An efficient automatic intensity based method for detection of macula in retinal images. - Arpit Bansal; Aashwin Vats; Akshita Jain; Malay Kishore Dutta; Radim Burget; Jiri Prinosil (TSP, 2016)

Product design methodology applied in developing a liquid petroleum gas level indicator using android technology. - Vishwas Mahesh; Vinyas Mahesh; Ishwarkumar Teggi; Arpit Bansal; S. Manjesh (Ingénierie des Systèmes d Inf., 2018)

PAG-Net: Progressive Attention Guided Depth Super-resolution Network. - Arpit Bansal; Sankaraganesh Jonna; Rajiv R. Sahay (CoRR, 2019)

Preventing Unauthorized Use of Proprietary Data: Poisoning for Secure Dataset Release. - Liam Fowl; Ping-yeh Chiang; Micah Goldblum; Jonas Geiping; Arpit Bansal; Wojtek Czaja; Tom Goldstein (CoRR, 2021)

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. - Arpit Bansal; Micah Goldblum; Valeriia Cherepanova; Avi Schwarzschild; C. Bayan Bruss; Tom Goldstein (CoRR, 2021)

Datasets for Studying Generalization from Easy to Hard Examples. - Avi Schwarzschild; Eitan Borgnia; Arjun Gupta; Arpit Bansal; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (CoRR, 2021)

Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. - Gowthami Somepalli; Liam Fowl; Arpit Bansal; Ping-Yeh Chiang; Yehuda Dar; Richard G. Baraniuk; Micah Goldblum; Tom Goldstein (CVPR, 2022)

Certified Neural Network Watermarks with Randomized Smoothing. - Arpit Bansal; Ping-Yeh Chiang; Michael J. Curry; Rajiv Jain; Curtis Wigington; Varun Manjunatha; John P. Dickerson; Tom Goldstein (ICML, 2022)

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. - Arpit Bansal; Avi Schwarzschild; Eitan Borgnia; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (NeurIPS, 2022)

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. - Arpit Bansal; Avi Schwarzschild; Eitan Borgnia; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (CoRR, 2022)

Universal Guidance for Diffusion Models. - Arpit Bansal; Hong-Min Chu; Avi Schwarzschild; Soumyadip Sengupta; Micah Goldblum; Jonas Geiping; Tom Goldstein (CVPR Workshops, 2023)

Loss Landscapes are All You Need: Neural Network Generalization Can Be Explained Without the Implicit Bias of Gradient Descent. - Ping-yeh Chiang; Renkun Ni; David Yu Miller; Arpit Bansal; Jonas Geiping; Micah Goldblum; Tom Goldstein (ICLR, 2023)

Transfer Learning with Deep Tabular Models. - Roman Levin; Valeriia Cherepanova; Avi Schwarzschild; Arpit Bansal; C. Bayan Bruss; Tom Goldstein; Andrew Gordon Wilson; Micah Goldblum (ICLR, 2023)

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. - Yuxin Wen; Arpit Bansal; Hamid Kazemi; Eitan Borgnia; Micah Goldblum; Jonas Geiping; Tom Goldstein (ICLR, 2023)

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. - Arpit Bansal; Eitan Borgnia; Hong-Min Chu; Jie Li 070; Hamid Kazemi; Furong Huang; Micah Goldblum; Jonas Geiping; Tom Goldstein (NeurIPS, 2023)

Transformers Can Do Arithmetic with the Right Embeddings. - Sean McLeish; Arpit Bansal; Alex Stein; Neel Jain; John Kirchenbauer; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Jonas Geiping; Avi Schwarzschild; Tom Goldstein (NeurIPS, 2024)

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. - Hossein Souri; Arpit Bansal; Hamid Kazemi; Liam Fowl; Aniruddha Saha; Jonas Geiping; Andrew Gordon Wilson; Rama Chellappa; Tom Goldstein; Micah Goldblum (CoRR, 2024)

Just How Flexible are Neural Networks in Practice? - Ravid Shwartz-Ziv; Micah Goldblum; Arpit Bansal; C. Bayan Bruss; Yann LeCun; Andrew Gordon Wilson (CoRR, 2024)