When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks. - Ankur Sikarwar; Arkil Patel; Navin Goyal (EMNLP, 2022)

On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering. - Ankur Sikarwar; Gabriel Kreiman (CoRR, 2022)

Reason from Context with Self-supervised Learning. - Xiao Liu; Ankur Sikarwar; Joo Hwee Lim; Gabriel Kreiman; Zenglin Shi; Mengmi Zhang (CoRR, 2022)

Human or Machine? Turing Tests for Vision and Language. - Mengmi Zhang; Giorgia Dellaferrera; Ankur Sikarwar; Marcelo Armendáriz; Noga Mudrik; Prachi Agrawal; Spandan Madan; Andrei Barbu; Haochen Yang; Tanishq Kumar; Meghna Sadwani; Stella Dellaferrera; et al. (CoRR, 2022)

Learning to Learn: How to Continuously Teach Humans and Machines. - Parantak Singh; You Li; Ankur Sikarwar; Weixian Lei; Daniel Gao; Morgan Bruce Talbot; Ying Sun 001; Mike Zheng Shou; Gabriel Kreiman; Mengmi Zhang (CoRR, 2022)

Learning to Learn: How to Continuously Teach Humans and Machines. - Parantak Singh; You Li; Ankur Sikarwar; Weixian Lei; Difei Gao; Morgan B. Talbot; Ying Sun 001; Mike Zheng Shou; Gabriel Kreiman; Mengmi Zhang (ICCV, 2023)

Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory. - Ankur Sikarwar; Mengmi Zhang (NeurIPS, 2023)

The Promise of RL for Autoregressive Image Editing. - Saba Ahmadi; Rabiul Awal; Ankur Sikarwar; Amirhossein Kazemnejad; Ge Ya Luo; Juan A. Rodríguez; Sai Rajeswar; Siva Reddy; Christopher Pal; Benno Krojer; Aishwarya Agrawal (CoRR, 2025)