82 citations · 121 across the 9 of their papers we have counts for
9 papers
CAT-Net: Category-Agnostic 3D Articulation Transfer from Single Image
Jasmine Collins, Anqi Liang, Jitendra Malik +2
We present a neural network approach to transfer the motion from a single image of an articulated object to a rest-state (i.e., unarticulated) 3D model. Our network learns to predi…
Towards Understanding How Machines Can Learn Causal Overhypotheses
Eliza Kosoy, David M. Chan, Adrian Liu +7
Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. The extensive literature…
Learning Causal Overhypotheses through Exploration in Children and Computational Models
Eliza Kosoy, Adrian Liu, Jasmine Collins +7
Despite recent progress in reinforcement learning (RL), RL algorithms for exploration still remain an active area of research. Existing methods often focus on state-based metrics,…
GANmouflage: 3D Object Nondetection with Texture Fields
Rui Guo, Jasmine Collins, Oscar de Lima +1
We propose a method that learns to camouflage 3D objects within scenes. Given an object's shape and a distribution of viewpoints from which it will be seen, we estimate a texture t…
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
Jasmine Collins, Shubham Goel, Kenan Deng +9
We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metad…
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Eliza Kosoy, Jasmine Collins, David M. Chan +6
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn. In turn, this ear…
Accelerating Training of Deep Neural Networks with a Standardization Loss
Jasmine Collins, Johannes Balle, Jonathon Shlens
A significant advance in accelerating neural network training has been the development of normalization methods, permitting the training of deep models both faster and with better…
Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning
Akosua Busia, Jasmine Collins, Navdeep Jaitly
Recently developed deep learning techniques have significantly improved the accuracy of various speech and image recognition systems. In this paper we adapt some of these technique…
Capacity and Trainability in Recurrent Neural Networks
Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo
Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store informat…