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Found 10 papers, 3 papers with code
MAD Speech: Measures of Acoustic Diversity of Speech
Using these datasets, we demonstrate that our proposed metrics achieve a stronger agreement with the ground-truth diversity than baselines.
MusicRL: Aligning Music Generation to Human Preferences
MusicRL is a pretrained autoregressive MusicLM (Agostinelli et al., 2023) model of discrete audio tokens finetuned with reinforcement learning to maximise sequence-level rewards.
Brain2Music: Reconstructing Music from Human Brain Activity
The process of reconstructing experiences from human brain activity offers a unique lens into how the brain interprets and represents the world.
SingSong: Generating musical accompaniments from singing
We present SingSong, a system that generates instrumental music to accompany input vocals, potentially offering musicians and non-musicians alike an intuitive new way to create music featuring their own voice.
MusicLM: Generating Music From Text
We introduce MusicLM, a model generating high-fidelity music from text descriptions such as "a calming violin melody backed by a distorted guitar riff".
Ranked #8 on
Text-to-Music Generation
on MusicCaps
How stable are Transferability Metrics evaluations?
Transferability metrics is a maturing field with increasing interest, which aims at providing heuristics for selecting the most suitable source models to transfer to a given target dataset, without fine-tuning them all.
Transferability Metrics for Selecting Source Model Ensembles
We address the problem of ensemble selection in transfer learning: Given a large pool of source models we want to select an ensemble of models which, after fine-tuning on the target training set, yields the best performance on the target test set.
Transferability Estimation using Bhattacharyya Class Separability
Then, we estimate their pairwise class separability using the Bhattacharyya coefficient, yielding a simple and effective measure of how well the source model transfers to the target task.
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches.
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control
Deep networks have enabled reinforcement learning to scale to more complex and challenging domains, but these methods typically require large quantities of training data.
