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06/23/2023
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy Imitation
In this paper we explore few-shot imitation learning for control problem...
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06/20/2022
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Recent years have seen a growth in user-centric applications that requir...
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06/17/2022
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
Modern deep learning systems are increasingly deployed in situations suc...
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10/26/2021
Non-Gaussian Gaussian Processes for Few-Shot Regression
Gaussian Processes (GPs) have been widely used in machine learning to mo...
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07/02/2021
Memory Efficient Meta-Learning with Large Images
Meta learning approaches to few-shot classification are computationally ...
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04/12/2021
How Sensitive are Meta-Learners to Dataset Imbalance?
Meta-Learning (ML) has proven to be a useful tool for training Few-Shot ...
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01/07/2021
Few-Shot Learning with Class Imbalance
Few-shot learning aims to train models on a limited number of labeled sa...
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06/10/2020
Self-Supervised Relational Reasoning for Representation Learning
In self-supervised learning, a system is tasked with achieving a surroga...
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10/11/2019
Deep Kernel Transfer in Gaussian Processes for Few-shot Learning
Humans tackle new problems by making inferences that go far beyond the i...
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08/15/2019
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction
Deep reinforcement learning has proven to be a great success in allowing...
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07/25/2019
Y-Autoencoders: disentangling latent representations via sequential-encoding
In the last few years there have been important advancements in generati...
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09/12/2017
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Group emotion recognition in the wild is a challenging problem, due to t...
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09/11/2017