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Found 25 papers, 6 papers with code
Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network
However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses.
Enhancing Data Quality through Self-learning on Imbalanced Financial Risk Data
In the financial risk domain, particularly in credit default prediction and fraud detection, accurate identification of high-risk class instances is paramount, as their occurrence can have significant economic implications.
Self Adaptive Threshold Pseudo-labeling and Unreliable Sample Contrastive Loss for Semi-supervised Image Classification
Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data.
Multiscale lubrication simulation based on fourier feature networks with trainable frequency
To date, no PINN methods have been reported for rough surface lubrication.
AMSNet: Netlist Dataset for AMS Circuits
Today's analog/mixed-signal (AMS) integrated circuit (IC) designs demand substantial manual intervention.
The Treasure Beneath Multiple Annotations: An Uncertainty-aware Edge Detector
Deep learning-based edge detectors heavily rely on pixel-wise labels which are often provided by multiple annotators.
Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEs
Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns.
Cutting-Splicing data augmentation: A novel technology for medical image segmentation
Background: Medical images are more difficult to acquire and annotate than natural images, which results in data augmentation technologies often being used in medical image segmentation tasks.
AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds
From this observation, we have devised a single-stage anchor-free network that can fulfill these requirements.
Human-Level Control through Directly-Trained Deep Spiking Q-Networks
Specifically, we propose a directly-trained deep spiking reinforcement learning architecture based on the Leaky Integrate-and-Fire (LIF) neurons and Deep Q-Network (DQN).
PHMOSpell: Phonological and Morphological Knowledge Guided Chinese Spelling Check
Chinese Spelling Check (CSC) is a challenging task due to the complex characteristics of Chinese characters.
Ranked #1 on
Chinese Spell Checking
on SIGHAN 2015
Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness
Extended from our last year's award-winning model AFDet, we have made a handful of modifications to the base model, to improve the accuracy and at the same time to greatly reduce the latency.
KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi
Recent progress in text classification has been focused on high-resource languages such as English and Chinese.
1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation
We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors.
1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking
This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges.
2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection
A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons.
AFDet: Anchor Free One Stage 3D Object Detection
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving.
Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Records
Electronic medical records (EMRs) supports the development of machine learning algorithms for predicting disease incidence, patient response to treatment, and other healthcare events.
LoAdaBoost: loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data
One challenge in applying federated machine learning is the possibly different distributions of data from diverse sources.
Naturalness Sum Rules and Their Collider Tests
We present the most general sum rules reflecting the cancellation of ultraviolet divergences in the Higgs potential in weakly-coupled, natural extensions of the Standard Model.
High Energy Physics - Phenomenology
Web-Scale Responsive Visual Search at Bing
In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing.
Recommender Engine for Continuous Time Quantum Monte Carlo Methods
Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions.
Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
Despite their exceptional flexibility and popularity, the Monte Carlo methods often suffer from slow mixing times for challenging statistical physics problems.
