Search Results for author:
Found 24 papers, 8 papers with code
Entropy-Assisted Quality Pattern Identification in Finance
Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge.
MAIDS: Malicious Agent Identification-based Data Security Model for Cloud Environments
To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system.
Selective Shot Learning for Code Explanation
Recent work shows that the performance of LLMs for code explanation improves in a few-shot setting, especially when the few-shot examples are selected intelligently.
Digital Twin Generators for Disease Modeling
We show that the same neural network architecture can be trained to generate accurate digital twins for patients across 13 different indications simply by changing the training set and tuning hyperparameters.
Deep Learning Based Named Entity Recognition Models for Recipes
Simultaneously, we systematically cleaned and analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER.
Sparse Graph Representations for Procedural Instructional Documents
We propose two approaches to model document similarity by representing document pairs as a directed and sparse JCIG that incorporates sequential information.
Exploring Large Language Models for Code Explanation
Automating code documentation through explanatory text can prove highly beneficial in code understanding.
Data-driven decision-focused surrogate modeling
We introduce the concept of decision-focused surrogate modeling for solving computationally challenging nonlinear optimization problems in real-time settings.
Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation
Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances.
Speaker Profiling in Multiparty Conversations
We have evaluated various baselines on this dataset and benchmarked it with a new neural model, SPOT, which we introduce in this paper.
Dimension reduction and redundancy removal through successive Schmidt decompositions
We show that data with distributions such as uniform, Poisson, exponential, or similar to these distributions can be approximated by using only a few terms which can be easily mapped onto quantum circuits.
Coherence and Diversity through Noise: Self-Supervised Paraphrase Generation via Structure-Aware Denoising
This allows for learning a denoising function that operates over both aspects and produces semantically equivalent and syntactically diverse outputs through grounded noise injection.
A Privacy-Preserving Outsourced Data Model in Cloud Environment
Therefore, a privacy-preserving model is proposed, which protects the privacy of the data without compromising machine learning efficiency.
Efficient Learning of Decision-Making Models: A Penalty Block Coordinate Descent Algorithm for Data-Driven Inverse Optimization
In this work, we consider the inverse problem where we use prior decision data to uncover the underlying decision-making process in the form of a mathematical optimization model.
'John ate 5 apples' != 'John ate some apples': Self-Supervised Paraphrase Quality Detection for Algebraic Word Problems
There is a need for paraphrase scoring methods in the context of AWP to enable the training of good paraphrasers.
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
Sampling is an established technique to scale graph neural networks to large graphs.
Evaluation of deep learning models for multi-step ahead time series prediction
The deep learning methods comprise simple recurrent neural networks, long short-term memory (LSTM) networks, bidirectional LSTM networks, encoder-decoder LSTM networks, and convolutional neural networks.
Ranked #1 on
Time Series Prediction
on Sunspot
(using extra training data)
SEMIE: SEMantically Infused Embeddings with Enhanced Interpretability for Domain-specific Small Corpus
Word embeddings are a basic building block of modern NLP pipelines.
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdown
Air pollution has a wide range of implications on agriculture, economy, road accidents, and health.
Decomposition and Adaptive Sampling for Data-Driven Inverse Linear Optimization
This work addresses inverse linear optimization where the goal is to infer the unknown cost vector of a linear program.
Towards Semantic Noise Cleansing of Categorical Data based on Semantic Infusion
Based on this technique, we propose an unsupervised text-preprocessing framework to filter the semantic noise using the context of the terms.
DiffQue: Estimating Relative Difficulty of Questions in Community Question Answering Services
In this paper, we propose DiffQue, a novel system that maps this problem to a network-aided edge directionality prediction problem.
Simple Features for Strong Performance on Named Entity Recognition in Code-Switched Twitter Data
In this work, we address the problem of Named Entity Recognition (NER) in code-switched tweets as a part of the Workshop on Computational Approaches to Linguistic Code-switching (CALCS) at ACL{'}18.
