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Found 10 papers, 6 papers with code
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
Using this likelihood, we propose the Dynamic Impact Single-Event Embedding model (DISEE).
Using Sequences of Life-events to Predict Human Lives
We can also represent human lives in a way that shares this structural similarity to language.
Structural Similarities Between Language Models and Neural Response Measurements
Human language processing is also opaque, but neural response measurements can provide (noisy) recordings of activation during listening or reading, from which we can extract similar representations of words and phrases.
Dialectograms: Machine Learning Differences between Discursive Communities
Word embeddings provide an unsupervised way to understand differences in word usage between discursive communities.
Scalable Hierarchical Embeddings of Complex Networks
Graph representation learning has become important in order to understand and predict intrinsic structures in complex networks.
Recovering lost and absent information in temporal networks
The full range of activity in a temporal network is captured in its edge activity data -- time series encoding the tie strengths or on-off dynamics of each edge in the network.
A Non-negative Matrix Factorization Based Method for Quantifying Rhythms of Activity and Sleep and Chronotypes Using Mobile Phone Data
We do not observe any clear emergent categories of people based on the weights, but individuals are rather placed on a continuous spectrum according to the timings of their activities.
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
NLP tasks are often limited by scarcity of manually annotated data.
Ranked #1 on
Transfer Learning
on Amazon Review Polarity
Modeling the Temporal Nature of Human Behavior for Demographics Prediction
Mobile phone metadata is increasingly used for humanitarian purposes in developing countries as traditional data is scarce.
Link communities reveal multiscale complexity in networks
Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society.
Physics and Society Data Analysis, Statistics and Probability Quantitative Methods
