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Found 24 papers, 13 papers with code
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets
More critically, these independent search processes cannot share their learned knowledge (i. e., the distribution of good architectures) with each other and thus often result in limited search results.
Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets
To this end, we propose a Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an arbitrary budget as input and produces the Pareto optimal architecture for the target budget.
Towards Accurate and Compact Architectures via Neural Architecture Transformer
To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization.
Pareto-Frontier-aware Neural Architecture Search
To find promising architectures under different budgets, existing methods may have to perform an independent search for each budget, which is very inefficient and unnecessary.
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach
In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.
A Surgery of the Neural Architecture Evaluators
A major challenge in NAS is to conduct a fast and accurate evaluation of neural architectures.
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method significantly improves the search efficiency and finds better architectures than existing NAS methods.
Towards Fast Adaptation of Neural Architectures with Meta Learning
Recently, Neural Architecture Search (NAS) has been successfully applied to multiple artificial intelligence areas and shows better performance compared with hand-designed networks.
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor.
FTT-NAS: Discovering Fault-Tolerant Convolutional Neural Architecture
By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models.
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures.
Semi-supervised Learning with Contrastive Predicative Coding
Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain.
RaFM: Rank-Aware Factorization Machines
Factorization machines (FM) are a popular model class to learn pairwise interactions by a low-rank approximation.
Fast Single Image Reflection Suppression via Convex Optimization
Removing undesired reflections from images taken through the glass is of great importance in computer vision.
Nonparametric Topic Modeling with Neural Inference
Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner.
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner.
Collaborative Filtering with User-Item Co-Autoregressive Models
Deep neural networks have shown promise in collaborative filtering (CF).
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative clustering approach within the framework of Variational Auto-Encoder (VAE).
A Neural Autoregressive Approach to Collaborative Filtering
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE).
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Document Neural Autoregressive Distribution Estimation
We present an approach based on feed-forward neural networks for learning the distribution of textual documents.
Dynamic Capacity Networks
The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks.
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data
Second, we propose a deep extension of our model and provide an efficient way of training the deep model.
Topic Modeling of Multimodal Data: An Autoregressive Approach
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks.
A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation.
