Search Results for author:
Found 48 papers, 31 papers with code
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware.
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
We propose fine-tuning large language models for generation of stable materials.
From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction
Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains.
Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
Graph neural networks that model 3D data, such as point clouds or atoms, are typically desired to be $SO(3)$ equivariant, i. e., equivariant to 3D rotations.
AdsorbML: A Leap in Efficiency for Adsorption Energy Calculations using Generalizable Machine Learning Potentials
Computational catalysis is playing an increasingly significant role in the design of catalysts across a wide range of applications.
Spherical Channels for Modeling Atomic Interactions
We propose the Spherical Channel Network (SCN) to model atomic energies and forces.
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials.
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset.
Ranked #1 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
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.
Ranked #2 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
Compositional Transformers for Scene Generation
We introduce the GANformer2 model, an iterative object-oriented transformer, explored for the task of generative modeling.
Rotation Invariant Graph Neural Networks using Spin Convolutions
We introduce a novel approach to modeling angular information between sets of neighboring atoms in a graph neural network.
Ranked #3 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
Graph Neural Network
Initial Structure to Relaxed Energy (IS2RE)
ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations
By not imposing explicit physical constraints, we can flexibly design expressive models while maintaining their computational efficiency.
Generative Adversarial Transformers
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)
Creative Sketch Generation
Sketching or doodling is a popular creative activity that people engage in.
The Open Catalyst 2020 (OC20) Dataset and Community Challenges
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
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.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major advances in representation learning and statistical generation.
Exploring Crowd Co-creation Scenarios for Sketches
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
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).
Ranked #1 on
MRI Reconstruction
on fastMRI Knee 4x
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
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.
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
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.
Why Build an Assistant in Minecraft?
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.
CraftAssist: A Framework for Dialogue-enabled Interactive Agents
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
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.
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
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.
Inferring and Executing Programs for Visual Reasoning
Existing methods for visual reasoning attempt to directly map inputs to outputs using black-box architectures without explicitly modeling the underlying reasoning processes.
Ranked #5 on
Visual Question Answering (VQA)
on CLEVR-Humans
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
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.
Measuring Machine Intelligence Through Visual Question Answering
As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence.
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
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.
Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
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.
Visual Storytelling
We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling.
Shuffle and Learn: Unsupervised Learning using Temporal Order Verification
With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN).
Ranked #48 on
Self-Supervised Action Recognition
on HMDB51
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
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.
We Are Humor Beings: Understanding and Predicting Visual Humor
We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level.
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest.
Ranked #230 on
Object Detection
on COCO test-dev
Learning Common Sense Through Visual Abstraction
We show that the commonsense knowledge we learn is complementary to what can be learnt from sources of text.
VISALOGY: Answering Visual Analogy Questions
In this paper, we study the problem of answering visual analogy questions.
Mind's Eye: A Recurrent Visual Representation for Image Caption Generation
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.
Exploring Nearest Neighbor Approaches for Image Captioning
We explore a variety of nearest neighbor baseline approaches for image captioning.
VQA: Visual Question Answering
Given an image and a natural language question about the image, the task is to provide an accurate natural language answer.
Microsoft COCO Captions: Data Collection and Evaluation Server
In this paper we describe the Microsoft COCO Caption dataset and evaluation server.
CIDEr: Consensus-based Image Description Evaluation
We propose a novel paradigm for evaluating image descriptions that uses human consensus.
Learning a Recurrent Visual Representation for Image Caption Generation
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.
From Captions to Visual Concepts and Back
The language model learns from a set of over 400, 000 image descriptions to capture the statistics of word usage.
Ranked #1 on
Image Captioning
on COCO Captions test
Collecting Image Description Datasets using Crowdsourcing
We describe our two new datasets with images described by humans.
Fast Edge Detection Using Structured Forests
We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests.
Microsoft COCO: Common Objects in Context
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.
