3 citations · 12 across the 10 of their papers we have counts for
5 papers · 1 filter
Feature Diversification and Adaptation for Federated Domain Generalization
Seunghan Yang, Seokeon Choi, Hyunsin Park +3
Federated learning, a distributed learning paradigm, utilizes multiple clients to build a robust global model. In real-world applications, local clients often operate within their…
Quadapter: Adapter for GPT-2 Quantization
Minseop Park, Jaeseong You, Markus Nagel +1
Transformer language models such as GPT-2 are difficult to quantize because of outliers in activations leading to a large quantization error. To adapt to the error, one must use qu…
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation
Jangho Kim, Simyung Chang, Nojun Kwak
As edge devices become prevalent, deploying Deep Neural Networks (DNN) on edge devices has become a critical issue. However, DNN requires a high computational resource which is rar…
Prototype-based Personalized Pruning
Jangho Kim, Simyung Chang, Sungrack Yun +1
Nowadays, as edge devices such as smartphones become prevalent, there are increasing demands for personalized services. However, traditional personalization methods are not suitabl…
Towards Governing Agent's Efficacy: Action-Conditional -VAE for Deep Transparent Reinforcement Learning
John Yang, Gyujeong Lee, Minsung Hyun +2
We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable w…