Search Results for author:
Found 330 papers, 160 papers with code
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unattainable fully annotated training data.
Efficient Relational Learning with Hidden Variable Detection
Markov networks (MNs) can incorporate arbitrarily complex features in modeling relational data.
Large Margin Learning of Upstream Scene Understanding Models
This paper presents a joint max-margin and max-likelihood learning method for upstream scene understanding models, in which latent topic discovery and prediction model estimation are closely coupled and well-balanced.
Adaptive Multi-Task Lasso: with Application to eQTL Detection
To understand the relationship between genomic variations among population and complex diseases, it is essential to detect eQTLs which are associated with phenotypic effects.
Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
Learning from multi-view data is important in many applications, such as image classification and annotation.
Infinite Latent SVM for Classification and Multi-task Learning
Unlike existing nonparametric Bayesian models, which rely solely on specially conceived priors to incorporate domain knowledge for discovering improved latent representations, we study nonparametric Bayesian inference with regularization on the desired posterior distributions.
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs
When the regularization is induced from a linear operator on the posterior distributions, such as the expectation operator, we present a general convex-analysis theorem to characterize the solution of RegBayes.
Monte Carlo Methods for Maximum Margin Supervised Topic Models
An effective strategy to exploit the supervising side information for discovering predictive topic representations is to impose discriminative constraints induced by such information on the posterior distributions under a topic model.
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
Supervised topic models with a logistic likelihood have two issues that potentially limit their practical use: 1) response variables are usually over-weighted by document word counts; and 2) existing variational inference methods make strict mean-field assumptions.
Discriminative Relational Topic Models
Many scientific and engineering fields involve analyzing network data.
Gibbs Max-margin Topic Models with Data Augmentation
Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected prediction rule.
Scalable Inference for Logistic-Normal Topic Models
Logistic-normal topic models can effectively discover correlation structures among latent topics.
Online Bayesian Passive-Aggressive Learning
Online Passive-Aggressive (PA) learning is an effective framework for performing max-margin online learning.
Dropout Training for Support Vector Machines
To deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques.
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields
We prove that the gradient of candidate features can be represented solely as a function of signals and errors, and that CFI is an efficient approximation of gradient-based evaluation methods.
Big Learning with Bayesian Methods
Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data.
Distributed Bayesian Posterior Sampling via Moment Sharing
We propose a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation.
Robust Bayesian Max-Margin Clustering
We present max-margin Bayesian clustering (BMC), a general and robust framework that incorporates the max-margin criterion into Bayesian clustering models, as well as two concrete models of BMC to demonstrate its flexibility and effectiveness in dealing with different clustering tasks.
Spectral Methods for Supervised Topic Models
Supervised topic models simultaneously model the latent topic structure of large collections of documents and a response variable associated with each document.
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm
We propose a novel learning algorithm called \emph{expectation loss SVM} (e-SVM) that is devoted to the problems where only the ``positiveness" instead of a binary label of each training sample is available.
Max-margin Deep Generative Models
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability.
Fast Sampling for Bayesian Max-Margin Models
Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, social network link prediction and crowdsourcing, and they conjoin the flexibility of Bayesian modeling and predictive strengths of max-margin learning.
Bounded-Distortion Metric Learning
Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering.
Learning Deep Generative Models with Doubly Stochastic MCMC
We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models (DGMs) in a collapsed continuous parameter space.
Dropout Training for SVMs with Data Augmentation
Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.
A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution
Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods.
Pose-Guided Human Parsing with Deep Learned Features
Parsing human body into semantic regions is crucial to human-centric analysis.
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation
We then develop WarpLDA, an LDA sampler which achieves both the best O(1) time complexity per token and the best O(K) scope of random access.
DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization
In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization.
Max-Margin Majority Voting for Learning from Crowds
Learning-from-crowds aims to design proper aggregation strategies to infer the unknown true labels from the noisy labels provided by ordinary web workers.
Building Memory with Concept Learning Capabilities from Large-scale Knowledge Base
We present a new perspective on neural knowledge base (KB) embeddings, from which we build a framework that can model symbolic knowledge in the KB together with its learning process.
Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization
Bayesian matrix completion has been studied based on a low-rank matrix factorization formulation with promising results.
Jointly Modeling Topics and Intents with Global Order Structure
Modeling document structure is of great importance for discourse analysis and related applications.
Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features.
Fast Parallel SVM using Data Augmentation
As one of the most popular classifiers, linear SVMs still have challenges in dealing with very large-scale problems, even though linear or sub-linear algorithms have been developed recently on single machines.
Streaming Gibbs Sampling for LDA Model
Streaming variational Bayes (SVB) is successful in learning LDA models in an online manner.
Spectral Learning for Supervised Topic Models
Spectral methods have been applied to learn unsupervised topic models, such as latent Dirichlet allocation (LDA), with provable guarantees.
Scaling up Dynamic Topic Models
Dynamic topic models (DTMs) are very effective in discovering topics and capturing their evolution trends in time series data.
Max-Margin Nonparametric Latent Feature Models for Link Prediction
Our approach attempts to unite the ideas of max-margin learning and Bayesian nonparametrics to discover discriminative latent features for link prediction.
Learning to Generate with Memory
Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data.
Towards Better Analysis of Deep Convolutional Neural Networks
Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification.
Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)
Word embedding maps words into a low-dimensional continuous embedding space by exploiting the local word collocation patterns in a small context window.
PSDVec: a Toolbox for Incremental and Scalable Word Embedding
PSDVec is a Python/Perl toolbox that learns word embeddings, i. e. the mapping of words in a natural language to continuous vectors which encode the semantic/syntactic regularities between the words.
Conditional Generative Moment-Matching Networks
Maximum mean discrepancy (MMD) has been successfully applied to learn deep generative models for characterizing a joint distribution of variables via kernel mean embedding.
Kernel Bayesian Inference with Posterior Regularization
We propose a vector-valued regression problem whose solution is equivalent to the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior distribution.
SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs
Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete count data such as text and images.
Max-Margin Deep Generative Models for (Semi-)Supervised Learning
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability.
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data
In this paper, we propose an extension of State Space Model to work with different sources of information together with its learning and inference algorithms.
Stochastic Gradient Geodesic MCMC Methods
We propose two stochastic gradient MCMC methods for sampling from Bayesian posterior distributions defined on Riemann manifolds with a known geodesic flow, e. g. hyperspheres.
A Communication-Efficient Parallel Method for Group-Lasso
Group-Lasso (gLasso) identifies important explanatory factors in predicting the response variable by considering the grouping structure over input variables.
Collaborative Filtering with User-Item Co-Autoregressive Models
Deep neural networks have shown promise in collaborative filtering (CF).
Towards Better Analysis of Machine Learning Models: A Visual Analytics Perspective
Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems.
Scalable Inference for Nested Chinese Restaurant Process Topic Models
Finally, we propose an efficient distributed implementation of PCGS through vectorization, pre-processing, and a careful design of the concurrent data structures and communication strategy.
Triple Generative Adversarial Nets
Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL).
Improving Interpretability of Deep Neural Networks with Semantic Information
Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems.
Kernel Implicit Variational Inference
Recent progress in variational inference has paid much attention to the flexibility of variational posteriors.
Towards Robust Detection of Adversarial Examples
Although the recent progress is substantial, deep learning methods can be vulnerable to the maliciously generated adversarial examples.
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
By simultaneously considering the layer-by-layer spatial domain (SD) and the timing-dependent temporal domain (TD) in the training phase, as well as an approximated derivative for the spike activity, we propose a spatio-temporal backpropagation (STBP) training framework without using any complicated technology.
The YouTube-8M Kaggle Competition: Challenges and Methods
We took part in the YouTube-8M Video Understanding Challenge hosted on Kaggle, and achieved the 10th place within less than one month's time.
Detecting Semantic Parts on Partially Occluded Objects
Our approach detects semantic parts by accumulating the confidence of local visual cues.
Identify the Nash Equilibrium in Static Games with Random Payoffs
We study the problem on how to learn the pure Nash Equilibrium of a two-player zero-sum static game with random payoffs under unknown distributions via efficient payoff queries.
Learning Accurate Low-Bit Deep Neural Networks with Stochastic Quantization
This procedure can greatly compensate the quantization error and thus yield better accuracy for low-bit DNNs.
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
Thompson sampling has impressive empirical performance for many multi-armed bandit problems.
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
We find that: (1) the neurons in DNNs do not truly detect semantic objects/parts, but respond to objects/parts only as recurrent discriminative patches; (2) deep visual representations are not robust distributed codes of visual concepts because the representations of adversarial images are largely not consistent with those of real images, although they have similar visual appearance, both of which are different from previous findings.
ZhuSuan: A Library for Bayesian Deep Learning
In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning.
Boosting Adversarial Attacks with Momentum
To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks.
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Previous attempts on reducing the receptive field size by subsampling neighbors do not have a convergence guarantee, and their receptive field size per node is still in the order of hundreds.
Smooth Neighbors on Teacher Graphs for Semi-supervised Learning
In SNTG, a graph is constructed based on the predictions of the teacher model, i. e., the implicit self-ensemble of models.
Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designated semantics or structures.
Visual Concepts and Compositional Voting
We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles.
Message Passing Stein Variational Gradient Descent
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods.
Diversity-Promoting Bayesian Learning of Latent Variable Models
We also extend our approach to "diversify" Bayesian nonparametric models where the number of components is infinite.
Riemannian Stein Variational Gradient Descent for Bayesian Inference
The benefits are two-folds: (i) for inference tasks in Euclidean spaces, RSVGD has the advantage over SVGD of utilizing information geometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the unique advantages of SVGD to the Riemannian world.
Population Matching Discrepancy and Applications in Deep Learning
In this paper, we propose population matching discrepancy (PMD) for estimating the distribution distance based on samples, as well as an algorithm to learn the parameters of the distributions using PMD as an objective.
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples
Automatically writing stylized Chinese characters is an attractive yet challenging task due to its wide applicabilities.
Learning Random Fourier Features by Hybrid Constrained Optimization
Since the algorithm consumes a major computation cost in the testing phase, we propose a novel teacher-learner framework of learning computation-efficient kernel embeddings from specific data.
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
First, with HGD as a defense, the target model is more robust to either white-box or black-box adversarial attacks.
A Hierarchical Recurrent Neural Network for Symbolic Melody Generation
In this paper, we present a hierarchical recurrent neural network for melody generation, which consists of three Long-Short-Term-Memory (LSTM) subnetworks working in a coarse-to-fine manner along time.
Sound Multimedia
Stochastic Training of Graph Convolutional Networks
Previous attempts on reducing the receptive field size by subsampling neighbors do not have any convergence guarantee, and their receptive field size per node is still in the order of hundreds.
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process
Crowd behavior understanding is crucial yet challenging across a wide range of applications, since crowd behavior is inherently determined by a sequential decision-making process based on various factors, such as the pedestrians' own destinations, interaction with nearby pedestrians and anticipation of upcoming events.
Max-Mahalanobis Linear Discriminant Analysis Networks
In this paper, we show that a properly designed classifier can improve robustness to adversarial attacks and lead to better prediction results.
Sparse Adversarial Perturbations for Videos
Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored.
Stochastic Gradient Hamiltonian Monte Carlo with Variance Reduction for Bayesian Inference
In this paper, we apply the variance reduction tricks on Hamiltonian Monte Carlo and achieve better theoretical convergence results compared with the variance-reduced Langevin dynamics.
Adversarial Attacks and Defences Competition
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them.
Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems
We also propose an extension to train pLSI and a method to prune the network to obey the limited fan-in of some NMSs.
Graphical Generative Adversarial Networks
We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data.
Textbook Question Answering Under Instructor Guidance With Memory Networks
The machine thus performs as an instructor to extract the essay-level contradictions as the Guidance.
Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various applications.
A Spectral Approach to Gradient Estimation for Implicit Distributions
Recently there have been increasing interests in learning and inference with implicit distributions (i. e., distributions without tractable densities).
Understanding and Accelerating Particle-Based Variational Inference
Particle-based variational inference methods (ParVIs) have gained attention in the Bayesian inference literature, for their capacity to yield flexible and accurate approximations.
Learning Implicit Generative Models by Teaching Explicit Ones
Implicit generative models are difficult to train as no explicit density functions are defined.
Deep Structured Generative Models
Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures.
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention.
Analyzing the Noise Robustness of Deep Neural Networks
Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples.
Lazy-CFR: fast and near optimal regret minimization for extensive games with imperfect information
In this paper, we present a novel technique, lazy update, which can avoid traversing the whole game tree in CFR, as well as a novel analysis on the regret of CFR with lazy update.
Semi-crowdsourced Clustering with Deep Generative Models
We propose a new approach that includes a deep generative model (DGM) to characterize low-level features of the data, and a statistical relational model for noisy pairwise annotations on its subset.
Composite Binary Decomposition Networks
Binary neural networks have great resource and computing efficiency, while suffer from long training procedure and non-negligible accuracy drops, when comparing to the full-precision counterparts.
Stochastic Expectation Maximization with Variance Reduction
However, sEM has a slower asymptotic convergence rate than batch EM, and requires a decreasing sequence of step sizes, which is difficult to tune.
To Relieve Your Headache of Training an MRF, Take AdVIL
We propose a black-box algorithm called {\it Adversarial Variational Inference and Learning} (AdVIL) to perform inference and learning on a general Markov random field (MRF).
Improving Adversarial Robustness via Promoting Ensemble Diversity
Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial attacks.
Towards Interpretable Deep Neural Networks by Leveraging Adversarial Examples
3) We propose to improve the consistency of neurons on adversarial example subset by an adversarial training algorithm with a consistent loss.
Reward Shaping via Meta-Learning
Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL).
Understanding MCMC Dynamics as Flows on the Wasserstein Space
It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs).
Batch Virtual Adversarial Training for Graph Convolutional Networks
We present batch virtual adversarial training (BVAT), a novel regularization method for graph convolutional networks (GCNs).
Function Space Particle Optimization for Bayesian Neural Networks
While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature.
Cluster Alignment with a Teacher for Unsupervised Domain Adaptation
Deep learning methods have shown promise in unsupervised domain adaptation, which aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution.
Ranked #3 on
Domain Adaptation
on SVNH-to-MNIST
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
In this paper, we propose a translation-invariant attack method to generate more transferable adversarial examples against the defense models.
Efficient Decision-based Black-box Adversarial Attacks on Face Recognition
In this paper, we evaluate the robustness of state-of-the-art face recognition models in the decision-based black-box attack setting, where the attackers have no access to the model parameters and gradients, but can only acquire hard-label predictions by sending queries to the target model.
$A^*$ sampling with probability matching
We provide insights into the relationship between $A^*$ sampling and probability matching by analyzing a nontrivial special case in which the state space is partitioned into two subsets.
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning
We can solve these tasks by first building models for other agents and then finding the optimal policy with these models.
Boosting Generative Models by Leveraging Cascaded Meta-Models
Besides, our framework can be extended to semi-supervised boosting, where the boosted model learns a joint distribution of data and labels.
Countering Noisy Labels By Learning From Auxiliary Clean Labels
We consider the learning from noisy labels (NL) problem which emerges in many real-world applications.
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e. g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models.
Scalable Training of Inference Networks for Gaussian-Process Models
Inference in Gaussian process (GP) models is computationally challenging for large data, and often difficult to approximate with a small number of inducing points.
Multi-objects Generation with Amortized Structural Regularization
Deep generative models (DGMs) have shown promise in image generation.
Improving Black-box Adversarial Attacks with a Transfer-based Prior
We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients.
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking
To the best of our knowledge, DashNet is the first framework that can integrate and process ANNs and SNNs in a hybrid paradigm, which provides a novel solution to achieve both effectiveness and efficiency for high-speed object tracking.
Cross-Lingual Contextual Word Embeddings Mapping With Multi-Sense Words In Mind
Recent work in cross-lingual contextual word embedding learning cannot handle multi-sense words well.
A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels
Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance.
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
Our experiments on CIFAR-10 and CIFAR-100 demonstrate that MI can further improve the adversarial robustness for the models trained by mixup and its variants.
Training Interpretable Convolutional Neural Networks towards Class-specific Filters
Convolutional neural networks (CNNs) have often been treated as “black-box” and successfully used in a range of tasks.
Deep Bayesian Structure Networks
Bayesian neural networks (BNNs) introduce uncertainty estimation to deep networks by performing Bayesian inference on network weights.
Understanding and Stabilizing GANs' Training Dynamics with Control Theory
There are existing efforts that model the training dynamics of GANs in the parameter space but the analysis cannot directly motivate practically effective stabilizing methods.
Ranked #37 on
Image Generation
on CIFAR-10
(Inception score metric)
Generative Well-intentioned Networks
We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers.
Measuring Uncertainty through Bayesian Learning of Deep Neural Network Structure
Bayesian neural networks (BNNs) augment deep networks with uncertainty quantification by Bayesian treatment of the network weights.
Design and Interpretation of Universal Adversarial Patches in Face Detection
We consider universal adversarial patches for faces -- small visual elements whose addition to a face image reliably destroys the performance of face detectors.
The Search for Sparse, Robust Neural Networks
Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters when compared to their dense counterparts.
Triple Generative Adversarial Networks
It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN).
Benchmarking Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which becomes one of the most important research problems in the development of deep learning.
SVQN: Sequential Variational Soft Q-Learning Networks
Partially Observable Markov Decision Processes (POMDPs) are popular and flexible models for real-world decision-making applications that demand the information from past observations to make optimal decisions.
Unifying Graph Convolutional Networks as Matrix Factorization
In recent years, substantial progress has been made on graph convolutional networks (GCN).
Analyzing the Noise Robustness of Deep Neural Networks
The key is to compare and analyze the datapaths of both the adversarial and normal examples.
Adversarial Distributional Training for Robust Deep Learning
Adversarial training (AT) is among the most effective techniques to improve model robustness by augmenting training data with adversarial examples.
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative.
Boosting Adversarial Training with Hypersphere Embedding
Adversarial training (AT) is one of the most effective defenses against adversarial attacks for deep learning models.
VFlow: More Expressive Generative Flows with Variational Data Augmentation
Generative flows are promising tractable models for density modeling that define probabilistic distributions with invertible transformations.
Ranked #30 on
Image Generation
on CIFAR-10
(bits/dimension metric)
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
Continual acquisition of novel experience without interfering previously learned knowledge, i. e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting.
Towards Face Encryption by Generating Adversarial Identity Masks
As billions of personal data being shared through social media and network, the data privacy and security have drawn an increasing attention.
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
Standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest.
Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
Posterior sampling for reinforcement learning (PSRL) is a useful framework for making decisions in an unknown environment.
Lazy-CFR: fast and near-optimal regret minimization for extensive games with imperfect information
In this paper, we present Lazy-CFR, a CFR algorithm that adopts a lazy update strategy to avoid traversing the whole game tree in each round.
Nonparametric Score Estimators
Estimating the score, i. e., the gradient of log density function, from a set of samples generated by an unknown distribution is a fundamental task in inference and learning of probabilistic models that involve flexible yet intractable densities.
Brain-inspired global-local learning incorporated with neuromorphic computing
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
Dynamic Window-level Granger Causality of Multi-channel Time Series
In this paper, we present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data.
Calibrated Reliable Regression using Maximum Mean Discrepancy
Accurate quantification of uncertainty is crucial for real-world applications of machine learning.
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities.
Efficient Learning of Generative Models via Finite-Difference Score Matching
Several machine learning applications involve the optimization of higher-order derivatives (e. g., gradients of gradients) during training, which can be expensive in respect to memory and computation even with automatic differentiation.
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Based on large-scale evaluations, the commercial FR API services fail to exhibit acceptable performance on robustness evaluation, and we also draw several important conclusions for understanding the adversarial robustness of FR models and providing insights for the design of robust FR models.
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters
Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.
Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks
In each iteration, SWITCH first tries to update the current sample along the direction of $\hat{\mathbf{g}}$, but considers switching to its opposite direction $-\hat{\mathbf{g}}$ if our algorithm detects that it does not increase the value of the attack objective function.
BayesAdapter: Being Bayesian, Inexpensively and Robustly, via Bayesian Fine-tuning
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are falling far behind in terms of adoption in real-world applications compared with normal NNs, mainly due to their limited scalability in training, and low fidelity in their uncertainty estimates.
Bag of Tricks for Adversarial Training
Adversarial training (AT) is one of the most effective strategies for promoting model robustness.
BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, mainly due to persistent concerns on their scalability, accessibility, and reliability.
Bi-level Score Matching for Learning Energy-based Latent Variable Models
This paper presents a bi-level score matching (BiSM) method to learn EBLVMs with general structures by reformulating SM as a bi-level optimization problem.
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable.
Further Analysis of Outlier Detection with Deep Generative Models
The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling.
Multi-label classification: do Hamming loss and subset accuracy really conflict with each other?
On the other hand, when directly optimizing SA with its surrogate loss, it has learning guarantees that depend on $O(\sqrt{c})$ for both HL and SA measures.
Understanding and Exploring the Network with Stochastic Architectures
In this work, we decouple the training of a network with stochastic architectures (NSA) from NAS and provide a first systematical investigation on it as a stand-alone problem.
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.
Series Saliency: Temporal Interpretation for Multivariate Time Series Forecasting
Though deep learning methods have recently been developed to give superior forecasting results, it is crucial to improve the interpretability of time series models.
Adaptive N-step Bootstrapping with Off-policy Data
In this work, we point out that the optimal value of n actually differs on each data point, while the fixed value n is a rough average of them.
Ranking Cost: One-Stage Circuit Routing by Directly Optimizing Global Objective Function
In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths to achieve the global object.
ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning
Continual learning usually assumes the incoming data are fully labeled, which might not be applicable in real applications.
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.
Cognitive Visual Inspection Service for LCD Manufacturing Industry
This paper discloses a novel visual inspection system for liquid crystal display (LCD), which is currently a dominant type in the FPD industry.
High-fidelity Prediction of Megapixel Longitudinal Phase-space Images of Electron Beams using Encoder-Decoder Neural Networks
We also show the scalability and interpretability of the model by sharing the same decoder with more than one encoder used for different setups of the photoinjector, and propose a pragmatic way to model a facility with various diagnostics and working points.
Rethinking Natural Adversarial Examples for Classification Models
A detection model based on the classification model EfficientNet-B7 achieved a top-1 accuracy of 53. 95%, surpassing previous state-of-the-art classification models trained on ImageNet, suggesting that accurate localization information can significantly boost the performance of classification models on ImageNet-A.
DNN2LR: Automatic Feature Crossing for Credit Scoring
Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR.
Implicit Normalizing Flows
Through theoretical analysis, we show that the function space of ImpFlow is strictly richer than that of ResFlows.
Black-box Detection of Backdoor Attacks with Limited Information and Data
Although deep neural networks (DNNs) have made rapid progress in recent years, they are vulnerable in adversarial environments.
LiBRe: A Practical Bayesian Approach to Adversarial Detection
Despite their appealing flexibility, deep neural networks (DNNs) are vulnerable against adversarial examples.
Accurate and Reliable Forecasting using Stochastic Differential Equations
It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments.
Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.
Few-shot Continual Learning: a Brain-inspired Approach
Our solution is based on the observation that continual learning of a task sequence inevitably interferes few-shot generalization, which makes it highly nontrivial to extend few-shot learning strategies to continual learning scenarios.
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering
We present Mixture of Contrastive Experts (MiCE), a unified probabilistic clustering framework that simultaneously exploits the discriminative representations learned by contrastive learning and the semantic structures captured by a latent mixture model.
Ranked #9 on
Image Clustering
on Imagenet-dog-15
Automated Decision-based Adversarial Attacks
Deep learning models are vulnerable to adversarial examples, which can fool a target classifier by imposing imperceptible perturbations onto natural examples.
Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
Our results show that learning algorithms with the consistent univariate loss have an error bound of $O(c)$ ($c$ is the number of labels), while algorithms with the inconsistent pairwise loss depend on $O(\sqrt{c})$ as shown in prior work.
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking.
Unsupervised Part Segmentation through Disentangling Appearance and Shape
We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results.
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.
Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations
Especially, the discovery of PDEs with highly nonlinear coefficients from low-quality data remains largely under-addressed.
Exploring Memorization in Adversarial Training
In this paper, we explore the memorization effect in adversarial training (AT) for promoting a deeper understanding of model capacity, convergence, generalization, and especially robust overfitting of the adversarially trained models.
Stability and Generalization of Bilevel Programming in Hyperparameter Optimization
Our results can explain some mysterious behaviours of the bilevel programming in practice, for instance, overfitting to the validation set.
Understanding Softmax Confidence and Uncertainty
It is often remarked that neural networks fail to increase their uncertainty when predicting on data far from the training distribution.
Nonlinear Hawkes Processes in Time-Varying System
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena.
Pre-Trained Models: Past, Present and Future
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI).
Quasi-Bayesian Dual Instrumental Variable Regression
Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking.
Strategically-timed State-Observation Attacks on Deep Reinforcement Learning Agents
Deep reinforcement learning (DRL) policies are vulnerable to the adversarial attack on their observations, which may mislead real-world RL agents to catastrophic failures.
Accumulative Poisoning Attacks on Real-time Data
Collecting training data from untrusted sources exposes machine learning services to poisoning adversaries, who maliciously manipulate training data to degrade the model accuracy.
Towards Safe Reinforcement Learning via Constraining Conditional Value at Risk
Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty caused by stochastic policies and environment variability.
PVGNet: A Bottom-Up One-Stage 3D Object Detector With Integrated Multi-Level Features
Quantization-based methods are widely used in LiDAR points 3D object detection for its efficiency in extracting context information.
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.
Improving Transferability of Adversarial Patches on Face Recognition with Generative Models
However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models.
Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning
In this paper, we provide a framework to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space.
Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting.
On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms
Finally, our theoretical results are confirmed by experiments on several numerical benchmarks as well as adversarial attacks.
