28 citations · 66 across the 12 of their papers we have counts for
13 papers
HyperMix: Out-of-Distribution Detection and Classification in Few-Shot Settings
Nikhil Mehta, Kevin J Liang, Jing Huang +3
Out-of-distribution (OOD) detection is an important topic for real-world machine learning systems, but settings with limited in-distribution samples have been underexplored. Such f…
Meta-Learned Attribute Self-Interaction Network for Continual and Generalized Zero-Shot Learning
Vinay K Verma, Nikhil Mehta, Kevin J Liang +2
Zero-shot learning (ZSL) is a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges remain. Recently, metho…
Recommender Systems with Generative Retrieval
Shashank Rajput, Nikhil Mehta, Anima Singh +10
Modern recommender systems perform large-scale retrieval by first embedding queries and item candidates in the same unified space, followed by approximate nearest neighbor search t…
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Vinay Kumar Verma, Nikhil Mehta, Shijing Si +2
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, the…
Pseudo-OOD training for robust language models
Dhanasekar Sundararaman, Nikhil Mehta, Lawrence Carin
While pre-trained large-scale deep models have garnered attention as an important topic for many downstream natural language processing (NLP) tasks, such models often make unreliab…
A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology
Aaditya Prasad, Nikhil Mehta, Matthew Horak +1
Automated plant diagnosis is a technology that promises large increases in cost-efficiency for agriculture. However, multiple problems reduce the effectiveness of drones, including…
Efficient Feature Transformations for Discriminative and Generative Continual Learning
Vinay Kumar Verma, Kevin J Liang, Nikhil Mehta +2
As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critica…
Meta-Learned Attribute Self-Gating for Continual Generalized Zero-Shot Learning
Vinay Kumar Verma, Kevin Liang, Nikhil Mehta +1
Zero-shot learning (ZSL) has been shown to be a promising approach to generalizing a model to categories unseen during training by leveraging class attributes, but challenges still…
Counterfactual Representation Learning with Balancing Weights
Serge Assaad, Shuxi Zeng, Chenyang Tao +5
A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation lea…
WAFFLe: Weight Anonymized Factorization for Federated Learning
Weituo Hao, Nikhil Mehta, Kevin J Liang +3
In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices. In light of…
Continual Learning using a Bayesian Nonparametric Dictionary of Weight Factors
Nikhil Mehta, Kevin J Liang, Vinay K Verma +1
Naively trained neural networks tend to experience catastrophic forgetting in sequential task settings, where data from previous tasks are unavailable. A number of methods, using v…
Survival Cluster Analysis
Paidamoyo Chapfuwa, Chunyuan Li, Nikhil Mehta +2
Conventional survival analysis approaches estimate risk scores or individualized time-to-event distributions conditioned on covariates. In practice, there is often great population…
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin, Piyush Rai
Stochastic blockmodels (SBM) and their variants, , mixed-membership and overlapping stochastic blockmodels, are latent variable based generative models for graphs. They have…