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Found 11 papers, 6 papers with code
ModWaveMLP: MLP-Based Mode Decomposition and Wavelet Denoising Model to Defeat Complex Structures in Traffic Forecasting
Additionally, when handling traffic data, researchers tend to manually design the model structure based on the data features, which makes the structure of traffic prediction redundant and the model generalizability limited.
Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification
Experimental results show that PseMix could often assist state-of-the-art MIL networks to refresh their classification performance on WSIs.
ProtoDiv: Prototype-guided Division of Consistent Pseudo-bags for Whole-slide Image Classification
Due to the limitations of inadequate Whole-Slide Image (WSI) samples with weak labels, pseudo-bag-based multiple instance learning (MIL) appears as a vibrant prospect in WSI classification.
Social4Rec: Distilling User Preference from Social Graph for Video Recommendation in Tencent
Despite recommender systems play a key role in network content platforms, mining the user's interests is still a significant challenge.
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images
To tackle these problems, we propose a novel adversarial multiple instance learning (AdvMIL) framework.
DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis
Our experiments and ablation studies verify that (i) the proposed DSCA could outperform existing state-of-the-art methods in cancer prognosis, by an average C-Index improvement of around 4. 6%; (ii) our DSCA network is more efficient in computation -- it has more learnable parameters (6. 31M vs. 860. 18K) but less computational costs (2. 51G vs. 4. 94G), compared to a typical existing multi-resolution network.
NCS4CVR: Neuron-Connection Sharing for Multi-Task Learning in Video Conversion Rate Prediction
To address this sharing&conflict problem, we propose a novel multi-task CVR modeling scheme with neuron-connection level sharing named NCS4CVR, which can automatically and flexibly learn which neuron weights are shared or not shared without artificial experience.
MMSE Channel Estimation for Two-Port Demodulation Reference Signals in New Radio
In this paper, we firstly propose a minimum mean square error (MMSE) scheme with full priori knowledge (F-MMSE) to achieve the channel estimation of two-port DMRS in NR.
Machine Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO
Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA).
Realtime Scheduling and Power Allocation Using Deep Neural Networks
With the increasing number of base stations (BSs) and network densification in 5G, interference management using link scheduling and power control are vital for better utilization of radio resources.
A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images
Recording atomic-resolution transmission electron microscopy (TEM) images is becoming increasingly routine.
Materials Science
