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Found 16 papers, 6 papers with code
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases such as Alzheimer Disease (AD).
Utilizing the LightGBM Algorithm for Operator User Credit Assessment Research
First, for the massive data related to user evaluation provided by operators, key features are extracted by data preprocessing and feature engineering methods, and a multi-dimensional feature set with statistical significance is constructed; then, linear regression, decision tree, LightGBM, and other machine learning algorithms build multiple basic models to find the best basic model; finally, integrates Averaging, Voting, Blending, Stacking and other integrated algorithms to refine multiple fusion models, and finally establish the most suitable fusion model for operator user evaluation.
Fostc3net:A Lightweight YOLOv5 Based On the Network Structure Optimization
In addition, a FasterNet module is introduced to replace the c3 module in the YOLOv5 Backbone.
BROW: Better featuRes fOr Whole slide image based on self-distillation
Hence, we proposed BROW, a foundation model for extracting better feature representations for WSIs, which can be conveniently adapted to downstream tasks without or with slight fine-tuning.
High Probability Analysis for Non-Convex Stochastic Optimization with Clipping
Gradient clipping is a commonly used technique to stabilize the training process of neural networks.
Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation
To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry.
Ranked #1 on
2D Object Detection
on CeyMo
Understanding the Generalization Performance of Spectral Clustering Algorithms
The theoretical analysis of spectral clustering mainly focuses on consistency, while there is relatively little research on its generalization performance.
Towards Sharper Generalization Bounds for Structured Prediction
In the smoothness scenario, we provide generalization bounds that are not only a logarithmic dependency on the label set cardinality but a faster convergence rate of order $\mathcal{O}(\frac{1}{n})$ on the sample size $n$.
Learning Rates for Nonconvex Pairwise Learning
We first successfully establish learning rates for these algorithms in a general nonconvex setting, where the analysis sheds insights on the trade-off between optimization and generalization and the role of early-stopping.
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
In this work, we revisit the role of discriminator in GAN compression and design a novel generator-discriminator cooperative compression scheme for GAN compression, termed GCC.
High Probability Generalization Bounds for Minimax Problems with Fast Rates
In this paper, we provide improved generalization analyses for almost all existing generalization measures of minimax problems, which enables the minimax problems to establish sharper bounds of order $\mathcal{O}\left( 1/n \right)$, significantly, with high probability.
Improved Learning Rates for Stochastic Optimization: Two Theoretical Viewpoints
the sample size $n$ for ERM and SGD with milder assumptions in convex learning and similar high probability rates of order $\mathcal{O} (1/n)$ in nonconvex learning, rather than in expectation.
Distilling a Powerful Student Model via Online Knowledge Distillation
Besides, a self-distillation module is adopted to convert the feature map of deeper layers into a shallower one.
Network Pruning using Adaptive Exemplar Filters
Inspired by the face recognition community, we use a message passing algorithm Affinity Propagation on the weight matrices to obtain an adaptive number of exemplars, which then act as the preserved filters.
Learning Efficient GANs for Image Translation via Differentiable Masks and co-Attention Distillation
The latter simultaneously distills informative attention maps from both the generator and discriminator of a pre-trained model to the searched generator, effectively stabilizing the adversarial training of our light-weight model.
Filter Sketch for Network Pruning
Our approach, referred to as FilterSketch, encodes the second-order information of pre-trained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure.
