Search Results for author:
Found 117 papers, 67 papers with code
Wasserstein Selective Transfer Learning for Cross-domain Text Mining
However, the source and target domains usually have different data distributions, which may lead to negative transfer.
Profanity-Avoiding Training Framework for Seq2seq Models with Certified Robustness
To address this problem, we propose a training framework with certified robustness to eliminate the causes that trigger the generation of profanity.
MindGYM: Enhancing Vision-Language Models via Synthetic Self-Challenging Questions
Large vision-language models (VLMs) face challenges in achieving robust, transferable reasoning abilities due to reliance on labor-intensive manual instruction datasets or computationally expensive self-supervised methods.
Do we Really Need Visual Instructions? Towards Visual Instruction-Free Fine-tuning for Large Vision-Language Models
Despite the success, as visual instructions require images as the input, it would leave the gap in inheriting the task-solving capabilities from the backbone LLMs, and make it costly to collect a large-scale dataset.
KIMAs: A Configurable Knowledge Integrated Multi-Agent System
Knowledge-intensive conversations supported by large language models (LLMs) have become one of the most popular and helpful applications that can assist people in different aspects.
Knowledge Graph-Guided Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs).
Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
Fine-tuning large language models (LLMs) using diverse datasets is crucial for enhancing their overall performance across various domains.
Talk to Right Specialists: Routing and Planning in Multi-agent System for Question Answering
Experimental results demonstrate that RopMura effectively handles both single-hop and multi-hop queries, with the routing mechanism enabling precise answers for single-hop queries and the combined routing and planning mechanisms achieving accurate, multi-step resolutions for complex queries.
Data-Juicer 2.0: Cloud-Scale Adaptive Data Processing for Foundation Models
The burgeoning field of foundation models necessitates advanced data processing mechanisms capable of harnessing vast valuable data with varied types utilized by these models.
HumanVBench: Exploring Human-Centric Video Understanding Capabilities of MLLMs with Synthetic Benchmark Data
In the domain of Multimodal Large Language Models (MLLMs), achieving human-centric video understanding remains a formidable challenge.
ArtAug: Enhancing Text-to-Image Generation through Synthesis-Understanding Interaction
This enables the synthesis model to directly produce aesthetically pleasing images without any extra computational cost.
A Simple and Provable Scaling Law for the Test-Time Compute of Large Language Models
We propose a general two-stage algorithm that enjoys a provable scaling law for the test-time compute of large language models (LLMs).
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models
The structural properties of naturally arising social graphs are extensively studied to understand their evolution.
GenSim: A General Social Simulation Platform with Large Language Model based Agents
With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior.
Agent-Oriented Planning in Multi-Agent Systems
Through the collaboration of multiple LLM-empowered agents possessing diverse expertise and tools, multi-agent systems achieve impressive progress in solving real-world problems.
Safety Layers in Aligned Large Language Models: The Key to LLM Security
Aligned LLMs are secure, capable of recognizing and refusing to answer malicious questions.
Exploring Selective Layer Fine-Tuning in Federated Learning
Federated learning (FL) has emerged as a promising paradigm for fine-tuning foundation models using distributed data in a privacy-preserving manner.
Understanding Byzantine Robustness in Federated Learning with A Black-box Server
Federated learning (FL) becomes vulnerable to Byzantine attacks where some of participators tend to damage the utility or discourage the convergence of the learned model via sending their malicious model updates.
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
We release our codes and dataset to encourage further research on multimodal data synthesis and MLLMs' fundamental capabilities for image understanding.
Ranked #88 on
Visual Question Answering
on MM-Vet
EIUP: A Training-Free Approach to Erase Non-Compliant Concepts Conditioned on Implicit Unsafe Prompts
Since image generation is conditioned on text, prompt purification serves as a straightforward solution for content safety.
Very Large-Scale Multi-Agent Simulation in AgentScope
Recent advances in large language models (LLMs) have opened new avenues for applying multi-agent systems in very large-scale simulations.
On the Design and Analysis of LLM-Based Algorithms
We initiate a formal investigation into the design and analysis of LLM-based algorithms, i. e. algorithms that contain one or multiple calls of large language models (LLMs) as sub-routines and critically rely on the capabilities of LLMs.
Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development
The emergence of large-scale multi-modal generative models has drastically advanced artificial intelligence, introducing unprecedented levels of performance and functionality.
The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective
As LLMs and MLLMs rely on vast amounts of model parameters and data to achieve emergent capabilities, the importance of data is receiving increasingly widespread attention and recognition.
FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model
Specifically, our method involves the server generating a compressed LLM and aligning its performance with the full model.
ExVideo: Extending Video Diffusion Models via Parameter-Efficient Post-Tuning
To evaluate the efficacy of our proposed post-tuning approach, we conduct extension training on the Stable Video Diffusion model.
BiMix: A Bivariate Data Mixing Law for Language Model Pretraining
Optimization of domain proportions yields superior model performance compared to existing methods.
Review of Data-centric Time Series Analysis from Sample, Feature, and Period
Data is essential to performing time series analysis utilizing machine learning approaches, whether for classic models or today's large language models.
Dynamic Demonstration Retrieval and Cognitive Understanding for Emotional Support Conversation
Emotional Support Conversation (ESC) systems are pivotal in providing empathetic interactions, aiding users through negative emotional states by understanding and addressing their unique experiences.
Improving LoRA in Privacy-preserving Federated Learning
Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency.
ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization
Data visualization serves as a critical means for presenting data and mining its valuable insights.
Less is More: High-value Data Selection for Visual Instruction Tuning
To investigate this issue, we conduct a series of empirical studies, which reveal a significant redundancy within the visual instruction datasets, and show that greatly reducing the amount of instructions from several tasks even do not affect the performance.
Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model Optimizers
By systematically analyzing a rich set of improvement strategies on the two aspects, we further develop a capable Gradient-inspired LLM-based Prompt Optimizer called GPO.
A Bargaining-based Approach for Feature Trading in Vertical Federated Learning
We analyze the proposed bargaining model under perfect and imperfect performance information settings, proving the existence of an equilibrium that optimizes the parties' objectives.
Double-I Watermark: Protecting Model Copyright for LLM Fine-tuning
To support various applications, a prevalent and efficient approach for business owners is leveraging their valuable datasets to fine-tune a pre-trained LLM through the API provided by LLM owners or cloud servers.
AgentScope: A Flexible yet Robust Multi-Agent Platform
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs).
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing.
An Auction-based Marketplace for Model Trading in Federated Learning
This FL market allows clients to gain monetary reward by selling their own models and improve local model performance through the purchase of others' models.
EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models
This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs).
From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information
Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visual elements.
Ranked #118 on
Visual Question Answering
on MM-Vet
Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning
Recently, multi-task instruction tuning has been applied into sentence representation learning, which endows the capability of generating specific representations with the guidance of task instruction, exhibiting strong generalization ability on new tasks.
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph
To better perform reasoning on KG, recent work typically adopts a pre-trained language model~(PLM) to model the question, and a graph neural network~(GNN) based module to perform multi-hop reasoning on the KG.
Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes
Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions.
EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs).
Tunable Soft Prompts are Messengers in Federated Learning
As the global model itself is not required to be shared and the local training is conducted based on an auxiliary model with fewer parameters than the global model, the proposed approach provides protection for the global model while reducing communication and computation costs in FL.
Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness
A prevailing belief in attack and defense community is that the higher flatness of adversarial examples enables their better cross-model transferability, leading to a growing interest in employing sharpness-aware minimization and its variants.
Data-Juicer: A One-Stop Data Processing System for Large Language Models
A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance.
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning.
Ranked #3 on
Text-To-SQL
on spider
TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Series
Given the lack of data, limited resources, semantic context requirements, and so on, this work focuses on TS-for-LLM, where we aim to activate LLM's ability for TS data by designing a TS embedding method suitable for LLM.
Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study
Different from previous studies focused on overall performance, this work aims to investigate the impact of quantization on \emph{emergent abilities}, which are important characteristics that distinguish LLMs from small language models.
Counterfactual Debiasing for Generating Factually Consistent Text Summaries
Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved.
Efficient Personalized Federated Learning via Sparse Model-Adaptation
To overcome these challenges, we propose a novel approach named pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models.
Multi-grained Hypergraph Interest Modeling for Conversational Recommendation
In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations.
HPN: Personalized Federated Hyperparameter Optimization
The client encoding is calculated with a random projection-based procedure to protect each client's privacy.
LON-GNN: Spectral GNNs with Learnable Orthonormal Basis
In recent years, a plethora of spectral graph neural networks (GNN) methods have utilized polynomial basis with learnable coefficients to achieve top-tier performances on many node-level tasks.
FS-Real: Towards Real-World Cross-Device Federated Learning
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in both academia and industry.
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks
We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments.
Collaborating Heterogeneous Natural Language Processing Tasks via Federated Learning
In this study, we further broaden the application scope of FL in NLP by proposing an Assign-Then-Contrast (denoted as ATC) framework, which enables clients with heterogeneous NLP tasks to construct an FL course and learn useful knowledge from each other.
Privacy-Preserved Neural Graph Similarity Learning
To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years.
Towards Universal Sequence Representation Learning for Recommender Systems
In order to develop effective sequential recommenders, a series of sequence representation learning (SRL) methods are proposed to model historical user behaviors.
FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization
Due to this uniqueness, existing HPO benchmarks no longer satisfy the need to compare HPO methods in the FL setting.
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning
Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients.
A Benchmark for Federated Hetero-Task Learning
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.
ID-Agnostic User Behavior Pre-training for Sequential Recommendation
Instead of explicitly learning representations for item IDs, IDA-SR directly learns item representations from rich text information.
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks
Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with limited capacity in generalizing to graphs of varied homophily levels.
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning
The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications.
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals.
Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling
Recently, Product Question Answering (PQA) on E-Commerce platforms has attracted increasing attention as it can act as an intelligent online shopping assistant and improve the customer shopping experience.
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression
In this paper, we target to compress PLMs with knowledge distillation, and propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information.
iFlood: A Stable and Effective Regularizer
However, our further studies uncover that the design of the loss function of Flooding can lead to a discrepancy between its objective and implementation, and cause the instability issue.
Coarformer: Transformer for large graph via graph coarsening
We get rid of these obstacles by exploiting the complementary natures of GNN and Transformer, and trade the fine-grained long-range information for the efficiency of Transformer.
Learned Index with Dynamic $\epsilon$
We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method.
Path-specific Causal Fair Prediction via Auxiliary Graph Structure Learning
To tackle these challenges, we propose a novel casual graph based fair prediction framework, which integrates graph structure learning into fair prediction to ensure that unfair pathways are excluded in the causal graph.
Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation
However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice.
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algorithm/model selection, and hyper-parameter tuning.
Automated Graph Learning via Population Based Self-Tuning GCN
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification.
Differential Privacy for Text Analytics via Natural Text Sanitization
The sanitized texts also contribute to our sanitization-aware pretraining and fine-tuning, enabling privacy-preserving natural language processing over the BERT language model with promising utility.
Unified Conversational Recommendation Policy Learning via Graph-based Reinforcement Learning
However, existing methods mainly target at solving one or two of these three decision-making problems in CRS with separated conversation and recommendation components, which restrict the scalability and generality of CRS and fall short of preserving a stable training procedure.
A Unified Transferable Model for ML-Enhanced DBMS
We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks.
Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge
Answer selection, which is involved in many natural language processing applications such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge.
Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation
Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be deployed in real-time applications.
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
In recent years, conversational recommender system (CRS) has received much attention in the research community.
A Pluggable Learned Index Method via Sampling and Gap Insertion
In this paper, we propose a formal machine learning based framework to quantify the index learning objective, and study two general and pluggable techniques to enhance the learning efficiency and learning effectiveness for learned indexes.
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains
However, the large model sizes, together with the long inference time, limit the deployment of such models in real-time applications.
Learning to Mutate with Hypergradient Guided Population
Computing the gradient of model hyperparameters, i. e., hypergradient, enables a promising and natural way to solve the hyperparameter optimization task.
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
The literature has witnessed the success of leveraging Pre-trained Language Models (PLMs) and Transfer Learning (TL) algorithms to a wide range of Natural Language Processing (NLP) applications, yet it is not easy to build an easy-to-use and scalable TL toolkit for this purpose.
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
In this library, we implement 73 recommendation models on 28 benchmark datasets, covering the categories of general recommendation, sequential recommendation, context-aware recommendation and knowledge-based recommendation.
Scalable Graph Neural Networks via Bidirectional Propagation
Most notably, GBP can deliver superior performance on a graph with over 60 million nodes and 1. 8 billion edges in less than half an hour on a single machine.
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data
Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph.
Simple and Deep Graph Convolutional Networks
We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping}.
Sequential Recommendation with Self-Attentive Multi-Adversarial Network
Recently, deep learning has made significant progress in the task of sequential recommendation.
Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
Distant supervision based methods for entity and relation extraction have received increasing popularity due to the fact that these methods require light human annotation efforts.
Practical Data Poisoning Attack against Next-Item Recommendation
In real-world recommendation systems, the cost of retraining recommendation models is high, and the interaction frequency between users and a recommendation system is restricted. Given these real-world restrictions, we propose to let the agent interact with a recommender simulator instead of the target recommendation system and leverage the transferability of the generated adversarial samples to poison the target system.
A Survey on Causal Inference
Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.
AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search
Motivated by the necessity and benefits of task-oriented BERT compression, we propose a novel compression method, AdaBERT, that leverages differentiable Neural Architecture Search to automatically compress BERT into task-adaptive small models for specific tasks.
Automated Relational Meta-learning
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.
Joint Learning of Answer Selection and Answer Summary Generation in Community Question Answering
Community question answering (CQA) gains increasing popularity in both academy and industry recently.
A Minimax Game for Instance based Selective Transfer Learning
To the best of our knowledge, this is the first to build a minimax game based model for selective transfer learning.
Multi-Grained Named Entity Recognition
This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
Multi-Grained Named Entity Recognition
named-entity-recognition
+5
Data Poisoning Attack against Knowledge Graph Embedding
Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently.
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.
Joint Slot Filling and Intent Detection via Capsule Neural Networks
Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.
Ranked #9 on
Intent Detection
on SNIPS
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.
Representation Learning for Treatment Effect Estimation from Observational Data
Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to the missing counterfactuals and the selection bias.
Finding Similar Medical Questions from Question Answering Websites
Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.
Towards Differentially Private Truth Discovery for Crowd Sensing Systems
To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic.
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation.
Databases
Multi-Grained Entity Proposal Network for Named Entity Recognition
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
SynonymNet: Multi-context Bilateral Matching for Entity Synonyms
Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery.
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge
In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains.
Cooperative Denoising for Distantly Supervised Relation Extraction
Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts.
Generative Discovery of Relational Medical Entity Pairs
Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies.
Bringing Semantic Structures to User Intent Detection in Online Medical Queries
The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.
Multi-source Hierarchical Prediction Consolidation
We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.
