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5 papers · 2 filters
Knowledge Distillation for Federated Learning: a Practical Guide
Alessio Mora, Irene Tenison, Paolo Bellavista +1
Federated Learning (FL) enables the training of Deep Learning models without centrally collecting possibly sensitive raw data. The most used algorithms for FL are parameter-averagi…
Using attention methods to predict judicial outcomes
Vithor Gomes Ferreira Bertalan, Evandro Eduardo Seron Ruiz
Legal Judgment Prediction is one of the most acclaimed fields for the combined area of NLP, AI, and Law. By legal prediction we mean an intelligent systems capable to predict speci…
KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification
Wei Ju, Junwei Yang, Meng Qu +3
This paper studies semi-supervised graph classification, which is an important problem with various applications in social network analysis and bioinformatics. This problem is typi…
A Highly Adaptive Acoustic Model for Accurate Multi-Dialect Speech Recognition
Sanghyun Yoo, Inchul Song, Yoshua Bengio
Despite the success of deep learning in speech recognition, multi-dialect speech recognition remains a difficult problem. Although dialect-specific acoustic models are known to per…
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
Vincent Mai, Kaustubh Mani, Liam Paull
In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency. As t…