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.
A new network-based method for high-level data classification without normalization.
problem Evaluation and classification of data with hidden attribute interactions.
method A new network-building methodology based on attribute-attribute interactions, avoiding normalization.
result Improvement in accuracy of high-level classification using betweenness centrality.
New method builds complex networks from attribute interactions without normalization.
problem Improving high-level classification algorithms by capturing hidden attribute interactions.
method Proposes a new complex network building methodology based on attribute-attribute interactions, avoiding normalization.
result Demonstrates improved performance in high-level classification techniques.
Music FaderNets learns high-level musical qualities from low-level attributes.
problem Learning high-level musical qualities from limited data and subjective labels.
method Model low-level attributes through feature disentanglement and latent regularization; infer high-level features from low-level representations using GM-VAEs.
result Model successfully learns intrinsic relationships between high-level features and low-level attributes with minimal labeled data.
X-Caps improves medical diagnosis explainability by encoding visual attributes in capsules.
problem Uninterpretable predictions from deep neural networks in healthcare.
method Teaches a novel multi-task capsule network to encode high-level visual attributes and malignancy scores.
result X-Caps outperforms state-of-the-art deep dense 3D CNNs in capturing visually interpretable attributes and malignancy prediction.
Following widely used in visual recognition concept of relative attributes, the article establishes definition of the relative PCA attributes for a class of objects defined by vectors of their parameters. A new rating model (RELARM) is built using relative PCA attribute ranking functions for rating object description a…
Proposes a method for explaining black-box models with nested feature attributions.
problem Making black-box models transparent and trustworthy.
method Model-agnostic local explanation method exploiting nested feature structure and consistency property.
result Accurate and consistent HiFAs and LoFAs estimated using fewer model queries.
A framework learns interpretable models with minimal accuracy loss.
problem Tackles interpretability in deep learning models.
method Jointly learns a predictive model and its interpretation model with minimal loss of accuracy.
result Generates interpretable models with minimal loss of accuracy.
A new network-based high-level data classification method using betweenness centrality.
problem Traditional data classification techniques focus on physical features, while high-level classification considers semantic meaning.
method Proposes a network-based high-level classification technique using betweenness centrality.
result Competent classification performance in nine real datasets compared to traditional models.
Attribute-aware CF models aims at rating prediction given not only the historical rating from users to items, but also the information associated with users (e.g. age), items (e.g. price), or even ratings (e.g. rating time). This paper surveys works in the past decade developing attribute-aware CF systems, and discover…
Obtaining compact and discriminative features is one of the major challenges in many of the real-world image classification tasks such as face verification and object recognition. One possible approach is to represent input image on the basis of high-level features that carry semantic meaning which humans can understan…
This paper applies machine learning techniques to student modeling. It presents a method for discovering high-level student behaviors from a very large set of low-level traces corresponding to problem-solving actions in a learning environment. Basic actions are encoded into sets of domain-dependent attribute-value patt…
In recent years numerous advanced malware, aka advanced persistent threats (APT) are allegedly developed by nation-states. The task of attributing an APT to a specific nation-state is extremely challenging for several reasons. Each nation-state has usually more than a single cyber unit that develops such advanced malwa…
Deep learning has brought an unprecedented progress in computer vision and significant advances have been made in predicting subjective properties inherent to visual data (e.g., memorability, aesthetic quality, evoked emotions, etc.). Recently, some research works have even proposed deep learning approaches to modify i…
The study tests and optimizes fairness in credit scoring models.
problem Discrimination in credit scoring models based on protected attributes.
method Formal testing and variable identification to optimize fairness and performance.
result Guidance on monitoring and improving algorithmic fairness in credit scoring.
Deep neural networks classify nation-state malware with high accuracy.
problem Classifying and attributing advanced persistent threats (APT) from nation-states.
method Used deep neural networks to analyze malware behavior in a sandbox, learning high-level feature abstractions.
result Achieved 98.6% accuracy in classifying 1000 Chinese and Russian APTs.
New method for visualizing high-level concepts in generative models.
problem Challenges in evaluating and visualizing concepts in generative models.
method Introduces a method to compute concept saliency maps for latent representations of known or novel high-level concepts.
result Concept saliency maps highlight input features important for high-level concepts.
Captum library simplifies model interpretability for PyTorch.
problem Improving model interpretability for various PyTorch models.
method Unified, open-source library with generic attribution algorithms and metrics.
result Unified and scalable model interpretability for multimodal inputs.
EigenGAN discovers interpretable dimensions in GAN layers for semantic control.
problem Lack of explicit dimensions to control semantic attributes in GAN layers.
method EigenGAN embeds linear subspaces with orthogonal bases into each generator layer, learning eigen-dimensions corresponding to semantic attributes via adversarial training.
result EigenGAN can produce samples with continuous changes corresponding to specific semantic attributes.
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.
Unsupervised ranking faces one critical challenge in evaluation applications, that is, no ground truth is available. When PageRank and its variants show a good solution in related subjects, they are applicable only for ranking from link-structure data. In this work, we focus on unsupervised ranking from multi-attribute…
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the g…
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end tra…
The abundance of data produced daily from large variety of sources has boosted the need of novel approaches on causal inference analysis from observational data. Observational data often contain noisy or missing entries. Moreover, causal inference studies may require unobserved high-level information which needs to be …
ZSL-KG learns class representations from common sense knowledge graphs.
problem Predicting classes without labeled examples using semantic class representations.
method TrGCN, a novel transformer graph convolutional network, embeds nodes from common sense knowledge graphs in a vector space.
result ZSL-KG improves over existing methods on five out of six zero-shot benchmark datasets.
CBMs improve interpretability in RUL prediction for aircraft engines.
problem Lack of interpretability in deep learning models for asset prognostics.
method Concept Bottleneck Models (CBMs) for RUL prediction.
result CBMs achieve comparable or superior performance to black-box models while being more interpretable.
Study investigates key design choices in on-policy RL algorithms.
problem Lack of transparency in RL algorithm implementations.
method Implemented >50 design choices in a unified RL framework.
result Insights and recommendations for on-policy RL training.
We present a novel solution to the problem of simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than focusing on minimizing the simulation-reality gap, we learn a set of diverse policies that are parameterized in a way that makes them easily reusable. This diversity and p…
Predicts routing congestion in FPGA designs using machine learning.
problem Routing congestion estimation is absent or inaccurate in HLS design methods.
method Machine learning to predict routing congestion in HLS.
result Accurately estimates routing congestion with errors of 6.71% and 10.05%.
The interpretation of deep learning models is a challenge due to their size, complexity, and often opaque internal state. In addition, many systems, such as image classifiers, operate on low-level features rather than high-level concepts. To address these challenges, we introduce Concept Activation Vectors (CAVs), whic…
Paper develops a new model for dynamic graph representation learning.
problem Learning over dynamic graphs with changing topology and node attributes.
method Hierarchical variational model with latent random variables and semi-implicit variational inference.
result SI-VGRNN and VGRNN outperform existing methods in dynamic link prediction.
Hierarchical decoupling improves sample efficiency for complex robots.
problem Learning long-range behaviors on complex robots.
method Two-part policy: low-level imitation and high-level transfer, with KL regularization.
result Hierarchical transfer significantly improves zero-shot high-level transfer and stabilizes learning.
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible a…
Model criticism tool evaluates text coherence and structure in generated long-form text.
problem Evaluate the high-level structure of generated text for coherence, coreference, and topicality.
method Apply model criticism in latent space to compare real and generated data distributions.
result Transformer-based models struggle with maintaining structural coherence and coreference.
Proposes a Taylor framework to unify and analyze attribution methods.
problem Lack of a unified guideline for feature contribution assignment in machine learning models.
method Introduces a Taylor attribution framework to model the attribution problem and reformulates fourteen mainstream methods.
result Empirically validates the Taylor reformulations and reveals a positive correlation between performance and principles followed.
Unified framework for analyzing machine learning model attributions.
problem Lack of a general and theoretical framework for understanding attribution methods.
method Proposes a Taylor attribution framework to unify and analyze seven mainstream attribution methods.
result Established three principles for good attribution and empirically validated the Taylor reformulations.
We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the …
Improved deep learning models using new attribution priors and expected gradients.
problem Improving interpretability and performance of deep learning models.
method Introducing new attribution priors and expected gradients method that satisfies interpretability axioms.
result Improves model performance across various real-world tasks.
HDGI learns node representations for heterogeneous graphs.
problem Challenges in learning node representations for heterogeneous graphs.
method HDGI uses meta-path structure, graph convolution, and semantic-level attention to maximize local-global mutual information.
result HDGI outperforms state-of-the-art methods on graph-related tasks.
Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results.…
Paper uses RL for high-level character control in 3D environments.
problem Creating intelligent characters with generalizable behavior.
method Combines traditional animations, heuristics, and reinforcement learning.
result Demonstrates learning of complex behaviors in a 3D environment.
Paper tackles attribute pattern learning in high-dimensional SLAMs.
problem Learning significant attribute patterns from high-dimensional SLAMs.
method Proposes a penalized likelihood method for selecting attribute patterns.
result Establishes selection consistency in overfitted SLAMs.
Wavelet Attribution Method (WAM) improves feature attribution for deep models.
problem Inability of pixel-based heatmaps to capture data structure and variability in feature attribution.
method Wavelet domain for feature attribution, leveraging spatial and scale-localized properties of wavelet coefficients.
result WAM provides quantitatively superior explanations across audio, image, and volume modalities.
Proposes VCLANC for attributed network clustering using node and attribute embeddings.
problem Lack of mutual affinity exploitation between nodes and attributes in graph convolution.
method Dual variational auto-encoders for node and attribute embeddings, Gaussian mixture model priors, mutual distance and clustering assignment hardening losses.
result Demonstrates effectiveness on real-world attributed network datasets.
Generates missing node attributes for better graph-based tasks.
problem Missing or incomplete node attributes degrade graph-based algorithms' performance.
method Deep adversarial learning-based method (NANG) to generate node attributes.
result Generated node attributes improve node classification and link prediction.
Proposes a method to predict node attributes using network topology.
problem Predicting node attributes in graphs for various applications.
method Creates a feature map using all attributes of neighbors to predict attributes values for a node.
result Significantly improves prediction accuracy compared to baseline approaches.
Investors seek to attribute performance to various features using Shapley value method.
problem Attributing performance to different features in an investment process.
method Use Shapley value method for attribution, either exactly or approximately.
result Shapley value method provides a preferred attribution approach.
Unified analysis of removal-based feature attributions robustness.
problem Robustness of removal-based feature attributions is not well understood.
method Theoretical analysis and upper bounds derivation for removal-based feature attributions under input and model perturbations.
result Upper bounds for the difference between intact and perturbed attributions derived under various perturbation settings.