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Found 18 papers, 5 papers with code
Explainable Deep Learning: A Field Guide for the Uninitiated
The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) finally elaborates on user-oriented explanation designing and potential future directions on explainable deep learning.
Contextual Grounding of Natural Language Entities in Images
Next, the model learns the contextual representations of the text tokens and image objects through layers of high-order interaction respectively.
Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection
To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14, 000 website samples.
Visual Entailment: A Novel Task for Fine-Grained Image Understanding
We evaluate various existing VQA baselines and build a model called Explainable Visual Entailment (EVE) system to address the VE task.
Ranked #8 on
Visual Entailment
on SNLI-VE test
Visual Entailment Task for Visually-Grounded Language Learning
We introduce a new inference task - Visual Entailment (VE) - which differs from traditional Textual Entailment (TE) tasks whereby a premise is defined by an image, rather than a natural language sentence as in TE tasks.
HELOC Applicant Risk Performance Evaluation by Topological Hierarchical Decomposition
Strong regulations in the financial industry mean that any decisions based on machine learning need to be explained.
Reasoning over RDF Knowledge Bases using Deep Learning
Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field.
Deep Neural Ranking for Crowdsourced Geopolitical Event Forecasting
We develop a forecast aggregation model that integrates topical information about a question, meta-data about a pair of forecasters, and their predictions in a deep siamese neural network that decides which forecasters' predictions are more likely to be close to the correct response.
Realistic Traffic Generation for Web Robots
Web traffic generation is a classic research problem, no generator accounts for the characteristics of web robots or crawlers that are now the dominant source of traffic to a web server.
Intrinsic Point of Interest Discovery from Trajectory Data
Spatial and temporal aspects are qualities of any trajectory database, making the framework applicable to data from any domain and of any resolution.
Relating Input Concepts to Convolutional Neural Network Decisions
Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN's decision.
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains.
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached.
A Semantics-Based Measure of Emoji Similarity
This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base.
EmojiNet: An Open Service and API for Emoji Sense Discovery
This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web.
Finding Street Gang Members on Twitter
A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population.
Word Embeddings to Enhance Twitter Gang Member Profile Identification
Gang affiliates have joined the masses who use social media to share thoughts and actions publicly.
EmojiNet: Building a Machine Readable Sense Inventory for Emoji
It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date.
