Posts in category: Apple

Apple Machine Learning Research

This paper was accepted at the Machine Learning for Audio Workshop at NeurIPS 2023.
Over the past several years, the synchronization between audio and visual signals has been leveraged to learn richer audio-visual representations. Aided by the large availability of unlabeled videos, many unsupervised training frameworks have demonstrated impressive results in various downstream audio and video tasks. Recently, Masked Audio-Video Learners (MAViL) has emerged as a state-of-the-art audio-video pre-training framework. MAViL couples contrastive learning with masked autoencoding to jointly…Apple Machine Learning Research

*=Equal Contributors
Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule; for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important machine learning tool is the model EMA, a functional copy of a target model whose parameters move towards those of its target model according to an Exponential Moving Average (EMA) at a rate parameterized by a momentum…Apple Machine Learning Research

Behavioral testing in NLP allows fine-grained evaluation of systems by examining their linguistic capabilities through the analysis of input-output behavior. Unfortunately, existing work on behavioral testing in Machine Translation (MT) is currently restricted to largely handcrafted tests covering a limited range of capabilities and languages. To address this limitation, we propose using Large Language Models (LLMs) to generate a diverse set of source sentences tailored to test the behavior of MT models in a range of situations. We can then verify whether the MT model exhibits the expected…Apple Machine Learning Research

Spotting user-defined flexible keyword in real-time is challenging because
the keyword is represented in text. In this work, we propose a novel architecture
to efficiently detect the flexible keywords based on the following ideas. We contsruct the representative acousting embeding of a keyword using graphene-to-phone conversion. The phone-to-embedding conversion is done by looking up the embedding dictionary which is built by averaging the corresponding embeddings (from audio encoder) of each phone during the training. The key benefit of our approach is that both text embedding and audio…Apple Machine Learning Research

Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant challenges for deployment on resource-constrained devices. Despite recent trends favoring alternative activation functions such as GELU or SiLU, known for increased computation, this study strongly advocates for reinstating ReLU activation in LLMs. We demonstrate that using the ReLU activation function has a negligible impact on convergence and performance while significantly reducing computation and weight transfer…Apple Machine Learning Research

We give the first result for agnostically learning Single-Index Models (SIMs) with arbitrary monotone and Lipschitz activations. All prior work either held only in the realizable setting or required the activation to be known. Moreover, we only require the marginal to have bounded second moments, whereas all prior work required stronger distributional assumptions (such as anticoncentration or boundedness). Our algorithm is based on recent work by [GHK+23] on omniprediction using predictors satisfying calibrated multiaccuracy. Our analysis is simple and relies on the relationship between…Apple Machine Learning Research

Using a vision-inspired keyword spotting framework, we propose an architecture with input-dependent dynamic depth capable of processing streaming audio. Specifically, we extend a Conformer encoder with trainable binary gates that allow to dynamically skip network modules according to the input audio. Our approach improves detection and localization accuracy on continuous speech using Librispeech’s 1,000 most frequent words while maintaining a small memory footprint. The inclusion of gates also allows the average amount of processing without affecting the overall performance to be reduced…Apple Machine Learning Research

Autoregressive models for text sometimes generate repetitive and low-quality output because errors accumulate during the steps of generation. This issue is often attributed to exposure bias – the difference between how a model is trained and how it is used during inference. Denoising diffusion models provide an alternative approach in which a model can revisit and revise its output. However, they can be computationally expensive, and prior efforts on text have led to models that produce less fluent output compared to autoregressive models, especially for longer text and paragraphs. In this…Apple Machine Learning Research

We propose using a Gaussian Mixture Model (GMM) as reverse transition operator (kernel) within the Denoising Diffusion Implicit Models (DDIM) framework, which is one of the most widely used approaches for accelerated sampling from pre-trained Denoising Diffusion Probabilistic Models (DDPM). Specifically we match the first and second order central moments of the DDPM forward marginals by constraining the parameters of the GMM. We see that moment matching is sufficient to obtain samples with equal or better quality than the original DDIM with Gaussian kernels. We provide experimental results…Apple Machine Learning Research