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Notes on pretraining
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Multimodal Pretraining for Generalizable EEG Representation Learning
Researchers have developed a multimodal EEG foundation model that can be used for various EEG-related tasks without being limited to specific datasets or tasks. The model uses a raw signal encoder, a time-frequency…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The paper proposes a new method called RAPTOR for temporal knowledge graph (TKG) reasoning under the extrapolation setting. This method aims to improve the efficiency of target-oriented path exploration by using…
Reachability-Aware Pretraining for Efficient Target-Oriented Path Exploration in Temporal Knowledge Graph Reasoning
The authors propose a new method called RAPTOR for improving the efficiency of target-oriented path exploration in temporal knowledge graphs (TKG). The method injects a reachability-aware inductive bias to the agent…
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
The paper presents RMISC, a large-scale, high-quality, open-access corpus of real-world multivariate time series data. The researchers pretrain four advanced Time Series Foundation Models (TSFMs) on univariate…
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
The authors propose a new approach called Task-Agnostic Pretraining (TAP) for Vision-Language-Action (VLA) models to overcome the scarcity of expert demonstrations. TAP aims to separate learning physical competence and…
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
Researchers propose Task-Agnostic Pretraining (TAP) as a solution to the scarcity of expert demonstrations in Vision-Language-Action (VLA) models. TAP aims to separate learning physical competence and semantic…
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