Search Results for author:
Found 130 papers, 44 papers with code
Learning Progressive Joint Propagation for Human Motion Prediction
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
基于相似度进行句子选择的机器阅读理解数据增强(Machine reading comprehension data Augmentation for sentence selection based on similarity)
“目前常见的机器阅读理解数据增强方法如回译, 单独对文章或者问题进行数据增强, 没有考虑文章、问题和选项三元组之间的联系。因此, 本文探索了一种利用三元组联系进行文章句子筛选的数据增强方法, 通过比较文章与问题以及选项的相似度, 选取文章中与二者联系紧密的句子。同时为了使不同选项的三元组区别增大, 我们选用了正则化Dropout的策略。实验结果表明, 在RACE数据集上的准确率可提高3. 8%。”
\textrm{DuReader}_{\textrm{vis}}: A Chinese Dataset for Open-domain Document Visual Question Answering
Open-domain question answering has been used in a wide range of applications, such as web search and enterprise search, which usually takes clean texts extracted from various formats of documents (e. g., web pages, PDFs, or Word documents) as the information source.
Deep Transferring Quantization
Specifically, we propose a method named deep transferring quantization (DTQ) to effectively exploit the knowledge in a pre-trained full-precision model.
A Survey on Efficient Training of Transformers
Recent advances in Transformers have come with a huge requirement on computing resources, highlighting the importance of developing efficient training techniques to make Transformer training faster, at lower cost, and to higher accuracy by the efficient use of computation and memory resources.
Planning-assisted autonomous swarm shepherding with collision avoidance
Robotic shepherding is a bio-inspired approach to autonomously guiding a swarm of agents towards a desired location and has earned increasing research interest recently.
Discover governing differential equations from evolving systems
Our proposal performs well in identifying the change points and discovering governing differential equations in two evolving systems.
Enhanced-rate Iterative Beamformers for Active IRS-assisted Wireless Communications
In this paper, in order to boost the achievable rate of user in such a wireless network, three enhanced-rate iterative beamforming methods are proposed by designing the amplifying factors and the corresponding phases at active IRS.
Three High-rate Beamforming Methods for Active IRS-aided Wireless Network
To fully exploit the amplifying gain achieved by active IRS, two high-rate methods, maximum ratio reflecting (MRR) and selective ratio reflecting (SRR) are presented, which are motivated by maximum ratio combining and selective ratio combining.
Federated deep clustering with GAN-based data synthesis
The necessity to invest more resources in improving federated clustering methods is twofold: 1) The performance of supervised federated learning models can benefit from clustering.
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes
In this way, our proposal can better keep the advantages of previous community detection results and transfer them to the next task.
Dense Text Retrieval based on Pretrained Language Models: A Survey
With powerful PLMs, we can effectively learn the representations of queries and texts in the latent representation space, and further construct the semantic matching function between the dense vectors for relevance modeling.
AgileAvatar: Stylized 3D Avatar Creation via Cascaded Domain Bridging
We propose a novel self-supervised learning framework to create high-quality stylized 3D avatars with a mix of continuous and discrete parameters.
LGN-Net: Local-Global Normality Network for Video Anomaly Detection
Existing unsupervised VAD methods tend to learn normality from training sets consisting of only normal videos and regard instances deviating from such normality as anomalies.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Federated clustering with GAN-based data synthesis
Federated clustering is an adaptation of centralized clustering in the federated settings, which aims to cluster data based on a global similarity measure while keeping all data local.
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning
Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover.
EcoFormer: Energy-Saving Attention with Linear Complexity
To this end, we propose a new binarization paradigm customized to high-dimensional softmax attention via kernelized hashing, called EcoFormer, to map the original queries and keys into low-dimensional binary codes in Hamming space.
FocusFormer: Focusing on What We Need via Architecture Sampler
During architecture search, these methods focus on finding architectures on the Pareto frontier of performance and resource consumption, which forms a gap between training and deployment.
Provably Tightest Linear Approximation for Robustness Verification of Sigmoid-like Neural Networks
Considerable efforts have been devoted to finding the so-called tighter approximations to obtain more precise verification results.
An Interpretability Evaluation Benchmark for Pre-trained Language Models
To fill in the gap, we propose a novel evaluation benchmark providing with both English and Chinese annotated data.
Learning Appearance-motion Normality for Video Anomaly Detection
Video anomaly detection is a challenging task in the computer vision community.
HIRE: Distilling High-order Relational Knowledge From Heterogeneous Graph Neural Networks
To the best of our knowledge, we are the first to propose a HIgh-order RElational (HIRE) knowledge distillation framework on heterogeneous graphs, which can significantly boost the prediction performance regardless of model architectures of HGNNs.
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly Detection
Existing methods for anomaly detection based on memory-augmented autoencoder (AE) have the following drawbacks: (1) Establishing a memory bank requires additional memory space.
Computationally Identifying Funneling and Focusing Questions in Classroom Discourse
Responsive teaching is a highly effective strategy that promotes student learning.
Reducing US Biofuels Requirements Mitigates Short-term Impacts of Global Population and Income Growth on Agricultural Environmental Outcomes
Biobased energy, particularly corn starch-based ethanol and other liquid renewable fuels, are a major element of federal and state energy policies in the United States.
Multi-scale Analysis of Nitrogen Loss Mitigation in the US Corn Belt
Reducing the size of the hypoxic zone in the Gulf of Mexico has proven to be a challenging task.
Contextual Adapters for Personalized Speech Recognition in Neural Transducers
Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data.
Less Learn Shortcut: Analyzing and Mitigating Learning of Spurious Feature-Label Correlation
Many recent works indicate that the deep neural networks tend to take dataset biases as shortcuts to make decision, rather than understand the tasks, which results in failures on the real-world applications.
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.
MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities
By comparing the similarities and differences between input samples and memory samples in the memory pool to give effective guesses about abnormal regions; In the inference phase, MemSeg directly determines the abnormal regions of the input image in an end-to-end manner.
Ranked #2 on
Anomaly Detection
on MVTec AD
A Thorough Examination on Zero-shot Dense Retrieval
Recent years have witnessed the significant advance in dense retrieval (DR) based on powerful pre-trained language models (PLM).
A Multi-Transformation Evolutionary Framework for Influence Maximization in Social Networks
Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes.
Dynamic Focus-aware Positional Queries for Semantic Segmentation
In this paper, we propose a simple yet effective query design for semantic segmentation termed Dynamic Focus-aware Positional Queries (DFPQ), which dynamically generates positional queries conditioned on the cross-attention scores from the preceding decoder block and the positional encodings for the corresponding image features, simultaneously.
Ranked #18 on
Semantic Segmentation
on ADE20K
(using extra training data)
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding
In addition, the NLU model in the two-stage system is not streamable, as it must wait for the audio segments to complete processing, which ultimately impacts the latency of the SLU system.
Automatic Speech Recognition
Natural Language Understanding
+2
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine
In this paper, we present DuReader_retrieval, a large-scale Chinese dataset for passage retrieval.
Network Collaborator: Knowledge Transfer Between Network Reconstruction and Community Detection
Community detection (CD) from dynamics and network reconstruction (NR) from dynamics are natural synergistic tasks that motivate the proposed evolutionary multitasking NR and CD framework, called network collaborator (NC).
Evolutionary Multitasking AUC Optimization
Inspired by the characteristic of pairwise learning, the cheap AUC optimization task with a small-scale dataset sampled from the large-scale dataset is constructed to promote the AUC accuracy of the original, large-scale, and expensive AUC optimization task.
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models
For this purpose, we create a Chinese dataset namely DuQM which contains natural questions with linguistic perturbations to evaluate the robustness of question matching models.
Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understanding
Recent years have seen significant advances in end-to-end (E2E) spoken language understanding (SLU) systems, which directly predict intents and slots from spoken audio.
Natural Language Understanding
Spoken Language Understanding
Sharpness-aware Quantization for Deep Neural Networks
Network quantization is a dominant paradigm of model compression.
Pruning Self-attentions into Convolutional Layers in Single Path
Vision Transformers (ViTs) have achieved impressive performance over various computer vision tasks.
Mesa: A Memory-saving Training Framework for Transformers
While Transformers have delivered significant performance improvements, training such networks is extremely memory intensive owing to storing all intermediate activations that are needed for gradient computation during backpropagation, especially for long sequences.
Context-Aware Transformer Transducer for Speech Recognition
We also leverage both BLSTM and pretrained BERT based models to encode contextual data and guide the network training.
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In this paper, we propose a novel joint training approach for dense passage retrieval and passage re-ranking.
RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery
Vertical Federated Learning (VFL) is a distributed learning paradigm that allows multiple agents to jointly train a global model when each agent holds a different subset of features for the same sample(s).
Exploiting Spatial-Temporal Semantic Consistency for Video Scene Parsing
Compared with image scene parsing, video scene parsing introduces temporal information, which can effectively improve the consistency and accuracy of prediction.
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval
Recently, dense passage retrieval has become a mainstream approach to finding relevant information in various natural language processing tasks.
Rapid Elastic Architecture Search under Specialized Classes and Resource Constraints
More critically, EAS is able to find compact architectures within 0. 1 second for 50 deployment scenarios.
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements.
Resisting Out-of-Distribution Data Problem in Perturbation of XAI
Besides, our solution also resolves a fundamental problem with the faithfulness indicator, a commonly used evaluation metric of XAI algorithms that appears to be sensitive to the OoD issue.
HANet: Hierarchical Alignment Networks for Video-Text Retrieval
Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text.
AgileGAN: stylizing portraits by inversion-consistent transfer learning
While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images.
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources.
Ranked #1 on
Image Retrieval
on Localized Narratives
Tensor networks for unsupervised machine learning
Despite the great potential, however, existing tensor network models for unsupervised machine learning only work as a proof of principle, as their performance is much worse than the standard models such as restricted Boltzmann machines and neural networks.
Exploiting Large-scale Teacher-Student Training for On-device Acoustic Models
We present results from Alexa speech teams on semi-supervised learning (SSL) of acoustic models (AM) with experiments spanning over 3000 hours of GPU time, making our study one of the largest of its kind.
Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions
In conversation, uptake happens when a speaker builds on the contribution of their interlocutor by, for example, acknowledging, repeating or reformulating what they have said.
Large-Scale Data-Driven Airline Market Influence Maximization
On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.
Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information
Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos.
Less is More: Pay Less Attention in Vision Transformers
Transformers have become one of the dominant architectures in deep learning, particularly as a powerful alternative to convolutional neural networks (CNNs) in computer vision.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
AAformer: Auto-Aligned Transformer for Person Re-Identification
In this paper, we introduce an alignment scheme in Transformer architecture for the first time and propose the Auto-Aligned Transformer (AAformer) to automatically locate both the human parts and non-human ones at patch-level.
Scalable Vision Transformers with Hierarchical Pooling
However, the routine of the current ViT model is to maintain a full-length patch sequence during inference, which is redundant and lacks hierarchical representation.
Ranked #667 on
Image Classification
on ImageNet
Temporal Memory Attention for Video Semantic Segmentation
Video semantic segmentation requires to utilize the complex temporal relations between frames of the video sequence.
Ranked #1 on
Video Semantic Segmentation
on CamVid
Global-Local Propagation Network for RGB-D Semantic Segmentation
Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images.
CPTR: Full Transformer Network for Image Captioning
Besides, we provide detailed visualizations of the self-attention between patches in the encoder and the "words-to-patches" attention in the decoder thanks to the full Transformer architecture.
Fast Sequence Generation with Multi-Agent Reinforcement Learning
These models are autoregressive in that they generate each word by conditioning on previously generated words, which leads to heavy latency during inference.
Single-path Bit Sharing for Automatic Loss-aware Model Compression
By jointly training the binary gates in conjunction with network parameters, the compression configurations of each layer can be automatically determined.
HAIR: Hierarchical Visual-Semantic Relational Reasoning for Video Question Answering
Specifically, we present a novel graph memory mechanism to perform relational reasoning, and further develop two types of graph memory: a) visual graph memory that leverages visual information of video for relational reasoning; b) semantic graph memory that is specifically designed to explicitly leverage semantic knowledge contained in the classes and attributes of video objects, and perform relational reasoning in the semantic space.
Magnetic field and gravitational waves from the first-order Phase Transition
We perform the three dimensional lattice simulation of the magnetic field and gravitational wave productions from bubble collisions during the first-order electroweak phase transition.
Cosmology and Nongalactic Astrophysics High Energy Physics - Lattice High Energy Physics - Phenomenology
AutoCaption: Image Captioning with Neural Architecture Search
The whole process involves a visual understanding module and a language generation module, which brings more challenges to the design of deep neural networks than other tasks.
DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection
In this paper, we investigate the complimentary roles of spatial and temporal information and propose a novel dynamic spatiotemporal network (DS-Net) for more effective fusion of spatiotemporal information.
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering
In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers.
Ranked #4 on
Passage Retrieval
on Natural Questions
Conditional Automated Channel Pruning for Deep Neural Networks
To address this issue, given a target compression rate for the whole model, one can search for the optimal compression rate for each layer.
Robust Mean Estimation in High Dimensions via $\ell_0$ Minimization
We study the robust mean estimation problem in high dimensions, where $\alpha <0. 5$ fraction of the data points can be arbitrarily corrupted.
Scene Segmentation with Dual Relation-aware Attention Network
We conduct extensive experiments to validate the effectiveness of our network and achieve new state-of-the-art segmentation performance on four challenging scene segmentation data sets, i. e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff data sets.
Ranked #9 on
Semantic Segmentation
on COCO-Stuff test
AQD: Towards Accurate Quantized Object Detection
Network quantization allows inference to be conducted using low-precision arithmetic for improved inference efficiency of deep neural networks on edge devices.
Not only Look, but also Listen: Learning Multimodal Violence Detection under Weak Supervision
Violence detection has been studied in computer vision for years.
NROWAN-DQN: A Stable Noisy Network with Noise Reduction and Online Weight Adjustment for Exploration
Effective exploration for noisy networks is one of the most important issues in deep reinforcement learning.
Non-Autoregressive Image Captioning with Counterfactuals-Critical Multi-Agent Learning
In this paper, we propose a Non-Autoregressive Image Captioning (NAIC) model with a novel training paradigm: Counterfactuals-critical Multi-Agent Learning (CMAL).
NTIRE 2020 Challenge on NonHomogeneous Dehazing
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
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.
DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications
Machine reading comprehension (MRC) is a crucial task in natural language processing and has achieved remarkable advancements.
Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs
In computer science, there exist a large number of optimization problems defined on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints.
Normalized and Geometry-Aware Self-Attention Network for Image Captioning
First, we propose Normalized Self-Attention (NSA), a reparameterization of SA that brings the benefits of normalization inside SA.
Micro-supervised Disturbance Learning: A Perspective of Representation Probability Distribution
To explore the representation learning capability under the continuous stimulation of the SPI, we present a deep Micro-supervised Disturbance Learning (Micro-DL) framework based on the Micro-DGRBM and Micro-DRBM models and compare it with a similar deep structure which has not any external stimulation.
Generative Low-bitwidth Data Free Quantization
More critically, our method achieves much higher accuracy on 4-bit quantization than the existing data free quantization method.
Ranked #1 on
Data Free Quantization
on ImageNet
(ImageNet W8A8 Top-1 Accuracy metric)
Learn to Generate Time Series Conditioned Graphs with Generative Adversarial Nets
Differently, in this paper, we are interested in a novel problem named Time Series Conditioned Graph Generation: given an input multivariate time series, we aim to infer a target relation graph modeling the underlying interrelationships between time series with each node corresponding to each time series.
Discrimination-aware Network Pruning for Deep Model Compression
In this paper, we propose a simple-yet-effective method called discrimination-aware channel pruning (DCP) to choose the channels that actually contribute to the discriminative power.
Semantic-Aware Label Placement for Augmented Reality in Street View
Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios.
CoKE: Contextualized Knowledge Graph Embedding
This work presents Contextualized Knowledge Graph Embedding (CoKE), a novel paradigm that takes into account such contextual nature, and learns dynamic, flexible, and fully contextualized entity and relation embeddings.
Adaptive Context Network for Scene Parsing
Recent works attempt to improve scene parsing performance by exploring different levels of contexts, and typically train a well-designed convolutional network to exploit useful contexts across all pixels equally.
Ranked #70 on
Semantic Segmentation
on ADE20K val
D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension
In this paper, we introduce a simple system Baidu submitted for MRQA (Machine Reading for Question Answering) 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Flash X-ray diffraction imaging in 3D: a proposed analysis pipeline
Modern Flash X-ray diffraction Imaging (FXI) acquires diffraction signals from single biomolecules at a high repetition rate from X-ray Free Electron Lasers (XFELs), easily obtaining millions of 2D diffraction patterns from a single experiment.
Vatex Video Captioning Challenge 2020: Multi-View Features and Hybrid Reward Strategies for Video Captioning
This report describes our solution for the VATEX Captioning Challenge 2020, which requires generating descriptions for the videos in both English and Chinese languages.
Gumbel-softmax Optimization: A Simple General Framework for Combinatorial Optimization Problems on Graphs
Many problems in real life can be converted to combinatorial optimization problems (COPs) on graphs, that is to find a best node state configuration or a network structure such that the designed objective function is optimized under some constraints.
Effective Training of Convolutional Neural Networks with Low-bitwidth Weights and Activations
Furthermore, we propose a second progressive quantization scheme which gradually decreases the bit-width from high-precision to low-precision during training.
Aligning Linguistic Words and Visual Semantic Units for Image Captioning
Image captioning attempts to generate a sentence composed of several linguistic words, which are used to describe objects, attributes, and interactions in an image, denoted as visual semantic units in this paper.
Quantum Fisher information matrix and multiparameter estimation
Quantum Fisher information matrix (QFIM) is a core concept in theoretical quantum metrology due to the significant importance of quantum Cram\'{e}r-Rao bound in quantum parameter estimation.
Quantum Physics
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension
In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC.
Multi-local Collaborative AutoEncoder
In mcrRBM and mcrGRBM models, the structure and multi-local collaborative relationships of unlabeled data are integrated into their encoding procedure.
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling
0-1 knapsack is of fundamental importance in computer science, business, operations research, etc.
Towards Time-Aware Distant Supervision for Relation Extraction
Time-DS is composed of a time series instance-popularity and two strategies.
A General Deep Learning Framework for Network Reconstruction and Dynamics Learning
We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.
Unsupervised Feature Learning Architecture with Multi-clustering Integration RBM
In this paper, we present a novel unsupervised feature learning architecture, which consists of a multi-clustering integration module and a variant of RBM termed multi-clustering integration RBM (MIRBM).
Discrimination-aware Channel Pruning for Deep Neural Networks
Channel pruning is one of the predominant approaches for deep model compression.
Supervised Classification Methods for Flash X-ray single particle diffraction Imaging
It is also straightforward to parallelize them so as to fully match the XFEL repetition rate, thereby enabling processing at site.
Aggregated Semantic Matching for Short Text Entity Linking
The task of entity linking aims to identify concepts mentioned in a text fragments and link them to a reference knowledge base.
Answer-focused and Position-aware Neural Question Generation
(2) The model copies the context words that are far from and irrelevant to the answer, instead of the words that are close and relevant to the answer.
Dual Attention Network for Scene Segmentation
Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively.
Ranked #6 on
Semantic Segmentation
on Trans10K
Neural Math Word Problem Solver with Reinforcement Learning
Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
Current evaluation metrics to question answering based machine reading comprehension (MRC) systems generally focus on the lexical overlap between the candidate and reference answers, such as ROUGE and BLEU.
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Machine reading comprehension (MRC) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine.
Ranked #3 on
Question Answering
on MS MARCO
DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
Experiments show that human performance is well above current state-of-the-art baseline systems, leaving plenty of room for the community to make improvements.
A Statistical Framework for Product Description Generation
We present in this paper a statistical framework that generates accurate and fluent product description from product attributes.
Stacked Deconvolutional Network for Semantic Segmentation
In SDN, multiple shallow deconvolutional networks, which are called as SDN units, are stacked one by one to integrate contextual information and guarantee the fine recovery of localization information.
Ranked #4 on
Semantic Segmentation
on PASCAL VOC 2012 test
Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks
Existing methods for arterial blood pressure (BP) estimation directly map the input physiological signals to output BP values without explicitly modeling the underlying temporal dependencies in BP dynamics.
Ranked #1 on
Blood pressure estimation
on MIMIC-III
A-Lamp: Adaptive Layout-Aware Multi-Patch Deep Convolutional Neural Network for Photo Aesthetic Assessment
However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input.
Ranked #2 on
Aesthetics Quality Assessment
on AVA
Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions
These two-dimensional diffraction patterns can be practically reconstructed and retrieved down to a resolution of a few \angstrom.
Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters
With the expected enormous amount of diffraction data to be produced in the foreseeable future, this is the required scale to approach real time processing of data at the beam site.
Weakly-Supervised Dual Clustering for Image Semantic Segmentation
In this paper, we propose a novel Weakly-Supervised Dual Clustering (WSDC) approach for image semantic segmentation with image-level labels, i. e., collaboratively performing image segmentation and tag alignment with those regions.
