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Found 46 papers, 21 papers with code
Repo4QA: Answering Coding Questions via Dense Retrieval on GitHub Repositories
Open-source platforms such as GitHub and Stack Overflow both play significant roles in current software ecosystems.
Treatment-Aware Hyperbolic Representation Learning for Causal Effect Estimation with Social Networks
Firstly, TAHyper employs the hyperbolic space to encode the social networks, thereby effectively reducing the distortion of confounder representation caused by Euclidean embeddings.
Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation
A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.
SlowTrack: Increasing the Latency of Camera-based Perception in Autonomous Driving Using Adversarial Examples
Our evaluation results show that the system-level effects can be significantly improved, i. e., the vehicle crash rate of SlowTrack is around 95% on average while existing works only have around 30%.
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP).
Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network
In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks.
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Specifically, our RUAD can more effectively alleviate the feature sensitivity of the uplift model through two customized modules, including a feature selection module with joint multi-label modeling to identify a key subset from the input features and an adversarial feature desensitization module using adversarial training and soft interpolation operations to enhance the robustness of the model against this selected subset of features.
Offline Imitation Learning with Variational Counterfactual Reasoning
We theoretically analyze the influence of the generated expert data and the improvement of generalization.
A Survey on Large Language Model based Autonomous Agents
In this paper, we present a comprehensive survey of these studies, delivering a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.
Mutually Guided Few-shot Learning for Relational Triple Extraction
Specifically, our method consists of an entity-guided relation proto-decoder to classify the relations firstly and a relation-guided entity proto-decoder to extract entities based on the classified relations.
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.
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender Systems
However, building a user simulator with no reality-gap, i. e., can predict user's feedback exactly, is unrealistic because the users' reaction patterns are complex and historical logs for each user are limited, which might mislead the simulator-based recommendation policy.
Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems
In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible.
Dynamically Expandable Graph Convolution for Streaming Recommendation
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives
Representation learning has been a critical topic in machine learning.
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.
Hyperspherical Quantization: Toward Smaller and More Accurate Models
Model quantization enables the deployment of deep neural networks under resource-constrained devices.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.
Gradient-based Bi-level Optimization for Deep Learning: A Survey
In this survey, we first give a formal definition of the gradient-based bi-level optimization.
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
Learning vectorized embeddings is at the core of various recommender systems for user-item matching.
Unbiased Implicit Feedback via Bi-level Optimization
Recent works reveal a huge gap between the implicit feedback and user-item relevance due to the fact that implicit feedback is also closely related to the item exposure.
Joint Multisided Exposure Fairness for Recommendation
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
In this paper, we propose a novel geometric-based approach called Tangent Attack (TA), which identifies an optimal tangent point of a virtual hemisphere located on the decision boundary to reduce the distortion of the attack.
Pruning Ternary Quantization
To simultaneously optimize bit-width, model size, and accuracy, we propose pruning ternary quantization (PTQ): a simple, effective, symmetric ternary quantization method.
Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation
To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.
TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
The model has to adapt to changes in the TKG for efficient training and inference while preserving its performance on historical knowledge.
Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation
Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.
Knowledge-Enhanced Top-K Recommendation in Poincaré Ball
To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks
In each iteration, SWITCH first tries to update the current sample along the direction of $\hat{\mathbf{g}}$, but considers switching to its opposite direction $-\hat{\mathbf{g}}$ if our algorithm detects that it does not increase the value of the attack objective function.
Simulating Unknown Target Models for Query-Efficient Black-box Attacks
The meta-gradients of this loss are then computed and accumulated from multiple tasks to update the Simulator and subsequently improve generalization.
Feature Statistics Guided Efficient Filter Pruning
Building compact convolutional neural networks (CNNs) with reliable performance is a critical but challenging task, especially when deploying them in real-world applications.
Universal Successor Features for Transfer Reinforcement Learning
Transfer in Reinforcement Learning (RL) refers to the idea of applying knowledge gained from previous tasks to solving related tasks.
Multi-Graph Convolution Collaborative Filtering
In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.
Memory Augmented Graph Neural Networks for Sequential Recommendation
In addition to the modeling of user interests, we employ a bilinear function to capture the co-occurrence patterns of related items.
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate.
Model-based Reinforcement Learning
reinforcement-learning
+1
The Hitchhiker's Guide to LDA
Latent Dirichlet Allocation (LDA) model is a famous model in the topic model field, it has been studied for years due to its extensive application value in industry and academia.
MetaAdvDet: Towards Robust Detection of Evolving Adversarial Attacks
To solve such few-shot problem with the evolving attack, we propose a meta-learning based robust detection method to detect new adversarial attacks with limited examples.
Hierarchical Gating Networks for Sequential Recommendation
However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed.
Ranked #1 on
Recommendation Systems
on Amazon-CDs
(Recall@10 metric)
AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
(2) We integrate various dynamic models (including convolutional long short-term memory, two stream network, conditional random field, and temporal action localization network) into AU R-CNN and then investigate and analyze the reason behind the performance of dynamic models.
Ranked #1 on
Action Unit Detection
on BP4D
Gated Attentive-Autoencoder for Content-Aware Recommendation
In particular, a word-level and a neighbor-level attention module are integrated with the autoencoder.
Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence
To incorporate the geographical context information, we propose a neighbor-aware decoder to make users' reachability higher on the similar and nearby neighbors of checked-in POIs, which is achieved by the inner product of POI embeddings together with the radial basis function (RBF) kernel.
Universal Successor Representations for Transfer Reinforcement Learning
The objective of transfer reinforcement learning is to generalize from a set of previous tasks to unseen new tasks.
Obstacle Avoidance through Deep Networks based Intermediate Perception
Second, predict the trajectory from the depth and normal.
