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Found 25 papers, 13 papers with code
Benchmarking Multimodal AutoML for Tabular Data with Text Fields
We consider the use of automated supervised learning systems for data tables that not only contain numeric/categorical columns, but one or more text fields as well.
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features
For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.
Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features
Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e. g., text-based, categorical, continuous, etc.).
Distiller: A Systematic Study of Model Distillation Methods in Natural Language Processing
We aim to identify how different components in the KD pipeline affect the resulting performance and how much the optimal KD pipeline varies across different datasets/tasks, such as the data augmentation policy, the loss function, and the intermediate representation for transferring the knowledge between teacher and student.
Deep Learning for Functional Data Analysis with Adaptive Basis Layers
Despite their widespread success, the application of deep neural networks to functional data remains scarce today.
Multimodal AutoML on Structured Tables with Text Fields
We design automated supervised learning systems for data tables that not only contain numeric/categorical columns, but text fields as well.
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Errors in test sets are numerous and widespread: we estimate an average of at least 3. 3% errors across the 10 datasets, where for example label errors comprise at least 6% of the ImageNet validation set.
Deep Quantile Aggregation
Conditional quantile estimation is a key statistical learning challenge motivated by the need to quantify uncertainty in predictions or to model a diverse population without being overly reductive.
Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforcement Learning (RL) algorithms have limited applicability in real-world settings, which can be too expensive to allow exploration.
TraDE: A Simple Self-Attention-Based Density Estimator
We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data.
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles by stacking, bagging, and boosting many individual models like trees, deep networks, and nearest neighbor estimators.
ResNeSt: Split-Attention Networks
It is well known that featuremap attention and multi-path representation are important for visual recognition.
Ranked #5 on
Instance Segmentation
on COCO test-dev
(APS metric)
TraDE: Transformers for Density Estimation
We present TraDE, a self-attention-based architecture for auto-regressive density estimation with continuous and discrete valued data.
Overinterpretation reveals image classification model pathologies
Here, we demonstrate that neural networks trained on CIFAR-10 and ImageNet suffer from overinterpretation, and we find models on CIFAR-10 make confident predictions even when 95% of input images are masked and humans cannot discern salient features in the remaining pixel-subsets.
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file.
Denoising Improves Latent Space Geometry in Text Autoencoders
Neural language models have recently shown impressive gains in unconditional text generation, but controllable generation and manipulation of text remain challenging.
Recognizing Variables from their Data via Deep Embeddings of Distributions
A key obstacle in automated analytics and meta-learning is the inability to recognize when different datasets contain measurements of the same variable.
Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensembles
The inaccuracy of neural network models on inputs that do not stem from the training data distribution is both problematic and at times unrecognized.
Educating Text Autoencoders: Latent Representation Guidance via Denoising
We prove that this simple modification guides the latent space geometry of the resulting model by encouraging the encoder to map similar texts to similar latent representations.
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation
Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content.
What made you do this? Understanding black-box decisions with sufficient input subsets
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model.
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing
We consider dynamic pricing with many products under an evolving but low-dimensional demand model.
Sequence to Better Sequence: Continuous Revision of Combinatorial Structures
Under this model, gradient methods can be used to efficiently optimize the continuous latent factors with respect to inferred outcomes.
Learning Optimal Interventions
Assuming the underlying relationship remains invariant under intervention, we develop efficient algorithms to identify the optimal intervention policy from limited data and provide theoretical guarantees for our approach in a Gaussian Process setting.
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions
We introduce principal differences analysis (PDA) for analyzing differences between high-dimensional distributions.
