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Found 247 papers, 112 papers with code
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples - a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify.
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
important object pixels in an image) to justify its classification and analogously what should be minimally and necessarily \emph{absent} (viz.
Leveraging Latent Features for Local Explanations
As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
Efficient Neural Network Robustness Certification with General Activation Functions
Finding minimum distortion of adversarial examples and thus certifying robustness in neural network classifiers for given data points is known to be a challenging problem.
Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning.
DBA: Distributed Backdoor Attacks against Federated Learning
Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data.
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples.
Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases
When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack).
Vision Transformers are Robust Learners
Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy.
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness.
Is Robustness the Cost of Accuracy? -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
The prediction accuracy has been the long-lasting and sole standard for comparing the performance of different image classification models, including the ImageNet competition.
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.
Ranked #1 on
Keyword Spotting
on Google Speech Commands
(10-keyword Speech Commands dataset metric)
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Word Mover's Embedding: From Word2Vec to Document Embedding
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings.
Principled Multilayer Network Embedding
Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship.
Social and Information Networks Physics and Society
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning.
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.
How to Backdoor Diffusion Models?
To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks.
Voice2Series: Reprogramming Acoustic Models for Time Series Classification
Learning to classify time series with limited data is a practical yet challenging problem.
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach
Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness.
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm
We apply extreme value theory on the new formal robustness guarantee and the estimated robustness is called second-order CLEVER score.
Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach
We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions.
Sign-OPT: A Query-Efficient Hard-label Adversarial Attack
We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input.
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks
Recent studies have shown that adversarial examples in state-of-the-art image classifiers trained by deep neural networks (DNN) can be easily generated when the target model is transparent to an attacker, known as the white-box setting.
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning
Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems.
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification.
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Variational Quantum Circuits for Deep Reinforcement Learning
To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep $Q$-value function for decision-making and policy-selection reinforcement learning with experience replay and target network.
On the Adversarial Robustness of Vision Transformers
Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision.
Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning
In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks.
Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.
Understanding and Improving Visual Prompting: A Label-Mapping Perspective
As highlighted below, we show that when reprogramming an ImageNet-pretrained ResNet-18 to 13 target tasks, our method outperforms baselines by a substantial margin, e. g., 7. 9% and 6. 7% accuracy improvements in transfer learning to the target Flowers102 and CIFAR100 datasets.
HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models
We make our results publicly accessible for reproducible pipelines with released pre-trained models, thus providing a new evaluation paradigm for ASR error correction with LLMs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
We show that AdvCL is able to enhance cross-task robustness transferability without loss of model accuracy and finetuning efficiency.
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples
In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities.
On the Limitation of MagNet Defense against $L_1$-based Adversarial Examples
In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerging research field in the conjunction of deep learning and security.
On the Limitation of Local Intrinsic Dimensionality for Characterizing the Subspaces of Adversarial Examples
Understanding and characterizing the subspaces of adversarial examples aid in studying the robustness of deep neural networks (DNNs) to adversarial perturbations.
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this setting.
Optimizing Molecules using Efficient Queries from Property Evaluations
Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery.
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example.
Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning
Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning.
Inference and Denoise: Causal Inference-based Neural Speech Enhancement
This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention.
Training a Resilient Q-Network against Observational Interference
Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications.
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Since the lack of robustness has become one of the main hurdles for CNNs, in this paper we ask: How to adversarially robustify a CNN-based MoE model?
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become standard.
CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks
This motivates us to propose a general and efficient framework, CNN-Cert, that is capable of certifying robustness on general convolutional neural networks.
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
Catastrophic Data Leakage in Vertical Federated Learning
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
In this paper we formulate the attacks with discrete input on a set function as an optimization task.
Adversarial T-shirt! Evading Person Detectors in A Physical World
To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts.
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system.
Reprogramming Pretrained Language Models for Antibody Sequence Infilling
Results on antibody design benchmarks show that our model on low-resourced antibody sequence dataset provides highly diverse CDR sequences, up to more than a two-fold increase of diversity over the baselines, without losing structural integrity and naturalness.
Adversarial Attack Generation Empowered by Min-Max Optimization
In this paper, we show how a general framework of min-max optimization over multiple domains can be leveraged to advance the design of different types of adversarial attacks.
Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$-ball to composite semantic perturbations, such as the combination of Hue, Saturation, Brightness, Contrast, and Rotation.
CARBEN: Composite Adversarial Robustness Benchmark
Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e. g., perturbations bounded in Lp ball.
MAML is a Noisy Contrastive Learner in Classification
Model-agnostic meta-learning (MAML) is one of the most popular and widely adopted meta-learning algorithms, achieving remarkable success in various learning problems.
Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustness
Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish.
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR).
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes; Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node-pair and a dissimilar node-pair.
Optimizing Mode Connectivity via Neuron Alignment
Yet, current curve finding algorithms do not consider the influence of symmetry in the loss surface created by model weight permutations.
Reinforcement Learning based Interconnection Routing for Adaptive Traffic Optimization
In this work, we demonstrate the promise of applying reinforcement learning (RL) to optimize NoC runtime performance.
Proper Network Interpretability Helps Adversarial Robustness in Classification
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks.
AutoVP: An Automated Visual Prompting Framework and Benchmark
To bridge this gap, we propose AutoVP, an end-to-end expandable framework for automating VP design choices, along with 12 downstream image-classification tasks that can serve as a holistic VP-performance benchmark.
Scalable Spectral Clustering Using Random Binning Features
Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.
Ranked #5 on
Image/Document Clustering
on pendigits
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis for DMs.
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
As application demands for zeroth-order (gradient-free) optimization accelerate, the need for variance reduced and faster converging approaches is also intensifying.
When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation.
How Robust are Randomized Smoothing based Defenses to Data Poisoning?
Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation\cite{cohen2019certified}, MACER\cite{zhai2020macer}, and SmoothAdv\cite{salman2019provably} that achieve high certified adversarial robustness.
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
In this work, we propose Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism.
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average cases but also in worst cases or adverse situations.
Visual Prompting for Adversarial Robustness
In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time.
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes
To address this challenge, we introduce NeuralFuse, a novel add-on module that addresses the accuracy-energy tradeoff in low-voltage regimes by learning input transformations to generate error-resistant data representations.
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored.
Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications
The von Neumann graph entropy (VNGE) facilitates measurement of information divergence and distance between graphs in a graph sequence.
Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from that of the target.
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses
However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e. g., large drop in test accuracy.
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation.
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks
In deep learning, mixture-of-experts (MoE) activates one or few experts (sub-networks) on a per-sample or per-token basis, resulting in significant computation reduction.
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
Diffusion models (DM) have become state-of-the-art generative models because of their capability to generate high-quality images from noises without adversarial training.
On Certifying and Improving Generalization to Unseen Domains
This highlights that the performance of DG methods on a few benchmark datasets may not be representative of their performance on unseen domains in the wild.
Self-Progressing Robust Training
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems.
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance.
Theoretical Error Performance Analysis for Variational Quantum Circuit Based Functional Regression
In this work, we first put forth an end-to-end quantum neural network, TTN-VQC, which consists of a quantum tensor network based on a tensor-train network (TTN) for dimensionality reduction and a VQC for functional regression.
Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
Towards Verifying Robustness of Neural Networks Against Semantic Perturbations
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task.
Don't Forget to Sign the Gradients!
Engineering a top-notch deep learning model is an expensive procedure that involves collecting data, hiring human resources with expertise in machine learning, and providing high computational resources.
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines.
NCTV: Neural Clamping Toolkit and Visualization for Neural Network Calibration
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks.
Better May Not Be Fairer: A Study on Subgroup Discrepancy in Image Classification
In this paper, we provide 20, 000 non-trivial human annotations on popular datasets as a first step to bridge gap to studying how natural semantic spurious features affect image classification, as prior works often study datasets mixing low-level features due to limitations in accessing realistic datasets.
Convex Bounds on the Softmax Function with Applications to Robustness Verification
The softmax function is a ubiquitous component at the output of neural networks and increasingly in intermediate layers as well.
Exploring the Benefits of Visual Prompting in Differential Privacy
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model.
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers
For machine learning with tabular data, Table Transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to ensure data privacy.
Controllability, Multiplexing, and Transfer Learning in Networks using Evolutionary Learning
Next, (2) the learned networks are technically controllable as only a small number of driver nodes are required to move the system to a new state.
Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent
Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability.
SChME at SemEval-2020 Task 1: A Model Ensemble for Detecting Lexical Semantic Change
Our results show evidence that the number of landmarks used for alignment has a directimpact on the predictive performance of the model.
Benchmarking Machine Learning Robustness in Covid-19 Genome Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and counting.
An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization
Molecule optimization is an important problem in chemical discovery and has been approached using many techniques, including generative modeling, reinforcement learning, genetic algorithms, and much more.
On the Supermodularity of Active Graph-based Semi-supervised Learning with Stieltjes Matrix Regularization
Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks.
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model.
Bypassing Feature Squeezing by Increasing Adversary Strength
Feature Squeezing is a recently proposed defense method which reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample.
Zeroth-Order Online Alternating Direction Method of Multipliers: Convergence Analysis and Applications
In this paper, we design and analyze a new zeroth-order online algorithm, namely, the zeroth-order online alternating direction method of multipliers (ZOO-ADMM), which enjoys dual advantages of being gradient-free operation and employing the ADMM to accommodate complex structured regularizers.
Accelerated Distributed Dual Averaging over Evolving Networks of Growing Connectivity
Our analysis reveals the connection between network topology design and the convergence rate of DDA, and provides quantitative evaluation of DDA acceleration for distributed optimization that is absent in the existing analysis.
Incremental Eigenpair Computation for Graph Laplacian Matrices: Theory and Applications
The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used in spectral clustering and community detection.
Revisiting Spectral Graph Clustering with Generative Community Models
The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs).
Learning Graph Topological Features via GAN
Inspired by the generation power of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs.
Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks.
Can GAN Learn Topological Features of a Graph?
This paper is first-line research expanding GANs into graph topology analysis.
Bias-Variance Tradeoff of Graph Laplacian Regularizer
This paper presents a bias-variance tradeoff of graph Laplacian regularizer, which is widely used in graph signal processing and semi-supervised learning tasks.
FEAST: An Automated Feature Selection Framework for Compilation Tasks
In this paper, we propose FEAture Selection for compilation Tasks (FEAST), an efficient and automated framework for determining the most relevant and representative features from a feature pool.
Multilayer Spectral Graph Clustering via Convex Layer Aggregation
Multilayer graphs are commonly used for representing different relations between entities and handling heterogeneous data processing tasks.
Incremental Method for Spectral Clustering of Increasing Orders
The smallest eigenvalues and the associated eigenvectors (i. e., eigenpairs) of a graph Laplacian matrix have been widely used for spectral clustering and community detection.
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection
Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful.
Supervised Collective Classification for Crowdsourcing
Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e. g., labels of an item) provided by labelers.
When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications.
Phase Transitions in Spectral Community Detection of Large Noisy Networks
We prove phase transitions in community detectability as a function of the external edge connection probability and the noisy edge presence probability under a general network model where two arbitrarily connected communities are interconnected by random external edges.
On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces
Recent studies have found that deep learning systems are vulnerable to adversarial examples; e. g., visually unrecognizable adversarial images can easily be crafted to result in misclassification.
Is Ordered Weighted $\ell_1$ Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR
Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks.
Characterizing Audio Adversarial Examples Using Temporal Dependency
In particular, our results reveal the importance of using the temporal dependency in audio data to gain discriminate power against adversarial examples.
PROVEN: Certifying Robustness of Neural Networks with a Probabilistic Approach
With deep neural networks providing state-of-the-art machine learning models for numerous machine learning tasks, quantifying the robustness of these models has become an important area of research.
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach
We study the problem of attacking machine learning models in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions.
signSGD via Zeroth-Order Oracle
Our study shows that ZO signSGD requires $\sqrt{d}$ times more iterations than signSGD, leading to a convergence rate of $O(\sqrt{d}/\sqrt{T})$ under mild conditions, where $d$ is the number of optimization variables, and $T$ is the number of iterations.
Toward A Neuro-inspired Creative Decoder
Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain.
Model Agnostic Contrastive Explanations for Structured Data
Recently, a method [7] was proposed to generate contrastive explanations for differentiable models such as deep neural networks, where one has complete access to the model.
hpGAT: High-order Proximity Informed Graph Attention Network
Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data.
Ranked #39 on
Node Classification
on Citeseer
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing
Moreover, the same classifier yields the lack of a trade-off with respect to ideal distributions while yielding a trade-off when accuracy is measured with respect to the given (possibly biased) dataset.
CAT: Customized Adversarial Training for Improved Robustness
Adversarial training has become one of the most effective methods for improving robustness of neural networks.
Block Switching: A Stochastic Approach for Deep Learning Security
Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models.
AdvMS: A Multi-source Multi-cost Defense Against Adversarial Attacks
Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars.
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e. g., autonomous navigation and continuous robot arm control.)
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner.
Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks.
Hidden Cost of Randomized Smoothing
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public.
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
A Primer on Zeroth-Order Optimization in Signal Processing and Machine Learning
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications.
A Dynamical Systems Approach for Convergence of the Bayesian EM Algorithm
Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorithms and its applications.
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.
Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources
Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data.
BIG-bench Machine Learning
Diabetic Retinopathy Detection
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ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics
Empowered by the disentangled latent space learning, the extrinsic latent embedding is successfully used for classification or property prediction of different drugs bound to a specific protein.
CAFE: Catastrophic Data Leakage in Federated Learning
In this paper, we revisit this defense premise and propose an advanced data leakage attack to efficiently recover batch data from the shared aggregated gradients.
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks
Moreover, as the algorithm for training a sparse neural network is specified as (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned model weights in the hidden layer.
Higher-Order Certification for Randomized Smoothing
We also provide a framework that generalizes the calculation for certification using higher-order information.
Reprogramming Language Models for Molecular Representation Learning
Recent advancements in transfer learning have made it a promising approach for domain adaptation via transfer of learned representations.
Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework
In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations.
Robust Text CAPTCHAs Using Adversarial Examples
At the second stage, we design and apply a highly transferable adversarial attack for text CAPTCHAs to better obstruct CAPTCHA solvers.
Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records
With the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems.
Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
Fake it Till You Make it: Self-Supervised Semantic Shifts for Monolingual Word Embedding Tasks
The goal of LSC is to characterize and quantify language variations with respect to word meaning, to measure how distinct two language sources are (that is, people or language models).
Fast Training of Provably Robust Neural Networks by SingleProp
Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees.
Meta Federated Learning
Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks.
Non-Singular Adversarial Robustness of Neural Networks
In this paper, we formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
Domain Adaptation for Learning Generator from Paired Few-Shot Data
We propose a Paired Few-shot GAN (PFS-GAN) model for learning generators with sufficient source data and a few target data.
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples
It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors.
Towards creativity characterization of generative models via group-based subset scanning
We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models.
Gi and Pal Scores: Deep Neural Network Generalization Statistics
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks.
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained.
High-Robustness, Low-Transferability Fingerprinting of Neural Networks
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models.
Simple Transparent Adversarial Examples
We propose two new aspects of adversarial image generation methods and evaluate them on the robustness of Google Cloud Vision API's optical character recognition service and object detection APIs deployed in real-world settings such as sightengine. com, picpurify. com, Google Cloud Vision API, and Microsoft Azure's Computer Vision API.
Predicting Deep Neural Network Generalization with Perturbation Response Curves
However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization.
Real-World Adversarial Examples involving Makeup Application
The Cycle-GAN is used to generate adversarial makeup, and the architecture of the victimized classifier is VGG 16.
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
QTN-VQC: An End-to-End Learning framework for Quantum Neural Networks
The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks.
Auto-Transfer: Learning to Route Transferable Representations
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications.
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks
It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors.
Benchmarking Machine Learning Robustness in Covid-19 Spike Sequence Classification
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 viral genome --- millions of sequences and counting.
Certified Robustness for Free in Differentially Private Federated Learning
Federated learning (FL) provides an efficient training paradigm to jointly train a global model leveraging data from distributed users.
Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
How unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer.
Robust Event Classification Using Imperfect Real-world PMU Data
Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e. g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers.
How and When Adversarial Robustness Transfers in Knowledge Distillation?
Our comprehensive analysis shows several novel insights that (1) With KDIGA, students can preserve or even exceed the adversarial robustness of the teacher model, even when their models have fundamentally different architectures; (2) KDIGA enables robustness to transfer to pre-trained students, such as KD from an adversarially trained ResNet to a pre-trained ViT, without loss of clean accuracy; and (3) Our derived local linearity bounds for characterizing adversarial robustness in KD are consistent with the empirical results.
Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
Meta Adversarial Perturbations
A plethora of attack methods have been proposed to generate adversarial examples, among which the iterative methods have been demonstrated the ability to find a strong attack.
Best Arm Identification in Contaminated Stochastic Bandits
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
Towards A Unified Min-Max Framework for Adversarial Exploration and Robustness
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.
Efficient Training of Robust and Verifiable Neural Networks
We propose that many common certified defenses can be viewed under a unified framework of regularization.
SPROUT: Self-Progressing Robust Training
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy and reliable machine learning systems.
Visual Interpretability Alone Helps Adversarial Robustness
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability, and interpretability is itself susceptible to adversarial attacks.
Optimizing Loss Landscape Connectivity via Neuron Alignment
Empirically, this initialization is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes.
On the Effectiveness of Poisoning against Unsupervised Domain Adaptation
However, the limited effect of poisoning is restricted to the setting where training and test data are from the same distribution.
Generalizing Adversarial Training to Composite Semantic Perturbations
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$ norm to composite semantic perturbations, such as Hue, Saturation, Brightness, Contrast, and Rotation.
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications.
Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis
Basically, given only a small set of detectors that are learned to recognize some manually annotated attributes (i. e., the seen attributes), we aim to synthesize the detectors of novel attributes in a zero-shot learning manner.
Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines
To alleviate this issue, we propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data.
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework
With the integrated framework, we achieve up to 6\% improvement on the standard accuracy and 17\% improvement on the robust accuracy.
Neural Capacitance: A New Perspective of Neural Network Selection via Edge Dynamics
To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
Auto-Transfer: Learning to Route Transferrable Representations
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications.
Holistic Adversarial Robustness of Deep Learning Models
Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability.
Towards Creativity Characterization of Generative Models via Group-based Subset Scanning
We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.
When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
