Chitwan Saharia

I am a Research Engineer at Google Brain, Toronto.

I finished my undergrad from Indian Institute of Technology (IIT), Bombay with a major in Computer Science and Engineering . I was advised by Prof. Preethi Jyothi for my Bachelor Thesis Project on Analysing the Impact of Modalities on Video Captioning. I previously interned at Mila, Montreal under the supervision of Prof. Yoshua Bengio , and Dzmitry Bahdanau .

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My research interest revolves around Machine Learning and its applications. At present, I am working on diffusion based image generative models with the amazing colleagues at Brain. I have also been actively working non-autoregressive generative models for text. When I was an undergrad, I interned at MILA and worked on analysing sample complexity of learning algorithms for grounded language learning.

We show that cascaded diffusion models are capable of generating high fidelity images on the class-conditional ImageNet generation challenge, without any assistance from auxiliary image classifiers to boost sample quality. We outperform BigGAN-deep and VQVAE-2 on FID and classification accuracy scores (CAS).

We adapt score matching based diffusion models for the image super-resolution. We achieve a fool rate of 50% on face super-resolution, and 40% on ImageNet super-resolution. We cascaded multiple super-resolution models to efficiently generate 1024x1024 unconditional faces, and 256x256 class conditional natural images.

We apply latent alignment based models for non-autoregressive machine translation. We achieve SOTA for WMT14 EnDe for single step generation using CTC, and SOTA for iterative generation using Imputer.

We introduce a semi-autoregressive model for speech recognition that uses a tractable dynamic programming algorithm to approximately marginalize over all latent alignments and generation orders.

We study the impact of False Negatives in GAIL algorithm, and present a method diagnose it. We further present a solution - Fake Conditioning and improve upon sample complexity of human demonstrations by an order of magnitude compared to Behavioral Cloning.

We study the impact audio and visual modalities in learning models for Video Captioning.

We present a platform to study sample efficiency of grounded language learning. We include a numer of tasks with varying complexity and present a rigid sample complexity benchmark on each task.

Huge thanks to Jon Barron for the template!