Search Results for author:
Found 140 papers, 60 papers with code
Multi-Stage Pre-training for Automated Chinese Essay Scoring
This paper proposes a pre-training based automated Chinese essay scoring method.
Decomposed Human Motion Prior for Video Pose Estimation via Adversarial Training
Estimating human pose from video is a task that receives considerable attention due to its applicability in numerous 3D fields.
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
In this paper, we introduce a unified and generalist Biomedical Generative Pre-trained Transformer (BiomedGPT) model, which leverages self-supervision on large and diverse datasets to accept multi-modal inputs and perform a range of downstream tasks.
PathAsst: Redefining Pathology through Generative Foundation AI Assistant for Pathology
As advances in large language models (LLMs) and multimodal techniques continue to mature, the development of general-purpose multimodal large language models (MLLMs) has surged, with significant applications in natural image interpretation.
Adaptive Chameleon or Stubborn Sloth: Unraveling the Behavior of Large Language Models in Knowledge Clashes
By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory.
Equivariant Multi-Modality Image Fusion
Multi-modality image fusion is a technique used to combine information from different sensors or modalities, allowing the fused image to retain complementary features from each modality, such as functional highlights and texture details.
Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors
Recent work has shown that fine-tuning large language models (LLMs) on large-scale instruction-following datasets substantially improves their performance on a wide range of NLP tasks, especially in the zero-shot setting.
Ranked #2 on
Relation Extraction
on TACRED-Revisited
Denoising Diffusion Models for Plug-and-Play Image Restoration
Although diffusion models have shown impressive performance for high-quality image synthesis, their potential to serve as a generative denoiser prior to the plug-and-play IR methods remains to be further explored.
Automatic Evaluation of Attribution by Large Language Models
To facilitate the evaluation, we manually curate a set of test examples covering 12 domains from a generative search engine, New Bing.
Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records
Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data.
Ray Conditioning: Trading Photo-consistency for Photo-realism in Multi-view Image Generation
Multi-view image generation attracts particular attention these days due to its promising 3D-related applications, e. g., image viewpoint editing.
A Scalable Test Problem Generator for Sequential Transfer Optimization
Lastly, a benchmark suite with 12 individual STOPs is developed using the proposed generator, which can serve as an arena for comparing different STO algorithms.
Exploring Causes of Demographic Variations In Face Recognition Accuracy
In recent years, media reports have called out bias and racism in face recognition technology.
Predicting multiple sclerosis disease severity with multimodal deep neural networks
Multiple Sclerosis (MS) is a chronic disease developed in human brain and spinal cord, which can cause permanent damage or deterioration of the nerves.
A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors
We propose a deep learning framework model to predict multiple risk factors after a liver transplant.
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure.
ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge
The autonomous ChatDoctor model based on Wikipedia and Database Brain can access real-time and authoritative information and answer patient questions based on this information, significantly improving the accuracy of the model's responses, which shows extraordinary potential for the medical field with a low tolerance for error.
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
To tackle these challenges, deep learning frameworks have been created to match patients to trials.
Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning
This survey paper tries to summarize and provide insights into the current research on instruction learning, particularly by answering the following questions: (i) What is task instruction, and what instruction types exist?
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM).
QVRF: A Quantization-error-aware Variable Rate Framework for Learned Image Compression
In this paper, we present a Quantization-error-aware Variable Rate Framework (QVRF) that utilizes a univariate quantization regulator a to achieve wide-range variable rates within a single model.
Memory-adaptive Depth-wise Heterogenous Federated Learning
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data.
Securing Biomedical Images from Unauthorized Training with Anti-Learning Perturbation
The volume of open-source biomedical data has been essential to the development of various spheres of the healthcare community since more `free' data can provide individual researchers more chances to contribute.
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks
Multiple recent studies show a paradox in graph convolutional networks (GCNs), that is, shallow architectures limit the capability of learning information from high-order neighbors, while deep architectures suffer from over-smoothing or over-squashing.
Event-Based Frame Interpolation with Ad-hoc Deblurring
The performance of video frame interpolation is inherently correlated with the ability to handle motion in the input scene.
Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images
Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs).
GreenEyes: An Air Quality Evaluating Model based on WaveNet
Accompanying rapid industrialization, humans are suffering from serious air pollution problems.
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features.
SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization
In this paper, we introduce joint importance, which integrates essential structural-aware interactions between components for the first time, to perform collaborative pruning.
Changes from Classical Statistics to Modern Statistics and Data Science
We urgently need to shift the paradigm for data analysis from the classical Euclidean data analysis to both Euclidean and non Euclidean data analysis and develop more and more innovative methods for describing, estimating and inferring non Euclidean geometries of modern real datasets.
BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models
Two linearly uncorrelated binary variables must be also independent because non-linear dependence cannot manifest with only two possible states.
DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data
Multivariate time series(MTS) is a universal data type related to many practical applications.
LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval
The alignment is achieved by weakened knowledge distillations to enlighten the retriever via two aspects -- 1) a lexicon-augmented contrastive objective to challenge the dense encoder and 2) a pair-wise rank-consistent regularization to make dense model's behavior incline to the other.
Learning Task-Oriented Flows to Mutually Guide Feature Alignment in Synthesized and Real Video Denoising
To this end, we propose a new multi-scale refined optical flow-guided video denoising method, which is more robust to different noise levels.
Ranked #1 on
Video Denoising
on VideoLQ
Hierarchical Reinforcement Learning Based Video Semantic Coding for Segmentation
In this paper, we take a step forward to video semantic compression and propose the Hierarchical Reinforcement Learning based task-driven Video Semantic Coding, named as HRLVSC.
Hierarchical Reinforcement Learning
reinforcement-learning
+3
CLOWER: A Pre-trained Language Model with Contrastive Learning over Word and Character Representations
However, most current models use Chinese characters as inputs and are not able to encode semantic information contained in Chinese words.
Query-Response Interactions by Multi-tasks in Semantic Search for Chatbot Candidate Retrieval
Semantic search for candidate retrieval is an important yet neglected problem in retrieval-based Chatbots, which aims to select a bunch of candidate responses efficiently from a large pool.
Unified Normalization for Accelerating and Stabilizing Transformers
To tackle these issues, we propose Unified Normalization (UN), which can speed up the inference by being fused with other linear operations and achieve comparable performance on par with LN.
Reference-based Image Super-Resolution with Deformable Attention Transformer
Reference-based image super-resolution (RefSR) aims to exploit auxiliary reference (Ref) images to super-resolve low-resolution (LR) images.
Towards Interpretable Video Super-Resolution via Alternating Optimization
These issues can be alleviated by a cascade of three separate sub-tasks, including video deblurring, frame interpolation, and super-resolution, which, however, would fail to capture the spatial and temporal correlations among video sequences.
Enhancing Multi-view Stereo with Contrastive Matching and Weighted Focal Loss
Learning-based multi-view stereo (MVS) methods have made impressive progress and surpassed traditional methods in recent years.
ARF: Artistic Radiance Fields
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene.
Recurrent Video Restoration Transformer with Guided Deformable Attention
Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature.
MORE: A Metric Learning Based Framework for Open-domain Relation Extraction
To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction).
WT-MVSNet: Window-based Transformers for Multi-view Stereo
In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo.
UnifieR: A Unified Retriever for Large-Scale Retrieval
Large-scale retrieval is to recall relevant documents from a huge collection given a query.
Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification
Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i. e., the part-of-speech tags and dependency relations).
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Sequential recommendation (SR) aims to model users' dynamic preferences from their historical interactions.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Adversarial Learning of Hard Positives for Place Recognition
Our method learns local and global augmentation policies which will increase the training loss, while the image retrieval network is forced to learn more powerful features for discriminating increasingly difficult examples.
Discussion of Multiscale Fisher's Independence Test for Multivariate Dependence
In this comment, we would like to discuss a general framework unifying the MULTIFIT and other tests and compare it with the binary expansion randomized ensemble test (BERET hereafter) proposed by Lee et al. (In press).
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching
Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching.
Indoor simultaneous localization and mapping based on fringe projection profilometry
Simultaneous Localization and Mapping (SLAM) plays an important role in outdoor and indoor applications ranging from autonomous driving to indoor robotics.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images
We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines.
Selecting task with optimal transport self-supervised learning for few-shot classification
Few-Shot classification aims at solving problems that only a few samples are available in the training process.
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence.
APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction
In many web applications, deep learning-based CTR prediction models (deep CTR models for short) are widely adopted.
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks toward solving image denoising, existing methods mostly rely on simple noise assumptions, such as additive white Gaussian noise (AWGN), JPEG compression noise and camera sensor noise, and a general-purpose blind denoising method for real images remains unsolved.
Phased Flight Trajectory Prediction with Deep Learning
The unprecedented increase of commercial airlines and private jets over the next ten years presents a challenge for air traffic control.
Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation
Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.
Self-supervised Transparent Liquid Segmentation for Robotic Pouring
Liquid state estimation is important for robotics tasks such as pouring; however, estimating the state of transparent liquids is a challenging problem.
VRT: A Video Restoration Transformer
Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.
Ranked #1 on
Deblurring
on BASED
An Adaptive Neuro-Fuzzy System with Integrated Feature Selection and Rule Extraction for High-Dimensional Classification Problems
The effectiveness of the FSRE-AdaTSK is demonstrated on 19 datasets of which five are in more than 2000 dimension including two with dimension greater than 7000.
Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology
There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate.
Sequential Recommendation with Bidirectional Chronological Augmentation of Transformer
Sequential recommendation can capture user chronological preferences from their historical behaviors, yet the learning of short sequences (cold-start problem) in many benchmark datasets is still an open challenge.
Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation
MFA basically considers three parallel information fusion strategies, i. e., the cross-model fusion, temporal fusion and a novel online-offline pseudo label fusion.
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.
Towards Flexible Blind JPEG Artifacts Removal
Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage.
GradTS: A Gradient-Based Automatic Auxiliary Task Selection Method Based on Transformer Networks
Compared to AUTOSEM, a strong baseline method, GradTS improves the performance of MT-DNN with a bert-base-cased backend model, from 0. 33% to 17. 93% on 8 natural language understanding (NLU) tasks in the GLUE benchmarks.
A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy
The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data.
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer
Each feature value is mapped to an embedding vector via an embedding learning process.
SwinIR: Image Restoration Using Swin Transformer
In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.
Ranked #2 on
Color Image Denoising
on urban100 sigma15
Federated Variational Learning for Anomaly Detection in Multivariate Time Series
Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS).
Contributions of Transformer Attention Heads in Multi- and Cross-lingual Tasks
Through extensive experiments, we show that (1) pruning a number of attention heads in a multi-lingual Transformer-based model has, in general, positive effects on its performance in cross-lingual and multi-lingual tasks and (2) the attention heads to be pruned can be ranked using gradients and identified with a few trial experiments.
Learning to Detect: A Data-driven Approach for Network Intrusion Detection
With massive data being generated daily and the ever-increasing interconnectivity of the world's Internet infrastructures, a machine learning based intrusion detection system (IDS) has become a vital component to protect our economic and national security.
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Specifically, we first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review.
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
More specifically, HCFlow learns a bijective mapping between HR and LR image pairs by modelling the distribution of the LR image and the rest high-frequency component simultaneously.
Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution
Extensive experiments on synthetic and real images show that the proposed MANet not only performs favorably for both spatially variant and invariant kernel estimation, but also leads to state-of-the-art blind SR performance when combined with non-blind SR methods.
Video Super-Resolution Transformer
Specifically, to tackle the first issue, we present a spatial-temporal convolutional self-attention layer with a theoretical understanding to exploit the locality information.
Open Hierarchical Relation Extraction
To establish the bidirectional connections between OpenRE and relation hierarchy, we propose the task of open hierarchical relation extraction and present a novel OHRE framework for the task.
LocalViT: Bringing Locality to Vision Transformers
The importance of locality mechanisms is validated in two ways: 1) A wide range of design choices (activation function, layer placement, expansion ratio) are available for incorporating locality mechanisms and all proper choices can lead to a performance gain over the baseline, and 2) The same locality mechanism is successfully applied to 4 vision transformers, which shows the generalization of the locality concept.
Ranked #554 on
Image Classification
on ImageNet
PQA: Perceptual Question Answering
Perceptual organization remains one of the very few established theories on the human visual system.
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images.
Flow-based Kernel Prior with Application to Blind Super-Resolution
Kernel estimation is generally one of the key problems for blind image super-resolution (SR).
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
It is widely acknowledged that single image super-resolution (SISR) methods would not perform well if the assumed degradation model deviates from those in real images.
Spatio-Temporal Data Mining for Aviation Delay Prediction
A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay.
BEAUTY Powered BEAST
To achieve a robust power, we study test statistics with data-adaptive weights, referred to as the binary expansion adaptive symmetry test (BEAST).
Local Change Point Detection and Cleaning of EEMD Signals with Application to Acoustic Shockwaves
Using this technique, we demonstrate improved signal cleaning performance for acoustic shockwave signal detection.
RpBERT: A Text-image Relation Propagation-based BERT Model for Multimodal NER
We integrate soft or hard gates to select visual clues and propose a multitask algorithm to train on the MNER datasets.
Ranked #5 on
Multi-modal Named Entity Recognition
on Twitter-15
Multi-modal Named Entity Recognition
named-entity-recognition
+2
Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network
We propose a novel in-time risk trajectory predictive model to handle the irregular sampling rate in the data, which follows the dynamics of risk of performing mechanical ventilation for individual patients.
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
We then discuss the influence of various parameters and operations on the CNN performance, with the utilization of autoencoder (AE).
Dimensionality Reduction
Fluid Dynamics
Computational Physics
Nanoscale spin detection of copper ions using double electron-electron resonance at room temperature
We report the nanoscale spin detection and electron paramagnetic resonance (EPR) spectrum of copper (Cu$^{2+}$) ions via double electron-electron resonance with single spins in diamond at room temperature and low magnetic fields.
Quantum Physics Mesoscale and Nanoscale Physics
Kinetic Energy Distribution of Fragments for Thermal Neutron-Induced $^{235}$U and $^{239}$Pu Fission Reactions
(2) Every complementary pair of the primary fission fragments is approximatively described as two ellipsoids with large deformation at scission moment.
Nuclear Theory
SDSS-IV/MaNGA: Can impulsive gaseous inflows explain steep oxygen abundance profiles \& anomalously-low-metallicity regions?
We reveal a mutual correlation between steep oxygen abundance profiles between $0. 25-1. 5 R_e$, increased variability of metallicity between $1. 25-1. 75 R_e$, and elevated HI content at fixed total galaxy stellar mass.
Astrophysics of Galaxies
Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction
In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.
GMOT-40: A Benchmark for Generic Multiple Object Tracking
Multiple Object Tracking (MOT) has witnessed remarkable advances in recent years.
NeRF++: Analyzing and Improving Neural Radiance Fields
Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes.
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.
Cascaded Semantic and Positional Self-Attention Network for Document Classification
In this work, we propose a new architecture to aggregate the two sources of information using cascaded semantic and positional self-attention network (CSPAN) in the context of document classification.
Plug-and-Play Image Restoration with Deep Denoiser Prior
Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems.
The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures
Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification
Graph neural networks are promising architecture for learning and inference with graph-structured data.
Learning Context-Based Non-local Entropy Modeling for Image Compression
To address this issue, we propose a non-local operation for context modeling by employing the global similarity within the context.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
Adaptive Structural Fingerprints for Graph Attention Networks
Yet, how to fully exploit rich structural information in the attention mechanism remains a challenge.
Depth Sensing Beyond LiDAR Range
Depth sensing is a critical component of autonomous driving technologies, but today's LiDAR- or stereo camera-based solutions have limited range.
A Method for Curation of Web-Scraped Face Image Datasets
The numbers of subjects and images acquired in web-scraped datasets are usually very large, with number of images on the millions scale.
Computational Performance of a Germline Variant Calling Pipeline for Next Generation Sequencing
With the booming of next generation sequencing technology and its implementation in clinical practice and life science research, the need for faster and more efficient data analysis methods becomes pressing in the field of sequencing.
DHP: Differentiable Meta Pruning via HyperNetworks
Passing the sparsified latent vectors through the hypernetworks, the corresponding slices of the generated weight parameters can be removed, achieving the effect of network pruning.
Deep Unfolding Network for Image Super-Resolution
As a result, the proposed network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods.
How Does Gender Balance In Training Data Affect Face Recognition Accuracy?
Deep learning methods have greatly increased the accuracy of face recognition, but an old problem still persists: accuracy is usually higher for men than women.
Analysis of Gender Inequality In Face Recognition Accuracy
We show that the female genuine distribution improves when only female images without facial cosmetics are used, but that the female impostor distribution also degrades at the same time.
The Binary Expansion Randomized Ensemble Test (BERET)
Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence.
Unsupervised Context Rewriting for Open Domain Conversation
Context modeling has a pivotal role in open domain conversation.
Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style
In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner.
Leveraging Vision Reconstruction Pipelines for Satellite Imagery
Reconstructing 3D geometry from satellite imagery is an important topic of research.
Neural Blind Deconvolution Using Deep Priors
To connect MAP and deep models, we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution.
A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry
Iterative methods for tomographic image reconstruction have great potential for enabling high quality imaging from low-dose projection data.
TOI-CNN: a Solution of Information Extraction on Chinese Insurance Policy
The advantage of TOI pooling layer is that the nested elements from one sentence could share computation and context in the forward and backward passes.
A Reference Vector based Many-Objective Evolutionary Algorithm with Feasibility-aware Adaptation
The infeasible parts of the objective space in difficult many-objective optimization problems cause trouble for evolutionary algorithms.
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
In this paper, we propose a principled formulation and framework by extending bicubic degradation based deep SISR with the help of plug-and-play framework to handle LR images with arbitrary blur kernels.
Generalizable Meta-Heuristic based on Temporal Estimation of Rewards for Large Scale Blackbox Optimization
The generalization abilities of heuristic optimizers may deteriorate with the increment of the search space dimensionality.
Toward Convolutional Blind Denoising of Real Photographs
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
Ranked #3 on
Denoising
on Darmstadt Noise Dataset
Multi-level Wavelet-CNN for Image Restoration
With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
Ranked #2 on
Grayscale Image Denoising
on Set12 sigma25
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR).
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.
Ranked #1 on
Grayscale Image Denoising
on BSD68 sigma75
Learning Deep CNN Denoiser Prior for Image Restoration
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
Ranked #1 on
Color Image Denoising
on BSD68 sigma5
Single Image Super-resolution via a Lightweight Residual Convolutional Neural Network
To tackle with the second problem, a lightweight CNN architecture which has carefully designed width, depth and skip connections was proposed.
BET on Independence
To avoid such power loss, we approach the nonparametric test of independence through the new framework of binary expansion statistics (BEStat) and binary expansion testing (BET), which examine dependence through a novel binary expansion filtration approximation of the copula.
Entity Embedding-based Anomaly Detection for Heterogeneous Categorical Events
Anomaly detection plays an important role in modern data-driven security applications, such as detecting suspicious access to a socket from a process.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
Seeing the Forest from the Trees in Two Looks: Matrix Sketching by Cascaded Bilateral Sampling
Given a matrix A of size m by n, state-of-the-art randomized algorithms take O(m * n) time and space to obtain its low-rank decomposition.
Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations
Structures and evolutions of the invariance network, in particular the vanishing correlations, can shed important light on locating causal anomalies and performing diagnosis.
Distributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multi-way data.
Spherical Cap Packing Asymptotics and Rank-Extreme Detection
Such probabilistic considerations result in an asymptotic sharp universal uniform bound on the maximal inner product between any set of unit vectors and a stochastically independent uniformly distributed unit vector.
