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Found 62 papers, 11 papers with code
Falcon: Faster and Parallel Inference of Large Language Models through Enhanced Semi-Autoregressive Drafting and Custom-Designed Decoding Tree
Striking an optimal balance between minimal drafting latency and high speculation accuracy to enhance the inference speed of Large Language Models remains a significant challenge in speculative decoding.
Distributed-Order Fractional Graph Operating Network
We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus.
Edge centrality and the total variation of graph distributional signals
This short note is a supplement to [1], in which the total variation of graph distributional signals is introduced and studied.
Control the GNN: Utilizing Neural Controller with Lyapunov Stability for Test-Time Feature Reconstruction
However, these approaches lack theoretical analysis of the proximity between predictions and ground truth at test time.
Generalized Graph Signal Reconstruction via the Uncertainty Principle
We introduce a novel uncertainty principle for generalized graph signals that extends classical time-frequency and graph uncertainty principles into a unified framework.
The Transferability of Downsamped Sparse Graph Convolutional Networks
By incorporating both sparsity and topological similarity into the model, this study establishes an upper bound on the transfer error for downsampling in the training of large-scale sparse graphs and provides insight into the influence of topological structure on transfer performance.
A Graph Signal Processing Perspective of Network Multiple Hypothesis Testing with False Discovery Rate Control
We consider a multiple hypothesis testing problem in a sensor network over the joint spatial-time domain.
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND
We introduce the FRactional-Order graph Neural Dynamical network (FROND), a new continuous graph neural network (GNN) framework.
Online Signed Sampling of Bandlimited Graph Signals
In this paper, we are interested in how to sample based on sign information in an online manner, by which the direction of the original graph signal can be estimated.
Node Flux-Linkage Synchronizing Control of Power Systems with 100% Wind Power Generation Based on Capacitor Voltage Balancing Scheme
In order to limit the short-circuit current of inverters, a logic-based bang-bang funnel control (LBFC) is designed to control the switches of inverter bridges when over-current is detected.
Lossless digraph signal processing via polar decomposition
In this paper, we present a signal processing framework for directed graphs.
Modeling Sparse Graph Sequences and Signals Using Generalized Graphons
Our signal processing framework provides a comprehensive approach to analyzing and processing signals on graph sequences, even if they are sparse.
Lagrangian Modelling and Motion Stability of Synchronous Generator Power Systems
This paper proposes to analyze the motion stability of synchro-nous generator power systems using a Lagrangian model derived in the configuration space of generalized position and speed.
Graph Neural Networks with a Distribution of Parametrized Graphs
Traditionally, graph neural networks have been trained using a single observed graph.
A sampling construction of graphon 1-norm convergence
The note also contains errata of the previous version of the note.
Spectral Convergence of Complexon Shift Operators
We recall the notion of a complexon as the limit of a simplicial complex sequence [1].
Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance
This random graph process converges to the generalized graphon in stretched cut distance.
FRGNN: Mitigating the Impact of Distribution Shift on Graph Neural Networks via Test-Time Feature Reconstruction
To address this challenge, we propose FR-GNN, a general framework for GNNs to conduct feature reconstruction.
The faces of Convolution: from the Fourier theory to algebraic signal processing
In this expository article, we provide a self-contained overview of the notion of convolution embedded in different theories: from the classical Fourier theory to the theory of algebraic signal processing.
AMTSS: An Adaptive Multi-Teacher Single-Student Knowledge Distillation Framework For Multilingual Language Inference
To support cost-effective language inference in multilingual settings, we propose AMTSS, an adaptive multi-teacher single-student distillation framework, which allows distilling knowledge from multiple teachers to a single student.
Generalized signals on simplicial complexes
Topological signal processing (TSP) over simplicial complexes typically assumes observations associated with the simplicial complexes are real scalars.
Leveraging Label Non-Uniformity for Node Classification in Graph Neural Networks
We introduce the key notion of label non-uniformity, which is derived from the Wasserstein distance between the softmax distribution of the logits and the uniform distribution.
Distributional Signals for Node Classification in Graph Neural Networks
In graph neural networks (GNNs), both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP).
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks
However, a graph can have hyperbolic and Euclidean geometries at different regions of the graph.
Graph signal processing with categorical perspective
In this paper, we propose a framework for graph signal processing using category theory.
On distributional graph signals
We develop signal processing tools to study the new notion of distributional graph signals.
Sampling of Correlated Bandlimited Continuous Signals by Joint Time-vertex Graph Fourier Transform
When sampling multiple signals, the correlation between the signals can be exploited to reduce the overall number of samples.
On semi shift invariant graph filters
In graph signal processing, one of the most important subjects is the study of filters, i. e., linear transformations that capture relations between graph signals.
SGAT: Simplicial Graph Attention Network
In this paper, we present Simplicial Graph Attention Network (SGAT), a simplicial complex approach to represent such high-order interactions by placing features from non-target nodes on the simplices.
Abstract message passing and distributed graph signal processing
Graph signal processing is a framework to handle graph structured data.
To further understand graph signals
Graph signal processing (GSP) is a framework to analyze and process graph-structured data.
Recovery of Graph Signals from Sign Measurements
Sampling and interpolation have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertexes, of which most achievements are about continuous signals.
Graph Signal Processing over a Probability Space of Shift Operators
Each graph topology gives rise to a different shift operator.
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference
In order to better understand the reason behind model behaviors (i. e., making predictions), most recent works have exploited generative models to provide complementary explanations.
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training
Pre-trained Language Models (PLMs) have achieved great success on Machine Reading Comprehension (MRC) over the past few years.
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
Experimental results demonstrate that our adapted margin cosine loss can greatly enhance the baseline models with an absolute performance gain of 15\% on average, strongly verifying the potential of tackling the language prior problem in VQA from the angle of the answer feature space learning.
Learning on heterogeneous graphs using high-order relations
A heterogeneous graph consists of different vertices and edges types.
Signal processing with a distribution of graph operators
In this paper, we develop a signal processing framework of a network without explicit knowledge of the network topology.
Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations
More specifically, we propose a reinforced selector to extract useful PRF terms to enhance response candidates and a BERT-based response ranker to rank the PRF-enhanced responses.
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources
More concretely, we first introduce a novel graph-based iterative knowledge retrieval module, which iteratively retrieves concepts and entities related to the given question and its choices from multiple knowledge sources.
Sampling Theory of Bandlimited Continuous-Time Graph Signals
A continuous-time graph signal can be viewed as a time series of graph signals.
AliMe KG: Domain Knowledge Graph Construction and Application in E-commerce
Pre-sales customer service is of importance to E-commerce platforms as it contributes to optimizing customers' buying process.
Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems
More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer.
MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert
How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response.
Subgraph Signal Processing
Graph signal processing, like the graph Fourier transform, requires the full graph signal at every vertex of the graph.
Signal processing on simplicial complexes
In this paper, we develop a signal processing framework on simplicial complexes, such that we recover the traditional GSP theory when restricted to signals on graphs.
"Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior Using Imagine-Then-Arbitrate Model
And the arbitrator decides whether to wait or to make a response to the user directly.
Transformation of Dense and Sparse Text Representations
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
Information-seeking conversation system aims at satisfying the information needs of users through conversations.
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans.
Learning from Positive and Unlabeled Data with Adversarial Training
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
Dialogue systems dealing with multi-domain tasks are highly required.
Simple and Effective Text Matching with Richer Alignment Features
In this paper, we present a fast and strong neural approach for general purpose text matching applications.
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Review-Driven Answer Generation for Product-Related Questions in E-Commerce
Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation.
GFCN: A New Graph Convolutional Network Based on Parallel Flows
In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data.
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector.
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages
Our approach is extended from a basic monolingual STS framework to a shared multilingual encoder pretrained with translation task to incorporate rich-resource language data.
A Deep Relevance Model for Zero-Shot Document Filtering
In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand.
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
Building multi-turn information-seeking conversation systems is an important and challenging research topic.
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems
Dialogue management (DM) decides the next action of a dialogue system according to the current dialogue state, and thus plays a central role in task-oriented dialogue systems.
