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Found 18 papers, 8 papers with code
Imputing Out-of-Vocabulary Embeddings with LOVE Makes LanguageModels Robust with Little Cost
State-of-the-art NLP systems represent inputs with word embeddings, but these are brittle when faced with Out-of-Vocabulary (OOV) words. To address this issue, we follow the principle of mimick-like models to generate vectors for unseen words, by learning the behavior of pre-trained embeddings using only the surface form of words. We present a simple contrastive learning framework, LOVE, which extends the word representation of an existing pre-trained language model (such as BERT) and makes it robust to OOV with few additional parameters. Extensive evaluations demonstrate that our lightweight model achieves similar or even better performances than prior competitors, both on original datasets and on corrupted variants.
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
We present an in-depth analysis of data discovery in data lakes, focusing on table augmentation for given machine learning tasks.
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities.
Why do tree-based models still outperform deep learning on typical tabular data?
While deep learning has enabled tremendous progress on text and image datasets, its superiority on tabular data is not clear.
What’s a good imputation to predict with missing values?
In fact, we show that on perfectly imputed data the best regression function will generally be discontinuous, which makes it hard to learn.
AI as statistical methods for imperfect theories
Science has progressed by reasoning on what models could not predict because they were missing important ingredients.
NeuMiss networks: differentiable programming for supervised learning with missing values.
We provide an upper bound on the Bayes risk of NeuMiss networks, and show that they have good predictive accuracy with both a number of parameters and a computational complexity independent of the number of missing data patterns.
Comparing distributions: \ell_1 geometry improves kernel two-sample testing
Here, we show that $L^p$ distances (with $p\geq 1$) between these distribution representatives give metrics on the space of distributions that are well-behaved to detect differences between distributions as they metrize the weak convergence.
Manifold-regression to predict from MEG/EEG brain signals without source modeling
We show that Wasserstein and geometric distances allow perfect out-of-sample prediction on the generative models.
Computational and informatics advances for reproducible data analysis in neuroimaging
We argue that openness and transparency are critical for reproducibility, and we outline an ecosystem for open and transparent science that has emerged within the human neuroimaging community.
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data
In many applications where collecting data is expensive, for example neuroscience or medical imaging, the sample size is typically small compared to the feature dimension.
Stochastic Subsampling for Factorizing Huge Matrices
We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns.
Learning brain regions via large-scale online structured sparse dictionary learning
We propose a multivariate online dictionary-learning method for obtaining decompositions of brain images with structured and sparse components (aka atoms).
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data
Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing datasets.
Fast clustering for scalable statistical analysis on structured images
The use of brain images as markers for diseases or behavioral differences is challenged by the small effects size and the ensuing lack of power, an issue that has incited researchers to rely more systematically on large cohorts.
Region segmentation for sparse decompositions: better brain parcellations from rest fMRI
Functional Magnetic Resonance Images acquired during resting-state provide information about the functional organization of the brain through measuring correlations between brain areas.
Mapping paradigm ontologies to and from the brain
Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies.
Brain covariance selection: better individual functional connectivity models using population prior
We describe subject-level brain functional connectivity structure as a multivariate Gaussian process and introduce a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population.
