Personalized text generation is an emerging research area that has attra...
Automated Machine Learning (AutoML) techniques have recently been introd...
Making personalized recommendation for cold-start users, who only have a...
Federated learning (FL) enables multiple clients to train models
collabo...
Time series forecasting has been a widely explored task of great importa...
Large-scale Transformer models bring significant improvements for variou...
Standard fine-tuning of large pre-trained language models (PLMs) for
dow...
While fine-tuning pre-trained networks has become a popular way to train...
Fine-tuning large-scale pre-trained language models to downstream tasks
...
Nonconvex regularization has been popularly used in low-rank matrix lear...
Robots excel at avoiding obstacles but still struggle to traverse comple...
Many applications require robots to move through terrain with large
obst...
Short text classification is a fundamental task in natural language
proc...
We present a new method LiST for efficient fine-tuning of large pre-trai...
We find that different Deep Neural Networks (DNNs) trained with the same...
The COVID-19 pandemic has imposed serious challenges in multiple perspec...
Federated Learning has shown great potentials for the distributed data
u...
In this work, we study the problem of named entity recognition (NER) in ...
Federated Semi-Supervised Learning (FedSSL) has gained rising attention ...
Molecular property prediction plays a fundamental role in drug discovery...
Fake news travels at unprecedented speeds, reaches global audiences and ...
Federated learning (FL) has emerged as an effective technique to co-trai...
In this paper, we introduce MedLane – a new human-annotated Medical Lang...
Neural sequence labeling is an important technique employed for many Nat...
Recent advances in information extraction have motivated the automatic
c...
Matrix learning is at the core of many machine learning problems. To
enc...
Can one build a knowledge graph (KG) for all products in the world? Know...
Product catalogs are valuable resources for eCommerce website. In the
ca...
Effective inference for a generative adversarial model remains an import...
Today social media has become the primary source for news. Via social me...
The quest of `can machines think' and `can machines do what human do' ar...
Convolutional sparse coding (CSC) can learn representative shift-invaria...
Convolutional sparse coding (CSC) has been popularly used for the learni...
Convolutional sparse coding (CSC) improves sparse coding by learning a
s...