activity
20182022
most citedIn-Distribution Interpretability for Challenging Modalities

5 citations · 5 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2022

Causal Scene BERT: Improving object detection by searching for challenging groups of data

Cinjon Resnick, Or Litany, Amlan Kar +4

Modern computer vision applications rely on learning-based perception modules parameterized with neural networks for tasks like object detection. These modules frequently have low…

cs.CV2021

Self-Supervised Equivariant Scene Synthesis from Video

Cinjon Resnick, Or Litany, Cosmas Heiß +3

We propose a self-supervised framework to learn scene representations from video that are automatically delineated into background, characters, and their animations. Our method cap…

cs.LG2020

Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian

Jack Parker-Holder, Luke Metz, Cinjon Resnick +6

Over the last decade, a single algorithm has changed many facets of our lives - Stochastic Gradient Descent (SGD). In the era of ever decreasing loss functions, SGD and its various…

cs.CV2020

Learned Equivariant Rendering without Transformation Supervision

Cinjon Resnick, Or Litany, Hugo Larochelle +2

We propose a self-supervised framework to learn scene representations from video that are automatically delineated into objects and background. Our method relies on moving objects…

cs.LG20205 cited

In-Distribution Interpretability for Challenging Modalities

Cosmas Heiß, Ron Levie, Cinjon Resnick +2

It is widely recognized that the predictions of deep neural networks are difficult to parse relative to simpler approaches. However, the development of methods to investigate the m…

cs.CL2019

Capacity, Bandwidth, and Compositionality in Emergent Language Learning

Cinjon Resnick, Abhinav Gupta, Jakob Foerster +2

Many recent works have discussed the propensity, or lack thereof, for emergent languages to exhibit properties of natural languages. A favorite in the literature is learning compos…