Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences
On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday...
Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models...
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
Inspired by the advancements in foundation models for language-vision modeling, we explore the utilization of transformers and large-scale pretraining on biosignals. In this...
OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into...
A Multi-signal Large Language Model for Device-directed Speech Detection
We present an architecture for device-directed speech detection that treats the task as a text-generation problem. We use a multi-modal fusion approach that...
Towards a World-English Language Model
Neural Network Language Models (NNLMs) of Virtual Assistants (VAs) are generally language-, region-, and in some cases, device-dependent, which increases the effort to...
Streaming Anchor Loss: Augmenting Supervision with Temporal Significance
Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the...
MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training
*Equal Contributors
Contrastive pretraining of image-text foundation models, such as CLIP, demonstrated excellent zero-shot performance and improved robustness on a wide range of downstream...
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2024
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Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization
Existing vision-language models exhibit strong generalization on a variety of visual domains and tasks. However, such models mainly perform zero-shot recognition in a...
