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Found 425 papers, 123 papers with code
Ranking Measures and Loss Functions in Learning to Rank
We show that these loss functions are upper bounds of the measure-based ranking errors.
Two-Layer Generalization Analysis for Ranking Using Rademacher Average
sampling of queries and the conditional i. i. d sampling of documents per query.
Influence Diffusion Dynamics and Influence Maximization in Social Networks with Friend and Foe Relationships
Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing.
Social and Information Networks Discrete Mathematics Physics and Society E.1; H.3.3
A Theoretical Analysis of NDCG Type Ranking Measures
We show that NDCG with logarithmic discount has consistent distinguishability although it converges to the same limit for all ranking functions.
Approximate dynamic programming using fluid and diffusion approximations with applications to power management
Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations.
IMRank: Influence Maximization via Finding Self-Consistent Ranking
Early methods mainly fall into two paradigms with certain benefits and drawbacks: (1)Greedy algorithms, selecting seed nodes one by one, give a guaranteed accuracy relying on the accurate approximation of influence spread with high computational cost; (2)Heuristic algorithms, estimating influence spread using efficient heuristics, have low computational cost but unstable accuracy.
Social and Information Networks Data Structures and Algorithms F.2.2; D.2.8
Real-time Topic-aware Influence Maximization Using Preprocessing
In this paper, we focus on the topic-aware influence maximization task.
Agent Behavior Prediction and Its Generalization Analysis
Then we prove a generalization bound for the machine learning algorithms on the behavior data generated by the new Markov chain, which depends on both the Markovian parameters and the covering number of the function class compounded by the loss function for behavior prediction and the behavior prediction model.
Actionness Ranking with Lattice Conditional Ordinal Random Fields
Action analysis in image and video has been attracting more and more attention in computer vision.
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search
Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers.
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
The objective of an online learning algorithm for CMAB is to minimize (\alpha,\beta)-approximation regret, which is the difference between the \alpha{\beta} fraction of the expected reward when always playing the optimal super arm, and the expected reward of playing super arms according to the algorithm.
Generalization Analysis for Game-Theoretic Machine Learning
For Internet applications like sponsored search, cautions need to be taken when using machine learning to optimize their mechanisms (e. g., auction) since self-interested agents in these applications may change their behaviors (and thus the data distribution) in response to the mechanisms.
Compositional Structure Learning for Action Understanding
The focus of the action understanding literature has predominately been classification, how- ever, there are many applications demanding richer action understanding such as mobile robotics and video search, with solutions to classification, localization and detection.
Combinatorial Pure Exploration of Multi-Armed Bandits
We also establish a general problem-dependent lower bound for the CPE problem.
On the Depth of Deep Neural Networks: A Theoretical View
First, we derive an upper bound for RA of DNN, and show that it increases with increasing depth.
From Competition to Complementarity: Comparative Influence Diffusion and Maximization
We study two natural optimization problems, Self Influence Maximization and Complementary Influence Maximization, in a novel setting with complementary entities.
Social and Information Networks Physics and Society H.2.8
Stochastic Online Greedy Learning with Semi-bandit Feedbacks
We further show that the bound is tight in $T$ and other problem instance parameters.
Action Detection by Implicit Intentional Motion Clustering
This paper hence seeks to understand the spatiotemporal properties of intentional movement and how to capture such intentional movement without relying on challenging human detection and tracking.
Robust Influence Maximization
In this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding $k$ seed nodes in a social network to maximize the influence spread.
Joint Sensing Matrix and Sparsifying Dictionary Optimization for Tensor Compressive Sensing
In this paper, we propose a joint optimization approach of the sensing matrix and dictionary for a TCS system.
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks
In this framework, we propose to aggregate the local models by ensemble, i. e., averaging the outputs of local models instead of the parameters.
Asynchronous Stochastic Gradient Descent with Delay Compensation
We propose a novel technology to compensate this delay, so as to make the optimization behavior of ASGD closer to that of sequential SGD.
Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction
The results verified our theoretical findings and demonstrated the practical efficiency of the asynchronous stochastic proximal algorithms with variance reduction.
Generalization Error Bounds for Optimization Algorithms via Stability
Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG).
Combinatorial Multi-Armed Bandit with General Reward Functions
Our framework enables a much larger class of reward functions such as the $\max()$ function and nonlinear utility functions.
A Communication-Efficient Parallel Algorithm for Decision Tree
After partitioning the training data onto a number of (e. g., $M$) machines, this algorithm performs both local voting and global voting in each iteration.
A Character-Aware Encoder for Neural Machine Translation
This article proposes a novel character-aware neural machine translation (NMT) model that views the input sequences as sequences of characters rather than words.
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
The proximal gradient algorithm has been popularly used for convex optimization.
Optimization and Control
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications
Finally, we provide lower bound results showing that the factor $1/p^*$ is unavoidable for general CMAB-T problems, suggesting that the TPM condition is crucial in removing this factor.
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator.
DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods.
Dual Supervised Learning
Many supervised learning tasks are emerged in dual forms, e. g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation.
Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space
We evaluate AES on three test examples and compare AES with two adaptive sampling methods -- the Neighborhood-Voronoi algorithm and the straddle heuristic -- that operate over fixed input variable bounds.
Towards Compact and Fast Neural Machine Translation Using a Combined Method
Neural Machine Translation (NMT) lays intensive burden on computation and memory cost.
Slim-DP: A Light Communication Data Parallelism for DNN
However, with the increasing size of DNN models and the large number of workers in practice, this typical data parallelism cannot achieve satisfactory training acceleration, since it usually suffers from the heavy communication cost due to transferring huge amount of information between workers and the parameter server.
Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling
First, we give a mathematical formulation for the practical data processing procedure in distributed machine learning, which we call data partition with global/local shuffling.
Low-Rank Tensor Completion: A Pseudo-Bayesian Learning Approach
Low rank tensor completion, which solves a linear inverse problem with the principle of parsimony, is a powerful technique used in many application domains in computer vision and pattern recognition.
Simplex Search Based Brain Storm Optimization
Through modeling human's brainstorming process, the brain storm optimization (BSO) algorithm has become a promising population-based evolutionary algorithm.
Influence Maximization with \varepsilon-Almost Submodular Threshold Functions
Influence maximization is the problem of selecting $k$ nodes in a social network to maximize their influence spread.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.
Learning for Disparity Estimation through Feature Constancy
The second part performs matching cost calculation, matching cost aggregation and disparity calculation to estimate the initial disparity using shared features.
Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration
Nonlinear machine learning techniques can identify such CVs but typically do not furnish an explicit relationship with the atomic coordinates necessary to perform biased sampling.
$\mathcal{G}$-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
Then, a natural question is: \emph{can we construct a new vector space that is positively scale-invariant and sufficient to represent ReLU neural networks so as to better facilitate the optimization process }?
Multi-Round Influence Maximization (Extended Version)
In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where influence propagates in multiple rounds independently from possibly different seed sets, and the goal is to select seeds for each round to maximize the expected number of nodes that are activated in at least one round.
Social and Information Networks
Scalable Influence Maximization with General Marketing Strategies
In this paper, we study scalable algorithms for influence maximization with general marketing strategies (IM-GMS), in which a marketing strategy mix is modeled as a vector $\mathbf{x}=(x_1, \ldots, x_d)$ and could activate a node $v$ in the social network with probability $h_v(\mathbf{x})$.
Social and Information Networks Data Structures and Algorithms
Train Feedfoward Neural Network with Layer-wise Adaptive Rate via Approximating Back-matching Propagation
This inconsistence of gradient magnitude across different layers renders optimization of deep neural network with a single learning rate problematic.
Thompson Sampling for Combinatorial Semi-Bandits
We first analyze the standard TS algorithm for the general CMAB model when the outcome distributions of all the base arms are independent, and obtain a distribution-dependent regret bound of $O(m\log K_{\max}\log T / \Delta_{\min})$, where $m$ is the number of base arms, $K_{\max}$ is the size of the largest super arm, $T$ is the time horizon, and $\Delta_{\min}$ is the minimum gap between the expected reward of the optimal solution and any non-optimal solution.
Unsupervised Neural Machine Translation with Weight Sharing
Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data.
Ranked #6 on
Machine Translation
on WMT2016 German-English
Combinatorial Pure Exploration with Continuous and Separable Reward Functions and Its Applications (Extended Version)
Each decision has a reward according to the distributions of arms.
Differential Equations for Modeling Asynchronous Algorithms
Then we conduct theoretical analysis on the convergence rates of ASGD algorithm based on the continuous approximation.
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji{'}s embedding is evaluated.
SupportNet: solving catastrophic forgetting in class incremental learning with support data
A plain well-trained deep learning model often does not have the ability to learn new knowledge without forgetting the previously learned knowledge, which is known as catastrophic forgetting.
Towards Binary-Valued Gates for Robust LSTM Training
Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling.
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin
End-to-end models have been showing superiority in Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors
Computer simulations often involve both qualitative and numerical inputs.
Semi-Supervised Disfluency Detection
While the disfluency detection has achieved notable success in the past years, it still severely suffers from the data scarcity.
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMS
Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics.
BézierGAN: Automatic Generation of Smooth Curves from Interpretable Low-Dimensional Parameters
Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e. g., airfoils) and hydrodynamic shapes (e. g., hulls) are designed.
Capacity Control of ReLU Neural Networks by Basis-path Norm
Motivated by this, we propose a new norm \emph{Basis-path Norm} based on a group of linearly independent paths to measure the capacity of neural networks more accurately.
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting
(2) The convergence rate is determined by the step size, with the mixing time of the Markov process as the coefficient.
Expressiveness in Deep Reinforcement Learning
Based on our observations, we formally define expressiveness of the state extractor as the rank of the matrix composed by representations.
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning
We then propose the dynamic Boltzmann softmax(DBS) operator to enable the convergence to the optimal value function in value iteration.
Improving the Robustness of Speech Translation
Although neural machine translation (NMT) has achieved impressive progress recently, it is usually trained on the clean parallel data set and hence cannot work well when the input sentence is the production of the automatic speech recognition (ASR) system due to the enormous errors in the source.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Exploring RNN-Transducer for Chinese Speech Recognition
End-to-end approaches have drawn much attention recently for significantly simplifying the construction of an automatic speech recognition (ASR) system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Community Exploration: From Offline Optimization to Online Learning
We introduce the community exploration problem that has many real-world applications such as online advertising.
An Online Attention-based Model for Speech Recognition
In previous work, researchers have shown that such architectures can acquire comparable results to state-of-the-art ASR systems, especially when using a bidirectional encoder and global soft attention (GSA) mechanism.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Modality Attention for End-to-End Audio-visual Speech Recognition
In this paper, we propose a novel multimodal attention based method for audio-visual speech recognition which could automatically learn the fused representation from both modalities based on their importance.
Audio-Visual Speech Recognition
Robust Speech Recognition
+2
Learning to Predict the Cosmological Structure Formation
We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory.
On the Local Hessian in Back-propagation
We study the Hessian of the local back-matching loss (local Hessian) and connect it to the efficiency of BP.
Vector and Line Quantization for Billion-scale Similarity Search on GPUs
The inverted index structure is amenable to GPU-based implementations, and the state-of-the-art systems such as Faiss are able to exploit the massive parallelism offered by GPUs.
Nonlinear Discovery of Slow Molecular Modes using State-Free Reversible VAMPnets
The success of enhanced sampling molecular simulations that accelerate along collective variables (CVs) is predicated on the availability of variables coincident with the slow collective motions governing the long-time conformational dynamics of a system.
Improved Algorithm on Online Clustering of Bandits
We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies.
Stochastic Online Learning with Probabilistic Graph Feedback
We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability $p_{ij}$.
Positively Scale-Invariant Flatness of ReLU Neural Networks
That is to say, the minimum with balanced values of basis paths will more likely to be flatter and generalize better.
Reinforcement Learning with Dynamic Boltzmann Softmax Updates
In this paper, we propose to update the value function with dynamic Boltzmann softmax (DBS) operator, which has good convergence property in the setting of planning and learning.
Stabilize Deep ResNet with A Sharp Scaling Factor $τ$
Moreover, for ResNets with normalization layer, adding such a factor $\tau$ also stabilizes the training and obtains significant performance gain for deep ResNet.
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesis
If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality.
Ranked #6 on
Text-to-Image Generation
on CUB
(Inception score metric)
The Roadmap to 6G -- AI Empowered Wireless Networks
The recent upsurge of diversified mobile applications, especially those supported by Artificial Intelligence (AI), is spurring heated discussions on the future evolution of wireless communications.
Optimization on Multiple Manifolds
Optimization on manifold has been widely used in machine learning, to handle optimization problems with constraint.
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space
Then, a natural question is: \emph{can we construct a new vector space that is positively scale-invariant and sufficient to represent ReLU neural networks so as to better facilitate the optimization process }?
An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE
Insights in power grid pixel maps (PGPMs) refer to important facility operating states and unexpected changes in the power grid.
Adaptive Influence Maximization with Myopic Feedback
We study the adaptive influence maximization problem with myopic feedback under the independent cascade model: one sequentially selects k nodes as seeds one by one from a social network, and each selected seed returns the immediate neighbors it activates as the feedback available for later selections, and the goal is to maximize the expected number of total activated nodes, referred as the influence spread.
Social and Information Networks
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data
Our theory captures how the convergence of distributed algorithms behaves as the number of machines and the size of local data vary.
Capabilities and Limitations of Time-lagged Autoencoders for Slow Mode Discovery in Dynamical Systems
We also compare the TAE results with those obtained using state-free reversible VAMPnets (SRVs) as a variational-based neural network approach for slow modes discovery, and show that SRVs can correctly discover slow modes where TAEs fail.
Self-Activation Influence Maximization
We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes.
Social and Information Networks
Factorization Bandits for Online Influence Maximization
We capitalize on an important property of the influence maximization problem named network assortativity, which is ignored by most existing works in online influence maximization.
High-resolution Markov state models for the dynamics of Trp-cage miniprotein constructed over slow folding modes identified by state-free reversible VAMPnets
State-free reversible VAMPnets (SRVs) are a neural network-based framework capable of learning the leading eigenfunctions of the transfer operator of a dynamical system from trajectory data.
Stochastic One-Sided Full-Information Bandit
In this paper, we study the stochastic version of the one-sided full information bandit problem, where we have $K$ arms $[K] = \{1, 2, \ldots, K\}$, and playing arm $i$ would gain reward from an unknown distribution for arm $i$ while obtaining reward feedback for all arms $j \ge i$.
On Adaptivity Gaps of Influence Maximization under the Independent Cascade Model with Full Adoption Feedback
In this paper, we study the adaptivity gap of the influence maximization problem under independent cascade model when full-adoption feedback is available.
Social and Information Networks
Data-Centric Mixed-Variable Bayesian Optimization For Materials Design
To this end, we present a data-centric, mixed-variable Bayesian Optimization framework that integrates data from literature, experiments, and simulations for knowledge discovery and computational materials design.
A Natural-language-based Visual Query Approach of Uncertain Human Trajectories
Visual querying is essential for interactively exploring massive trajectory data.
STABILITY AND CONVERGENCE THEORY FOR LEARNING RESNET: A FULL CHARACTERIZATION
We show that for standard initialization used in practice, $\tau =1/\Omega(\sqrt{L})$ is a sharp value in characterizing the stability of forward/backward process of ResNet, where $L$ is the number of residual blocks.
P-BN: Towards Effective Batch Normalization in the Path Space
Hence, some new algorithms that conduct optimizations directly in the path space (the path space is proven to be PSI) were developed, such as Stochastic Gradient Descent (SGD) in the path space, and it was shown that SGD in the path space is superior to that in the weight space.
THE EFFECT OF ADVERSARIAL TRAINING: A THEORETICAL CHARACTERIZATION
It has widely shown that adversarial training (Madry et al., 2018) is effective in defending adversarial attack empirically.
Path Space for Recurrent Neural Networks with ReLU Activations
Optimization algorithms like stochastic gradient descent that optimize the neural networks in the vector space of weights, which are not positively scale-invariant.
Good Semi-supervised VAE Requires Tighter Evidence Lower Bound
(2) Good semi-supervised learning results and good generative performance can not be obtained at the same time.
Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables
We present in this paper the integration of a novel latent-variable (LV) approach for mixed-variable GP modeling with the BO framework for materials design.
A Conditional Generative Model for Predicting Material Microstructures from Processing Methods
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications.
Interpreting Basis Path Set in Neural Networks
Based on basis path set, G-SGD algorithm significantly outperforms conventional SGD algorithm in optimizing neural networks.
Improving Generalization of Transformer for Speech Recognition with Parallel Schedule Sampling and Relative Positional Embedding
Transformer has shown promising results in many sequence to sequence transformation tasks recently.
Online Second Price Auction with Semi-bandit Feedback Under the Non-Stationary Setting
The problem is more challenging than the standard online learning scenario since the private value distribution is non-stationary, meaning that the distribution of bidders' private values may change over time, and we need to use the \emph{non-stationary regret} to measure the performance of our algorithm.
Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization
On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a $(1-1/e)$-approximation to the expected number of adoptions in the optimal non-adaptive seed choice.
Social and Information Networks
Gradient Perturbation is Underrated for Differentially Private Convex Optimization
By using the \emph{expected curvature}, we show that gradient perturbation can achieve a significantly improved utility guarantee that can theoretically justify the advantage of gradient perturbation over other perturbation methods.
Adaptive Expansion Bayesian Optimization for Unbounded Global Optimization
Bayesian optimization is normally performed within fixed variable bounds.
Combinatorial Semi-Bandit in the Non-Stationary Environment
In a special case where the reward function is linear and we have an exact oracle, we design a parameter-free algorithm that achieves nearly optimal regret both in the switching case and in the dynamic case without knowing the parameters in advance.
PaDGAN: A Generative Adversarial Network for Performance Augmented Diverse Designs
With this new loss function, we develop a variant of the Generative Adversarial Network, named "Performance Augmented Diverse Generative Adversarial Network" or PaDGAN, which can generate novel high-quality designs with good coverage of the design space.
G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation.
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation
In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
In this paper, we identify a new phenomenon called activation-divergence which occurs in Federated Learning (FL) due to data heterogeneity (i. e., data being non-IID) across multiple users.
MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning
This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
Intelligent Internet-of-Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence".
Efficient Approximation Algorithms for Adaptive Influence Maximization
In this paper, we propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of $1-\mathrm{e}^{\rho_b(\varepsilon-1)}$, where $\rho_b=1-(1-1/b)^b$ and $\varepsilon \in (0, 1)$ is a user-specified parameter.
Social and Information Networks
Lightweight Mask R-CNN for Long-Range Wireless Power Transfer Systems
The proposed approach makes the object detection much easier to be transplanted on mobile devices and reduce the burden of hardware computation.
Hierarchical Reinforcement Learning for Automatic Disease Diagnosis
Existing approaches usually employ a flat policy structure that treat all symptoms and diseases equally for action making.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
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.
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.
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
(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.
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.
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.
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 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.
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.
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.
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Molecular Latent Space Simulators
Small integration time steps limit molecular dynamics (MD) simulations to millisecond time scales.
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.
MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement
Deep generative models have proven useful for automatic design synthesis and design space exploration.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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).
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
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.
Latent Causal Invariant Model
To avoid spurious correlation, we propose a Latent Causal Invariance Model (LaCIM) which pursues causal prediction.
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.
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.
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.
A universal simulating framework for quantum key distribution systems
Our framework focuses on realistic characters of optical devices and system structures.
Quantum Physics Optics
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
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.
Phase of Nonlinear Systems
A nonlinear small phase theorem is then established for feedback stability analysis of semi-sectorial systems.
Maximizing Social Welfare in a Competitive Diffusion Model
Competitive IM focuses on the propagation of competing items in the network.
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.
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.
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.
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.
A Plug-and-Play Priors Framework for Hyperspectral Unmixing
Spectral unmixing is a widely used technique in hyperspectral image processing and analysis.
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.
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.
Deep Generative Model for Efficient 3D Airfoil Parameterization and Generation
We demonstrate FFD-GAN's performance using a wing shape design example.
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.
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
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
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.
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
Multi-Scale Cost Volumes Cascade Network for Stereo Matching
This is because different cost volumes play a crucial role in balancing speed and accuracy.
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).
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.
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
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.
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.
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 #7 on
6D Pose Estimation using RGBD
on REAL275
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.
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
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.
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.
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.
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).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
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.
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.
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.
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.
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.
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.
