Search Results for author:
Found 18 papers, 7 papers with code
Learning to Discover Efficient Mathematical Identities
In this paper we explore how machine learning techniques can be applied to the discovery of efficient mathematical identities.
Neural Random-Access Machines
In this paper, we propose and investigate a new neural network architecture called Neural Random Access Machine.
Adding Gradient Noise Improves Learning for Very Deep Networks
This success is partially attributed to architectural innovations such as convolutional and long short-term memory networks.
Learning Efficient Algorithms with Hierarchical Attentive Memory
In this paper, we propose and investigate a novel memory architecture for neural networks called Hierarchical Attentive Memory (HAM).
Smart Reply: Automated Response Suggestion for Email
In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply.
Better Text Understanding Through Image-To-Text Transfer
Generic text embeddings are successfully used in a variety of tasks.
Critical Hyper-Parameters: No Random, No Cry
The selection of hyper-parameters is critical in Deep Learning.
Toward Optimal Run Racing: Application to Deep Learning Calibration
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand.
Are GANs Created Equal? A Large-Scale Study
Generative adversarial networks (GAN) are a powerful subclass of generative models.
A Large-Scale Study on Regularization and Normalization in GANs
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
Investigating Object Compositionality in Generative Adversarial Networks
We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects.
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
Towards Accurate Generative Models of Video: A New Metric & Challenges
To this extent we propose Fr\'{e}chet Video Distance (FVD), a new metric for generative models of video, and StarCraft 2 Videos (SCV), a benchmark of game play from custom starcraft 2 scenarios that challenge the current capabilities of generative models of video.
FVD: A new Metric for Video Generation
While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.
The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Generative adversarial networks (GANs) are a class of deep generative models which aim to learn a target distribution in an unsupervised fashion.
A Case for Object Compositionality in Deep Generative Models of Images
In this work we propose to structure the generator of a GAN to consider objects and their relations explicitly, and generate images by means of composition.
Google Research Football: A Novel Reinforcement Learning Environment
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.
