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Found 7 papers, 3 papers with code
Road Rutting Detection using Deep Learning on Images
The proposed road rutting dataset and the results of our research study will help accelerate the research on detection of road rutting using deep learning.
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks
While fine-tuning pre-trained models for downstream classification is the conventional paradigm in NLP, often task-specific nuances may not get captured in the resultant models.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Transients generate memory and break hyperbolicity in stochastic enzymatic networks
We propose new statistical measures, defined in terms of turnover times, to distinguish between the transient and steady states and apply these to experimental data from a landmark experiment that first observed molecular memory in a single enzyme with multiple binding sites.
Syntax-guided Controlled Generation of Paraphrases
In these methods, syntactic-guidance is sourced from a separate exemplar sentence.
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.
eCommerceGAN : A Generative Adversarial Network for E-commerce
Exploring the space of all plausible orders could help us better understand the relationships between the various entities in an e-commerce ecosystem, namely the customers and the products they purchase.
