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Found 68 papers, 22 papers with code
End-to-end training of Multimodal Model and ranking Model
In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption.
BEM: Balanced and Entropy-based Mix for Long-Tailed Semi-Supervised Learning
To this end, this paper introduces the Balanced and Entropy-based Mix (BEM), a pioneering mixing approach to re-balance the class distribution of both data quantity and uncertainty.
Knowledge Condensation and Reasoning for Knowledge-based VQA
We condense the retrieved knowledge passages from two perspectives.
Skeleton Supervised Airway Segmentation
Then, we construct a geometry-aware dual-path propagation framework (GDP) to further promote complementary propagation learning, composed of hard geometry-aware propagation learning and soft geometry-aware propagation guidance.
Understanding Public Perceptions of AI Conversational Agents: A Cross-Cultural Analysis
We find Chinese participants tended to view CAs hedonically, perceived voice-based and physically embodied CAs as warmer and more competent, and generally expressed positive emotions.
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition
Grounded Multimodal Named Entity Recognition (GMNER) is a nascent multimodal task that aims to identify named entities, entity types and their corresponding visual regions.
Optimal dynamic climate adaptation pathways: a case study of New York City
We model the severity of extreme sea level events using the block maxima approach from extreme value theory, and then develop a real options framework, factoring in climate change, sea level rise uncertainty, and the growth in asset exposure.
RIDERS: Radar-Infrared Depth Estimation for Robust Sensing
Dense depth recovery is crucial in autonomous driving, serving as a foundational element for obstacle avoidance, 3D object detection, and local path planning.
Future Impact Decomposition in Request-level Recommendations
Furthermore, we show that a reward-based future decomposition strategy can better express the item-wise future impact and improve the recommendation accuracy in the long term.
PepGB: Facilitating peptide drug discovery via graph neural networks
In summary, our proposed frameworks can serve as potent tools to facilitate peptide early drug discovery.
RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale
To circumvent this issue, we learn to augment versatile and robust monocular depth prediction with the dense metric scale induced from sparse and noisy Radar data.
Towards Efficient and Effective Text-to-Video Retrieval with Coarse-to-Fine Visual Representation Learning
To address this issue, we adopt multi-granularity visual feature learning, ensuring the model's comprehensiveness in capturing visual content features spanning from abstract to detailed levels during the training phase.
LoCo: Locally Constrained Training-Free Layout-to-Image Synthesis
We further propose a Padding Token Constraint (PTC) to leverage the semantic information embedded in previously neglected padding tokens, improving the consistency between object appearance and layout instructions.
Frequency-Aware Transformer for Learned Image Compression
Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years.
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively.
Interpretable Diffusion via Information Decomposition
For diffusion models, we show that a natural non-negative decomposition of mutual information emerges, allowing us to quantify informative relationships between words and pixels in an image.
Discovering Sounding Objects by Audio Queries for Audio Visual Segmentation
Explicit object-level semantic correspondence between audio and visual modalities is established by gathering object information from visual features with predefined audio queries.
Cross-Domain Product Representation Learning for Rich-Content E-Commerce
It is the first dataset to cover product pages, short videos, and live streams simultaneously, providing the basis for establishing a unified product representation across different media domains.
Cross-view Semantic Alignment for Livestreaming Product Recognition
Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the image and video views of the products.
ActionPrompt: Action-Guided 3D Human Pose Estimation With Text and Pose Prompting
Recent 2D-to-3D human pose estimation (HPE) utilizes temporal consistency across sequences to alleviate the depth ambiguity problem but ignore the action related prior knowledge hidden in the pose sequence.
LEST: Large-scale LiDAR Semantic Segmentation with Transformer
Large-scale LiDAR-based point cloud semantic segmentation is a critical task in autonomous driving perception.
D3L: Decomposition of 3D Rotation and Lift from 2D Joint to 3D for Human Mesh Recovery
However, these methods present rotation semantic ambiguity, rotation error accumulation, and shape estimation overfitting, which also leads to errors in the estimated pose.
Multi-Epoch Learning for Deep Click-Through Rate Prediction Models
However, it is still unknown whether a multi-epoch training paradigm could achieve better results, as the best performance is usually achieved by one-epoch training.
Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge
However, these methods either neglect the necessity of providing the model with external knowledge, or encounter issues of high redundancy in the retrieved knowledge.
Ranked #1 on
Multi-modal Named Entity Recognition
on Twitter-2017
(using extra training data)
Multi-modal Named Entity Recognition
named-entity-recognition
+1
PELE scores: Pelvic X-ray Landmark Detection by Pelvis Extraction and Enhancement
The pelvis, the lower part of the trunk, supports and balances the trunk.
DiffULD: Diffusive Universal Lesion Detection
But they are still ineffective for ULD due to the insufficient training targets.
Pose-Oriented Transformer with Uncertainty-Guided Refinement for 2D-to-3D Human Pose Estimation
There has been a recent surge of interest in introducing transformers to 3D human pose estimation (HPE) due to their powerful capabilities in modeling long-term dependencies.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
Mixed-order self-paced curriculum learning for universal lesion detection
Most SCL methods commonly adopt a loss-based strategy of estimating data difficulty and deweighting the `hard' samples in the early training stage.
Fairness in Medical Image Analysis and Healthcare: A Literature Survey
Machine learning-enabled medical imaging analysis has become a vital part of the automatic diagnosis system.
From Static to Dynamic Structures: Improving Binding Affinity Prediction with a Graph-Based Deep Learning Model
Accurate prediction of the protein-ligand binding affinities is an essential challenge in the structure-based drug design.
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction
In this paper, we argue that there exist two major issues hindering current self-supervised learning methods from obtaining desired performance on molecular property prediction, that is, the ill-defined pre-training tasks and the limited model capacity.
Graph Representation Learning
Molecular Property Prediction
+2
SATr: Slice Attention with Transformer for Universal Lesion Detection
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.
DNN Training Acceleration via Exploring GPGPU Friendly Sparsity
The training phases of Deep neural network~(DNN) consumes enormous processing time and energy.
MVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Clouds
The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification.
Pareto Domain Adaptation
Domain adaptation (DA) attempts to transfer the knowledge from a labeled source domain to an unlabeled target domain that follows different distribution from the source.
Incentive Compatible Pareto Alignment for Multi-Source Large Graphs
Challenges of the problem include 1) how to align large-scale entities between sources to share information and 2) how to mitigate negative transfer from joint learning multi-source data.
Hierarchical Graph Networks for 3D Human Pose Estimation
Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton.
Ranked #42 on
3D Human Pose Estimation
on MPI-INF-3DHP
(AUC metric)
Context-aware Tree-based Deep Model for Recommender Systems
Vertically, a parent fusion layer is designed in M to transmit the user preference representation in higher levels of T to the current level, grasping the essence that tree-based methods are generating the candidate set from coarse to detail during the beam search retrieval.
Deep learning for blocking in entity matching: a design space exploration
In this paper, we develop the DeepBlocker framework that significantly advances the state of the art in applying DL to blocking for EM.
Ranked #5 on
Blocking
on Abt-Buy
Learning Slice-Aware Representations with Mixture of Attentions
Real-world machine learning systems are achieving remarkable performance in terms of coarse-grained metrics like overall accuracy and F-1 score.
Conditional Training with Bounding Map for Universal Lesion Detection
Universal Lesion Detection (ULD) in computed tomography plays an essential role in computer-aided diagnosis.
Ranked #3 on
Medical Object Detection
on DeepLesion
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration
Ensuring model robustness or resilience in the skill routing component is an important problem since skills may dynamically change their subscription in the ontology after the skill routing model has been deployed to production.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Phase Diagram of Triangular Lattice Quantum Ising Model under External Field
Quantum Ising model on a triangular lattice hosts a finite temperature Berezinskii-Kosterlitz-Thouless (BKT) phase with emergent U(1) symmetry, and it will transit into an up-up-down (UUD) phase with $C_3$ symmetry breaking upon an infinitesimal external field along the longitudinal direction, but the overall phase diagram spanned by the axes of external field and temperature remains opaque due to the lack of systematic invesitgations with controlled methodologies.
Strongly Correlated Electrons Statistical Mechanics
A scalable framework for learning from implicit user feedback to improve natural language understanding in large-scale conversational AI systems
Natural Language Understanding (NLU) is an established component within a conversational AI or digital assistant system, and it is responsible for producing semantic understanding of a user request.
Learning to Infer User Hidden States for Online Sequential Advertising
To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
Bounding Maps for Universal Lesion Detection
The bounding maps (BMs) are used in two-stage anchor-based ULD frameworks to reduce the FP rate.
The Statistical Characteristics of Power-Spectrum Subband Energy Ratios under Additive Gaussian White Noise
When Gaussian white noise was mixed with the known signal, the resulting PSER followed a doubly non-central beta distribution.
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.
Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
In E-commerce, advertising is essential for merchants to reach their target users.
Learning Optimal Tree Models Under Beam Search
Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising
Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc.
Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition
We hereby propose a decomposition generative adversarial network (DecGAN) to anatomically decompose a CXR image but with unpaired data.
Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
We refer to this process as Leverage.
Spectral-based Graph Convolutional Network for Directed Graphs
Our approach can work directly on directed graph data in semi-supervised nodes classification tasks.
Detecting Customer Complaint Escalation with Recurrent Neural Networks and Manually-Engineered Features
Consumers dissatisfied with the normal dispute resolution process provided by an e-commerce company{'}s customer service agents have the option of escalating their complaints by filing grievances with a government authority.
Continuous Learning for Large-scale Personalized Domain Classification
This is a major component in mainstream IPDAs in industry.
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning} greatly improves recommendation accuracy using tree index.
Learning Adaptive Display Exposure for Real-Time Advertising
In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?
Deep Learning for Entity Matching: A Design Space Exploration
Entity matching (EM) finds data instances that refer to the same real-world entity.
Ranked #8 on
Entity Resolution
on Amazon-Google
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
Real-time advertising allows advertisers to bid for each impression for a visiting user.
Learning Tree-based Deep Model for Recommender Systems
In systems with large corpus, however, the calculation cost for the learnt model to predict all user-item preferences is tremendous, which makes full corpus retrieval extremely difficult.
Toward a System Building Agenda for Data Integration
They provide tools to address the "pain points" of the steps, and tools are built on top of the Python data science and Big Data ecosystem (PyData).
Databases
Deep Interest Network for Click-Through Rate Prediction
In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.
Ranked #1 on
Click-Through Rate Prediction
on Amazon
Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.
Optimized Cost per Click in Taobao Display Advertising
Moreover, the platform has to be responsible for the business revenue and user experience.
