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Found 13 papers, 7 papers with code
Exploring Length Generalization in Large Language Models
The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning from datasets where longer problem instances are rare.
Automated Theorem Proving
Out-of-Distribution Generalization
Solving Quantitative Reasoning Problems with Language Models
Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(STEM metric)
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning
Although machine learning models typically experience a drop in performance on out-of-distribution data, accuracies on in- versus out-of-distribution data are widely observed to follow a single linear trend when evaluated across a testbed of models.
Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data.
Understanding the Failure Modes of Out-of-Distribution Generalization
Empirical studies suggest that machine learning models often rely on features, such as the background, that may be spuriously correlated with the label only during training time, resulting in poor accuracy during test-time.
Parameter Estimation using Neural Networks in the Presence of Detector Effects
Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators.
Asymptotics of Wide Convolutional Neural Networks
Wide neural networks have proven to be a rich class of architectures for both theory and practice.
Simulation Assisted Likelihood-free Anomaly Detection
For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands.
OmniFold: A Method to Simultaneously Unfold All Observables
Collider data must be corrected for detector effects ("unfolded") to be compared with many theoretical calculations and measurements from other experiments.
Neural Networks for Full Phase-space Reweighting and Parameter Tuning
We therefore introduce Deep neural networks using Classification for Tuning and Reweighting (DCTR), a neural network-based approach to reweight and fit simulations using all kinematic and flavor information -- the full phase space.
Binary JUNIPR: an interpretable probabilistic model for discrimination
We refer to this refined approach as Binary JUNIPR.
High Energy Physics - Phenomenology
JUNIPR: a Framework for Unsupervised Machine Learning in Particle Physics
As a third application, JUNIPR models can reweight events from one (e. g. simulated) data set to agree with distributions from another (e. g. experimental) data set.
