49 citations · 172 across the 25 of their papers we have counts for
27 papers
Joint Causal Structure and Cluster Discovery Using Variational Inference
Avni Rajpal, Anubhav Kumar, Rishabh Karnad +2
Causal discovery aims to understand the relationships between individual random variables. In many applications, such as brain imaging and climate modeling, it is more meaningful t…
Galaxy Morphology Classification: Uncertainty Modeling and Out of Distribution Detection
Prem Prakash, Shantanu Desai, P. K. Srijith
We present a comprehensive framework for galaxy morphology classification that combines enhanced ``out-of-distribution (OOD)'' detection with improved uncertainty quantification. U…
Toward Efficient Uncertainty in LLMs through Evidential Knowledge Distillation
Lakshmana Sri Harsha Nemani, P. K. Srijith, Tomasz Kuśmierczyk
Accurate uncertainty quantification remains a key challenge for standard LLMs, prompting the adoption of Bayesian and ensemble-based methods. However, such methods typically necess…
Linked Adapters: Linking Past and Future to Present for Effective Continual Learning
Dupati Srikar Chandra, P. K. Srijith, Dana Rezazadegan +1
Continual learning allows the system to learn and adapt to new tasks while retaining the knowledge acquired from previous tasks. However, deep learning models suffer from catastrop…
Transformer based Multitask Learning for Image Captioning and Object Detection
Debolena Basak, P. K. Srijith, Maunendra Sankar Desarkar
In several real-world scenarios like autonomous navigation and mobility, to obtain a better visual understanding of the surroundings, image captioning and object detection play a c…
Continuous Depth Recurrent Neural Differential Equations
Srinivas Anumasa, Geetakrishnasai Gunapati, P. K. Srijith
Recurrent neural networks (RNNs) have brought a lot of advancements in sequence labeling tasks and sequence data. However, their effectiveness is limited when the observations in t…
DialoGen: Generalized Long-Range Context Representation for Dialogue Systems
Suvodip Dey, Maunendra Sankar Desarkar, Asif Ekbal +1
Long-range context modeling is crucial to both dialogue understanding and generation. The most popular method for dialogue context representation is to concatenate the last- utt…
HyperHawkes: Hypernetwork based Neural Temporal Point Process
Manisha Dubey, P. K. Srijith, Maunendra Sankar Desarkar
Temporal point process serves as an essential tool for modeling time-to-event data in continuous time space. Despite having massive amounts of event sequence data from various doma…
Continual Learning with Dependency Preserving Hypernetworks
Dupati Srikar Chandra, Sakshi Varshney, P. K. Srijith +1
Humans learn continually throughout their lifespan by accumulating diverse knowledge and fine-tuning it for future tasks. When presented with a similar goal, neural networks suffer…
Cosmic Ray Rejection with Attention Augmented Deep Learning
S. R. Bhavanam, Sumohana S. Channappayya, P. K. Srijith +1
Cosmic Ray (CR) hits are the major contaminants in astronomical imaging and spectroscopic observations involving solid-state detectors. Correctly identifying and masking them is a…
Bayesian Neural Hawkes Process for Event Uncertainty Prediction
Manisha Dubey, Ragja Palakkadavath, P. K. Srijith
Event data consisting of time of occurrence of the events arises in several real-world applications. Recent works have introduced neural network based point processes for modeling…
Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification
Maunika Tamire, Srinivas Anumasa, P. K. Srijith
Classification of posts in social media such as Twitter is difficult due to the noisy and short nature of texts. Sequence classification models based on recurrent neural networks (…
Latent Time Neural Ordinary Differential Equations
Srinivas Anumasa, P. K. Srijith
Neural ordinary differential equations (NODE) have been proposed as a continuous depth generalization to popular deep learning models such as Residual networks (ResNets). They prov…
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations
Srinivas Anumasa, P. K. Srijith
Neural ordinary differential equations (NODE) have been proposed as a continuous depth generalization to popular deep learning models such as Residual networks (ResNets). They prov…
Monte Carlo DropBlock for Modelling Uncertainty in Object Detection
Kumari Deepshikha, Sai Harsha Yelleni, P. K. Srijith +1
With the advancements made in deep learning, computer vision problems like object detection and segmentation have seen a great improvement in performance. However, in many real-wor…
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan
Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes but their training remains challenging. Sparse approximations simplify the training but of…
Adiabatic Quantum Feature Selection for Sparse Linear Regression
Surya Sai Teja Desu, P. K. Srijith, M. V. Panduranga Rao +1
Linear regression is a popular machine learning approach to learn and predict real valued outputs or dependent variables from independent variables or features. In many real world…
Delay Differential Neural Networks
Srinivas Anumasa, P. K. Srijith
Neural ordinary differential equations (NODEs) treat computation of intermediate feature vectors as trajectories of ordinary differential equation parameterized by a neural network…
Galaxy Morphology Classification using Neural Ordinary Differential Equations
Raghav Gupta, P. K. Srijith, Shantanu Desai
We introduce a continuous depth version of the Residual Network (ResNet) called Neural ordinary differential equations (NODE) for the purpose of galaxy morphology classification. W…
Hawkes Process Classification through Discriminative Modeling of Text
Rohan Tondulkar, Manisha Dubey, P. K. Srijith +1
Social media has provided a platform for users to gather and share information and stay updated with the news. Such networks also provide a platform to users where they can engage…
HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process
Manisha Dubey, P. K. Srijith, Maunendra Sankar Desarkar
The prevalence of location-based social networks (LBSNs) has eased the understanding of human mobility patterns. Knowledge of human dynamics can aid in various ways like urban plan…
Learning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence
P. Jayashree, Ballijepalli Shreya, P. K. Srijith
Learning word representations has garnered greater attention in the recent past due to its diverse text applications. Word embeddings encapsulate the syntactic and semantic regular…
Deep Gaussian Processes with Convolutional Kernels
Vinayak Kumar, Vaibhav Singh, P. K. Srijith +1
Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alternative to standard parametric deep learning models. A DGP is formed by stacking multiple GPs resulting in a we…
Variational Inference as an alternative to MCMC for parameter estimation and model selection
Geetakrishnasai Gunapati, Anirudh Jain, P. K. Srijith +1
Most applications of Bayesian Inference for parameter estimation and model selection in astrophysics involve the use of Monte Carlo techniques such as Markov Chain Monte Carlo (MCM…