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Found 11 papers, 8 papers with code
The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features
Foundation models have become popular in forecasting due to their ability to make accurate predictions, even with minimal fine-tuning on specific datasets.
Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data
To model shifts of these causal models, we use a secondary SCM, that specifies changes in the primary model parameters.
Bayes' Power for Explaining In-Context Learning Generalizations
In this paper, we argue that a more useful interpretation of neural network behavior in this era is as an approximation of the true posterior, as defined by the data-generating process.
PFNs4BO: In-Context Learning for Bayesian Optimization
In this paper, we use Prior-data Fitted Networks (PFNs) as a flexible surrogate for Bayesian Optimization (BO).
Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering
Specifically, we introduce Context-Aware Automated Feature Engineering (CAAFE), a feature engineering method for tabular datasets that utilizes an LLM to iteratively generate additional semantically meaningful features for tabular datasets based on the description of the dataset.
On the Importance of Hyperparameters and Data Augmentation for Self-Supervised Learning
Self-Supervised Learning (SSL) has become a very active area of Deep Learning research where it is heavily used as a pre-training method for classification and other tasks.
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods.
Transformers Can Do Bayesian Inference
Our method restates the objective of posterior approximation as a supervised classification problem with a set-valued input: it repeatedly draws a task (or function) from the prior, draws a set of data points and their labels from it, masks one of the labels and learns to make probabilistic predictions for it based on the set-valued input of the rest of the data points.
In-Loop Meta-Learning with Gradient-Alignment Reward
To do this, we optimize the loss of the next training step.
Byte-Pair Encoding for Text-to-SQL Generation
In this work, we adapt BPE for text-to-SQL generation.
Robust techniques for polarization and detection of nuclear spin ensembles
Here we demonstrate the polarization and read out of a nuclear spin bath consisting of $^{13}$C nuclear spins in diamond by using a single nitrogen-vacancy (NV) center.
Quantum Physics
