Search Results for author: C. Lawrence Zitnick

Found 41 papers, 22 papers with code

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

no code implementations6 Apr 2022 Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ulissi, C. Lawrence Zitnick, Abhishek Das

Based on this analysis, we identify a smaller dataset that correlates well with the full OC20 dataset, and propose the GemNet-OC model, which outperforms the previous state-of-the-art on OC20 by 16%, while reducing training time by a factor of 10.

Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations

no code implementations ICLR 2022 Anuroop Sriram, Abhishek Das, Brandon M. Wood, Siddharth Goyal, C. Lawrence Zitnick

Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat climate change.

Compositional Transformers for Scene Generation

1 code implementation NeurIPS 2021 Drew A. Hudson, C. Lawrence Zitnick

We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling.

Disentanglement Scene Generation

Rotation Invariant Graph Neural Networks using Spin Convolutions

no code implementations17 Jun 2021 Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, C. Lawrence Zitnick

We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network.

Frame

ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations

no code implementations2 Mar 2021 Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, C. Lawrence Zitnick

By not imposing explicit physical constraints, we can flexibly design expressive models while maintaining their computational efficiency.

Data Augmentation

Generative Adversarial Transformers

2 code implementations1 Mar 2021 Drew A. Hudson, C. Lawrence Zitnick

We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling.

 Ranked #1 on Image Generation on FFHQ (FID-10k-training-steps metric)

Disentanglement Image Generation +1

Creative Sketch Generation

1 code implementation ICLR 2021 Songwei Ge, Vedanuj Goswami, C. Lawrence Zitnick, Devi Parikh

Sketching or doodling is a popular creative activity that people engage in.

The Open Catalyst 2020 (OC20) Dataset and Community Challenges

2 code implementations20 Oct 2020 Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, Zachary Ulissi

Catalyst discovery and optimization is key to solving many societal and energy challenges including solar fuels synthesis, long-term energy storage, and renewable fertilizer production.

An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storage

no code implementations14 Oct 2020 C. Lawrence Zitnick, Lowik Chanussot, Abhishek Das, Siddharth Goyal, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Thibaut Lavril, Aini Palizhati, Morgane Riviere, Muhammed Shuaibi, Anuroop Sriram, Kevin Tran, Brandon Wood, Junwoong Yoon, Devi Parikh, Zachary Ulissi

As we increase our reliance on renewable energy sources such as wind and solar, which produce intermittent power, storage is needed to transfer power from times of peak generation to peak demand.

Exploring Crowd Co-creation Scenarios for Sketches

no code implementations15 May 2020 Devi Parikh, C. Lawrence Zitnick

As a first step towards studying the ability of human crowds and machines to effectively co-create, we explore several human-only collaborative co-creation scenarios.

End-to-End Variational Networks for Accelerated MRI Reconstruction

3 code implementations14 Apr 2020 Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick, Nafissa Yakubova, Florian Knoll, Patricia Johnson

The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).

MRI Reconstruction

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge

1 code implementation6 Jan 2020 Florian Knoll, Tullie Murrell, Anuroop Sriram, Nafissa Yakubova, Jure Zbontar, Michael Rabbat, Aaron Defazio, Matthew J. Muckley, Daniel K. Sodickson, C. Lawrence Zitnick, Michael P. Recht

Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.

Image Reconstruction

GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction

1 code implementation CVPR 2020 Anuroop Sriram, Jure Zbontar, Tullie Murrell, C. Lawrence Zitnick, Aaron Defazio, Daniel K. Sodickson

In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors.

MRI Reconstruction

Why Build an Assistant in Minecraft?

1 code implementation22 Jul 2019 Arthur Szlam, Jonathan Gray, Kavya Srinet, Yacine Jernite, Armand Joulin, Gabriel Synnaeve, Douwe Kiela, Haonan Yu, Zhuoyuan Chen, Siddharth Goyal, Demi Guo, Danielle Rothermel, C. Lawrence Zitnick, Jason Weston

In this document we describe a rationale for a research program aimed at building an open "assistant" in the game Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.

Natural Language Understanding

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

3 code implementations19 Jul 2019 Jonathan Gray, Kavya Srinet, Yacine Jernite, Haonan Yu, Zhuoyuan Chen, Demi Guo, Siddharth Goyal, C. Lawrence Zitnick, Arthur Szlam

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions.

ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

1 code implementation12 Feb 2019 Yuandong Tian, Jerry Ma, Qucheng Gong, Shubho Sengupta, Zhuoyuan Chen, James Pinkerton, C. Lawrence Zitnick

The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy.

Game of Go

ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

2 code implementations NeurIPS 2017 Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick

In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment.

Atari Games Frame +2

Inferring and Executing Programs for Visual Reasoning

5 code implementations ICCV 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.

Visual Question Answering Visual Reasoning

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

4 code implementations CVPR 2017 Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, Ross Girshick

When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings.

Question Answering Visual Question Answering +1

Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?

no code implementations17 Jun 2016 Abhishek Das, Harsh Agrawal, C. Lawrence Zitnick, Devi Parikh, Dhruv Batra

We conduct large-scale studies on `human attention' in Visual Question Answering (VQA) to understand where humans choose to look to answer questions about images.

Question Answering Visual Question Answering +1

Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels

no code implementations CVPR 2016 Ishan Misra, C. Lawrence Zitnick, Margaret Mitchell, Ross Girshick

When human annotators are given a choice about what to label in an image, they apply their own subjective judgments on what to ignore and what to mention.

Image Captioning Image Classification

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

no code implementations CVPR 2016 Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick

In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest.

Small Object Detection

Mind's Eye: A Recurrent Visual Representation for Image Caption Generation

no code implementations CVPR 2015 Xinlei Chen, C. Lawrence Zitnick

Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.

Image Retrieval

Learning a Recurrent Visual Representation for Image Caption Generation

no code implementations20 Nov 2014 Xinlei Chen, C. Lawrence Zitnick

Results are better than or comparable to state-of-the-art results on the image and sentence retrieval tasks for methods using similar visual features.

Image Retrieval

Fast Edge Detection Using Structured Forests

no code implementations20 Jun 2014 Piotr Dollár, C. Lawrence Zitnick

We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests.

BSDS500 Edge Detection +1

Microsoft COCO: Common Objects in Context

26 code implementations1 May 2014 Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.

Instance Segmentation Object Localization +3

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