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Found 10 papers, 1 papers with code
MolMole: Molecule Mining from Scientific Literature
To address this, we introduce MolMole, a vision-based deep learning framework that unifies molecule detection, reaction diagram parsing, and optical chemical structure recognition (OCSR) into a single pipeline for automating the extraction of chemical data directly from page-level documents.
ImagePiece: Content-aware Re-tokenization for Efficient Image Recognition
Vision Transformers (ViTs) have achieved remarkable success in various computer vision tasks.
EXAONE 3.5: Series of Large Language Models for Real-world Use Cases
This technical report introduces the EXAONE 3. 5 instruction-tuned language models, developed and released by LG AI Research.
Diffusion based Semantic Outlier Generation via Nuisance Awareness for Out-of-Distribution Detection
Out-of-distribution (OOD) detection, which determines whether a given sample is part of the in-distribution (ID), has recently shown promising results through training with synthetic OOD datasets.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
EXAONE 3.0 7.8B Instruction Tuned Language Model
We introduce EXAONE 3. 0 instruction-tuned language model, the first open model in the family of Large Language Models (LLMs) developed by LG AI Research.
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets.
Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?
Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning.
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks
Transfer learning is a crucial technique for handling a small amount of data that is potentially related to other abundant data.
Gradient Surgery for One-shot Unlearning on Generative Model
Recent regulation on right-to-be-forgotten emerges tons of interest in unlearning pre-trained machine learning models.
Semi-supervised regression with skewed data via adversarially forcing the distribution of predicted values
We assume the existence of enough unlabeled data that follow the true distribution, and that the true distribution can be roughly estimated from domain knowledge or a few samples.
