Search Results for author:
Found 27 papers, 7 papers with code
Machine Learning Techniques for Data Reduction of CFD Applications
We present an approach called guaranteed block autoencoder that leverages Tensor Correlations (GBATC) for reducing the spatiotemporal data generated by computational fluid dynamics (CFD) and other scientific applications.
Graph Attention Network for Lane-Wise and Topology-Invariant Intersection Traffic Simulation
Traffic congestion has significant economic, environmental, and social ramifications.
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids.
Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
Additionally, we integrate spatiotemporal feature detection with data compression and demonstrate that performing adaptive error-bounded compression in higher dimensional space enables greater compression ratios, leveraging the error propagation theory of a transformation-based compressor.
A Spatiotemporal Correspondence Approach to Unsupervised LiDAR Segmentation with Traffic Applications
We address the problem of unsupervised semantic segmentation of outdoor LiDAR point clouds in diverse traffic scenarios.
An Efficient Semi-Automated Scheme for Infrastructure LiDAR Annotation
Specifically, we introduce a human-in-the-loop schema in which annotators recursively fix and refine annotations imperfectly predicted by our tool and incrementally add them to the training dataset to obtain better SOT and MOT models.
Scalable Hybrid Learning Techniques for Scientific Data Compression
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery.
Expressing linear equality constraints in feedforward neural networks
Elimination of the auxiliary variables leads to a dual minimization problem on the Lagrange multipliers introduced to satisfy the linear constraints.
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations
We present a new approach to unsupervised shape correspondence learning between pairs of point clouds.
Towards Improving the Generation Quality of Autoregressive Slot VAEs
We propose two improvements that strengthen object correlation learning.
Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions
Scene flow estimation is therefore converted into the problem of recovering motion from the alignment of probability density functions, which we achieve using a closed-form expression of the classic Cauchy-Schwarz divergence.
Self-Supervised Learning
Self-supervised Scene Flow Estimation
Learning Scene Dynamics from Point Cloud Sequences
Our experimental evaluation confirms that recurrent processing of point cloud sequences results in significantly better SSFE compared to using only two frames.
Generating Scenes with Latent Object Models
We introduce a structured latent variable model that learns the underlying data-generating process for a dataset of scenes.
Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations
Unsupervised multi-object representation learning depends on inductive biases to guide the discovery of object-centric representations that generalize.
Hybrid Generative Models for Two-Dimensional Datasets
Two-dimensional array-based datasets are pervasive in a variety of domains.
Neural data compression for physics plasma simulation
We present a VAE-based data compression method, called VAe Physics Optimized Reduction (VAPOR), to compress scientific data while preserving physics constraints.
SparsePipe: Parallel Deep Learning for 3D Point Clouds
Besides, it suitably partitions the model when the GPUs are heterogeneous such that the computing is load-balanced with reduced communication overhead.
Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories
This study aims to develop a real-time intersection optimization (RIO) control algorithm to efficiently serve traffic of Connected and Automated Vehicles (CAVs) and conventional vehicles (CNVs).
Intelligent Intersection: Two-Stream Convolutional Networks for Real-time Near Accident Detection in Traffic Video
In this paper, we propose a two-stream Convolutional Network architecture that performs real-time detection, tracking, and near accident detection of road users in traffic video data.
Learning Permutations with Sinkhorn Policy Gradient
Many problems at the intersection of combinatorics and computer science require solving for a permutation that optimally matches, ranks, or sorts some data.
Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer's Disease Classification
Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convolutional layer.
Machine Learning Methods for Data Association in Multi-Object Tracking
Data association is a key step within the multi-object tracking pipeline that is notoriously challenging due to its combinatorial nature.
Global Model Interpretation via Recursive Partitioning
To generate the interpretation tree, a unified process recursively partitions the input variable space by maximizing the difference in the average contribution of the split variable between the divided spaces.
Spatial Scaling of Satellite Soil Moisture using Temporal Correlations and Ensemble Learning
A novel algorithm is developed to downscale soil moisture (SM), obtained at satellite scales of 10-40 km by utilizing its temporal correlations to historical auxiliary data at finer scales.
Disaggregation of SMAP L3 Brightness Temperatures to 9km using Kernel Machines
In this study, a machine learning algorithm is used for disaggregation of SMAP brightness temperatures (T$_{\textrm{B}}$) from 36km to 9km.
Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models
A novel algorithm is proposed to downscale microwave brightness temperatures ($\mathrm{T_B}$), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications.
Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes using Holistic Structure based Models
The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, while the computational time needed was reduced by three times.
