Temporal Gaussian Mixture Layer for Videos

ICLR 2019 2 code implementations

We introduce a new convolutional layer named the Temporal Gaussian Mixture (TGM) layer and present how it can be used to efficiently capture longer-term temporal information in continuous activity videos.

 SOTA for Action Detection on THUMOS' 14 (using extra training data)

ACTION DETECTION

E3: Entailment-driven Extracting and Editing for Conversational Machine Reading

ACL 2019 1 code implementation

Conversational machine reading systems help users answer high-level questions (e. g. determine if they qualify for particular government benefits) when they do not know the exact rules by which the determination is made(e. g. whether they need certain income levels or veteran status).

READING COMPREHENSION

Multi$^{\mathbf{3}}$Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

5 Dec 20181 code implementation

We propose a novel approach for rapid segmentation of flooded buildings by fusing multiresolution, multisensor, and multitemporal satellite imagery in a convolutional neural network.

FLOODED BUILDING SEGMENTATION

Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Data

13 Aug 20182 code implementations

We present hidden fluid mechanics (HFM), a physics informed deep learning framework capable of encoding an important class of physical laws governing fluid motions, namely the Navier-Stokes equations.

Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data

3 Jan 20191 code implementation

2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs.

Adversarial Uncertainty Quantification in Physics-Informed Neural Networks

9 Nov 20182 code implementations

We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks.

Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

18 Feb 20191 code implementation

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges.

Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning

6 Jul 20161 code implementation

Our method is open source and freely available for the community to use.

Hidden Talents of the Variational Autoencoder

16 Jun 20171 code implementation

Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution.

DIMENSIONALITY REDUCTION

A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime

11 Feb 20191 code implementation

The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design tasks related to uncertainty quantification.