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Found 44 papers, 4 papers with code
Fast Sparse View Guided NeRF Update for Object Reconfigurations
Neural Radiance Field (NeRF), as an implicit 3D scene representation, lacks inherent ability to accommodate changes made to the initial static scene.
A Quantitative Evaluation of Score Distillation Sampling Based Text-to-3D
However, the SDS method is also the source of several artifacts, such as the Janus problem, the misalignment between the text prompt and the generated 3D model, and 3D model inaccuracies.
Anchor function: a type of benchmark functions for studying language models
However, language model research faces significant challenges, especially for academic research groups with constrained resources.
BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation
The purpose of remote sensing image change detection (RSCD) is to detect differences between bi-temporal images taken at the same place.
IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization
Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services.
Group Pose: A Simple Baseline for End-to-End Multi-person Pose Estimation
State-of-the-art solutions adopt the DETR-like framework, and mainly develop the complex decoder, e. g., regarding pose estimation as keypoint box detection and combining with human detection in ED-Pose, hierarchically predicting with pose decoder and joint (keypoint) decoder in PETR.
Towards Visual Foundational Models of Physical Scenes
We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion.
Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation
Instead of learning a single prototype for each class, in this paper, we propose to use an adaptive number of prototypes to dynamically describe the different point patterns within a semantic class.
Ranked #17 on
3D Semantic Segmentation
on SemanticKITTI
Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild
We for the first time propose a point cloud based hand joint tracking network, HandTrackNet, to estimate the inter-frame hand joint motion.
Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation
Category-level object pose estimation aims to find 6D object poses of previously unseen object instances from known categories without access to object CAD models.
R4: A Framework for Route Representation and Route Recommendation
For user representation, we utilize a series of historical navigation to extract user preference.
Denoising User-aware Memory Network for Recommendation
Specifically, the framework: (i) proposes a feature purification module based on orthogonal mapping, which use the representation of explicit feedback to purify the representation of implicit feedback, and effectively denoise the implicit feedback; (ii) designs a user memory network to model the long-term interests in a fine-grained way by improving the memory network, which is ignored by the existing methods; and (iii) develops a preference-aware interactive representation component to fuse the long-term and short-term interests of users based on gating to understand the evolution of unbiased preferences of users.
Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds
To reduce the huge amount of pose annotations needed for category-level learning, we propose for the first time a self-supervised learning framework to estimate category-level 6D object pose from single 3D point clouds.
Super exponential divergence of periodic points for C^1-generic partially hyperbolic homoclinic classes
A diffeomorphism f is called super exponential divergent if for every r>1, the lower limit of #Per_n(f)/r^n diverges to infinity as n tends to infinity, where Per_n(f) is the set of all periodic points of f with period n. This property is stronger than the usual super exponential growth of the number of periodic points.
Dynamical Systems 37C20, 37C25, 37C29, 37D30
Interactive Question Clarification in Dialogue via Reinforcement Learning
In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.
LRC-BERT: Latent-representation Contrastive Knowledge Distillation for Natural Language Understanding
In this work, we propose a knowledge distillation method LRC-BERT based on contrastive learning to fit the output of the intermediate layer from the angular distance aspect, which is not considered by the existing distillation methods.
Handling Rare Entities for Neural Sequence Labeling
One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases.
Slot-consistent NLG for Task-oriented Dialogue Systems with Iterative Rectification Network
Data-driven approaches using neural networks have achieved promising performances in natural language generation (NLG).
Unpack Local Model Interpretation for GBDT
Besides the commonly used feature importance as a global interpretation, feature contribution is a local measure that reveals the relationship between a specific instance and the related output.
NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay
Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.
Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization
However, existing MF approaches suffer from two major problems: (1) Expensive computations and storages due to the centralized model training mechanism: the centralized learners have to maintain the whole user-item rating matrix, and potentially huge low rank matrices.
Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition
We revise the attention distribution to focus on the local and contextual semantic information by incorporating the relative position information between utterances.
RNE: A Scalable Network Embedding for Billion-scale Recommendation
However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.
Long Short-Term Sample Distillation
The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.
Uncovering Insurance Fraud Conspiracy with Network Learning
In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.
How Much Can A Retailer Sell? Sales Forecasting on Tmall
By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).
Heterogeneous Graph Neural Networks for Malicious Account Detection
We present, GEM, the first heterogeneous graph neural network approach for detecting malicious accounts at Alipay, one of the world's leading mobile cashless payment platform.
Category-Level Articulated Object Pose Estimation
We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space.
A Time Attention based Fraud Transaction Detection Framework
With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security.
Kingsoft's Neural Machine Translation System for WMT19
This paper describes the Kingsoft AI Lab{'}s submission to the WMT2019 news translation shared task.
TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business.
Review Helpfulness Prediction with Embedding-Gated CNN
Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions.
Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators
With the growing amount of reviews in e-commerce websites, it is critical to assess the helpfulness of reviews and recommend them accordingly to consumers.
Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud
This model can block fraud transactions in a large amount of money each day.
Feature Propagation on Graph: A New Perspective to Graph Representation Learning
In this paper, we first define the concept of feature propagation on graph formally, and then study its convergence conditions to equilibrium states.
A Boosting Framework of Factorization Machine
However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets.
Distributed Collaborative Hashing and Its Applications in Ant Financial
The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.
Time-sensitive Customer Churn Prediction based on PU Learning
With the fast development of Internet companies throughout the world, customer churn has become a serious concern.
Non-Local Graph-Based Prediction For Reversible Data Hiding In Images
If sufficiently smooth, we pose a maximum a posteriori (MAP) problem using either a quadratic Laplacian regularizer or a graph total variation (GTV) term as signal prior.
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.
Secure Detection of Image Manipulation by means of Random Feature Selection
We address the problem of data-driven image manipulation detection in the presence of an attacker with limited knowledge about the detector.
Cryptography and Security
Small-footprint Keyword Spotting Using Deep Neural Network and Connectionist Temporal Classifier
Mainly for the sake of solving the lack of keyword-specific data, we propose one Keyword Spotting (KWS) system using Deep Neural Network (DNN) and Connectionist Temporal Classifier (CTC) on power-constrained small-footprint mobile devices, taking full advantage of general corpus from continuous speech recognition which is of great amount.
