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Found 66 papers, 22 papers with code
#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate
We collect a large dataset of tweets during the 1st U. S. Presidential Debate in 2016 (#DebateNight) and we analyze its 1. 5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness.
Social and Information Networks
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness
Emotion cause analysis has been a key topic in natural language processing.
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning
Then the attention-based CNN model is incorporated into a novel adversarial cross-lingual learning framework, in which with the help of user properties as bridge between languages, we can extract the language-specific features and language-independent features to enrich the user post representation so as to alleviate the data insufficiency problem.
Doc2hash: Learning Discrete Latent variables for Documents Retrieval
However, the discrete stochastic layer is usually incompatible with the backpropagation in the training stage, and thus causes a gradient flow problem because of non-differentiable operators.
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation
We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation.
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension
Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
Adversarial Synthesis of Human Pose from Text
This work focuses on synthesizing human poses from human-level text descriptions.
Progressive Cluster Purification for Unsupervised Feature Learning
In this work, we propose a novel clustering based method, which, by iteratively excluding class inconsistent samples during progressive cluster formation, alleviates the impact of noise samples in a simple-yet-effective manner.
Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning
For this scheme, we propose a novel privacy-preserving architecture where two parties can collaboratively train a deep learning model efficiently while preserving the privacy of each party's data.
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
We investigate response selection for multi-turn conversation in retrieval-based chatbots.
Exploring Instance Relations for Unsupervised Feature Embedding
Despite the great progress achieved in unsupervised feature embedding, existing contrastive learning methods typically pursue view-invariant representations through attracting positive sample pairs and repelling negative sample pairs in the embedding space, while neglecting to systematically explore instance relations.
Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks
In this paper, we propose Multi-channel Graph Neural Networks with Sentiment-awareness (MGNNS) for image-text sentiment detection.
Dense Semantic Contrast for Self-Supervised Visual Representation Learning
Concretely, these downstream tasks require more accurate representation, in other words, the pixels from the same object must belong to a shared semantic category, which is lacking in the previous methods.
Sample Complexity of the Robust LQG Regulator with Coprime Factors Uncertainty
The robust LQG synthesis procedure is performed by considering bounded additive model uncertainty on the coprime factors of the plant.
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
Graph-adaptive Rectified Linear Unit for Graph Neural Networks
However, in the updating stage, all nodes share the same updating function.
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
In this paper, we show that the node embedding obtained via the graph augmentations is highly biased, somewhat limiting contrastive models from learning discriminative features for downstream tasks.
Graph Component Contrastive Learning for Concept Relatedness Estimation
As it is intractable for data augmentation to fully capture the structural information of the ConcreteGraph due to a large amount of potential concept pairs, we further introduce a novel Graph Component Contrastive Learning framework to implicitly learn the complete structure of the ConcreteGraph.
MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation
Building dialogue generation systems in a zero-shot scenario remains a huge challenge, since the typical zero-shot approaches in dialogue generation rely heavily on large-scale pre-trained language generation models such as GPT-3 and T5.
Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems
The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature.
DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms.
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
In this paper, we present relation-aware contrastive self-supervised learning (ReCo) to integrate instance relations, i. e., global distribution relation and local interpolation relation, into the CSL framework in a plug-and-play fashion.
Alleviating Sparsity of Open Knowledge Graphs with Ternary Contrastive Learning
Sparsity of formal knowledge and roughness of non-ontological construction make sparsity problem particularly prominent in Open Knowledge Graphs (OpenKGs).
Dimensionality-Varying Diffusion Process
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension.
Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty
Refitting the expression of the relevant closed loop allows for the optimal linear observer problem given a fixed state feedback gain to be recast as a convex problem in the Youla parameter.
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
Although augmentations (e. g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, complementary yet not well researched strategy.
Learning to Select Prototypical Parts for Interpretable Sequential Data Modeling
Prototype-based interpretability methods provide intuitive explanations of model prediction by comparing samples to a reference set of memorized exemplars or typical representatives in terms of similarity.
PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism
We investigate response generation for multi-turn dialogue in generative-based chatbots.
MAGI: Multi-Annotated Explanation-Guided Learning
This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase.
oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation
We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for high performance code generation of the deep neural network graph.
A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society.
Cones: Concept Neurons in Diffusion Models for Customized Generation
Concatenating multiple clusters of concept neurons can vividly generate all related concepts in a single image.
Bipartite Graph Convolutional Hashing for Effective and Efficient Top-N Search in Hamming Space
We propose an end-to-end Bipartite Graph Convolutional Hashing approach, namely BGCH, which consists of three novel and effective modules: (1) adaptive graph convolutional hashing, (2) latent feature dispersion, and (3) Fourier serialized gradient estimation.
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models.
Evaluate What You Can't Evaluate: Unassessable Quality for Generated Response
There are risks in using eference-free evaluators based on LLMs to evaluate the quality of dialogue responses.
Cones 2: Customizable Image Synthesis with Multiple Subjects
Synthesizing images with user-specified subjects has received growing attention due to its practical applications.
Eliminating Lipschitz Singularities in Diffusion Models
Diffusion models, which employ stochastic differential equations to sample images through integrals, have emerged as a dominant class of generative models.
InRank: Incremental Low-Rank Learning
To remedy this, we design a new training algorithm Incremental Low-Rank Learning (InRank), which explicitly expresses cumulative weight updates as low-rank matrices while incrementally augmenting their ranks during training.
RoCar: A Relationship Network-based Evaluation Method to Large Language Models
Large language models (LLMs) have received increasing attention.
Large Language Models Are Also Good Prototypical Commonsense Reasoners
Commonsense reasoning is a pivotal skill for large language models, yet it presents persistent challenges in specific tasks requiring this competence.
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
The application of CEs encounters two main challenges: general user preferences and variable ML systems.
Rationality and connectivity in stochastic learning for networked coordination games
Our analysis shows that there is a relationship between the connectivity of the network, and the rationality of the agents.
Multi-Prompt Fine-Tuning of Foundation Models for Enhanced Medical Image Segmentation
The Segment Anything Model (SAM) is a powerful foundation model that introduced revolutionary advancements in natural image segmentation.
Saliency-Guided Hidden Associative Replay for Continual Learning
Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning.
SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation
Explainable AI seeks to bring light to the decision-making processes of black-box models.
Controllable Data Generation Via Iterative Data-Property Mutual Mappings
To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement.
XAI Benchmark for Visual Explanation
Our proposed benchmarks facilitate a fair evaluation and comparison of visual explanation methods.
Visual Attention-Prompted Prediction and Learning
To tackle these challenges, we propose a novel framework called Visual Attention-Prompted Prediction and Learning, which seamlessly integrates visual attention prompts into the model's decision-making process and adapts to images both with and without attention prompts for prediction.
MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks
Consequently, our work complements research on the performance of MLLMs in multimodal comprehension tasks, achieving a more comprehensive and holistic evaluation of MLLMs.
Alignment and Outer Shell Isotropy for Hyperbolic Graph Contrastive Learning
We show that in the hyperbolic space one has to address the leaf- and height-level uniformity which are related to properties of trees, whereas in the ambient space of the hyperbolic manifold, these notions translate into imposing an isotropic ring density towards boundaries of Poincar\'e ball.
Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models
We categorize methods based on their optimization focus: computational, memory, energy, financial, and network resources and their applicability across various stages of an LLM's lifecycle, including architecture design, pretraining, finetuning, and system design.
CodePrompt: Improving Source Code-Related Classification with Knowledge Features through Prompt Learning
Researchers have explored the potential of utilizing pre-trained language models, such as CodeBERT, to improve source code-related tasks.
STICKERCONV: Generating Multimodal Empathetic Responses from Scratch
Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets.
HiFT: A Hierarchical Full Parameter Fine-Tuning Strategy
As LMs grow in size, fine-tuning the full parameters of LMs requires a prohibitively large amount of GPU memory.
Distilling Large Language Models for Text-Attributed Graph Learning
To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale.
ELAD: Explanation-Guided Large Language Models Active Distillation
The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences.
FastMAC: Stochastic Spectral Sampling of Correspondence Graph
As such, the core of our method is the stochastic spectral sampling of correspondence graph.
DUE: Dynamic Uncertainty-Aware Explanation Supervision via 3D Imputation
Explanation supervision aims to enhance deep learning models by integrating additional signals to guide the generation of model explanations, showcasing notable improvements in both the predictability and explainability of the model.
Is Mamba Effective for Time Series Forecasting?
Furthermore, we conduct extensive experiments to delve deeper into the potential of Mamba compared to the Transformer in the TSF.
DreamLIP: Language-Image Pre-training with Long Captions
Motivated by this, we propose to dynamically sample sub-captions from the text label to construct multiple positive pairs, and introduce a grouping loss to match the embeddings of each sub-caption with its corresponding local image patches in a self-supervised manner.
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
This work presents FlashFace, a practical tool with which users can easily personalize their own photos on the fly by providing one or a few reference face images and a text prompt.
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features
Moreover, to verify the generality of the model, we also conduct experiments on two common sentiment analysis datasets.
Learning to Improve Persona Consistency in Multi-party Dialogue Generation via Text Knowledge Enhancement
In an open-domain dialogue system, the consistent persona is a key factor to generate real and coherent dialogues.
