Search Results for author: Sanjana Srivastava

Found 10 papers, 3 papers with code

BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments

no code implementations6 Aug 2021 Sanjana Srivastava, Chengshu Li, Michael Lingelbach, Roberto Martín-Martín, Fei Xia, Kent Vainio, Zheng Lian, Cem Gokmen, Shyamal Buch, C. Karen Liu, Silvio Savarese, Hyowon Gweon, Jiajun Wu, Li Fei-Fei

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation.

iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks

1 code implementation6 Aug 2021 Chengshu Li, Fei Xia, Roberto Martín-Martín, Michael Lingelbach, Sanjana Srivastava, Bokui Shen, Kent Vainio, Cem Gokmen, Gokul Dharan, Tanish Jain, Andrey Kurenkov, C. Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, Silvio Savarese

We evaluate the new capabilities of iGibson 2. 0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI.

Imitation Learning

The Foes of Neural Network's Data Efficiency Among Unnecessary Input Dimensions

no code implementations13 Jul 2021 Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki, Xavier Boix

Datasets often contain input dimensions that are unnecessary to predict the output label, e. g. background in object recognition, which lead to more trainable parameters.

Object Recognition

The Foes of Neural Network’s Data Efficiency Among Unnecessary Input Dimensions

no code implementations1 Jan 2021 Vanessa D'Amario, Sanjana Srivastava, Tomotake Sasaki, Xavier Boix

In this paper, we investigate the impact of unnecessary input dimensions on one of the central issues of machine learning: the number of training examples needed to achieve high generalization performance, which we refer to as the network's data efficiency.

Foveation Image Classification +3

Identifying Learning Rules From Neural Network Observables

2 code implementations NeurIPS 2020 Aran Nayebi, Sanjana Srivastava, Surya Ganguli, Daniel L. K. Yamins

We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes, and that these results generalize to limited access to the trajectory and held-out architectures and learning curricula.

Open-Ended Question Answering

Minimal Images in Deep Neural Networks: Fragile Object Recognition in Natural Images

no code implementations ICLR 2019 Sanjana Srivastava, Guy Ben-Yosef, Xavier Boix

Ullman et al. 2016 show that a slight modification of the location and size of the visible region of the minimal image produces a sharp drop in human recognition accuracy.

Object Recognition

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