Search Results for author: Fabrizio Silvestri

Found 21 papers, 9 papers with code

Detecting and Understanding Harmful Memes: A Survey

1 code implementation9 May 2022 Shivam Sharma, Firoj Alam, Md. Shad Akhtar, Dimitar Dimitrov, Giovanni Da San Martino, Hamed Firooz, Alon Halevy, Fabrizio Silvestri, Preslav Nakov, Tanmoy Chakraborty

One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.

ReLACE: Reinforcement Learning Agent for Counterfactual Explanations of Arbitrary Predictive Models

no code implementations22 Oct 2021 Ziheng Chen, Fabrizio Silvestri, Gabriele Tolomei, He Zhu, Jia Wang, Hongshik Ahn

However, existing CF generation strategies either exploit the internals of specific models (e. g., random forests or neural networks), or depend on each sample's neighborhood, which makes them hard to be generalized for more complex models and inefficient for larger datasets.

Decision Making reinforcement-learning

Detecting Propaganda Techniques in Memes

1 code implementation ACL 2021 Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino

We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.

Database Reasoning Over Text

1 code implementation ACL 2021 James Thorne, Majid Yazdani, Marzieh Saeidi, Fabrizio Silvestri, Sebastian Riedel, Alon Halevy

Neural models have shown impressive performance gains in answering queries from natural language text.

SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images

1 code implementation SEMEVAL 2021 Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, Giovanni Da San Martino

We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.

CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN

no code implementations1 Apr 2021 Giorgio Barnabò, Giovanni Trappolini, Lorenzo Lastilla, Cesare Campagnano, Angela Fan, Fabio Petroni, Fabrizio Silvestri

The two main research threads in computer-based music generation are: the construction of autonomous music-making systems, and the design of computer-based environments to assist musicians.

Image-to-Image Translation Music Generation +2

CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks

1 code implementation5 Feb 2021 Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, Fabrizio Silvestri

In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes.

Neural Databases

no code implementations14 Oct 2020 James Thorne, Majid Yazdani, Marzieh Saeidi, Fabrizio Silvestri, Sebastian Riedel, Alon Halevy

We describe NeuralDB, a database system with no pre-defined schema, in which updates and queries are given in natural language.

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

3 code implementations20 Jun 2017 Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, Mounia Lalmas

There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model.

Feature Engineering

Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising

no code implementations7 Jul 2016 Mihajlo Grbovic, Nemanja Djuric, Vladan Radosavljevic, Fabrizio Silvestri, Ricardo Baeza-Yates, Andrew Feng, Erik Ordentlich, Lee Yang, Gavin Owens

For this reason search engines often provide a service of advanced matching, which automatically finds additional relevant queries for advertisers to bid on.

Neighborhood Sensitive Mapping for Zero-Shot Classification using Independently Learned Semantic Embeddings

no code implementations26 May 2016 Gaurav Singh, Fabrizio Silvestri, John Shawe-Taylor

In a traditional setting, classifiers are trained to approximate a target function $f:X \rightarrow Y$ where at least a sample for each $y \in Y$ is presented to the training algorithm.

General Classification Zero-Shot Learning

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