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Found 28 papers, 8 papers with code
A Preliminary Exploration of GANs for Keyphrase Generation
We observed that our model achieves state-of-the-art performance in the generation of abstractive keyphrases and is comparable to the best performing extractive techniques.
A Learnable Agent Collaboration Network Framework for Personalized Multimodal AI Search Engine
This paper proposes a novel AI Search Engine framework called the Agent Collaboration Network (ACN).
CTR-KAN: KAN for Adaptive High-Order Feature Interaction Modeling
Modeling high-order feature interactions is critical for click-through rate (CTR) prediction, yet traditional approaches often face challenges in balancing predictive accuracy and computational efficiency.
Unsupervised Part Discovery via Dual Representation Alignment
Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vision tasks.
Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
These four RAG modules synergistically improve the response quality and efficiency of the RAG system.
Differential Encoding for Improved Representation Learning over Graphs
The message-passing paradigm and the global attention mechanism fundamentally generate node embeddings based on information aggregated from a node's local neighborhood or from the whole graph.
Conditional Local Feature Encoding for Graph Neural Networks
The key mechanism of current GNNs is message passing, where a node's feature is updated based on the information passing from its local neighbourhood.
ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization
The efficiency and personalization characteristics of ERAGent are supported by the Experiential Learner module which makes the AI assistant being capable of expanding its knowledge and modeling user profile incrementally.
Neighbour-level Message Interaction Encoding for Improved Representation Learning on Graphs
To address this issue, we propose a neighbour-level message interaction information encoding method for improving graph representation learning.
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks
Intuitively, we can expect the generated embeddings become smooth asymptotically layerwisely, that is each layer of graph convolution generates a smoothed version of embeddings as compared to that generated by the previous layer.
Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion
Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i. e, breath and heartbeat), is an attractive solution to health and security.
Multimodal Hyperspectral Image Classification via Interconnected Fusion
More specifically, they overlook the contextual information across modalities of HSI and LiDAR and the intra-modality characteristics of LiDAR.
On the Use of Context for Predicting Citation Worthiness of Sentences in Scholarly Articles
In this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles.
An Efficient Multitask Neural Network for Face Alignment, Head Pose Estimation and Face Tracking
Moreover, we generate a cheap heatmap based on the face alignment result and fuse it with features to improve the performance of the other two tasks.
Ranked #28 on
Face Alignment
on WFLW
Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations
Based on this observation, we propose a method for counteracting adversarial perturbations to improve adversarial robustness.
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks
We show that applying stochastic scaling at the gradient level is complementary to that applied at the feature level to improve the overall performance.
Towards Counteracting Adversarial Perturbations to Resist Adversarial Examples
We validate the proposed method on CIFAR-10 and CIFAR-100.
Two-Step Classification using Recasted Data for Low Resource Settings
We further improve the performance by using a joint-objective for classification and textual entailment.
MIDAS at SemEval-2020 Task 10: Emphasis Selection using Label Distribution Learning and Contextual Embeddings
This paper presents our submission to the SemEval 2020 - Task 10 on emphasis selection in written text.
An Annotated Dataset of Discourse Modes in Hindi Stories
In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative.
Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings
In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings.
Keyphrase Generation for Scientific Articles using GANs
In this paper, we present a keyphrase generation approach using conditional Generative Adversarial Networks (GAN).
Dialogue Act Classification in Group Chats with DAG-LSTMs
In this paper, we introduce a new model architecture, directed-acyclic-graph LSTM (DAG-LSTM) for DA classification.
Identifying Offensive Posts and Targeted Offense from Twitter
In this paper we present our approach and the system description for Sub-task A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive Language in Social Media.
Suggestion Mining from Online Reviews using ULMFiT
In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.
Dual Pattern Learning Networks by Empirical Dual Prediction Risk Minimization
This architecture forces the network to learn discriminative class-specific features by analyzing and comparing two input images.
Modelling Temporal Information Using Discrete Fourier Transform for Video Classification
In this way, static image features extracted from a pre-trained deep CNN and temporal information represented by DFT features are jointly considered for video classification.
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos
By this way, static image features extracted from a pre-trained deep CNN and temporal information represented by DFT features are jointly considered for video emotion recognition.
