no code implementations • 9 Sep 2024 • Xiao Li, Jaemoon Lee, Anand Rangarajan, Sanjay Ranka

To address this challenge, data compression or reduction techniques are crucial.

no code implementations • 4 Aug 2024 • Jacques P. Fleischer, Ryan Pallack, Ahan Mishra, Gustavo Riente de Andrade, Subhadipto Poddar, Emmanuel Posadas, Robert Schenck, Tania Banerjee, Anand Rangarajan, Sanjay Ranka

This paper utilizes video analytics to study pedestrian and vehicle traffic behavior, focusing on analyzing traffic patterns during football gamedays.

no code implementations • 2 May 2024 • Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati, Anand Rangarajan, Sanjay Ranka

MTDT enables accurate, fine-grained estimation of loop detector waveform time series for each lane of movement, alongside successful estimation of several MOEs for each lane group associated with a traffic phase concurrently and for all approaches of an arbitrary urban intersection.

no code implementations • 1 May 2024 • Xiao Li, Qian Gong, Jaemoon Lee, Scott Klasky, Anand Rangarajan, Sanjay Ranka

Scientists conduct large-scale simulations to compute derived quantities-of-interest (QoI) from primary data.

no code implementations • 28 Apr 2024 • Jaemoon Lee, Ki Sung Jung, Qian Gong, Xiao Li, Scott Klasky, Jacqueline Chen, Anand Rangarajan, Sanjay Ranka

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.

1 code implementation • 11 Apr 2024 • Nooshin Yousefzadeh, Rahul Sengupta, Yashaswi Karnati, Anand Rangarajan, Sanjay Ranka

Traffic congestion has significant economic, environmental, and social ramifications.

no code implementations • 11 Jan 2024 • Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak, Tania Banerjee, Jaemoon Lee, Anand Rangarajan, Lipeng Wan, Nicolas Vidal, Qing Liu, Ana Gainaru, Norbert Podhorszki, Richard Archibald, Sanjay Ranka, Scott Klasky

We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids.

no code implementations • 6 Jan 2024 • Qian Gong, Chengzhu Zhang, Xin Liang, Viktor Reshniak, Jieyang Chen, Anand Rangarajan, Sanjay Ranka, Nicolas Vidal, Lipeng Wan, Paul Ullrich, Norbert Podhorszki, Robert Jacob, Scott Klasky

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.

no code implementations • 23 Aug 2023 • Xiao Li, Pan He, Aotian Wu, Sanjay Ranka, Anand Rangarajan

We address the problem of unsupervised semantic segmentation of outdoor LiDAR point clouds in diverse traffic scenarios.

no code implementations • 25 Jan 2023 • Aotian Wu, Pan He, Xiao Li, Ke Chen, Sanjay Ranka, Anand Rangarajan

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.

1 code implementation • 21 Dec 2022 • Tania Banerjee, Jong Choi, Jaemoon Lee, Qian Gong, Jieyang Chen, Scott Klasky, Anand Rangarajan, Sanjay Ranka

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.

1 code implementation • 8 Nov 2022 • Anand Rangarajan, Pan He, Jaemoon Lee, Tania Banerjee, Sanjay Ranka

Elimination of the auxiliary variables leads to a dual minimization problem on the Lagrange multipliers introduced to satisfy the linear constraints.

no code implementations • 5 Sep 2022 • Pan He, Patrick Emami, Sanjay Ranka, Anand Rangarajan

We present a new approach to unsupervised shape correspondence learning between pairs of point clouds.

1 code implementation • 3 Jun 2022 • Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan

We propose two improvements that strengthen object correlation learning.

no code implementations • 23 Mar 2022 • Pan He, Patrick Emami, Sanjay Ranka, Anand Rangarajan

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

no code implementations • 16 Nov 2021 • Pan He, Patrick Emami, Sanjay Ranka, Anand Rangarajan

Our experimental evaluation confirms that recurrent processing of point cloud sequences results in significantly better SSFE compared to using only two frames.

no code implementations • 29 Sep 2021 • Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan

We introduce a structured latent variable model that learns the underlying data-generating process for a dataset of scenes.

no code implementations • 24 Jun 2021 • George Worrall, Anand Rangarajan, Jasmeet Judge

Results from this study exhibit both the viability of NNs in crop growth stage estimation (CGSE) and the benefits of using domain knowledge.

1 code implementation • 7 Jun 2021 • Patrick Emami, Pan He, Sanjay Ranka, Anand Rangarajan

Unsupervised multi-object representation learning depends on inductive biases to guide the discovery of object-centric representations that generalize.

no code implementations • 1 Jun 2021 • Hoda Shajari, Jaemoon Lee, Sanjay Ranka, Anand Rangarajan

Two-dimensional array-based datasets are pervasive in a variety of domains.

no code implementations • ICLR Workshop Neural_Compression 2021 • Jong Choi, Michael Churchill, Qian Gong, Seung-Hoe Ku, Jaemoon Lee, Anand Rangarajan, Sanjay Ranka, Dave Pugmire, CS Chang, Scott Klasky

We present a VAE-based data compression method, called VAe Physics Optimized Reduction (VAPOR), to compress scientific data while preserving physics constraints.

no code implementations • 27 Dec 2020 • Keke Zhai, Pan He, Tania Banerjee, Anand Rangarajan, Sanjay Ranka

Besides, it suitably partitions the model when the GPUs are heterogeneous such that the computing is load-balanced with reduced communication overhead.

no code implementations • 25 Mar 2020 • Hoda Shajari, Anand Rangarajan

We propose a straightforward extension to the SVM to cope with multiclass and multilabel classification problems within a unified framework.

no code implementations • 4 Jan 2019 • Xiaohui Huang, Pan He, Anand Rangarajan, Sanjay Ranka

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.

1 code implementation • 7 Mar 2018 • Chengliang Yang, Anand Rangarajan, Sanjay Ranka

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.

1 code implementation • 11 Feb 2018 • Chengliang Yang, Anand Rangarajan, Sanjay Ranka

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.

1 code implementation • 29 Sep 2017 • Yuan Zhou, Anand Rangarajan, Paul D. Gader

We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives).

no code implementations • 27 Oct 2016 • Anthony O. Smith, Anand Rangarajan

To this end, we set up a model in which each class is represented as a 1D subspace of the vector space formed by the features.

no code implementations • 1 Apr 2016 • Kushal Arora, Anand Rangarajan

Traditional language models treat language as a finite state automaton on a probability space over words.

no code implementations • 28 Jan 2016 • Mark Moyou, John Corring, Adrian Peter, Anand Rangarajan

In this work, we present a novel and practical approach to address one of the longstanding problems in computer vision: 2D and 3D affine invariant feature matching.

no code implementations • 21 Jan 2016 • Subit Chakrabarti, Jasmeet Judge, Tara Bongiovanni, Anand Rangarajan, Sanjay Ranka

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.

no code implementations • 20 Jan 2016 • Subit Chakrabarti, Tara Bongiovanni, Jasmeet Judge, Anand Rangarajan, Sanjay Ranka

In this study, a machine learning algorithm is used for disaggregation of SMAP brightness temperatures (T$_{\textrm{B}}$) from 36km to 9km.

no code implementations • 3 Jan 2016 • Kushal Arora, Anand Rangarajan

In this paper, we address the last problem and propose a new discriminative entropy based intrinsic metric that works for both traditional word level models and unnormalized language models like sentence level models.

no code implementations • 30 Sep 2015 • Yuan Zhou, Anand Rangarajan, Paul Gader

In this paper, we show that NCM can be used for calculating the uncertainty of the estimated endmembers with spatial priors incorporated for better unmixing.

no code implementations • 30 Jan 2015 • Subit Chakrabarti, Jasmeet Judge, Anand Rangarajan, Sanjay Ranka

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.

no code implementations • 30 Jan 2015 • Subit Chakrabarti, Jasmeet Judge, Anand Rangarajan, Sanjay Ranka

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.

no code implementations • CVPR 2014 • Yan Deng, Anand Rangarajan, Stephan Eisenschenk, Baba C. Vemuri

In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm.

no code implementations • 8 Mar 2014 • Karthik S. Gurumoorthy, Adrian M. Peter, Birmingham Hang Guan, Anand Rangarajan

In our framework, a solution to the eikonal equation is obtained in the limit as Planck's constant $\hbar$ (treated as a free parameter) tends to zero of the solution to the corresponding linear Schr\"odinger equation.

no code implementations • 13 Nov 2012 • Karthik S. Gurumoorthy, Anand Rangarajan, John Corring

We prove that the density function of the gradient of a sufficiently smooth function $S : \Omega \subset \mathbb{R}^d \rightarrow \mathbb{R}$, obtained via a random variable transformation of a uniformly distributed random variable, is increasingly closely approximated by the normalized power spectrum of $\phi=\exp\left(\frac{iS}{\tau}\right)$ as the free parameter $\tau \rightarrow 0$.

no code implementations • 13 Dec 2011 • Karthik S. Gurumoorthy, Anand Rangarajan

In other words, when $S$ and $\phi$ are related by $\phi = \exp \left(-\frac{S}{\tau} \right)$ and $\phi$ satisfies a specific linear differential equation corresponding to the extremum of a variational problem, we obtain the approximate Euclidean distance function $S = -\tau \log(\phi)$ which converges to the true solution in the limit as $\tau \rightarrow 0$.

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