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04/21/2023
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
We propose an evolution strategies-based algorithm for estimating gradie...
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10/13/2022
CUF: Continuous Upsampling Filters
Neural fields have rapidly been adopted for representing 3D signals, but...
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08/09/2022
Learning to Improve Code Efficiency
Improvements in the performance of computing systems, driven by Moore's ...
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07/07/2022
Pre-training helps Bayesian optimization too
Bayesian optimization (BO) has become a popular strategy for global opti...
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10/20/2021
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Industry has gradually moved towards application-specific hardware accel...
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09/16/2021
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers
The performance of deep neural networks can be highly sensitive to the c...
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02/12/2021
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Most graph neural networks (GNN) perform poorly in graphs where neighbor...
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02/08/2021
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
We propose a general and scalable approximate sampling strategy for prob...
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02/02/2021
Apollo: Transferable Architecture Exploration
The looming end of Moore's Law and ascending use of deep learning drives...
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12/18/2020
Human 3D keypoints via spatial uncertainty modeling
We introduce a technique for 3D human keypoint estimation that directly ...
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10/08/2020
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
Energy-Based Models (EBMs) present a flexible and appealing way to repre...
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10/05/2020
Learned Hardware/Software Co-Design of Neural Accelerators
The use of deep learning has grown at an exponential rate, giving rise t...
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07/31/2020
Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Most recommender systems (RS) research assumes that a user's utility can...
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06/29/2020
An Imitation Learning Approach for Cache Replacement
Program execution speed critically depends on increasing cache hits, as ...
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06/17/2020
Big Self-Supervised Models are Strong Semi-Supervised Learners
One paradigm for learning from few labeled examples while making best us...
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06/15/2020
Neural Execution Engines: Learning to Execute Subroutines
A significant effort has been made to train neural networks that replica...
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02/18/2020
SentenceMIM: A Latent Variable Language Model
We introduce sentenceMIM, a probabilistic auto-encoder for language mode...
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12/06/2019
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
We propose to reinterpret a standard discriminative classifier of p(y|x)...
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10/08/2019
MIM: Mutual Information Machine
We introduce the Mutual Information Machine (MIM), an autoencoder model ...
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10/04/2019
High Mutual Information in Representation Learning with Symmetric Variational Inference
We introduce the Mutual Information Machine (MIM), a novel formulation o...
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06/17/2019
Learning Execution through Neural Code Fusion
As the performance of computer systems stagnates due to the end of Moore...
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06/06/2019
Flexibly Fair Representation Learning by Disentanglement
We consider the problem of learning representations that achieve group a...
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05/31/2019
Learning Sparse Networks Using Targeted Dropout
Neural networks are easier to optimise when they have many more weights ...
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05/30/2019
Graph Normalizing Flows
We introduce graph normalizing flows: a new, reversible graph neural net...
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04/04/2019
Neural Networks for Modeling Source Code Edits
Programming languages are emerging as a challenging and interesting doma...
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03/07/2019
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Few-shot classification refers to learning a classifier for new classes ...
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03/06/2018
Learning Memory Access Patterns
The explosion in workload complexity and the recent slow-down in Moore's...
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03/02/2018
Meta-Learning for Semi-Supervised Few-Shot Classification
In few-shot classification, we are interested in learning algorithms tha...
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06/16/2017
An online sequence-to-sequence model for noisy speech recognition
Generative models have long been the dominant approach for speech recogn...
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05/16/2017
Learning Hard Alignments with Variational Inference
There has recently been significant interest in hard attention models fo...
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03/15/2017
Prototypical Networks for Few-shot Learning
We propose prototypical networks for the problem of few-shot classificat...
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11/03/2015
The Variational Fair Autoencoder
We investigate the problem of learning representations that are invarian...
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06/01/2015
Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions
One of the main challenges in Zero-Shot Learning of visual categories is...
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02/19/2015
Scalable Bayesian Optimization Using Deep Neural Networks
Bayesian optimization is an effective methodology for the global optimiz...
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02/10/2015
Generative Moment Matching Networks
We consider the problem of learning deep generative models from data. We...
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12/17/2014
Learning unbiased features
A key element in transfer learning is representation learning; if repres...
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02/05/2014
Input Warping for Bayesian Optimization of Non-stationary Functions
Bayesian optimization has proven to be a highly effective methodology fo...
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10/16/2012
Fast Exact Inference for Recursive Cardinality Models
Cardinality potentials are a generally useful class of high order potent...
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06/27/2012