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Found 13 papers, 4 papers with code
TF-Label: a Topological-Folding Labeling Scheme for Reachability Querying in a Large Graph
We propose TF-label, an efficient and scalable labeling scheme for processing reachability queries.
Integration of LiDAR and Hyperspectral Data for Land-cover Classification: A Case Study
In this paper, an approach is proposed to fuse LiDAR and hyperspectral data, which considers both spectral and spatial information in a single framework.
Deep Learning for Epidemiological Predictions
Predicting new and urgent trends in epidemiological data is an important problem for public health, and has attracted increasing attention in the data mining and machine learning communities.
AdaBoost-assisted Extreme Learning Machine for Efficient Online Sequential Classification
In this paper, we propose an AdaBoost-assisted extreme learning machine for efficient online sequential classification (AOS-ELM).
Causal Mosaic: Cause-Effect Inference via Nonlinear ICA and Ensemble Method
We address the problem of distinguishing cause from effect in bivariate setting.
High-resolution global irrigation prediction with Sentinel-2 30m data
An accurate and precise understanding of global irrigation usage is crucial for a variety of climate science efforts.
MAUS: A Dataset for Mental Workload Assessmenton N-back Task Using Wearable Sensor
Besides, we also presents a reproducible baseline system as a preliminary benchmark (The code of the baseline system on MAUS dataset is available on Github: https://github. com/rickwu11/MAUS\_dataset\_baseline\_system), which testing accuracy are 71. 6 %, 66. 7 %, and 59. 9 % in ECG, fingertip PPG, wristband PPG, respectively.
Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation
Recently, deep convolutional neural networks (CNNs) have achieved many eye-catching results.
C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learning
Based on the simulation results on CIFAR-10 and CIFAR-100, our method achieves a 16x compression ratio with negligible accuracy drops compared with the vanilla SL.
TSPTQ-ViT: Two-scaled post-training quantization for vision transformer
Due to the non-normally distributed values after Softmax and GeLU, post-training quantization on ViTs results in severe accuracy degradation.
Music ControlNet: A model similar to SD ControlNetD that can accurately control music generation
While the image-domain Uni-ControlNet method already allows generation with any subset of controls, we devise a new strategy to allow creators to input controls that are only partially specified in time.
MPTQ-ViT: Mixed-Precision Post-Training Quantization for Vision Transformer
However, without considering the asymmetry in activations and relying on hand-crafted settings, these methods often struggle to maintain performance under low-bit quantization.
LATTE: Low-Precision Approximate Attention with Head-wise Trainable Threshold for Efficient Transformer
With the rise of Transformer models in NLP and CV domain, Multi-Head Attention has been proven to be a game-changer.
