This paper tackles visual artifacts in GAN-generated images by manipulating input spaces.
problem Large scale visual artifacts in GAN-generated images.
method Manipulating the input space by applying small changes to noise vectors to avoid error-prone areas.
result A simple residual module significantly reduces visual artifacts with minimal cost.
VarNet learns and manipulates high-level attributes from inputs.
problem Manipulating high-level attributes of inputs.
method Generative model that learns attributes from data and can handle predefined attributes.
result VarNet can learn and manipulate relevant attributes from datasets.
New model improves neural network robustness against input manipulations.
problem Improving neural network robustness against input manipulations.
method Causal view and deep causal manipulation augmented model (deep CAMA) with data augmentation and test-time fine-tuning.
result Deep CAMA shows superior robustness against unseen manipulations compared to traditional models.
Noise added to deep Q-networks reduces adversarial attacks.
problem Vulnerability of deep reinforcement learning to policy manipulation attacks.
method Addition of noise to the parameter space of deep reinforcement learners during training.
result Noise reduces the transferability of adversarial examples.
Improved autoencoder for realistic face manipulation.
problem Face identity conservation and disentanglement in high-resolution images.
method Modified progressively growing autoencoder (PIONEER) with new normalization schemes.
result Significantly improved visual and quantitative results for face identity conservation.
Defends against strategic data manipulation in machine learning.
problem Adversaries can tamper with datasets to influence learning outcomes.
method Uses multiple learners and strategic activation to counteract attacks.
result Demonstrates effectiveness of a game-theoretic approach to defense.
Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image wit…
Paper addresses hypothesis space misspecification in learning from human demonstrations and corrections.
problem Hypothesis space misspecification in learning from human demonstrations and corrections.
method Reason explicitly about how well the robot can explain human inputs given its hypothesis space.
result Demonstrates method on a 7 DOF robot manipulator.
Diffusion models enhance robotic manipulation through probabilistic multi-modal learning.
problem Enhancing robotic manipulation through robust and multi-modal learning.
method Probabilistic diffusion models integrating imitation and reinforcement learning.
result Diffusion models improve grasp learning, trajectory planning, and data augmentation in robotics.
Manipulated explanations can be made imperceptible to the naked eye.
problem The trustworthiness and interpretability of neural networks can be compromised by manipulable explanations.
method The paper demonstrates that explanations can be altered by applying small, imperceptible input changes that do not affect the network's output.
result An upper bound on the susceptibility of explanations to manipulation has been derived, and effective mechanisms to enhance explanation robustness have been proposed.
This work proposes a RL approach to learn versatile robotic manipulation tasks.
problem Challenging manipulation tasks in robotics and vision.
method Reinforcement learning (RL) to combine primitive skills, no intermediate rewards, few demonstrations, and efficient skill learning.
result Versatile robotic manipulation in challenging settings with temporary occlusions and dynamic scene changes.
Adversarial attacks can fool algorithmic trading systems.
problem Adversarial perturbations can manipulate algorithmic trading models.
method Real-time adversarial attacks on trading algorithms using universal perturbations.
result Perturbations can fool trading algorithms at unseen data points.
Paper proposes detecting video manipulation using stream descriptors.
problem Misuse of manipulated video content.
method Binary classifiers on multimedia stream descriptors.
result Scalable approach can detect high-quality manipulations.
P3I learns holistic scene representations from a single image.
problem Inferring camera poses, object locations, and global scene structures from a single image.
method Combines search-based and gradient-based algorithms.
result P3I outperforms baselines on various image manipulation tasks.
Paper discovers and manipulates artistic styles in paintings without supervision.
problem Automatic discovery and manipulation of artistic styles in large art collections.
method Unsupervised learning using archetypal analysis on deep image representations.
result Learned dictionary of archetypal styles can be used for style interpretation and manipulation.
Recent work has shown that state-of-the-art classifiers are quite brittle, in the sense that a small adversarial change of an originally with high confidence correctly classified input leads to a wrong classification again with high confidence. This raises concerns that such classifiers are vulnerable to attacks and ca…
Adversaries manipulate wireless power allocation to reduce user rates.
problem Adversaries exploit deep learning for power control to decrease communication rates.
method Adversaries craft perturbations to inputs of a DNN to minimize power allocation.
result Adversarial attacks are highly effective and robust to uncertainties.
The method learns disentangled representations for localized image manipulations.
problem Image generating neural networks are viewed as black boxes with global effects.
method Localized ResNet Autoencoder with multiple loss functions.
result The network can transfer specific facial attributes like shape and color of eyes, hair, mouth, etc. between persons.
A new method for disentangled latent spaces in VAEs that can manipulate attributes.
problem Disentangled representation of attributes in latent spaces of VAEs.
method Attribute-based regularization loss to enforce monotonic relationships between attributes and latent codes.
result Manipulation of attributes in latent spaces post-training.
Can we manipulate multiple deep neural networks simultaneously?
problem Selective fooling of multiple machine learning systems.
method Formulated as a novel optimization problem.
result It is easy to selectively manipulate multiple MNIST classifiers simultaneously.
Trojans can be inserted into deep neural networks without altering training data.
problem Inserting malicious code into deep learning models without changing training data.
method Patches model parameters in memory to achieve desired behavior on specific inputs.
result Efficient patches can induce trojan behavior with minimal risk of detection.
Paper presents a new port-Hamiltonian model for vehicle manipulators.
problem Complex mechanical systems' energy flow and conservation.
method Derives port-Hamiltonian dynamics from Hamiltonian reduction theory.
result Establishes mathematical equivalence with existing formulations.
Defense against adversarial attacks by manipulating feature thickness.
problem Vulnerability of machine learning models to adversarial attacks.
method Feature Manipulation (FM)-Defense using a combo-variational autoencoder.
result Detection and purification of adversarial examples with high accuracy.
We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors' social networks influence the stock price dynamics and vice versa. An investor is modeled as a Gaussian fuzzy set (a fuzzy opinion) with the center and standar…
HO2 learns options from data efficiently, improving robot manipulation tasks.
problem Learning options from raw pixel inputs in 3D robot manipulation tasks.
method HO2 infers likely option choices and trains all policy components off-policy.
result HO2 outperforms existing methods on 3D robot manipulation tasks.
Simpler method for separating and manipulating latent attributes in autoencoders.
problem Separating and manipulating latent attributes in autoencoders.
method Matrix subspace projection
result Our method allows for changing selected attributes while preserving other information.
This work improves RL for complex robotic tasks by guiding exploration with task-specific goal distributions.
problem Solving long-horizon, complex sequential tasks in robotics with sparse rewards.
method Extends hindsight relabelling to task-specific goal distributions using a small set of demonstrations.
result Significantly higher overall performance on complex robotic manipulation tasks.
ANNs solve complex inverse kinematics of a Tricept parallel robot.
problem Solving inverse kinematics for a Tricept parallel robot.
method Developed kinematic equations, used ANNs (MLP and RBF) for solving.
result ANNs provided proper accuracy and speed in solving complex inverse kinematics.
Camera stickers can fool deep learning systems by manipulating the lens, achieving 49.6% misclassification rate.
problem The vulnerability of deep learning systems to physical adversarial attacks.
method Iterative procedure to update attack perturbation and threat model for physical realizability.
result Achieved 49.6% misclassification rate for targeted attacks on ImageNet classifiers.
The paper examines how social inequality affects algorithmic classification outcomes.
problem Social inequality impacts algorithmic classification outcomes.
method Adapting models of strategic manipulation to account for social inequality and subsidy effects.
result Subsidizing disadvantaged groups can paradoxically harm both groups and the learner.
Workshop reviews techniques to understand neural NLP models.
problem Understanding the inner workings of neural networks in natural language processing.
method Systematic manipulation of inputs, decoding intermediate representations, modifying architectures, and testing on simplified languages.
result Various techniques can improve explainability of neural network models.
Paper tackles multi-object reinforcement learning, improving skill extrapolation.
problem Learning robust manipulation tasks in multi-object settings.
method Introduces a linear relation network module to enhance skill generalization.
result Agents can extrapolate and generalize to any new object number, scaling linearly.
New research shows input-gradients can be manipulated without changing model's core function, challenging their use for model interpretation.
problem Current methods for model interpretability using input-gradients are flawed due to their arbitrary manipulability.
method Investigated by reinterpreting logits as unnormalized log-densities, proposing novel approximations for score-matching.
result Improving alignment between implicit density model and data distribution enhances gradient structure and explanatory power.
New method uses explicit human demonstrations to teach missing features in reward learning.
problem Reward learning methods rely on handcrafted features, limiting their ability to adapt to new or unexplained corrections.
method Introduces human input guiding the robot from states with missing features to states without, teaching the feature explicitly and integrating it into the reward function.
result Decreases sample complexity and improves generalization of the learned reward over deep IRL baseline.
TF-Coder simplifies tensor manipulation programming in TensorFlow.
problem Difficulty in programming with TensorFlow due to steep learning curve.
method Bottom-up weighted enumerative search with value-based pruning and type/value filtering.
result Solves 63 out of 70 real-world tasks within 5 minutes.
A new method improves target selection for manipulating complex systems like the brain.
problem Improper incorporation of low-variance outcomes into latent space of predictive models.
method Developed a novel objective based on supervised variational autoencoders (SVAEs) for PPCA (Probabilistic Principal Component Analysis).
result gPCR (Generative Principal Component Regression) dramatically improves target selection in manipulation compared to standard PCR and SVAEs.
This work tackles force control for contact-rich manipulation tasks with rigid robots using RL.
problem Challenges in working with real robotic hardware, especially position-controlled robots.
method Combines RL with traditional force control techniques, implementing parallel position/force control and admittance control.
result Validated methods on both simulation and real robot (UR3 e-series) for force control.
Graph neural networks are vulnerable to adversarial attacks by manipulating graph structure.
problem Vulnerability of Graph Neural Networks to adversarial attacks.
method Categorization and review of existing attacks and defenses.
result Developed a repository for empirical studies on graph adversarial attacks and defenses.
Paper analyzes robustness of non-Lipschitz networks, proving powerful adversarial attacks but offering solutions.
problem Adversarial attacks on deep networks, especially non-Lipschitz networks.
method Developed an attack model that abstracts the challenge of adversarial robustness, proving the power of such attacks and offering solutions.
result Proves powerful adversarial attacks on non-Lipschitz networks but offers solutions with abstention.
A novel RL approach learns robotic manipulation without human demonstrations.
problem Learning robotic manipulation policies efficiently and effectively.
method Introducing simulated locomotion demonstration rewards (SLDRs) to enable RL learning.
result The approach achieves higher success rates and faster learning compared to alternatives.
The paper formalizes how concepts are encoded in text-guided generative models and provides a method to manipulate them.
problem Encoding and manipulating concepts in text-guided generative models.
method Formalizing concepts as subspaces of a representation space, developing algebraic manipulation methods.
result The ability to manipulate concepts in generative models through algebraic operations on the representation.
We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers…
Researchers create a flickering attack to fool video recognition networks.
problem Adversarial manipulation of video classification networks.
method Introducing a flickering temporal perturbation to fool video classifiers.
result Achieved high fooling ratio and temporal-invariant perturbation.
Paper shows how to manipulate RL agents with sequential small perturbations.
problem Sequential attacks on reinforcement learning agents to change their behavior.
method Adversarial Transformer Network (ATN) to generate and integrate attacks.
result RL agents can be misled to optimise for adversarial rewards over time.
This research analyzes how input and output layers affect deep neural networks' resistance to adversarial attacks.
problem The vulnerability of deep neural networks to adversarial inputs, especially non-gradient based attacks.
method Analysis of three different fully connected dense network classes with manipulated input and output layers.
result Manipulating input and output layers can significantly enhance a deep neural network's robustness against adversarial attacks.
Bayesian methods improve adversarial machine learning robustness.
problem Improving machine learning systems' security against adversarial manipulation.
method Integrating Bayesian perspectives to relax unrealistic assumptions in game-theoretic models.
result Bayesian approach provides more robust inferences about opponent's beliefs and interests.
Concept bottleneck models enable concept manipulation for model interpretation.
problem Training models to directly predict labels without intermediate concepts.
method Train models to predict intermediate concepts at training time, then use these concepts to predict labels.
result Concept bottleneck models achieve competitive accuracy with standard models while enabling concept manipulation.
In this paper, we consider the streaming memory-limited matrix completion problem when the observed entries are noisy versions of a small random fraction of the original entries. We are interested in scenarios where the matrix size is very large so the matrix is very hard to store and manipulate. Here, columns of the o…