Search Results for author:
Found 17 papers, 12 papers with code
Incremental Extractive Opinion Summarization Using Cover Trees
In this work, we study the task of extractive opinion summarization in an incremental setting, where the underlying review set evolves over time.
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Distributed representations provide a vector space that captures meaningful relationships between data instances.
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e. g., forward/backward passes) than the task-specific objective at every time step.
Efficient Graph Field Integrators Meet Point Clouds
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds.
Unsupervised Opinion Summarization Using Approximate Geodesics
We then use these representations to quantify the relevance of review sentences using a novel approximate geodesic distance based scoring mechanism.
Sustaining Fairness via Incremental Learning
In this work, we propose to address this issue by introducing the problem of learning fair representations in an incremental learning setting.
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document.
Unsupervised Extractive Opinion Summarization Using Sparse Coding
A semantic unit is supposed to capture an abstract semantic concept.
Learning Fair Representations via Rate-Distortion Maximization
Text representations learned by machine learning models often encode undesirable demographic information of the user.
Does Commonsense help in detecting Sarcasm?
For this, we incorporate commonsense knowledge into the prediction process using a graph convolution network with pre-trained language model embeddings as input.
Adversarial Scrubbing of Demographic Information for Text Classification
Contextual representations learned by language models can often encode undesirable attributes, like demographic associations of the users, while being trained for an unrelated target task.
Is Everything in Order? A Simple Way to Order Sentences
We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets.
Learning beyond datasets: Knowledge Graph Augmented Neural Networks for Natural language Processing
In this work, we propose to enhance learning models with world knowledge in the form of Knowledge Graph (KG) fact triples for Natural Language Processing (NLP) tasks.
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing
With our proposed cross dataset learning procedure we show that one can achieve competitive/better performance than learning from a single dataset.
SIMILARnet: Simultaneous Intelligent Localization and Recognition Network
Global Average Pooling (GAP) [4] has been used previously to generate class activation for image classification tasks.
Handwriting Profiling using Generative Adversarial Networks
Handwriting is a skill learned by humans from a very early age.
SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks
Autonomous driving is one of the most recent topics of interest which is aimed at replicating human driving behavior keeping in mind the safety issues.
