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Found 17 papers, 13 papers with code
Code Llama: Open Foundation Models for Code
We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks.
Ranked #13 on
Code Generation
on HumanEval
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding1.
From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion
Recently, such models have been used to synthesize audio waveforms conditioned on highly compressed representations.
Simple and Controllable Music Generation
We tackle the task of conditional music generation.
Ranked #4 on
Text-to-Music Generation
on MusicCaps
Hybrid Transformers for Music Source Separation
While it performs poorly when trained only on MUSDB, we show that it outperforms Hybrid Demucs (trained on the same data) by 0. 45 dB of SDR when using 800 extra training songs.
Ranked #1 on
Music Source Separation
on MUSDB18
(using extra training data)
High Fidelity Neural Audio Compression
We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks.
AudioGen: Textually Guided Audio Generation
Finally, we explore the ability of the proposed method to generate audio continuation conditionally and unconditionally.
Ranked #9 on
Audio Generation
on AudioCaps
Decoding speech perception from non-invasive brain recordings
Overall, this effective decoding of perceived speech from non-invasive recordings delineates a promising path to decode language from brain activity, without putting patients at risk for brain surgery.
Hybrid Spectrogram and Waveform Source Separation
Source separation models either work on the spectrogram or waveform domain.
Ranked #6 on
Music Source Separation
on MUSDB18
Music Demixing Challenge 2021
The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.
Differentiable Model Compression via Pseudo Quantization Noise
DiffQ is differentiable both with respect to the unquantized weights and the number of bits used.
Ranked #28 on
Language Modelling
on WikiText-103
A Simple Convergence Proof of Adam and Adagrad
We provide a simple proof of convergence covering both the Adam and Adagrad adaptive optimization algorithms when applied to smooth (possibly non-convex) objective functions with bounded gradients.
Music Source Separation in the Waveform Domain
Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song.
Ranked #3 on
Multi-task Audio Source Seperation
on MTASS
Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed
We study the problem of source separation for music using deep learning with four known sources: drums, bass, vocals and other accompaniments.
SING: Symbol-to-Instrument Neural Generator
On the generalization task of synthesizing notes for pairs of pitch and instrument not seen during training, SING produces audio with significantly improved perceptual quality compared to a state-of-the-art autoencoder based on WaveNet as measured by a Mean Opinion Score (MOS), and is about 32 times faster for training and 2, 500 times faster for inference.
AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods
We study a new aggregation operator for gradients coming from a mini-batch for stochastic gradient (SG) methods that allows a significant speed-up in the case of sparse optimization problems.
Constant Step Size Least-Mean-Square: Bias-Variance Trade-offs and Optimal Sampling Distributions
Our analysis leads to new insights into stochastic approximation algorithms: (a) it gives a tighter bound on the allowed step-size; (b) the generalization error may be divided into a variance term which is decaying as O(1/n), independently of the step-size $\gamma$, and a bias term that decays as O(1/$\gamma$ 2 n 2); (c) when allowing non-uniform sampling, the choice of a good sampling density depends on whether the variance or bias terms dominate.
