no code implementations • 11 Jan 2025 • Xiaoying Xing, Avinab Saha, Junfeng He, Susan Hao, Paul Vicol, MoonKyung Ryu, Gang Li, Sahil Singla, Sarah Young, Yinxiao Li, Feng Yang, Deepak Ramachandran
Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety.
no code implementations • 17 Sep 2024 • Paul Dütting, Thomas Kesselheim, Brendan Lucier, Rebecca Reiffenhäuser, Sahil Singla
In particular, for submodular/XOS valuations, we know 2-competitive algorithms/mechanisms that set a fixed price for each item and the arriving bidders take their favorite subset of the remaining items given these prices.
2 code implementations • 13 Aug 2024 • Imagen-Team-Google, :, Jason Baldridge, Jakob Bauer, Mukul Bhutani, Nicole Brichtova, Andrew Bunner, Lluis Castrejon, Kelvin Chan, YiChang Chen, Sander Dieleman, Yuqing Du, Zach Eaton-Rosen, Hongliang Fei, Nando de Freitas, Yilin Gao, Evgeny Gladchenko, Sergio Gómez Colmenarejo, Mandy Guo, Alex Haig, Will Hawkins, Hexiang Hu, Huilian Huang, Tobenna Peter Igwe, Siavash Khodadadeh, Yelin Kim, Ksenia Konyushkova, Karol Langner, Eric Lau, Rory Lawton, Shixin Luo, Soňa Mokrá, Henna Nandwani, Yasumasa Onoe, Aäron van den Oord, Zarana Parekh, Jordi Pont-Tuset, Hang Qi, Rui Qian, Deepak Ramachandran, Poorva Rane, Abdullah Rashwan, Robert Riachi, Hansa Srinivasan, Srivatsan Srinivasan, Robin Strudel, Benigno Uria, Oliver Wang, Su Wang, Austin Waters, Chris Wolff, Auriel Wright, Zhisheng Xiao, Hao Xiong, Keyang Xu, Marc van Zee, Junlin Zhang, Katie Zhang, Wenlei Zhou, Konrad Zolna, Ola Aboubakar, Canfer Akbulut, Oscar Akerlund, Isabela Albuquerque, Nina Anderson, Marco Andreetto, Lora Aroyo, Ben Bariach, David Barker, Sherry Ben, Dana Berman, Courtney Biles, Irina Blok, Pankil Botadra, Jenny Brennan, Karla Brown, John Buckley, Rudy Bunel, Elie Bursztein, Christina Butterfield, Ben Caine, Viral Carpenter, Norman Casagrande, Ming-Wei Chang, Solomon Chang, Shamik Chaudhuri, Tony Chen, John Choi, Dmitry Churbanau, Nathan Clement, Matan Cohen, Forrester Cole, Mikhail Dektiarev, Vincent Du, Praneet Dutta, Tom Eccles, Ndidi Elue, Ashley Feden, Shlomi Fruchter, Frankie Garcia, Roopal Garg, Weina Ge, Ahmed Ghazy, Bryant Gipson, Andrew Goodman, Dawid Górny, Sven Gowal, Khyatti Gupta, Yoni Halpern, Yena Han, Susan Hao, Jamie Hayes, Jonathan Heek, Amir Hertz, Ed Hirst, Emiel Hoogeboom, Tingbo Hou, Heidi Howard, Mohamed Ibrahim, Dirichi Ike-Njoku, Joana Iljazi, Vlad Ionescu, William Isaac, Reena Jana, Gemma Jennings, Donovon Jenson, Xuhui Jia, Kerry Jones, Xiaoen Ju, Ivana Kajic, Christos Kaplanis, Burcu Karagol Ayan, Jacob Kelly, Suraj Kothawade, Christina Kouridi, Ira Ktena, Jolanda Kumakaw, Dana Kurniawan, Dmitry Lagun, Lily Lavitas, Jason Lee, Tao Li, Marco Liang, Maggie Li-Calis, Yuchi Liu, Javier Lopez Alberca, Matthieu Kim Lorrain, Peggy Lu, Kristian Lum, Yukun Ma, Chase Malik, John Mellor, Thomas Mensink, Inbar Mosseri, Tom Murray, Aida Nematzadeh, Paul Nicholas, Signe Nørly, João Gabriel Oliveira, Guillermo Ortiz-Jimenez, Michela Paganini, Tom Le Paine, Roni Paiss, Alicia Parrish, Anne Peckham, Vikas Peswani, Igor Petrovski, Tobias Pfaff, Alex Pirozhenko, Ryan Poplin, Utsav Prabhu, Yuan Qi, Matthew Rahtz, Cyrus Rashtchian, Charvi Rastogi, Amit Raul, Ali Razavi, Sylvestre-Alvise Rebuffi, Susanna Ricco, Felix Riedel, Dirk Robinson, Pankaj Rohatgi, Bill Rosgen, Sarah Rumbley, MoonKyung Ryu, Anthony Salgado, Tim Salimans, Sahil Singla, Florian Schroff, Candice Schumann, Tanmay Shah, Eleni Shaw, Gregory Shaw, Brendan Shillingford, Kaushik Shivakumar, Dennis Shtatnov, Zach Singer, Evgeny Sluzhaev, Valerii Sokolov, Thibault Sottiaux, Florian Stimberg, Brad Stone, David Stutz, Yu-Chuan Su, Eric Tabellion, Shuai Tang, David Tao, Kurt Thomas, Gregory Thornton, Andeep Toor, Cristian Udrescu, Aayush Upadhyay, Cristina Vasconcelos, Alex Vasiloff, Andrey Voynov, Amanda Walker, Luyu Wang, Miaosen Wang, Simon Wang, Stanley Wang, Qifei Wang, Yuxiao Wang, Ágoston Weisz, Olivia Wiles, Chenxia Wu, Xingyu Federico Xu, Andrew Xue, Jianbo Yang, Luo Yu, Mete Yurtoglu, Ali Zand, Han Zhang, Jiageng Zhang, Catherine Zhao, Adilet Zhaxybay, Miao Zhou, Shengqi Zhu, Zhenkai Zhu, Dawn Bloxwich, Mahyar Bordbar, Luis C. Cobo, Eli Collins, Shengyang Dai, Tulsee Doshi, Anca Dragan, Douglas Eck, Demis Hassabis, Sissie Hsiao, Tom Hume, Koray Kavukcuoglu, Helen King, Jack Krawczyk, Yeqing Li, Kathy Meier-Hellstern, Andras Orban, Yury Pinsky, Amar Subramanya, Oriol Vinyals, Ting Yu, Yori Zwols
We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts.
no code implementations • 24 Jun 2024 • Katherine M. Collins, Najoung Kim, Yonatan Bitton, Verena Rieser, Shayegan Omidshafiei, Yushi Hu, Sherol Chen, Senjuti Dutta, Minsuk Chang, Kimin Lee, Youwei Liang, Georgina Evans, Sahil Singla, Gang Li, Adrian Weller, Junfeng He, Deepak Ramachandran, Krishnamurthy Dj Dvijotham
Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate reward function has not been conclusively established.
no code implementations • 13 Jun 2024 • Akhil Agnihotri, Rahul Jain, Deepak Ramachandran, Sahil Singla
In this paper, we present the $\texttt{e-COP}$ algorithm, the first policy optimization algorithm for constrained Reinforcement Learning (RL) in episodic (finite horizon) settings.
no code implementations • 22 May 2024 • Tarun Kalluri, Jihyeon Lee, Kihyuk Sohn, Sahil Singla, Manmohan Chandraker, Joseph Xu, Jeremiah Liu
We present a simple and efficient method to leverage emerging text-to-image generative models in creating large-scale synthetic supervision for the task of damage assessment from aerial images.
no code implementations • 20 Dec 2023 • Sahil Singla, Yifan Wang
To overcome this assumption, we study sequential posted pricing in the bandit learning model, where the seller interacts with $n$ buyers over $T$ rounds: In each round the seller posts $n$ prices for the $n$ buyers and the first buyer with a valuation higher than the price takes the item.
no code implementations • 17 Nov 2022 • Sahil Singla, Atoosa Malemir Chegini, Mazda Moayeri, Soheil Feiz
Our Data-Centric Debugging (DCD) framework carefully creates a debug-train set by selecting images from $\mathcal{F}$ that are perceptually similar to the images in $\mathcal{E}_{sample}$.
no code implementations • 16 Nov 2022 • Khashayar Gatmiry, Thomas Kesselheim, Sahil Singla, Yifan Wang
The goal is to minimize the regret, which is the difference over $T$ rounds in the total value of the optimal algorithm that knows the distributions vs. the total value of our algorithm that learns the distributions from the partial feedback.
1 code implementation • 15 Nov 2022 • Sahil Singla, Soheil Feizi
In this work, we reduce this gap by introducing (a) a procedure to certify robustness of 1-Lipschitz CNNs by replacing the last linear layer with a 1-hidden layer MLP that significantly improves their performance for both standard and provably robust accuracy, (b) a method to significantly reduce the training time per epoch for Skew Orthogonal Convolution (SOC) layers (>30\% reduction for deeper networks) and (c) a class of pooling layers using the mathematical property that the $l_{2}$ distance of an input to a manifold is 1-Lipschitz.
no code implementations • 28 Mar 2022 • Sahil Singla, Mazda Moayeri, Soheil Feizi
Deep neural networks can be unreliable in the real world especially when they heavily use spurious features for their predictions.
2 code implementations • 8 Oct 2021 • Sahil Singla, Soheil Feizi
Our methodology is based on this key idea: to identify spurious or core \textit{visual features} used in model predictions, we identify spurious or core \textit{neural features} (penultimate layer neurons of a robust model) via limited human supervision (e. g., using top 5 activating images per feature).
no code implementations • ICLR 2022 • Sahil Singla, Soheil Feizi
Focusing on image classifications, we define causal attributes as the set of visual features that are always a part of the object while spurious attributes are the ones that are likely to {\it co-occur} with the object but not a part of it (e. g., attribute ``fingers" for class ``band aid").
1 code implementation • ICLR 2022 • Sahil Singla, Surbhi Singla, Soheil Feizi
While $1$-Lipschitz CNNs can be designed by enforcing a $1$-Lipschitz constraint on each layer, training such networks requires each layer to have an orthogonal Jacobian matrix (for all inputs) to prevent the gradients from vanishing during backpropagation.
1 code implementation • 24 May 2021 • Sahil Singla, Soheil Feizi
Then, we use the Taylor series expansion of the Jacobian exponential to construct the SOC layer that is orthogonal.
1 code implementation • ICCV 2021 • Vasu Singla, Sahil Singla, David Jacobs, Soheil Feizi
In particular, we show that using activation functions with low (exact or approximate) curvature values has a regularization effect that significantly reduces both the standard and robust generalization gaps in adversarial training.
no code implementations • ICLR 2021 • Sahil Singla, Soheil Feizi
Through experiments on MNIST and CIFAR-10, we demonstrate the effectiveness of our spectral bound in improving generalization and robustness of deep networks.
no code implementations • ICLR 2021 • Cassidy Laidlaw, Sahil Singla, Soheil Feizi
We call this threat model the neural perceptual threat model (NPTM); it includes adversarial examples with a bounded neural perceptual distance (a neural network-based approximation of the true perceptual distance) to natural images.
1 code implementation • CVPR 2021 • Sahil Singla, Besmira Nushi, Shital Shah, Ece Kamar, Eric Horvitz
Traditional evaluation metrics for learned models that report aggregate scores over a test set are insufficient for surfacing important and informative patterns of failure over features and instances.
no code implementations • 14 Oct 2020 • Thomas Kesselheim, Sahil Singla
We study \OLVCp in both stochastic and adversarial arrival settings, and give a general procedure to reduce the problem from $d$ dimensions to a single dimension.
2 code implementations • 22 Jun 2020 • Cassidy Laidlaw, Sahil Singla, Soheil Feizi
We call this threat model the neural perceptual threat model (NPTM); it includes adversarial examples with a bounded neural perceptual distance (a neural network-based approximation of the true perceptual distance) to natural images.
1 code implementation • 17 Jun 2020 • Vedant Nanda, Samuel Dooley, Sahil Singla, Soheil Feizi, John P. Dickerson
In this paper, we argue that traditional notions of fairness that are only based on models' outputs are not sufficient when the model is vulnerable to adversarial attacks.
no code implementations • ICML 2020 • Sahil Singla, Soheil Feizi
Second, we derive a computationally-efficient differentiable upper bound on the curvature of a deep network.
1 code implementation • 22 Nov 2019 • Sahil Singla, Soheil Feizi
Through experiments on MNIST and CIFAR-10, we demonstrate the effectiveness of our spectral bound in improving generalization and provable robustness of deep networks.
no code implementations • 25 Sep 2019 • Sahil Singla, Soheil Feizi
We also use the curvature bound as a regularization term during the training of the network to boost its certified robustness against adversarial examples.
no code implementations • 28 May 2019 • Alexander Levine, Sahil Singla, Soheil Feizi
Deep learning interpretation is essential to explain the reasoning behind model predictions.
1 code implementation • 1 Feb 2019 • Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi
Second, we compute the importance of group-features in deep learning interpretation by introducing a sparsity regularization term.
no code implementations • 1 Feb 2019 • Sahil Singla, Soheil Feizi
These robustness certificates leverage the piece-wise linear structure of ReLU networks and use the fact that in a polyhedron around a given sample, the prediction function is linear.