Search Results for author:
Found 16 papers, 10 papers with code
How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks
This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.
Graph Federated Learning Based on the Decentralized Framework
Graph-federated learning is mainly based on the classical federated learning framework i. e., the Client-Server framework.
Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns
LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
Then, we propose a graph matching approach to select correspondence objects based on the structure layout and semantic property similarity of vertices' neighbors.
Optimally Controllable Perceptual Lossy Compression
We prove that arbitrary points of the D-P tradeoff bound can be achieved by a simple linear interpolation between the outputs of a minimum MSE decoder and a specifically constructed perfect perceptual decoder.
An Experimental Comparison Between Temporal Difference and Residual Gradient with Neural Network Approximation
In this work, we perform extensive experiments to show that TD outperforms RG, that is, when the training leads to a small Bellman residual error, the solution found by TD has a better policy and is more robust against the perturbation of neural network parameters.
Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition
To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of skeleton sequence and RGB frame is close to that of the skeleton sequence and RGB video.
Masks Fusion with Multi-Target Learning For Speech Enhancement
Typically, a single T-F mask is first estimated based on DNN and then used to mask the spectrogram of noisy speech in an order to suppress the noise.
Optimal Transport for Unsupervised Denoising Learning
Toward answering this question, this work proposes a criterion for unsupervised denoising learning based on the optimal transport theory.
On Perceptual Lossy Compression: The Cost of Perceptual Reconstruction and An Optimal Training Framework
This paper provides nontrivial results theoretically revealing that, \textit{1}) the cost of achieving perfect perception quality is exactly a doubling of the lowest achievable MSE distortion, \textit{2}) an optimal encoder for the "classic" rate-distortion problem is also optimal for the perceptual compression problem, \textit{3}) distortion loss is unnecessary for training a perceptual decoder.
Revisiting Robust Model Fitting Using Truncated Loss
Furthermore, the new algorithms are applied to various 2D/3D registration problems.
Matrix Completion via Nonconvex Regularization: Convergence of the Proximal Gradient Algorithm
Besides the convergence to a stationary point for a generalized nonconvex penalty, we provide more deep analysis on a popular and important class of nonconvex penalties which have discontinuous thresholding functions.
3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN
Deep learning on point clouds has made a lot of progress recently.
A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning
In recent, nonconvex regularization based sparse and low-rank recovery is of considerable interest and it in fact is a main driver of the recent progress in nonconvex and nonsmooth optimization.
Efficient Outlier Removal in Large Scale Global Structure-from-Motion
This work addresses the outlier removal problem in large-scale global structure-from-motion.
Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties.
