25 citations · 38 across the 5 of their papers we have counts for
5 papers
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia +4
Machine learning systems perform well on pattern matching tasks, but their ability to perform algorithmic or logical reasoning is not well understood. One important reasoning capab…
Active Learning at the ImageNet Scale
Zeyad Ali Sami Emam, Hong-Min Chu, Ping-Yeh Chiang +4
Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this l…
Datasets for Studying Generalization from Easy to Hard Examples
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta +5
We describe new datasets for studying generalization from easy to hard examples.
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
Zeyad Emam, Andrew Kondrich, Sasha Harrison +4
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL). However, since the e…
Understanding Generalization through Visualizations
W. Ronny Huang, Zeyad Emam, Micah Goldblum +4
The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive. Numerous rigorous attempts have been…