Search Results for author:
Found 16 papers, 2 papers with code
Combining Satellite and Weather Data for Crop Type Mapping: An Inverse Modelling Approach
We finally discuss the impact of weather by correlating our results with crop phenology to show that WSTATT is able to capture physical properties of crop growth.
Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Heterogeneous Systems
We present a Task-aware modulation using Representation Learning (TAM-RL) framework that enhances personalized predictions in few-shot settings for heterogeneous systems when individual task characteristics are not known.
Message Propagation Through Time: An Algorithm for Sequence Dependency Retention in Time Series Modeling
Time series modeling, a crucial area in science, often encounters challenges when training Machine Learning (ML) models like Recurrent Neural Networks (RNNs) using the conventional mini-batch training strategy that assumes independent and identically distributed (IID) samples and initializes RNNs with zero hidden states.
Entity Aware Modelling: A Survey
However, these entity characteristics are not readily available in many real-world scenarios, and different ML methods have been proposed to infer these characteristics from the data.
Mini-Batch Learning Strategies for modeling long term temporal dependencies: A study in environmental applications
To address this issue, we further propose a new strategy which augments a training segment with an initial value of the target variable from the timestep right before the starting of the training segment.
Spatiotemporal Classification with limited labels using Constrained Clustering for large datasets
Using this large unlabelled dataset, we first show how a spatiotemporal representation is better compared to just spatial or temporal representation.
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Furthermore, we show that KGSSL is relatively more robust to distortion than baseline methods, and outperforms the baseline model by 35\% when plugging in KGSSL inferred characteristics.
CalCROP21: A Georeferenced multi-spectral dataset of Satellite Imagery and Crop Labels
Mapping and monitoring crops is a key step towards sustainable intensification of agriculture and addressing global food security.
Physics Guided Machine Learning Methods for Hydrology
The goal of this work is to incorporate our understanding of physical processes and constraints in hydrology into machine learning algorithms, and thus bridge the performance gap while reducing the need for large amounts of data compared to traditional data-driven approaches.
Automated Monitoring Cropland Using Remote Sensing Data: Challenges and Opportunities for Machine Learning
This paper provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to automatically map croplands over long period and over large regions.
Gender Prediction in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
In this paper, we analyze the task of author's gender prediction in code-mixed content and present a corpus of English-Hindi texts collected from Twitter which is annotated with author's gender.
Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System
In this paper, we analyze the task of humor detection in texts and describe a freely available corpus containing English-Hindi code-mixed tweets annotated with humorous(H) or non-humorous(N) tags.
An English-Hindi Code-Mixed Corpus: Stance Annotation and Baseline System
Social media has become one of the main channels for peo- ple to communicate and share their views with the society.
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm Detection
Social media platforms like twitter and facebook have be- come two of the largest mediums used by people to express their views to- wards different topics.
Discovery of Shifting Patterns in Sequence Classification
The experiments demonstrate the superiority of our proposed method in sequence classification performance and in detecting discriminative shifting patterns.
ORBIT: Ordering Based Information Transfer Across Space and Time for Global Surface Water Monitoring
Various data fusion methods have been proposed in the literature that mainly rely on individual timesteps when both datasets are available to learn a mapping between features values at different resolutions using local relationships between pixels.
