1 citations · 1 across the 6 of their papers we have counts for
6 papers
EgoBabyVLM: Benchmarking Cross-Modal Learning from Naturalistic Egocentric Video Data
Dongyan Lin, Phillip Rust, Angel Villar Corrales +19
Children acquire language grounding with remarkable robustness from limited visuo-linguistic input in ways that surpass today's best large multimodal models. Recent research sugges…
SpidR: Learning Fast and Stable Linguistic Units for Spoken Language Models Without Supervision
Maxime Poli, Mahi Luthra, Youssef Benchekroun +8
The parallel advances in language modeling and speech representation learning have raised the prospect of learning language directly from speech without textual intermediates. This…
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation
Mahi Luthra, Jiayi Shen, Maxime Poli +14
Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compared to the data-hungry self-super…
LongTail-Swap: benchmarking language models' abilities on rare words
Robin Algayres, Charles-Éric Saint-James, Mahi Luthra +6
Children learn to speak with a low amount of data and can be taught new words on a few-shot basis, making them particularly data-efficient learners. The BabyLM challenge aims at ex…
MusicFlow: Cascaded Flow Matching for Text Guided Music Generation
K R Prajwal, Bowen Shi, Matthew Lee +8
We introduce MusicFlow, a cascaded text-to-music generation model based on flow matching. Based on self-supervised representations to bridge between text descriptions and music aud…
On The Open Prompt Challenge In Conditional Audio Generation
Ernie Chang, Sidd Srinivasan, Mahi Luthra +8
Text-to-audio generation (TTA) produces audio from a text description, learning from pairs of audio samples and hand-annotated text. However, commercializing audio generation is ch…