Search Results for author: Zalán Borsos

Found 16 papers, 8 papers with code

MusicRL: Aligning Music Generation to Human Preferences

no code implementations6 Feb 2024 Geoffrey Cideron, Sertan Girgin, Mauro Verzetti, Damien Vincent, Matej Kastelic, Zalán Borsos, Brian McWilliams, Victor Ungureanu, Olivier Bachem, Olivier Pietquin, Matthieu Geist, Léonard Hussenot, Neil Zeghidour, Andrea Agostinelli

MusicRL is a pretrained autoregressive MusicLM (Agostinelli et al., 2023) model of discrete audio tokens finetuned with reinforcement learning to maximise sequence-level rewards.

Music Generation

Disentangling speech from surroundings with neural embeddings

no code implementations29 Mar 2022 Ahmed Omran, Neil Zeghidour, Zalán Borsos, Félix de Chaumont Quitry, Malcolm Slaney, Marco Tagliasacchi

We present a method to separate speech signals from noisy environments in the embedding space of a neural audio codec.

Attribute

SpeechPainter: Text-conditioned Speech Inpainting

no code implementations15 Feb 2022 Zalán Borsos, Matt Sharifi, Marco Tagliasacchi

We propose SpeechPainter, a model for filling in gaps of up to one second in speech samples by leveraging an auxiliary textual input.

Data Summarization via Bilevel Optimization

no code implementations26 Sep 2021 Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause

We show the effectiveness of our framework for a wide range of models in various settings, including training non-convex models online and batch active learning.

Active Learning Bilevel Optimization +2

MicAugment: One-shot Microphone Style Transfer

1 code implementation19 Oct 2020 Zalán Borsos, Yunpeng Li, Beat Gfeller, Marco Tagliasacchi

A crucial aspect for the successful deployment of audio-based models "in-the-wild" is the robustness to the transformations introduced by heterogeneous acquisition conditions.

Data Augmentation Style Transfer

Semi-supervised Batch Active Learning via Bilevel Optimization

1 code implementation19 Oct 2020 Zalán Borsos, Marco Tagliasacchi, Andreas Krause

Active learning is an effective technique for reducing the labeling cost by improving data efficiency.

Active Learning Bilevel Optimization +1

Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents

no code implementations19 Jun 2019 Zalán Borsos, Andrey Khorlin, Andrea Gesmundo

Recent advances in Neural Architecture Search (NAS) have produced state-of-the-art architectures on several tasks.

Neural Architecture Search Transfer Learning

Online Variance Reduction with Mixtures

1 code implementation29 Mar 2019 Zalán Borsos, Sebastian Curi, Kfir. Y. Levy, Andreas Krause

Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction.

Stochastic Optimization

Inference of the three-dimensional chromatin structure and its temporal behavior

no code implementations22 Nov 2018 Bianca-Cristina Cristescu, Zalán Borsos, John Lygeros, María Rodríguez Martínez, Maria Anna Rapsomaniki

In this work, we explore the idea of manifold learning for the 3D chromatin structure inference and present a novel method, REcurrent Autoencoders for CHromatin 3D structure prediction (REACH-3D).

Online Variance Reduction for Stochastic Optimization

2 code implementations13 Feb 2018 Zalán Borsos, Andreas Krause, Kfir. Y. Levy

Modern stochastic optimization methods often rely on uniform sampling which is agnostic to the underlying characteristics of the data.

Stochastic Optimization

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