no code implementations • 18 Apr 2024 • Guangyu Sun, Matias Mendieta, Aritra Dutta, Xin Li, Chen Chen
Multi-modal transformers mark significant progress in different domains, but siloed high-quality data hinders their further improvement.
no code implementations • 28 Dec 2023 • Aritra Dutta, Dr. G Suseela, Asmita Sood
Multi-modal image stitching can be a difficult feat.
no code implementations • 7 Dec 2023 • Aritra Dutta, Srijan Das, Jacob Nielsen, Rajatsubhra Chakraborty, Mubarak Shah
Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality.
no code implementations • 19 Oct 2023 • Aritra Dutta, El Houcine Bergou, Soumia Boucherouite, Nicklas Werge, Melih Kandemir, Xin Li
Additionally, our analyses allow us to measure the density of the $\epsilon$-stationary points in the final iterates of SGD, and we recover the classical $O(\frac{1}{\sqrt{T}})$ asymptotic rate under various existing assumptions on the objective function and the bounds on the stochastic gradient.
no code implementations • 31 Aug 2023 • El Houcine Bergou, Soumia Boucherouite, Aritra Dutta, Xin Li, Anna Ma
In this paper, we analyze the convergence of RK for noisy linear systems when the coefficient matrix, $A$, is corrupted with both additive and multiplicative noise, along with the noisy vector, $b$.
no code implementations • 12 Sep 2022 • El Houcine Bergou, Konstantin Burlachenko, Aritra Dutta, Peter Richtárik
Recently, Hanzely and Richt\'{a}rik (2020) proposed a new formulation for training personalized FL models aimed at balancing the trade-off between the traditional global model and the local models that could be trained by individual devices using their private data only.
no code implementations • NeurIPS 2021 • Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem, Trambak Banerjee, Marco Canini, Panos Kalnis
Unlike with Top-$k$ sparsifier, we show that hard-threshold has the same asymptotic convergence and linear speedup property as SGD in the convex case and has no impact on the data-heterogeneity in the non-convex case.
1 code implementation • NeurIPS 2021 • Hang Xu, Kelly Kostopoulou, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis
DeepReduce is orthogonal to existing gradient sparsifiers and can be applied in conjunction with them, transparently to the end-user, to significantly lower the communication overhead.
1 code implementation • NeurIPS 2021 • Kelly Kostopoulou, Hang Xu, Aritra Dutta, Xin Li, Alexandros Ntoulas, Panos Kalnis
This paper introduces DeepReduce, a versatile framework for the compressed communication of sparse tensors, tailored for distributed deep learning.
1 code implementation • 19 Nov 2019 • Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem, Chen-Yu Ho, Atal Narayan Sahu, Marco Canini, Panos Kalnis
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of deep neural networks.
1 code implementation • 28 May 2019 • Aritra Dutta, El Houcine Bergou, Yunming Xiao, Marco Canini, Peter Richtárik
In contrast to RNA which computes extrapolation coefficients by (approximately) setting the gradient of the objective function to zero at the extrapolated point, we propose a more direct approach, which we call direct nonlinear acceleration (DNA).
no code implementations • 25 May 2019 • Aritra Dutta, Filip Hanzely, Jingwei Liang, Peter Richtárik
The best pair problem aims to find a pair of points that minimize the distance between two disjoint sets.
no code implementations • 21 May 2018 • Aritra Dutta, Filip Hanzely, Peter Richtárik
Robust principal component analysis (RPCA) is a well-studied problem with the goal of decomposing a matrix into the sum of low-rank and sparse components.
no code implementations • 15 Apr 2018 • Aritra Dutta, Xin Li, Peter Richtarik
We primarily study a special a weighted low-rank approximation of matrices and then apply it to solve the background modeling problem.
no code implementations • 23 Nov 2017 • Aritra Dutta, Peter Richtarik
We propose a surprisingly simple model for supervised video background estimation.
no code implementations • 4 Jul 2017 • Aritra Dutta, Xin Li
Classical principal component analysis (PCA) is not robust to the presence of sparse outliers in the data.
no code implementations • 2 Jul 2017 • Aritra Dutta, Xin Li, Peter Richtárik
Principal component pursuit (PCP) is a state-of-the-art approach for background estimation problems.