Search Results for author:
Found 52 papers, 20 papers with code
Rotation-robust Intersection over Union for 3D Object Detection
In this paper, we propose a Rotation-robust Intersection over Union ($ extit{RIoU}$) for 3D object detection, which aims to jointly learn the overlap of rotated bounding boxes.
HyperDet3D: Learning a Scene-conditioned 3D Object Detector
A bathtub in a library, a sink in an office, a bed in a laundry room -- the counter-intuition suggests that scene provides important prior knowledge for 3D object detection, which instructs to eliminate the ambiguous detection of similar objects.
Deep AutoAugment
In this work, instead of fixing a set of hand-picked default augmentations alongside the searched data augmentations, we propose a fully automated approach for data augmentation search named Deep AutoAugment (DeepAA).
Ranked #1 on
Data Augmentation
on ImageNet
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
In this paper, we propose a weakly-supervised approach for 3D object detection, which makes it possible to train a strong 3D detector with position-level annotations (i. e. annotations of object centers).
Disentangling Long and Short-Term Interests for Recommendation
Modeling user's long-term and short-term interests is crucial for accurate recommendation.
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
Towards Unsupervised Deep Graph Structure Learning
To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning.
Crowd-sensing Enhanced Parking Patrol using Trajectories of Sharing Bikes
Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents.
Poformer: A simple pooling transformer for speaker verification
Most recent speaker verification systems are based on extracting speaker embeddings using a deep neural network.
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.
Enhancing semi-supervised learning via self-interested coalitional learning
Jointly solving these two tasks allows full utilization of information from both labeled and unlabeled data, thus alleviating the problem of over-reliance on labeled data.
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs
Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.
Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Recommender system is one of the most important information services on today's Internet.
POI Alias Discovery in Delivery Addresses using User Locations
People often refer to a place of interest (POI) by an alias.
NSP-BERT: A Prompt-based Zero-Shot Learner Through an Original Pre-training Task--Next Sentence Prediction
Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune paradigm.
Spatio-Temporal Graph Contrastive Learning
Second, data augmentations that are used for defeating noise are less explored for STG data.
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.
DGCN: Diversified Recommendation with Graph Convolutional Networks
These years much effort has been devoted to improving the accuracy or relevance of the recommendation system.
Sequential Recommendation with Graph Neural Networks
This helps explicitly distinguish users' core interests, by forming dense clusters in the interest graph.
CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System
We propose a correlation structure-based collective anomaly detection (CSCAD) model for high-dimensional anomaly detection problem in large systems, which is also generalizable to semi-supervised or supervised settings.
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring
To accurately pour drinks into various containers is an essential skill for service robots.
Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently.
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
Optimizing the combustion efficiency of a thermal power generating unit (TPGU) is a highly challenging and critical task in the energy industry.
Topological Transformations in Pentagonal 2D Materials Induced by Stone-Wales Defects
We discover two distinct topological pathways through which the pentagonal Cairo tiling (P5), a structural model for single-layer $AB_2$ pyrite materials, respectively transforms into a crystalline rhombus-hexagon (C46) tiling and random rhombus-pentagon-hexagon (R456) tilings, by continuously introducing the Stone-Wales (SW) topological defects.
Soft Condensed Matter Disordered Systems and Neural Networks Materials Science
Disentangling User Interest and Conformity for Recommendation with Causal Embedding
We further demonstrate that the learned embeddings successfully capture the desired causes, and show that DICE guarantees the robustness and interpretability of recommendation.
Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Existing Neural Architecture Search (NAS) methods either encode neural architectures using discrete encodings that do not scale well, or adopt supervised learning-based methods to jointly learn architecture representations and optimize architecture search on such representations which incurs search bias.
Urban Anomaly Analytics: Description, Detection, and Prediction
Urban anomalies may result in loss of life or property if not handled properly.
Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment
Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integers dilated kernels is used to cope with the misalignment of fractional sampling.
Price-aware Recommendation with Graph Convolutional Networks
Price, an important factor in marketing --- which determines whether a user will make the final purchase decision on an item --- surprisingly, has received relatively little scrutiny.
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.
Fine-Grained Urban Flow Inference
To tackle these issues, we develop a model entitled UrbanFM which consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs that uses a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influence of different external factors.
Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach
Compared with the benchmarked models, our model has the lowest tracking error, across a range of portfolio sizes.
HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking
Without the constraint imposed by the hand-designed heuristics, our searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.
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.
Reward Advancement: Transforming Policy under Maximum Causal Entropy Principle
In this paper, we define and investigate a general reward trans-formation problem (namely, reward advancement): Recovering the range of additional reward functions that transform the agent's policy from original policy to a predefined target policy under MCE principle.
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.
Modeling cell migration regulated by cell-ECM micromechanical coupling
During migration, individual cells can generate active pulling forces via actin filament contraction, which are transmitted to the ECM fibers through focal adhesion complexes, remodel the ECM, and eventually propagate to and can be sensed by other cells in the system.
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training.
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.
COIN: A Large-scale Dataset for Comprehensive Instructional Video Analysis
There are substantial instructional videos on the Internet, which enables us to acquire knowledge for completing various tasks.
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.
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting
We cast the weather forecasting problem as an end-to-end deep learning problem and solve it by proposing a novel negative log-likelihood error (NLE) loss function.
HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting
In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models.
Diversity and Sparsity: A New Perspective on Index Tracking
We introduce the first index tracking method that explicitly optimises both diversity and sparsity in a single joint framework.
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.
pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}.
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.
Gated Neural Networks for Option Pricing: Rationality by Design
We propose a neural network approach to price EU call options that significantly outperforms some existing pricing models and comes with guarantees that its predictions are economically reasonable.
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
+2
T-Drive: Driving Directions Based on Taxi Trajectories
GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge.
