2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
Learning to Learn: How to Continuously Teach Humans and Machines
Parantak Singh, You Li, Ankur Sikarwar +7
Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These…
Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap
Mengmi Zhang, Elisa Pavarino, Xiao Liu +20
As AI becomes increasingly embedded in daily life, ascertaining whether an agent is human is critical. We systematically benchmark AI's ability to imitate humans in three language…
Learning to See the Elephant in the Room: Self-Supervised Context Reasoning in Humans and AI
Xiao Liu, Soumick Sarker, Ankur Sikarwar +4
Humans rarely perceive objects in isolation but interpret scenes through relationships among co-occurring elements. How such contextual knowledge is acquired without explicit super…
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks
Ankur Sikarwar, Arkil Patel, Navin Goyal
Humans can reason compositionally whilst grounding language utterances to the real world. Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whe…
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering
Ankur Sikarwar, Gabriel Kreiman
In recent years, multi-modal transformers have shown significant progress in Vision-Language tasks, such as Visual Question Answering (VQA), outperforming previous architectures by…