Search Results for author: Vikram Voleti

Found 19 papers, 5 papers with code

Conditional Generative Modeling for Images, 3D Animations, and Video

no code implementations19 Oct 2023 Vikram Voleti

Overall, our research aims to make a meaningful contribution to the pursuit of more efficient and flexible generative models, with the potential to shape the future of computer vision.

Denoising Gaussian Processes +2

Score-based Diffusion Models in Function Space

no code implementations14 Feb 2023 Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar

They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.


Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification

no code implementations18 Dec 2022 Daniel Zhang, Vikram Voleti, Alexander Wong, Jason Deglint

In this study, we explore the feasibility of leveraging federated learning for privacy-preserving training of deep neural networks for phytoplankton classification.

Federated Learning Privacy Preserving

Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models

no code implementations21 Oct 2022 Vikram Voleti, Christopher Pal, Adam Oberman

Generative models based on denoising diffusion techniques have led to an unprecedented increase in the quality and diversity of imagery that is now possible to create with neural generative models.


SMPL-IK: Learned Morphology-Aware Inverse Kinematics for AI Driven Artistic Workflows

1 code implementation16 Aug 2022 Vikram Voleti, Boris N. Oreshkin, Florent Bocquelet, Félix G. Harvey, Louis-Simon Ménard, Christopher Pal

Inverse Kinematics (IK) systems are often rigid with respect to their input character, thus requiring user intervention to be adapted to new skeletons.

Pose Estimation

Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms

no code implementations3 Aug 2022 Nitpreet Bamra, Vikram Voleti, Alexander Wong, Jason Deglint

Thus, this work demonstrates the ability of GANs to create large synthetic datasets of phytoplankton from small training datasets, accomplishing a key step towards sustainable systematic monitoring of harmful algal blooms.


Generative Models of Brain Dynamics -- A review

no code implementations22 Dec 2021 Mahta Ramezanian Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti, Irina Rish, Guillaume Dumas

The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century.

Simple Video Generation using Neural ODEs

no code implementations7 Sep 2021 David Kanaa, Vikram Voleti, Samira Ebrahimi Kahou, Christopher Pal

Despite having been studied to a great extent, the task of conditional generation of sequences of frames, or videos, remains extremely challenging.

Video Generation

SALT: Sea lice Adaptive Lattice Tracking -- An Unsupervised Approach to Generate an Improved Ocean Model

no code implementations24 Jun 2021 Ju An Park, Vikram Voleti, Kathryn E. Thomas, Alexander Wong, Jason L. Deglint

Warming oceans due to climate change are leading to increased numbers of ectoparasitic copepods, also known as sea lice, which can cause significant ecological loss to wild salmon populations and major economic loss to aquaculture sites.


Multi-Resolution Continuous Normalizing Flows

1 code implementation15 Jun 2021 Vikram Voleti, Chris Finlay, Adam Oberman, Christopher Pal

In this work we introduce a Multi-Resolution variant of such models (MRCNF), by characterizing the conditional distribution over the additional information required to generate a fine image that is consistent with the coarse image.

Density Estimation Image Generation

Frustratingly Easy Uncertainty Estimation for Distribution Shift

no code implementations7 Jun 2021 Tiago Salvador, Vikram Voleti, Alexander Iannantuono, Adam Oberman

While the primary goal is to improve accuracy under distribution shift, an important secondary goal is uncertainty estimation: evaluating the probability that the prediction of a model is correct.

Image Classification Unsupervised Domain Adaptation

FairCal: Fairness Calibration for Face Verification

no code implementations ICLR 2022 Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam Oberman

However, they still have drawbacks: they reduce accuracy (AGENDA, PASS, FTC), or require retuning for different false positive rates (FSN).

Face Recognition Face Verification +1

Improving Continuous Normalizing Flows using a Multi-Resolution Framework

no code implementations ICML Workshop INNF 2021 Vikram Voleti, Chris Finlay, Adam M Oberman, Christopher Pal

Recent work has shown that Continuous Normalizing Flows (CNFs) can serve as generative models of images with exact likelihood calculation and invertible generation/density estimation.

Density Estimation

Accounting for Variance in Machine Learning Benchmarks

no code implementations1 Mar 2021 Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.

Benchmarking BIG-bench Machine Learning +1

Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules

1 code implementation ICML 2020 Sarthak Mittal, Alex Lamb, Anirudh Goyal, Vikram Voleti, Murray Shanahan, Guillaume Lajoie, Michael Mozer, Yoshua Bengio

To effectively utilize the wealth of potential top-down information available, and to prevent the cacophony of intermixed signals in a bidirectional architecture, mechanisms are needed to restrict information flow.

Language Modelling Open-Ended Question Answering +2

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