Search Results for author:
Found 143 papers, 55 papers with code
Knowledge Graph Embedding by Translating on Hyperplanes
Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling.
Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator.
Insensitive Stochastic Gradient Twin Support Vector Machine for Large Scale Problems
In this paper, stochastic gradient descent algorithm is investigated to twin support vector machines for classification.
On the Effectiveness of Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss for both the discriminator and the generator.
Generalized two-dimensional linear discriminant analysis with regularization
However, 2DLDA may encounter the singularity issue theoretically and the sensitivity to outliers.
A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning
This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).
Joint Training of Candidate Extraction and Answer Selection for Reading Comprehension
While sophisticated neural-based techniques have been developed in reading comprehension, most approaches model the answer in an independent manner, ignoring its relations with other answer candidates.
Robust Bhattacharyya bound linear discriminant analysis through adaptive algorithm
In this paper, we propose a novel linear discriminant analysis criterion via the Bhattacharyya error bound estimation based on a novel L1-norm (L1BLDA) and L2-norm (L2BLDA).
Ramp-based Twin Support Vector Clustering
Traditional plane-based clustering methods measure the cost of within-cluster and between-cluster by quadratic, linear or some other unbounded functions, which may amplify the impact of cost.
A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i. e., seismic volume labeling.
A general model for plane-based clustering with loss function
In this paper, we propose a general model for plane-based clustering.
Characterization of migrated seismic volumes using texture attributes: a comparative study
It is our hope that this comparative study will help acquaint the seismic interpretation community with the many available powerful image texture analysis techniques, providing more alternative attributes for their seismic exploration.
Artificial Intelligence for Prosthetics - challenge solutions
In the NeurIPS 2018 Artificial Intelligence for Prosthetics challenge, participants were tasked with building a controller for a musculoskeletal model with a goal of matching a given time-varying velocity vector.
Fisher-Bures Adversary Graph Convolutional Networks
We try to minimize the loss wrt the perturbed $G+\Delta{G}$ while making $\Delta{G}$ to be effective in terms of the Fisher information of the neural network.
Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations
Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data
To solve the problem, we propose a new framework SurfCon that leverages two important types of information in the privacy-aware clinical data, i. e., the surface form information, and the global context information for synonym discovery.
A Distributed Fair Machine Learning Framework with Private Demographic Data Protection
In this paper, we propose a distributed fair learning framework for protecting the privacy of demographic data.
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences.
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.
Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD).
Multiple Flat Projections for Cross-manifold Clustering
In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems.
Tree++: Truncated Tree Based Graph Kernels
At the heart of Tree++ is a graph kernel called the path-pattern graph kernel.
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.
Rationalizing Medical Relation Prediction from Corpus-level Statistics
Nowadays, the interpretability of machine learning models is becoming increasingly important, especially in the medical domain.
Training Noise-Robust Deep Neural Networks via Meta-Learning
Label noise may significantly degrade the performance of Deep Neural Networks (DNNs).
HGKT: Introducing Hierarchical Exercise Graph for Knowledge Tracing
To solve the above problems, we propose a hierarchical graph knowledge tracing model called HGKT to explore the latent hierarchical relations between exercises.
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.
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary
Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence.
Unseen Target Stance Detection with Adversarial Domain Generalization
Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets.
Exercise Hierarchical Feature Enhanced Knowledge Tracing
Knowledge tracing is a fundamental task in the computer-aid educational system.
Exploring Common and Individual Characteristics of Students via Matrix Recovering
We assume that the characteristics matrix of students' is composed of two parts: one is a low-rank matrix representing the common characteristics of students; the other is a sparse matrix representing individual characteristics of students.
Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention
We finally apply our model on PAKDD papers published from 2009-2019 to mine insightful results from scientific papers published in a longer time span.
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling
However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression.
EvaLDA: Efficient Evasion Attacks Towards Latent Dirichlet Allocation
As one of the most powerful topic models, Latent Dirichlet Allocation (LDA) has been used in a vast range of tasks, including document understanding, information retrieval and peer-reviewer assignment.
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries
This work proposes a generic evaluation metric ROBY, a novel attack-independent robustness measure based on the model's decision boundaries.
Semantic Inference Network for Few-shot Streaming Label Learning
In this work, we formulate and study few-shot streaming label learning (FSLL), which models emerging new labels with only a few annotated examples by utilizing the knowledge learned from past labels.
Ensemble and Random Collaborative Representation-Based Anomaly Detector for Hyperspectral Imagery
To alleviate this problem, we proposed a novel ensemble and random collaborative representation-based detector (ERCRD) for HAD, which comprises two closely related stages.
Equilibrium and Socially optimal of a double-sided queueing system with two-mass point matching time
The numerical scenarios illustrate the influence of parameters on the equilibrium strategy and socially optimal strategy under two information levels.
Probability 60K25, 91B50, 91A35
Fuzzy Discriminant Clustering with Fuzzy Pairwise Constraints
In semi-supervised fuzzy clustering, this paper extends the traditional pairwise constraint (i. e., must-link or cannot-link) to fuzzy pairwise constraint.
Chinese Sentences Similarity via Cross-Attention Based Siamese Network
Measuring sentence similarity is a key research area nowadays as it allows machines to better understand human languages.
Overcoming Difficulty in Obtaining Dark-skinned Subjects for Remote-PPG by Synthetic Augmentation
Camera-based remote photoplethysmography (rPPG) provides a non-contact way to measure physiological signals (e. g., heart rate) using facial videos.
MedAI at SemEval-2021 Task 5: Start-to-end Tagging Framework for Toxic Spans Detection
This paper describes the system submitted to SemEval 2021 Task 5: Toxic Spans Detection.
Synchronization of Power Systems under Stochastic Disturbances
The synchronization of power generators is an important condition for the proper functioning of a power system, in which the fluctuations in frequency and the phase angle differences between the generators are sufficiently small when subjected to stochastic disturbances.
N24News: A New Dataset for Multimodal News Classification
Current news datasets merely focus on text features on the news and rarely leverage the feature of images, excluding numerous essential features for news classification.
Ranked #1 on
News Classification
on N15News
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI.
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
In addition to algorithmic analysis, we provide a comprehensive and unified empirical comparison of different exploration methods for DRL on a set of commonly used benchmarks.
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.
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.
Coherence boosting: When your pretrained language model is not paying enough attention
Long-range semantic coherence remains a challenge in automatic language generation and understanding.
Graph-Based Similarity of Neural Network Representations
Understanding the black-box representations in Deep Neural Networks (DNN) is an essential problem in deep learning.
Understanding the Dynamics of DNNs Using Graph Modularity
Specifically, we model the process of class separation of intermediate representations in pre-trained DNNs as the evolution of communities in dynamic graphs.
ED2: Environment Dynamics Decomposition World Models for Continuous Control
Model-based reinforcement learning (MBRL) achieves significant sample efficiency in practice in comparison to model-free RL, but its performance is often limited by the existence of model prediction error.
Continual Learning With Lifelong Vision Transformer
Continual learning methods aim at training a neural network from sequential data with streaming labels, relieving catastrophic forgetting.
Synthetic Generation of Face Videos With Plethysmograph Physiology
Accelerated by telemedicine, advances in Remote Photoplethysmography (rPPG) are beginning to offer a viable path toward non-contact physiological measurement.
MR-SVS: Singing Voice Synthesis with Multi-Reference Encoder
However, they face several challenges: 1) the fixed-size speaker embedding is not powerful enough to capture full details of the target timbre; 2) single reference audio does not contain sufficient timbre information of the target speaker; 3) the pitch inconsistency between different speakers also leads to a degradation in the generated voice.
Compactness Score: A Fast Filter Method for Unsupervised Feature Selection
For feature engineering, feature selection seems to be an important research content in which is anticipated to select "excellent" features from candidate ones.
Adversarial Attacks and Defense Methods for Power Quality Recognition
To this end, we first propose a signal-specific method and a universal signal-agnostic method to attack power systems using generated adversarial examples.
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data
To address the two problems, we propose RBLDA for MTS data classification, where each of the two within-class matrices is regularized via one parameter.
Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework
To break this curse, we propose a unified agent permutation framework that exploits the permutation invariance (PI) and permutation equivariance (PE) inductive biases to reduce the multiagent state space.
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration
However, we reveal sub-optimal collaborative behaviors also emerge with strong correlations, and simply maximizing the MI can, surprisingly, hinder the learning towards better collaboration.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Experimental quantum adversarial learning with programmable superconducting qubits
Our results reveal experimentally a crucial vulnerability aspect of quantum learning systems under adversarial scenarios and demonstrate an effective defense strategy against adversarial attacks, which provide a valuable guide for quantum artificial intelligence applications with both near-term and future quantum devices.
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
In online adaptation phase, with the environment context inferred from few experiences collected in new environments, the policy is optimized by gradient ascent with respect to the PDVF.
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.
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.
Towards Fine-grained Causal Reasoning and QA
Understanding causality is key to the success of NLP applications, especially in high-stakes domains.
PrEF: Percolation-based Evolutionary Framework for the diffusion-source-localization problem in large networks
We assume that the state of a number of nodes in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the diffusion-source-localization (DSL) problem.
D4: a Chinese Dialogue Dataset for Depression-Diagnosis-Oriented Chat
In a depression-diagnosis-directed clinical session, doctors initiate a conversation with ample emotional support that guides the patients to expose their symptoms based on clinical diagnosis criteria.
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.
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.
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.
Modern Question Answering Datasets and Benchmarks: A Survey
Question Answering (QA) is one of the most important natural language processing (NLP) tasks.
Generalization-Memorization Machines
Under this mechanism, error-based learning machines improve their memorization abilities of training data without over-fitting.
Bootstrapping a User-Centered Task-Oriented Dialogue System
We present TacoBot, a task-oriented dialogue system built for the inaugural Alexa Prize TaskBot Challenge, which assists users in completing multi-step cooking and home improvement tasks.
Explicit Image Caption Editing
Given an image and a reference caption, the image caption editing task aims to correct the misalignment errors and generate a refined caption.
Online Continual Learning with Contrastive Vision Transformer
Based on the learnable focuses, we design a focal contrastive loss to rebalance contrastive learning between new and past classes and consolidate previously learned representations.
Balancing Stability and Plasticity through Advanced Null Space in Continual Learning
Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i. e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well.
A Game-Theoretic Perspective of Generalization in Reinforcement Learning
However, there is not a unified formulation of the various schemes, as well as the comprehensive comparisons of methods across different schemes.
Compressed Sensing Based RFI Mitigation and Restoration for Pulsar Signals
In pulsar signal processing, two primary difficulties are (1) radio-frequency interference (RFI) mitigation and (2) information loss due to preprocessing and mitigation itself.
Hiding Visual Information via Obfuscating Adversarial Perturbations
Growing leakage and misuse of visual information raise security and privacy concerns, which promotes the development of information protection.
Cross Task Neural Architecture Search for EEG Signal Classifications
Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition.
ThinkSum: Probabilistic reasoning over sets using large language models
Our results suggest that because the probabilistic inference in ThinkSum is performed outside of calls to the LLM, ThinkSum is less sensitive to prompt design, yields more interpretable predictions, and can be flexibly combined with latent variable models to extract structured knowledge from LLMs.
A Comprehensive Survey of Data Augmentation in Visual Reinforcement Learning
Visual reinforcement learning (RL), which makes decisions directly from high-dimensional visual inputs, has demonstrated significant potential in various domains.
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding
Cross-task knowledge transfer via multi-task learning has recently made remarkable progress in general NLP tasks.
Search to Pass Messages for Temporal Knowledge Graph Completion
In particular, we develop a generalized framework to explore topological and temporal information in TKGs.
Ranked #1 on
Link Prediction
on GDELT
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation
Although substantial efforts have been made using graph neural networks (GNNs) for AI-driven drug discovery (AIDD), effective molecular representation learning remains an open challenge, especially in the case of insufficient labeled molecules.
Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification
In this paper, we propose a Neighbour Consistency guided Pseudo Label Refinement (NCPLR) framework, which can be regarded as a transductive form of label propagation under the assumption that the prediction of each example should be similar to its nearest neighbours'.
ALTO: Alternating Latent Topologies for Implicit 3D Reconstruction
One school of thought is to encode a latent vector for each point (point latents).
Hard Sample Aware Network for Contrastive Deep Graph Clustering
Moreover, under the guidance of the carefully collected high-confidence clustering information, our proposed weight modulating function will first recognize the positive and negative samples and then dynamically up-weight the hard sample pairs while down-weighting the easy ones.
Exit options sustain altruistic punishment and decrease the second-order free-riders, but it is not a panacea
Altruistic punishment, where individuals incur personal costs to punish others who have harmed third parties, presents an evolutionary conundrum as it undermines individual fitness.
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning
Prompt tuning, in which a base pretrained model is adapted to each task via conditioning on learned prompt vectors, has emerged as a promising approach for efficiently adapting large language models to multiple downstream tasks.
Learning Combinatorial Prompts for Universal Controllable Image Captioning
To this end, we propose a novel prompt-based framework for CIC by learning Combinatorial Prompts, dubbed as ComPro.
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.
GPT is becoming a Turing machine: Here are some ways to program it
We demonstrate that, through appropriate prompting, GPT-3 family of models can be triggered to perform iterative behaviours necessary to execute (rather than just write or recall) programs that involve loops, including several popular algorithms found in computer science curricula or software developer interviews.
HPN: Personalized Federated Hyperparameter Optimization
The client encoding is calculated with a random projection-based procedure to protect each client's privacy.
Delving into Shape-aware Zero-shot Semantic Segmentation
Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy.
Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning
Furthermore, most methods concentrate on a single attack goal and lack a generalizable adversary to develop distinct attack strategies for diverse goals, thus limiting precise control over victim model behavior in real-world scenarios.
Behavior Contrastive Learning for Unsupervised Skill Discovery
Under mild assumptions, our objective maximizes the MI between different behaviors based on the same skill, which serves as an upper bound of the previous MI objective.
ReDirTrans: Latent-to-Latent Translation for Gaze and Head Redirection
Then both the initial and edited embeddings are projected back (deprojected) to the initial latent space as residuals to modify the input latent vectors by subtraction and addition, representing old status removal and new status addition.
ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to solving complex problems.
Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID
To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters.
Reasoning with Language Model is Planning with World Model
RAP on LLAMA-33B surpasses CoT on GPT-4 with 33% relative improvement in a plan generation setting.
On the Value of Myopic Behavior in Policy Reuse
Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence.
MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuning
Fine-tuning large-scale pre-trained language models has been demonstrated effective for various natural language processing (NLP) tasks.
Sequential Attention Source Identification Based on Feature Representation
Snapshot observation based source localization has been widely studied due to its accessibility and low cost.
Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model
Therefore, the attack models in existing MIAs are difficult to effectively train with the knowledge of the target model, resulting in sub-optimal attacks.
Roll Up Your Sleeves: Working with a Collaborative and Engaging Task-Oriented Dialogue System
We introduce TacoBot, a user-centered task-oriented digital assistant designed to guide users through complex real-world tasks with multiple steps.
IOB: Integrating Optimization Transfer and Behavior Transfer for Multi-Policy Reuse
Humans have the ability to reuse previously learned policies to solve new tasks quickly, and reinforcement learning (RL) agents can do the same by transferring knowledge from source policies to a related target task.
Least Squares Maximum and Weighted Generalization-Memorization Machines
Furthermore, we propose some different memory impact functions for the MIMM and WIMM.
Learning Point-wise Abstaining Penalty for Point Cloud Anomaly Detection
We demonstrate that learning different abstaining penalties, apart from point-wise penalty, for different types of (synthesized) outliers can further improve the performance.
Node-Aligned Graph-to-Graph (NAG2G): Elevating Template-Free Deep Learning Approaches in Single-Step Retrosynthesis
Single-step retrosynthesis (SSR) in organic chemistry is increasingly benefiting from deep learning (DL) techniques in computer-aided synthesis design.
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
Experimental results illustrate the superiority of RO2O in facilitating stable offline-to-online learning and achieving significant improvement with limited online interactions.
Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages
Plasticity, the ability of a neural network to evolve with new data, is crucial for high-performance and sample-efficient visual reinforcement learning (VRL).
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instincts of large language models (LLMs) and the intricacies of the target task.
Octavius: Mitigating Task Interference in MLLMs via LoRA-MoE
While this phenomenon has been overlooked in previous work, we propose a novel and extensible framework, called Octavius, for comprehensive studies and experimentation on multimodal learning with Multimodal Large Language Models (MLLMs).
DECap: Towards Generalized Explicit Caption Editing via Diffusion Mechanism
The denoising process involves the explicit predictions of edit operations and corresponding content words, refining reference captions through iterative step-wise editing.
Control of the Power Flows of a Stochastic Power System
The critical subset is related to the power flows of all power lines of a power system and to transient stability.
MVDD: Multi-View Depth Diffusion Models
State-of-the-art results from extensive experiments demonstrate MVDD's excellent ability in 3D shape generation, depth completion, and its potential as a 3D prior for downstream tasks.
GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking
With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability.
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation
In this work, we focus on zero-shot point cloud semantic segmentation and propose a simple yet effective baseline to transfer the visual-linguistic knowledge implied in CLIP to point cloud encoder at both feature and output levels.
OVD-Explorer: Optimism Should Not Be the Sole Pursuit of Exploration in Noisy Environments
In reinforcement learning, the optimism in the face of uncertainty (OFU) is a mainstream principle for directing exploration towards less explored areas, characterized by higher uncertainty.
Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms
Theoretical analyses show that our algorithm succeeds with high probability as long as the sample probability exceeds the aforementioned threshold, and this theoretical result is further validated by synthetic experiments.
Community Detection in the Multi-View Stochastic Block Model
This paper considers the problem of community detection on multiple potentially correlated graphs from an information-theoretical perspective.
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
To the best of our knowledge, SKR is the first attempt to address the time-consuming nature of data generation for learning neural operators.
Diffusion-based Data Augmentation for Object Counting Problems
Our proposed smoothed density map input for ControlNet significantly improves ControlNet's performance in generating crowds in the correct locations.
Improving Data Augmentation for Robust Visual Question Answering with Effective Curriculum Learning
Compared to training on the entire augmented dataset, our ECL strategy can further enhance VQA models' performance with fewer training samples.
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
While such methods could improve GNN predictions, they usually don't perform well on explanations.
GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion
In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i. e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge.
Multi-Agent, Human-Agent and Beyond: A Survey on Cooperation in Social Dilemmas
Third, we review the emergent field of leveraging AI agents to enhance cooperation among humans.
Emergence of Social Norms in Large Language Model-based Agent Societies
The emergence of social norms has attracted much interest in a wide array of disciplines, ranging from social science and cognitive science to artificial intelligence.
Graph Regularized NMF with L20-norm for Unsupervised Feature Learning
Nonnegative Matrix Factorization (NMF) is a widely applied technique in the fields of machine learning and data mining.
AUEditNet: Dual-Branch Facial Action Unit Intensity Manipulation with Implicit Disentanglement
Our experiments demonstrate AUEditNet's superior accuracy in editing AU intensities, affirming its capability in disentangling facial attributes and identity within a limited subject pool.
LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
(2) We develop LLM Reasoners, a library for standardized modular implementation of existing and new reasoning algorithms, under a unified formulation of the search, reward, and world model components.
Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning
Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions.
ASCM: An Answer Space Clustered Prompting Method without Answer Engineering
We also propose a stable semi-supervised method named stair learning (SL) that orderly distills knowledge from better models to weaker models.
Deep Streaming Label Learning
In order to fill in these research gaps, we propose a novel deep neural network (DNN) based framework, Deep Streaming Label Learning (DSLL), to classify instances with newly emerged labels effectively.
Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering
More fine-grained analyses on transfer behaviors reveal the types of transferred knowledge and transfer patterns.
