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Found 6 papers, 2 papers with code
Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders
The offline and online evaluation metrics for recommender systems are ambiguous in their true objectives.
Learning to Optimize with Dynamic Mode Decomposition
Designing faster optimization algorithms is of ever-growing interest.
SimpleTRON: Simple Transformer with O(N) Complexity
In this paper, we propose that the dot product pairwise matching attention layer, which is widely used in Transformer-based models, is redundant for the model performance.
Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks
However, such models face several problems during the learning process, mainly due to the redundancy of the individual neurons, which results in sub-optimal accuracy or the need for additional training steps.
Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
Recently introduced EASE algorithm presents a simple and elegant way, how to solve the top-N recommendation task.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(using extra training data)
Transfer learning based few-shot classification using optimal transport mapping from preprocessed latent space of backbone neural network
MetaDL Challenge 2020 focused on image classification tasks in few-shot settings.
