3 code implementations • 30 Apr 2020 • Zihang Meng, Sathya N. Ravi, Vikas Singh
We describe our development and show the use of our solver in a video segmentation task and meta-learning for few-shot learning.
1 code implementation • CVPR 2022 • Ronak Mehta, Sourav Pal, Vikas Singh, Sathya N. Ravi
For models which require no training (k-NN), simply deleting the closest original sample can be effective.
4 code implementations • 21 Mar 2018 • Sathya N. Ravi, Ronak Mehta, Vikas Singh
We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties.
1 code implementation • 18 Nov 2021 • Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh
In this paper, we show that a Bernoulli sampling attention mechanism based on Locality Sensitive Hashing (LSH), decreases the quadratic complexity of such models to linear.
1 code implementation • 17 Mar 2018 • Sathya N. Ravi, Tuan Dinh, Vishnu Sai Rao Lokhande, Vikas Singh
We provide convergence guarantees and show a suite of immediate benefits that are possible -- from training ResNets with fewer layers but better accuracy simply by substituting in our version of CG to faster training of GANs with 50% fewer epochs in image inpainting applications to provably better generalization guarantees using efficiently implementable forms of recently proposed regularizers.
1 code implementation • CVPR 2020 • Vishnu Suresh Lokhande, Songwong Tasneeyapant, Abhay Venkatesh, Sathya N. Ravi, Vikas Singh
Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision.
1 code implementation • 16 Feb 2021 • Aditya Kumar Akash, Vishnu Suresh Lokhande, Sathya N. Ravi, Vikas Singh
Learning invariant representations is a critical first step in a number of machine learning tasks.
1 code implementation • ICCV 2021 • Zihang Meng, Vikas Singh, Sathya N. Ravi
We study how stochastic differential equation (SDE) based ideas can inspire new modifications to existing algorithms for a set of problems in computer vision.
1 code implementation • ECCV 2020 • Vishnu Suresh Lokhande, Aditya Kumar Akash, Sathya N. Ravi, Vikas Singh
We provide a detailed technical analysis and present experiments demonstrating that various fairness measures from the literature can be reliably imposed on a number of training tasks in vision in a manner that is interpretable.
1 code implementation • 11 Feb 2023 • Zhu Wang, Sourav Medya, Sathya N. Ravi
Often, deep network models are purely inductive during training and while performing inference on unseen data.
Ranked #7 on Visual Question Answering on VQA v2 test-dev
1 code implementation • CVPR 2017 • Sathya N. Ravi, Yunyang Xiong, Lopamudra Mukherjee, Vikas Singh
This paper is inspired by a relatively recent work of Seitz and Baker which introduced the so-called Filter Flow model.
no code implementations • 22 Aug 2017 • Sathya N. Ravi, Maxwell D. Collins, Vikas Singh
We present a new Frank-Wolfe (FW) type algorithm that is applicable to minimization problems with a nonsmooth convex objective.
no code implementations • 28 Feb 2017 • Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh
We seek to analyze whether network architecture and input data statistics may guide the choices of learning parameters and vice versa.
no code implementations • 17 Nov 2015 • Vamsi K. Ithapu, Sathya N. Ravi, Vikas Singh
The regularization and output consistency behavior of dropout and layer-wise pretraining for learning deep networks have been fairly well studied.
no code implementations • CVPR 2018 • Seong Jae Hwang, Sathya N. Ravi, Zirui Tao, Hyunwoo J. Kim, Maxwell D. Collins, Vikas Singh
Visual relationships provide higher-level information of objects and their relations in an image â this enables a semantic understanding of the scene and helps downstream applications.
no code implementations • CVPR 2018 • Lopamudra Mukherjee, Sathya N. Ravi, Jiming Peng, Vikas Singh
In this paper, we study the quantization problem in the setting where subspaces are orthogonal and show that this problem is intricately related to a specific type of spectral decomposition of the data.
no code implementations • CVPR 2016 • Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function.
no code implementations • ICCV 2015 • Won Hwa Kim, Sathya N. Ravi, Sterling C. Johnson, Ozioma C. Okonkwo, Vikas Singh
A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases.
no code implementations • ICCV 2015 • Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.
no code implementations • ICCV 2015 • Lopamudra Mukherjee, Sathya N. Ravi, Vamsi K. Ithapu, Tyler Holmes, Vikas Singh
In this paper, we first derive an Augmented Lagrangian approach to optimize the standard binary Hashing objective (i. e., maintain fidelity with a given distance matrix).
no code implementations • 26 Sep 2019 • Sathya N. Ravi, Abhay Venkatesh, Glenn Moo Fung, Vikas Singh
Data dependent regularization is known to benefit a wide variety of problems in machine learning.
no code implementations • NeurIPS 2021 • Zihang Meng, Lopamudra Mukherjee, Vikas Singh, Sathya N. Ravi
We propose a framework which makes it feasible to directly train deep neural networks with respect to popular families of task-specific non-decomposable per- formance measures such as AUC, multi-class AUC, F -measure and others, as well as models such as non-negative matrix factorization.
no code implementations • 18 Feb 2022 • Jurijs Nazarovs, Rudrasis Chakraborty, Songwong Tasneeyapant, Sathya N. Ravi, Vikas Singh
Panel data involving longitudinal measurements of the same set of participants taken over multiple time points is common in studies to understand childhood development and disease modeling.
1 code implementation • CVPR 2022 • Vishnu Suresh Lokhande, Rudrasis Chakraborty, Sathya N. Ravi, Vikas Singh
Pooling multiple neuroimaging datasets across institutions often enables improvements in statistical power when evaluating associations (e. g., between risk factors and disease outcomes) that may otherwise be too weak to detect.
no code implementations • 5 Feb 2023 • Harsh Mishra, Jurijs Nazarovs, Manmohan Dogra, Sathya N. Ravi
In score-based models, a generative task is formulated using a parametric model (such as a neural network) to directly learn the gradient of such high dimensional distributions, instead of the density functions themselves, as is done traditionally.
1 code implementation • 12 Feb 2023 • Hamidreza Almasi, Harsh Mishra, Balajee Vamanan, Sathya N. Ravi
Therefore, to scale computation and data, these models are inevitably trained in a distributed manner in clusters of nodes, and their updates are aggregated before being applied to the model.
no code implementations • 5 Oct 2023 • Zhu Wang, Praveen Raj Veluswami, Harsh Mishra, Sathya N. Ravi
Furthermore, we illustrate applications of our novel rooted loss function in generative modeling based downstream applications, such as finetuning StyleGAN model with the rooted loss.
no code implementations • 2 Apr 2024 • Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi, Sourav Medya
This progress extends to the field of patent analysis and innovation, where AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle such as classification, retrieval, and valuation prediction.