Machine Learning

Balaraman Ravindran
Arun Rajkumar
Arun RajKumar
Harish Guruprasad
Applied Data Science and Machine Learning (ADSML)
Accelerated Sequence Design of Star Block Copolymers: An Unbiased Exploration Strategy via Fusion of Molecular Dynamics Simulations and Machine Learning
TOMBoost: a topic modeling based boosting approach for learning with class imbalance
Hypergraph partitioning using tensor eigenvalue decomposition
MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication
Q4 2022
Q3 2022
AI and data science centers in top Indian academic institutions
Byzantine Spectral Ranking
Multi-Variate Time Series Forecasting on Variable Subsets
Domain-Agnostic Constrastive Representations for Learning from Label Proportions
Q2 2022
Integration of machine learning and first principles models
Q1 2022
Scaling graph representation learning algorithms in an implementation agnostic fashion
On Combining Bags to Better Learn from Label Proportions
Inductive Bias of Gradient Descent for Weight Normalized Smooth Homogeneous Neural Nets
Accurate and Interpretable AI models: Towards Deployable AI
Q4 2021
Q3 2021
Interpretable AI
Machine Learning for Robot Locomotion: Grounded Learning and Adaptive Parameter Learning
Ritwiz Kamal
Q2 2021
Q1 2021
An Enhanced Advising Model in Teacher-Student framework using State Categorization
Revisiting Link Prediction on Heterogeneous Graphs with A Multi-view Perspective
Q4 2020
Data Driven Monitoring of Water Distribution Networks
Domain agnostic methods for integration of prior knowledge in learning algorithms
Aided Selection of Sampling Methods for Imbalanced Data Classification
Q4 2020-2021
Q3 2020
Consistent plug-in classifiers for complex objectives and constraints
Hypergraph clustering by iteratively reweighted modularity maximization
Q2 2020
Inductive Bias of Gradient Descent for Exponentially Weight Normalized Smooth Homogeneous Neural Nets
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks
Brintha Vijayakumar Padmavathy
Abdul Bakey Mir
Yadav Mahesh Lorik
HPRA: Hyperedge Prediction using Resource Allocation
Machine Learning Applications for Mass Spectrometry-Based Metabolomics
D y VED eep
Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
Conducting Non-adaptive Experiments in a Live Setting: A Bayesian Approach to Determining Optimal Sample Size
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
PlotQA: Reasoning over Scientific Plots
Predicting software defect type using concept-based classification
Interpretability With Accurate Small Models
Novel ratio-metric features enable the identification of new driver genes across cancer types
Comparison of first trimester dating methods for gestational age estimation and their implication on preterm birth classification in a North Indian cohort
A New Measure of Modularity in Hypergraphs: Theoretical Insights and Implications for Effective Clustering
Rate of change analysis for interestingness measures
Graph Convolutional Network with Sequential Attention for Goal-Oriented Dialogue Systems
Q4 2019
Extra: Transfer-guided exploration
Studying the plasticity in deep convolutional neural networks using random pruning
Pack and Detect
Network-based features enable prediction of essential genes across diverse organisms
Q4 2018
On Controllable Sparse Alternatives to Softmax
Consistent algorithms for multiclass classification with a reject option
Dynamic Action Repetition for Deep Reinforcement Learning
An autoencoder approach to learning bilingual word representations