CATEGORY

Apple

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023

Apple is sponsoring the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), which will take place in person from June 18 to...

Less Is More: A Unified Architecture for Device-Directed Speech Detection with Multiple Invocation Types

Suppressing unintended invocation of the device because of the speech that sounds like wake-word, or accidental button presses, is critical for a good...

Collaborative Machine Learning Model Building with Families Using Co-ML

Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to...

Collaborative Machine Learning Model Building with Families Using Co-ML

Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to...

Efficient Multimodal Neural Networks for Trigger-less Voice Assistants

The adoption of multimodal interactions by Voice Assistants (VAs) is growing rapidly to enhance human-computer interactions. Smartwatches have now incorporated trigger-less methods of...

Fast Class-Agnostic Salient Object Segmentation

In 2022, we launched a new systemwide capability that allows users to automatically and instantly lift the subject from an image or isolate...

Application-Agnostic Language Modeling for On-Device ASR

On-device automatic speech recognition systems face several challenges compared to server-based systems. They have to meet stricter constraints in terms of speed, disk...

Robustness in Multimodal Learning under Train-Test Modality Mismatch

Multimodal learning is defined as learning over multiple heterogeneous input modalities such as video, audio, and text. In this work, we are concerned...

Unconstrained Channel Pruning – Apple Machine Learning Research

Modern neural networks are growing not only in size and complexity but also in inference time. One of the most effective compression techniques...

Growing and Serving Large Open-domain Knowledge Graphs

*= Equal Contributors Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to...

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