Search Results for author: Vikram Voleti

Found 12 papers, 2 papers with code

Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation

no code implementations19 May 2022 Vikram Voleti, Alexia Jolicoeur-Martineau, Christopher Pal

We train the model in a manner where we randomly and independently mask all the past frames or all the future frames.

Denoising Video Prediction

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.

Frame 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.

 Ranked #1 on Image Generation on ImageNet 128x128 (bpd metric)

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.

Data Augmentation

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 Sequential Image Classification +1

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