activity
20152025
most citedFoodX-251: A Dataset for Fine-grained Food Classification

66 citations · 104 across the 18 of their papers we have counts for

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Showing cs.LGShow all

7 papers · 1 filter

cs.LG2023

Confidence Calibration for Systems with Cascaded Predictive Modules

Yunye Gong, Yi Yao, Xiao Lin +2

Existing conformal prediction algorithms estimate prediction intervals at target confidence levels to characterize the performance of a regression model on new test samples. Howeve…

cs.LG20222 cited

System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games

Indranil Sur, Zachary Daniels, Abrar Rahman +16

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adap…

cs.LG2021

Confidence Calibration for Domain Generalization under Covariate Shift

Yunye Gong, Xiao Lin, Yi Yao +3

Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation. However, these methods suffer from the following limitations: 1) they req…

cs.LG20203 cited

Detecting Trojaned DNNs Using Counterfactual Attributions

Karan Sikka, Indranil Sur, Susmit Jha +2

We target the problem of detecting Trojans or backdoors in DNNs. Such models behave normally with typical inputs but produce specific incorrect predictions for inputs poisoned with…

cs.LG20204 cited

Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition, and Selective Transfer

Aswin Raghavan, Jesse Hostetler, Indranil Sur +2

We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can sample…

cs.LG2020

Progressive Growing of Neural ODEs

Hammad A. Ayyubi, Yi Yao, Ajay Divakaran

Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time…