13 citations · 16 across the 6 of their papers we have counts for
6 papers
Putting People in Their Place: Affordance-Aware Human Insertion into Scenes
Sumith Kulal, Tim Brooks, Alex Aiken +5
We study the problem of inferring scene affordances by presenting a method for realistically inserting people into scenes. Given a scene image with a marked region and an image of…
Training with Mixed-Precision Floating-Point Assignments
Wonyeol Lee, Rahul Sharma, Alex Aiken
When training deep neural networks, keeping all tensors in high precision (e.g., 32-bit or even 16-bit floats) is often wasteful. However, keeping all tensors in low precision (e.g…
On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters
Wonyeol Lee, Sejun Park, Alex Aiken
Recent work has shown that forward- and reverse- mode automatic differentiation (AD) over the reals is almost always correct in a mathematically precise sense. However, actual prog…
Programmatic Concept Learning for Human Motion Description and Synthesis
Sumith Kulal, Jiayuan Mao, Alex Aiken +1
We introduce Programmatic Motion Concepts, a hierarchical motion representation for human actions that captures both low-level motion and high-level description as motion concepts.…
Quartz: Superoptimization of Quantum Circuits (Extended Version)
Mingkuan Xu, Zikun Li, Oded Padon +8
Existing quantum compilers optimize quantum circuits by applying circuit transformations designed by experts. This approach requires significant manual effort to design and impleme…
Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identities
Jessica Pointing, Oded Padon, Zhihao Jia +4
Existing quantum compilers focus on mapping a logical quantum circuit to a quantum device and its native quantum gates. Only simple circuit identities are used to optimize the quan…