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Found 47 papers, 11 papers with code
Understanding Gender Bias in Knowledge Base Embeddings
To exemplify the potential applications of our study, we also present two strategies (by adding and removing KB triples) to mitigate gender biases in KB embeddings.
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals
Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation.
SRGS: Super-Resolution 3D Gaussian Splatting
This approach relies on the representation power of Gaussian primitives to provide a high-quality rendering.
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space
To address the aforementioned problems, we propose a trustworthy multimodal sentiment ordinal network (TMSON) to improve performance in sentiment analysis.
LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace
As a result, the constructed subspace is able to dynamically and incrementally approximate the Hessian inverse vector product with less effort and thus leads to a favorable estimate of the hyper-gradient.
Tastle: Distract Large Language Models for Automatic Jailbreak Attack
In this work, we propose Tastle, a novel black-box jailbreak framework for automated red teaming of LLMs.
Blockage-Aware Robust Beamforming in RIS-Aided Mobile Millimeter Wave MIMO Systems
For the rate maximization problem, an accelerated projected gradient descent (PGD) algorithm is developed to solve the computational challenge of high-dimensional RIS phase-shift matrix (PSM) optimization.
State Value Generation with Prompt Learning and Self-Training for Low-Resource Dialogue State Tracking
Experimental results on the MultiWOZ 2. 1 dataset show that our method which has only less than 1 billion parameters achieves state-of-the-art performance under the data ratio settings of 5%, 10%, and 25% when limited to models under 100 billion parameters.
Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction
Spatial transcriptomics (ST) captures gene expression within distinct regions (i. e., windows) of a tissue slide.
Language-driven All-in-one Adverse Weather Removal
Then, with the guidance of degradation prior, we sparsely select restoration experts from a candidate list dynamically based on a Mixture-of-Experts (MoE) structure.
Event Camera Data Dense Pre-training
This paper introduces a self-supervised learning framework designed for pre-training neural networks tailored to dense prediction tasks using event camera data.
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling
Multi-view or even multi-modal data is appealing yet challenging for real-world applications.
Mapping EEG Signals to Visual Stimuli: A Deep Learning Approach to Match vs. Mismatch Classification
Existing approaches to modeling associations between visual stimuli and brain responses are facing difficulties in handling between-subject variance and model generalization.
LCCo: Lending CLIP to Co-Segmentation
With a backbone segmentation network that independently processes each image from the set, we introduce semantics from CLIP into the backbone features, refining them in a coarse-to-fine manner with three key modules: i) an image set feature correspondence module, encoding global consistent semantic information of the image set; ii) a CLIP interaction module, using CLIP-mined common semantics of the image set to refine the backbone feature; iii) a CLIP regularization module, drawing CLIP towards this co-segmentation task, identifying the best CLIP semantic and using it to regularize the backbone feature.
LDP: Language-driven Dual-Pixel Image Defocus Deblurring Network
In this paper, we propose, to the best of our knowledge, the first framework that introduces the contrastive language-image pre-training framework (CLIP) to accurately estimate the blur map from a DP pair unsupervisedly.
Multi-task Paired Masking with Alignment Modeling for Medical Vision-Language Pre-training
To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge.
Event Camera Data Pre-training
Our model is a self-supervised learning framework, and uses paired event camera data and natural RGB images for training.
Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances
Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors.
Ranked #2 on
Low-Light Image Enhancement
on VV
K3DN: Disparity-Aware Kernel Estimation for Dual-Pixel Defocus Deblurring
It estimates a disparity feature map, which is used to query a trainable kernel set to estimate a blur kernel that best describes the spatially-varying blur.
ISG: I can See Your Gene Expression
Instead, we present ISG framework that harnesses interactions among discriminative features from texture-abundant regions by three new modules: 1) a Shannon Selection module, based on the Shannon information content and Solomonoff's theory, to filter out textureless image regions; 2) a Feature Extraction network to extract expressive low-dimensional feature representations for efficient region interactions among a high-resolution image; 3) a Dual Attention network attends to regions with desired gene expression features and aggregates them for the prediction task.
Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction
This paper proposes an Exemplar Guided Network (EGN) to accurately and efficiently predict gene expression directly from each window of a tissue slide image.
Less is More: Facial Landmarks can Recognize a Spontaneous Smile
Smile veracity classification is a task of interpreting social interactions.
A Missing Value Filling Model Based on Feature Fusion Enhanced Autoencoder
We first incorporate into an autoencoder a hidden layer that consists of de-tracking neurons and radial basis function neurons, which can enhance the ability of learning interrelated features and common features.
K-textures, a self-supervised hard clustering deep learning algorithm for satellite image segmentation
The model detects $k$ hard clustering classes represented in the model as $k$ discrete binary masks and their associated $k$ independently generated textures, that combined are a simulation of the original image.
DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers
De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data.
Feature Modulation to Improve Struggle Detection in Web Search: A Psychological Approach
Existing Web search struggle detection methods rely on effort-based features to identify the struggling moments.
Adversarial Reweighting for Partial Domain Adaptation
To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data.
Ranked #1 on
Partial Domain Adaptation
on DomainNet
Prediction of properties of metal alloy materials based on machine learning
The experimental results show that the machine learning can predict the material properties accurately.
Seismic wave propagation and inversion with Neural Operators
We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations.
Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation
To reach this goal, we construct the nearest neighborhood for every target data and take it as the fundamental clustering unit by building our objective on the geometry.
Molecular distance matrix prediction based on graph convolutional networks
In order to indicate the effect of DMGCN model, the model is compared with the model DeeperGCN-DAGNN and the method of calculating molecular conformation in RDKit.
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems.
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification
After each sentiment classification task is learned, its knowledge is retained to help future task learning.
S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation
Instead of constraining the translation process by using a reference image, the users can command the model to retouch the generated images by involving the semantic information in the generation process.
RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition
Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways.
DeepSTCL: A Deep Spatio-temporal ConvLSTM for Travel Demand Prediction
Urban resource scheduling is an important part of the development of a smart city, and transportation resources are the main components of urban resources.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Dialogue state tracker is responsible for inferring user intentions through dialogue history.
Learning with Noisy Labels for Sentence-level Sentiment Classification
We propose a novel DNN model called NetAb (as shorthand for convolutional neural Networks with Ab-networks) to handle noisy labels during training.
Fast Calculation of Probabilistic Optimal Power Flow: A Deep Learning Approach
Taking advantage of the deep structure and reconstructive strategy of stacked denoising auto encoders (SDAE), a SDAE-based optimal power flow (OPF) is developed to extract the high-level nonlinear correlations between the system operating condition and the OPF solution.
Fast Calculation of Probabilistic Power Flow: A Model-based Deep Learning Approach
A deep neural network (DNN) is used to approximate the power flow calculation and is trained according to the physical power flow equations to improve its learning ability.
Forward and Backward Knowledge Transfer for Sentiment Classification
That is, it is possible to improve the NB classifier for a task by improving its model parameters directly by using the retained knowledge from other tasks.
Spectral Perturbation Meets Incomplete Multi-view Data
In this work, we show a strong link between perturbation risk bounds and incomplete multi-view clustering.
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management.
MI\&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification
A CNN method for sentiment classification task in Task 4A of SemEval 2017 is presented.
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
Due to the combination of the advantages in model-based approach and deep learning approach, the ADMM-Nets achieve state-of-the-art reconstruction accuracies with fast computational speed.
Deep ADMM-Net for Compressive Sensing MRI
Compressive Sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI).
Multi-Shot Person Re-Identification via Relational Stein Divergence
The use of similarity vectors is in contrast to the traditional approach of embedding manifolds into tangent spaces, which can suffer from representing the manifold structure inaccurately.
