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Found 17 papers, 8 papers with code
Supervised Quantile Normalization for Low Rank Matrix Factorization
Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts.
AudioLM: a Language Modeling Approach to Audio Generation
We introduce AudioLM, a framework for high-quality audio generation with long-term consistency.
Learning strides in convolutional neural networks
In particular, we show that introducing our layer into a ResNet-18 architecture allows keeping consistent high performance on CIFAR10, CIFAR100 and ImageNet even when training starts from poor random stride configurations.
Optimal Transport Tools (OTT): A JAX Toolbox for all things Wasserstein
Optimal transport tools (OTT-JAX) is a Python toolbox that can solve optimal transport problems between point clouds and histograms.
DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding
Our neural algorithm presents the diarization task as an iterative process: it repeatedly builds a representation for each speaker before predicting the voice activity of each speaker conditioned on the extracted representations.
Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking
Second, we show that inverting permutations is a meaningful pretext task for learning audio representations in an unsupervised fashion.
LEAF: A Learnable Frontend for Audio Classification
In this work we show that we can train a single learnable frontend that outperforms mel-filterbanks on a wide range of audio signals, including speech, music, audio events and animal sounds, providing a general-purpose learned frontend for audio classification.
Shuffle to Learn: Self-supervised learning from permutations via differentiable ranking
In particular, we also improve music understanding by reordering spectrogram patches in the frequency space, as well as video classification by reordering frames along the time axis.
A Universal Learnable Audio Frontend
Mel-filterbanks are fixed, engineered audio features which emulate human perception and have lived through the history of audio understanding up to today.
Learning with Differentiable Pertubed Optimizers
Machine learning pipelines often rely on optimizers procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).
Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting (tests can be mistaken) to decide adaptively (looking at past results) which groups to test next, with the goal to converge to a good detection, as quickly, and with as few tests as possible.
Fast Differentiable Sorting and Ranking
While numerous works have proposed differentiable proxies to sorting and ranking, they do not achieve the $O(n \log n)$ time complexity one would expect from sorting and ranking operations.
Learning with Differentiable Perturbed Optimizers
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e. g., sorting, picking closest neighbors, or shortest paths).
Supervised Quantile Normalization for Low-rank Matrix Approximation
Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts.
Differentiable Ranking and Sorting using Optimal Transport
From this observation, we propose extended rank and sort operators by considering optimal transport (OT) problems (the natural relaxation for assignments) where the auxiliary measure can be any weighted measure supported on $m$ increasing values, where $m \ne n$.
MULEX: Disentangling Exploitation from Exploration in Deep RL
An agent learning through interactions should balance its action selection process between probing the environment to discover new rewards and using the information acquired in the past to adopt useful behaviour.
Differentiable Ranks and Sorting using Optimal Transport
Sorting an array is a fundamental routine in machine learning, one that is used to compute rank-based statistics, cumulative distribution functions (CDFs), quantiles, or to select closest neighbors and labels.
