Search Results for author: Jasmine Collins

Found 10 papers, 6 papers with code

CommonCanvas: An Open Diffusion Model Trained with Creative-Commons Images

1 code implementation25 Oct 2023 Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, Volodymyr Kuleshov

This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce.

Transfer Learning

CA$^2$T-Net: Category-Agnostic 3D Articulation Transfer from Single Image

no code implementations5 Jan 2023 Jasmine Collins, Anqi Liang, Jitendra Malik, Hao Zhang, Frédéric Devernay

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.

Towards Understanding How Machines Can Learn Causal Overhypotheses

1 code implementation16 Jun 2022 Eliza Kosoy, David M. Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Han Huang, Jessica B. Hamrick, John Canny, Nan Rosemary Ke, Alison Gopnik

Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence.

BIG-bench Machine Learning Causal Inference

GANmouflage: 3D Object Nondetection with Texture Fields

no code implementations CVPR 2023 Rui Guo, Jasmine Collins, Oscar de Lima, Andrew Owens

Our model learns to camouflage a variety of object shapes from randomly sampled locations and viewpoints within the input scene, and is the first to address the problem of hiding complex object shapes.

Exploring Exploration: Comparing Children with RL Agents in Unified Environments

1 code implementation6 May 2020 Eliza Kosoy, Jasmine Collins, David M. Chan, Sandy Huang, Deepak Pathak, Pulkit Agrawal, John Canny, Alison Gopnik, Jessica B. Hamrick

Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn.

Accelerating Training of Deep Neural Networks with a Standardization Loss

1 code implementation3 Mar 2019 Jasmine Collins, Johannes Balle, Jonathon Shlens

We find that a standardization loss accelerates training on both small- and large-scale image classification experiments, works with a variety of architectures, and is largely robust to training across different batch sizes.

Image Classification Test

Capacity and Trainability in Recurrent Neural Networks

1 code implementation29 Nov 2016 Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo

They can store an amount of task information which is linear in the number of parameters, and is approximately 5 bits per parameter.

Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning

no code implementations4 Nov 2016 Akosua Busia, Jasmine Collins, Navdeep Jaitly

We first train a series of deep neural networks to predict eight-class secondary structure labels given a protein's amino acid sequence information and find that using recent methods for regularization, such as dropout and weight-norm constraining, leads to measurable gains in accuracy.

Protein Secondary Structure Prediction Protein Structure Prediction

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