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Found 89 papers, 32 papers with code
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding
Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases. However, debias on the Chinese language, one of the most spoken languages, has been less explored. Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming. In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data. Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i. e., a kind of component in Chinese characters, during the training procedure. This consequently alleviates discriminative gender biases. Experimental results on public benchmark datasets show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.
Distance Metric Learning with Joint Representation Diversification
Distance metric learning (DML) is to learn a representation space equipped with a metric, such that examples from the same class are closer than examples from different classes with respect to the metric.
Language-specific Effects on Automatic Speech Recognition Errors for World Englishes
Despite recent advancements in automated speech recognition (ASR) technologies, reports of unequal performance across speakers of different demographic groups abound.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
Character Animation aims to generating character videos from still images through driving signals.
Benefiting from Bias: Delegating to Encourage Information Acquisition
A principal delegates decisions to a biased agent.
PepLand: a large-scale pre-trained peptide representation model for a comprehensive landscape of both canonical and non-canonical amino acids
In recent years, the scientific community has become increasingly interested on peptides with non-canonical amino acids due to their superior stability and resistance to proteolytic degradation.
CryoAlign: feature-based method for global and local 3D alignment of EM density maps
Advances on cryo-electron imaging technologies have led to a rapidly increasing number of density maps.
DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision
However, it is difficult to restore the lost details in the dark area by relying only on the RGB domain.
Automated Bioinformatics Analysis via AutoBA
With the fast-growing and evolving omics data, the demand for streamlined and adaptable tools to handle the analysis continues to grow.
Discovering Mental Health Research Topics with Topic Modeling
In this study, our goal is to identify general trends in the field and pinpoint high-impact research topics by analyzing a large dataset of mental health research papers.
SkipcrossNets: Adaptive Skip-cross Fusion for Road Detection
Multi-modal fusion is increasingly being used for autonomous driving tasks, as images from different modalities provide unique information for feature extraction.
PE-YOLO: Pyramid Enhancement Network for Dark Object Detection
Current object detection models have achieved good results on many benchmark datasets, detecting objects in dark conditions remains a large challenge.
Path to Medical AGI: Unify Domain-specific Medical LLMs with the Lowest Cost
Medical artificial general intelligence (AGI) is an emerging field that aims to develop systems specifically designed for medical applications that possess the ability to understand, learn, and apply knowledge across a wide range of tasks and domains.
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health.
Improving the Robustness of Summarization Systems with Dual Augmentation
The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states.
DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation
In fully automatic mode, the presence of inevitable poor prompts (such as points outside the mask or boxes significantly larger than the mask) can significantly mislead mask generation.
A Topic-aware Summarization Framework with Different Modal Side Information
To address these two challenges, we first propose a unified topic encoder, which jointly discovers latent topics from the document and various kinds of side information.
Informative Data Selection with Uncertainty for Multi-modal Object Detection
To quantify the correlation in multi-modal information, we model the uncertainty, as the inverse of data information, in different modalities and embed it in the bounding box generation.
SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model
Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population.
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
Optimization of binding affinities in chemical space with generative pretrained transformer and deep reinforcement learning
The SGPT-RL method achieved better results than Reinvent on the ACE2 task, where molecular docking was used as the optimization goal.
Learning towards Selective Data Augmentation for Dialogue Generation
As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples.
Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare
Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries.
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI
However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data.
Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning
While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines.
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior
This is particularly evident for the learning-based multi-target landmark detection, where algorithms could be misleading to learn primarily the variation of background due to the varying FOV, failing the detection of targets.
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs
Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting.
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs
Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns.
Scientific Paper Extractive Summarization Enhanced by Citation Graphs
We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.
Prediction of Geometric Transformation on Cardiac MRI via Convolutional Neural Network
In our work, we propose to learn features in medical images by training ConvNets to recognize the geometric transformation applied to images and present a simple self-supervised task that can easily predict the geometric transformation.
Towards Improving Faithfulness in Abstractive Summarization
The evaluation of factual consistency also shows that our model generates more faithful summaries than baselines.
Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
Generalized Complete Intersection Calabi-Yau Manifold (gCICY) is a new construction of Calabi-Yau manifolds established recently.
Graph Neural Network and Spatiotemporal Transformer Attention for 3D Video Object Detection from Point Clouds
In this paper, we propose to detect 3D objects by exploiting temporal information in multiple frames, i. e., the point cloud videos.
Learning Towards the Largest Margins
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power.
Prototype-Anchored Learning for Learning with Imperfect Annotations
We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
Target-aware Abstractive Related Work Generation with Contrastive Learning
The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers.
Context Attention Network for Skeleton Extraction
Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image.
ProNet DB: A proteome-wise database for protein surface property representations and RNA-binding profiles
The rapid growth in the number of experimental and predicted protein structures and more complicated protein structures challenge users in computational biology for utilizing the structural information and protein surface property representation.
Modeling COVID-19 vaccine-induced immunological memory development and its links to antibody level and infectiousness
By introducing the maximum viral load and recovery time after viral infection, we quantitatively studied the protective effect of vaccines against viral infection.
Weakly Supervised High-Fidelity Clothing Model Generation
The development of online economics arouses the demand of generating images of models on product clothes, to display new clothes and promote sales.
Ranked #2 on
Virtual Try-on
on MPV
Towards artificial general intelligence via a multimodal foundation model
To overcome this limitation and take a solid step towards artificial general intelligence (AGI), we develop a foundation model pre-trained with huge multimodal data, which can be quickly adapted for various downstream cognitive tasks.
Shape Controllable Virtual Try-on for Underwear Models
Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect.
Protein-RNA interaction prediction with deep learning: Structure matters
Protein-RNA interactions are of vital importance to a variety of cellular activities.
Double Similarity Distillation for Semantic Image Segmentation
In this paper, motivated by the residual learning and global aggregation, we propose a simple yet general and effective knowledge distillation framework called double similarity distillation (DSD) to improve the classification accuracy of all existing compact networks by capturing the similarity knowledge in pixel and category dimensions, respectively.
2nd Place Solution for Waymo Open Dataset Challenge - Real-time 2D Object Detection
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images.
Ranked #1 on
Object Detection
on Waymo Open Dataset
2nd Place Solution for Waymo Open Dataset Challenge -- Real-time 2D Object Detection
In an autonomous driving system, it is essential to recognize vehicles, pedestrians and cyclists from images.
Asymmetric Loss Functions for Learning with Noisy Labels
Symmetric loss functions are confirmed to be robust to label noise.
The Reconfiguration Pattern of Individual Brain Metabolic Connectome for Parkinson's Disease Identification
We aim to establish an individual metabolic connectome method to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and their diagnostic value in PD.
Person Re-Identification by Context-aware Part Attention and Multi-Head Collaborative Learning
In addition, we propose a novel multi-head collaborative training scheme to improve the performance, which is collaboratively supervised by multiple heads with the same structure but different parameters.
A Data-Centric Framework for Composable NLP Workflows
Empirical natural language processing (NLP) systems in application domains (e. g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.
A Framework For Differentiable Discovery Of Graph Algorithms
Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms.
One-sample Guided Object Representation Disassembling
In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.
Object sieving and morphological closing to reduce false detections in wide-area aerial imagery
We use two wide-area aerial videos to compare the performance of five object detection algorithms in the absence and in the presence of our post-processing scheme.
Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control
In this paper, we formulate such a task offloading problem with unknown system dynamics as a combinatorial multi-armed bandit (CMAB) problem with long-term constraints on time-averaged energy consumptions.
History-Aware Online Cache Placement in Fog-Assisted IoT Systems: An Integration of Learning and Control
Due to uncertainties in practice such as unknown file popularities, cache placement scheme design is still an open problem with unresolved challenges: 1) how to maintain time-averaged storage costs under budgets, 2) how to incorporate online learning to aid cache placement to minimize performance loss (a. k. a.
Networking and Internet Architecture Signal Processing
Service Chain Composition with Failures in NFV Systems: A Game-Theoretic Perspective
In this paper, we formulate the problem of service chain composition in NFV systems with failures as a non-cooperative game.
SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic
Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories.
Learning to Stop While Learning to Predict
Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already.
Computational Drug Repositioning and Elucidation of Mechanism of Action of Compounds against SARS-CoV-2
The COVID-19 crisis called for rapid reaction from all the fields of biomedical research.
Disassembling Object Representations without Labels
In this paper, we study a new representation-learning task, which we termed as disassembling object representations.
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Then a dual generator is trained by taking the output from the former generator as input.
RNA Secondary Structure Prediction By Learning Unrolled Algorithms
The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints.
Intermittent Pulling with Local Compensation for Communication-Efficient Federated Learning
A major bottleneck on the performance of distributed Stochastic Gradient Descent (SGD) algorithm for large-scale Federated Learning is the communication overhead on pushing local gradients and pulling global model.
AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration
It also models the correlation between clinical features to enhance the ones which strongly indicate the health status and thus can maintain a state-of-the-art performance in terms of prediction accuracy while providing qualitative interpretability.
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics.
Ship Instance Segmentation From Remote Sensing Images Using Sequence Local Context Module
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network.
Deep learning in bioinformatics: introduction, application, and perspective in big data era
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics.
Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes.
PromID: human promoter prediction by deep learning
In this work we further develop our deep learning approach that was relatively successful to discriminate short promoter and non-promoter sequences.
Hybrid Policies Using Inverse Rewards for Reinforcement Learning
This paper puts forward a broad-spectrum improvement for reinforcement learning algorithms, which combines the policies using original rewards and inverse (negative) rewards.
On the Decision Boundary of Deep Neural Networks
We demonstrate, both theoretically and empirically, that the last weight layer of a neural network converges to a linear SVM trained on the output of the last hidden layer, for both the binary case and the multi-class case with the commonly used cross-entropy loss.
SupportNet: solving catastrophic forgetting in class incremental learning with support data
A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting.
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image.
OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction
Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations.
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies.
Predicting Discharge Medications at Admission Time Based on Deep Learning
Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen to plan for and what possible changes on initial medication may occur during an inpatient stay.
RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning
Anonymized electronic medical records are an increasingly popular source of research data.
Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback
Morever, the proposed algorithm can be scaled up to large-sized datasets after a relaxation.
Robust Online Multi-Task Learning with Correlative and Personalized Structures
Multi-Task Learning (MTL) can enhance a classifier's generalization performance by learning multiple related tasks simultaneously.
Regularized maximum correntropy machine
To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework.
Learning manifold to regularize nonnegative matrix factorization
Recently, manifold regularized NMF used a nearest neighbor graph to regulate the learning of factorization parameter matrices and has shown its advantage over traditional NMF methods for data representation problems.
Maximum mutual information regularized classification
In this paper, a novel pattern classification approach is proposed by regularizing the classifier learning to maximize mutual information between the classification response and the true class label.
Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization
To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively.
When coding meets ranking: A joint framework based on local learning
In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm.
Large Margin Image Set Representation and Classification
In this paper, we propose a novel image set representation and classification method by maximizing the margin of image sets.
Semi-Supervised Sparse Coding
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations.
Multiple graph regularized protein domain ranking
However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.
