7 citations · 16 across the 6 of their papers we have counts for
8 papers
Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation
Yixin Wan, Arjun Subramonian, Anaelia Ovalle +6
The recent advancement of large and powerful models with Text-to-Image (T2I) generation abilities -- such as OpenAI's DALLE-3 and Google's Gemini -- enables users to generate high-…
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?
Hritik Bansal, Da Yin, Masoud Monajatipoor +1
Text-to-image generative models have achieved unprecedented success in generating high-quality images based on natural language descriptions. However, it is shown that these models…
Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting Models
Hritik Bansal, Gantavya Bhatt, Pankaj Malhotra +1
Systematic generalization aims to evaluate reasoning about novel combinations from known components, an intrinsic property of human cognition. In this work, we study systematic gen…
Can RNNs trained on harder subject-verb agreement instances still perform well on easier ones?
Hritik Bansal, Gantavya Bhatt, Sumeet Agarwal
Previous work suggests that RNNs trained on natural language corpora can capture number agreement well for simple sentences but perform less well when sentences contain agreement a…
Resting state-fMRI approach towards understanding impairments in mTLE
Nishad Singhi, Hritik Bansal
Mesial temporal lobe epilepsy (mTLE) is the most common form of epilepsy. While it is characterized by an epileptogenic focus in the mesial temporal lobe, it is increasingly unders…
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?
Gantavya Bhatt, Hritik Bansal, Rishubh Singh +1
Long short-term memory (LSTM) networks and their variants are capable of encapsulating long-range dependencies, which is evident from their performance on a variety of linguistic t…