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- Proceedings of Machine Learning Research
Proceedings of the 39th International Conference on Machine Learning Held in Baltimore, Maryland, USA on 17-23 July 2022 Published as Volume 162 by the Proceedings of Machine Learning Research on 28 June 2022 Volume Edited by: Kamalika Chaudhuri Stefanie Jegelka Le Song Csaba Szepesvari Gang Niu Sivan Sabato Series Editors: Neil D Lawrence
- Let Invariant Rationale Discovery Inspire Graph Contrastive Learning - PMLR
Leading graph contrastive learning (GCL) methods perform graph augmentations in two fashions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic information, or (2) us
- Let Invariant Rationale Discovery Inspire Graph Contrastive Learning
Leading graph contrastive learning (GCL) meth- ods perform graph augmentations in two fash- ions: (1) randomly corrupting the anchor graph, which could cause the loss of semantic informa- tion, or (2) using domain knowledge to maintain salient features, which undermines the general- ization to other domains Taking an invariance look at GCL, we argue that a high-performing augmentation should
- The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
Recent years have seen the emergence of pre-trained representations as a powerful abstraction for AI applications in computer vision, natural language, and speech However, policy learning for cont
- Dataset Condensation with Contrastive Signals - PMLR
Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation (DC), methods can achieve state-of-theart performance when applied to data-efficient learnin
- State Transition of Dendritic Spines Improves Learning of Sparse . . . - PMLR
State Transition of Dendritic Spines Improves Learning of Sparse Spiking Neural NetworksYanqi Chen, Zhaofei Yu, Wei Fang, Zhengyu Ma, Tie
- HousE: Knowledge Graph Embedding with Householder Parameterization - PMLR
The effectiveness of knowledge graph embedding (KGE) largely depends on the ability to model intrinsic relation patterns and mapping properties However, existing approaches can only capture some of them with insufficient modeling capacity In this work, we propose a more powerful KGE framework named HousE, which involves a novel parameterization based on two kinds of Householder
- NLP From Scratch Without Large-Scale Pretraining: A Simple and . . . - PMLR
Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train We propose a simple and efficient learning framework, TLM, that does not rely on large-scale pretraining Given some labeled task data and a large general corpus, TLM uses task data as queries to retrieve a tiny subset of the general corpus and
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