Posts in category: Apple

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 learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames. More specifically, our SAL and its focal variations dynamically modulate the frame-wise cross entropy…Apple Machine Learning Research

Neural Network Language Models (NNLMs) of Virtual Assistants (VAs) are generally language-, region-, and in some cases, device-dependent, which increases the effort to scale and maintain them. Combining NNLMs for one or more of the categories could be one way to improve scalability. In this work, we combine regional variants of English by building a “World English” NNLM. We examine three data sampling techniques and we experiment with adding adapter bottlenecks to the existing production NNLMs to model dialect-specific characteristics and investigate different strategies to train adapters. We…Apple Machine Learning Research

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 combines acoustic information from the recorded audio waveform with text and confidence information obtained from an automatic speech recognition system. The audio waveform is represented as a sequence of continuous embeddings by an audio encoder and presented as a prefix token to a pretrained large language model (LLM). We demonstrate that using multi-modal information within LLMs yields equal error rate improvements over text-only and…Apple Machine Learning Research

Keeping large foundation models up to date on latest data is inherently expensive. To avoid the prohibitive costs of constantly retraining, it is imperative to continually train these models. This problem is exacerbated by the lack of any large scale continual learning benchmarks or baselines. We introduce the first set of web-scale Time-Continual (TiC) benchmarks for training vision-language models: TiC-DataComp, TiC-YFCC, and TiC-Redcaps. TiC-DataComp, our largest dataset, contains over 12.7B timestamped image-text pairs spanning 9 years (2014-2022). We first use our benchmarks to curate…Apple Machine Learning Research

Apple Machine Learning Research

Apple Machine Learning Research

Datasets that pair Knowledge Graphs (KG) and text together (KG-T) can be used to train forward and reverse neural models that generate text from KG and vice versa. However models trained on datasets where KG and text pairs are not equivalent can suffer from more hallucination and poorer recall. In this paper, we verify this empirically by generating datasets with different levels of noise and find that noisier datasets do indeed lead to more hallucination. We argue that the ability of forward and reverse models trained on a dataset to cyclically regenerate source KG or text is a proxy for the…Apple Machine Learning Research

Wearable sensors have permeated into people’s lives, ushering impactful applications in interactive systems and activity recognition. However, practitioners face significant obstacles when dealing with sensing heterogeneities, requiring custom models for different platforms. In this paper, we conduct a comprehensive evaluation of the generalizability of motion models across sensor locations. Our analysis highlights this challenge and identifies key on-body locations for building location-invariant models that can be integrated on any device. For this, we introduce the largest multi-location…Apple Machine Learning Research

While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data from these domains. However, target-domain data is usually not readily available in many scenarios. In this paper, we propose a new strategy for adapting ASR models to new target domains without any text or speech from those domains. To accomplish this, we propose a novel data synthesis pipeline that uses a Large Language Model (LLM) to generate a target domain text corpus, and a state-of-the-art controllable speech…Apple Machine Learning Research

This paper was accepted at The 5th AAAI Workshop on Privacy-Preserving Artificial Intelligence.
Personalized recommendations form an important part of today’s internet ecosystem, helping artists and creators to reach interested users, and helping users to discover new and engaging content. However, many users today are skeptical of platforms that personalize recommendations, in part due to historically careless treatment of personal data and data privacy. Now, businesses that rely on personalized recommendations are entering a new paradigm, where many of their systems must be overhauled to be…Apple Machine Learning Research