narayan:
Is Timing Critical to Trace Reconstruction? SMC 2021: 2700-2705 - 2020
- [j3]Dhruv Bhandari, Sandeep Paul, Apurva Narayan:
Deep neural networks for multimodal data fusion and affect recognition. Int. J. Artif. Intell. Soft Comput. 7(2): 130-145 (2020) - [c16]Pradeep K. Mahato
QMine: A Framework for Mining Quantitative Regular Expressions from System Traces. QRS Companion 2020: 370-377 - [c15]Yohen Thounaojam, Wiliam Setiawan, Apurva Narayan:
MA2DF: A Multi-Agent Anomaly Detection Framework. SMC 2020: 30-36 - [c14]Pranav Khanna, Apurva Narayan:
Light Weight Dilated CNN for Time Series Classification and Prediction. SMC 2020: 2179-2183 - [c13]Wiliam Setiawan, Yohen Thounaojam, Apurva Narayan:
GWAD: Greedy Workflow Graph Anomaly Detection Framework for System Traces. SMC 2020: 2790-2796 - [c12]Pradeep K. Mahato
Robust Supply Chains with Gradient Boosted Trees. SSCI 2020: 2633-2639
2010 – 2019
- 2019
- [j2]Ilia Sucholutsky
Pay attention and you won't lose it: a deep learning approach to sequence imputation. PeerJ Comput. Sci. 5: e210 (2019) - [c11]Ilia Sucholutsky
Deep Learning for System Trace Restoration. IJCNN 2019: 1-8 - [c10]Apurva Narayan, Sebastian Fischmeister:
Mining Time for Timed Regular Specifications. SMC 2019: 63-69 - [c9]Karuna Lakhani, Apurva Narayan:
A Neural Word Embedding Approach to System Trace Reconstruction. SMC 2019: 285-291 - [i1]Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau, Sebastian Fischmeister:
Deep Learning for System Trace Restoration. CoRR abs/1904.05411 (2019) - 2018
- [j1]Apurva Narayan
Mining Timed Regular Specifications from System Traces. ACM Trans. Embed. Comput. Syst. 17(2): 46:1-46:21 (2018) - 2017
- [c8]Lukas Schmidt, Apurva Narayan, Sebastian Fischmeister:
TREM: a tool for mining timed regular specifications from system traces. ASE 2017: 901-906 - [c7]Apurva Narayan, Keith W. Hipel:
Long short term memory networks for short-term electric load forecasting. SMC 2017: 2573-2578 - 2016
- [c6]Greta Cutulenco, Yogi Joshi, Apurva Narayan, Sebastian Fischmeister:
Mining timed regular expressions from system traces. SoftwareMining@ASE 2016: 3-10 - 2011
- [c5]Apurva Narayan, Keith W. Hipel, K. Ponnambalam, Sandeep Paul
Neuro-fuzzy inference system (ASuPFuNIS) model for intervention time series prediction of electricity prices. SMC 2011: 2121-2126
2000 – 2009
- 2009
- [c4]Apurva Narayan, Chellapilla Patvardhan:
A Novel Quantum Evolutionary Algorithm for Quadratic Knapsack Problem. SMC 2009: 1388-1392 - [c3]Apurva Narayan, Tamanna Srivastava:
Dynamic Fuzzy Controller Based Multilayered Architecture for Extended Battery Life in Mobile Handheld Devices. SMC 2009: 3035-3040 - 2008
- [c2]Lotika Singh, Apurva Narayan, Satish Kumar:
Dynamic fuzzy load balancing on LAM/MPI clusters with applications in parallel master-slave implementations of an evolutionary neuro-fuzzy learning system. FUZZ-IEEE 2008: 1782-1788 - 2007
- [c1]C. Patvardhan, Apurva Narayan, Anand Srivastav:
Enhanced Quantum Evolutionary Algorithms for Difficult Knapsack Problems. PReMI 2007: 252-260
Coauthor Index
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