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Found 36 papers, 17 papers with code
Memory Sharing for Large Language Model based Agents
In the realm of artificial intelligence, the adaptation of Large Language Model (LLM)-based agents to execute tasks via natural language prompts represents a significant advancement, notably eliminating the need for explicit retraining or fine tuning for fixed-answer tasks such as common sense questions and yes/no queries.
Graph Partial Label Learning with Potential Cause Discovering
PLL is a critical weakly supervised learning problem, where each training instance is associated with a set of candidate labels, including both the true label and additional noisy labels.
LinkNER: Linking Local Named Entity Recognition Models to Large Language Models using Uncertainty
Named Entity Recognition (NER) serves as a fundamental task in natural language understanding, bearing direct implications for web content analysis, search engines, and information retrieval systems.
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning
To this end, we propose a novel hierarchical topology isomorphism expertise embedded graph contrastive learning, which introduces knowledge distillations to empower GCL models to learn the hierarchical topology isomorphism expertise, including the graph-tier and subgraph-tier.
Rethinking Causal Relationships Learning in Graph Neural Networks
In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels.
Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering
To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner.
Combat Urban Congestion via Collaboration: Heterogeneous GNN-based MARL for Coordinated Platooning and Traffic Signal Control
Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way.
Joint Optimization of Traffic Signal Control and Vehicle Routing in Signalized Road Networks using Multi-Agent Deep Reinforcement Learning
In this paper, we propose a joint optimization approach for traffic signal control and vehicle routing in signalized road networks.
Adversarial Driving Behavior Generation Incorporating Human Risk Cognition for Autonomous Vehicle Evaluation
Autonomous vehicle (AV) evaluation has been the subject of increased interest in recent years both in industry and in academia.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the perspective of information theory and then propose a novel information theoretical framework for generalized multi-view learning.
Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction
We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens.
NerfAcc: Efficient Sampling Accelerates NeRFs
Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering.
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance.
Monocular Dynamic View Synthesis: A Reality Check
We study the recent progress on dynamic view synthesis (DVS) from monocular video.
Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation
By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.
Ranked #3 on
Aspect-Based Sentiment Analysis (ABSA)
on ACOS
Classical Sequence Match is a Competitive Few-Shot One-Class Learner
Nowadays, transformer-based models gradually become the default choice for artificial intelligence pioneers.
Robust Causal Graph Representation Learning against Confounding Effects
This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence.
Bootstrapping Informative Graph Augmentation via A Meta Learning Approach
To this end, we propose a novel approach to learning a graph augmenter that can generate an augmentation with uniformity and informativeness.
A Two-Stage Data-Free Adversarial Patch Generation Framework
The proxy dataset selection stage calculates the proposed average patch saliency (APS) of each available dataset to select a high-APS proxy dataset that can guarantee patches' fooling abilities.
Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement
A mind-map is a diagram that represents the central concept and key ideas in a hierarchical way.
Information Theory-Guided Heuristic Progressive Multi-View Coding
To this end, we rethink the existing multi-view learning paradigm from the information theoretical perspective and then propose a novel information theoretical framework for generalized multi-view learning.
Learning with Holographic Reduced Representations
HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space.
Multi-Label Few-Shot Learning for Aspect Category Detection
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence.
Hierarchical Ranking for Answer Selection
Answer selection is a task to choose the positive answers from a pool of candidate answers for a given question.
Long-term Human Motion Prediction with Scene Context
Human movement is goal-directed and influenced by the spatial layout of the objects in the scene.
Deep Learning on Knowledge Graph for Recommender System: A Survey
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS).
Using Neural Networks for Programming by Demonstration
A low time-complexity and data requirement favoring framework for reproducing emergent behavior, given an abstract demonstration, is discussed in [1], [2].
Universal Adversarial Perturbation for Text Classification
Given a state-of-the-art deep neural network text classifier, we show the existence of a universal and very small perturbation vector (in the embedding space) that causes natural text to be misclassified with high probability.
Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation
This is typically done by augmenting static operators with learned free-form sampling grids in the image space, dynamically tuned to the data and task for adapting the receptive field.
Ranked #172 on
Object Detection
on COCO test-dev
Spatio-Temporal Action Graph Networks
Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep model to generalize to future object appearance.
Disentangling Propagation and Generation for Video Prediction
A dynamic scene has two types of elements: those that move fluidly and can be predicted from previous frames, and those which are disoccluded (exposed) and cannot be extrapolated.
Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data.
AutoLoc: Weakly-supervised Temporal Action Localization in Untrimmed Videos
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Ranked #16 on
Weakly Supervised Action Localization
on ActivityNet-1.2
(mAP@0.5 metric)
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
AutoLoc: Weakly-supervised Temporal Action Localization
In this paper, we first develop a novel weakly-supervised TAL framework called AutoLoc to directly predict the temporal boundary of each action instance.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
