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Found 78 papers, 43 papers with code
Adaptive Variance Based Label Distribution Learning For Facial Age Estimation
Some existing methods adopt distribution learning to tackle this issue by exploiting the semantic correlation between age labels.
Ranked #6 on
Age Estimation
on MORPH album2 (Caucasian)
Are Pre-trained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Pre-trained Transformer-based models were reported to be robust in intent classification.
PDALN: Progressive Domain Adaptation over a Pre-trained Model for Low-Resource Cross-Domain Named Entity Recognition
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.
Instruction-based Hypergraph Pretraining
However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrained knowledge.
MetaAligner: Conditional Weak-to-Strong Correction for Generalizable Multi-Objective Alignment of Language Models
Recent advancements in large language models (LLMs) aim to tackle heterogeneous human expectations and values via multi-objective preference alignment.
E4C: Enhance Editability for Text-Based Image Editing by Harnessing Efficient CLIP Guidance
Diffusion-based image editing is a composite process of preserving the source image content and generating new content or applying modifications.
ConspEmoLLM: Conspiracy Theory Detection Using an Emotion-Based Large Language Model
Driven by a comprehensive analysis of conspiracy text that reveals its distinctive affective features, we propose ConspEmoLLM, the first open-source LLM that integrates affective information and is able to perform diverse tasks relating to conspiracy theories.
AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
It meticulously standardizes and unifies these trajectories into a consistent format, streamlining the creation of a generic data loader optimized for agent training.
AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System
Thus, we open-source a new AI agent library, AgentLite, which simplifies this process by offering a lightweight, user-friendly platform for innovating LLM agent reasoning, architectures, and applications with ease.
AFPR-CIM: An Analog-Domain Floating-Point RRAM-based Compute-In-Memory Architecture with Dynamic Range Adaptive FP-ADC
Moreover, a novel FP-DAC is also implemented which reconstructs FP digital codes into analog values to perform analog computation.
The FinBen: An Holistic Financial Benchmark for Large Language Models
This along with the rapid development of LLMs, highlights the urgent need for a systematic financial evaluation benchmark for LLMs.
PFDM: Parser-Free Virtual Try-on via Diffusion Model
Virtual try-on can significantly improve the garment shopping experiences in both online and in-store scenarios, attracting broad interest in computer vision.
EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis
In this paper, we propose EmoLLMs, the first series of open-sourced instruction-following LLMs for comprehensive affective analysis based on fine-tuning various LLMs with instruction data, the first multi-task affective analysis instruction dataset (AAID) with 234K data samples based on various classification and regression tasks to support LLM instruction tuning, and a comprehensive affective evaluation benchmark (AEB) with 14 tasks from various sources and domains to test the generalization ability of LLMs.
Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task
In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios.
Semi-Supervised Clustering via Structural Entropy with Different Constraints
In this work, we present Semi-supervised clustering via Structural Entropy (SSE), a novel method that can incorporate different types of constraints from diverse sources to perform both partitioning and hierarchical clustering.
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation
With our principle, we managed to outperform GPT-Turbo-3. 5 on three datasets using 7b models e. g., Vicuna-7b and Openchat-7b on NDCG@10.
Multi-view Graph Convolution for Participant Recommendation
Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate.
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems.
Emotion Detection for Misinformation: A Review
The emotions and sentiments of netizens, as expressed in social media posts and news, constitute important factors that can help to distinguish fake news from genuine news and to understand the spread of rumors.
Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection
While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance.
Knowledge Graph Context-Enhanced Diversified Recommendation
Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.
Unified Pretraining for Recommendation via Task Hypergraphs
On the other hand, pretraining and finetuning on the same dataset leads to a high risk of overfitting.
Collaborative Semantic Alignment in Recommendation Systems
However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.
CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model
It is specifically designed for code-related tasks with both English and Chinese prompts and supports over 40 programming languages.
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.
Graph-based Alignment and Uniformity for Recommendation
To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph.
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
With the proliferation of social media, a growing number of users search for and join group activities in their daily life.
Enhancing Performance on Seen and Unseen Dialogue Scenarios using Retrieval-Augmented End-to-End Task-Oriented System
End-to-end task-oriented dialogue (TOD) systems have achieved promising performance by leveraging sophisticated natural language understanding and natural language generation capabilities of pre-trained models.
BOLAA: Benchmarking and Orchestrating LLM-augmented Autonomous Agents
The massive successes of large language models (LLMs) encourage the emerging exploration of LLM-augmented Autonomous Agents (LAAs).
Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization
This demonstrates that using policy gradient optimization to improve language agents, for which we believe our work is one of the first, seems promising and can be applied to optimize other models in the agent architecture to enhance agent performances over time.
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI
Despite advancements in conversational AI, language models encounter challenges to handle diverse conversational tasks, and existing dialogue dataset collections often lack diversity and comprehensiveness.
REX: Rapid Exploration and eXploitation for AI Agents
In this paper, we propose an enhanced approach for Rapid Exploration and eXploitation for AI Agents called REX.
Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation
We explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation.
Addressing the Rank Degeneration in Sequential Recommendation via Singular Spectrum Smoothing
We also establish a correlation between the ranks of sequence and item embeddings and the rank of the user-item preference prediction matrix, which can affect recommendation diversity.
Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training
This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs.
Conditional Denoising Diffusion for Sequential Recommendation
Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions.
Towards More Robust and Accurate Sequential Recommendation with Cascade-guided Adversarial Training
Two properties unique to the nature of sequential recommendation models may impair their robustness - the cascade effects induced during training and the model's tendency to rely too heavily on temporal information.
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Wasserstein Discrepancy Measurement builds upon the 2-Wasserstein distance, which is more robust, more efficient in small batch sizes, and able to model the uncertainty of stochastic augmentation processes.
MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System
To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec).
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph
PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users.
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer
However, it remains a significant challenge to model auxiliary item relationships in SR. To simultaneously model high-order item-item transitions in sequences and auxiliary item relationships, we propose a Multi-relational Transformer capable of modeling auxiliary item relationships for SR (MT4SR).
LSAP: Rethinking Inversion Fidelity, Perception and Editability in GAN Latent Space
In this work, we first point out that these two characteristics are related to the degree of alignment (or disalignment) of the inverse codes with the synthetic distribution.
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
Explanation Guided Contrastive Learning for Sequential Recommendation
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user adoptions.
ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
Generating Negative Samples for Sequential Recommendation
Due to the dynamics of users' interests and model updates during training, considering randomly sampled items from a user's non-interacted item set as negatives can be uninformative.
Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory
To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.
Improving Contrastive Learning with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering
Among several interesting findings, it is important to highlight that (1) the generative readers perform better in long context QA, (2) the extractive readers perform better in short context while also showing better out-of-domain generalization, and (3) the encoder of encoder-decoder PrLMs (e. g., T5) turns out to be a strong extractive reader and outperforms the standard choice of encoder-only PrLMs (e. g., RoBERTa).
Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network
We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.
Intent Contrastive Learning for Sequential Recommendation
Specifically, we introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering.
Sequential Recommendation via Stochastic Self-Attention
We further argue that BPR loss has no constraint on positive and sampled negative items, which misleads the optimization.
Multi-initialization Optimization Network for Accurate 3D Human Pose and Shape Estimation
In the second stage, we design a mesh refinement transformer (MRT) to respectively refine each coarse reconstruction result via a self-attention mechanism.
Ranked #64 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
Federated Social Recommendation with Graph Neural Network
However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns.
Pre-training Graph Neural Network for Cross Domain Recommendation
Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain.
Deep Fraud Detection on Non-attributed Graph
The successes of most previous methods heavily rely on rich node features and high-fidelity labels.
Self-supervised Learning for Sequential Recommendation with Model Augmentation
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar.
DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.
Contrastive Self-supervised Sequential Recommendation with Robust Augmentation
In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.
Quality-Aware Network for Face Parsing
This is a very short technical report, which introduces the solution of the Team BUPT-CASIA for Short-video Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021.
Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
We use Elliptical Gaussian distributions to describe items and sequences with uncertainty.
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Pre-trained Transformer-based models were reported to be robust in intent classification.
Relational Graph Neural Network Design via Progressive Neural Architecture Search
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful information embedded in long-distance nodes to improve the performance of nodes with low homophily without leading to performance degradation in nodes with high homophily.
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer
We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.
Quality-Aware Network for Human Parsing
How to estimate the quality of the network output is an important issue, and currently there is no effective solution in the field of human parsing.
Deoscillated Graph Collaborative Filtering
Stacking multiple cross-hop propagation layers and locality layers constitutes the DGCF model, which models high-order CF signals adaptively to the locality of nodes and layers.
Basket Recommendation with Multi-Intent Translation Graph Neural Network
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket.
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Finally, the selected neighbors across different relations are aggregated together.
Ranked #5 on
Fraud Detection
on Amazon-Fraud
Identity-Guided Human Semantic Parsing for Person Re-Identification
In this paper, we propose the identity-guided human semantic parsing approach (ISP) to locate both the human body parts and personal belongings at pixel-level for aligned person re-ID only with person identity labels.
Ranked #40 on
Person Re-Identification
on DukeMTMC-reID
CodeMatcher: Searching Code Based on Sequential Semantics of Important Query Words
CodeMatcher first collects metadata for query words to identify irrelevant/noisy ones, then iteratively performs fuzzy search with important query words on the codebase that is indexed by the Elasticsearch tool, and finally reranks a set of returned candidate code according to how the tokens in the candidate code snippet sequentially matched the important words in a query.
Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection
In this paper, we introduce these inconsistencies and design a new GNN framework, $\mathsf{GraphConsis}$, to tackle the inconsistency problem: (1) for the context inconsistency, we propose to combine the context embeddings with node features, (2) for the feature inconsistency, we design a consistency score to filter the inconsistent neighbors and generate corresponding sampling probability, and (3) for the relation inconsistency, we learn a relation attention weights associated with the sampled nodes.
BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Network
By defining a basket entity to represent the basket intent, we can model this problem as a basket-item link prediction task in the User-Basket-Item~(UBI) graph.
A Large-Scale Deep Architecture for Personalized Grocery Basket Recommendations
In this paper, we introduce a production within-basket grocery recommendation system, RTT2Vec, which generates real-time personalized product recommendations to supplement the user's current grocery basket.
JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation
JSCN will simultaneously operate multi-layer spectral convolutions on different graphs, and jointly learn a domain-invariant user representation with a domain adaptive user mapping module.
Semantic Alignment: Finding Semantically Consistent Ground-truth for Facial Landmark Detection
Despite this, we notice that the semantic ambiguity greatly degrades the detection performance.
Ranked #1 on
Face Alignment
on 300W
(NME_inter-pupil (%, Full) metric)
