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Found 38 papers, 21 papers with code
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
Recent years have witnessed intensive research interests on training deep neural networks (DNNs) more efficiently by quantization-based compression methods, which facilitate DNNs training in two ways: (1) activations are quantized to shrink the memory consumption, and (2) gradients are quantized to decrease the communication cost.
Can LLMs be Good Graph Judger for Knowledge Graph Construction?
We seek to utilize the capacity of LLMs to function as a graph judger, a capability superior to their role only as a predictor for KG construction problems.
Corporate Fundamentals and Stock Price Co-Movement
We introduce an innovative framework that leverages advanced big data techniques to analyze dynamic co-movement between stocks and their underlying fundamentals using high-frequency stock market data.
Retrofitting Temporal Graph Neural Networks with Transformer
We also adapt Transformer codebase to train TF-TGN efficiently with multiple GPUs.
Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph
Text matching retrieves texts with similar embeddings to match with a node.
TreeCSS: An Efficient Framework for Vertical Federated Learning
As such, we propose TreeCSS as an efficient VFL framework that accelerates the two main steps.
Open-CD: A Comprehensive Toolbox for Change Detection
We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules.
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System
Our results demonstrate three major findings: 1) The UPMEM PIM system can be a viable alternative to state-of-the-art CPUs and GPUs for many memory-bound ML training workloads, especially when operations and datatypes are natively supported by PIM hardware, 2) it is important to carefully choose the optimization algorithms that best fit PIM, and 3) the UPMEM PIM system does not scale approximately linearly with the number of nodes for many data-intensive ML training workloads.
Class Similarity Transition: Decoupling Class Similarities and Imbalance from Generalized Few-shot Segmentation
This paper focuses on the relevance between base and novel classes, and improves GFSS in two aspects: 1) mining the similarity between base and novel classes to promote the learning of novel classes, and 2) mitigating the class imbalance issue caused by the volume difference between the support set and the training set.
Distribution-Aware Data Expansion with Diffusion Models
In this paper, we propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model.
Jointly Learning Representations for Map Entities via Heterogeneous Graph Contrastive Learning
Developing effective Map Entity Representation Learning (MERL) methods is crucial to extracting embedding information from electronic maps and converting map entities into representation vectors for downstream applications.
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
Recently, neural networks have proven to be effective in performing speech coding task at low bitrates.
A Beam-Segmenting Polar Format Algorithm Based on Double PCS for Video SAR Persistent Imaging
Through processing the sub-beam data and mosaicking the refocused subimages, the full image in GOCS without distortion and defocus is obtained.
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning
For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the generative paradigm and learns to reconstruct masked graph edges or node features.
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed.
Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark]
The field of urban spatial-temporal prediction is advancing rapidly with the development of deep learning techniques and the availability of large-scale datasets.
LightCDNet: Lightweight Change Detection Network Based on VHR Images
Reducing the model size while maintaining high accuracy is a key challenge in developing lightweight change detection models.
Building change detection for remote sensing images
Change Detection
+1
LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
As deep learning technology advances and more urban spatial-temporal data accumulates, an increasing number of deep learning models are being proposed to solve urban spatial-temporal prediction problems.
Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism
In response to this problem, we introduce the Coordinate Attention (CA) module to replace the Res Block to reduce the number of parameters, and cooperate with the spatial information extraction network above to strengthen the information extraction ability.
GA-HQS: MRI reconstruction via a generically accelerated unfolding approach
Deep unfolding networks (DUNs) are the foremost methods in the realm of compressed sensing MRI, as they can employ learnable networks to facilitate interpretable forward-inference operators.
BUAA_BIGSCity: Spatial-Temporal Graph Neural Network for Wind Power Forecasting in Baidu KDD CUP 2022
Therefore, organizers provide a wind power dataset containing historical data from 134 wind turbines and launch the Baidu KDD Cup 2022 to examine the limitations of current methods for wind power forecasting.
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
However, GNN-based models have three major limitations for traffic prediction: i) Most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic urban traffic patterns; ii) Most methods only consider short-range spatial information and are unable to capture long-range spatial dependencies; iii) These methods ignore the fact that the propagation of traffic conditions between locations has a time delay in traffic systems.
Ranked #5 on
Traffic Prediction
on PeMSD4
Continuous Trajectory Generation Based on Two-Stage GAN
Simulating the human mobility and generating large-scale trajectories are of great use in many real-world applications, such as urban planning, epidemic spreading analysis, and geographic privacy protect.
Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics
TRL aims to convert complicated raw trajectories into low-dimensional representation vectors, which can be applied to various downstream tasks, such as trajectory classification, clustering, and similarity computation.
A THz Video SAR Imaging Algorithm Based on Chirp Scaling
In video synthetic aperture radar (SAR) imaging mode, the polar format algorithm (PFA) is more computational effective than the backprojection algorithm (BPA).
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities.
Physics-informed machine learning
Spatio-Temporal Forecasting
+1
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates
Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e. g., organizations or enterprises) to collaboratively build machine learning models with privacy protection.
Stochastic Gradient Descent without Full Data Shuffle
In this paper, we first conduct a systematic empirical study on existing data shuffling strategies, which reveals that all existing strategies have room for improvement -- they all suffer in terms of I/O performance or convergence rate.
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Apart from rule-based and machine learning filters that are already deployed in production, we want to enable efficient real-time inference with graph neural networks (GNNs), which is useful to catch multihop risk propagation in a transaction graph.
LibCity: An Open Library for Traffic Prediction
This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework.
Multivariate Time Series Forecasting
Spatio-Temporal Forecasting
+2
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
BAGUA: Scaling up Distributed Learning with System Relaxations
Recent years have witnessed a growing list of systems for distributed data-parallel training.
OpenBox: A Generalized Black-box Optimization Service
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
Towards Demystifying Serverless Machine Learning Training
The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML).
Efficient Automatic CASH via Rising Bandits
In this framework, the BO methods are used to solve the HPO problem for each ML algorithm separately, incorporating a much smaller hyperparameter space for BO methods.
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Instead of sampling configurations randomly in HB, BOHB samples configurations based on a BO surrogate model, which is constructed with the high-fidelity measurements only.
An Experimental Evaluation of Large Scale GBDT Systems
Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications.
Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates
The performance of deep neural networks crucially depends on good hyperparameter configurations.
