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Found 87 papers, 21 papers with code
AbsInstruct: Eliciting Abstraction Ability from LLMs through Explanation Tuning with Plausibility Estimation
Abstraction ability is crucial in human intelligence, which can also benefit various tasks in NLP study.
Addressing Distribution Shift in Time Series Forecasting with Instance Normalization Flows
Due to non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting.
A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network
SEFF utilizes saliency maps of the neighboring scales to enhance the necessary features for fusing, resulting in more representative fused features.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
P-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models
Lastly, P-Bench performs existing privacy attacks on LMs with pre-defined privacy objectives as the empirical evaluation results.
Chain-of-Choice Hierarchical Policy Learning for Conversational Recommendation
Conversational Recommender Systems (CRS) illuminate user preferences via multi-round interactive dialogues, ultimately navigating towards precise and satisfactory recommendations.
Dual-stage Flows-based Generative Modeling for Traceable Urban Planning
We design several experiments to indicate that our framework can outperform compared to other generative models for the urban planning task.
Crack-Net: Prediction of Crack Propagation in Composites
Computational solid mechanics has become an indispensable approach in engineering, and numerical investigation of fracture in composites is essential as composites are widely used in structural applications.
Deep Reinforcement Learning for Artificial Upwelling Energy Management
The potential of artificial upwelling (AU) as a means of lifting nutrient-rich bottom water to the surface, stimulating seaweed growth, and consequently enhancing ocean carbon sequestration, has been gaining increasing attention in recent years.
Multi-step Jailbreaking Privacy Attacks on ChatGPT
With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts.
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecasting
The distribution shift in Time Series Forecasting (TSF), indicating series distribution changes over time, largely hinders the performance of TSF models.
Approximate message passing from random initialization with applications to $\mathbb{Z}_{2}$ synchronization
This paper is concerned with the problem of reconstructing an unknown rank-one matrix with prior structural information from noisy observations.
A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations.
HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval
Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module, which enhances the semantic corresponding relations between the local and global information, and obtains more accurate feature representations for the image and text modalities.
Background-Mixed Augmentation for Weakly Supervised Change Detection
Change detection (CD) is to decouple object changes (i. e., object missing or appearing) from background changes (i. e., environment variations) like light and season variations in two images captured in the same scene over a long time span, presenting critical applications in disaster management, urban development, etc.
DiMBERT: Learning Vision-Language Grounded Representations with Disentangled Multimodal-Attention
To enhance the correlation between vision and language in disentangled spaces, we introduce the visual concepts to DiMBERT which represent visual information in textual format.
Prophet Attention: Predicting Attention with Future Attention for Image Captioning
Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.
Graph Soft-Contrastive Learning via Neighborhood Ranking
Moreover, as the neighborhood size exponentially expands with more hops considered, we propose neighborhood sampling strategies to improve learning efficiency.
A Knowledge Distillation-Based Backdoor Attack in Federated Learning
With knowledge distillation, we can reduce the abnormal characteristics in model result from the label flipping, thus the model can bypass the defenses.
Omni-directional Pathloss Measurement Based on Virtual Antenna Array with Directional Antennas
Omni-directional pathloss, which refers to the pathloss when omni-directional antennas are used at the link ends, are essential for system design and evaluation.
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Feature selection and instance selection are two important techniques of data processing.
TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction
However, such a method strictly restricts entities on the hyper-ellipsoid surfaces which limits the optimization of entity distribution, leading to suboptimal performance of knowledge graph completion.
DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.
EndHiC: assemble large contigs into chromosomal-level scaffolds using the Hi-C links from contig ends
As the Hi-C links of two adjacent contigs concentrate only at the neighbor ends of the contigs, larger contig size will reduce the power to differentiate adjacent (signal) and non-adjacent (noise) contig linkages, leading to a higher rate of mis-assembly.
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
Space-time video super-resolution (STVSR) aims to construct a high space-time resolution video sequence from the corresponding low-frame-rate, low-resolution video sequence.
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection
Anomaly detection or outlier detection is a common task in various domains, which has attracted significant research efforts in recent years.
Machine Learning-Based 3D Channel Modeling for U2V mmWave Communications
Moreover, the impact of 3D rotations, which has rarely been reported in previous works, can be observed in the generated CCF and ACF, which are also consistent with the theoretical and measurement results.
Multiplex Graph Neural Network for Extractive Text Summarization
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary.
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model
As a result, the proposed approach can handle various tasks including: Audio-Oriented Multimodal Machine Comprehension, Machine Reading Comprehension and Machine Listening Comprehension, in a single model, making fair comparisons possible between our model and the existing unimodal MC models.
Object-Guided Instance Segmentation With Auxiliary Feature Refinement for Biological Images
To deal with this problem, in this paper, we propose an object-guided instance segmentation method.
Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation
In detail, PoKE explores the posterior knowledge, which provides explicit abnormal visual regions to alleviate visual data bias; PrKE explores the prior knowledge from the prior medical knowledge graph (medical knowledge) and prior radiology reports (working experience) to alleviate textual data bias.
Research on Optimization Method of Multi-scale Fish Target Fast Detection Network
The fish target detection algorithm lacks a good quality data set, and the algorithm achieves real-time detection with lower power consumption on embedded devices, and it is difficult to balance the calculation speed and identification ability.
Test-Time Training for Deformable Multi-Scale Image Registration
Registration is a fundamental task in medical robotics and is often a crucial step for many downstream tasks such as motion analysis, intra-operative tracking and image segmentation.
DGGAN: Depth-image Guided Generative Adversarial Networks for Disentangling RGB and Depth Images in 3D Hand Pose Estimation
Estimating3D hand poses from RGB images is essentialto a wide range of potential applications, but is challengingowing to substantial ambiguity in the inference of depth in-formation from RGB images.
Prophet Attention: Predicting Attention with Future Attention
Especially for image captioning, the attention based models are expected to ground correct image regions with proper generated words.
Automatic Distractor Generation for Multiple Choice Questions in Standard Tests
To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests.
Interactive Reinforcement Learning for Feature Selection with Decision Tree in the Loop
In this journal version, we propose a novel interactive and closed-loop architecture to simultaneously model interactive reinforcement learning (IRL) and decision tree feedback (DTF).
MM-Hand: 3D-Aware Multi-Modal Guided Hand Generative Network for 3D Hand Pose Synthesis
Estimating the 3D hand pose from a monocular RGB image is important but challenging.
Simplifying Reinforced Feature Selection via Restructured Choice Strategy of Single Agent
To address the question, we develop a single-agent reinforced feature selection approach integrated with restructured choice strategy.
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement Learning
Motivated by such a computational dilemma, this study is to develop a novel feature space navigation method.
Dynamic mmWave Channel Emulation in a Cost-Effective MPAC with Dominant-Cluster Concept
In this contribution, we firstly illustrate the dominant-cluster(s) concept, where the non-dominant clusters in the mmWave channels are pruned, for mmWave 5G devices applying massive MIMO beamforming.
Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
Scoliosis is a congenital disease that causes lateral curvature in the spine.
Commonsense Evidence Generation and Injection in Reading Comprehension
Human tackle reading comprehension not only based on the given context itself but often rely on the commonsense beyond.
Partly Supervised Multitask Learning
Leveraging self-supervision and adversarial training, we propose a novel general purpose semi-supervised, multiple-task model---namely, self-supervised, semi-supervised, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification.
An Extension of LIME with Improvement of Interpretability and Fidelity
As an extension of LIME, this paper proposes an high-interpretability and high-fidelity local explanation method, known as Local Explanation using feature Dependency Sampling and Nonlinear Approximation (LEDSNA).
Analysis of Scoliosis From Spinal X-Ray Images
Leveraging a carefully-adjusted U-Net model with progressive side outputs, we propose an end-to-end segmentation model that provides a fully automatic and reliable segmentation of the vertebrae associated with scoliosis measurement.
Estimation of genome size using k-mer frequencies from corrected long reads
We show that corrected third-generation data can be used to count k-mer frequencies and estimate genome size reliably, in replacement of using second-generation data.
A Modified Perturbed Sampling Method for Local Interpretable Model-agnostic Explanation
Explainability is a gateway between Artificial Intelligence and society as the current popular deep learning models are generally weak in explaining the reasoning process and prediction results.
Nonparametric Structure Regularization Machine for 2D Hand Pose Estimation
We propose a novel Nonparametric Structure Regularization Machine (NSRM) for 2D hand pose estimation, adopting a cascade multi-task architecture to learn hand structure and keypoint representations jointly.
Shape-Aware Organ Segmentation by Predicting Signed Distance Maps
In this work, we propose to resolve the issue existing in current deep learning based organ segmentation systems that they often produce results that do not capture the overall shape of the target organ and often lack smoothness.
Object-Guided Instance Segmentation for Biological Images
Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects.
Explaining the Predictions of Any Image Classifier via Decision Trees
Despite outstanding contribution to the significant progress of Artificial Intelligence (AI), deep learning models remain mostly black boxes, which are extremely weak in explainability of the reasoning process and prediction results.
Multi-Grained Named Entity Recognition
This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested.
Ranked #5 on
Nested Mention Recognition
on ACE 2005
Multi-Grained Named Entity Recognition
named-entity-recognition
+5
Neural Multi-Scale Self-Supervised Registration for Echocardiogram Dense Tracking
In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking.
Entity Synonym Discovery via Multipiece Bilateral Context Matching
Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization.
Joint Slot Filling and Intent Detection via Capsule Neural Networks
Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding.
Ranked #7 on
Slot Filling
on ATIS
Multi-Task Learning with Multi-View Attention for Answer Selection and Knowledge Base Question Answering
Second, these two tasks can benefit each other: answer selection can incorporate the external knowledge from knowledge base (KB), while KBQA can be improved by learning contextual information from answer selection.
Generating Realistic Training Images Based on Tonality-Alignment Generative Adversarial Networks for Hand Pose Estimation
Hand pose estimation from a monocular RGB image is an important but challenging task.
On the Generation of Medical Question-Answer Pairs
Question answering (QA) has achieved promising progress recently.
Finding Similar Medical Questions from Question Answering Websites
Patients who have medical information demands tend to post questions about their health conditions on these crowdsourced Q&A websites and get answers from other users.
Multi-Grained Entity Proposal Network for Named Entity Recognition
In this paper, we focus on a new Named Entity Recognition (NER) task, i. e., the Multi-grained NER task.
MedTruth: A Semi-supervised Approach to Discovering Knowledge Condition Information from Multi-Source Medical Data
In the light of these challenges, we propose a new truth discovery method, MedTruth, for medical knowledge condition discovery, which incorporates prior source quality information into the source reliability estimation procedure, and also utilizes the knowledge triple information for trustworthy information computation.
Databases
SynonymNet: Multi-context Bilateral Matching for Entity Synonyms
Being able to automatically discover synonymous entities from a large free-text corpus has transformative effects on structured knowledge discovery.
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot.
Knowledge as A Bridge: Improving Cross-domain Answer Selection with External Knowledge
In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains.
Cooperative Denoising for Distantly Supervised Relation Extraction
Distantly supervised relation extraction greatly reduces human efforts in extracting relational facts from unstructured texts.
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).
Ranked #5 on
Lung Nodule Classification
on LIDC-IDRI
Generative Discovery of Relational Medical Entity Pairs
Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies.
Bringing Semantic Structures to User Intent Detection in Online Medical Queries
The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries.
DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification
Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively.
Automated Pulmonary Nodule Detection And Classification
Classification
+1
Lesion detection and Grading of Diabetic Retinopathy via Two-stages Deep Convolutional Neural Networks
We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN).
An Automated CNN Recommendation System for Image Classification Tasks
In order to solve this problem, we proposed an automated CNN recommendation system for image classification task.
Multi-source Hierarchical Prediction Consolidation
We propose a novel multi-source hierarchical prediction consolidation method to effectively exploits the complicated hierarchical label structures to resolve the noisy and conflicting information that inherently originates from multiple imperfect sources.
Variational hybridization and transformation for large inaccurate noisy-or networks
In addition, we propose a transformation ranking algorithm that is very stable to large variances in network prior probabilities, a common issue that arises in medical applications of Bayesian networks.
On Study of the Binarized Deep Neural Network for Image Classification
Unlike those trials, in this paper, we focused on the basic propagation function of the artificial neural network and proposed the binarized deep neural network.
Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization
Stochastic gradient descent (SGD) holds as a classical method to build large scale machine learning models over big data.
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion
Then the Schatten 1-norm of the core tensor is used to replace that of the whole tensor, which leads to a much smaller-scale matrix SNM problem.
Stochastic Coordinate Coding and Its Application for Drosophila Gene Expression Pattern Annotation
The effectiveness of gene expression pattern annotation relies on the quality of feature representation.
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Unlike prior works, our method can effectively and efficiently consider missing labels and label correlations simultaneously, and is very scalable, that has linear time complexities over the size of the data.
Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification
This paper presents a novel algorithm which uses compact hash bits to greatly improve the efficiency of non-linear kernel SVM in very large scale visual classification problems.
Orthogonal Rank-One Matrix Pursuit for Low Rank Matrix Completion
Numerical results show that our proposed algorithm is more efficient than competing algorithms while achieving similar or better prediction performance.
Generalization Bounds for Representative Domain Adaptation
Finally, we analyze the asymptotic convergence and the rate of convergence of the learning process for representative domain adaptation.
Multilabel Consensus Classification
Nonetheless, data nowadays are usually multilabeled, such that more than one label have to be predicted at the same time.
Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects
Therefore, it is necessary to develop efficient assembly-independent methods for accurate estimation of these genomic characteristics.
Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models
First, we can boost the diversity of classification ensemble by incorporating multiple clustering outputs, each of which provides grouping constraints for the joint label predictions of a set of related objects.
