2 citations · 4 across the 5 of their papers we have counts for
6 papers
Uncertainty Quantification in Continual Open-World Learning
Amanda S. Rios, Ibrahima J. Ndiour, Parual Datta +3
AI deployed in the real-world should be capable of autonomously adapting to novelties encountered after deployment. Yet, in the field of continual learning, the reliance on novelty…
CONCLAD: COntinuous Novel CLAss Detector
Amanda Rios, Ibrahima Ndiour, Parual Datta +2
In the field of continual learning, relying on so-called oracles for novelty detection is commonplace albeit unrealistic. This paper introduces CONCLAD ("COntinuous Novel CLAss Det…
CUAL: Continual Uncertainty-aware Active Learner
Amanda Rios, Ibrahima Ndiour, Parual Datta +3
AI deployed in many real-world use cases should be capable of adapting to novelties encountered after deployment. Here, we consider a challenging, under-explored and realistic cont…
Rate-Distortion Theory in Coding for Machines and its Application
Alon Harell, Yalda Foroutan, Nilesh Ahuja +6
Recent years have seen a tremendous growth in both the capability and popularity of automatic machine analysis of images and video. As a result, a growing need for efficient compre…
A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computing
Parual Datta, Nilesh Ahuja, V. Srinivasa Somayazulu +1
Split computing has emerged as a recent paradigm for implementation of DNN-based AI workloads, wherein a DNN model is split into two parts, one of which is executed on a mobile/cli…
A Greedy Part Assignment Algorithm for Real-time Multi-person 2D Pose Estimation
Srenivas Varadarajan, Parual Datta, Omesh Tickoo
Human pose-estimation in a multi-person image involves detection of various body parts and grouping them into individual person clusters. While the former task is challenging due t…