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
Improvement of Credit Scoring by LSTM Autoencoder Model
Milad Sattari Maleki - Seyedeh Niusha Motevallian - Faezehsadat Hosseini - Mohammad Sabokrou - Hamidreza Soltanalizadeh Maleki
An Efficient Planning Method for Autonomous Navigation of a Wheeled-Robot based on Deep Reinforcement Learning
Ali Salimi Sadr - Mahdi Shahbazi Khojasteh - Hamed Malek - Armin Salimi-Badr
MultiPath ViT OCR: A Lightweight Visual Transformer-based License Plate Optical Character Recognition
Alireza Azadbakht - Saeed Reza Kheradpisheh - Hadi Farahani
A Cost-Sensitive Genetic Algorithm for Customer Segmentation in Auto Insurances
Alireza Khajenoori - Mohammad Saniee Abadeh - Mohsen Mohammadzadeh
Area-Efficient VLSI Implementation of Bit-Serial Multiplier Using Polynomial Basis over GF(2m)
Saeideh Nabipour - Javad Javidan - Gholamreza Zare Fatin
An Interactive Approach for Query-based Multi-Document Scientific Text Summarization
Mohammadsadra Nejati - Azadeh Mohebi - Abbas Ahmadi
Segmentation of Coronary Artery Stenosis in X-ray Angiography using Mamba Models
Fatemeh Fouladi - Ali Rostami - Hedieh Sajedi
Blind image quality assessment based on Multi-resolution Local Structures
Seyed Majid Khorashadizadeh - Mehdi Sadeghi Bakhi - Fatemeh Seifishahpar - AliMohammad Latif
Cluster Sampling: A Cluster-Driven Sampling Strategy for Deep Metric Learning
Hamideh Rafiee - Ahmad Ali Abin - Seyed Soroush Majd
FarSick: A Persian Semantic Textual Similarity And Natural Language Inference Dataset
Zahra Ghasemi - Mohammad Ali Keyvanrad
more
Samin Hamayesh - Version 42.4.1