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Found 175 papers, 44 papers with code
ReDro: Efficiently Learning Large-sized SPD Visual Representation
When learning this representation in deep networks, eigen-decomposition of covariance matrix is usually needed for a key step called matrix normalisation.
Progressive Hard-case Mining across Pyramid Levels in Object Detection
To mediate the influence of level imbalance, we propose a Unified Multi-level Optimization Paradigm (UMOP) consisting of two components: 1) an independent classification loss supervising each pyramid level with individual resampling considerations; 2) a progressive hard-case mining loss defining all losses across the pyramid levels without extra level-wise settings.
LibFewShot: A Comprehensive Library for Few-shot Learning
Furthermore, based on LibFewShot, we provide comprehensive evaluations on multiple benchmark datasets with multiple backbone architectures to evaluate common pitfalls and effects of different training tricks.
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers
Over the last few years, there are a growing number of datasets and deep learning-based methods proposed for effectively solving MWPs.
Observability is Sufficient for the Design of Globally Exponentially Convergent State Observers for State-affine Nonlinear Systems
In this paper we are interested in the problem of state observation of state-affine nonlinear systems.
Identifiability Implies Robust, Globally Exponentially Convergent On-line Parameter Estimation: Application to Model Reference Adaptive Control
The estimators are shown to be robust to additive measurement noise and--not necessarily slow--parameter variations.
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive Training
One-shot neural architecture search (NAS) applies weight-sharing supernet to reduce the unaffordable computation overhead of automated architecture designing.
Cascade Bagging for Accuracy Prediction with Few Training Samples
To alleviate this problem, we propose a novel framework to train an accuracy predictor under few training samples.
Few-shot Unsupervised Domain Adaptation with Image-to-class Sparse Similarity Encoding
Inspired by the recent local descriptor based few-shot learning (FSL), our general UDA model is fully built upon local descriptors (LDs) for image classification and domain adaptation.
Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Adaptive Refined Labeling
Price movement forecasting aims at predicting the future trends of financial assets based on the current market conditions and other relevant information.
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net.
Hand Image Understanding via Deep Multi-Task Learning
To further improve the performance of these tasks, we propose a novel Hand Image Understanding (HIU) framework to extract comprehensive information of the hand object from a single RGB image, by jointly considering the relationships between these tasks.
Triplet is All You Need with Random Mappings for Unsupervised Visual Representation Learning
However, this type of methods, such as SimCLR and MoCo, relies heavily on a large number of negative pairs and thus requires either large batches or memory banks.
A Self-Boosting Framework for Automated Radiographic Report Generation
On one hand, the image-text matching branch helps to learn highly text-correlated visual features for the report generation branch to output high quality reports.
Signal Acquisition of Luojia-1A Low Earth Orbit Navigation Augmentation System with Software Defined Receiver
The performance of those integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite.
RFCBF: enhance the performance and stability of Fast Correlation-Based Filter
Feature selection is a preprocessing step which plays a crucial role in the domain of machine learning and data mining.
Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification
In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes's group using a multi-objective optimization genetic algorithm.
A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe
The identification of protein-ligand interaction plays a key role in biochemical research and drug discovery.
Investigating Math Word Problems using Pretrained Multilingual Language Models
In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective.
Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition
Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance.
Ranked #3 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
Learning discriminative image representations plays a vital role in long-tailed image classification because it can ease the classifier learning in imbalanced cases.
Ranked #3 on
Long-tail Learning
on CIFAR-100-LT (ρ=50)
Shift-and-Balance Attention
The attention branch is gated using the Sigmoid function and multiplied by the feature map's trunk branch.
DeepStyle: User Style Embedding for Authorship Attribution of Short Texts
Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications.
Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation
For learning pseudo mask generator from the auxiliary dataset, we present a bi-level optimization formulation.
Network Representation Learning: From Traditional Feature Learning to Deep Learning
Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data.
Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link
Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets.
HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality.
Image Classification
Performance
Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events
Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.
Unified First-Principles Study of the Anomalous Hall Effect Based on Exact Muffin-Tin Orbitals
Based on the exact muffin-tin orbitals (EMTOs), we developed a first-principles method to calculate the current operators and investigated the anomalous Hall effect in bcc Fe as an example, with which we successfully separated the skew scattering contribution from the side jump and intrinsic contributions by fitting the scaling law with the introduction of sparse impurities.
Materials Science Mesoscale and Nanoscale Physics
Pruning and Quantization for Deep Neural Network Acceleration: A Survey
We discuss trade-offs in element-wise, channel-wise, shape-wise, filter-wise, layer-wise and even network-wise pruning.
Pretrain-to-Finetune Adversarial Training via Sample-wise Randomized Smoothing
For certification, we carefully allocate specific robust regions for each test sample.
Optimizing Information Bottleneck in Reinforcement Learning: A Stein Variational Approach
We also show that our method achieves better performance than VIB and mutual information neural estimation (MINE), two other popular approaches to optimize the information bottleneck framework in supervised learning.
Tensor Representations for Action Recognition
In this paper, we propose novel tensor representations for compactly capturing such higher-order relationships between visual features for the task of action recognition.
The exact factorizations of almost simple groups
This paper presents a classification of exact factorizations of almost simple groups, which have been a long-standing open problem initiated by the work of Wiegold and Williamson in 1980 and significantly progressed by Liebeck, Praeger and Saxl in 2010.
Group Theory
Learning geometry-image representation for 3D point cloud generation
We study the problem of generating point clouds of 3D objects.
Visual place recognition: A survey from deep learning perspective
Visual place recognition has attracted widespread research interest in multiple fields such as computer vision and robotics.
Decoding PPP Corrections from BDS B2b Signals Using a Software-defined Receiver: an Initial Performance Evaluation
With the rapid development of China's BeiDou Navigation Satellite System(BDS), the application of real-time precise point positioning (RTPPP) based on BDS has become an active research area in the field of Global Navigation Satellite System (GNSS).
w-Net: Dual Supervised Medical Image Segmentation Model with Multi-Dimensional Attention and Cascade Multi-Scale Convolution
Non-local attention and feature learning by multi-scale methods are widely used to model network, which drives progress in medical image segmentation.
ASMFS: Adaptive-Similarity-based Multi-modality Feature Selection for Classification of Alzheimer's Disease
With the increasing amounts of high-dimensional heterogeneous data to be processed, multi-modality feature selection has become an important research direction in medical image analysis.
S3ML: A Secure Serving System for Machine Learning Inference
We present S3ML, a secure serving system for machine learning inference in this paper.
Supervised Learning with Projected Entangled Pair States
Tensor networks, a model that originated from quantum physics, has been gradually generalized as efficient models in machine learning in recent years.
Biomedical named entity recognition using BERT in the machine reading comprehension framework
Using the sequence labeling framework to implement biomedical named entity recognition (BioNER) is currently a conventional method.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines.
Tropical Tensor Network for Ground States of Spin Glasses
We present a unified exact tensor network approach to compute the ground state energy, identify the optimal configuration, and count the number of solutions for spin glasses.
Statistical Mechanics Quantum Physics Computation
MODEL: Motif-based Deep Feature Learning for Link Prediction
Link prediction plays an important role in network analysis and applications.
Robust Collaborative Learning of Patch-level and Image-level Annotations for Diabetic Retinopathy Grading from Fundus Image
As a result, it exploits more discriminative features for DR grading.
Robust Retinal Vessel Segmentation from a Data Augmentation Perspective
In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation.
Residual-CycleGAN based Camera Adaptation for Robust Diabetic Retinopathy Screening
How can we train a classification model on labeled fundus images ac-quired from only one camera brand, yet still achieves good performance on im-ages taken by other brands of cameras?
Graph-to-Tree Learning for Solving Math Word Problems
While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well.
Ranked #2 on
Math Word Problem Solving
on Math23K
Human Activity Recognition based on Dynamic Spatio-Temporal Relations
In the proposed method, an activity is divided into several successive actions represented by spatio temporal patterns, and the evolution of these actions are captured by a sequential model.
SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors
We not only give the visual denoising effect of the filter but also use two metrics for quantitatively analyzing the filter's performance.
Large-Scale Secure XGB for Vertical Federated Learning
Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.
Finet: Using Fine-grained Batch Normalization to Train Light-weight Neural Networks
To build light-weight network, we propose a new normalization, Fine-grained Batch Normalization (FBN).
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Due to increasing amounts of data and compute resources, deep learning achieves many successes in various domains.
AIBench Scenario: Scenario-distilling AI Benchmarking
We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
We use real-world benchmarks to cover the factors space that impacts the learning dynamics to the most considerable extent.
Continuous matrix product operator approach to finite temperature quantum states
We present an algorithm for studying quantum systems at finite temperature using continuous matrix product operator representation.
Strongly Correlated Electrons Statistical Mechanics Computational Physics
Class Distribution Alignment for Adversarial Domain Adaptation
To address this issue, we propose a novel approach called Conditional ADversarial Image Translation (CADIT) to explicitly align the class distributions given samples between the two domains.
Secret Sharing based Secure Regressions with Applications
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.
A Noise Filter for Dynamic Vision Sensors using Self-adjusting Threshold
Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers.
Emerging Technologies Signal Processing
A Neural Architecture Search based Framework for Liquid State Machine Design
By exploring the design space in network architectures and parameters, recent works have demonstrated great potential for improving the accuracy of LSM model with low complexity.
Exploration of Input Patterns for Enhancing the Performance of Liquid State Machines
To mitigate the difficulty in effectively dealing with huge input spaces of LSM, and to find that whether input reduction can enhance LSM performance, we explore several input patterns, namely fullscale, scanline, chessboard, and patch.
DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentation
Neural Architecture Search (NAS) has shown great potentials in automatically designing scalable network architectures for dense image predictions.
Ranked #6 on
Semantic Segmentation
on Cityscapes test
Multi-AI competing and winning against humans in iterated Rock-Paper-Scissors game
We model and predict human competition behavior by combining many Markov Models with different fixed memory lengths (abbreviated as "multi-AI"), and develop an architecture of multi-AI with changeable parameters to adapt to different competition strategies.
Extended Batch Normalization
To alleviate the problem caused by small batch size, extended batch normalization computes the standard deviation along the (N, C, H, W) dimensions, thus enlarges the number of samples from which the standard deviation is computed.
Ranked #56 on
Image Classification
on STL-10
Industrial Scale Privacy Preserving Deep Neural Network
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.
Exploration of Surgeons' Natural Skills for Robotic Catheterization
Despite having the robotic catheter systems which have recently emerged as safe way of performing cardiovascular interventions, a number of important challenges are yet to be investigated.
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
An end-to-end benchmark is a distillation of the essential attributes of an industry-scale application.
Asymmetric Distribution Measure for Few-shot Learning
Given the natural asymmetric relation between a query image and a support class, we argue that an asymmetric measure is more suitable for metric-based few-shot learning.
Self-supervising Action Recognition by Statistical Moment and Subspace Descriptors
In this paper, we build on a concept of self-supervision by taking RGB frames as input to learn to predict both action concepts and auxiliary descriptors e. g., object descriptors.
Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits
We extend the ability of unitary quantum circuits by interfacing it with classical autoregressive neural networks.
Quantum Physics
Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design
We introduce Yao, an extensible, efficient open-source framework for quantum algorithm design.
Quantum Physics Strongly Correlated Electrons Computational Physics
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
Application of Word2vec in Phoneme Recognition
At the same time, in order to solve the problem of overfitting in the 61 phoneme recognition model on TIMIT dataset, we propose a new training method.
BenchCouncil's View on Benchmarking AI and Other Emerging Workloads
This paper outlines BenchCouncil's view on the challenges, rules, and vision of benchmarking modern workloads like Big Data, AI or machine learning, and Internet Services.
Differentiable Meta-learning Model for Few-shot Semantic Segmentation
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm.
Chemical-protein Interaction Extraction via Gaussian Probability Distribution and External Biomedical Knowledge
Furthermore, the Gaussian probability distribution can effectively improve the extraction performance of sentences with overlapping relations in biomedical relation extraction tasks.
Deep Learning based HEp-2 Image Classification: A Comprehensive Review
This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods.
Defensive Few-shot Adversarial Learning
In this paper, instead of assuming such a distribution consistency, we propose to make this assumption at a task-level in the episodic training paradigm in order to better transfer the defense knowledge.
End-to-end speech enhancement based on discrete cosine transform
Previous speech enhancement methods focus on estimating the short-time spectrum of speech signals due to its short-term stability.
Ranked #11 on
Speech Enhancement
on DEMAND
Sound Audio and Speech Processing
Neural Canonical Transformation with Symplectic Flows
We construct flexible and powerful canonical transformations as generative models using symplectic neural networks.
Region-wise Generative Adversarial ImageInpainting for Large Missing Areas
To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both continuous and discontinuous large missing areas, in an adversarial manner.
Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image
Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images.
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
Progressive Cross-camera Soft-label Learning for Semi-supervised Person Re-identification
In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels.
GreyReID: A Two-stream Deep Framework with RGB-grey Information for Person Re-identification
Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch.
AIBench: An Industry Standard Internet Service AI Benchmark Suite
On the basis of the AIBench framework, abstracting the real-world data sets and workloads from one of the top e-commerce providers, we design and implement the first end-to-end Internet service AI benchmark, which contains the primary modules in the critical paths of an industry scale application and is scalable to deploy on different cluster scales.
Edge AIBench: Towards Comprehensive End-to-end Edge Computing Benchmarking
In edge computing scenarios, the distribution of data and collaboration of workloads on different layers are serious concerns for performance, privacy, and security issues.
Performance Distributed, Parallel, and Cluster Computing
Adversarial Camera Alignment Network for Unsupervised Cross-camera Person Re-identification
To achieve the camera alignment, we develop a Multi-Camera Adversarial Learning (MCAL) to map images of different cameras into a shared subspace.
HPC AI500: A Benchmark Suite for HPC AI Systems
In this paper, we propose HPC AI500 --- a benchmark suite for evaluating HPC systems that running scientific DL workloads.
NPSA: Nonorthogonal Principal Skewness Analysis
Principal skewness analysis (PSA) has been introduced for feature extraction in hyperspectral imagery.
Tracking system of Mine Patrol Robot for Low Illumination Environment
The experimental results show that the novel tracker performance in low-illumination environment is better than that of general trackers.
Modeling Intra-Relation in Math Word Problems with Different Functional Multi-Head Attentions
Several deep learning models have been proposed for solving math word problems (MWPs) automatically.
Ranked #4 on
Math Word Problem Solving
on Math23K
Loss Switching Fusion with Similarity Search for Video Classification
We define our classification problem as classifying background and foreground motions using the same feature representation for outdoor scenes.
A Comparative Review of Recent Kinect-based Action Recognition Algorithms
Video-based human action recognition is currently one of the most active research areas in computer vision.
Ranked #56 on
Skeleton Based Action Recognition
on NTU RGB+D
Hallucinating IDT Descriptors and I3D Optical Flow Features for Action Recognition with CNNs
Thus, we propose an end-to-end trainable network with streams which learn the IDT-based BoW/FV representations at the training stage and are simple to integrate with the I3D model.
Ranked #5 on
Action Recognition
on HMDB-51
(using extra training data)
A Semantic-based Medical Image Fusion Approach
Medical image fusion is a promising approach to providing overall information from medical images of different modalities.
Graph Attention Convolution for Point Cloud Semantic Segmentation
It neglects the structure of an object, results in poor object delineation and small spurious regions in the segmentation result.
Ranked #9 on
Semantic Segmentation
on Semantic3D
Monge-Amp\`ere Flow for Generative Modeling
We present a deep generative model, named Monge-Amp\`ere flow, which builds on continuous-time gradient flow arising from the Monge-Amp\`ere equation in optimal transport theory.
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction.
Ranked #1 on
Named Entity Recognition
on BC4CHEMD
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification
For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains.
Ranked #16 on
Unsupervised Domain Adaptation
on Market to Duke
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning
Its key difference from the literature is the replacement of the image-level feature based measure in the final layer by a local descriptor based image-to-class measure.
Differentiable Programming Tensor Networks
Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and trains them using automatic differentiation (AD).
Strongly Correlated Electrons Quantum Physics
Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval.
Variational Quantum Eigensolver with Fewer Qubits
One can obtain a matrix product state (MPS) representation of the ground state using a number of qubits smaller than the physical degrees of freedom.
Quantum Physics Strongly Correlated Electrons
A kernel-independent treecode based on barycentric Lagrange interpolation
A kernel-independent treecode (KITC) is presented for fast summation of pairwise particle interactions.
Numerical Analysis
Tree Tensor Networks for Generative Modeling
Matrix product states (MPS), a tensor network designed for one-dimensional quantum systems, has been recently proposed for generative modeling of natural data (such as images) in terms of `Born machine'.
Dynamic Runtime Feature Map Pruning
Of the networks considered, those using ReLU (AlexNet, SqueezeNet, VGG16) contain a high percentage of 0-valued parameters and can be statically pruned.
Wider Channel Attention Network for Remote Sensing Image Super-resolution
In this letter, we propose a novel single-image super-resolution (SISR) algorithm named Wider Channel Attention Network (WCAN) for remote sensing images.
A Treecode Algorithm for 3D Stokeslets and Stresslets
Evaluating the velocity induced by a collection of Stokeslets and stresslets by direct summation requires $O(N^2)$ operations, where $N$ is the system size.
Numerical Analysis
Translating a Math Word Problem to an Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
Performance assessment of the deep learning technologies in grading glaucoma severity
Among these CNNs, the DenseNet had the highest classification accuracy (i. e., 75. 50%) based on pre-trained weights when using global ROIs, as compared to 65. 50% when using local ROIs.
SDFN: Segmentation-based Deep Fusion Network for Thoracic Disease Classification in Chest X-ray Images
Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images.
Translating a Math Word Problem to a Expression Tree
Moreover, we analyze the performance of three popular SEQ2SEQ models on the math word problem solving.
Solving Statistical Mechanics Using Variational Autoregressive Networks
We propose a general framework for solving statistical mechanics of systems with finite size.
Monge-Ampère Flow for Generative Modeling
We present a deep generative model, named Monge-Amp\`ere flow, which builds on continuous-time gradient flow arising from the Monge-Amp\`ere equation in optimal transport theory.
The Gap of Semantic Parsing: A Survey on Automatic Math Word Problem Solvers
Solving mathematical word problems (MWPs) automatically is challenging, primarily due to the semantic gap between human-readable words and machine-understandable logics.
Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms
We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context.
Learning and Inference on Generative Adversarial Quantum Circuits
Quantum mechanics is inherently probabilistic in light of Born's rule.
Toward Better Loanword Identification in Uyghur Using Cross-lingual Word Embeddings
Neural machine translation models integrating results of loanword identification experiments achieve the best results on OOV translation(with 0. 5-0. 9 BLEU improvements)
Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings
In this paper, we present a new large-scale benchmark dataset termed Aerial Imagery for Roof Segmentation (AIRS).
A Simple and Space Efficient Segment Tree Implementation
The segment tree is an extremely versatile data structure.
Data Structures and Algorithms
Online Progressive Deep Metric Learning
Furthermore, in a progressively and nonlinearly learning way, ODML has a stronger learning ability than traditional shallow online metric learning in the case of limited available training data.
Interactive Medical Image Segmentation via Point-Based Interaction and Sequential Patch Learning
In this paper, we present a novel method for interactive medical image segmentation with the following merits.
Anchor-based Nearest Class Mean Loss for Convolutional Neural Networks
Discriminative features are obtained by constraining the deep CNNs to map training samples to the corresponding anchors as close as possible.
MaskReID: A Mask Based Deep Ranking Neural Network for Person Re-identification
Lastly, considering person retrieval is a special image retrieval task, we propose a novel ranking loss to optimize the whole network.
Differentiable Learning of Quantum Circuit Born Machine
However, similar to the leading implicit generative models in deep learning, such as the generative adversarial networks, the quantum circuits cannot provide the likelihood of the generated samples, which poses a challenge to the training.
BigDataBench: A Scalable and Unified Big Data and AI Benchmark Suite
On the basis of our previous work that identifies eight data motifs taking up most of the run time of a wide variety of big data and AI workloads, we propose a scalable benchmarking methodology that uses the combination of one or more data motifs---to represent diversity of big data and AI workloads.
Understanding Membership Inferences on Well-Generalized Learning Models
Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.
Neural Network Renormalization Group
We present a variational renormalization group (RG) approach using a deep generative model based on normalizing flows.
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience
We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce.
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition
Our first subnet is a two-stream network which could explore both temporal and spatial information.
Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data.
Scalable Quantum Tomography with Fidelity Estimation
We propose a quantum tomography scheme for pure qudit systems which adopts random base measurements and generative learning methods, along with a built-in fidelity estimation approach to assess the reliability of the tomographic states.
Quantum Physics
DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition
Being symmetric positive-definite (SPD), covariance matrix has traditionally been used to represent a set of local descriptors in visual recognition.
Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging
Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging based 3D topographic brain glucose metabolism patterns from normal controls (NC) and individuals with dementia of Alzheimer's type (DAT) are used to train a novel multi-scale ensemble classification model.
Deep Convolutional Neural Networks for Anomaly Event Classification on Distributed Systems
The increasing popularity of server usage has brought a plenty of anomaly log events, which have threatened a vast collection of machines.
When Traffic Flow Prediction Meets Wireless Big Data Analytics
Traffic congestion is one of the most serious problems in a city, which can be predicted in advance by analyzing traffic flow patterns.
Unsupervised Generative Modeling Using Matrix Product States
Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence.
Log-linear Models for Uyghur Segmentation in Spoken Language Translation
To alleviate data sparsity in spoken Uyghur machine translation, we proposed a log-linear based morphological segmentation approach.
Revisiting Metric Learning for SPD Matrix Based Visual Representation
The proposed method has been tested on multiple SPD-based visual representation data sets used in the literature, and the results demonstrate its interesting properties and attractive performance.
Training Triplet Networks with GAN
Triplet networks are widely used models that are characterized by good performance in classification and retrieval tasks.
X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM
An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed.
Can Boltzmann Machines Discover Cluster Updates ?
Boltzmann machines are physics informed generative models with wide applications in machine learning.
Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks
We compare cosine normalization with batch, weight and layer normalization in fully-connected neural networks as well as convolutional networks on the data sets of MNIST, 20NEWS GROUP, CIFAR-10/100 and SVHN.
Equivalence of restricted Boltzmann machines and tensor network states
Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures.
Recommender Engine for Continuous Time Quantum Monte Carlo Methods
Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions.
Exploiting Structure Sparsity for Covariance-based Visual Representation
A variety of methods have been proposed to boost its efficacy, with some recent ones resorting to nonlinear kernel technique.
Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
Despite their exceptional flexibility and popularity, the Monte Carlo methods often suffer from slow mixing times for challenging statistical physics problems.
OPML: A One-Pass Closed-Form Solution for Online Metric Learning
To achieve a low computational cost when performing online metric learning for large-scale data, we present a one-pass closed-form solution namely OPML in this paper.
Discovering Phase Transitions with Unsupervised Learning
Unsupervised learning is a discipline of machine learning which aims at discovering patterns in big data sets or classifying the data into several categories without being trained explicitly.
LiDAR Ground Filtering Algorithm for Urban Areas Using Scan Line Based Segmentation
It utilizes the scan line information in LiDAR data to segment the LiDAR data.
Compositional Model based Fisher Vector Coding for Image Classification
To handle this limitation, in this paper we break the convention which assumes that a local feature is drawn from one of few Gaussian distributions.
Beyond Covariance: Feature Representation With Nonlinear Kernel Matrices
It proposes an open framework to use the kernel matrix over feature dimensions as a generic representation and discusses its properties and advantages.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
The NNI Query-by-Example System for MediaEval 2015
This paper describes the system developed by the NNI team for the Query-by-Example Search on Speech Task (QUESST) in the MediaEval 2015 evaluation.
Ranked #9 on
Keyword Spotting
on QUESST
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN).
Parallel Spectral Clustering Algorithm Based on Hadoop
Spectral clustering and cloud computing is emerging branch of computer science or related discipline.
Boosting-like Deep Learning For Pedestrian Detection
This paper proposes boosting-like deep learning (BDL) framework for pedestrian detection.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
The quality of the generated answers of our mQA model on this dataset is evaluated by human judges through a Turing Test.
HEp-2 Cell Image Classification with Deep Convolutional Neural Networks
Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases.
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
By calculating the gradient vector of the proposed model, we derive a new fisher vector encoding strategy, termed Sparse Coding based Fisher Vector Coding (SCFVC).
The NNI Query-by-Example System for MediaEval 2014
For both symbolic and DTW search, partial sequence matching is performed to reduce missing rate, especially for query type 2 and 3.
Ranked #6 on
Keyword Spotting
on QUESST
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
A comprehensive experimental study is conducted on a variety of image classification tasks to compare our proposed discriminative Stein kernel with the original Stein kernel and other commonly used methods for evaluating the similarity between SPD matrices.
Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification
In this paper, we propose a learning framework to effectively improve the discriminative power of SICEs by taking advantage of the samples in the opposite class.
A Generalized Probabilistic Framework for Compact Codebook Creation
In the third criterion, which shows the best merging performance, we propose a max-margin-based parameter estimation method and apply it with multinomial distribution.
Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network
Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivitybased biomarkers for the Alzheimer's disease (AD).
A Fast Approximate AIB Algorithm for Distributional Word Clustering
Distributional word clustering merges the words having similar probability distributions to attain reliable parameter estimation, compact classification models and even better classification performance.
Feature Selection via Sparse Approximation for Face Recognition
Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades.
Feature selection via simultaneous sparse approximation for person specific face verification
There is an increasing use of some imperceivable and redundant local features for face recognition.
Positive Semidefinite Metric Learning with Boosting
In this work, we propose a boosting-based technique, termed BoostMetric, for learning a Mahalanobis distance metric.