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Found 5 papers, 2 papers with code
Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models
On text benchmarks, Core not only performs competitively to other frontier models on a set of well-established benchmarks (e. g. MMLU, GSM8K) but also outperforms GPT4-0613 on human evaluation.
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics.
Decoupling the Depth and Scope of Graph Neural Networks
We propose a design principle to decouple the depth and scope of GNNs -- to generate representation of a target entity (i. e., a node or an edge), we first extract a localized subgraph as the bounded-size scope, and then apply a GNN of arbitrary depth on top of the subgraph.
Ranked #3 on
Node Classification
on Reddit
Deep Graph Neural Networks with Shallow Subgraph Samplers
We propose a simple "deep GNN, shallow sampler" design principle to improve both the GNN accuracy and efficiency -- to generate representation of a target node, we use a deep GNN to pass messages only within a shallow, localized subgraph.
Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review
Autonomous vehicles were experiencing rapid development in the past few years.
