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Found 7 papers, 3 papers with code
FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data Mixtures for Legal Reasoning
In this work, we curate LawInstruct, a large legal instruction dataset, covering 17 jurisdictions, 24 languages and a total of 12M examples.
Translation between Molecules and Natural Language
We present $\textbf{MolT5}$ $-$ a self-supervised learning framework for pretraining models on a vast amount of unlabeled natural language text and molecule strings.
Ranked #4 on
Text-based de novo Molecule Generation
on ChEBI-20
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
Representation learning for improved interpretability and classification accuracy of clinical factors from EEG
Despite extensive standardization, diagnostic interviews for mental health disorders encompass substantial subjective judgment.
Using Deep Networks and Transfer Learning to Address Disinformation
We apply an ensemble pipeline composed of a character-level convolutional neural network (CNN) and a long short-term memory (LSTM) as a general tool for addressing a range of disinformation problems.
Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks
Simulated data containing a set of base classes is first used to learn an initial set of weights.
Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings
This paper describes an abstractive summarization method for tabular data which employs a knowledge base semantic embedding to generate the summary.
