Customer-obsessed science

Evaluating open-domain conversation models has been an open challenge due to the open-ended nature of conversations. In addition to static evaluations, recent work has started to explore a variety of per-turn and per-dialog interactive evaluation mechanisms and provide advice on the best setup. In this work, we adopt the interactive evaluation framework and further apply to multiple models with a focus

Computational notebooks are widely used for machine learning (ML). However, notebooks raise new correctness concerns beyond those found in traditional programming environments. ML library APIs are easy to misuse, and the notebook execution model raises entirely new problems concerning reproducibility. It is common to use static analyses to detect bugs and enforce best practices in software applications.

Synthesizing high-fidelity talking head videos of an arbitrary identity, lip-synced to a target speech segment, is a challenging problem. Recent GAN-based methods succeed by training a model on a large amount of videos, allowing the generator to learn a variety of audio-lip representations. However, they are unable to handle head pose changes. On the other hand, Neural Radiance Fields (NeRFs) model the

Existing general purpose frameworks for gigantic model training, i.e., dense models with billions of parameters, cannot scale efficiently on cloud environment with various networking conditions due to large communication overheads. In this paper, we propose MiCS, which Minimizes the Communication Scale to bring down communication overhead. Specifically, by decreasing the number of participants in a communication

Biodiversity loss and ecosystem degradation are global challenges demanding creative and scalable solutions. Recent increases in data collection coupled with machine learning have the potential to expand landscape monitoring capabilities. We present a computer vision solution to the problem of identifying invasive species. The Australian Tree Fern (Cyathea cooperi) is a fast growing species that is displacing