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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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Generalized Adversarially Learned Inference
Allowing effective inference of latent vectors while training GANs can g...
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Quantile Regularization: Towards Implicit Calibration of Regression Models
Recent works have shown that most deep learning models are often poorly ...
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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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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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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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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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Nonparametric Bayesian Structure Adaptation for Continual Learning
Continual Learning is a learning paradigm where machine learning models ...
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Refined α-Divergence Variational Inference via Rejection Sampling
We present an approximate inference method, based on a synergistic combi...
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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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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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Stochastic Blockmodels meet Graph Neural Networks
Stochastic blockmodels (SBM) and their variants, e.g., mixed-membership ...
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Play and Prune: Adaptive Filter Pruning for Deep Model Compression
While convolutional neural networks (CNN) have achieved impressive perfo...
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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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Generative Model for Zero-Shot Sketch-Based Image Retrieval
We present a probabilistic model for Sketch-Based Image Retrieval (SBIR)...
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HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs
We present a novel deep learning architecture in which the convolution o...
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Leveraging Filter Correlations for Deep Model Compression
We present a filter correlation based model compression approach for dee...
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Graph Convolutional Networks based Word Embeddings
Recently, word embeddings have been widely adopted across several NLP ap...
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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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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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Generalized Zero-Shot Learning via Synthesized Examples
We present a generative framework for generalized zero-shot learning whe...
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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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Leveraging Distributional Semantics for Multi-Label Learning
We present a novel and scalable label embedding framework for large-scal...
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A Deep Generative Framework for Paraphrase Generation
Paraphrase generation is an important problem in NLP, especially in ques...
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A Simple Exponential Family Framework for Zero-Shot Learning
We present a simple generative framework for learning to predict previou...
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Deep Generative Models for Relational Data with Side Information
We present a probabilistic framework for overlapping community discovery...
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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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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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Flexible Modeling of Latent Task Structures in Multitask Learning
Multitask learning algorithms are typically designed assuming some fixed...
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