
MetaLearning surrogate models for sequential decision making
Metalearning methods leverage past experience to learn datadriven indu...
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Structured agents for physical construction
Physical construction  the ability to compose objects, subject to phys...
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Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcementlearning agents can learn ...
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Degenerate Feedback Loops in Recommender Systems
Machine learning is used extensively in recommender systems deployed in ...
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Verification of NonLinear Specifications for Neural Networks
Prior work on neural network verification has focused on specifications ...
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Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoninga core ability within human intelligencepres...
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REGAL: Transfer Learning For Fast Optimization of Computation Graphs
We present a deep reinforcement learning approach to optimizing the exec...
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Making sense of sensory input
This paper attempts to answer a central question in unsupervised learnin...
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Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Recent research has made the surprising finding that stateoftheart de...
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The NeuroSymbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the NeuroSymbolic Concept Learner (NSCL), a model that lear...
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CLEVRER: CoLlision Events for Video REpresentation and Reasoning
The ability to reason about temporal and causal events from videos lies ...
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Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
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Branch and Bound for Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safetycritic...
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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...
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An Alternative Surrogate Loss for PGDbased Adversarial Testing
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
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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...
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Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
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Knowing When to Stop: Evaluation and Verification of Conformity to Outputsize Specifications
Models such as SequencetoSequence and ImagetoSequence are widely use...
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A Hierarchical Probabilistic UNet for Modeling MultiScale Ambiguities
Medical imaging only indirectly measures the molecular identity of the t...
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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...
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Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
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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...
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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...
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Neural Program MetaInduction
Most recently proposed methods for Neural Program Induction work under t...
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TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; g...
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ZeroShot Task Generalization with MultiTask Deep Reinforcement Learning
As a step towards developing zeroshot task generalization capabilities ...
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Deep API Programmer: Learning to Program with APIs
We present DAPIP, a ProgrammingByExample system that learns to program...
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Ensemble Bayesian Optimization
Bayesian Optimization (BO) has been shown to be a very effective paradig...
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Learning Disentangled Representations with SemiSupervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
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Summary  TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; t...
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NeuroSymbolic Program Synthesis
Recent years have seen the proposal of a number of neural architectures ...
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Learning Continuous Semantic Representations of Symbolic Expressions
Combining abstract, symbolic reasoning with continuous neural reasoning ...
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Batched Highdimensional Bayesian Optimization via Structural Kernel Learning
Optimization of highdimensional blackbox functions is an extremely cha...
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TimeSensitive Bayesian Information Aggregation for Crowdsourcing Systems
Crowdsourcing systems commonly face the problem of aggregating multiple ...
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Information Gathering in Networks via Active Exploration
How should we gather information in a network, where each node's visibil...
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Multiway Particle Swarm Fusion
This paper proposes a novel MAP inference framework for Markov Random Fi...
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Deep MultiModal Image Correspondence Learning
Inference of correspondences between images from different modalities is...
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Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
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Memoryaugmented Attention Modelling for Videos
We present a method to improve video description generation by modeling ...
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Deep disentangled representations for volumetric reconstruction
We introduce a convolutional neural network for inferring a compact dise...
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PartitionMerge: Distributed Inference and Modularity Optimization
This paper presents a novel meta algorithm, PartitionMerge (PM), which ...
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Multidimensional Parametric Mincuts for Constrained MAP Inference
In this paper, we propose novel algorithms for inferring the Maximum a P...
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Efficient Continuous Relaxations for Dense CRF
Dense conditional random fields (CRF) with Gaussian pairwise potentials ...
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Learning to Navigate the Energy Landscape
In this paper, we present a novel and efficient architecture for address...
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DeepContext: ContextEncoding Neural Pathways for 3D Holistic Scene Understanding
While deep neural networks have led to humanlevel performance on comput...
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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 ...
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Efficient nongreedy optimization of decision trees
Decision trees and randomized forests are widely used in computer vision...
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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...
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PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
We propose a novel approach to reduce the computational cost of evaluati...
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Memory Bounded Deep Convolutional Networks
In this work, we investigate the use of sparsityinducing regularizers d...
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