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Found 32 papers, 16 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.
Regression as Classification: Influence of Task Formulation on Neural Network Features
Neural networks can be trained to solve regression problems by using gradient-based methods to minimize the square loss.
Scaling ResNets in the Large-depth Regime
initializations, the only non-trivial dynamics is for $\alpha_L = 1/\sqrt{L}$ (other choices lead either to explosion or to identity mapping).
Reverse-Complement Equivariant Networks for DNA Sequences
As DNA sequencing technologies keep improving in scale and cost, there is a growing need to develop machine learning models to analyze DNA sequences, e. g., to decipher regulatory signals from DNA fragments bound by a particular protein of interest.
Framing RNN as a kernel method: A neural ODE approach
Building on the interpretation of a recurrent neural network (RNN) as a continuous-time neural differential equation, we show, under appropriate conditions, that the solution of a RNN can be viewed as a linear function of a specific feature set of the input sequence, known as the signature.
Efficient and Modular Implicit Differentiation
In this paper, we propose automatic implicit differentiation, an efficient and modular approach for implicit differentiation of optimization problems.
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).
Differentiable Divergences Between Time Series
Soft-DTW addresses these issues, but it is not a positive definite divergence: due to the bias introduced by entropic regularization, it can be negative and it is not minimized when the time series are equal.
On Mixup Regularization
We show that Mixup can be interpreted as standard empirical risk minimization estimator subject to a combination of data transformation and random perturbation of the transformed data.
Ranked #75 on
Image Classification
on ObjectNet
(using extra training data)
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.
MissDeepCausal: Causal Inference from Incomplete Data Using Deep Latent Variable Models
Inferring causal effects of a treatment, intervention or policy from observational data is central to many applications.
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 $m \ne n$.
Differentiable Deep Clustering with Cluster Size Constraints
Clustering is a fundamental unsupervised learning approach.
ASNI: Adaptive Structured Noise Injection for shallow and deep neural networks
Dropout is a regularisation technique in neural network training where unit activations are randomly set to zero with a given probability \emph{independently}.
Deep multi-class learning from label proportions
We propose a learning algorithm capable of learning from label proportions instead of direct data labels.
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.
Relating Leverage Scores and Density using Regularized Christoffel Functions
Statistical leverage scores emerged as a fundamental tool for matrix sketching and column sampling with applications to low rank approximation, regression, random feature learning and quadrature.
DropLasso: A robust variant of Lasso for single cell RNA-seq data
Single-cell RNA sequencing (scRNA-seq) is a fast growing approach to measure the genome-wide transcriptome of many individual cells in parallel, but results in noisy data with many dropout events.
The Weighted Kendall and High-order Kernels for Permutations
We propose new positive definite kernels for permutations.
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Learning sparse linear models with two-way interactions is desirable in many application domains such as genomics.
Supervised Quantile Normalisation
Quantile normalisation is a popular normalisation method for data subject to unwanted variations such as images, speech, or genomic data.
Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data
Structured machine learning methods were recently proposed for taking into account the structure embedded in a hierarchy and using it as additional a priori information, and could therefore allow to improve microbial identification systems.
Large-scale Machine Learning for Metagenomics Sequence Classification
In this work, we investigate the potential of modern, large-scale machine learning implementations for taxonomic affectation of next-generation sequencing reads based on their k-mers profile.
Tight convex relaxations for sparse matrix factorization
Based on a new atomic norm, we propose a new convex formulation for sparse matrix factorization problems in which the number of nonzero elements of the factors is assumed fixed and known.
Consistency of random forests
What has greatly contributed to the popularity of forests is the fact that they can be applied to a wide range of prediction problems and have few parameters to tune.
The group fused Lasso for multiple change-point detection
We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals.
Fast detection of multiple change-points shared by many signals using group LARS
We present a fast algorithm for the detection of multiple change-points when each is frequently shared by members of a set of co-occurring one-dimensional signals.
A bagging SVM to learn from positive and unlabeled examples
We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting.
White Functionals for Anomaly Detection in Dynamical Systems
The candidate functionals are estimated in a subset of a reproducing kernel Hilbert space associated with the set where the process takes values.
Clustered Multi-Task Learning: A Convex Formulation
In multi-task learning several related tasks are considered simultaneously, with the hope that by an appropriate sharing of information across tasks, each task may benefit from the others.
