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Found 4 papers, 0 papers with code
IGOT: Information Gain Optimized Tokenizer on Domain Adaptive Pretraining
In this article, we proposed Information Gain Optimized Tokenizer (IGOT), which analyzes the special token set of downstream tasks, constructs a new subset using heuristic function $\phi$ with the special token and its information gain, to build new domain-specific tokenizer, and continues pretraining on the downstream task data.
ManiFoundation Model for General-Purpose Robotic Manipulation of Contact Synthesis with Arbitrary Objects and Robots
Our work introduces a comprehensive framework to develop a foundation model for general robotic manipulation that formalizes a manipulation task as contact synthesis.
Object-centric Inference for Language Conditioned Placement: A Foundation Model based Approach
We focus on the task of language-conditioned object placement, in which a robot should generate placements that satisfy all the spatial relational constraints in language instructions.
CasGCN: Predicting future cascade growth based on information diffusion graph
In this paper, we propose a novel deep learning architecture for cascade growth prediction, called CasGCN, which employs the graph convolutional network to extract structural features from a graphical input, followed by the application of the attention mechanism on both the extracted features and the temporal information before conducting cascade size prediction.
