203 citations · 440 across the 14 of their papers we have counts for
14 papers
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs
Clara H. McCreery, Namit Katariya, Anitha Kannan +2
People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified pe…
COVID-19 in differential diagnosis of online symptom assessments
Anitha Kannan, Richard Chen, Vignesh Venkataraman +2
The COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. One class of solutions are symptom checkers, which have become very…
The accuracy vs. coverage trade-off in patient-facing diagnosis models
Anitha Kannan, Jason Alan Fries, Eric Kramer +3
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis…
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue
Sam Shleifer, Manish Chablani, Anitha Kannan +2
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a…
Domain-Relevant Embeddings for Medical Question Similarity
Clara McCreery, Namit Katariya, Anitha Kannan +2
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-quest…
Open Set Medical Diagnosis
Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso +4
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they ha…
Prototypical Clustering Networks for Dermatological Disease Diagnosis
Viraj Prabhu, Anitha Kannan, Murali Ravuri +3
We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis. Dermatological diagnosis poses two major challenges for standard off-…
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Ashwin Kalyan, Stefan Lee, Anitha Kannan +1
Many structured prediction problems (particularly in vision and language domains) are ambiguous, with multiple outputs being correct for an input - e.g. there are many ways of desc…
Learning from the experts: From expert systems to machine-learned diagnosis models
Murali Ravuri, Anitha Kannan, Geoffrey J. Tso +1
Expert diagnostic support systems have been extensively studied. The practical applications of these systems in real-world scenarios have been somewhat limited due to well-understo…
Tackling Over-pruning in Variational Autoencoders
Serena Yeung, Anitha Kannan, Yann Dauphin +1
Variational autoencoders (VAE) are directed generative models that learn factorial latent variables. As noted by Burda et al. (2015), these models exhibit the problem of factor ove…
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model
Jiasen Lu, Anitha Kannan, Jianwei Yang +2
We present a novel training framework for neural sequence models, particularly for grounded dialog generation. The standard training paradigm for these models is maximum likelihood…
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
Jianwei Yang, Anitha Kannan, Dhruv Batra +1
We present LR-GAN: an adversarial image generation model which takes scene structure and context into account. Unlike previous generative adversarial networks (GANs), the proposed…
Transformation-Based Models of Video Sequences
Joost van Amersfoort, Anitha Kannan, Marc'Aurelio Ranzato +3
In this work we propose a simple unsupervised approach for next frame prediction in video. Instead of directly predicting the pixels in a frame given past frames, we predict the tr…
Structured Query Reformulations in Commerce Search
Sreenivas Gollapudi, Samuel Ieong, Anitha Kannan
Recent work in commerce search has shown that understanding the semantics in user queries enables more effective query analysis and retrieval of relevant products. However, due to…