Search Results for author:
Found 40 papers, 20 papers with code
Point-PC: Point Cloud Completion Guided by Prior Knowledge via Causal Inference
To retrieve similar shapes from the partial input, we also apply a contrastive learning-based pre-training scheme to transfer features of incomplete shapes into the domain of complete shape features.
T2TD: Text-3D Generation Model based on Prior Knowledge Guidance
Meanwhile, to effectively integrate multi-modal prior knowledge into textual information, we adopt a novel multi-layer transformer structure to progressively fuse related shape and textual information, which can effectively compensate for the lack of structural information in the text and enhance the final performance of the 3D generation model.
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data.
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Following a fast initial breakthrough in graph based learning, Graph Neural Networks (GNNs) have reached a widespread application in many science and engineering fields, prompting the need for methods to understand their decision process.
Smooth image-to-image translations with latent space interpolations
Multi-domain image-to-image (I2I) translations can transform a source image according to the style of a target domain.
BIASeD: Bringing Irrationality into Automated System Design
Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions.
Rethinking the Learning Paradigm for Facial Expression Recognition
Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation.
Spatial Entropy as an Inductive Bias for Vision Transformers
In this work, we propose a different and complementary direction, in which a local bias is introduced using an auxiliary self-supervised task, performed jointly with standard supervised training.
Personalized Algorithmic Recourse with Preference Elicitation
It is paramount that the sequence of actions does not require too much effort for users to implement.
A Framework for Verifiable and Auditable Federated Anomaly Detection
Federated Leaning is an emerging approach to manage cooperation between a group of agents for the solution of Machine Learning tasks, with the goal of improving each agent's performance without disclosing any data.
Reprogramming FairGANs with Variational Auto-Encoders: A New Transfer Learning Model
Fairness-aware GANs (FairGANs) exploit the mechanisms of Generative Adversarial Networks (GANs) to impose fairness on the generated data, freeing them from both disparate impact and disparate treatment.
Trajectory Test-Train Overlap in Next-Location Prediction Datasets
Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo-marketing, and disease spreading.
Generating Synthetic Mobility Networks with Generative Adversarial Networks
We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks.
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Being able to provide counterfactual interventions - sequences of actions we would have had to take for a desirable outcome to happen - is essential to explain how to change an unfavourable decision by a black-box machine learning model (e. g., being denied a loan request).
ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation
The ISF manipulates the semantics of an input latent code to make the image generated from it lying in the desired visual domain.
Click to Move: Controlling Video Generation with Sparse Motion
This paper introduces Click to Move (C2M), a novel framework for video generation where the user can control the motion of the synthesized video through mouse clicks specifying simple object trajectories of the key objects in the scene.
Smoothing the Disentangled Latent Style Space for Unsupervised Image-to-Image Translation
In this paper, we propose a new training protocol based on three specific losses which help a translation network to learn a smooth and disentangled latent style space in which: 1) Both intra- and inter-domain interpolations correspond to gradual changes in the generated images and 2) The content of the source image is better preserved during the translation.
Efficient Training of Visual Transformers with Small Datasets
This task encourages the VTs to learn spatial relations within an image and makes the VT training much more robust when training data are scarce.
Detecting discriminatory risk through data annotation based on Bayesian inferences
For this reason, we propose a method of data annotation based on Bayesian statistical inference that aims to warn about the risk of discriminatory results of a given data set.
A Survey on Deep Learning for Human Mobility
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more.
Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation
Inspired by recent works on image inpainting, our proposed method leverages the semantic segmentation to model the content and structure of the image, and learn the best shape and location of the object to insert.
Retrieval Guided Unsupervised Multi-domain Image-to-Image Translation
In this paper we propose the use of an image retrieval system to assist the image-to-image translation task.
Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach
Our proposed model disentangles the image content from the visual attributes, and it learns to modify the latter using the textual description, before generating a new image from the content and the modified attribute representation.
Learning Mobility Flows from Urban Features with Spatial Interaction Models and Neural Networks
A fundamental problem of interest to policy makers, urban planners, and other stakeholders involved in urban development projects is assessing the impact of planning and construction activities on mobility flows.
Social and Information Networks Physics and Society
From code to market: Network of developers and correlated returns of cryptocurrencies
"Code is law" is the funding principle of cryptocurrencies.
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling
Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images.
Gesture-to-Gesture Translation in the Wild via Category-Independent Conditional Maps
In this work, we propose a novel GAN architecture that decouples the required annotations into a category label - that specifies the gesture type - and a simple-to-draw category-independent conditional map - that expresses the location, rotation and size of the hand gesture.
PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms
Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions.
The invisible power of fairness. How machine learning shapes democracy
In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture.
BIG-bench Machine Learning
Cultural Vocal Bursts Intensity Prediction
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The economic value of neighborhoods: Predicting real estate prices from the urban environment
In this paper, we use multiple sources of data to entangle the economic contribution of the neighborhood's characteristics such as walkability and security perception.
Computers and Society
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey.
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
This measure can be used by policy makers to evaluate the effect of policies and change them dynamically.
What your Facebook Profile Picture Reveals about your Personality
We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals.
Stigmergy-based modeling to discover urban activity patterns from positioning data
However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling.
RAMBLE ON: Tracing Movements of Popular Historical Figures
We present RAMBLE ON, an application integrating a pipeline for frame-based information extraction and an interface to track and display movement trajectories.
Are Safer Looking Neighborhoods More Lively? A Multimodal Investigation into Urban Life
But in a world where the preference for safe looking neighborhoods is small, the connection between the perception of safety and liveliness will be either weak or nonexistent.
Computers and Society Social and Information Networks Physics and Society
The Death and Life of Great Italian Cities: A Mobile Phone Data Perspective
This is mainly because it is hard to collect data about "city life".
Computers and Society Social and Information Networks Physics and Society
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social settings (e. g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels.
Generalized Compression Dictionary Distance as Universal Similarity Measure
We present a new similarity measure based on information theoretic measures which is superior than Normalized Compression Distance for clustering problems and inherits the useful properties of conditional Kolmogorov complexity.
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits
Research has proven that stress reduces quality of life and causes many diseases.
