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Found 7 papers, 5 papers with code
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).
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
