9 citations · 18 across the 10 of their papers we have counts for
10 papers
AI, Take the Wheel: What Drives Delegation and Trust in Human-Computer Cooperative Question Answering?
Maharshi Gor, Yoo Yeon Sung, Yu Hou +4
AI systems are fallible, and humans can make mistakes in deciding whether to trust AI over their own judgment. Thus, improving human-AI collaboration requires understanding when, w…
Do great minds think alike? Investigating Human-AI Complementarity in Question Answering with CAIMIRA
Maharshi Gor, Hal Daumé, Tianyi Zhou +1
Recent advancements of large language models (LLMs) have led to claims of AI surpassing humans in natural language processing (NLP) tasks such as textual understanding and reasonin…
Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness
Yoo Yeon Sung, Maharshi Gor, Eve Fleisig +2
Adversarial datasets should validate AI robustness by providing samples on which humans perform well, but models do not. However, as models evolve, datasets can become obsolete. Me…
MetaDIP: Accelerating Deep Image Prior with Meta Learning
Kevin Zhang, Mingyang Xie, Maharshi Gor +3
Deep image prior (DIP) is a recently proposed technique for solving imaging inverse problems by fitting the reconstructed images to the output of an untrained convolutional neural…
Investigating Information Inconsistency in Multilingual Open-Domain Question Answering
Shramay Palta, Haozhe An, Yifan Yang +2
Retrieval based open-domain QA systems use retrieved documents and answer-span selection over retrieved documents to find best-answer candidates. We hypothesize that multilingual Q…
MATE: Multi-view Attention for Table Transformer Efficiency
Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller +1
This work presents a sparse-attention Transformer architecture for modeling documents that contain large tables. Tables are ubiquitous on the web, and are rich in information. Howe…
Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy
Maharshi Gor, Kellie Webster, Jordan Boyd-Graber
The goal of question answering (QA) is to answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, mod…
GAN-Tree: An Incrementally Learned Hierarchical Generative Framework for Multi-Modal Data Distributions
Jogendra Nath Kundu, Maharshi Gor, Dakshit Agrawal +1
Despite the remarkable success of generative adversarial networks, their performance seems less impressive for diverse training sets, requiring learning of discontinuous mapping fu…
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
Jogendra Nath Kundu, Maharshi Gor, Phani Krishna Uppala +1
An unsupervised human action modeling framework can provide useful pose-sequence representation, which can be utilized in a variety of pose analysis applications. In this work we p…
BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
Jogendra Nath Kundu, Maharshi Gor, R. Venkatesh Babu
Human motion prediction model has applications in various fields of computer vision. Without taking into account the inherent stochasticity in the prediction of future pose dynamic…