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Found 33 papers, 19 papers with code
A Universal Sets-level Optimization Framework for Next Set Recommendation
Next Set Recommendation (NSRec), encompassing related tasks such as next basket recommendation and temporal sets prediction, stands as a trending research topic.
Learning k-Determinantal Point Processes for Personalized Ranking
There are many personalized ranking approaches for item recommendation from implicit feedback like Bayesian Personalized Ranking (BPR) and listwise ranking.
Sampled Transformer for Point Sets
The sparse transformer can reduce the computational complexity of the self-attention layers to $O(n)$, whilst still being a universal approximator of continuous sequence-to-sequence functions.
Determinantal Point Process Likelihoods for Sequential Recommendation
Sequential recommendation is a popular task in academic research and close to real-world application scenarios, where the goal is to predict the next action(s) of the user based on his/her previous sequence of actions.
Fair Wrapping for Black-box Predictions
We introduce a new family of techniques to post-process ("wrap") a black-box classifier in order to reduce its bias.
Factorized Fourier Neural Operators
We propose the Factorized Fourier Neural Operator (F-FNO), a learning-based approach for simulating partial differential equations (PDEs).
Interval-censored Hawkes processes
We propose the multi-impulse exogenous function - for when the exogenous events are observed as event time - and the latent homogeneous Poisson process exogenous function - for when the exogenous events are presented as interval-censored volumes.
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series
We introduce Radflow, a novel model that embodies three key ideas: a recurrent neural network to obtain node embeddings that depend on time, the aggregation of the flow of influence from neighboring nodes with multi-head attention, and the multi-layer decomposition of time series.
AttentionFlow: Visualising Influence in Networks of Time Series
The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest.
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy
Most work on multi-document summarization has focused on generic summarization of information present in each individual document set.
UNIPoint: Universally Approximating Point Processes Intensities
The proof connects the well known Stone-Weierstrass Theorem for function approximation, the uniform density of non-negative continuous functions using a transfer functions, the formulation of the parameters of a piece-wise continuous functions as a dynamic system, and a recurrent neural network implementation for capturing the dynamics.
Transform and Tell: Entity-Aware News Image Captioning
We address the first challenge by associating words in the caption with faces and objects in the image, via a multi-modal, multi-head attention mechanism.
Variation across Scales: Measurement Fidelity under Twitter Data Sampling
This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades).
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
We show that QP matches quantile functions rather than moments as in EP and has the same mean update but a smaller variance update than EP, thereby alleviating EP's tendency to over-estimate posterior variances.
Estimating Attention Flow in Online Video Networks
In this paper, we first construct the Vevo network -- a YouTube video network with 60, 740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics.
ASNets: Deep Learning for Generalised Planning
In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets).
Comparative Document Summarisation via Classification
This paper considers extractive summarisation in a comparative setting: given two or more document groups (e. g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups.
Efficient Non-parametric Bayesian Hawkes Processes
On two large-scale Twitter diffusion datasets, we show that our methods outperform the current state-of-the-art in goodness-of-fit and that the time complexity is linear in the size of the dataset.
D-PAGE: Diverse Paraphrase Generation
In this paper, we investigate the diversity aspect of paraphrase generation.
SemStyle: Learning to Generate Stylised Image Captions using Unaligned Text
We develop a model that learns to generate visually relevant styled captions from a large corpus of styled text without aligned images.
Simplifying Sentences with Sequence to Sequence Models
We simplify sentences with an attentive neural network sequence to sequence model, dubbed S4.
Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks
We find that results depend on the type of content being promoted: superusers are more successful in promoting Howto and Gaming videos, whereas the cohort of regular users are more influential for Activism videos.
#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate
We collect a large dataset of tweets during the 1st U. S. Presidential Debate in 2016 (#DebateNight) and we analyze its 1. 5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness.
Social and Information Networks
Describing Natural Images Containing Novel Objects with Knowledge Guided Assitance
Images in the wild encapsulate rich knowledge about varied abstract concepts and cannot be sufficiently described with models built only using image-caption pairs containing selected objects.
Action Schema Networks: Generalised Policies with Deep Learning
In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems.
Beyond Views: Measuring and Predicting Engagement in Online Videos
The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance.
Social and Information Networks Human-Computer Interaction
A Tutorial on Hawkes Processes for Events in Social Media
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time.
Revisiting revisits in trajectory recommendation
Trajectory recommendation is the problem of recommending a sequence of places in a city for a tourist to visit.
Learning Points and Routes to Recommend Trajectories
Information about POIs are used to learn a POI ranking model that accounts for the start and end points of tours.
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical analysis on the extracted information (knowledge completion).
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity
Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption.
Social and Information Networks
SentiCap: Generating Image Descriptions with Sentiments
We design a system to describe an image with emotions, and present a model that automatically generates captions with positive or negative sentiments.
AutoRec: Autoencoders Meet Collaborative Filtering
This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF).
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