1 citations · 1 across the 3 of their papers we have counts for
4 papers · 1 filter
MI-MIDI 2: From Observation to Control with Sparse Autoencoders in Text-to-MIDI Generation
Jakub Poćwiardowski, Mateusz Modrzejewski
Sparse autoencoders (SAEs) can expose features of a generative model without predefined labels, but in language models the evidence used to interpret these features has proven an u…
DINO-A: Adapting Self-Distillation Vision Transformers to General Audio Representation Learning
Tomasz Radzikowski, Mateusz Modrzejewski, Przemysław Rokita
We present DINO-A, an adaptation of self-distillation from vision to general audio representation learning. While DINO has become a canonical method in self-supervised vision and p…
Prevailing Research Areas for Music AI in the Era of Foundation Models
Megan Wei, Mateusz Modrzejewski, Aswin Sivaraman +1
Parallel to rapid advancements in foundation model research, the past few years have witnessed a surge in music AI applications. As AI-generated and AI-augmented music become incre…
The Biased Journey of MSD_AUDIO.ZIP
Haven Kim, Keunwoo Choi, Mateusz Modrzejewski +1
The equitable distribution of academic data is crucial for ensuring equal research opportunities, and ultimately further progress. Yet, due to the complexity of using the API for a…