Multi-plateau Ensemble For Endoscopic Artefact Segmentation And Detection. - Suyog Jadhav; Udbhav Bamba; Arnav Chavan; Rishabh Tiwari; Aryan Raj (EndoCV@ISBI, 2020)

A translational pathway of deep learning methods in GastroIntestinal Endoscopy. - Sharib Ali; Mariia Dmitrieva; Noha M. Ghatwary; Sophia Bano; Gorkem Polat; Alptekin Temizel; Adrian Krenzer; Amar Hekalo; Yun Bo Guo; Bogdan J. Matuszewski; Mourad Gridach; Irina Voiculescu; et al. (CoRR, 2020)

Rescaling CNN Through Learnable Repetition of Network Parameters. - Arnav Chavan; Udbhav Bamba; Rishabh Tiwari; Deepak K. Gupta (ICIP, 2021)

ChipNet: Budget-Aware Pruning with Heaviside Continuous Approximations. - Rishabh Tiwari; Udbhav Bamba; Arnav Chavan; Deepak K. Gupta (ICLR, 2021)

Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy. - Sharib Ali; Mariia Dmitrieva; Noha M. Ghatwary; Sophia Bano; Gorkem Polat; Alptekin Temizel; Adrian Krenzer; Amar Hekalo; Yun Bo Guo; Bogdan J. Matuszewski; Mourad Gridach; Irina Voiculescu; et al. (Medical Image Anal., 2021)

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space. - Arnav Chavan; Zhiqiang Shen; Zhuang Liu 003; Zechun Liu; Kwang-Ting Cheng; Eric P. Xing (CVPR, 2022)

Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning. - Arnav Chavan; Rishabh Tiwari; Udbhav Bamba; Deepak K. Gupta (CVPR, 2022)

Transfer Learning Gaussian Anomaly Detection by Fine-tuning Representations. - Oliver Rippel; Arnav Chavan; Chucai Lei; Dorit Merhof (IMPROVE, 2022)

A Comparative Study on the Impact of Model Compression Techniques on Fairness in Language Models. - Krithika Ramesh; Arnav Chavan; Shrey Pandit; Sunayana Sitaram (ACL (1), 2023)

On Designing Light-Weight Object Trackers Through Network Pruning: Use CNNS or Transformers? - Saksham Aggarwal; Taneesh Gupta; Pawan Kumar Sahu; Arnav Chavan; Rishabh Tiwari; Dilip K. Prasad; Deepak K. Gupta (ICASSP, 2023)

RCV2023 Challenges: Benchmarking Model Training and Inference for Resource-Constrained Deep Learning. - Rishabh Tiwari; Arnav Chavan; Deepak K. Gupta; Gowreesh Mago; Animesh Gupta; Akash Gupta; Suraj Sharan; Yukun Yang; Shanwei Zhao; Shihao Wang; Youngjun Kwak; Seonghun Jeong; et al. (ICCV (Workshops), 2023)

Patch Gradient Descent: Training Neural Networks on Very Large Images. - Deepak K. Gupta; Gowreesh Mago; Arnav Chavan; Dilip K. Prasad (CoRR, 2023)

One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning. - Arnav Chavan; Zhuang Liu 003; Deepak K. Gupta; Eric P. Xing; Zhiqiang Shen (CoRR, 2023)

Surgical Feature-Space Decomposition of LLMs: Why, When and How? - Arnav Chavan; Nahush Lele; Deepak K. Gupta (ACL (1), 2024)

Rethinking Compression: Reduced order modelling of Latent Features in Large Language Models. - Arnav Chavan; Nahush Lele; Deepak K. Gupta (Tiny Papers @ ICLR, 2024)

Beyond Uniform Scaling: Exploring Depth Heterogeneity in Neural Architectures. - Akash Guna R. T.; Arnav Chavan; Deepak K. Gupta (Tiny Papers @ ICLR, 2024)

Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward. - Arnav Chavan; Raghav Magazine; Shubham Kushwaha; Mérouane Debbah; Deepak K. Gupta (IJCAI, 2024)

Parameter Efficient Fine-Tuning for Deep Learning-Based Full-Waveform Inversion. - Koustav Ghosal; Abhranta Panigrahi; Arnav Chavan; ArunSingh; Deepak K. Gupta (CoRR, 2024)

Pushing the Limits of Gradient Descent for Efficient Learning on Large Images. - Deepak K. Gupta; Gowreesh Mago; Arnav Chavan; Dilip K. Prasad; Rajat Mani Thomas (Trans. Mach. Learn. Res., 2024)

Rethinking the Value of Training-Free Structured Pruning of LLMs. - Nahush Lele; Arnav Chavan; Aryamaan Thakur; Deepak K. Gupta (Trans. Mach. Learn. Res., 2025)