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Found 8 papers, 5 papers with code
Dynamic layer selection in decoder-only transformers
The vast size of Large Language Models (LLMs) has prompted a search to optimize inference.
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
We propose the Enhanced Cluster-aware Graph Network (ECGN), a novel method that addresses these issues by integrating cluster-specific training with synthetic node generation.
MODL: Multilearner Online Deep Learning
This further leads us to the cascaded multilearner design, in which multiple shallow and deep learners are co-trained to solve the online learning problem in a cooperative, synergistic fashion.
Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Every day the volume of training data is expanding and the number of user interactions is constantly increasing.
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.
Contrastive Learning for Time Series on Dynamic Graphs
We propose a framework called GraphTNC for unsupervised learning of joint representations of the graph and the time-series.
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Since a meaningful graph representing dependencies between bags is rarely available, we propose to use a Bayesian GNN framework that can generate a likely graph structure for scenarios where there is uncertainty in the graph or when no graph is available.
Motion Inbetweening via Deep $Δ$-Interpolator
We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates in the delta mode using the spherical linear interpolator as a baseline.
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Motion Synthesis
on LaFAN1
