1 code implementation • IJCNLP 2019 • Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rocktäschel, Douwe Kiela, Arthur Szlam, Jason Weston
We analyze the ingredients necessary for successful grounding in this setting, and how each of these factors relate to agents that can talk and act successfully.
1 code implementation • 15 Feb 2023 • Joshua Vendrow, Saachi Jain, Logan Engstrom, Aleksander Madry
In this work, we introduce the notion of a dataset interface: a framework that, given an input dataset and a user-specified shift, returns instances from that input distribution that exhibit the desired shift.
1 code implementation • 29 Jun 2022 • Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry
Moreover, by combining our framework with off-the-shelf diffusion models, we can generate images that are especially challenging for the analyzed model, and thus can be used to perform synthetic data augmentation that helps remedy the model's failure modes.
1 code implementation • CVPR 2022 • Hadi Salman, Saachi Jain, Eric Wong, Aleksander Mądry
Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region.
1 code implementation • CVPR 2023 • Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, Aleksander Madry
It is commonly believed that in transfer learning including more pre-training data translates into better performance.
1 code implementation • 6 Sep 2018 • Saachi Jain, David Hallac, Rok Sosic, Jure Leskovec
Such data can be interpreted as a sequence of states, where each state represents a prototype of system behavior.
1 code implementation • 6 Jul 2022 • Hadi Salman, Saachi Jain, Andrew Ilyas, Logan Engstrom, Eric Wong, Aleksander Madry
Using transfer learning to adapt a pre-trained "source model" to a downstream "target task" can dramatically increase performance with seemingly no downside.
1 code implementation • 15 Oct 2021 • Saachi Jain, Dimitris Tsipras, Aleksander Madry
To improve model generalization, model designers often restrict the features that their models use, either implicitly or explicitly.
1 code implementation • ICLR 2022 • Saachi Jain, Hadi Salman, Eric Wong, Pengchuan Zhang, Vibhav Vineet, Sai Vemprala, Aleksander Madry
Missingness, or the absence of features from an input, is a concept fundamental to many model debugging tools.
no code implementations • 29 Jun 2021 • Saachi Jain, Adityanarayanan Radhakrishnan, Caroline Uhler
Aligned latent spaces, where meaningful semantic shifts in the input space correspond to a translation in the embedding space, play an important role in the success of downstream tasks such as unsupervised clustering and data imputation.