Posts in category: Apple

Momentum based optimizers are central to a wide range of machine learning applications. These typically rely on an Exponential Moving Average (EMA) of gradients, which decays exponentially the present contribution of older gradients. This accounts for gradients being local linear approximations which lose their relevance as the iterate moves along the loss landscape. This work questions the use of a single EMA to accumulate past gradients and empirically demonstrates how this choice can be sub-optimal: a single EMA cannot simultaneously give a high weight to the immediate past, and a…Apple Machine Learning Research

Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limitation. In this paper, we introduce Ferret-UI 2, a multimodal large language model (MLLM) designed for universal UI understanding across a wide range of platforms, including iPhone, Android, iPad, Webpage, and AppleTV. Building on the foundation of Ferret-UI, Ferret-UI 2 introduces three key innovations: support for multiple platform types, high-resolution perception through adaptive scaling, and advanced task…Apple Machine Learning Research

Large generative models are becoming increasingly capable and more widely deployed to power production applications, but getting these models to produce exactly what’s desired can still be challenging. Fine-grained control over these models’ outputs is important to meet user expectations and to mitigate potential misuses, ensuring the models’ reliability and safety. To address these issues, Apple machine learning researchers have developed a new technique that is modality-agnostic and provides fine-grained control over the model’s behavior with negligible computational overhead, while…Apple Machine Learning Research

There is a gap between finding a first-order stationary point (FOSP) and a second-order stationary point (SOSP) under differential privacy constraints, and it remains unclear whether privately finding an SOSP is more challenging than finding an FOSP. Specifically, Ganesh et al. (2023) claimed that an αalphaα-SOSP can be found with α=O~(1n1/3+(dnϵ)3/7)alpha=tilde{O}(frac{1}{n^{1/3}}+(frac{sqrt{d}}{nepsilon})^{3/7})α=O~(n1/31​+(nϵd​​)3/7), where nnn is the dataset size, ddd is the dimension, and ϵepsilonϵ is the differential privacy parameter.
However, a recent analysis revealed an issue…Apple Machine Learning Research

We present RelCon, a novel self-supervised Relative Contrastive learning approach for training a motion foundation model from wearable accelerometry sensors. First, a learnable distance measure is trained to capture motif similarity and domain-specific semantic information such as rotation invariance. Then, the learned distance provides a measurement of semantic similarity between a pair of accelerometry time-series, which we use to train our foundation model to model relative relationships across time and across subjects. The foundation model is trained on 1 billion segments from 87,376…Apple Machine Learning Research

Instruction-following is crucial for building AI agents with large language models (LLMs), as these models must adhere strictly to user-provided constraints and guidelines. However, LLMs often fail to follow even simple and clear instructions. To improve instruction-following behavior and prevent undesirable outputs, a deeper understanding of how LLMs’ internal states relate to these outcomes is required. In this work, we investigate whether LLMs encode information in their representations that correlates with instruction-following success—a property we term “knowing internally”. Our analysis…Apple Machine Learning Research

Nearest neighbour search over dense vector collections has important applications in information retrieval, retrieval augmented generation (RAG), and content ranking. Performing efficient search over large vector collections is a well studied problem with many existing approaches and open source implementations. However, most state-of-the-art systems are generally targeted towards scenarios using large servers with an abundance of memory, static vector collections that are not updatable, and nearest neighbour search in isolation of other search criteria. We present Micro Nearest Neighbour…Apple Machine Learning Research

This paper was accepted at the Scalable Continual Learning for Lifelong Foundation Models (SCLLFM) Workshop at NeurIPS 2024.
Large Language Models (LLMs) trained on historical web data inevitably become outdated. We investigate evaluation strategies and update methods for LLMs as new data becomes available. We introduce a web-scale dataset for time-continual pretraining of LLMs derived from 114 dumps of Common Crawl (CC) – orders of magnitude larger than previous continual language modeling benchmarks. We also design time-stratified evaluations across both general CC data and specific domains…Apple Machine Learning Research

Large language models (LLMs) could be valuable personal AI agents across various domains, provided they can precisely follow user instructions. However, recent studies have shown significant limitations in LLMs’ instruction-following capabilities, raising concerns about their reliability in high-stakes applications. Accurately estimating LLMs’ uncertainty in adhering to instructions is critical to mitigating deployment risks. We present, to our knowledge, the first systematic evaluation of uncertainty estimation abilities of LLMs in the context of instruction-following. Our study identifies…Apple Machine Learning Research

Recent advancements in multimodal models highlight the value of rewritten captions for improving performance, yet key challenges remain. Notably, the role of synthetic captions and their interaction with original web-crawled AltTexts in pre-training is still unclear. Additionally, different multimodal foundation models may have distinct preferences for specific caption formats while the efforts of studying the optimal captions for each foundation model remain limited. In this work, we introduce a novel, controllable, and scalable captioning pipeline that generates diverse caption formats…Apple Machine Learning Research