Search Results for author:
Found 12 papers, 1 papers with code
Question Calibration and Multi-Hop Modeling for Temporal Question Answering
In the real world, many facts contained in KGs are time-constrained thus temporal KGQA has received increasing attention.
Poisson Process for Bayesian Optimization
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP).
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation
Few-shot image generation aims to train generative models using a small number of training images.
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal human effort.
Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts
To alleviate this problem, we propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences in sentence pairs by separately modeling affinity and difference semantics.
Deep Transformers Thirst for Comprehensive-Frequency Data
We study an NLP model called LSRA, which introduces IB with a pyramid-free structure.
Universal Semi-Supervised Learning
Universal Semi-Supervised Learning (UniSSL) aims to solve the open-set problem where both the class distribution (i. e., class set) and feature distribution (i. e., feature domain) are different between labeled dataset and unlabeled dataset.
Multi-Label Few-Shot Learning for Aspect Category Detection
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence.
Automatic low-bit hybrid quantization of neural networks through meta learning
With the best searched quantization policy, we subsequently retrain or finetune to further improve the performance of the quantized target network.
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
In this work, we propose to employ tools inspired by the Differentiable Neural Architecture Search (D-NAS) literature in order to optimize the architecture for FSL without over-fitting.
Transferable AutoML by Model Sharing Over Grouped Datasets
This paper presents a so-called transferable AutoML approach that leverages previously trained models to speed up the search process for new tasks and datasets.
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice.
