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Found 8 papers, 4 papers with code
uaMix-MAE: Efficient Tuning of Pretrained Audio Transformers with Unsupervised Audio Mixtures
Masked Autoencoders (MAEs) learn rich low-level representations from unlabeled data but require substantial labeled data to effectively adapt to downstream tasks.
Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond $1+α$ Moments
The state of the art results for mean estimation in $\mathbb{R}$ are 1) the optimal sub-Gaussian mean estimator by [LV22], with the tight sub-Gaussian constant for all distributions with finite but unknown variance, and 2) the analysis of the median-of-means algorithm by [BCL13] and a lower bound by [DLLO16], characterizing the big-O optimal errors for distributions for which only a $1+\alpha$ moment exists for $\alpha \in (0, 1)$.
Accelerating Diffusion-Based Text-to-Audio Generation with Consistency Distillation
Diffusion models power a vast majority of text-to-audio (TTA) generation methods.
Ranked #10 on
Audio Generation
on AudioCaps
A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks
Persistence diagrams (PDs), often characterized as sets of death and birth of homology class, have been known for providing a topological representation of a graph structure, which is often useful in machine learning tasks.
A Study on Self-Supervised Object Detection Pretraining
In this work, we study different approaches to self-supervised pretraining of object detection models.
Training Robust Zero-Shot Voice Conversion Models with Self-supervised Features
Unsupervised Zero-Shot Voice Conversion (VC) aims to modify the speaker characteristic of an utterance to match an unseen target speaker without relying on parallel training data.
Revealing and Protecting Labels in Distributed Training
Prior works have demonstrated that labels can be revealed analytically from the last layer of certain models (e. g., ResNet), or they can be reconstructed jointly with model inputs by using Gradients Matching [Zhu et al'19] with additional knowledge about the current state of the model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It
We show that a dropout rate of 0. 2 can reduce the speaker identity accuracy to 0% top-1 (0. 5% top-5).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
