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Found 62 papers, 32 papers with code
Synthnet: Learning synthesizers end-to-end
Learning synthesizers and generating music in the raw audio domain is a challenging task.
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored.
Visual-Text Cross Alignment: Refining the Similarity Score in Vision-Language Models
The local visual areas are then cross-aligned with the finer descriptions by creating a similarity matrix using the pre-trained VLM.
Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting
Recent innovations in diffusion probabilistic models have paved the way for significant progress in image, text and audio generation, leading to their applications in generative time series forecasting.
DDA: Dimensionality Driven Augmentation Search for Contrastive Learning in Laparoscopic Surgery
Self-supervised learning (SSL) has potential for effective representation learning in medical imaging, but the choice of data augmentation is critical and domain-specific.
Bidirectional Adversarial Autoencoders for the design of Plasmonic Metasurfaces
An example of this is the design of photonic metasurfaces by using their photoluminescent spectrum as the input data to predict their topology.
Mitigating Challenges of the Space Environment for Onboard Artificial Intelligence: Design Overview of the Imaging Payload on SpIRIT
Artificial intelligence (AI) and autonomous edge computing in space are emerging areas of interest to augment capabilities of nanosatellites, where modern sensors generate orders of magnitude more data than can typically be transmitted to mission control.
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
We show that the model performs with high reliability and efficiency on the online CPD problem ($\sim$98\% and $\sim$97\% area under precision-recall curve respectively).
Whose Side Are You On? Investigating the Political Stance of Large Language Models
Large Language Models (LLMs) have gained significant popularity for their application in various everyday tasks such as text generation, summarization, and information retrieval.
Unlearnable Examples For Time Series
In this work, we introduce the first UE generation method to protect time series data from unauthorized training by deep learning models.
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset.
End-to-End Anti-Backdoor Learning on Images and Time Series
Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security.
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation
Experimental results demonstrate that our model can outperform existing state-of-the-art models in 5 out of 6 datasets, specifically on those with data containing both global and local properties.
PELP: Pioneer Event Log Prediction Using Sequence-to-Sequence Neural Networks
Process mining, a data-driven approach for analyzing, visualizing, and improving business processes using event logs, has emerged as a powerful technique in the field of business process management.
Distilling Cognitive Backdoor Patterns within an Image
We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks.
Backdoor Attacks on Time Series: A Generative Approach
We find that, compared to images, it can be more challenging to achieve the two goals on time series.
Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition
Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant to actions.
IGFormer: Interaction Graph Transformer for Skeleton-based Human Interaction Recognition
Human interaction recognition is very important in many applications.
Ranked #2 on
Human Interaction Recognition
on SBU / SBU-Refine
A Survey of Automated Data Augmentation Algorithms for Deep Learning-based Image Classification Tasks
To mitigate such problem, a novel direction is to automatically learn the image augmentation policies from the given dataset using Automated Data Augmentation (AutoDA) techniques.
On the Convergence and Robustness of Adversarial Training
In this paper, we propose such a criterion, namely First-Order Stationary Condition for constrained optimization (FOSC), to quantitatively evaluate the convergence quality of adversarial examples found in the inner maximization.
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
Bounding box (bbox) regression is a fundamental task in computer vision.
De Novo Molecular Generation with Stacked Adversarial Model
A second stage model then takes these features to learn properties of the molecules and refine more valid molecules.
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Specifically, we make the following key observations: 1) more parameters (higher model capacity) does not necessarily help adversarial robustness; 2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness; and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness.
FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS
In FAHGNN, we explore the influence of node features for the expressive power of GNNs and augment features by introducing common features and personal features to model information.
Semantic-Preserving Adversarial Text Attacks
In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models.
Adversarial Interaction Attacks: Fooling AI to Misinterpret Human Intentions
Understanding the actions of both humans and artificial intelligence (AI) agents is important before modern AI systems can be fully integrated into our daily life.
$\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
Bounding box (bbox) regression is a fundamental task in computer vision.
Dual Head Adversarial Training
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples/attacks, raising concerns about their reliability in safety-critical applications.
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
In this paper, we focus on image classification and propose a method to visualize and understand the class-wise knowledge (patterns) learned by DNNs under three different settings including natural, backdoor and adversarial.
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions
Understanding the actions of both humans and artificial intelligence (AI) agents is important before modern AI systems can be fully integrated into our daily life.
Unlearnable Examples: Making Personal Data Unexploitable
This paper raises the question: \emph{can data be made unlearnable for deep learning models?}
Neural Architecture Search via Combinatorial Multi-Armed Bandit
NAS can be performed via policy gradient, evolutionary algorithms, differentiable architecture search or tree-search methods.
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
We compare our approach withother approaches to the predict+optimize problem and showwe can successfully tackle some hard combinatorial problemsbetter than other predict+optimize methods.
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research.
MurTree: Optimal Classification Trees via Dynamic Programming and Search
Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
A backdoor attack installs a backdoor into the victim model by injecting a backdoor pattern into a small proportion of the training data.
Normalized Loss Functions for Deep Learning with Noisy Labels
However, in practice, simply being robust is not sufficient for a loss function to train accurate DNNs.
Ranked #30 on
Image Classification
on mini WebVision 1.0
(ImageNet Top-1 Accuracy metric)
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in adversarial robustness research.
Improving Adversarial Robustness Requires Revisiting Misclassified Examples
In this paper, we investigate the distinctive influence of misclassified and correctly classified examples on the final robustness of adversarial training.
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples.
Clean-Label Backdoor Attacks on Video Recognition Models
We propose the use of a universal adversarial trigger as the backdoor trigger to attack video recognition models, a situation where backdoor attacks are likely to be challenged by the above 4 strict conditions.
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
We find that using more gradients from the skip connections rather than the residual modules according to a decay factor, allows one to craft adversarial examples with high transferability.
Symmetric Cross Entropy for Robust Learning with Noisy Labels
In this paper, we show that DNN learning with Cross Entropy (CE) exhibits overfitting to noisy labels on some classes ("easy" classes), but more surprisingly, it also suffers from significant under learning on some other classes ("hard" classes).
Ranked #43 on
Image Classification
on Clothing1M
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
This raises safety concerns about the deployment of these systems in clinical settings.
FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs
Generative Adversarial Networks (GANs) are a powerful class of generative models.
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality.
Black-box Adversarial Attacks on Video Recognition Models
Using three benchmark video datasets, we demonstrate that V-BAD can craft both untargeted and targeted attacks to fool two state-of-the-art deep video recognition models.
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem since the individuals observed may be different at different time points, generally following an aggregate behaviour.
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs).
Ranked #39 on
Image Classification
on mini WebVision 1.0
Iterative Learning with Open-set Noisy Labels
We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions.
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction.
Online Cluster Validity Indices for Streaming Data
An important part of cluster analysis is validating the quality of computationally obtained clusters.
Providing Effective Real-time Feedback in Simulation-based Surgical Training
Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT.
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections
Our contributions are as follows; we first show that constraining the transition matrix to be unitary is a special case of an orthogonal constraint.
TopicResponse: A Marriage of Topic Modelling and Rasch Modelling for Automatic Measurement in MOOCs
This paper explores the suitability of using automatically discovered topics from MOOC discussion forums for modelling students' academic abilities.
Ground Truth Bias in External Cluster Validity Indices
For example, NCinc bias in the RI can be changed to NCdec bias by skewing the distribution of clusters in the ground truth partition.
Adjusting for Chance Clustering Comparison Measures
In particular, the Adjusted Rand Index (ARI) based on pair-counting, and the Adjusted Mutual Information (AMI) based on Shannon information theory are very popular in the clustering community.
MOOCs Meet Measurement Theory: A Topic-Modelling Approach
Such models infer latent skill levels by relating them to individuals' observed responses on a series of items such as quiz questions.
A Framework to Adjust Dependency Measure Estimates for Chance
For example: non-linear dependencies between two continuous variables can be explored with the Maximal Information Coefficient (MIC); and categorical variables that are dependent to the target class are selected using Gini gain in random forests.
Invariant backpropagation: how to train a transformation-invariant neural network
In many classification problems a classifier should be robust to small variations in the input vector.
