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Found 26 papers, 9 papers with code
Graph Embedding Techniques, Applications, and Performance: A Survey
Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications.
Modeling Psychotherapy Dialogues with Kernelized Hashcode Representations: A Nonparametric Information-Theoretic Approach
We also derive a novel lower bound on mutual information, used as a model-selection criterion favoring representations with better alignment between the utterances of participants in a collaborative dialogue setting, as well as higher predictability of the generated responses.
Capturing Edge Attributes via Network Embedding
Here we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations.
Social and Information Networks
DynGEM: Deep Embedding Method for Dynamic Graphs
The major advantages of DynGEM include: (1) the embedding is stable over time, (2) it can handle growing dynamic graphs, and (3) it has better running time than using static embedding methods on each snapshot of a dynamic graph.
Social and Information Networks
Discovering Signals from Web Sources to Predict Cyber Attacks
Cyber attacks are growing in frequency and severity.
Future Automation Engineering using Structural Graph Convolutional Neural Networks
The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graphs.
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning
Capturing such evolution is key to predicting the properties of unseen networks.
Ranked #5 on
Dynamic Link Prediction
on Enron Emails
DynamicGEM: A Library for Dynamic Graph Embedding Methods
DynamicGEM is an open-source Python library for learning node representations of dynamic graphs.
Pykg2vec: A Python Library for Knowledge Graph Embedding
Python library for knowledge graph embedding and representation learning.
Tracking Temporal Evolution of Graphs using Non-Timestamped Data
Datasets to study the temporal evolution of graphs are scarce.
Social and Information Networks
Benchmarks for Graph Embedding Evaluation
We use the comparisons on our 100 benchmark graphs to define GFS-score, that can be applied to any embedding method to quantify its performance.
Graph Representation Ensemble Learning
In this work, we introduce the problem of graph representation ensemble learning and provide a first of its kind framework to aggregate multiple graph embedding methods efficiently.
ArduCode: Predictive Framework for Automation Engineering
We show that machine learning can be leveraged to assist the automation engineer in classifying automation, finding similar code snippets, and reasoning about the hardware selection of sensors and actuators.
Exploiting Temporal Coherence for Multi-modal Video Categorization
Multimodal ML models can process data in multiple modalities (e. g., video, images, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding).
Cross-modal Learning for Multi-modal Video Categorization
Multi-modal machine learning (ML) models can process data in multiple modalities (e. g., video, audio, text) and are useful for video content analysis in a variety of problems (e. g., object detection, scene understanding, activity recognition).
Hierarchical Class-Based Curriculum Loss
We theoretically show that the proposed loss function is a tighter bound of 0-1 loss compared to any other loss satisfying the hierarchical constraints.
Enhancing Transformer for Video Understanding Using Gated Multi-Level Attention and Temporal Adversarial Training
GAT uses a multi-level attention gate to model the relevance of a frame based on local and global contexts.
Leveraging Local Temporal Information for Multimodal Scene Classification
In this paper, we propose a novel self-attention block that leverages both local and global temporal relationships between the video frames to obtain better contextualized representations for the individual frames.
Can't Fool Me: Adversarially Robust Transformer for Video Understanding
To address this, several techniques have been proposed to increase robustness of a model for image classification tasks.
Is the Elephant Flying? Resolving Ambiguities in Text-to-Image Generative Models
Natural language often contains ambiguities that can lead to misinterpretation and miscommunication.
"I'm fully who I am": Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation
Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life.
FLIRT: Feedback Loop In-context Red Teaming
Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation.
On the steerability of large language models toward data-driven personas
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented.
JAB: Joint Adversarial Prompting and Belief Augmentation
With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance.
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies
Our paper is the first to link LLM misgendering to tokenization and deficient neopronoun grammar, indicating that LLMs unable to correctly treat neopronouns as pronouns are more prone to misgender.
Faithful Model Evaluation for Model-Based Metrics
Statistical significance testing is used in natural language processing (NLP) to determine whether the results of a study or experiment are likely to be due to chance or if they reflect a genuine relationship.
