
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
This paper demonstrates a fatal vulnerability in natural language infere...
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Modulating Scalable Gaussian Processes for Expressive Statistical Learning
For a learning task, Gaussian process (GP) is interested in learning the...
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Delving into InterImage Invariance for Unsupervised Visual Representations
Contrastive learning has recently shown immense potential in unsupervise...
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Defending Adversarial Attacks without Adversarial Attacks in Deep Reinforcement Learning
Many recent studies in deep reinforcement learning (DRL) have proposed t...
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Online Deep Clustering for Unsupervised Representation Learning
Joint clustering and feature learning methods have shown remarkable perf...
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CoCon: A SelfSupervised Approach for Controlled Text Generation
Pretrained Transformerbased language models (LMs) display remarkable na...
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Deep LatentVariable Kernel Learning
Deep kernel learning (DKL) leverages the connection between Gaussian pro...
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Heterogeneous Representation Learning: A Review
The realworld data usually exhibits heterogeneous properties such as mo...
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Jacobian Adversarially Regularized Networks for Robustness
Adversarial examples are crafted with imperceptible perturbations with t...
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What it Thinks is Important is Important: Robustness Transfers through Input Gradients
Adversarial perturbations are imperceptible changes to input pixels that...
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Poison as a Cure: Detecting Neutralizing VariableSized Backdoor Attacks in Deep Neural Networks
Deep learning models have recently shown to be vulnerable to backdoor po...
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A MultiTask Gradient Descent Method for MultiLabel Learning
Multilabel learning studies the problem where an instance is associated...
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Minimalistic Attacks: How Little it Takes to Fool a Deep Reinforcement Learning Policy
Recent studies have revealed that neural networkbased policies can be e...
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DEVDAN: Deep Evolving Denoising Autoencoder
The Denoising Autoencoder (DAE) enhances the flexibility of the data str...
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Automatic Construction of Multilayer Perceptron Network from Streaming Examples
Autonomous construction of deep neural network (DNNs) is desired for dat...
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Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Gaussian process classification (GPC) provides a flexible and powerful s...
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A Survey on Multioutput Learning
Multioutput learning aims to simultaneously predict multiple outputs gi...
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AIR5: Five Pillars of Artificial Intelligence Research
In this article, we provide and overview of what we consider to be some ...
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Towards Safer Smart Contracts: A Sequence Learning Approach to Detecting Vulnerabilities
Symbolic analysis of security exploits in smart contracts has demonstrat...
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Largescale Heteroscedastic Regression via Gaussian Process
Heteroscedastic regression which considers varying noises across input d...
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Understanding and Comparing Scalable Gaussian Process Regression for Big Data
As a nonparametric Bayesian model which produces informative predictive...
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Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams
The generative learning phase of Autoencoder (AE) and its successor Deno...
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Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer
Deep neural networks (DNN), while becoming the driving force of many nov...
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When Gaussian Process Meets Big Data: A Review of Scalable GPs
The vast quantity of information brought by big data as well as the evol...
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Generalized Robust Bayesian Committee Machine for Largescale Gaussian Process Regression
In order to scale standard Gaussian process (GP) regression to largesca...
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Addressing Expensive Multiobjective Games with Postponed Preference Articulation via Memetic Coevolution
This paper presents algorithmic and empirical contributions demonstratin...
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Evolutionary Multitasking for Singleobjective Continuous Optimization: Benchmark Problems, Performance Metric, and Baseline Results
In this report, we suggest nine test problems for multitask singleobje...
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Evolutionary Multitasking for Multiobjective Continuous Optimization: Benchmark Problems, Performance Metrics and Baseline Results
In this report, we suggest nine test problems for multitask multiobjec...
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MIMLFCN+: Multiinstance Multilabel Learning via Fully Convolutional Networks with Privileged Information
Multiinstance multilabel (MIML) learning has many interesting applicat...
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Coevolutionary multitask learning for dynamic time series prediction
Multitask learning employs shared representation of knowledge for learn...
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Genetic Transfer or Population Diversification? Deciphering the Secret Ingredients of Evolutionary Multitask Optimization
Evolutionary multitasking has recently emerged as a novel paradigm that ...
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Meme as Building Block for Evolutionary Optimization of Problem Instances
A significantly underexplored area of evolutionary optimization in the ...
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Discovering Support and Affiliated Features from Very High Dimensions
In this paper, a novel learning paradigm is presented to automatically i...
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