b'Piyush Rai'

research

∙ 12/19/2022

A Probabilistic Framework for Lifelong Test-Time Adaptation

Test-time adaptation is the problem of adapting a source pre-trained mod...

0 Dhanajit Brahma, et al. ∙

research

∙ 07/21/2022

Novel Class Discovery without Forgetting

Humans possess an innate ability to identify and differentiate instances...

5 K J Joseph, et al. ∙

research

∙ 06/15/2022

Bayesian Federated Learning via Predictive Distribution Distillation

For most existing federated learning algorithms, each round consists of ...

15 Shrey Bhatt, et al. ∙

research

∙ 04/22/2022

Spacing Loss for Discovering Novel Categories

Novel Class Discovery (NCD) is a learning paradigm, where a machine lear...

11 K J Joseph, et al. ∙

research

∙ 04/18/2022

Semi-Supervised Super-Resolution

Super-Resolution is the technique to improve the quality of a low-resolu...

0 Ankur Singh, et al. ∙

research

∙ 12/04/2021

SITA: Single Image Test-time Adaptation

In Test-time Adaptation (TTA), given a model trained on some source data...

0 Ansh Khurana, et al. ∙

research

∙ 11/07/2021

NeurInt : Learning to Interpolate through Neural ODEs

A wide range of applications require learning image generation models wh...

20 Avinandan Bose, et al. ∙

research

∙ 10/05/2021

Hypernetworks for Continual Semi-Supervised Learning

Learning from data sequentially arriving, possibly in a non i.i.d. way, ...

0 Dhanajit Brahma, et al. ∙

research

∙ 07/26/2021

Fine-Grained Emotion Prediction by Modeling Emotion Definitions

In this paper, we propose a new framework for fine-grained emotion predi...

8 Gargi Singh, et al. ∙

research

∙ 06/12/2021

Knowledge Consolidation based Class Incremental Online Learning with Limited Data

We propose a novel approach for class incremental online learning in a l...

0 Mohammed Asad Karim, et al. ∙

research

∙ 03/30/2021

Rectification-based Knowledge Retention for Continual Learning

Deep learning models suffer from catastrophic forgetting when trained in...

22 Pravendra Singh, et al. ∙

research

∙ 03/29/2021

Variational Rejection Particle Filtering

We present a variational inference (VI) framework that unifies and lever...

19 Rahul Sharma, et al. ∙

research

∙ 03/25/2021

Efficient Feature Transformations for Discriminative and Generative Continual Learning

As neural networks are increasingly being applied to real-world applicat...

0 Vinay Kumar Verma, et al. ∙

research

∙ 03/06/2021

Efficient Continual Adaptation for Generative Adversarial Networks

We present a continual learning approach for generative adversarial netw...

0 Sakshi Varshney, et al. ∙

research

∙ 03/01/2021

Few-Shot Lifelong Learning

Many real-world classification problems often have classes with very few...

5 Pratik Mazumder, et al. ∙

research

∙ 11/14/2020

Towards Zero-Shot Learning with Fewer Seen Class Examples

We present a meta-learning based generative model for zero-shot learning...

1 Vinay Kumar Verma, et al. ∙

research

∙ 06/15/2020

Generalized Adversarially Learned Inference

Allowing effective inference of latent vectors while training GANs can g...

0 Yatin Dandi, et al. ∙

research

∙ 02/28/2020

Quantile Regularization: Towards Implicit Calibration of Regression Models

Recent works have shown that most deep learning models are often poorly ...

0 Saiteja Utpala, et al. ∙

research

∙ 01/15/2020

A "Network Pruning Network" Approach to Deep Model Compression

We present a filter pruning approach for deep model compression, using a...

0 Vinay Kumar Verma, et al. ∙

research

∙ 12/31/2019

Deep Attentive Ranking Networks for Learning to Order Sentences

We present an attention-based ranking framework for learning to order se...

8 Pawan Kumar, et al. ∙

research

∙ 12/17/2019

Jointly Trained Image and Video Generation using Residual Vectors

In this work, we propose a modeling technique for jointly training image...

17 Yatin Dandi, et al. ∙

research

∙ 12/12/2019

On the relationship between multitask neural networks and multitask Gaussian Processes

Despite the effectiveness of multitask deep neural network (MTDNN), ther...

0 Karthikeyan K, et al. ∙

research

∙ 12/08/2019

Nonparametric Bayesian Structure Adaptation for Continual Learning

Continual Learning is a learning paradigm where machine learning models ...

0 Abhishek Kumar, et al. ∙

research

∙ 09/17/2019

Refined α-Divergence Variational Inference via Rejection Sampling

We present an approximate inference method, based on a synergistic combi...

0 Rahul Sharma, et al. ∙

research

∙ 09/10/2019

A Meta-Learning Framework for Generalized Zero-Shot Learning

Learning to classify unseen class samples at test time is popularly refe...

30 Vinay Kumar Verma, et al. ∙

research

∙ 06/07/2019

A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation

In this paper, we present a domain adaptation based generative framework...

0 Varun Khare, et al. ∙

research

∙ 05/14/2019

Stochastic Blockmodels meet Graph Neural Networks

Stochastic blockmodels (SBM) and their variants, e.g., mixed-membership ...

0 Nikhil Mehta, et al. ∙

research

∙ 05/11/2019

Play and Prune: Adaptive Filter Pruning for Deep Model Compression

While convolutional neural networks (CNN) have achieved impressive perfo...

0 Pravendra Singh, et al. ∙

research

∙ 05/02/2019

Deep Generative Models for Sparse, High-dimensional, and Overdispersed Discrete Data

Many applications, such as text modelling, high-throughput sequencing, a...

0 He Zhao, et al. ∙

research

∙ 04/18/2019

Generative Model for Zero-Shot Sketch-Based Image Retrieval

We present a probabilistic model for Sketch-Based Image Retrieval (SBIR)...

0 Vinay Kumar Verma, et al. ∙

research

∙ 03/11/2019

HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs

We present a novel deep learning architecture in which the convolution o...

0 Pravendra Singh, et al. ∙

research

∙ 11/26/2018

Leveraging Filter Correlations for Deep Model Compression

We present a filter correlation based model compression approach for dee...

0 Pravendra Singh, et al. ∙

research

∙ 09/12/2018

Graph Convolutional Networks based Word Embeddings

Recently, word embeddings have been widely adopted across several NLP ap...

0 Shikhar Vashishth, et al. ∙

research

∙ 07/10/2018

Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels

The latent feature relational model (LFRM) is a generative model for gra...

0 Gundeep Arora, et al. ∙

research

∙ 01/27/2018

A Generative Approach to Zero-Shot and Few-Shot Action Recognition

We present a generative framework for zero-shot action recognition where...

0 Ashish Mishra, et al. ∙

research

∙ 12/11/2017

Generalized Zero-Shot Learning via Synthesized Examples

We present a generative framework for generalized zero-shot learning whe...

0 Gundeep Arora, et al. ∙

research

∙ 11/15/2017

Zero-Shot Learning via Class-Conditioned Deep Generative Models

We present a deep generative model for learning to predict classes not s...

0 Wenlin Wang, et al. ∙

research

∙ 09/18/2017

Leveraging Distributional Semantics for Multi-Label Learning

We present a novel and scalable label embedding framework for large-scal...

0 Rahul Wadbude, et al. ∙

research

∙ 09/15/2017

A Deep Generative Framework for Paraphrase Generation

Paraphrase generation is an important problem in NLP, especially in ques...

0 Ankush Gupta, et al. ∙

research

∙ 07/25/2017

A Simple Exponential Family Framework for Zero-Shot Learning

We present a simple generative framework for learning to predict previou...

0 Vinay Kumar Verma, et al. ∙

research

∙ 06/16/2017

Deep Generative Models for Relational Data with Side Information

We present a probabilistic framework for overlapping community discovery...

0 Changwei Hu, et al. ∙

research

∙ 08/18/2015

Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data

We present a Bayesian non-negative tensor factorization model for count-...

0 Changwei Hu, et al. ∙

research

∙ 08/18/2015

Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors

We present a scalable Bayesian model for low-rank factorization of massi...

0 Changwei Hu, et al. ∙

research

∙ 06/27/2012

Flexible Modeling of Latent Task Structures in Multitask Learning

Multitask learning algorithms are typically designed assuming some fixed...

0 Alexandre Passos, et al. ∙

Piyush Rai

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