Zobrazeno 1 - 10
of 442
pro vyhledávání: '"Lu, Songtao"'
Autor:
Fang, Minghong, Zhang, Zifan, Hairi, Khanduri, Prashant, Liu, Jia, Lu, Songtao, Liu, Yuchen, Gong, Neil
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system follows the server-assisted architecture (server-assisted FL), where the t
Externí odkaz:
http://arxiv.org/abs/2406.10416
Stochastic bilevel optimization tackles challenges involving nested optimization structures. Its fast-growing scale nowadays necessitates efficient distributed algorithms. In conventional distributed bilevel methods, each worker must transmit full-di
Externí odkaz:
http://arxiv.org/abs/2405.18858
Autor:
Zhang, Shuai, Fernando, Heshan Devaka, Liu, Miao, Murugesan, Keerthiram, Lu, Songtao, Chen, Pin-Yu, Chen, Tianyi, Wang, Meng
This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics. In this setting, the Q-function of each RL problem (task) can be decompo
Externí odkaz:
http://arxiv.org/abs/2405.15920
Transformer-based large language models have displayed impressive in-context learning capabilities, where a pre-trained model can handle new tasks without fine-tuning by simply augmenting the query with some input-output examples from that task. Desp
Externí odkaz:
http://arxiv.org/abs/2402.15607
Stochastic bilevel optimization (SBO) is becoming increasingly essential in machine learning due to its versatility in handling nested structures. To address large-scale SBO, decentralized approaches have emerged as effective paradigms in which nodes
Externí odkaz:
http://arxiv.org/abs/2402.03167
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization
In this paper, we present a novel bilevel optimization-based training approach to training acoustic models for automatic speech recognition (ASR) tasks that we term {bi-level joint unsupervised and supervised training (BL-JUST)}. {BL-JUST employs a l
Externí odkaz:
http://arxiv.org/abs/2401.06980
Soft random sampling (SRS) is a simple yet effective approach for efficient training of large-scale deep neural networks when dealing with massive data. SRS selects a subset uniformly at random with replacement from the full data set in each epoch. I
Externí odkaz:
http://arxiv.org/abs/2311.12727
Ontology revision aims to seamlessly incorporate a new ontology into an existing ontology and plays a crucial role in tasks such as ontology evolution, ontology maintenance, and ontology alignment. Similar to repair single ontologies, resolving logic
Externí odkaz:
http://arxiv.org/abs/2310.18378
Autor:
Zhang, Shuai, Li, Hongkang, Wang, Meng, Liu, Miao, Chen, Pin-Yu, Lu, Songtao, Liu, Sijia, Murugesan, Keerthiram, Chaudhury, Subhajit
Publikováno v:
Neurips 2023
This paper provides a theoretical understanding of Deep Q-Network (DQN) with the $\varepsilon$-greedy exploration in deep reinforcement learning. Despite the tremendous empirical achievement of the DQN, its theoretical characterization remains undere
Externí odkaz:
http://arxiv.org/abs/2310.16173
Neuro-symbolic learning (NSL) models complex symbolic rule patterns into latent variable distributions by neural networks, which reduces rule search space and generates unseen rules to improve downstream task performance. Centralized NSL learning inv
Externí odkaz:
http://arxiv.org/abs/2308.15324