
Contrastive Training for Improved OutofDistribution Detection
Reliable detection of outofdistribution (OOD) inputs is increasingly u...
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Evaluating the Apperception Engine
The Apperception Engine is an unsupervised learning system. Given a sequ...
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Strong Generalization and Efficiency in Neural Programs
We study the problem of learning efficient algorithms that strongly gene...
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Lagrangian Decomposition for Neural Network Verification
A fundamental component of neural network verification is the computatio...
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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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Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Recent improvements in largescale language models have driven progress ...
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Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
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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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Making sense of sensory input
This paper attempts to answer a central question in unsupervised learnin...
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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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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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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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Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
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Are Labels Required for Improving Adversarial Robustness?
Recent work has uncovered the interesting (and somewhat surprising) find...
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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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REGAL: Transfer Learning For Fast Optimization of Computation Graphs
We present a deep reinforcement learning approach to optimizing the exec...
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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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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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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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Structured agents for physical construction
Physical construction  the ability to compose objects, subject to phys...
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Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoninga core ability within human intelligencepres...
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MetaLearning surrogate models for sequential decision making
Metalearning methods leverage past experience to learn datadriven indu...
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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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Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
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Verification of deep probabilistic models
Probabilistic models are a critical part of the modern deep learning too...
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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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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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Strength in Numbers: Tradingoff Robustness and Computation via AdversariallyTrained Ensembles
While deep learning has led to remarkable results on a number of challen...
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On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Recent works have shown that it is possible to train models that are ver...
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NeuralSymbolic VQA: Disentangling Reasoning from Vision and Language Understanding
We marry two powerful ideas: deep representation learning for visual rec...
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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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Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
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Value Propagation Networks
We present Value Propagation (VProp), a parameterefficient differentiab...
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Training verified learners with learned verifiers
This paper proposes a new algorithmic framework,predictorverifier train...
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Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) with Gaussian pairwise potentials...
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Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Program synthesis is the task of automatically generating a program cons...
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Programmatically Interpretable Reinforcement Learning
We study the problem of generating interpretable and verifiable policies...
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A Dual Approach to Scalable Verification of Deep Networks
This paper addresses the problem of formally verifying desirable propert...
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Can Neural Networks Understand Logical Entailment?
We introduce a new dataset of logical entailments for the purpose of mea...
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Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
This paper investigates recently proposed approaches for defending again...
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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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Semantic Code Repair using NeuroSymbolic Transformation Networks
We study the problem of semantic code repair, which can be broadly defin...
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Neural Program MetaInduction
Most recently proposed methods for Neural Program Induction work under t...
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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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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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Deep API Programmer: Learning to Program with APIs
We present DAPIP, a ProgrammingByExample system that learns to program...
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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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Stabilising Experience Replay for Deep MultiAgent Reinforcement Learning
Many realworld problems, such as network packet routing and urban traff...
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