Search Results for author:
Found 138 papers, 45 papers with code
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention
It is hypothesized that one’s interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts).
基于人物特征增强的拟人句要素抽取方法研究(Research on Element Extraction of Personified Sentences Based on Enhanced Characters)
“在散文阅读理解的鉴赏类问题中, 对拟人句赏析考查比较频繁。目前, 已有的工作仅对拟人句中的本体要素进行识别并抽取, 存在要素抽取不完整的问题, 尤其是当句子中出现多个本体时, 需要确定拟人词与各个本体的对应关系。为解决这些问题, 本文提出了基于人物特征增强的拟人句要素抽取方法。该方法利用特定领域的特征, 增强句子的向量表示, 再利用条件随机场模型对拟人句中的本体和拟人词要素进行识别。在此基础上, 利用自注意力机制对要素之间的关系进行检测, 使用要素同步机制和关系同步机制进行信息交互, 用于要素识别和关系检测的输入更新。在自建的拟人数据集上进行<本体, 拟人词>抽取的比较实验, 结果表明本文提出的模型性能优于其他比较模型。”
Hashtags, Emotions, and Comments: A Large-Scale Dataset to Understand Fine-Grained Social Emotions to Online Topics
This paper studies social emotions to online discussion topics.
Prompt-Driven Neural Machine Translation
Neural machine translation (NMT) has obtained significant performance improvement over the recent years.
MLEC-QA: A Chinese Multi-Choice Biomedical Question Answering Dataset
Question Answering (QA) has been successfully applied in scenarios of human-computer interaction such as chatbots and search engines.
Continuous sign language recognition based on cross-resolution knowledge distillation
It is then used to combine cross-resolution knowledge distillation and traditional knowledge distillation methods to form a CSLR model based on cross-resolution knowledge distillation (CRKD).
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Integrating SAM with adaptive learning rate and momentum acceleration, dubbed AdaSAM, has already been explored empirically to train large-scale deep neural networks without theoretical guarantee due to the triple difficulties in analyzing the coupled perturbation step, adaptive learning rate and momentum step.
P$^2$SDF for Neural Indoor Scene Reconstruction
As the unsupervised plane segments are usually noisy and inaccurate, we propose to assign different weights to the sampled points on the plane in plane estimation as well as the regularization loss.
Understanding Social Media Cross-Modality Discourse in Linguistic Space
To fill in the gap, we present a novel concept of cross-modality discourse, reflecting how human readers couple image and text understandings.
Audio2Gestures: Generating Diverse Gestures from Audio
Finally, we demonstrate that our method can be readily used to generate motion sequences with user-specified motion clips on the timeline.
A Fine-grained Chinese Software Privacy Policy Dataset for Sequence Labeling and Regulation Compliant Identification
Privacy protection raises great attention on both legal levels and user awareness.
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
Traffic signal control is safety-critical for our daily life.
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind
When reading a story, humans can rapidly understand new fictional characters with a few observations, mainly by drawing analogy to fictional and real people they met before in their lives.
Temporal superimposed crossover module for effective continuous sign language
The ultimate goal of continuous sign language recognition(CSLR) is to facilitate the communication between special people and normal people, which requires a certain degree of real-time and deploy-ability of the model.
Exploiting Spatial-temporal Correlations for Video Anomaly Detection
Video anomaly detection (VAD) remains a challenging task in the pattern recognition community due to the ambiguity and diversity of abnormal events.
2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study
We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks.
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
However, these data augmentation methods either potentially cause shifts in decision boundaries (representation interpolation), are not expressive enough (token replacement), or introduce too much computational overhead (augmentation with models).
MBTI Personality Prediction for Fictional Characters Using Movie Scripts
An NLP model that understands stories should be able to understand the characters in them.
An Attention-Guided and Wavelet-Constrained Generative Adversarial Network for Infrared and Visible Image Fusion
The GAN-based infrared and visible image fusion methods have gained ever-increasing attention due to its effectiveness and superiority.
Time Will Change Things: An Empirical Study on Dynamic Language Understanding in Social Media Classification
Language features are ever-evolving in the real-world social media environment.
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks
To tackle this problem, in this paper, we proposed an efficient Coarse-to-Fine Facet-Aware Ranking (C2F-FAR) framework for unsupervised long document summarization, which is based on the semantic block.
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.
Recognition of Handwritten Chinese Text by Segmentation: A Segment-annotation-free Approach
A novel weakly supervised learning method is proposed to enable the network to be trained using only transcript annotations; thus, the expensive character segmentation annotations required by previous segmentation-based methods can be avoided.
Handwritten Chinese Text Recognition
Weakly-supervised Learning
Continuous Sign Language Recognition via Temporal Super-Resolution Network
The sparse frame-level features are fused through the features obtained by the two designed branches as the reconstructed dense frame-level feature sequence, and the connectionist temporal classification(CTC) loss is used for training and optimization after the time-series feature extraction part.
Attention-based Multimodal Feature Representation Model for Micro-video Recommendation
Therefore, this paper adopts a multi-headed self-attentive mechanism to mine the internal correlations between features and thus learn the internal representation of features.
The Cross-lingual Conversation Summarization Challenge
We propose the shared task of cross-lingual conversation summarization, \emph{ConvSumX Challenge}, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation.
Abstractive Dialogue Summarization
Cross-Lingual Abstractive Summarization
+3
A Survey of Machine Narrative Reading Comprehension Assessments
As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks.
Domain Invariant Model with Graph Convolutional Network for Mammogram Classification
To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains.
Analyzing the Intensity of Complaints on Social Media
Furthermore, we conduct a comprehensive linguistic analysis around complaints, including the connections between complaints and sentiment, and a cross-lingual comparison for complaints expressions used by Chinese and English speakers.
Saliency in Augmented Reality
Therefore, in this paper, we mainly analyze the interaction effect between background (BG) scenes and AR contents, and study the saliency prediction problem in AR.
TVShowGuess: Character Comprehension in Stories as Speaker Guessing
We propose a new task for assessing machines' skills of understanding fictional characters in narrative stories.
Multi-scale temporal network for continuous sign language recognition
The time-wise feature extraction part performs temporal feature learning by first extracting temporal receptive field features of different scales using the proposed multi-scale temporal block (MST-block) to improve the temporal modeling capability, and then further encoding the temporal features of different scales by the transformers module to obtain more accurate temporal features.
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
Doctor Recommendation in Online Health Forums via Expertise Learning
Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task.
Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmap
In this study, we developed a single-stage rotating object detector via two points with a solar corona heatmap (ROTP) to detect oriented objects.
Adversarial Attacks and Defense Methods for Power Quality Recognition
To this end, we first propose a signal-specific method and a universal signal-agnostic method to attack power systems using generated adversarial examples.
Decoupled IoU Regression for Object Detection
Prior works propose to predict Intersection-over-Union (IoU) between bounding boxes and corresponding ground-truths to improve NMS, while accurately predicting IoU is still a challenging problem.
Rethinking Attention-Model Explainability through Faithfulness Violation Test
Attention mechanisms are dominating the explainability of deep models.
Towards Noiseless Object Contours for Weakly Supervised Semantic Segmentation
Existing methods usually generate pseudo labels from class activation map (CAM) and then train a segmentation model.
A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
In this paper, we comprehensively study three architecture design choices on ViT -- spatial reduction, doubled channels, and multiscale features -- and demonstrate that a vanilla ViT architecture can fulfill this goal without handcrafting multiscale features, maintaining the original ViT design philosophy.
SPTS: Single-Point Text Spotting
For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance.
Taming Overconfident Prediction on Unlabeled Data from Hindsight
The prediction uncertainty is typically expressed as the \emph{entropy} computed by the transformed probabilities in output space.
Solving the Data Sparsity Problem in Predicting the Success of the Startups with Machine Learning Methods
With data platforms like Crunchbase aggregating the information of startup companies, it is possible to predict with machine learning algorithms.
Cross-domain Trajectory Prediction with CTP-Net
Further, considering the consistency between the observed and the predicted trajectories, a target domain offset discriminator is utilized to adversarially regularize the future trajectory predictions to be in line with the observed trajectories.
EncT5: A Framework for Fine-tuning T5 as Non-autoregressive Models
Pre-trained encoder-decoder transformer architectures have become increasingly popular recently with the advent of T5 models.
Proxy-bridged Image Reconstruction Network for Anomaly Detection in Medical Images
To mitigate this problem, in this paper, we propose a novel Proxy-bridged Image Reconstruction Network (ProxyAno) for anomaly detection in medical images.
Fine-Tuning from Limited Feedbacks
Instead of learning from scratch, fine-tuning a pre-trained model to fit a related target dataset of interest or downstream tasks has been a handy trick to achieve the desired performance.
A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict
Based on this dual-channel framework, we design the Dual-Channel Network~(DC-Net) to recognize sentiment conflict.
Revisiting 3D ResNets for Video Recognition
A recent work from Bello shows that training and scaling strategies may be more significant than model architectures for visual recognition.
Ranked #33 on
Action Classification
on Kinetics-600
Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders
In order to overcome this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code.
Ranked #3 on
Gesture Generation
on BEAT
Engage the Public: Poll Question Generation for Social Media Posts
This paper presents a novel task to generate poll questions for social media posts.
OLR 2021 Challenge: Datasets, Rules and Baselines
In addition to the Language Identification (LID) tasks, multilingual Automatic Speech Recognition (ASR) tasks are introduced to OLR 2021 Challenge for the first time.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transmission Dynamics of COVID-19 Pandemic Non-pharmaceutical Interventions and Vaccination
By simulating the relation ships of the basic reproduction number $\mathscr{R}_{0}$, the vaccination rate and the efficiency of vaccine, we find that it is impossible to achieve the herd immunity without NPIs when the efficiency of vaccine is lower than $76. 9\%$.
Oriental Language Recognition (OLR) 2020: Summary and Analysis
The fifth Oriental Language Recognition (OLR) Challenge focuses on language recognition in a variety of complex environments to promote its development.
An Integrated Framework for Two-pass Personalized Voice Trigger
For the SV system, we proposed a multi-task learning network, where phonetic branch is trained with the character label of the utterance, and speaker branch is trained with the label of the speaker.
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
Quotations are crucial for successful explanations and persuasions in interpersonal communications.
Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure
Numerous online conversations are produced on a daily basis, resulting in a pressing need to conversation understanding.
Causal Hidden Markov Model for Time Series Disease Forecasting
We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages.
Dilated SpineNet for Semantic Segmentation
Scale-permuted networks have shown promising results on object bounding box detection and instance segmentation.
Ranked #4 on
Semantic Segmentation
on PASCAL VOC 2012 val
Global Existence of Strong and Weak Solutions to 2D Compressible Navier-Stokes System in Bounded Domains with Large Data and Vacuum
We study the barotropic compressible Navier-Stokes system where the shear viscosity is a positive constant and the bulk one proportional to a power of the density with the power bigger than one and a third.
Analysis of PDEs
RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks with Fine-Grain Utilization
In this paper, we propose RTGPU, which can schedule the execution of multiple GPU applications in real-time to meet hard deadlines.
Exploring the Galactic Anticenter substructure with LAMOST & Gaia DR2
Their location in [$\alpha$/M] vs. [M/H] space is more metal poor than typical thin disk stars, with [$\alpha$/M] \textbf{lower} than the thick disk.
Astrophysics of Galaxies
Crowd Counting With Partial Annotations in an Image
To fully leverage the data captured from different scenes with different view angles while reducing the annotation cost, this paper studies a novel crowd counting setting, i. e. only using partial annotations in each image as training data.
Dispersively probed microwave spectroscopy of a silicon hole double quantum dot
Owing to ever increasing gate fidelities and to a potential transferability to industrial CMOS technology, silicon spin qubits have become a compelling option in the strive for quantum computation.
Mesoscale and Nanoscale Physics
Constructive and Destructive Interference of Kerker-type Scattering in an Ultra-thin Silicon Huygens Metasurface
These modes are provided by a designed and fabricated CMOS compatible ultra-thin Silicon nanodisk metasurface with a suppressed magnetic dipole contribution, and verified through multipole decomposition.
Optics Classical Physics
Forecasting Irreversible Disease via Progression Learning
In order to account for the degree of progression of the disease, we propose a temporal generative model to accurately generate the future image and compare it with the current one to get a residual image.
Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks
Recently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance.
Modeling Evolution of Message Interaction for Rumor Resolution
Previous work for rumor resolution concentrates on exploiting time-series characteristics or modeling topology structure separately.
Cross-Media Keyphrase Prediction: A Unified Framework with Multi-Modality Multi-Head Attention and Image Wordings
Further analyses show that our multi-head attention is able to attend information from various aspects and boost classification or generation in diverse scenarios.
On the Transformer Growth for Progressive BERT Training
Due to the excessive cost of large-scale language model pre-training, considerable efforts have been made to train BERT progressively -- start from an inferior but low-cost model and gradually grow the model to increase the computational complexity.
Strategy for Boosting Pair Comparison and Improving Quality Assessment Accuracy
The development of rigorous quality assessment model relies on the collection of reliable subjective data, where the perceived quality of visual multimedia is rated by the human observers.
Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification
Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations.
Fairness in Semi-supervised Learning: Unlabeled Data Help to Reduce Discrimination
A set of experiments on real-world and synthetic datasets show that our method is able to use unlabeled data to achieve a better trade-off between accuracy and discrimination.
Fairness Constraints in Semi-supervised Learning
Extensive experiments show that our method is able to achieve fair semi-supervised learning, and reach a better trade-off between accuracy and fairness than fair supervised learning.
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
Multiscale modeling of glioma pseudopalisades: contributions from the tumor microenvironment
Moreover, we study two different types of scaling and compare the behavior of the obtained macroscopic PDEs by way of simulations.
Dynamic Online Conversation Recommendation
Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner.
Minority Oversampling for Imbalanced Time Series Classification
Many important real-world applications involve time-series data with skewed distribution.
Secure Metric Learning via Differential Pairwise Privacy
This paper studies, for the first time, how pairwise information can be leaked to attackers during distance metric learning, and develops differential pairwise privacy (DPP), generalizing the definition of standard differential privacy, for secure metric learning.
UFTR: A Unified Framework for Ticket Routing
In short, our results demonstrate that the UFTR is a superior solution to the ticket routing problem because it takes into account previously unexploited interrelationships between the group assignment and group transfer problems.
GPM: A Generic Probabilistic Model to Recover Annotator's Behavior and Ground Truth Labeling
In the big data era, data labeling can be obtained through crowdsourcing.
What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process
In our world with full of uncertainty, debates and argumentation contribute to the progress of science and society.
SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning
We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability.
Pose-Assisted Multi-Camera Collaboration for Active Object Tracking
In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images.
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis
Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices.
Interleaved Composite Quantization for High-Dimensional Similarity Search
A method often used to reduce this computational cost is quantization of the vector space and location-based encoding of the dataset vectors.
Deep Image-to-Video Adaptation and Fusion Networks for Action Recognition
For the same action, the knowledge learned from different media types, e. g., videos and images, may be related and complementary.
Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning
In this paper, we present a graph-aware encoder-decoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data.
A New Ensemble Adversarial Attack Powered by Long-term Gradient Memories
Deep neural networks are vulnerable to adversarial attacks.
Discrete Argument Representation Learning for Interactive Argument Pair Identification
In this paper, we focus on extracting interactive argument pairs from two posts with opposite stances to a certain topic.
Coupling Global and Local Context for Unsupervised Aspect Extraction
Aspect words, indicating opinion targets, are essential in expressing and understanding human opinions.
Neural Conversation Recommendation with Online Interaction Modeling
The prevalent use of social media leads to a vast amount of online conversations being produced on a daily basis.
Subtopic-driven Multi-Document Summarization
In multi-document summarization, a set of documents to be summarized is assumed to be on the same topic, known as the underlying topic in this paper.
Global Temporal Representation based CNNs for Infrared Action Recognition
Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video, is not considered.
Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition
However, the infrared action data is limited until now, which degrades the performance of infrared action recognition.
Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling
Recently, there has been growing interest in developing learning-based methods to detect and utilize salient semi-global or global structures, such as junctions, lines, planes, cuboids, smooth surfaces, and all types of symmetries, for 3D scene modeling and understanding.
Intrinsic Weight Learning Approach for Multi-view Clustering
Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering.
Topic-Aware Neural Keyphrase Generation for Social Media Language
Further discussions show that our model learns meaningful topics, which interprets its superiority in social media keyphrase generation.
Joint Effects of Context and User History for Predicting Online Conversation Re-entries
We hypothesize that both the context of the ongoing conversations and the users' previous chatting history will affect their continued interests in future engagement.
Multimodal Transformer with Multi-View Visual Representation for Image Captioning
Despite the success of existing studies, current methods only model the co-attention that characterizes the inter-modal interactions while neglecting the self-attention that characterizes the intra-modal interactions.
Microblog Hashtag Generation via Encoding Conversation Contexts
Automatic hashtag annotation plays an important role in content understanding for microblog posts.
SSoC: Learning Spontaneous and Self-Organizing Communication for Multi-Agent Collaboration
By treating the communication behaviour as an explicit action, SSoC learns to organize communication in an effective and efficient way.
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.
Ranked #11 on
Question Answering
on SQuAD1.1 dev
(F1 metric)
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
GANs-NQM: A Generative Adversarial Networks based No Reference Quality Assessment Metric for RGB-D Synthesized Views
In this paper, we proposed a no-reference (NR) quality metric for RGB plus image-depth (RGB-D) synthesis images based on Generative Adversarial Networks (GANs), namely GANs-NQM.
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
This paper presents an unsupervised framework for jointly modeling topic content and discourse behavior in microblog conversations.
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets
Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems.
A Survey on Deep Learning for Named Entity Recognition
In this paper, we provide a comprehensive review on existing deep learning techniques for NER.
Polarization Fraction Measurement in same sign WW scattering using Deep Learning
Studying the longitudinally polarized fraction of $W^\pm W^\pm$ scattering at the LHC is crucial to examine the unitarization mechanism of the vector boson scattering amplitude through Higgs and possible new physics.
High Energy Physics - Phenomenology High Energy Physics - Experiment
When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
In this work, we propose a framework that combines the approximation-theory-based multifidelity method and Gaussian-process-regression-based multifidelity method to achieve data-model convergence when stochastic simulation models and sparse accurate observation data are available.
RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation
RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time.
A Joint Model of Conversational Discourse Latent Topics on Microblogs
To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: (1) different roles of conversational discourse, and (2) various latent topics in reflecting content information.
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
In this paper we present a hybrid active sampling strategy for pairwise preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labelling.
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples{'} daily lives.
A Joint Model of Conversational Discourse and Latent Topics on Microblogs
To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: 1) different roles of conversational discourse, 2) various latent topics in reflecting content information.
Topic Memory Networks for Short Text Classification
Many classification models work poorly on short texts due to data sparsity.
Hierarchically Learned View-Invariant Representations for Cross-View Action Recognition
In order to make the feature representations of videos across views transferable, we then learn a transferable dictionary pair simultaneously from pairs of videos taken at different views to encourage each action video across views to have the same sparse representation.
Two-stage iterative Procrustes match algorithm and its application for VQ-based speaker verification
In this paper, we propose a two-stage iterative Procrustes algorithm (TIPM) to address the feature mismatch problem for VQ-based applications.
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA
The proposed method significantly outperforms state-of-the-art methods on CPU and GPU for high dimensional nearest neighbor queries on billion-scale datasets in terms of query time and accuracy regardless of the batch size.
Microblog Conversation Recommendation via Joint Modeling of Topics and Discourse
We propose a statistical model that jointly captures: (1) topics for representing user interests and conversation content, and (2) discourse modes for describing user replying behavior and conversation dynamics.
Encoding Conversation Context for Neural Keyphrase Extraction from Microblog Posts
Existing keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages.
Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings
In this paper, we present directional skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction.
A Manually Annotated Chinese Corpus for Non-task-oriented Dialogue Systems
This paper presents a large-scale corpus for non-task-oriented dialogue response selection, which contains over 27K distinct prompts more than 82K responses collected from social media.
SD-CNN: a Shallow-Deep CNN for Improved Breast Cancer Diagnosis
To evaluate the validity of our approach, we first develop a deep-CNN using 49 CEDM cases collected from Mayo Clinic to prove the contributions from recombined images for improved breast cancer diagnosis (0. 86 in accuracy using LE imaging vs. 0. 90 in accuracy using both LE and recombined imaging).
CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data
To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e. g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city.
DNN Model Compression Under Accuracy Constraints
In this paper, we propose a technique that directly minimizes both the model complexity and the changes in the loss function.
Adaptive Quantization of Neural Networks
The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly.
Reservation-Based Federated Scheduling for Parallel Real-Time Tasks
We provide general constraints for the design of such systems and prove that reservation-based federated scheduling has a constant speedup factor with respect to any optimal DAG task scheduler.
Operating Systems Distributed, Parallel, and Cluster Computing
Neural Attentive Session-based Recommendation
Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later.
Ranked #9 on
Session-Based Recommendations
on yoochoose1/64
Neural Att entive Session-based Recommendation
Specifically, we explore a hybrid encoder with an attention mechanism to model the user’s sequential behavior and capture the user’s main purpose in the current session, which are combined as a unified session representation later.
Gaussian Filter in CRF Based Semantic Segmentation
Artificial intelligence is making great changes in academy and industry with the fast development of deep learning, which is a branch of machine learning and statistical learning.
On 3D Face Reconstruction via Cascaded Regression in Shape Space
Cascaded regression has been recently applied to reconstructing 3D faces from single 2D images directly in shape space, and achieved state-of-the-art performance.
Mimicing the Kane-Mele type spin orbit interaction by spin-flexual phonon coupling in graphene devices
On the efforts of enhancing the spin orbit interaction (SOI) of graphene for seeking the dissipationless quantum spin Hall devices, unique Kane-Mele type SOI and high mobility samples are desired.
Mesoscale and Nanoscale Physics
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution
This paper describes a patchwork assembly algorithm for depth image super-resolution.
Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis
This is especially true for early AD, at which stage the disease-related regions are most likely to be weak-effect regions that are difficult to be detected from a single modality alone.
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.
