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Meta-Learning surrogate models for sequential decision making
Meta-learning methods leverage past experience to learn data-driven indu...
03/28/2019 ∙ by Alexandre Galashov, et al. ∙22 ∙
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Structured agents for physical construction
Physical construction -- the ability to compose objects, subject to phys...
04/05/2019 ∙ by Victor Bapst, et al. ∙20 ∙
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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Recent research has made the surprising finding that state-of-the-art de...
12/06/2019 ∙ by Sven Gowal, et al. ∙15 ∙
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Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcement-learning agents can learn ...
06/05/2018 ∙ by Dzmitry Bahdanau, et al. ∙14 ∙
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Degenerate Feedback Loops in Recommender Systems
Machine learning is used extensively in recommender systems deployed in ...
02/27/2019 ∙ by Ray Jiang, et al. ∙14 ∙
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Verification of Non-Linear Specifications for Neural Networks
Prior work on neural network verification has focused on specifications ...
02/25/2019 ∙ by Chongli Qin, et al. ∙14 ∙
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Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoning---a core ability within human intelligence---pres...
04/02/2019 ∙ by David Saxton, et al. ∙14 ∙
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REGAL: Transfer Learning For Fast Optimization of Computation Graphs
We present a deep reinforcement learning approach to optimizing the exec...
05/07/2019 ∙ by Aditya Paliwal, et al. ∙14 ∙
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Making sense of sensory input
This paper attempts to answer a central question in unsupervised learnin...
10/05/2019 ∙ by Richard Evans, et al. ∙14 ∙
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The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that lear...
04/26/2019 ∙ by Jiayuan Mao, et al. ∙12 ∙
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CLEVRER: CoLlision Events for Video REpresentation and Reasoning
The ability to reason about temporal and causal events from videos lies ...
10/03/2019 ∙ by Kexin Yi, et al. ∙12 ∙
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Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
10/28/2019 ∙ by Hanjun Dai, et al. ∙12 ∙
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Branch and Bound for Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critic...
09/14/2019 ∙ by Rudy Bunel, et al. ∙11 ∙
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Graph Matching Networks for Learning the Similarity of Graph Structured Objects
This paper addresses the challenging problem of retrieval and matching o...
04/29/2019 ∙ by Yujia Li, et al. ∙8 ∙
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An Alternative Surrogate Loss for PGD-based Adversarial Testing
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
10/21/2019 ∙ by Sven Gowal, et al. ∙8 ∙
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Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
This paper addresses the problem of evaluating learning systems in safet...
12/04/2018 ∙ by Jonathan Uesato, et al. ∙6 ∙
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Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
12/14/2018 ∙ by Miljan Martic, et al. ∙6 ∙
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Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...
04/26/2019 ∙ by Chenglong Wang, et al. ∙6 ∙
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A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Medical imaging only indirectly measures the molecular identity of the t...
05/30/2019 ∙ by Simon A. A. Kohl, et al. ∙5 ∙
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Compositional Imitation Learning: Explaining and executing one task at a time
We introduce a framework for Compositional Imitation Learning and Execut...
12/04/2018 ∙ by Thomas Kipf, et al. ∙4 ∙
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Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
07/04/2019 ∙ by Chongli Qin, et al. ∙3 ∙
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Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Neural networks are part of many contemporary NLP systems, yet their emp...
09/03/2019 ∙ by Po-Sen Huang, et al. ∙1 ∙
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Piecewise Linear Neural Network verification: A comparative study
The success of Deep Learning and its potential use in many important saf...
11/01/2017 ∙ by Rudy Bunel, et al. ∙0 ∙
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Neural Program Meta-Induction
Most recently proposed methods for Neural Program Induction work under t...
10/11/2017 ∙ by Jacob Devlin, et al. ∙0 ∙
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TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; g...
08/15/2016 ∙ by Alexander L. Gaunt, et al. ∙0 ∙
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Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning
As a step towards developing zero-shot task generalization capabilities ...
06/15/2017 ∙ by Junhyuk Oh, et al. ∙0 ∙
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Deep API Programmer: Learning to Program with APIs
We present DAPIP, a Programming-By-Example system that learns to program...
04/14/2017 ∙ by Surya Bhupatiraju, et al. ∙0 ∙
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Ensemble Bayesian Optimization
Bayesian Optimization (BO) has been shown to be a very effective paradig...
06/05/2017 ∙ by Zi Wang, et al. ∙0 ∙
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Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
06/01/2017 ∙ by N. Siddharth, et al. ∙0 ∙
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Summary - TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; t...
12/02/2016 ∙ by Alexander L. Gaunt, et al. ∙0 ∙
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Neuro-Symbolic Program Synthesis
Recent years have seen the proposal of a number of neural architectures ...
11/06/2016 ∙ by Emilio Parisotto, et al. ∙0 ∙
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Learning Continuous Semantic Representations of Symbolic Expressions
Combining abstract, symbolic reasoning with continuous neural reasoning ...
11/04/2016 ∙ by Miltiadis Allamanis, et al. ∙0 ∙
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Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Optimization of high-dimensional black-box functions is an extremely cha...
03/06/2017 ∙ by Zi Wang, et al. ∙0 ∙
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Time-Sensitive Bayesian Information Aggregation for Crowdsourcing Systems
Crowdsourcing systems commonly face the problem of aggregating multiple ...
10/21/2015 ∙ by Matteo Venanzi, et al. ∙0 ∙
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Information Gathering in Networks via Active Exploration
How should we gather information in a network, where each node's visibil...
04/24/2015 ∙ by Adish Singla, et al. ∙0 ∙
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Multi-way Particle Swarm Fusion
This paper proposes a novel MAP inference framework for Markov Random Fi...
12/05/2016 ∙ by Chen Liu, et al. ∙0 ∙
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Deep Multi-Modal Image Correspondence Learning
Inference of correspondences between images from different modalities is...
12/05/2016 ∙ by Chen Liu, et al. ∙0 ∙
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Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
11/22/2016 ∙ by N. Siddharth, et al. ∙0 ∙
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Memory-augmented Attention Modelling for Videos
We present a method to improve video description generation by modeling ...
11/07/2016 ∙ by Rasool Fakoor, et al. ∙0 ∙
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Deep disentangled representations for volumetric reconstruction
We introduce a convolutional neural network for inferring a compact dise...
10/12/2016 ∙ by Edward Grant, et al. ∙0 ∙
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Partition-Merge: Distributed Inference and Modularity Optimization
This paper presents a novel meta algorithm, Partition-Merge (PM), which ...
09/24/2013 ∙ by Vincent Blondel, et al. ∙0 ∙
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Multi-dimensional Parametric Mincuts for Constrained MAP Inference
In this paper, we propose novel algorithms for inferring the Maximum a P...
07/30/2013 ∙ by Yongsub Lim, et al. ∙0 ∙
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Efficient Continuous Relaxations for Dense CRF
Dense conditional random fields (CRF) with Gaussian pairwise potentials ...
08/22/2016 ∙ by Alban Desmaison, et al. ∙0 ∙
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Learning to Navigate the Energy Landscape
In this paper, we present a novel and efficient architecture for address...
03/18/2016 ∙ by Julien Valentin, et al. ∙0 ∙
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DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding
While deep neural networks have led to human-level performance on comput...
03/16/2016 ∙ by Yinda Zhang, et al. ∙0 ∙
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Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts
Markov Networks are widely used through out computer vision and machine ...
03/15/2012 ∙ by Chris Russell, et al. ∙0 ∙
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Efficient non-greedy optimization of decision trees
Decision trees and randomized forests are widely used in computer vision...
11/12/2015 ∙ by Mohammad Norouzi, et al. ∙0 ∙
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CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits
We propose a novel algorithm for optimizing multivariate linear threshol...
06/19/2015 ∙ by Mohammad Norouzi, et al. ∙0 ∙
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PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
We propose a novel approach to reduce the computational cost of evaluati...
04/30/2015 ∙ by Michael Figurnov, et al. ∙0 ∙
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Memory Bounded Deep Convolutional Networks
In this work, we investigate the use of sparsity-inducing regularizers d...
12/03/2014 ∙ by Maxwell D. Collins, et al. ∙0 ∙
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