Search Results for author:
Found 12 papers, 9 papers with code
Combining Evidence Across Filtrations
An e-process quantifies the accumulated evidence against a composite null hypothesis over a sequence of outcomes.
The Linear Representation Hypothesis and the Geometry of Large Language Models
Using this causal inner product, we show how to unify all notions of linear representation.
Counterfactually Comparing Abstaining Classifiers
When evaluating black-box abstaining classifier(s), however, we lack a principled approach that accounts for what the classifier would have predicted on its abstentions.
Comparing Sequential Forecasters
Consider two forecasters, each making a single prediction for a sequence of events over time.
An Empirical Study of Invariant Risk Minimization
Invariant risk minimization (IRM) (Arjovsky et al., 2019) is a recently proposed framework designed for learning predictors that are invariant to spurious correlations across different training environments.
KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding
Although several benchmark datasets for those tasks have been released in English and a few other languages, there are no publicly available NLI or STS datasets in the Korean language.
Natural Language Inference
Natural Language Understanding
+2
Jejueo Datasets for Machine Translation and Speech Synthesis
Jejueo was classified as critically endangered by UNESCO in 2010.
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs
We wrap our dataset and model in an easy-to-use Python library, which supports downloading and retrieving top-k word translations in any of the supported language pairs as well as computing top-k word translations for custom parallel corpora.
A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning
The resulting parallel corpora are subsequently used to pre-train Transformer models.
Ranked #14 on
Grammatical Error Correction
on BEA-2019 (test)
Predicting drug-target interaction using 3D structure-embedded graph representations from graph neural networks
Accurate prediction of drug-target interaction (DTI) is essential for in silico drug design.
Discovery of Natural Language Concepts in Individual Units of CNNs
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret.
Local White Matter Architecture Defines Functional Brain Dynamics
If communication efficiency is fundamentally constrained by the integrity along the entire length of a white matter bundle, then variability in the functional dynamics of brain networks should be associated with variability in the local connectome.
