66 citations · 168 across the 35 of their papers we have counts for
6 papers · 1 filter
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…
Zero-Shot Learning with Knowledge Enhanced Visual Semantic Embeddings
Karan Sikka, Jihua Huang, Andrew Silberfarb +6
We improve zero-shot learning (ZSL) by incorporating common-sense knowledge in DNNs. We propose Common-Sense based Neuro-Symbolic Loss (CSNL) that formulates prior knowledge as nov…
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning
Meng Ye, Xiao Lin, Giedrius Burachas +2
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes. Recent FSL works have made steady progress via metric learning, meta learning, repr…
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…
Deep Adaptive Semantic Logic (DASL): Compiling Declarative Knowledge into Deep Neural Networks
Karan Sikka, Andrew Silberfarb, John Byrnes +4
We introduce Deep Adaptive Semantic Logic (DASL), a novel framework for automating the generation of deep neural networks that incorporates user-provided formal knowledge to improv…
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…