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Andy Ly

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    Noise2Music: Text-conditioned Music Generation with Diffusion Models
    Qingqing Huang
    Daniel S. Park
    Tao Wang
    Nanxin Chen
    Zhengdong Zhang
    Zhishuai Zhang
    Jiahui Yu
    Christian Frank
    William Chan
    Wei Han
    Preview abstract We introduce Noise2Music, where a series of diffusion models are trained to generate high-quality 30-second music clips from text prompts. Two types of diffusion models, a generator model, which generates an intermediate representation conditioned on text, and a cascader model, which generates high-fidelity audio conditioned on the intermediate representation and possibly the text, are trained and utilized in succession to generate high-fidelity music. We explore two options for the intermediate representation, one in which it is a spectrogram and the other in which it is audio with lower fidelity. We find that the generated audio is able to faithfully reflect key elements of the text prompt such as genre, mood, tempo and instruments. Language models play a key role in this story---they are used to generate paired text for the audio of the training set and to extract embeddings of the text prompts ingested by the diffusion models. View details
    Learned Indexes for a Google-scale Disk-based Database
    Hussam Abu-Libdeh
    Alex Beutel
    Lyric Pankaj Doshi
    Tim Klas Kraska
    Chris Olston
    Preview abstract There is great excitement about learned index structures, but understandable skepticism about the practicality of a new method uprooting decades of research on B-Trees. In this paper, we work to remove some of that uncertainty by demonstrating how a learned index can be integrated in a distributed, disk-based database system: Google’s Bigtable. We detail several design decisions we made to integrate learned indexes in Bigtable. Our results show that integrating learned index significantly improves the end-to-end read latency and throughput for Bigtable. View details
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