Search Results for author:
Found 21 papers, 9 papers with code
Detecting and Understanding Harmful Memes: A Survey
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
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.
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure.
Detecting Propaganda Techniques in Memes
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
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
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
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.
A Survey on Multimodal Disinformation Detection
Recent years have witnessed the proliferation of fake news, propaganda, misinformation, and disinformation online.
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
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.
Automatic Music Production Using Generative Adversarial Networks
In this work, we propose a novel framework for $\textit{automatic music arrangement from raw audio in the frequency domain}$.
Neural Databases
We describe NeuralDB, a database system with no pre-defined schema, in which updates and queries are given in natural language.
Preserving Integrity in Online Social Networks
Online social networks provide a platform for sharing information and free expression.
Concept Matching for Low-Resource Classification
We propose a model to tackle classification tasks in the presence of very little training data.
Node Masking: Making Graph Neural Networks Generalize and Scale Better
Graph Neural Networks (GNNs) have received a lot of interest in the recent times.
How Decoding Strategies Affect the Verifiability of Generated Text
A generated sentence is verifiable if it can be corroborated or disproved by Wikipedia, and we find that the verifiability of generated text strongly depends on the decoding strategy.
Soft Token Matching for Interpretable Low-Resource Classification
We propose a model to tackle classification tasks in the presence of very little training data.
Misspelling Oblivious Word Embeddings
In this paper we present a method to learn word embeddings that are resilient to misspellings.
Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking
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.
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
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
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.
