1.1k citations · 4.4k across the 113 of their papers we have counts for
9 papers · 1 filter
Contrastive Distillation Is a Sample-Efficient Self-Supervised Loss Policy for Transfer Learning
Chris Lengerich, Gabriel Synnaeve, Amy Zhang +4
Traditional approaches to RL have focused on learning decision policies directly from episodic decisions, while slowly and implicitly learning the semantics of compositional repres…
Audio Language Modeling using Perceptually-Guided Discrete Representations
Felix Kreuk, Yaniv Taigman, Adam Polyak +4
In this work, we study the task of Audio Language Modeling, in which we aim at learning probabilistic models for audio that can be used for generation and completion. We use a stat…
High Fidelity Neural Audio Compression
Alexandre Défossez, Jade Copet, Gabriel Synnaeve +1
We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent spac…
Leveraging Demonstrations with Latent Space Priors
Jonas Gehring, Deepak Gopinath, Jungdam Won +3
Demonstrations provide insight into relevant state or action space regions, bearing great potential to boost the efficiency and practicality of reinforcement learning agents. In th…
Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling
Itai Gat, Felix Kreuk, Tu Anh Nguyen +5
Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual supervision. Such speech LM…
AudioGen: Textually Guided Audio Generation
Felix Kreuk, Gabriel Synnaeve, Adam Polyak +6
We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates a…
Code Translation with Compiler Representations
Marc Szafraniec, Baptiste Roziere, Hugh Leather +3
In this paper, we leverage low-level compiler intermediate representations (IR) to improve code translation. Traditional transpilers rely on syntactic information and handcrafted r…
Star Temporal Classification: Sequence Classification with Partially Labeled Data
Vineel Pratap, Awni Hannun, Gabriel Synnaeve +1
We develop an algorithm which can learn from partially labeled and unsegmented sequential data. Most sequential loss functions, such as Connectionist Temporal Classification (CTC),…
Flashlight: Enabling Innovation in Tools for Machine Learning
Jacob Kahn, Vineel Pratap, Tatiana Likhomanenko +11
As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become chall…