0% Complete
Home
/
12th International Conference on Computer and Knowledge Engineering
AVID: A VARIATIONAL INFERENCE DELIBERATION FOR META-LEARNING
Authors :
Alireza Javaheri
1
Arsham Gholamzadeh Khoee
2
Saeed Reza Kheradpisheh
3
Hadi Farahani
4
Mohammad Ganjtabesh
5
1- Department of Computer Sciences, Shahid Beheshti University, G.C., Tehran, Iran
2- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran
3- Department of Computer Sciences, Shahid Beheshti University, G.C., Tehran, Iran
4- Department of Computer Sciences, Shahid Beheshti University, G.C., Tehran, Iran
5- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran
Keywords :
Machine Learning،Meta-Learning،Few-shot Learning،Deep Learning،Variational Inference،Bayesian Methods
Abstract :
Meta-learning techniques enable quick learning of new tasks by using few samples with utilizing prior knowledge learned from previous tasks. Gradient-based models are widely used because of their simplicity and ability to solve a wide range of problems. However, they only succeed in solving tasks with a very similar structure since they adapt the model with a shared meta-parameter across all tasks. In recent years, some models have been proposed to enhance the gradient-based models to deal with task uncertainty and heterogeneity via sharing knowledge among similar tasks by using task clustering. Nevertheless, the high-dimensional parameter space of gradient-based models hinders them from achieving their full potential in low-data regimes. Bayesian meta-learning algorithms address this issue by learning a data-dependent latent generative representation of model parameters. Our proposed model bypasses the aforementioned limitations by leveraging Bayesian algorithms as well as clustering input tasks. The final analysis demonstrates the effectiveness of the proposed model for few-shot image classification problems.
Papers List
List of archived papers
Enhancing Lighter Neural Network Performance with Layer-wise Knowledge Distillation and Selective Pixel Attention
Siavash Zaravashan - Sajjad Torabi - Hesam Zaravashan
A Hybrid Echo State Network for Hypercomplex Pattern Recognition, Classification, and Big Data Analysis
Mohammad Jamshidi - Fatemeh Daneshfar
An influence maximization algorithm based on community detection using topological features
Zahra Aghaee - Afsaneh Fatemi
FarSick: A Persian Semantic Textual Similarity And Natural Language Inference Dataset
Zahra Ghasemi - Mohammad Ali Keyvanrad
BioBERT-based SNP-traits Associations Extraction from Biomedical Literature
Mohammad Dehghani - Behrouz Bokharaeian - Zahra Yazdanparast
SAT Based Analogy Evaluation Framework For Persian Word Embeddings
Seyed Ehsan Mahmoudi - Mehrnoush Shamsfard
A Novel Density-Based KNN in Pattern Recognition
Sajad Haghzad Klidbary - Abazar Arabameri
The Effect of Network Environment on Traffic Classification
Abolghasem Rezaei Khesal - Mehdi Teimouri
Improved TrustChain for Lightweight Devices
Seyed Salar Ghazi - Haleh Amintoosi
Analysis of Address Lifespans in Bitcoin and Ethereum
Amir Mohammad Karimi Mamaghan - Amin Setayesh - Behnam Bahrak
more
Samin Hamayesh - Version 41.7.6