research
∙
09/18/2023
AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation
Test-time adaptation is a promising research direction that allows the s...
share
research
∙
09/18/2023
Looking through the past: better knowledge retention for generative replay in continual learning
In this work, we improve the generative replay in a continual learning s...
research
∙
09/14/2023
FedFNN: Faster Training Convergence Through Update Predictions in Federated Recommender Systems
Federated Learning (FL) has emerged as a key approach for distributed ma...
research
∙
09/12/2023
Plasticity-Optimized Complementary Networks for Unsupervised Continual Learning
Continuous unsupervised representation learning (CURL) research has grea...
research
∙
08/18/2023
Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
In this work, we investigate exemplar-free class incremental learning (C...
research
∙
08/14/2023
MM-GEF: Multi-modal representation meet collaborative filtering
In modern e-commerce, item content features in various modalities offer ...
research
∙
03/14/2023
ICICLE: Interpretable Class Incremental Continual Learning
Continual learning enables incremental learning of new tasks without for...
research
∙
02/12/2023
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain Recommendation
Multi-domain recommender systems benefit from cross-domain representatio...
research
∙
08/30/2022
Online Meta-Learning for Model Update Aggregation in Federated Learning for Click-Through Rate Prediction
In Federated Learning (FL) of click-through rate (CTR) prediction, users...
research
∙
02/16/2022
Planckian jitter: enhancing the color quality of self-supervised visual representations
Several recent works on self-supervised learning are trained by mapping ...
research
∙
12/30/2021
Continually Learning Self-Supervised Representations with Projected Functional Regularization
Recent self-supervised learning methods are able to learn high-quality i...
research
∙
08/25/2021
Reducing Label Effort: Self-Supervised meets Active Learning
Active learning is a paradigm aimed at reducing the annotation effort by...
research
∙
01/07/2021
Metric Learning for Session-based Recommendations
Session-based recommenders, used for making predictions out of users' un...
research
∙
07/13/2020
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning
Research on continual learning has led to a variety of approaches to mit...
research
∙
04/01/2020
Semantic Drift Compensation for Class-Incremental Learning
Class-incremental learning of deep networks sequentially increases the n...
research
∙
11/22/2019
