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Found 40 papers, 19 papers with code
Deep Learning for Cross-Domain Data Fusion in Urban Computing: Taxonomy, Advances, and Outlook
As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e. g., geographical, traffic, social media, and environmental data) and modalities (e. g., spatio-temporal, visual, and textual modalities).
Federated Continual Learning via Knowledge Fusion: A Survey
The key objective of FCL is to fuse heterogeneous knowledge from different clients and retain knowledge of previous tasks while learning on new ones.
MLPST: MLP is All You Need for Spatio-Temporal Prediction
To accomplish the above goals, we propose an intuitive and novel framework, MLPST, a pure multi-layer perceptron architecture for traffic prediction.
Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference
To address these challenges, we recast the crowd flow inference problem as a self-supervised attributed graph representation learning task and introduce a novel Contrastive Self-learning framework for Spatio-Temporal data (CSST).
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly.
Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey
STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods.
CLIP-FO3D: Learning Free Open-world 3D Scene Representations from 2D Dense CLIP
Training a 3D scene understanding model requires complicated human annotations, which are laborious to collect and result in a model only encoding close-set object semantics.
Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?
The success of deep learning heavily relies on large-scale data with comprehensive labels, which is more expensive and time-consuming to fetch in 3D compared to 2D images or natural languages.
Ranked #5 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 10-way (10-shot)
(using extra training data)
Few-Shot 3D Point Cloud Classification
Knowledge Distillation
+1
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods.
Ranked #1 on
Traffic Prediction
on BJTaxi
AirFormer: Predicting Nationwide Air Quality in China with Transformers
Air pollution is a crucial issue affecting human health and livelihoods, as well as one of the barriers to economic and social growth.
Language-Assisted 3D Feature Learning for Semantic Scene Understanding
To guide 3D feature learning toward important geometric attributes and scene context, we explore the help of textual scene descriptions.
Contrastive Deep Supervision
The success of deep learning is usually accompanied by the growth in neural network depth.
Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views
A data augmentation module is utilized in contrastive learning to transform the given data example into two views, which is considered essential and irreplaceable.
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling
To address the above challenging problems, we propose a novel Cross-city Federated Transfer Learning framework (CcFTL) to cope with the data insufficiency and privacy problems.
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Federated distillation (FD) is proposed to simultaneously address the above two problems, which exchanges knowledge between the server and clients, supporting heterogeneous local models while significantly reducing communication overhead.
Spatio-Temporal Dynamic Graph Relation Learning for Urban Metro Flow Prediction
Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix.
HiSTGNN: Hierarchical Spatio-temporal Graph Neural Networks for Weather Forecasting
In this paper, we propose a novel Hierarchical Spatio-Temporal Graph Neural Network (HiSTGNN) to model cross-regional spatio-temporal correlations among meteorological variables in multiple stations.
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment.
POI Alias Discovery in Delivery Addresses using User Locations
People often refer to a place of interest (POI) by an alias.
GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio
This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition corpus with 10, 000 hours of high quality labeled audio suitable for supervised training, and 40, 000 hours of total audio suitable for semi-supervised and unsupervised training.
Ranked #1 on
Speech Recognition
on GigaSpeech
speechocean762: An Open-Source Non-native English Speech Corpus For Pronunciation Assessment
This paper introduces a new open-source speech corpus named "speechocean762" designed for pronunciation assessment use, consisting of 5000 English utterances from 250 non-native speakers, where half of the speakers are children.
Ranked #7 on
Phone-level pronunciation scoring
on speechocean762
Fairness and Accuracy in Federated Learning
An appropriate weight selection algorithm that combines the information quantity of training accuracy and training frequency to measure the weights is proposed.
Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
This framework consists of three parts: 1) a local feature extraction module to learn representations for each region; 2) a global context module to extract global contextual priors and upsample them to generate the global features; and 3) a region-specific predictor based on tensor decomposition to provide customized predictions for each region, which is very parameter-efficient compared to previous methods.
Federated Extra-Trees with Privacy Preserving
It is commonly observed that the data are scattered everywhere and difficult to be centralized.
Urban flows prediction from spatial-temporal data using machine learning: A survey
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc.
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning
Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging because of two aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locations along with temporal correlations among timestamps; 2) diversity of such spatiotemporal correlations, which vary from location to location and depend on the surrounding geographical information, e. g., points of interests and road networks.
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction
This paper focuses on two related subtasks of aspect-based sentiment analysis, namely aspect term extraction and aspect sentiment classification, which we call aspect term-polarity co-extraction.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Federated Forest
In this paper, we tackle these challenges and propose a privacy-preserving machine learning model, called Federated Forest, which is a lossless learning model of the traditional random forest method, i. e., achieving the same level of accuracy as the non-privacy-preserving approach.
Arbitrage of Energy Storage in Electricity Markets with Deep Reinforcement Learning
In this letter, we address the problem of controlling energy storage systems (ESSs) for arbitrage in real-time electricity markets under price uncertainty.
Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks
In this paper, we formulate crowd flow forecasting in irregular regions as a spatio-temporal graph (STG) prediction problem in which each node represents a region with time-varying flows.
UrbanFM: Inferring Fine-Grained Urban Flows
In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations.
Ranked #2 on
Fine-Grained Urban Flow Inference
on TaxiBJ-P4
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models.
Attention-based End-to-End Models for Small-Footprint Keyword Spotting
In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system.
Empirical Evaluation of Speaker Adaptation on DNN based Acoustic Model
Speaker adaptation aims to estimate a speaker specific acoustic model from a speaker independent one to minimize the mismatch between the training and testing conditions arisen from speaker variabilities.
Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition
First, we study the effectiveness of different dereverberation networks (the generator in GAN) and find that LSTM leads a significant improvement as compared with feed-forward DNN and CNN in our dataset.
Attention-Based End-to-End Speech Recognition on Voice Search
Previous attempts have shown that applying attention-based encoder-decoder to Mandarin speech recognition was quite difficult due to the logographic orthography of Mandarin, the large vocabulary and the conditional dependency of the attention model.
Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks
We propose a deep-learning-based approach, called ST-ResNet, to collectively forecast two types of crowd flows (i. e. inflow and outflow) in each and every region of a city.
Parallel Large-Scale Attribute Reduction on Cloud Systems
The rapid growth of emerging information technologies and application patterns in modern society, e. g., Internet, Internet of Things, Cloud Computing and Tri-network Convergence, has caused the advent of the era of big data.
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
The aggregation is further combined with external factors, such as weather and day of the week, to predict the final traffic of crowds in each and every region.
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data
In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collectively fill missing readings in a collection of geosensory time series data, considering 1) the temporal correlation between readings at different timestamps in the same series and 2) the spatial correlation between different time series.
Collaborative Filtering
Multivariate Time Series Imputation
+3
