Search Results for author:
Found 20 papers, 9 papers with code
GeomGS: LiDAR-Guided Geometry-Aware Gaussian Splatting for Robot Localization
Furthermore, we propose a novel localization method that fully utilizes both the geometric and photometric properties of GeomGS.
Self-supervised denoising of visual field data improves detection of glaucoma progression
This denoising model can be integrated into future models for visual field analysis to enhance detection of glaucoma progression.
Certifiable Reachability Learning Using a New Lipschitz Continuous Value Function
Our framework has two main parts: offline learning of a newly designed reachavoid value function, and post-learning certification.
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #3 on
Question Answering
on PeerQA
SUTRA: Scalable Multilingual Language Model Architecture
In this paper, we introduce SUTRA, multilingual Large Language Model architecture capable of understanding, reasoning, and generating text in over 50 languages.
Implicit Neural Image Stitching
Existing frameworks for image stitching often provide visually reasonable stitchings.
Semantic-Aware Implicit Template Learning via Part Deformation Consistency
In this paper, we highlight the importance of part deformation consistency and propose a semantic-aware implicit template learning framework to enable semantically plausible deformation.
Chemical Property-Guided Neural Networks for Naphtha Composition Prediction
In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction.
Semantic-aware Occlusion Filtering Neural Radiance Fields in the Wild
We present a learning framework for reconstructing neural scene representations from a small number of unconstrained tourist photos.
Robust Camera Pose Refinement for Multi-Resolution Hash Encoding
Multi-resolution hash encoding has recently been proposed to reduce the computational cost of neural renderings, such as NeRF.
Domain Generalization Emerges from Dreaming
Recent studies have proven that DNNs, unlike human vision, tend to exploit texture information rather than shape.
B-Spline Texture Coefficients Estimator for Screen Content Image Super-Resolution
Our network outperforms both a transformer-based reconstruction and an implicit Fourier representation method in almost upscaling factor, thanks to the positive constraint and compact support of the B-spline basis.
Learning Local Implicit Fourier Representation for Image Warping
In this paper, we propose a local texture estimator for image warping (LTEW) followed by an implicit neural representation to deform images into continuous shapes.
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
We show that our general performance model not only achieves low prediction error on DLRM, which has highly customized configurations and is dominated by multiple factors but also yields comparable accuracy on other compute-bound ML models targeted by most previous methods.
Local Texture Estimator for Implicit Representation Function
Recent works with an implicit neural function shed light on representing images in arbitrary resolution.
Ranked #10 on
Image Super-Resolution
on Urban100 - 3x upscaling
Point Cloud Augmentation with Weighted Local Transformations
Although data augmentation is a standard approach to compensate for the scarcity of data, it has been less explored in the point cloud literature.
Ranked #11 on
Point Cloud Classification
on PointCloud-C
A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity
Although unlabeled data is readily available in pool-based AL, AL algorithms are usually evaluated by measuring the increase in supervised learning (SL) performance at consecutive acquisition steps.
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control
We name this AL scheme convergence rate control (CRC), and our experiments show that a deep neural network trained using a combination of CRC and a recently proposed SSL algorithm can quickly achieve high performance using far less labeled samples than SL.
Applying GPGPU to Recurrent Neural Network Language Model based Fast Network Search in the Real-Time LVCSR
Recurrent Neural Network Language Models (RNNLMs) have started to be used in various fields of speech recognition due to their outstanding performance.
Accelerating recurrent neural network language model based online speech recognition system
This paper presents methods to accelerate recurrent neural network based language models (RNNLMs) for online speech recognition systems.
