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12/19/2022
A Probabilistic Framework for Lifelong Test-Time Adaptation
Test-time adaptation is the problem of adapting a source pre-trained mod...
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07/21/2022
Novel Class Discovery without Forgetting
Humans possess an innate ability to identify and differentiate instances...
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06/15/2022
Bayesian Federated Learning via Predictive Distribution Distillation
For most existing federated learning algorithms, each round consists of ...
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04/22/2022
Spacing Loss for Discovering Novel Categories
Novel Class Discovery (NCD) is a learning paradigm, where a machine lear...
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04/18/2022
Semi-Supervised Super-Resolution
Super-Resolution is the technique to improve the quality of a low-resolu...
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12/04/2021
SITA: Single Image Test-time Adaptation
In Test-time Adaptation (TTA), given a model trained on some source data...
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11/07/2021
NeurInt : Learning to Interpolate through Neural ODEs
A wide range of applications require learning image generation models wh...
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10/05/2021
Hypernetworks for Continual Semi-Supervised Learning
Learning from data sequentially arriving, possibly in a non i.i.d. way, ...
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07/26/2021
Fine-Grained Emotion Prediction by Modeling Emotion Definitions
In this paper, we propose a new framework for fine-grained emotion predi...
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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...
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03/30/2021
Rectification-based Knowledge Retention for Continual Learning
Deep learning models suffer from catastrophic forgetting when trained in...
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03/29/2021
Variational Rejection Particle Filtering
We present a variational inference (VI) framework that unifies and lever...
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03/25/2021
Efficient Feature Transformations for Discriminative and Generative Continual Learning
As neural networks are increasingly being applied to real-world applicat...
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03/06/2021
Efficient Continual Adaptation for Generative Adversarial Networks
We present a continual learning approach for generative adversarial netw...
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03/01/2021
Few-Shot Lifelong Learning
Many real-world classification problems often have classes with very few...
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11/14/2020
Towards Zero-Shot Learning with Fewer Seen Class Examples
We present a meta-learning based generative model for zero-shot learning...
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06/15/2020
Generalized Adversarially Learned Inference
Allowing effective inference of latent vectors while training GANs can g...
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02/28/2020
Quantile Regularization: Towards Implicit Calibration of Regression Models
Recent works have shown that most deep learning models are often poorly ...
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01/15/2020
A "Network Pruning Network" Approach to Deep Model Compression
We present a filter pruning approach for deep model compression, using a...
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12/31/2019
Deep Attentive Ranking Networks for Learning to Order Sentences
We present an attention-based ranking framework for learning to order se...
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12/17/2019
Jointly Trained Image and Video Generation using Residual Vectors
In this work, we propose a modeling technique for jointly training image...
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12/12/2019
On the relationship between multitask neural networks and multitask Gaussian Processes
Despite the effectiveness of multitask deep neural network (MTDNN), ther...
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12/08/2019
Nonparametric Bayesian Structure Adaptation for Continual Learning
Continual Learning is a learning paradigm where machine learning models ...
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09/17/2019
Refined α-Divergence Variational Inference via Rejection Sampling
We present an approximate inference method, based on a synergistic combi...
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09/10/2019
A Meta-Learning Framework for Generalized Zero-Shot Learning
Learning to classify unseen class samples at test time is popularly refe...
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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...
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05/14/2019
Stochastic Blockmodels meet Graph Neural Networks
Stochastic blockmodels (SBM) and their variants, e.g., mixed-membership ...
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05/11/2019
Play and Prune: Adaptive Filter Pruning for Deep Model Compression
While convolutional neural networks (CNN) have achieved impressive perfo...
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05/02/2019
Deep Generative Models for Sparse, High-dimensional, and Overdispersed Discrete Data
Many applications, such as text modelling, high-throughput sequencing, a...
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04/18/2019
Generative Model for Zero-Shot Sketch-Based Image Retrieval
We present a probabilistic model for Sketch-Based Image Retrieval (SBIR)...
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03/11/2019
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs
We present a novel deep learning architecture in which the convolution o...
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11/26/2018
Leveraging Filter Correlations for Deep Model Compression
We present a filter correlation based model compression approach for dee...
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09/12/2018
Graph Convolutional Networks based Word Embeddings
Recently, word embeddings have been widely adopted across several NLP ap...
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07/10/2018
Small-Variance Asymptotics for Nonparametric Bayesian Overlapping Stochastic Blockmodels
The latent feature relational model (LFRM) is a generative model for gra...
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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...
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12/11/2017
Generalized Zero-Shot Learning via Synthesized Examples
We present a generative framework for generalized zero-shot learning whe...
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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...
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09/18/2017
Leveraging Distributional Semantics for Multi-Label Learning
We present a novel and scalable label embedding framework for large-scal...
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09/15/2017
A Deep Generative Framework for Paraphrase Generation
Paraphrase generation is an important problem in NLP, especially in ques...
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07/25/2017
A Simple Exponential Family Framework for Zero-Shot Learning
We present a simple generative framework for learning to predict previou...
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06/16/2017
Deep Generative Models for Relational Data with Side Information
We present a probabilistic framework for overlapping community discovery...
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08/18/2015
Scalable Bayesian Non-Negative Tensor Factorization for Massive Count Data
We present a Bayesian non-negative tensor factorization model for count-...
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08/18/2015
Zero-Truncated Poisson Tensor Factorization for Massive Binary Tensors
We present a scalable Bayesian model for low-rank factorization of massi...
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06/27/2012