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Found 10 papers, 6 papers with code
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data.
The MiniPile Challenge for Data-Efficient Language Models
MiniPile is a 6GB subset of the deduplicated 825GB The Pile corpus.
Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases
The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data.
DAG Learning on the Permutahedron
We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data.
Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averaging
Training vision or language models on large datasets can take days, if not weeks.
Causal Machine Learning: A Survey and Open Problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM).
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Graph Self-Supervised Learning (GSSL) paves the way for learning graph embeddings without expert annotation, which is particularly impactful for molecular graphs since the number of possible molecules is enormous and labels are expensive to obtain.
When Do Flat Minima Optimizers Work?
Recently, flat-minima optimizers, which seek to find parameters in low-loss neighborhoods, have been shown to improve a neural network's generalization performance over stochastic and adaptive gradient-based optimizers.
Causal Effect Inference for Structured Treatments
We address the estimation of conditional average treatment effects (CATEs) for structured treatments (e. g., graphs, images, texts).
Probabilistic Active Meta-Learning
However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner?
