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Home » Archives for Ajay Divakaran
Ajay Divakaran

Ajay Divakaran

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Publications

Human behavior modeling publications July 15, 2022

Towards Understanding Confusion and Affective States Under Communication Failures in Voice-Based Human-Machine Interaction

Ajay Divakaran

We present a series of two studies conducted to understand user’s affective states during voice-based human-machine interactions. Emphasis is placed on the cases of communication errors or failures.

Machine learning publications April 8, 2022

Broadening AI Ethics Narratives: An Indic Arts View

Ajay Divakaran

In this paper, we investigate uncovering the unique socio-cultural perspectives embedded in human-made art, which in turn, can be valuable in expanding the horizon of AI ethics.

Machine learning publications October 25, 2021

Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models

Ajay Divakaran, Yi Yao

Error maps can indicate when a correctly attended region may be processed incorrectly leading to an incorrect answer, and hence, improve users’ understanding of those cases.

Machine learning publications October 22, 2021

Challenges in Procedural Multimodal Machine Comprehension: A Novel Way to Benchmark

Ajay Divakaran

We identify three critical biases stemming from the question-answer generation process and memorization capabilities of large deep models.

ajay divakaran bio pic
Other Innovators September 8, 2021

Ajay Divakaran

Ajay Divakaran

Senior Technical Director, Vision and Learning Laboratory, Center for Vision Technologies

Machine learning publications June 8, 2021

Comprehension Based Question Answering Using Bloom’s Taxonomy

Ajay Divakaran, Sara Rutherford-Quach

Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions.

Machine learning publications April 1, 2021

Modular Adaptation for Cross-Domain Few-Shot Learning

Ajay Divakaran, Yi Yao

While literature has demonstrated great successes via representation learning, in this work, we show that improvement of downstream tasks can also be achieved by appropriate designs of the adaptation process.

Machine learning publications April 1, 2021

Confidence Calibration for Domain Generalization under Covariate Shift

Yi Yao, Ajay Divakaran, Melinda Gervasio

We present novel calibration solutions via domain generalization. Our core idea is to leverage multiple calibration domains to reduce the effective distribution disparity between the target and calibration domains for improved calibration transfer without needing any data from the target domain.

Machine learning publications November 19, 2020

Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning

Yi Yao, Ajay Divakaran

We introduce Hybrid Consistency Training to jointly leverage interpolation consistency, including interpolating hidden features, that imposes linear behavior locally and data augmentation consistency that learns robust embeddings against sample variations.

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