Search Results for author: Sahil Singla

Found 28 papers, 11 papers with code

Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation

no code implementations11 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.

Text-to-Image Generation

Online Combinatorial Allocations and Auctions with Few Samples

no code implementations17 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.

Imagen 3

2 code implementations13 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.

Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation

no code implementations24 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.

Text-to-Image Generation

e-COP : Episodic Constrained Optimization of Policies

no code implementations13 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.

LEMMA reinforcement-learning +2

Robust Disaster Assessment from Aerial Imagery Using Text-to-Image Synthetic Data

no code implementations22 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.

Humanitarian

Bandit Sequential Posted Pricing via Half-Concavity

no code implementations20 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.

Data-Centric Debugging: mitigating model failures via targeted data collection

no code implementations17 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 $\mathcal{E}_{sample}$.

Image Classification

Bandit Algorithms for Prophet Inequality and Pandora's Box

no code implementations16 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.

Multi-Armed Bandits Stochastic Optimization

Improved techniques for deterministic l2 robustness

1 code implementation15 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.

Adversarial Robustness

Core Risk Minimization using Salient ImageNet

no code implementations28 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.

Salient ImageNet: How to discover spurious features in Deep Learning?

2 code implementations8 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).

Attribute Deep Learning

Causal ImageNet: How to discover spurious features in Deep Learning?

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").

Attribute Deep Learning

Improved deterministic l2 robustness on CIFAR-10 and CIFAR-100

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.

Adversarial Robustness

Skew Orthogonal Convolutions

1 code implementation24 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.

Adversarial Robustness

Low Curvature Activations Reduce Overfitting in Adversarial Training

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.

Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers

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.

Perceptual Adversarial Robustness: Generalizable Defenses Against Unforeseen Threat Models

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.

Adversarial Defense Adversarial Robustness +1

Understanding Failures of Deep Networks via Robust Feature Extraction

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.

Online Learning with Vector Costs and Bandits with Knapsacks

no code implementations14 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.

Scheduling

Perceptual Adversarial Robustness: Defense Against Unseen Threat Models

2 code implementations22 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.

Adversarial Defense Adversarial Robustness +1

Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning

1 code implementation17 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.

Decision Making Deep Learning +2

Second-Order Provable Defenses against 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.

Fantastic Four: Differentiable Bounds on Singular Values of Convolution Layers

1 code implementation22 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.

Curvature-based Robustness Certificates against Adversarial Examples

no code implementations25 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.

Certifiably Robust Interpretation in Deep Learning

no code implementations28 May 2019 Alexander Levine, Sahil Singla, Soheil Feizi

Deep learning interpretation is essential to explain the reasoning behind model predictions.

Deep Learning

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

1 code implementation1 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.

Deep Learning Feature Importance +1

Robustness Certificates Against Adversarial Examples for ReLU Networks

no code implementations1 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.

General Classification Multi-Label Classification +1

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