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Found 294 papers, 82 papers with code
Combinatorial Pure Exploration for Dueling Bandit
For Borda winner, we establish a reduction of the problem to the original CPE-MAB setting and design PAC and exact algorithms that achieve both the sample complexity similar to that in the CPE-MAB setting (which is nearly optimal for a subclass of problems) and polynomial running time per round.
The Sogou-TIIC Speech Translation System for IWSLT 2018
Our final average result on speech translation is 31. 02 BLEU.
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning
Generating informative and appropriate responses is challenging but important for building human-like dialogue systems.
A Structure-Aware Argument Encoder for Literature Discourse Analysis
Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context.
Dual Refinement Underwater Object Detection Network
Due to the complex underwater environment, underwater imaging often encounters some problems such as blur, scale variation, color shift, and texture distortion.
Combinatorial Causal Bandits without Graph Skeleton
This paper studies the CCB problem without the graph structure on binary general causal models and BGLMs.
Does Federated Learning Really Need Backpropagation?
BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server.
XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries.
Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery
Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction, surface reconstruction (or CityGML model reconstruction).
Decoding Structure-Spectrum Relationships with Physically Organized Latent Spaces
A new semi-supervised machine learning method for the discovery of structure-spectrum relationships is developed and demonstrated using the specific example of interpreting X-ray absorption near-edge structure (XANES) spectra.
MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding
Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets.
Towards Efficient Visual Simplification of Computational Graphs in Deep Neural Networks
A computational graph in a deep neural network (DNN) denotes a specific data flow diagram (DFD) composed of many tensors and operators.
Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation
The proposed rotation representation has two major advantages: 1) decoupled characteristic that makes the rotation estimation easier; 2) flexible length and rotated angle of the vectors allow us to find a more suitable vector representation for specific pose estimation task.
CSI-PPPNet: A One-Sided Deep Learning Framework for Massive MIMO CSI Feedback
This paper presents a novel method for massive MIMO CSI feedback via a one-sided deep learning framework.
Fourier-Net: Fast Image Registration with Band-limited Deformation
Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain.
A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images
Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality.
ElegantSeg: End-to-End Holistic Learning for Extra-Large Image Semantic Segmentation
This paper presents a new paradigm for Extra-large image semantic Segmentation, called ElegantSeg, that capably processes holistic extra-large image semantic segmentation (ELISS).
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data
To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.
Synchronization of Diverse Agents via Phase Analysis
They can also model the controllers of the agents which may be different for each agent or uniform for all the agents.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Fully Bayesian inference for latent variable Gaussian process models
However, this plug-in approach will not account for uncertainty in estimation of the LVs, which can be significant especially with limited training data.
Global-to-local Expression-aware Embeddings for Facial Action Unit Detection
Then, AU feature maps and their corresponding AU masks are multiplied to generate AU masked features focusing on local facial region.
Facial Action Units Detection Aided by Global-Local Expression Embedding
We find that AUs and facial expressions are highly associated, and existing facial expression datasets often contain a large number of identities.
Feature-Proxy Transformer for Few-Shot Segmentation
With a rethink of recent advances, we find that the current FSS framework has deviated far from the supervised segmentation framework: Given the deep features, FSS methods typically use an intricate decoder to perform sophisticated pixel-wise matching, while the supervised segmentation methods use a simple linear classification head.
Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control
This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments to maximize EV users' benefits.
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models
A ranking model is said to be Certified Top-$K$ Robust on a ranked list when it is guaranteed to keep documents that are out of the top $K$ away from the top $K$ under any attack.
Optimal $(0,1)$-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya)
We propose and examine two optimal $(0, 1)$-matrix completion problems with majorization ordered objectives.
MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning
Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related multi-modal auxiliary data (i. e., texts and images), which enhance the diversity of knowledge.
Disconnected Emerging Knowledge Graph Oriented Inductive Link Prediction
A more challenging scenario is that emerging KGs consist of only unseen entities, called as disconnected emerging KGs (DEKGs).
Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms
Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications.
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis
Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification).
Provable Adaptivity in Adam
In particular, the existing analysis of Adam cannot clearly demonstrate the advantage of Adam over SGD.
Global Consistent Point Cloud Registration Based on Lie-algebraic Cohomology
We present a novel, effective method for global point cloud registration problems by geometric topology.
D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights
We present a trajectory prediction approach with respect to traffic lights, D2-TPred, which uses a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) to handle the problem of discontinuous dependency in the spatial-temporal space.
Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods.
Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization
By ensuring differential privacy in the learning algorithms, one can rigorously mitigate the risk of large models memorizing sensitive training data.
Combinatorial Pure Exploration of Causal Bandits
The combinatorial pure exploration of causal bandits is the following online learning task: given a causal graph with unknown causal inference distributions, in each round we choose a subset of variables to intervene or do no intervention, and observe the random outcomes of all random variables, with the goal that using as few rounds as possible, we can output an intervention that gives the best (or almost best) expected outcome on the reward variable $Y$ with probability at least $1-\delta$, where $\delta$ is a given confidence level.
Combinatorial Causal Bandits
For the special case of linear models with hidden variables, we apply causal inference techniques such as the do-calculus to convert the original model into a Markovian model, and then show that our BGLM-OFU algorithm and another algorithm based on the linear regression both solve such linear models with hidden variables.
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret
We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on their different tolerance on exploration risks and should be treated separately.
Mutual Distillation Learning Network for Trajectory-User Linking
In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL.
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations
Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient.
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance
Based on this inspiration, we investigate the domain-invariant unweighted sparse associative structures and the domain-variant strengths of the structures.
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation
Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses.
Shape-Aware Monocular 3D Object Detection
In this paper, a single-stage monocular 3D object detection model is proposed.
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks and Datasets
In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience.
Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs
Stochastic partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics.
Automated Sleep Staging via Parallel Frequency-Cut Attention
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance.
TO-FLOW: Efficient Continuous Normalizing Flows with Temporal Optimization adjoint with Moving Speed
In this paper, a temporal optimization is proposed by optimizing the evolutionary time for forward propagation of the neural ODE training.
t-METASET: Tailoring Property Bias of Large-Scale Metamaterial Datasets through Active Learning
Distinctly, we seek a solution to a commonplace yet frequently overlooked scenario at early stages of data-driven design of metamaterials: when a massive (~O(10^4 )) shape-only library has been prepared with no properties evaluated.
GAN-DUF: Hierarchical Deep Generative Models for Design Under Free-Form Geometric Uncertainty
To address this issue, we propose a Generative Adversarial Network-based Design under Uncertainty Framework (GAN-DUF), which contains a deep generative model that simultaneously learns a compact representation of nominal (ideal) designs and the conditional distribution of fabricated designs given any nominal design.
Branching Reinforcement Learning
In this paper, we propose a novel Branching Reinforcement Learning (Branching RL) model, and investigate both Regret Minimization (RM) and Reward-Free Exploration (RFE) metrics for this model.
When Small Gain Meets Small Phase
In this paper, we investigate the feedback stability of multiple-input multiple-output linear time-invariant systems with combined gain and phase information.
REST: Debiased Social Recommendation via Reconstructing Exposure Strategies
By explicitly Reconstructing Exposure STrategies (REST in short), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method.
Using Deep Learning with Large Aggregated Datasets for COVID-19 Classification from Cough
The Covid-19 pandemic has been one of the most devastating events in recent history, claiming the lives of more than 5 million people worldwide.
Baihe: SysML Framework for AI-driven Databases
Based on the high level architecture, we then describe a concrete implementation of Baihe for PostgreSQL and present example use cases for learned query optimizers.
Deep Generative Models for Geometric Design Under Uncertainty
Deep generative models have demonstrated effectiveness in learning compact and expressive design representations that significantly improve geometric design optimization.
Glue: Adaptively Merging Single Table Cardinality to Estimate Join Query Size
Cardinality estimation (CardEst), a central component of the query optimizer, plays a significant role in generating high-quality query plans in DBMS.
Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending
We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i. e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility.
Recovering Latent Causal Factor for Generalization to Distributional Shifts
To avoid such a spurious correlation, we propose \textbf{La}tent \textbf{C}ausal \textbf{I}nvariance \textbf{M}odels (LaCIM) that specifies the underlying causal structure of the data and the source of distributional shifts, guiding us to pursue only causal factor for prediction.
CCSL: A Causal Structure Learning Method from Multiple Unknown Environments
Specifically, for the former, we provide a Causality-related Chinese Restaurant Process to cluster samples based on the similarity of the causal structure; for the latter, we introduce a variational-inference-based approach to learn the causal structures.
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle
These are the first theoretical results for TS to solve CMAB with a common approximation oracle and break the misconception that TS cannot work with approximation oracles.
Availability Attacks Create Shortcuts
We are the first to unveil an important population property of the perturbations of these attacks: they are almost \textbf{linearly separable} when assigned with the target labels of the corresponding samples, which hence can work as \emph{shortcuts} for the learning objective.
Collaborative Pure Exploration in Kernel Bandit
In this paper, we formulate a Collaborative Pure Exploration in Kernel Bandit problem (CoPE-KB), which provides a novel model for multi-agent multi-task decision making under limited communication and general reward functions, and is applicable to many online learning tasks, e. g., recommendation systems and network scheduling.
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
In this paper, we propose a framework to construct SE(3) equivariant graph neural networks that can approximate the geometric quantities efficiently.
Optimizing Information-theoretical Generalization Bounds via Anisotropic Noise in SGLD
We prove that with constraint to guarantee low empirical risk, the optimal noise covariance is the square root of the expected gradient covariance if both the prior and the posterior are jointly optimized.
TESSERACT: Gradient Flip Score to Secure Federated Learning Against Model Poisoning Attacks
The attack uses the intuition that simply by changing the sign of the gradient updates that the optimizer is computing, for a set of malicious clients, a model can be diverted from the optima to increase the test error rate.
Does Momentum Change the Implicit Regularization on Separable Data?
The momentum acceleration technique is widely adopted in many optimization algorithms.
Gradient Information Matters in Policy Optimization by Back-propagating through Model
Then we proposed a two-model-based learning method to control the prediction error and the gradient error.
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles
In contextual bandit, one major challenge is to develop theoretically solid and empirically efficient algorithms for general function classes.
Continual Learning with Filter Atom Swapping
In this paper, we first enforce a low-rank filter subspace by decomposing convolutional filters within each network layer over a small set of filter atoms.
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations
We study the problem of coarse-grained response selection in retrieval-based dialogue systems.
Online Influence Maximization under the Independent Cascade Model with Node-Level Feedback
We study the online influence maximization (OIM) problem in social networks, where the learner repeatedly chooses seed nodes to generate cascades, observes the cascade feedback, and gradually learns the best seeds that generate the largest cascade in multiple rounds.
End-to-End Conversational Search for Online Shopping with Utterance Transfer
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers.
The Singular Angle of Nonlinear Systems
It is, thus, different from the recently appeared nonlinear system phase which adopts the complexification of real-valued signals using the Hilbert transform.
Real-Time Visual Analysis of High-Volume Social Media Posts
In contrast to previous work, our system also works with non-geolocated posts and avoids extensive preprocessing such as detecting events.
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit
However, it is in general unknown how to deriveefficient and effective EE trade-off methods for non-linearcomplex tasks, suchas contextual bandit with deep neural network as the reward function.
R-Drop: Regularized Dropout for Neural Networks
Dropout is a powerful and widely used technique to regularize the training of deep neural networks.
Ranked #4 on
Machine Translation
on WMT2014 English-French
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors
In addition, an interpretable latent space is obtained to draw insights into the effect of categorical factors, such as those associated with building blocks of architectures and element choices in metamaterial and materials design.
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation
It is desirable to transfer the knowledge stored in a well-trained source model onto non-annotated target domain in the absence of source data.
Large Scale Private Learning via Low-rank Reparametrization
We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise suffering notorious dimensional dependence.
Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization
Such methods, however, cannot guarantee that the input measurements are satisfied in the recovered image, since the learned parameters by the network are applied to every test image.
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior
Denoising diffusion probabilistic models have been recently proposed to generate high-quality samples by estimating the gradient of the data density.
Data-Driven Multiscale Design of Cellular Composites with Multiclass Microstructures for Natural Frequency Maximization
After integrating LVGP with the density-based TO, an efficient data-driven cellular composite optimization process is developed to enable concurrent exploration of microstructure concepts and the associated volume fractions for natural frequency optimization.
Multi-layered Network Exploration via Random Walks: From Offline Optimization to Online Learning
For the online learning setting, neither the network structure nor the node weights are known initially.
Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics
In this paper, to reduce the reliance on the numerical solver, we propose to enhance the supervised signal in the training of NODE.
DINs: Deep Interactive Networks for Neurofibroma Segmentation in Neurofibromatosis Type 1 on Whole-Body MRI
In this study, we propose deep interactive networks (DINs) to address the above limitations.
Network Inference and Influence Maximization from Samples
Our IMS algorithms enhance the learning-and-then-optimization approach by allowing a constant approximation ratio even when the diffusion parameters are hard to learn, and we do not need any assumption related to the network structure or diffusion parameters.
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Engineering design tasks often require synthesizing new designs that meet desired performance requirements.
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network
Specifically, we first structure the sequence of EMR into a hierarchical graph network and then obtain the causal relationship between multi-granularity features and diagnosis results through counterfactual intervention on the graph.
UAV Aided Over-the-Air Computation
Over-the-air computation (AirComp) seamlessly integrates communication and computation by exploiting the waveform superposition property of multiple-access channels.
Prior-Enhanced Few-Shot Segmentation with Meta-Prototypes
The hierarchical features help the model highlight the decision boundary and focus on hard pixels, and the structural information learned from base classes is treated as the prior knowledge for novel classes.
Machine-Learning Non-Conservative Dynamics for New-Physics Detection
Energy conservation is a basic physics principle, the breakdown of which often implies new physics.
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
Along with this routine, we find that confidence and a rectified confidence (R-Con) can form two coupled rejection metrics, which could provably distinguish wrongly classified inputs from correctly classified ones.
Learning a Model-Driven Variational Network for Deformable Image Registration
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD
We prove that with constraint to guarantee low empirical risk, the optimal noise covariance is the square root of the expected gradient covariance if both the prior and the posterior are jointly optimized.
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation
However, existing methods for empathetic response generation usually either consider only one empathy factor or ignore the hierarchical relationships between different factors, leading to a weak ability of empathy modeling.
A Graph Neural Network Approach for Product Relationship Prediction
By representing products as nodes and their relationships as edges of a graph, we show how an inductive graph neural network approach, named GraphSAGE, can efficiently learn continuous representations for nodes and edges.
Over-the-Air Computation via Reconfigurable Intelligent Surface
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition property of multiple-access channels.
Geometrical Characterization of Sensor Placement for Cone-Invariant and Multi-Agent Systems against Undetectable Zero-Dynamics Attacks
Undetectable attacks are an important class of malicious attacks threatening the security of cyber-physical systems, which can modify a system's state but leave the system output measurements unaffected, and hence cannot be detected from the output.
A Phase Theory of MIMO LTI Systems
In this paper, we define the phase response for a class of multi-input multi-output (MIMO) linear time-invariant (LTI) systems whose frequency responses are (semi-)sectorial at all frequencies.
Pure Exploration Bandit Problem with General Reward Functions Depending on Full Distributions
In this paper, we study the pure exploration bandit model on general distribution functions, which means that the reward function of each arm depends on the whole distribution, not only its mean.
Integrating Information Theory and Adversarial Learning for Cross-modal Retrieval
Moreover, feature encoders (as a generator) project uni-modal features into a commonly shared space and attempt to fool the discriminator by maximizing its output information entropy.
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition
Recently, attention-based encoder-decoder (AED) end-to-end (E2E) models have drawn more and more attention in the field of automatic speech recognition (ASR).
Delay Analysis of Wireless Federated Learning Based on Saddle Point Approximation and Large Deviation Theory
Federated learning (FL) is a collaborative machine learning paradigm, which enables deep learning model training over a large volume of decentralized data residing in mobile devices without accessing clients' private data.
FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders
The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results.
Lifelong Person Re-Identification via Adaptive Knowledge Accumulation
In this work we explore a new and challenging ReID task, namely lifelong person re-identification (LReID), which enables to learn continuously across multiple domains and even generalise on new and unseen domains.
Magnetoelectric torque and edge currents in spin-orbit coupled graphene nanoribbons
Even without the magnetization, an out-of-plane polarized chiral edge spin current is produced, resembling that in the quantum spin Hall effect.
Mesoscale and Nanoscale Physics
Two Mirroring And Interpolating Methods To Estimate Peak Position For Symmetric Signals With Single Peak
Signals with single peak and symmetry property are very common in various fields, such as probability density function of normal distribution.
FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image.
Ranked #3 on
6D Pose Estimation using RGBD
on REAL275
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
The output of such simulations, in particular the calculated flow fields, are usually very complex and hard to interpret for realistic three-dimensional real-world applications, especially if time-dependent simulations are investigated.
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
In this work we propose a weight soft-regularization method based on the Oblique manifold.
A Sequential Variational Mode Decomposition Method
And in such a way, the mode number also can be determined during the separation procedure.
Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis
This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space.
A Note on the Boundedness of Doob Maximal Operators on a Filtered Measure Space
Let $M$ be the Doob maximal operator on a filtered measure space and let $v$ be an $A_p$ weight with $1<p<+\infty$.
Probability 60G46
Realizing Majorana fermion modes in the Kitaev model
We study the possibility to realize Majorana zero mode that's robust and may be easily manipulated for braiding in quantum computing in the ground state of the Kitaev model in this work.
Strongly Correlated Electrons
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning
The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism.
Combinatorial Pure Exploration with Bottleneck Reward Function
For the FC setting, we propose novel algorithms with optimal sample complexity for a broad family of instances and establish a matching lower bound to demonstrate the optimality (within a logarithmic factor).
Multi-Scale Cost Volumes Cascade Network for Stereo Matching
This is because different cost volumes play a crucial role in balancing speed and accuracy.
Aharonov-Bohm Effect in Three-dimensional Higher-order Topological Insulator
Unlike AB interferometer of 3D topological insulator(TI), we find that there are different AB oscillation frequencies for a given direction of magnetic field in 3D HOTI.
Mesoscale and Nanoscale Physics
Deep Learning for Instance Retrieval: A Survey
In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics.
UAV-Assisted Over-the-Air Computation
Over-the-air computation (AirComp) provides a promising way to support ultrafast aggregation of distributed data.
Optimize the trajectory of UAV which plays a BS in communication system
Search for the elusive jet-induced diffusion wake in $Z/γ$-jets with 2D jet tomography in high-energy heavy-ion collisions
Diffusion wake is an unambiguous part of the jet-induced medium response in high-energy heavy-ion collisions that leads to a depletion of soft hadrons in the opposite direction of the jet propagation.
High Energy Physics - Phenomenology
BN-invariant sharpness regularizes the training model to better generalization
However, it has been pointed out that the usual definitions of sharpness, which consider either the maxima or the integral of loss over a $\delta$ ball of parameters around minima, cannot give consistent measurement for scale invariant neural networks, e. g., networks with batch normalization layer.
Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation
We demonstrate FFD-GAN's performance using a wing shape design example.
On the Stability of Multi-branch Network
More importantly, we propose a new design ``STAM aggregation" that can guarantee to STAbilize the forward/backward process of Multi-branch networks irrespective of the number of branches.
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.
A Plug-and-Play Priors Framework for Hyperspectral Unmixing
Spectral unmixing is a widely used technique in hyperspectral image processing and analysis.
Time Series Domain Adaptation via Sparse Associative Structure Alignment
To reduce the difficulty in the discovery of causal structure, we relax it to the sparse associative structure and propose a novel sparse associative structure alignment model for domain adaptation.
Identifying Invariant Texture Violation for Robust Deepfake Detection
To learn such an invariance for deepfake detection, our InTeLe introduces an auto-encoder framework with different decoders for pristine and fake images, which are further appended with a shallow classifier in order to separate out the obvious artifact-effect.
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks
Except GD, adaptive algorithms such as AdaGrad, RMSProp and Adam are popular owing to their rapid training process.
Multitask machine learning of collective variables for enhanced sampling of rare events
Computing accurate reaction rates is a central challenge in computational chemistry and biology because of the high cost of free energy estimation with unbiased molecular dynamics.
Maximizing Social Welfare in a Competitive Diffusion Model
Competitive IM focuses on the propagation of competing items in the network.
Phase of Nonlinear Systems
A nonlinear small phase theorem is then established for feedback stability analysis of semi-sectorial systems.
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results.
KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
(2) A dynamic weighting strategy named Consensus Focus to identify both the malicious and irrelevant domains.
Knowledge Distillation
Multi-Source Unsupervised Domain Adaptation
+2
A universal simulating framework for quantum key distribution systems
Our framework focuses on realistic characters of optical devices and system structures.
Quantum Physics Optics
Online Influence Maximization under Linear Threshold Model
Based on the linear structure in node activations, we incorporate ideas from linear bandits and design an algorithm LT-LinUCB that is consistent with the observed feedback.
Federated Learning via Intelligent Reflecting Surface
To optimize the learning performance, we formulate an optimization problem that jointly optimizes the device selection, the aggregation beamformer at the base station (BS), and the phase shifts at the IRS to maximize the number of devices participating in the model aggregation of each communication round under certain mean-squared-error (MSE) requirements.
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance
Video surveillance is gaining increasing popularity to assist in railway intrusion detection in recent years.
Latent Causal Invariant Model
To avoid spurious correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
Learning Causal Semantic Representation for Out-of-Distribution Prediction
Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes the semantic factor with the variation factor due to their domain-specific correlation, while only the semantic factor causes the output.
Establishing the first hidden-charm pentaquark with strangeness
We study the $P_{cs}(4459)^0$ recently observed by LHCb using the method of QCD sum rules.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Fast Convergence Algorithm for Analog Federated Learning
In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end devices through over-the-air computation (AirComp).
Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth
Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications.
New Ideas and Trends in Deep Multimodal Content Understanding: A Review
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text.
On the Exploration of Incremental Learning for Fine-grained Image Retrieval
On the other hand, fine-tuning the learned representation only with the new classes leads to catastrophic forgetting.
Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors
In our work, we propose an NMF based unmixing framework which jointly uses a handcrafting regularizer and a learnt regularizer from data.
Fully Automatic Intervertebral Disc Segmentation Using Multimodal 3D U-Net
With the advance of deep learning, various neural network models have gained great success in image analysis including the recognition of intervertebral discs.
ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit
Meanwhile, human choreographers design dance motions from music in a two-stage manner: they firstly devise multiple choreographic dance units (CAUs), each with a series of dance motions, and then arrange the CAU sequence according to the rhythm, melody and emotion of the music.
MO-PaDGAN: Reparameterizing Engineering Designs for Augmented Multi-objective Optimization
Despite their success in capturing complex distributions, existing generative models face three challenges when used for design problems: 1) generated designs have limited design space coverage, 2) the generator ignores design performance, and 3)~the new parameterization is unable to represent designs beyond training data.
GraphFederator: Federated Visual Analysis for Multi-party Graphs
This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs.
Human-Computer Interaction Cryptography and Security Graphics
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices.
Global Context Aware Convolutions for 3D Point Cloud Understanding
We found that a key reason is that compared to point coordinates, rotation-invariant features consumed by point cloud convolution are not as distinctive.
Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification
To solve the problem, we present a carefully designed dual Gaussian-based variational auto-encoder (DG-VAE), which disentangles an identity-discriminable and an identity-ambiguous cross-modality feature subspace, following a mixture-of-Gaussians (MoG) prior and a standard Gaussian distribution prior, respectively.
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization
We build a prototype system based on our method, SMAP, to support the organization, computation, and exploration of secure joint embedding.
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics
Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks.
Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
The linear inverse problem is fundamental to the development of various scientific areas.
How Does Data Augmentation Affect Privacy in Machine Learning?
Even further, we show that the proposed approach can achieve higher MI attack success rates on models trained with some data augmentation than the existing methods on models trained without data augmentation.
MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement
Deep generative models have proven useful for automatic design synthesis and design space exploration.
Optimization from Structured Samples for Coverage Functions
We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data.
Molecular Latent Space Simulators
Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales.
Robust Neural Machine Translation with ASR Errors
In this paper, we propose a method to handle the two problems so as to generate robust translation to ASR errors.
Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process
With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization.
Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems
For microstructure design, the tuning of mechanical properties and complex manipulations of microstructures are easily achieved by simple vector operations in the latent space.
Dynamic of Stochastic Gradient Descent with State-Dependent Noise
Specifically, we show that the covariance of the noise of SGD in the local region of the local minima is a quadratic function of the state.
Online Competitive Influence Maximization
In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e. g., products, news stories) propagate in the same network and influence probabilities on edges are unknown.
Combinatorial Pure Exploration of Dueling Bandit
For Borda winner, we establish a reduction of the problem to the original CPE-MAB setting and design PAC and exact algorithms that achieve both the sample complexity similar to that in the CPE-MAB setting (which is nearly optimal for a subclass of problems) and polynomial running time per round.
Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
The resulted new parameterization can accelerate design optimization convergence by improving the representation compactness while maintaining sufficient representation capacity.
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback
In this paper, we first study the problem of combinatorial pure exploration with full-bandit feedback (CPE-BL), where a learner is given a combinatorial action space $\mathcal{X} \subseteq \{0, 1\}^d$, and in each round the learner pulls an action $x \in \mathcal{X}$ and receives a random reward with expectation $x^{\top} \theta$, with $\theta \in \mathbb{R}^d$ a latent and unknown environment vector.
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.
(Locally) Differentially Private Combinatorial Semi-Bandits
In this paper, we study Combinatorial Semi-Bandits (CSB) that is an extension of classic Multi-Armed Bandits (MAB) under Differential Privacy (DP) and stronger Local Differential Privacy (LDP) setting.
METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design
In answer, we posit that a smaller yet diverse set of unit cells leads to scalable search and unbiased learning.
Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech
In this paper, we propose multi-band MelGAN, a much faster waveform generation model targeting to high-quality text-to-speech.
Sound Audio and Speech Processing
Quda: Natural Language Queries for Visual Data Analytics
To address this challenge, we present a new dataset, called Quda, that aims to help V-NLIs recognize analytic tasks from free-form natural language by training and evaluating cutting-edge multi-label classification models.
EnsembleGAN: Adversarial Learning for Retrieval-Generation Ensemble Model on Short-Text Conversation
Aiming at generating responses that approximate the ground-truth and receive high ranking scores from the discriminator, the two generators learn to generate improved highly relevant responses and competitive unobserved candidates respectively, while the discriminative ranker is trained to identify true responses from adversarial ones, thus featuring the merits of both generator counterparts.