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Found 13 papers, 2 papers with code
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
We introduce Seed1. 5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks.
Deformable Registration Framework for Augmented Reality-based Surgical Guidance in Head and Neck Tumor Resection
In tongue specimens, the proposed framework improved TRE by up to 33% as compared to prior deformable registration.
External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation
Second, large-volume data arrive in a streaming mode with data distributions dynamically shifting, as new users/ads join and existing users/ads leave the system.
MultiBalance: Multi-Objective Gradient Balancing in Industrial-Scale Multi-Task Recommendation System
In industrial recommendation systems, multi-task learning (learning multiple tasks simultaneously on a single model) is a predominant approach to save training/serving resources and improve recommendation performance via knowledge transfer between the joint learning tasks.
Weaver: Foundation Models for Creative Writing
Weaver is pre-trained on a carefully selected corpus that focuses on improving the writing capabilities of large language models.
Minimum observability of probabilistic Boolean networks
This paper studies the minimum observability of probabilistic Boolean networks (PBNs), the main objective of which is to add the fewest measurements to make an unobservable PBN become observable.
An Empirical Study on the Holiday Effect of China's Time-Honored Companies
The stock segment of China's time-honored brand enterprises has an important position in our securities stock market.
VMap: An Interactive Rectangular Space-filling Visualization for Map-like Vertex-centric Graph Exploration
The resulting rectangular layout has better aspect ratio quality on synthetic data compared with the existing method for the rectangular partitioning of 2D points.
IDLat: An Importance-Driven Latent Generation Method for Scientific Data
We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.
VDL-Surrogate: A View-Dependent Latent-based Model for Parameter Space Exploration of Ensemble Simulations
In the model inference stage, we predict the latent representations at previously selected viewpoints and decode the latent representations to data space.
GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations
Our approach improves the efficiency of parameter space exploration with a surrogate model that predicts the simulation outputs accurately and efficiently.
Reinforcement Learning for Load-balanced Parallel Particle Tracing
Second, we propose a workload estimation model, helping RL agents estimate the workload distribution of processes in future computations.
Deep Hierarchical Super Resolution for Scientific Data
We present a novel technique for hierarchical super resolution (SR) with neural networks (NNs), which upscales volumetric data represented with an octree data structure to a high-resolution uniform grid with minimal seam artifacts on octree node boundaries.
