Search Results for author:
Found 75 papers, 18 papers with code
p-Norm Flow Diffusion for Local Graph Clustering
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.
An Empirical Study of Remote Sensing Pretraining
To this end, we train different networks from scratch with the help of the largest RS scene recognition dataset up to now -- MillionAID, to obtain a series of RS pretrained backbones, including both convolutional neural networks (CNN) and vision transformers such as Swin and ViTAE, which have shown promising performance on computer vision tasks.
High Dimensional Statistical Estimation under One-bit Quantization
Compared with data with high precision, one-bit (binary) data are preferable in many applications because of the efficiency in signal storage, processing, transmission, and enhancement of privacy.
Differentially Private $\ell_1$-norm Linear Regression with Heavy-tailed Data
Firstly, we study the case where the $\ell_2$ norm of data has bounded second order moment.
VDPC: Variational Density Peak Clustering Algorithm
Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN.
A Survey of Large-Scale Deep Learning Serving System Optimization: Challenges and Opportunities
With both scaling trends, new problems and challenges emerge in DL inference serving systems, which gradually trends towards Large-scale Deep learning Serving systems (LDS).
Fed2: Feature-Aligned Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes.
Single volume lung biomechanics from chest computed tomography using a mode preserving generative adversarial network
Once trained, the framework can be used as a registration-free method for predicting local tissue expansion.
Carousel Memory: Rethinking the Design of Episodic Memory for Continual Learning
In particular, in mobile and IoT devices, real-time data can be stored not just in high-speed RAMs but in internal storage devices as well, which offer significantly larger capacity than the RAMs.
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification
In this paper, we propose a novel concept-based label embedding method that can explicitly represent the concept and model the sharing mechanism among classes for the hierarchical text classification.
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction
In this paper, we propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) for CSC, which jointly learns how to understand language and correct spelling errors.
Faster Rates of Private Stochastic Convex Optimization
Specifically, we first show that under some mild assumptions on the loss functions, there is an algorithm whose output could achieve an upper bound of $\tilde{O}((\frac{1}{\sqrt{n}}+\frac{\sqrt{d\log \frac{1}{\delta}}}{n\epsilon})^\frac{\theta}{\theta-1})$ for $(\epsilon, \delta)$-DP when $\theta\geq 2$, here $n$ is the sample size and $d$ is the dimension of the space.
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data
To better understand the challenges arising from irregular data distribution, in this paper we provide the first study on the problem of DP-SCO with heavy-tailed data in the high dimensional space.
Spectral-Spatial Graph Reasoning Network for Hyperspectral Image Classification
At last, by combining the extracted spatial and spectral graph contexts, we obtain the SSGRN to achieve a high accuracy classification.
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction
Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document.
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits
Finally, we establish the lower bound to show that the instance-dependent regret of our improved algorithm is optimal.
$\ell_2$-norm Flow Diffusion in Near-Linear Time
Diffusion is a fundamental graph procedure and has been a basic building block in a wide range of theoretical and empirical applications such as graph partitioning and semi-supervised learning on graphs.
GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention
This task is challenging as models need not only to capture spatial dependency and temporal dependency within the data, but also to leverage useful auxiliary information for accurate predictions.
3DMNDT:3D multi-view registration method based on the normal distributions transform
Under the NDT framework, this paper proposes a novel multi-view registration method, named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the K-means clustering and Lie algebra solver to achieve multi-view registration.
Minimum Cost Flows, MDPs, and $\ell_1$-Regression in Nearly Linear Time for Dense Instances
In the special case of the minimum cost flow problem on $n$-vertex $m$-edge graphs with integer polynomially-bounded costs and capacities we obtain a randomized method which solves the problem in $\tilde{O}(m+n^{1. 5})$ time.
Data Structures and Algorithms Optimization and Control
CARE: Commonsense-Aware Emotional Response Generation with Latent Concepts
Experimental results on two large-scale datasets support our hypothesis and show that our model can produce more accurate and commonsense-aware emotional responses and achieve better human ratings than state-of-the-art models that only specialize in one aspect.
Third ArchEdge Workshop: Exploring the Design Space of Efficient Deep Neural Networks
This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs).
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy
In our second attempt, we show that for any $1$-Lipschitz generalized linear convex loss function, there is an $(\epsilon, \delta)$-LDP algorithm whose sample complexity for achieving error $\alpha$ is only linear in the dimensionality $p$.
Deep Learning Analysis and Age Prediction from Shoeprints
We collected 100, 000 shoeprints of subjects ranging from 7 to 80 years old and used the data to develop a deep learning end-to-end model ShoeNet to analyze age-related patterns and predict age.
Differentially Private (Gradient) Expectation Maximization Algorithm with Statistical Guarantees
To address this issue, we propose in this paper the first DP version of (Gradient) EM algorithm with statistical guarantees.
On Differentially Private Stochastic Convex Optimization with Heavy-tailed Data
For this case, we propose a method based on the sample-and-aggregate framework, which has an excess population risk of $\tilde{O}(\frac{d^3}{n\epsilon^4})$ (after omitting other factors), where $n$ is the sample size and $d$ is the dimensionality of the data.
Estimating Stochastic Linear Combination of Non-linear Regressions Efficiently and Scalably
To the best of our knowledge, this is the first work that studies and provides theoretical guarantees for the stochastic linear combination of non-linear regressions model.
Robust High Dimensional Expectation Maximization Algorithm via Trimmed Hard Thresholding
In this paper, we study the problem of estimating latent variable models with arbitrarily corrupted samples in high dimensional space ({\em i. e.,} $d\gg n$) where the underlying parameter is assumed to be sparse.
Learning Robust Algorithms for Online Allocation Problems Using Adversarial Training
In this paper, we focus on the AdWords problem, which is a classical online budgeted matching problem of both theoretical and practical significance.
ECG Beats Fast Classification Base on Sparse Dictionaries
In VQ method, a set of dictionaries corresponding to segments of ECG beats is trained, and VQ codes are used to represent each heartbeat.
Heterogeneous Federated Learning
Specifically, we design a feature-oriented regulation method ({$\Psi$-Net}) to ensure explicit feature information allocation in different neural network structures.
AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency
Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework including (1) testing-phase channel and column feature map pruning, as well as (2) training-phase optimization by targeted dropout.
Raising Expectations: Automating Expected Cost Analysis with Types
The effectiveness of the technique is evaluated by analyzing the sample complexity of discrete distributions and with a novel average-case estimation for deterministic programs that combines expected cost analysis with statistical methods.
Programming Languages
$p$-Norm Flow Diffusion for Local Graph Clustering
Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.
Towards Assessment of Randomized Smoothing Mechanisms for Certifying Adversarial Robustness
Based on our framework, we assess the Gaussian and Exponential mechanisms by comparing the magnitude of additive noise required by these mechanisms and the lower bounds (criteria).
Distributed Kernel Ridge Regression with Communications
This paper focuses on generalization performance analysis for distributed algorithms in the framework of learning theory.
Robust Feature-Based Point Registration Using Directional Mixture Model
This paper presents a robust probabilistic point registration method for estimating the rigid transformation (i. e. rotation matrix and translation vector) between two pointcloud dataset.
Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning
In this work, we show that such adversarial-based methods can only reduce the domain style gap, but cannot address the domain content distribution gap that is shown to be important for object detectors.
Facility Location Problem in Differential Privacy Model Revisited
On the negative side, we show that the approximation ratio of any $\epsilon$-DP algorithm is lower bounded by $\Omega(\frac{1}{\sqrt{\epsilon}})$, even for instances on HST metrics with uniform facility cost, under the super-set output setting.
Estimating Smooth GLM in Non-interactive Local Differential Privacy Model with Public Unlabeled Data
Then with high probability, the sample complexity of the public and private data, for the algorithm to achieve an $\alpha$ estimation error (in $\ell_\infty$-norm), is $O(p^2\alpha^{-2})$ and ${O}(p^2\alpha^{-2}\epsilon^{-2})$, respectively, if $\alpha$ is not too small ({\em i. e.,} $\alpha\geq \Omega(\frac{1}{\sqrt{p}})$), where $p$ is the dimensionality of the data.
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants.
Faster width-dependent algorithm for mixed packing and covering LPs
As a special case of our result, we report a $1+\eps$ approximation algorithm for the densest subgraph problem which runs in time $O(md/ \eps)$, where $m$ is the number of edges in the graph and $d$ is the maximum graph degree.
A Unified framework for randomized smoothing based certified defenses
We answer the above two questions by first demonstrating that Gaussian mechanism and Exponential mechanism are the (near) optimal options to certify the $\ell_2$ and $\ell_\infty$-normed robustness.
YaoGAN: Learning Worst-case Competitive Algorithms from Self-generated Inputs
To answer this question, we draw insights from classic results in game theory, analysis of algorithms, and online learning to introduce a novel framework.
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations
Messages in human conversations inherently convey emotions.
Ranked #8 on
Emotion Recognition in Conversation
on EC
Heterogeneous-Temporal Graph Convolutional Networks: Make the Community Detection Much Better
Community detection has long been an important yet challenging task to analyze complex networks with a focus on detecting topological structures of graph data.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features.
Compact Autoregressive Network
Autoregressive networks can achieve promising performance in many sequence modeling tasks with short-range dependence.
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications
With the rapid adoption of machine learning techniques for large-scale applications in science and engineering comes the convergence of two grand challenges in visualization.
EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition.
Ranked #1 on
Emotion Recognition
on SEED-IV
Single-Path Mobile AutoML: Efficient ConvNet Design and NAS Hyperparameter Optimization
In this work, we alleviate the NAS search cost down to less than 3 hours, while achieving state-of-the-art image classification results under mobile latency constraints.
Neural Learning of Online Consumer Credit Risk
This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase.
Single-Path NAS: Device-Aware Efficient ConvNet Design
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the latency constraint of a mobile device?
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device?
Ranked #532 on
Image Classification
on ImageNet
Density Matching for Bilingual Word Embedding
Recent approaches to cross-lingual word embedding have generally been based on linear transformations between the sets of embedding vectors in the two languages.
Differentially Private High Dimensional Sparse Covariance Matrix Estimation
In this paper, we study the problem of estimating the covariance matrix under differential privacy, where the underlying covariance matrix is assumed to be sparse and of high dimensions.
Expander Decomposition and Pruning: Faster, Stronger, and Simpler
Our result achieve both nearly linear running time and the strong expander guarantee for clusters.
Data Structures and Algorithms
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations
For the case of \emph{generalized linear losses} (such as hinge and logistic losses), we give an LDP algorithm whose sample complexity is only linear in the dimensionality $p$ and quasipolynomial in other terms (the privacy parameters $\epsilon$ and $\delta$, and the desired excess risk $\alpha$).
Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited
In this paper, we revisit the Empirical Risk Minimization problem in the non-interactive local model of differential privacy.
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
Affect conveys important implicit information in human communication.
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
The versatile toolkit also fosters technique sharing across different text generation tasks.
Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
The features make Texar particularly suitable for technique sharing and generalization across different text generation applications.
Tackling Adversarial Examples in QA via Answer Sentence Selection
Given a paragraph of an article and a corresponding query, instead of directly feeding the whole paragraph to the single BiDAF system, a sentence that most likely contains the answer to the query is first selected, which is done via a deep neural network based on TreeLSTM (Tai et al., 2015).
Neural Machine Translation with Key-Value Memory-Augmented Attention
Although attention-based Neural Machine Translation (NMT) has achieved remarkable progress in recent years, it still suffers from issues of repeating and dropping translations.
Differentially Private Empirical Risk Minimization Revisited: Faster and More General
In this paper we study the differentially private Empirical Risk Minimization (ERM) problem in different settings.
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case
In the case of constant or low dimensionality ($p\ll n$), we first show that if the ERM loss function is $(\infty, T)$-smooth, then we can avoid a dependence of the sample complexity, to achieve error $\alpha$, on the exponential of the dimensionality $p$ with base $1/\alpha$ (i. e., $\alpha^{-p}$), which answers a question in [smith 2017 interaction].
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
In this paper, we revisit the large-scale constrained linear regression problem and propose faster methods based on some recent developments in sketching and optimization.
Steering Output Style and Topic in Neural Response Generation
We propose simple and flexible training and decoding methods for influencing output style and topic in neural encoder-decoder based language generation.
Capacity Releasing Diffusion for Speed and Locality.
As an application, we use our CRD Process to develop an improved local algorithm for graph clustering.
Capacity Releasing Diffusion for Speed and Locality
Thus, our CRD Process is the first local graph clustering algorithm that is not subject to the well-known quadratic Cheeger barrier.
The Language Application Grid
The Language Application (LAPPS) Grid project is establishing a framework that enables language service discovery, composition, and reuse and promotes sustainability, manageability, usability, and interoperability of natural language Processing (NLP) components.
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
In this context, the state-of-the-art algorithms RASL'' and "TILT'' can be viewed as two special cases of our work, and yet each only performs part of the function of our method."
