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Found 10 papers, 2 papers with code
Measuring Robustness for NLP
The quality of Natural Language Processing (NLP) models is typically measured by the accuracy or error rate of a predefined test set.
Can Data Diversity Enhance Learning Generalization?
This paper introduces our Diversity Advanced Actor-Critic reinforcement learning (A2C) framework (DAAC) to improve the generalization and accuracy of Natural Language Processing (NLP).
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition
The use of low-rank adaptation (LoRA) with frozen pretrained language models (PLMs) has become increasing popular as a mainstream, resource-efficient modeling approach for memory-constrained hardware.
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition
We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring.
Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
Despite the recent success achieved by several two-stage prototypical networks in few-shot named entity recognition (NER) task, the overdetected false spans at the span detection stage and the inaccurate and unstable prototypes at the type classification stage remain to be challenging problems.
Ranked #2 on
Few-shot NER
on Few-NERD (INTRA)
Using AntiPatterns to avoid MLOps Mistakes
We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications.
The Weak Lensing Peak Statistics in the Mocks by the inverse-Gaussianization Method
We apply the inverse-Gaussianization method proposed in \citealt{arXiv:1607. 05007} to fast produce weak lensing convergence maps and investigate the peak statistics, including the peak height counts and peak steepness counts, in these mocks.
Cosmology and Nongalactic Astrophysics
Unsupervised Representation Learning for Gaze Estimation
Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse data (annotating 3D gaze is expensive and existing datasets use different setups).
Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis
In this work, we address the problem of person-specific gaze model adaptation from only a few reference training samples.
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image.
