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Found 14 papers, 3 papers with code
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift
The former normalizes the input to fix its distribution in terms of the mean and variance, while the latter returns the output to the original distribution.
ST-GRAT: A Novel Spatio-temporal Graph Attention Network for Accurately Forecasting Dynamically Changing Road Speed
Predicting road traffic speed is a challenging task due to different types of roads, abrupt speed change and spatial dependencies between roads; it requires the modeling of dynamically changing spatial dependencies among roads and temporal patterns over long input sequences.
Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints
The effects of PLUMCOT are shown to be remarkable in "unseen" constraints.
SANVis: Visual Analytics for Understanding Self-Attention Networks
Attention networks, a deep neural network architecture inspired by humans' attention mechanism, have seen significant success in image captioning, machine translation, and many other applications.
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning
To address this issue, this paper presents a novel meta-learning algorithm for unsupervised neural machine translation (UNMT) that trains the model to adapt to another domain by utilizing only a small amount of training data.
An Empirical Experiment on Deep Learning Models for Predicting Traffic Data
For example, it is difficult to figure out which models provide state-of-the-art performance, as recently proposed models have often been evaluated with different datasets and experiment environments.
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation
Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies on a pre-trained general NMT model which is adapted to the new domain on a sample of in-domain parallel data.
A Visual Analytics System for Improving Attention-based Traffic Forecasting Models
With deep learning (DL) outperforming conventional methods for different tasks, much effort has been devoted to utilizing DL in various domains.
Residual Correction in Real-Time Traffic Forecasting
Extensive experiments on METR-LA and PEMS-BAY demonstrate that our ResCAL can correctly capture the correlation of errors and correct the failures of various traffic forecasting models in event situations.
PePe: Personalized Post-editing Model utilizing User-generated Post-edits
Incorporating personal preference is crucial in advanced machine translation tasks.
Specializing Multi-domain NMT via Penalizing Low Mutual Information
Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains.
PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction
Understanding the movement patterns of objects (e. g., humans and vehicles) in a city is essential for many applications, including city planning and management.
KMMLU: Measuring Massive Multitask Language Understanding in Korean
We propose KMMLU, a new Korean benchmark with 35, 030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM.
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
