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Found 19 papers, 5 papers with code
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation
While the optimization can draw photometric reference from the input videos or be regulated by generative models, directly supervising Gaussian motions remains underexplored.
The Landscape and Challenges of HPC Research and LLMs
Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning.
OMPGPT: A Generative Pre-trained Transformer Model for OpenMP
Large language models (LLMs), as epitomized by models like ChatGPT, have revolutionized the field of natural language processing (NLP).
Identifying Policy Gradient Subspaces
Policy gradient methods hold great potential for solving complex continuous control tasks.
LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry
Visual odometry estimates the motion of a moving camera based on visual input.
FlashVideo: A Framework for Swift Inference in Text-to-Video Generation
In the evolving field of machine learning, video generation has witnessed significant advancements with autoregressive-based transformer models and diffusion models, known for synthesizing dynamic and realistic scenes.
CompCodeVet: A Compiler-guided Validation and Enhancement Approach for Code Dataset
The evaluation of CompCodeVet on two open-source code datasets shows that CompCodeVet has the ability to improve the training dataset quality for LLMs.
Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization
As a promising fashion for visual localization, scene coordinate regression (SCR) has seen tremendous progress in the past decade.
HPC-GPT: Integrating Large Language Model for High-Performance Computing
Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks.
Data Race Detection Using Large Language Models
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation.
Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C++
In this study, we present a novel dataset for training machine learning models translating between OpenMP Fortran and C++ code.
LM4HPC: Towards Effective Language Model Application in High-Performance Computing
In recent years, language models (LMs), such as GPT-4, have been widely used in multiple domains, including natural language processing, visualization, and so on.
PERFOGRAPH: A Numerical Aware Program Graph Representation for Performance Optimization and Program Analysis
The remarkable growth and significant success of machine learning have expanded its applications into programming languages and program analysis.
Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation
However, applying machine learning techniques to parallelism detection presents several challenges, such as the lack of an adequate dataset for training, an effective code representation with rich information, and a suitable machine learning model to learn the latent features of code for diverse analyses.
Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields
In this paper, we tackle the problem of active robotic 3D reconstruction of an object.
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning
Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration.
Multi-scale super-resolution generation of low-resolution scanned pathological images
We design a strategy to scan slides with low resolution (5X) and a super-resolution method is proposed to restore the image details when in diagnosis.
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.
