Search Results for author:
Found 17 papers, 2 papers with code
Gestures Are Used Rationally: Information Theoretic Evidence from Neural Sequential Models
Verbal communication is companied by rich non-verbal signals.
Untie the Knots: An Efficient Data Augmentation Strategy for Long-Context Pre-Training in Language Models
Large language models (LLM) have prioritized expanding the context window from which models can incorporate more information.
Voltage-Controlled Magnetoelectric Devices for Neuromorphic Diffusion Process
Stochastic diffusion processes are pervasive in nature, from the seemingly erratic Brownian motion to the complex interactions of synaptically-coupled spiking neurons.
On Large Language Models' Hallucination with Regard to Known Facts
We reveal the different dynamics of the output token probabilities along the depths of layers between the correct and hallucinated cases.
Truck Parking Usage Prediction with Decomposed Graph Neural Networks
However, these studies focus on a single parking site, and few approaches have been proposed to predict the usage of multiple truck parking sites considering spatio-temporal dependencies, due to the lack of data.
Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph Approach
This paper proposes the incorporation of techniques from stereophotoclinometry (SPC) into a keypoint-based structure-from-motion (SfM) system to estimate the surface normal and albedo at detected landmarks to improve autonomous surface and shape characterization of small celestial bodies from in-situ imagery.
PL-UNeXt: Per-stage Edge Detail and Line Feature Guided Segmentation for Power Line Detection
Power line detection is a critical inspection task for electricity companies and is also useful in avoiding drone obstacles.
LunarNav: Crater-based Localization for Long-range Autonomous Lunar Rover Navigation
In previous work, we developed crater detection algorithms for three different sensing modalities.
Rega-Net:Retina Gabor Attention for Deep Convolutional Neural Networks
Extensive research works demonstrate that the attention mechanism in convolutional neural networks (CNNs) effectively improves accuracy.
Error-Covariance Analysis of Monocular Pose Estimation Using Total Least Squares
First, the optimization framework is formulated for the pose estimation problem with observation vectors extracted from unit vectors from the camera center-of-projection, pointing towards the image features.
Emoji-based Fine-grained Attention Network for Sentiment Analysis in the Microblog Comments
The ablation experiments show that compared with ALBERT-AET, the proposed model ALBERT-FAET is better in the metrics, indicating that the fine-grained attention network can understand the diversified information of emoticons.
Lunar Rover Localization Using Craters as Landmarks
At the end of each drive, a ground-in-the-loop (GITL) interaction is used to get a position update from human operators in a more global reference frame, by matching images or local maps from onboard the rover to orbital reconnaissance images or maps of a large region around the rover's current position.
Window Filtering Algorithm for Pulsed Light Coherent Combining of Low Repetition Frequency
The multi-dithering method has been well verified in phase locking of polarization coherent combination experiment.
Rover Relocalization for Mars Sample Return by Virtual Template Synthesis and Matching
This work informs the Mars Sample Return campaign on the choice of a site where Perseverance (R0) will place a set of sample tubes for future retrieval by another rover (R1).
The Blessing and the Curse of the Noise behind Facial Landmark Annotations
The evolving algorithms for 2D facial landmark detection empower people to recognize faces, analyze facial expressions, etc.
BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training
In distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training.
Object Recognition by Using Multi-level Feature Point Extraction
In this paper, we present a novel approach for object recognition in real-time by employing multilevel feature analysis and demonstrate the practicality of adapting feature extraction into a Naive Bayesian classification framework that enables simple, efficient, and robust performance.
