Posts in category: Apple

This paper was accepted at the workshop on Regulatable ML at NeurIPS 2023.
Conformal Prediction (CP) is a method of estimating risk or uncertainty when using Machine Learning to help abide by common Risk Management regulations often seen in fields like healthcare and finance. CP for regression can be challenging, especially when the output distribution is heteroscedastic, multimodal, or skewed. Some of the issues can be addressed by estimating a distribution over the output, but in reality, such approaches can be sensitive to estimation error and yield unstable intervals. Here, we circumvent…Apple Machine Learning Research

This paper was accepted at the workshop Machine Learning and the Physical Sciences at NeurIPS 2023.
Over the past decades, hemodynamics simulators have steadily evolved and have become tools of choice for studying cardiovascular systems in-silico. This comes naturally at the cost of increasing complexity since state-of-the-art models are non-linear partial differential equations depending on many parameters. While such tools are routinely used to simulate hemodynamics given physiological parameters, solving the related inverse problems — mapping waveforms to physiological parameters — has…Apple Machine Learning Research

Super-resolution (SR) techniques have recently been proposed to upscale the outputs of neural radiance fields (NeRF) and generate high-quality images with enhanced inference speeds. However, existing NeRF+SR methods increase training overhead by using extra input features, loss functions, and/or expensive training procedures such as knowledge distillation. In this paper, we aim to leverage SR for efficiency gains without costly training or architectural changes. Specifically, we build a simple NeRF+SR pipeline that directly combines existing modules, and we propose a lightweight augmentation…Apple Machine Learning Research

Two salient limitations have long hindered the relevance of optimal transport methods to machine learning. First, the computational cost of standard sample-based solvers (when used on batches of samples) is prohibitive. Second, the mass conservation constraint makes OT solvers too rigid in practice: because they must match textit{all} points from both measures, their output can be heavily influenced by outliers. A flurry of recent works has addressed these computational and modeling limitations. Still it has resulted in two separate strains of methods: While the computational outlook was…Apple Machine Learning Research

Tracking biosignals is crucial for monitoring wellness and preempting the development of severe medical conditions. Today, wearable devices can conveniently record various biosignals, creating the opportunity to monitor health status without disruption to one’s daily routine. Despite the widespread use of wearable devices and existing digital biomarkers, the absence of curated data with annotated medical labels hinders the development of new biomarkers to measure common health conditions. In fact, medical datasets are usually small in comparison to other domains, which is an obstacle for…Apple Machine Learning Research

Recent advances in deep learning and automatic speech recognition have boosted the accuracy of end-to-end speech recognition to a new level. However, recognition of personal content such as contact names remains a challenge. In this work, we present a personalization solution for an end-to-end system based on connectionist temporal classification. Our solution uses class-based language model, in which a general language model provides modeling of the context for named entity classes, and personal named entities are compiled in a separate finite state transducer. We further introduce a…Apple Machine Learning Research

Apple Machine Learning Research

*= Equal Contributors
Federated Learning (FL) is a technique to train models using data distributed across devices. Differential Privacy (DP) provides a formal privacy guarantee for sensitive data. Our goal is to train a large neural network language model (NNLM) on compute-constrained devices while preserving privacy using FL and DP. However, the DP-noise introduced to the model increases as the model size grows, which often prevents convergence. We propose Partial Embedding Updates (PEU), a novel technique to decrease noise by decreasing payload size. Furthermore, we adopt Low Rank…Apple Machine Learning Research

With the help of creative prompt engineering and in-context learning, large language models (LLMs) are known to generalize well on a variety of text-based natural language processing (NLP) tasks. However, for performing well on spoken language understanding (SLU) tasks, LLMs either need to be equipped with in-built speech modality or they need to rely on speech-to-text conversion from an off-the-shelf automation speech recognition (ASR) system. In this work, we focus on the latter setup where the accuracy of LLM on SLU tasks is constrained by the accuracy of a frozen ASR system on the given…Apple Machine Learning Research

*=Equal Contributors
Multimodal datasets are a critical component in recent breakthroughs such as Stable Diffusion and GPT-4, yet their design does not receive the same research attention as model architectures or training algorithms. To address this shortcoming in the ML ecosystem, we introduce DataComp, a testbed for dataset experiments centered around a new candidate pool of 12.8 billion image-text pairs from Common Crawl. Participants in our benchmark design new filtering techniques or curate new data sources and then evaluate their new dataset by running our standardized CLIP training…Apple Machine Learning Research