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Scaling laws dictate that the performance of AI models is proportional to the amount of available data.
Predicting protein folding kinetics via temporal logic model checking
Christopher James Langmead and Sumit Kumar Jha · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Torchvision: Pytorch’s computer vision library
TorchVision maintainers and contributors · 2016
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Adversarial attacks on computer vision algorithms using natural perturbations
Arvind Ramanathan, Laura Pullum, Zubir Husein, Sunny Raj, Neslisah Torosdagli, Sumanta Pattanaik, and Sumit K Jha · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Auggan: Cross domain adaptation with gan-based data augmentation
Sheng-Wei Huang, Che-Tsung Lin, Shu-Ping Chen, Yen-Yi Wu, Po-Hao Hsu, and Shang-Hong Lai · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Detecting adversarial examples using data manifolds
Susmit Jha, Uyeong Jang, Somesh Jha, and Brian Jalaian · 2018
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Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
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On the susceptibility of deep neural networks to natural perturbations
Mesut Ozdag, Sunny Raj, Steven Fernandes, Laura L Pullum, and Sumit Kumar Jha · 2019
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A survey on image data augmentation for deep learning
Connor Shorten and Taghi M Khoshgoftaar · 2019
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A survey on data collection for machine learning: a big data-ai integration perspective
Yuji Roh, Geon Heo, and Steven Euijong Whang · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Explaining ai decisions using efficient methods for learning sparse boolean formulae
Susmit Jha, Tuhin Sahai, Vasumathi Raman, Alessandro Pinto, and Michael Francis · 2019
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Attribution-driven causal analysis for detection of adversarial examples
Susmit Jha, Sunny Raj, Steven Lawrence Fernandes, Sumit Kumar Jha, Somesh Jha, Gunjan Verma, Brian Jalaian, and Ananthram Swami · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Xrai: Better attributions through regions
A. Kapishnikov, T. Bolukbasi, F. Viegas, and M. Terry · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2020
Cited alongside, same era.
Model-centered assurance for autonomous systems
Susmit Jha, John Rushby, and Natarajan Shankar · 2020
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Projected gans converge faster
Axel Sauer, Kashyap Chitta, Jens Müller, and Andreas Geiger · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al · 2022
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Shaping noise for robust attributions in neural stochastic differential equations
Sumit Kumar Jha, Rickard Ewetz, Alvaro Velasquez, Arvind Ramanathan, and Susmit Jha · 2022
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Explainit!: A tool for computing robust attributions of dnns
Sumit Kumar Jha, Alvaro Velasquez, Rickard Ewetz, Laura Pullum, and Susmit Jha · 2022
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Diffusers: State-of-the-art diffusion models
Patrick von Platen, Suraj Patil, Anton Lozhkov, Pedro Cuenca, Nathan Lambert, Kashif Rasul, Mishig Davaadorj, Dhruv Nair, Sayak Paul, William Berman, Yiyi Xu, Steven Liu, and Thomas Wolf · 2022
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 2023
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Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
On smoother attributions using neural stochastic differential equations
Sumit Jha, Rickard Ewetz, Alvaro Velasquez, and Susmit Jha · 2021
Cited alongside, same era.
Protein folding neural networks are not robust
Sumit Kumar Jha, Arvind Ramanathan, Rickard Ewetz, Alvaro Velasquez, and Susmit Jha · 2021
Cited alongside, same era.
Detecting oods as datapoints with high uncertainty
Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Oleg Sokolsky, and Insup Lee · 2021
Cited alongside, same era.
Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
Cited alongside, same era.
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Synthetic data from diffusion models improves imagenet classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, and David J Fleet · 2023
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Diversify, don’t fine-tune: Scaling up visual recognition training with synthetic images
Zhuoran Yu, Chenchen Zhu, Sean Culatana, Raghuraman Krishnamoorthi, Fanyi Xiao, and Yong Jae Lee · 2023
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Leaving reality to imagination: Robust classification via generated datasets
Hritik Bansal and Aditya Grover · 2023
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Explore the power of synthetic data on few-shot object detection
Shaobo Lin, Kun Wang, Xingyu Zeng, and Rui Zhao · 2023
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Fake it till you make it: Learning transferable representations from synthetic imagenet clones
Mert Bülent Sarıyıldız, Karteek Alahari, Diane Larlus, and Yannis Kalantidis · 2023
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Sumit Kumar Jha, Susmit Jha, Rickard Ewetz, and Alvaro Velasquez · 2023
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Principled ood detection via multiple testing
Akshayaa Magesh, Venugopal V Veeravalli, Anirban Roy, and Susmit Jha · 2023
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Predicting out-of-distribution performance of deep neural networks using model conformance
Ramneet Kaur, Susmit Jha, Anirban Roy, Oleg Sokolsky, and Insup Lee · 2023
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Segment anything
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Grounding dino: Marrying dino with grounded pre-training for open-set object detection
Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al · 2023
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Task-agnostic detector for insertion-based backdoor attacks
Weimin Lyu, Xiao Lin, Songzhu Zheng, Lu Pang, Haibin Ling, Susmit Jha, and Chao Chen · 2024
Closest in time.
Diversify your vision datasets with automatic diffusion-based augmentation
Lisa Dunlap, Alyssa Umino, Han Zhang, Jiezhi Yang, Joseph E Gonzalez, and Trevor Darrell · 2024
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Integrated decision gradients: Compute your attributions where the model makes its decision
Chase Walker, Sumit Jha, Kenny Chen, and Rickard Ewetz · 2024
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Towards a game-theoretic understanding of explanation-based membership inference attacks
Kavita Kumari, Murtuza Jadliwala, Sumit Kumar Jha, and Anindya Maiti · 2024
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Concept-based analysis of neural networks via vision-language models
Ravi Mangal, Nina Narodytska, Divya Gopinath, Boyue Caroline Hu, Anirban Roy, Susmit Jha, and Corina S Păsăreanu · 2024
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https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/#baseline
How to Train State-Of-The-Art Models Using TorchVision’s Latest Primitives — pytorch.org · 2024
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