Posts in category: Apple

Apple researchers are advancing the field of ML through fundamental research that improves the world’s understanding of this technology and helps to redefine what is possible with it. This work may lead to advancements in Apple’s products and services, and the benefits of the research extend beyond the Apple ecosystem as it is shared with the broader research community through publication, open source resources, and engagement at industry and research community events.
Next week, the 38th annual Conference on Neural Information Processing Systems (NeurIPS), will be held in Vancouver, Canada…Apple Machine Learning Research

*Equal Contributors
Motivated by the problem of next word prediction on user devices we introduce and study the problem of personalized frequency histogram estimation in a federated setting. In this problem, over some domain, each user observes a number of samples from a distribution which is specific to that user. The goal is to compute for all users a personalized estimate of the user’s distribution with error measured in KL divergence. We focus on addressing two central challenges: statistical heterogeneity and protection of user privacy. Our approach to the problem relies on discovering…Apple Machine Learning Research

The remarkable advancements in Multimodal Large Language Models (MLLMs) have not rendered them immune to challenges, particularly in the context of handling deceptive information in prompts, thus producing hallucinated responses under such conditions. To quantitatively assess this vulnerability, we present MAD-Bench, a carefully curated benchmark that contains 1000 test samples divided into 5 categories, such as non-existent objects, count of objects, and spatial relationship. We provide a comprehensive analysis of popular MLLMs, ranging from GPT-4v, Reka, Gemini-Pro, to open-sourced models…Apple Machine Learning Research

Multi-modal large language models (MLLMs) have enabled numerous advances in understanding and reasoning in domains like vision, but we have not yet seen this broad success for time-series. Although prior works on time-series MLLMs have shown promising performance in time-series forecasting, very few works show how an LLM could be used for time-series reasoning in natural language. We propose a novel multi-modal time-series LLM approach that learns generalizable information across various domains with powerful zero-shot performance. First, we train a lightweight time-series encoder on top of an…Apple Machine Learning Research

Given a source and a target probability measure supported on Rdmathbb{R}^dRd, the Monge problem aims for the most efficient way to map one distribution to the other.
This efficiency is quantified by defining a cost function between source and target data.
Such a cost is often set by default in the machine learning literature to the squared-Euclidean distance, ℓ22(x,y)=12∥x−y∥22ell^2_2(x,y)=tfrac12|x-y|_2^2ℓ22​(x,y)=21​∥x−y∥22​.
The benefits of using elastic costs, defined through a regularizer τtauτ as c(x,y)=ℓ22(x,y)+τ(x−y)c(x, y)=ell^2_2(x,y)+tau(x-y)c(x,y)=ℓ22​(x,y)+τ(x−y), was…Apple Machine Learning Research

Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However, single-cell sequencing technologies are inherently destructive and can only measure a limited array of data modalities simultaneously. This limitation underscores the need for new methods capable of realigning cells. Optimal transport (OT) has emerged as a potent solution, but traditional discrete solvers are hampered by scalability, privacy, and out-of-sample estimation issues. These challenges have spurred the development of neural…Apple Machine Learning Research

Motivated by the phenomenon of strategic agents gaming a recommendation system to maximize the number of times they are recommended to users, we study a strategic variant of the linear contextual bandit problem, where the arms strategically misreport privately observed contexts to the learner. % under strategic context manipulation. We treat the algorithm design problem as one of emph{mechanism design} under uncertainty and propose the Optimistic Grim Trigger Mechanism (OptGTM) that minimizes regret while simultaneously incentivizing the agents to be approximately truthful. We show that…Apple Machine Learning Research

*Equal Contributors
Data from wearable sensors (e.g., heart rate, step count) can be used to model mood patterns. We characterize feature representations and modeling strategies with multi-modal discrete time series data for mood pattern classification with a large dataset with naturalistic missingness (n=116,819 participants) using 12 wearable data streams, with a focus on capturing periodic trends in data. Considering both performance and robustness, periodicity-based aggregate feature representations with gradient boosting models outperformed other representations and architectures…Apple Machine Learning Research

Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity in the sampled images, particularly when sampling with a high classifier-free guidance weight. To address this issue, we present Kaleido, a novel approach that enhances the diversity of samples by incorporating autoregressive latent priors. Kaleido integrates an autoregressive language model that encodes the original caption and generates latent variables, serving as abstract and intermediary representations for…Apple Machine Learning Research

This paper was accepted at the Ninth Conference on Machine Translation (WMT24) at EMNLP 2024.
The prosody of a spoken utterance, including features like stress, intonation and rhythm, can significantly affect the underlying semantics, and as a consequence can also affect its textual translation. Nevertheless, prosody is rarely studied within the context of speech-to-text translation (S2TT) systems. In particular, end-to-end (E2E) systems have been proposed as well-suited for prosody-aware translation because they have direct access to the speech signal when making translation decisions, but…Apple Machine Learning Research