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Found 6 papers, 1 papers with code
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases.
Probabilistic Forecasting: A Level-Set Approach
By recognizing the connection of our algorithm to random forests (RFs) and quantile regression forests (QRFs), we are able to prove consistency guarantees of our approach under mild assumptions on the underlying point estimator.
GOPHER: Categorical probabilistic forecasting withgraph structure via local continuous-time dynamics
We consider the problem of probabilistic forecasting over categories with graph structure, where the dynamics at a vertex depends on its local connectivity structure.
Improving Context-Based Meta-Reinforcement Learning with Self-Supervised Trajectory Contrastive Learning
To address this, we propose a novel self-supervised learning task, which we named Trajectory Contrastive Learning (TCL), to improve meta-training.
Recurrent Exploration Networks for Recommender Systems
Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems.
LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking
2) One-click annotation: Instead of drawing 3D bounding boxes or point-wise labels, we simplify the annotation to just one click on the target object, and automatically generate the bounding box for the target.
