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Found 130 papers, 56 papers with code
When does Further Pre-training MLM Help? An Empirical Study on Task-Oriented Dialog Pre-training
Further pre-training language models on in-domain data (domain-adaptive pre-training, DAPT) or task-relevant data (task-adaptive pre-training, TAPT) before fine-tuning has been shown to improve downstream tasks’ performances.
MorphSAM: Learning the Morphological Prompts from Atlases for Spine Image Segmentation
Spine image segmentation is crucial for clinical diagnosis and treatment of spine diseases.
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs
A modified UCB algorithm is leveraged to solve this problem.
KDRL: Post-Training Reasoning LLMs via Unified Knowledge Distillation and Reinforcement Learning
Recent advances in large language model (LLM) post-training have leveraged two distinct paradigms to enhance reasoning capabilities: reinforcement learning (RL) and knowledge distillation (KD).
How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study
In this paper, we present a comprehensive empirical study on how to enhance the safety of LRMs through Supervised Fine-Tuning (SFT).
ViEEG: Hierarchical Neural Coding with Cross-Modal Progressive Enhancement for EEG-Based Visual Decoding
Understanding and decoding brain activity into visual representations is a fundamental challenge at the intersection of neuroscience and artificial intelligence.
DAST: Difficulty-Aware Self-Training on Large Language Models
Present Large Language Models (LLM) self-training methods always under-sample on challenging queries, leading to inadequate learning on difficult problems which limits LLMs' ability.
CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games
Recent advances in Vision-Language-Action models (VLAs) have expanded the capabilities of embodied intelligence.
DAMM-Diffusion: Learning Divergence-Aware Multi-Modal Diffusion Model for Nanoparticles Distribution Prediction
Specifically, the MMFM is proposed to fuse features from multiple modalities, while the UAFM module is introduced to learn the uncertainty map for cross-attention computation.
When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning
To preserve image details while reducing computational complexity, we propose a text-guided token pruning method with Dynamic Image Pyramid (DIP) integration.
Split Adaptation for Pre-trained Vision Transformers
SA, inspired by split learning (SL), segments the pre-trained ViT into a frontend and a backend, with only the frontend shared with the client for data representation extraction.
Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization
In this work, we propose Mutual Reinforcing Data Synthesis (MRDS) within LLMs to improve few-shot dialogue summarization task.
Spectro-Riemannian Graph Neural Networks
Can integrating spectral and curvature signals unlock new potential in graph representation learning?
SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling
Open-world interpretation aims to accurately localize and recognize all objects within images by vision-language models (VLMs).
Where am I? Cross-View Geo-localization with Natural Language Descriptions
In this work, we introduce a novel task for cross-view geo-localization with natural language descriptions, which aims to retrieve corresponding satellite images or OSM database based on scene text descriptions.
HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases
Given a semi-structured knowledge base (SKB), where text documents are interconnected by relations, how can we effectively retrieve relevant information to answer user questions?
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models
Despite demonstrating impressive capabilities, Large Language Models (LLMs) still often struggle to accurately express the factual knowledge they possess, especially in cases where the LLMs' knowledge boundaries are ambiguous.
Inverse Delayed Reinforcement Learning
Instead of relying on direct observations, our approach employs an efficient off-policy adversarial training framework to derive expert features and recover optimal policies from augmented delayed observations.
Theta and/or alpha? Neural oscillational substrates for dynamic inter-brain synchrony during mother-child cooperation
Conversely, the {\alpha}-IBS state, accompanied by the EEG frequency-shift to the {\alpha} range, is more prominent during mother-led interactions.
Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
In this paper, we aim to tackle the limitation of the Adversarial Inverse Reinforcement Learning (AIRL) method in stochastic environments where theoretical results cannot hold and performance is degraded.
On Large Language Model Continual Unlearning
The results indicate that OOO consistently achieves the best unlearning effectiveness and utility preservation, especially when facing continuous unlearning requests.
Prototype Clustered Diffusion Models for Versatile Inverse Problems
To address this obstacle, we show that the measurement-based likelihood can be renovated with restoration-based likelihood via the opposite probabilistic graphic direction, licencing the patronage of various off-the-shelf restoration models and extending the strictly deterministic deterioration process to adaptable clustered processes with the supposed prototype, in what we call restorer guidance.
Spatially Resolved Gene Expression Prediction from Histology via Multi-view Graph Contrastive Learning with HSIC-bottleneck Regularization
The rapid development of spatial transcriptomics(ST) enables the measurement of gene expression at spatial resolution, making it possible to simultaneously profile the gene expression, spatial locations of spots, and the matched histopathological images.
Hierarchical Compression of Text-Rich Graphs via Large Language Models
Large Language Models (LLMs), noted for their superior text understanding abilities, offer a solution for processing the text in graphs but face integration challenges due to their limitation for encoding graph structures and their computational complexities when dealing with extensive text in large neighborhoods of interconnected nodes.
Missingness-resilient Video-enhanced Multimodal Disfluency Detection
In experiments across five disfluency-detection tasks, our unified multimodal approach significantly outperforms Audio-only unimodal methods, yielding an average absolute improvement of 10% (i. e., 10 percentage point increase) when both video and audio modalities are always available, and 7% even when video modality is missing in half of the samples.
GraphStorm: all-in-one graph machine learning framework for industry applications
GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code.
Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems
Specifically, we propose three distinct attack mechanisms: word injection, scenario manipulation, and knowledge injection, targeting various components in the LLM-based decision-making pipeline.
Variational Delayed Policy Optimization
In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning (RL).
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
Inspired by this, we introduce Graph-aware Parameter-Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning with LLMs on text-rich graphs.
Infusion: Preventing Customized Text-to-Image Diffusion from Overfitting
Text-to-image (T2I) customization aims to create images that embody specific visual concepts delineated in textual descriptions.
MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
In this paper, we address the challenge of multi-object tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT.
Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval
Cutting-edge studies focus on achieving unsupervised CDR but typically assume that the category spaces across domains are identical, an assumption that is often unrealistic in real-world scenarios.
Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models
2) Role Prompting assigns a central prompt to the general domain and a unique role prompt to each specific domain to minimize inter-domain confusion during training.
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Our work presents a digital twin (DT) framework for real-time predictive control of DED process parameters to meet specific design objectives.
Adaptive Convolutional Neural Network for Image Super-resolution
The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution.
Can GNN be Good Adapter for LLMs?
In terms of efficiency, the GNN adapter introduces only a few trainable parameters and can be trained with low computation costs.
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
To address these challenges, we present a novel Auxiliary-Delayed Reinforcement Learning (AD-RL) method that leverages auxiliary tasks involving short delays to accelerate RL with long delays, without compromising performance in stochastic environments.
Phase-driven Domain Generalizable Learning for Nonstationary Time Series
Monitoring and recognizing patterns in continuous sensing data is crucial for many practical applications.
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios
The recent trend of using Large Language Models (LLMs) as tool agents in real-world applications underscores the necessity for comprehensive evaluations of their capabilities, particularly in complex scenarios involving planning, creating, and using tools.
YODA: Teacher-Student Progressive Learning for Language Models
With the teacher's guidance, the student learns to iteratively refine its answer with feedback, and forms a robust and comprehensive understanding of the posed questions.
DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection
Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications.
Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware Guidance
At the second stage the image-wise compensatory information is derived with the compensatory kernels and embedded into the rescaled input images.
Fine-tuning Graph Neural Networks by Preserving Graph Generative Patterns
In this paper, we identify the fundamental cause of structural divergence as the discrepancy of generative patterns between the pre-training and downstream graphs.
Ranked #1 on
Graph Classification
on IMDb-M
(Accuracy (10-fold) metric)
Federated Continual Novel Class Learning
In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique.
Data Management For Training Large Language Models: A Survey
This survey aims to provide a comprehensive overview of current research in data management within both the pretraining and supervised fine-tuning stages of LLMs, covering various aspects of data management strategy design.
Empowering Autonomous Driving with Large Language Models: A Safety Perspective
Autonomous Driving (AD) encounters significant safety hurdles in long-tail unforeseen driving scenarios, largely stemming from the non-interpretability and poor generalization of the deep neural networks within the AD system, particularly in out-of-distribution and uncertain data.
State-Wise Safe Reinforcement Learning With Pixel Observations
In the context of safe exploration, Reinforcement Learning (RL) has long grappled with the challenges of balancing the tradeoff between maximizing rewards and minimizing safety violations, particularly in complex environments with contact-rich or non-smooth dynamics, and when dealing with high-dimensional pixel observations.
SELF: Self-Evolution with Language Feedback
SELF initiates with a meta-skill learning process that equips the LLMs with capabilities for self-feedback and self-refinement.
Parallelizing non-linear sequential models over the sequence length
Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature.
Ranked #5 on
Time Series Classification
on EigenWorms
Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling
However, it remains a challenging problem for these methods to ensure that the generated/predicted trajectories are physically realistic.
Cramer-Rao Bound Optimization for Active RIS-Empowered ISAC Systems
Integrated sensing and communication (ISAC), which simultaneously performs sensing and communication functions within a shared frequency band and hardware platform, has emerged as a promising technology for future wireless systems.
PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer
Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods and the efficiency of empirical force fields.
Effect of Attention and Self-Supervised Speech Embeddings on Non-Semantic Speech Tasks
different share of people perceive the same speech segment as a non-unanimous emotion.
Explaining and Adapting Graph Conditional Shift
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data.
Collaborative Multi-Agent Video Fast-Forwarding
We show that compared with other approaches in the literature, our frameworks achieve better coverage of important frames, while significantly reducing the number of frames processed at each agent.
Patton: Language Model Pretraining on Text-Rich Networks
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e. g., academic papers in a bibliographic network are linked by citations and co-authorships).
Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT
To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks.
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.
Leapfrog Diffusion Model for Stochastic Trajectory Prediction
The core of the proposed LED is to leverage a trainable leapfrog initializer to directly learn an expressive multi-modal distribution of future trajectories, which skips a large number of denoising steps, significantly accelerating inference speed.
Learning Representation for Anomaly Detection of Vehicle Trajectories
We conduct extensive experiments to demonstrate that our supervised method based on contrastive learning and unsupervised method based on reconstruction with semantic latent space can significantly improve the performance of anomalous trajectory detection in their corresponding settings over various baseline methods.
Inter-brain substrates of role switching during mother-child interaction
Mother-child interaction is highly dynamic and reciprocal.
Joint Transceiver Beamforming and Reflecting Design for Active RIS-Aided ISAC Systems
Integrated sensing and communication (ISAC) is recognized as a promising technology with great potential in saving hardware and spectrum resources, since it simultaneously realizes radar detection and user communication functions in the fully-shared platform.
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature.
DEJA VU: Continual Model Generalization For Unseen Domains
To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains.
Exploring Temporal Frequency Spectrum in Deep Video Deblurring
Video deblurring aims to restore the latent video frames from their blurred counterparts.
Semi-supervised Semantics-guided Adversarial Training for Robust Trajectory Prediction
In this paper, we present a novel adversarial training method for trajectory prediction.
KPT: Keyword-guided Pre-training for Grounded Dialog Generation
For the former, the grounding knowledge consists of keywords extracted from the response.
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data Format
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants.
Rectified Pessimistic-Optimistic Learning for Stochastic Continuum-armed Bandit with Constraints
This paper studies the problem of stochastic continuum-armed bandit with constraints (SCBwC), where we optimize a black-box reward function $f(x)$ subject to a black-box constraint function $g(x)\leq 0$ over a continuous space $\mathcal X$.
Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph
To answer this question, we theoretically study the concentration property of features obtained by neighborhood aggregation on homophilic and heterophilic graphs, introduce the single-pass augmentation-free graph contrastive learning loss based on the property, and provide performance guarantees for the minimizer of the loss on downstream tasks.
Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
In this work, we show that by simply applying consistency training with random data augmentation, state-of-the-art results on domain adaptation (DA) and generalization (DG) can be obtained.
Joint Beamforming Designs for Active Reconfigurable Intelligent Surface: A Sub-Connected Array Architecture
Affected by the "double fading" effect, however, conventional passive RIS cannot bring considerable performance improvement when users are not close enough to RIS.
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments
It is quite challenging to ensure the safety of reinforcement learning (RL) agents in an unknown and stochastic environment under hard constraints that require the system state not to reach certain specified unsafe regions.
A Tool for Neural Network Global Robustness Certification and Training
Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.
Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images
Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic.
Source-Free Domain Adaptation for Real-world Image Dehazing
Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction
In addition, experiments show that our method can significantly improve the system's robust generalization to unseen patterns of attacks.
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure.
Generative Adversarial Networks for Image Super-Resolution: A Survey
Second, we present popular architectures for GANs in big and small samples for image applications.
Augmentation-Free Graph Contrastive Learning with Performance Guarantee
Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data.
Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding
The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output is under input perturbations.
Federated Class-Incremental Learning
It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes.
Continual Prompt Tuning for Dialog State Tracking
A desirable dialog system should be able to continually learn new skills without forgetting old ones, and thereby adapt to new domains or tasks in its life cycle.
Shift-Robust Node Classification via Graph Adversarial Clustering
Then a shift-robust classifier is optimized on training graph and adversarial samples on target graph, which are generated by cluster GNN.
TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor
Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making.
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning
In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties (e. g., safety, stability) under the learned controller.
Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training Data
In this work we present a method, Shift-Robust GNN (SR-GNN), designed to account for distributional differences between biased training data and the graph's true inference distribution.
Co-designing Intelligent Control of Building HVACs and Microgrids
Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure.
Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning
As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption.
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization
Our NTL-based authorization approach instead provides data-centric protection, which we call applicability authorization, by significantly degrading the performance of the model on unauthorized data.
Verification in the Loop: Correct-by-Construction Control Learning with Reach-avoid Guarantees
Specifically, we leverage the verification results (computed reachable set of the system state) to construct feedback metrics for control learning, which measure how likely the current design of control parameters can meet the required reach-avoid property for safety and goal-reaching.
Chop Chop BERT: Visual Question Answering by Chopping VisualBERT's Heads
Based on this observation, we design a dynamic chopping module that can automatically remove heads and layers of the VisualBERT at an instance level when dealing with different questions.
End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering
The experiment results demonstrate that our approach can effectively mitigate the impact of adversarial attacks and can achieve 55% to 90% improvement over the original OpenPilot.
Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator
Data quantity and quality are crucial factors for data-driven learning methods.
Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework
While connected vehicle (CV) applications have the potential to revolutionize traditional transportation system, cyber and physical attacks on them could be devastating.
Cryptography and Security
Weak Adaptation Learning: Addressing Cross-Domain Data Insufficiency With Weak Annotator
Data quantity and quality are crucial factors for data-driven learning methods.
Simple is not Easy: A Simple Strong Baseline for TextVQA and TextCaps
Texts appearing in daily scenes that can be recognized by OCR (Optical Character Recognition) tools contain significant information, such as street name, product brand and prices.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.
Addressing Class Imbalance in Federated Learning
Our experiments demonstrate the importance of acknowledging class imbalance and taking measures as early as possible in FL training, and the effectiveness of our method in mitigating the impact.
Distributed Multi-agent Video Fast-forwarding
In many intelligent systems, a network of agents collaboratively perceives the environment for better and more efficient situation awareness.
One for Many: Transfer Learning for Building HVAC Control
Traditional HVAC control methods are typically based on creating explicit physical models for building thermal dynamics, which often require significant effort to develop and are difficult to achieve sufficient accuracy and efficiency for runtime building control and scalability for field implementations.
Unsupervised Differentiable Multi-aspect Network Embedding
To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.
Structured Multimodal Attentions for TextVQA
In this paper, we propose an end-to-end structured multimodal attention (SMA) neural network to mainly solve the first two issues above.
Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations.
Recent Advances and Challenges in Task-oriented Dialog System
Due to the significance and value in human-computer interaction and natural language processing, task-oriented dialog systems are attracting more and more attention in both academic and industrial communities.
CrossWOZ: A Large-Scale Chinese Cross-Domain Task-Oriented Dialogue Dataset
To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
ConvLab-2: An Open-Source Toolkit for Building, Evaluating, and Diagnosing Dialogue Systems
We present ConvLab-2, an open-source toolkit that enables researchers to build task-oriented dialogue systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
Eavesdrop the Composition Proportion of Training Labels in Federated Learning
Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devices.
Discovering Hypernymy in Text-Rich Heterogeneous Information Network by Exploiting Context Granularity
Extensive experiments on two large real-world datasets demonstrate the effectiveness of HyperMine and the utility of modeling context granularity.
Facet-Aware Evaluation for Extractive Summarization
In this paper, we present a facet-aware evaluation setup for better assessment of the information coverage in extracted summaries.
MaskPlus: Improving Mask Generation for Instance Segmentation
Instance segmentation is a promising yet challenging topic in computer vision.
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i. e., as long as they ensure that the neural networks are Lipschitz continuous.
Task-Guided Pair Embedding in Heterogeneous Network
In this paper, we propose a novel task-guided pair embedding framework in heterogeneous network, called TaPEm, that directly models the relationship between a pair of nodes that are related to a specific task (e. g., paper-author relationship in author identification).
ConvLab: Multi-Domain End-to-End Dialog System Platform
We present ConvLab, an open-source multi-domain end-to-end dialog system platform, that enables researchers to quickly set up experiments with reusable components and compare a large set of different approaches, ranging from conventional pipeline systems to end-to-end neural models, in common environments.
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics.
FFNet: Video Fast-Forwarding via Reinforcement Learning
The first group is supported by video summarization techniques, which require processing of the entire video to select an important subset for showing to users.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
However, current Open IE systems focus on modeling local context information in a sentence to extract relation tuples, while ignoring the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions.
Expert Finding in Heterogeneous Bibliographic Networks with Locally-trained Embeddings
We study the task of expert finding in heterogeneous bibliographical networks based on two aspects: textual content analysis and authority ranking.
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks
Therefore, we are motivated to propose a novel embedding learning framework---AspEm---to preserve the semantic information in HINs based on multiple aspects.
Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations
The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and playing Atari have set a good example in handling large state and actions spaces of complicated control problems.
Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance.
Adaptive Algorithm and Platform Selection for Visual Detection and Tracking
In this paper, we focus on this problem and propose a framework to adaptively select the "best" algorithm-parameter combination and the computation platform under performance and cost constraints at design time, and adapt the algorithms at runtime based on real-time inputs.
