Search Results for author:
Found 30 papers, 7 papers with code
Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives
Training of these TGNNs is enumerated by uniform random sampling based unsupervised loss.
Navigating the Structured What-If Spaces: Counterfactual Generation via Structured Diffusion
Generating counterfactual explanations is one of the most effective approaches for uncovering the inner workings of black-box neural network models and building user trust.
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows
We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions.
Retrieving Continuous Time Event Sequences using Neural Temporal Point Processes with Learnable Hashing
In detail, by CTES retrieval we mean that for an input query sequence, a retrieval system must return a ranked list of relevant sequences from a large corpus.
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
In this work, we address these limitations through a novel GNN framework called GRAFENNE.
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Despite their initial success, these techniques, like much of the existing deep generative methods, require a large number of training samples to learn a good model.
Embeddings for Tabular Data: A Survey
The classical learning phase consists of the models such as SVMs, linear and logistic regression, and tree-based methods.
DetAIL : A Tool to Automatically Detect and Analyze Drift In Language
The goal of this work is to ensure that machine learning and deep learning-based systems are as trusted as traditional software.
Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs
In this paper, we present REVAMP, a sequential POI recommendation approach that utilizes the user activity on smartphone applications (or apps) to identify their mobility preferences.
A Survey on Temporal Graph Representation Learning and Generative Modeling
Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e commerce, communication, road networks, biological systems, and many more.
Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes
In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
Plug and Play Counterfactual Text Generation for Model Robustness
We also show that the generated counterfactuals from CASPer can be used for augmenting the training data and thereby fixing and making the test model more robust.
TransDrift: Modeling Word-Embedding Drift using Transformer
In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines.
ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences
In this paper, we present ProActive, a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems -- next action prediction, sequence-goal prediction, and end-to-end sequence generation.
TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs
There has been a recent surge in learning generative models for graphs.
Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences
To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences.
Doing More with Less: Overcoming Data Scarcity for POI Recommendation via Cross-Region Transfer
Variability in social app usage across regions results in a high skew of the quantity and the quality of check-in data collected, which in turn is a challenge for effective location recommender systems.
VizAI : Selecting Accurate Visualizations of Numerical Data
In this paper, we present VizAI, a generative-discriminative framework that first generates various statistical properties of the data from a number of alternative visualizations of the data.
Region Invariant Normalizing Flows for Mobility Transfer
Later, we transfer the model parameters of spatial and temporal flows trained on a data-rich origin region for the next check-in and time prediction in a target region with scarce checkin data.
BERT Meets Relational DB: Contextual Representations of Relational Databases
In this paper, we address the problem of learning low dimension representation of entities on relational databases consisting of multiple tables.
Tracking entities in technical procedures -- a new dataset and baselines
We introduce TechTrack, a new dataset for tracking entities in technical procedures.
FROCC: Fast Random projection-based One-Class Classification
We present Fast Random projection-based One-Class Classification (FROCC), an extremely efficient method for one-class classification.
A Survey on Semantic Parsing from the perspective of Compositionality
In section 3 we will consider systems that uses formal languages e. g. $\lambda$-calculus (Steedman, 1996), $\lambda$-DCS (Liang, 2013).
IterefinE: Iterative KG Refinement Embeddings using Symbolic Knowledge
Knowledge Graphs (KGs) extracted from text sources are often noisy and lead to poor performance in downstream application tasks such as KG-based question answering. While much of the recent activity is focused on addressing the sparsity of KGs by using embeddings for inferring new facts, the issue of cleaning up of noise in KGs through KG refinement task is not as actively studied.
On Embeddings in Relational Databases
We address the problem of learning a distributed representation of entities in a relational database using a low-dimensional embedding.
Regex Queries over Incomplete Knowledge Bases
In response, we develop RotatE-Box -- a novel combination of RotatE and box embeddings.
Aspect-based Academic Search using Domain-specific KB
Often, the user is also aware of the "aspect" to retrieve a relevant document.
Discovering Topical Interactions in Text-based Cascades using Hidden Markov Hawkes Processes
We show using experiments on real and semi-synthetic data that HMHP is able to generalize better and recover the network strengths, topics and diffusion paths more accurately than state-of-the-art baselines.
A Machine Learning Approach to Quantitative Prosopography
Prosopography is an investigation of the common characteristics of a group of people in history, by a collective study of their lives.
DataVizard: Recommending Visual Presentations for Structured Data
Selecting the appropriate visual presentation of the data such that it preserves the semantics of the underlying data and at the same time provides an intuitive summary of the data is an important, often the final step of data analytics.
