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Found 464 papers, 153 papers with code
Riemannian kernel based Nyström method for approximate infinite-dimensional covariance descriptors with application to image set classification
We propose a novel framework for representing image sets by approximating infinite-dimensional CovDs in the paradigm of the Nystr\"om method based on a Riemannian kernel.
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect
Monocular visual odometry (VO) and simultaneous localization and mapping (SLAM) have seen tremendous improvements in accuracy, robustness and efficiency, and have gained increasing popularity over recent years.
Monocular Visual Odometry
Simultaneous Localization and Mapping
A Survey of Domain Adaptation for Neural Machine Translation
Neural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available.
Multiple Manifolds Metric Learning with Application to Image Set Classification
In image set classification, a considerable advance has been made by modeling the original image sets by second order statistics or linear subspace, which typically lie on the Riemannian manifold.
A Simple Riemannian Manifold Network for Image Set Classification
The proposed RieMNet and DRieMNet are evaluated on three tasks: video-based face recognition, set-based object categorization, and set-based cell identification.
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video
Our method produces superior results to the state-of-the-art learning-based, single- or two-view depth estimation methods on both indoor and outdoor benchmark datasets.
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation
Traditional Neural machine translation (NMT) involves a fixed training procedure where each sentence is sampled once during each epoch.
Application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery
In this work, we employ a state-of-the-art convolutional neural network architecture, called SegNet (Badrinarayanan et.
Syntax-Directed Attention for Neural Machine Translation
In this paper, we extend local attention with syntax-distance constraint, to focus on syntactically related source words with the predicted target word, thus learning a more effective context vector for word prediction.
Online Photometric Calibration for Auto Exposure Video for Realtime Visual Odometry and SLAM
We further show that our calibration can improve the performance of a state-of-the-art direct visual odometry method that works solely on pixel intensities, calibrating for photometric parameters in an online fashion in realtime.
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras
We propose Stereo Direct Sparse Odometry (Stereo DSO) as a novel method for highly accurate real-time visual odometry estimation of large-scale environments from stereo cameras.
Shape Generation using Spatially Partitioned Point Clouds
We propose to use the expressive power of neural networks to learn a distribution over the shape coefficients in a generative-adversarial framework.
3D Shape Induction from 2D Views of Multiple Objects
In this paper we investigate the problem of inducing a distribution over three-dimensional structures given two-dimensional views of multiple objects taken from unknown viewpoints.
A Novel Bilingual Word Embedding Method for Lexical Translation Using Bilingual Sense Clique
Most of the existing methods for bilingual word embedding only consider shallow context or simple co-occurrence information.
Connecting Phrase based Statistical Machine Translation Adaptation
Although more additional corpora are now available for Statistical Machine Translation (SMT), only the ones which belong to the same or similar domains with the original corpus can indeed enhance SMT performance directly.
Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification
The core of the method is a new discriminant function for metric learning and dimensionality reduction.
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
To this end, we incorporate deep depth predictions into Direct Sparse Odometry (DSO) as direct virtual stereo measurements.
LDSO: Direct Sparse Odometry with Loop Closure
In this paper we present an extension of Direct Sparse Odometry (DSO) to a monocular visual SLAM system with loop closure detection and pose-graph optimization (LDSO).
A Deeper Look at 3D Shape Classifiers
We investigate the role of representations and architectures for classifying 3D shapes in terms of their computational efficiency, generalization, and robustness to adversarial transformations.
NICT's Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task
Using the clean data of the WMT18 shared news translation task, we designed several features and trained a classifier to score each sentence pairs in the noisy data.
NICT's Neural and Statistical Machine Translation Systems for the WMT18 News Translation Task
Our systems are ranked first for the Estonian-English and Finnish-English language pairs (constraint) according to BLEU-cased.
Band Assignment in Dual Band Systems: A Learning-based Approach
In this work, we use a machine learning framework to provide an efficient and practical solution to the band assignment problem.
ATM:Adversarial-neural Topic Model
The proposed ATM models topics with Dirichlet prior and employs a generator network to capture the semantic patterns among latent topics.
A Deep Cascade Model for Multi-Document Reading Comprehension
To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension.
Ranked #2 on
Question Answering
on MS MARCO
Sentence Embedding for Neural Machine Translation Domain Adaptation
Although new corpora are becoming increasingly available for machine translation, only those that belong to the same or similar domains are typically able to improve translation performance.
Neural Machine Translation with Source Dependency Representation
Source dependency information has been successfully introduced into statistical machine translation.
Context-Aware Smoothing for Neural Machine Translation
In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information.
SketchNet: Sketch Classification With Web Images
In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images.
Binarized Mode Seeking for Scalable Visual Pattern Discovery
Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset.
Image Deblurring via Extreme Channels Prior
The proposed method takes advantage of both Bright and Dark Channel Prior.
Aligning Predicate-Argument Structures for Paraphrase Fragment Extraction
Paraphrases and paraphrasing algorithms have been found of great importance in various natural language processing tasks.
Joint Grammar and Treebank Development for Mandarin Chinese with HPSG
We present the ongoing development of MCG, a linguistically deep and precise grammar for Mandarin Chinese together with its accompanying treebank, both based on the linguistic framework of HPSG, and using MRS as the semantic representation.
Constructing a Question Corpus for Textual Semantic Relations
Finding useful questions is a challenging task in Community Question Answering (CQA).
Supervised ML Solution for Band Assignment in Dual-Band Systems with Omnidirectional and Directional Antennas
This paper considers the band assignment (BA) problem in dual-band systems, where the basestation (BS) chooses one of the two available frequency bands (centimeter-wave and millimeter-wave bands) to communicate with the user equipment (UE).
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
Scene understanding from images is a challenging problem encountered in autonomous driving.
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth
Deep learning-based, single-view depth estimation methods have recently shown highly promising results.
Ranked #3 on
Monocular Depth Estimation
on Mid-Air Dataset
Discriminative Clustering for Robust Unsupervised Domain Adaptation
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source dataset.
Lattice-Based Transformer Encoder for Neural Machine Translation
To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training.
Deep Reinforcement Learning for Multi-objective Optimization
The solutions can be directly obtained by a simple forward calculation of the neural network; thereby, no iteration is required and the MOP can be always solved in a reasonable time.
Inferring 3D Shapes from Image Collections using Adversarial Networks
To this end, we present new differentiable projection operators that can be used by PrGAN to learn better 3D generative models.
Distilling with Residual Network for Single Image Super Resolution
Recently, the deep convolutional neural network (CNN) has made remarkable progress in single image super resolution(SISR).
Deep learning scheme for recovery of broadband microwave photonic receiving systems in transceivers without expert knowledge and system priors
However, the quality of the signals may be degraded by defective photonic analog links, especially in a complicated MWP system.
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT.
Neural Machine Translation with Reordering Embeddings
The reordering model plays an important role in phrase-based statistical machine translation.
Sentence-Level Agreement for Neural Machine Translation
The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning
Specifically, the covariance matrix, linear subspace, and Gaussian distribution are applied for set representation simultaneously.
NICT's Supervised Neural Machine Translation Systems for the WMT19 News Translation Task
In this paper, we describe our supervised neural machine translation (NMT) systems that we developed for the news translation task for Kazakh↔English, Gujarati↔English, Chinese↔English, and English→Finnish translation directions.
NICT's Unsupervised Neural and Statistical Machine Translation Systems for the WMT19 News Translation Task
This paper presents the NICT{'}s participation in the WMT19 unsupervised news translation task.
Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation
However, it has not been well-studied for unsupervised neural machine translation (UNMT), although UNMT has recently achieved remarkable results in several domain-specific language pairs.
Open Event Extraction from Online Text using a Generative Adversarial Network
Experimental results show that our model outperforms the baseline approaches on all the datasets, with more significant improvements observed on the news article dataset where an increase of 15\% is observed in F-measure.
Regularized Context Gates on Transformer for Machine Translation
This paper first provides a method to identify source and target contexts and then introduce a gate mechanism to control the source and target contributions in Transformer.
Fake Colorized Image Detection
Based on our observations, i. e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE).
Multimedia
Machine learning for the recognition of emotion in the speech of couples in psychotherapy using the Stanford Suppes Brain Lab Psychotherapy Dataset
Our results show that couple therapy is a rich context for the study of emotion in naturally occurring speech.
Sound Human-Computer Interaction Audio and Speech Processing
First-Order Preconditioning via Hypergradient Descent
Standard gradient descent methods are susceptible to a range of issues that can impede training, such as high correlations and different scaling in parameter space. These difficulties can be addressed by second-order approaches that apply a pre-conditioning matrix to the gradient to improve convergence.
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling
As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence.
Attention Optimization for Abstractive Document Summarization
Attention plays a key role in the improvement of sequence-to-sequence-based document summarization models.
Literature Review: Human Segmentation with Static Camera
Our research topic is Human segmentation with static camera.
Document-level Neural Machine Translation with Associated Memory Network
Most existing document-level NMT approaches are satisfied with a smattering sense of global document-level information, while this work focuses on exploiting detailed document-level context in terms of a memory network.
English-Myanmar Supervised and Unsupervised NMT: NICT's Machine Translation Systems at WAT-2019
This paper presents the NICT{'}s participation (team ID: NICT) in the 6th Workshop on Asian Translation (WAT-2019) shared translation task, specifically Myanmar (Burmese) - English task in both translation directions.
SJTU-NICT at MRP 2019: Multi-Task Learning for End-to-End Uniform Semantic Graph Parsing
This paper describes our SJTU-NICT{'}s system for participating in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).
SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT
Our final submission ranks the third on the overall MRP evaluation metric, the first on EDS and the second on UCCA.
Recurrent Positional Embedding for Neural Machine Translation
To address this issue, this work proposes a recurrent positional embedding approach based on word vector.
Probing Contextualized Sentence Representations with Visual Awareness
We present a universal framework to model contextualized sentence representations with visual awareness that is motivated to overcome the shortcomings of the multimodal parallel data with manual annotations.
Machine Intelligence at the Edge with Learning Centric Power Allocation
However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient.
Deep Audio-Visual Learning: A Survey
Audio-visual learning, aimed at exploiting the relationship between audio and visual modalities, has drawn considerable attention since deep learning started to be used successfully.
Modeling Future Cost for Neural Machine Translation
Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible.
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training
Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community.
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation.
Fake Generated Painting Detection via Frequency Analysis
With the development of deep neural networks, digital fake paintings can be generated by various style transfer algorithms. To detect the fake generated paintings, we analyze the fake generated and real paintings in Fourier frequency domain and observe statistical differences and artifacts.
Knowledge Graph Embedding via Graph Attenuated Attention Networks
However, these methods assign the same weights on the relation path in the knowledge graph and ignore the rich information presented in neighbor nodes, which result in incomplete mining of triple features.
Ranked #12 on
Link Prediction
on WN18RR
Learning Generative Models of Shape Handles
We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations.
Explicit Reordering for Neural Machine Translation
Thus, we propose a novel reordering method to explicitly model this reordering information for the Transformer-based NMT.
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.
Deep Manifold Prior
We present a prior for manifold structured data, such as surfaces of 3D shapes, where deep neural networks are adopted to reconstruct a target shape using gradient descent starting from a random initialization.
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs.
Syntax-aware Data Augmentation for Neural Machine Translation
Data augmentation is an effective performance enhancement in neural machine translation (NMT) by generating additional bilingual data.
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack
In this paper, we first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks.
Aortic Pressure Forecasting with Deep Sequence Learning
The aim of this study was to forecast the mean aortic pressure five minutes in advance, using the 25 Hz time series data of previous five minutes as input.
VPLNet: Deep Single View Normal Estimation With Vanishing Points and Lines
Starting from a color image and a Manhattan line map, we use a deep neural network to regress on a dense normal map, and a dense Manhattan label map that identifies planar regions aligned with the Manhattan directions.
Content Word Aware Neural Machine Translation
Neural machine translation (NMT) encodes the source sentence in a universal way to generate the target sentence word-by-word.
Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks
The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9. 29 over the syntax-agnostic baseline.
SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
To this end, we have developed SenseCare research platform, which is designed to facilitate translational research on intelligent diagnosis and treatment planning in various clinical scenarios.
Human-Computer Interaction Image and Video Processing
DeepNetQoE: Self-adaptive QoE Optimization Framework of Deep Networks
A self-adaptive QoE model is set up that relates the model's accuracy with the computing resources required for training which will allow the experience value of the model to improve.
Learning Centric Power Allocation for Edge Intelligence
While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power.
Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses
This work proposes first a perception process for 6D pose estimation, which returns a discrete distribution of object poses in a scene.
What leads to generalization of object proposals?
It is lucrative to train a good proposal model, that generalizes to unseen classes.
Data-dependent Gaussian Prior Objective for Language Generation
However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.
Estimating Q(s,s') with Deterministic Dynamics Gradients
In this paper, we introduce a novel form of a value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving.
Graph-to-Sequence Neural Machine Translation
In the light of the current NMT models more or less capture graph information among the sequence in a latent way, we present a graph-to-sequence model facilitating explicit graph information capturing.
Neural Topic Modeling by Incorporating Document Relationship Graph
Graph Neural Networks (GNNs) that capture the relationships between graph nodes via message passing have been a hot research direction in the natural language processing community.
Neural Topic Modeling with Cycle-Consistent Adversarial Training
ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics.
Prior Knowledge Representation for Self-Attention Networks
Self-attention networks (SANs) have shown promising empirical results in various natural language processing tasks.
Cross-lingual Transfer Learning for Pre-trained Contextualized Language Models
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
Investigating Constraint Relationship in Evolutionary Many-Constraint Optimization
This paper contributes to the treatment of extensive constraints in evolutionary many-constraint optimization through consideration of the relationships between pair-wise constraints.
SJTU-NICT's Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT 's participation in the WMT 2020 machine translation shared task.
Learning Centric Wireless Resource Allocation for Edge Computing: Algorithm and Experiment
Edge intelligence is an emerging network architecture that integrates sensing, communication, computing components, and supports various machine learning applications, where a fundamental communication question is: how to allocate the limited wireless resources (such as time, energy) to the simultaneous model training of heterogeneous learning tasks?
Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time
Route planning is important in transportation.
Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network
Simulation results reveal that compared with traditional DRL method, the HRL training algorithm can reach convergence 30 training iterations earlier and reduce the outage probability by 5% in two-hop relay network with the same outage threshold.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Decoding SARS-CoV-2 transmission, evolution and ramification on COVID-19 diagnosis, vaccine, and medicine
Tremendous effort has been given to the development of diagnostic tests, preventive vaccines, and therapeutic medicines for coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Hardware-irrelevant parallel processing system
We propose a hardware-irrelevant PPS of which the performance is unaffected by hardware deviations.
Characterizing SARS-CoV-2 mutations in the United States
Using genotyping, sequence-alignment, time-evolution, $k$-means clustering, protein-folding stability, algebraic topology, and network theory, we reveal that the US SARS-CoV-2 has four substrains and five top US SARS-CoV-2 mutations were first detected in China (2 cases), Singapore (2 cases), and the United Kingdom (1 case).
Host immune response driving SARS-CoV-2 evolution
Additionally, we show that children under age five and the elderly may be at high risk from COVID-19 because of their overreacting to the viral infection.
Prediction and mitigation of mutation threats to COVID-19 vaccines and antibody therapies
This discovery implies the vulnerability of current vaccines and antibody drugs to new mutations.
SG-Net: Syntax Guided Transformer for Language Representation
In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention.
Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks
The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks.
Accurate Word Representations with Universal Visual Guidance
Word representation is a fundamental component in neural language understanding models.
Turbulence suppression by streamwise-varying wall rotation in pipe flow
Two control parameters, which are velocity amplitude and wavelength, are considered.
Fluid Dynamics
Theory of competing Chern-Simons orders and emergent phase transitions
Namely, the Chern-Simons superconductor describes the planar N\'{e}el state, while the Chern-Simons exciton insulator corresponds to the non-uniform chiral spin-liquid.
Strongly Correlated Electrons
Deep Deterministic Policy Gradient for Relay Selection and Power Allocation in Cooperative Communication Network
Perfect channel state information (CSI) is usually required when considering relay selection and power allocation in cooperative communication.
Information Theory Systems and Control Systems and Control Information Theory
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
Methodology-centered review of molecular modeling, simulation, and prediction of SARS-CoV-2
To provide the reader a quick update about the status of molecular modeling, simulation, and prediction of SARS-CoV-2, we present a comprehensive and systematic methodology-centered narrative in the nick of time.
Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry
To achieve map-based relocalization for direct methods, we integrate image features into Direct Sparse Odometry (DSO) and rely on feature matching to associate online visual odometry (VO) with a previously built map.
Text Compression-aided Transformer Encoding
In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily.
High-α-Metal-Rich stars in the LAMOST-MRS survey and its connection with the galactic bulge
A tentative interpretation of this special group is that its stars were formed in the X-shaped bar/bulge region, close to its corotation radius, where radial migration is the most intense, and brings them to present locations at 9 kpc and beyond.
Astrophysics of Galaxies Solar and Stellar Astrophysics
On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback
This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M, M, N, N) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled vehicles-to-vehicles (V2V).
Information Theory Information Theory
Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control
Key to such capability is hand-eye coordination, a cognitive ability that enables humans to adaptively direct their movements at task-relevant objects and be invariant to the objects' absolute spatial location.
Blockchain-Based Federated Learning in Mobile Edge Networks with Application in Internet of Vehicles
The rapid increase of the data scale in Internet of Vehicles (IoV) system paradigm, hews out new possibilities in boosting the service quality for the emerging applications through data sharing.
Exploring Imitation Learning for Autonomous Driving with Feedback Synthesizer and Differentiable Rasterization
Detailed ablation and visualization analysis are included to further demonstrate each of our proposed modules' effectiveness in our method.
Autonomous Driving
Data Augmentation
+1
Robotics
Decoding asymptomatic COVID-19 infection and transmission
By analyzing the distribution of 11083G>T in various countries, we unveil that 11083G>T may correlate with the hypotoxicity of SARS-CoV-2.
Mutations on COVID-19 diagnostic targets
Effective, sensitive, and reliable diagnostic reagents are of paramount importance for combating the ongoing coronavirus disease 2019 (COVID-19) pandemic at a time there is no preventive vaccine nor specific drug available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Vaccine-escape and fast-growing mutations in the United Kingdom, the United States, Singapore, Spain, South Africa, and other COVID-19-devastated countries
We further unveil that mutation N501Y involved in United Kingdom (UK), South Africa, and Brazil variants may moderately weaken the binding between the RBD and many known antibodies, while mutations E484K and K417N found in South Africa and Brazilian variants can potentially disrupt the binding between the RDB and many known antibodies.
Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach
By utilizing the Stein's unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data.
Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis
The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA.
Wireless Sensing With Deep Spectrogram Network and Primitive Based Autoregressive Hybrid Channel Model
Although wireless channel models can be adopted for dataset generation, current channel models are mostly designed for communication rather than sensing.
Long Short-Term Temporal Meta-learning in Online Recommendation
An effective online recommendation system should jointly capture users' long-term and short-term preferences in both users' internal behaviors (from the target recommendation task) and external behaviors (from other tasks).
Model Pruning Based on Quantified Similarity of Feature Maps
In this paper, we propose a novel theory to find redundant information in three-dimensional tensors, namely Quantified Similarity between Feature Maps (QSFM), and utilize this theory to guide the filter pruning procedure.
Variational Gaussian Topic Model with Invertible Neural Projections
Based on the variational auto-encoder, the proposed VaGTM models each topic with a multivariate Gaussian in decoder to incorporate word relatedness.
Cross-domain Contrastive Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain.
Advances and Challenges in Unsupervised Neural Machine Translation
Unsupervised cross-lingual language representation initialization methods, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results.
Chunk-based Chinese Spelling Check with Global Optimization
Chinese spelling check is a challenging task due to the characteristics of the Chinese language, such as the large character set, no word boundary, and short word length.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
A New Channel Estimation Strategy in Intelligent Reflecting Surface Assisted Networks
Specifically, in Phase I, the correlation coefficients between the channels of a typical BS antenna and those of the other antennas are estimated; while in Phase II, the cascaded channel of the typical antenna is estimated.
Patient-independent Schizophrenia Relapse Prediction Using Mobile Sensor based Daily Behavioral Rhythm Changes
In this work, we investigated a machine learning based schizophrenia relapse prediction model using mobile sensing data to characterize behavioral features.
Physics-Guided Deep Learning for Dynamical Systems: A Survey
Modeling complex physical dynamics is a fundamental task in science and engineering.
A Survey on Low-Resource Neural Machine Translation
Neural approaches have achieved state-of-the-art accuracy on machine translation but suffer from the high cost of collecting large scale parallel data.
MINERVAS: Massive INterior EnviRonments VirtuAl Synthesis
With the rapid development of data-driven techniques, data has played an essential role in various computer vision tasks.
Accelerating Edge Intelligence via Integrated Sensing and Communication
Conventionally, the sensing and communication stages are executed sequentially, which results in excessive amount of dataset generation and uploading time.
Integrated Sensing and Communication from Learning Perspective: An SDP3 Approach
Characterizing the sensing and communication performance tradeoff in integrated sensing and communication (ISAC) systems is challenging in the applications of learning-based human motion recognition.
Cross-lingual Transferring of Pre-trained Contextualized Language Models
Though the pre-trained contextualized language model (PrLM) has made a significant impact on NLP, training PrLMs in languages other than English can be impractical for two reasons: other languages often lack corpora sufficient for training powerful PrLMs, and because of the commonalities among human languages, computationally expensive PrLM training for different languages is somewhat redundant.
Discriminating modelling approaches for Point in Time Economic Scenario Generation
We introduce the notion of Point in Time Economic Scenario Generation (PiT ESG) with a clear mathematical problem formulation to unify and compare economic scenario generation approaches conditional on forward looking market data.
PIVODL: Privacy-preserving vertical federated learning over distributed labels
Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data.
A Multimodal Framework for Video Ads Understanding
There is a growing trend in placing video advertisements on social platforms for online marketing, which demands automatic approaches to understand the contents of advertisements effectively.
Unit-Modulus Wireless Federated Learning Via Penalty Alternating Minimization
Wireless federated learning (FL) is an emerging machine learning paradigm that trains a global parametric model from distributed datasets via wireless communications.
Emerging vaccine-breakthrough SARS-CoV-2 variants
The molecular mechanism underlying such surge is elusive due to 4, 653 non-degenerate mutations on the spike protein, which is the target of most COVID-19 vaccines.
Cross Modification Attention Based Deliberation Model for Image Captioning
The conventional encoder-decoder framework for image captioning generally adopts a single-pass decoding process, which predicts the target descriptive sentence word by word in temporal order.
Review of the mechanisms of SARS-CoV-2 evolution and transmission
We anticipate that viral evolution will combine RBD co-mutations at these two sites, creating future variants that are tens of times more infectious than the original SARS-CoV-2.
PreDet: Large-Scale Weakly Supervised Pre-Training for Detection
State-of-the-art object detection approaches typically rely on pre-trained classification models to achieve better performance and faster convergence.
Testing-Time Adaptation through Online Normalization Estimation
We present a simple and effective way to estimate the batch-norm statistics during test time, to fast adapt a source model to target test samples.
FEVERLESS: Fast and Secure Vertical Federated Learning based on XGBoost for Decentralized Labels
Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without revealing private local information.
Fast Adaptive Anomaly Detection
Anything outside of a given normal population is by definition an anomaly.
Multi-View Self-Attention Based Transformer for Speaker Recognition
In this work, we propose a novel multi-view self-attention mechanism and present an empirical study of different Transformer variants with or without the proposed attention mechanism for speaker recognition.
Differential Motion Evolution for Fine-Grained Motion Deformation in Unsupervised Image Animation
Key findings are twofold: (1) by capturing the motion transfer with an ordinary differential equation (ODE), it helps to regularize the motion field, and (2) by utilizing the source image itself, we are able to inpaint occluded/missing regions arising from large motion changes.
The evolution of the mechanisms of SARS-CoV-2 evolution revealing vaccine-resistant mutations in Europe and America
The importance of understanding SARS-CoV-2 evolution cannot be overemphasized.
Tackling the Imbalance for GNNs
Different from deep neural networks for non-graph data classification, graph neural networks (GNNs) leverage the information exchange between nodes (or samples) when representing nodes.
Entity Relation Extraction as Dependency Parsing in Visually Rich Documents
Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i. e., semantic entity), while the relations in-between are largely unexplored.
Ranked #4 on
Entity Linking
on FUNSD
A Geometry-Based Stochastic Model for Truck Communication Channels in Freeway Scenarios
We validate this model by contrasting the root-mean-square delay spread and the angular spreads of departure/arrival derived from the channel model with the outcomes directly derived from the measurements.
MixNorm: Test-Time Adaptation Through Online Normalization Estimation
We present a simple and effective way to estimate the batch-norm statistics during test time, to fast adapt a source model to target test samples.
Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition
Unsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data.
Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning
For the correction subtask, we utilize the masked language model, the seq2seq model and the spelling check model to generate corrections based on the detection results.
SJTU-NICT’s Supervised and Unsupervised Neural Machine Translation Systems for the WMT20 News Translation Task
In this paper, we introduced our joint team SJTU-NICT ‘s participation in the WMT 2020 machine translation shared task.
Syntax in End-to-End Natural Language Processing
This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results.
Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art
In most cases, the datasets for small object detection in remote sensing images are inadequate.
ProSTformer: Pre-trained Progressive Space-Time Self-attention Model for Traffic Flow Forecasting
When pre-trained on the big scale datasets and transferred to the medium and small scale datasets, ProSTformer achieves a significant enhancement and behaves best.
Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance
The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B. 1. 1. 529) has ushered panic responses around the world due to its contagious and vaccine escape mutations.
Gram-SLD: Automatic Self-labeling and Detection for Instance Objects
Instance object detection plays an important role in intelligent monitoring, visual navigation, human-computer interaction, intelligent services and other fields.
Bayesian User Localization and Tracking for Reconfigurable Intelligent Surface Aided MIMO Systems
In this paper, we study the user localization and tracking problem in the reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) system, where a multi-antenna base station (BS) and multiple RISs are deployed to assist the localization and tracking of a multi-antenna user.
Evaluation of Four Black-box Adversarial Attacks and Some Query-efficient Improvement Analysis
With the fast development of machine learning technologies, deep learning models have been deployed in almost every aspect of everyday life.
Collaborative Representation for SPD Matrices with Application to Image-Set Classification
Recent advances illustrate that how to effectively model these nonlinear variational information and learn invariant representations is an open challenge in the community of computer vision and pattern recognition To this end, we try to design a new algorithm to handle this problem.
On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR
Based on the above intuition, we first investigate types of end-to-end encoder-decoder based models in the single-input dual-output (SIDO) multi-task framework, after which a novel asynchronous decoding with fuzzy Pinyin sampling method is proposed according to the one-to-one correspondence characteristics between Pinyin and Character.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
More is Better (Mostly): On the Backdoor Attacks in Federated Graph Neural Networks
To further explore the properties of two backdoor attacks in Federated GNNs, we evaluate the attack performance for a different number of clients, trigger sizes, poisoning intensities, and trigger densities.
Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs.
VU-BERT: A Unified framework for Visual Dialog
The visual dialog task attempts to train an agent to answer multi-turn questions given an image, which requires the deep understanding of interactions between the image and dialog history.
Normal and Visibility Estimation of Human Face from a Single Image
Recent work on the intrinsic image of humans starts to consider the visibility of incident illumination and encodes the light transfer function by spherical harmonics.
Efficient conditioned face animation using frontally-viewed embedding
As the quality of few shot facial animation from landmarks increases, new applications become possible, such as ultra low bandwidth video chat compression with a high degree of realism.
An Interactive Image-based Modeling System
This paper propose a interactive 3D modeling method and corresponding system based on single or multiple uncalibrated images.
Passive Motion Detection via mmWave Communication System
Relying on the passive sensing system, a dataset of received signals, where three types of hand gestures are sensed, is collected by using Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) paths as the reference channel respectively.
Accelerating Federated Edge Learning via Topology Optimization
In this paper, a novel topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency.
Deep Reinforcement Learning for Online Routing of Unmanned Aerial Vehicles with Wireless Power Transfer
The model is trained using a deep reinforcement learning method offline, and is used to optimize the UAV routing problem online.
Deep Reinforcement Learning for Orienteering Problems Based on Decomposition
A dynamic pointer network (DYPN) is introduced as the TSP solver, which takes city locations as inputs and immediately outputs a permutation of nodes.
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new dominating variants
In particular, BA. 4 and BA. 5 are about 36\% more infectious than BA. 2 and are projected to become new dominating variants by natural selection.
The AISP-SJTU Simultaneous Translation System for IWSLT 2022
This paper describes AISP-SJTU’s submissions for the IWSLT 2022 Simultaneous Translation task.
Synchronous Refinement for Neural Machine Translation
Machine translation typically adopts an encoder-to-decoder framework, in which the decoder generates the target sentence word-by-word in an auto-regressive manner.
Few-Shot Class-Incremental Learning for Named Entity Recognition
In this work, we study a more challenging but practical problem, i. e., few-shot class-incremental learning for NER, where an NER model is trained with only few labeled samples of the new classes, without forgetting knowledge of the old ones.
TRT-ViT: TensorRT-oriented Vision Transformer
We revisit the existing excellent Transformers from the perspective of practical application.
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review
Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI).
An Indoor Environment Sensing and Localization System via mmWave Phased Array
At the end, we show that the reconstructed room layout can be utilized to locate a mobile device according to its AoA spectrum, even with single access point.
DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification
Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric positive definite (SPD) manifold.
