
Lagrangian Decomposition for Neural Network Verification
A fundamental component of neural network verification is the computatio...
read it

MetaLearning surrogate models for sequential decision making
Metalearning methods leverage past experience to learn datadriven indu...
read it

Structured agents for physical construction
Physical construction  the ability to compose objects, subject to phys...
read it

Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcementlearning agents can learn ...
read it

Degenerate Feedback Loops in Recommender Systems
Machine learning is used extensively in recommender systems deployed in ...
read it

Verification of NonLinear Specifications for Neural Networks
Prior work on neural network verification has focused on specifications ...
read it

Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoninga core ability within human intelligencepres...
read it

REGAL: Transfer Learning For Fast Optimization of Computation Graphs
We present a deep reinforcement learning approach to optimizing the exec...
read it

Making sense of sensory input
This paper attempts to answer a central question in unsupervised learnin...
read it

Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Recent research has made the surprising finding that stateoftheart de...
read it

The NeuroSymbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the NeuroSymbolic Concept Learner (NSCL), a model that lear...
read it

CLEVRER: CoLlision Events for Video REpresentation and Reasoning
The ability to reason about temporal and causal events from videos lies ...
read it

Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
read it

Branch and Bound for Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safetycritic...
read it

Graph Matching Networks for Learning the Similarity of Graph Structured Objects
This paper addresses the challenging problem of retrieval and matching o...
read it

An Alternative Surrogate Loss for PGDbased Adversarial Testing
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
read it

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
This paper addresses the problem of evaluating learning systems in safet...
read it

Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
read it

Knowing When to Stop: Evaluation and Verification of Conformity to Outputsize Specifications
Models such as SequencetoSequence and ImagetoSequence are widely use...
read it

A Hierarchical Probabilistic UNet for Modeling MultiScale Ambiguities
Medical imaging only indirectly measures the molecular identity of the t...
read it

Compositional Imitation Learning: Explaining and executing one task at a time
We introduce a framework for Compositional Imitation Learning and Execut...
read it

Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
read it

Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Neural networks are part of many contemporary NLP systems, yet their emp...
read it

Piecewise Linear Neural Network verification: A comparative study
The success of Deep Learning and its potential use in many important saf...
read it

Neural Program MetaInduction
Most recently proposed methods for Neural Program Induction work under t...
read it

TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; g...
read it

ZeroShot Task Generalization with MultiTask Deep Reinforcement Learning
As a step towards developing zeroshot task generalization capabilities ...
read it

Deep API Programmer: Learning to Program with APIs
We present DAPIP, a ProgrammingByExample system that learns to program...
read it

Ensemble Bayesian Optimization
Bayesian Optimization (BO) has been shown to be a very effective paradig...
read it

Learning Disentangled Representations with SemiSupervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
read it

Summary  TerpreT: A Probabilistic Programming Language for Program Induction
We study machine learning formulations of inductive program synthesis; t...
read it

NeuroSymbolic Program Synthesis
Recent years have seen the proposal of a number of neural architectures ...
read it

Learning Continuous Semantic Representations of Symbolic Expressions
Combining abstract, symbolic reasoning with continuous neural reasoning ...
read it

Batched Highdimensional Bayesian Optimization via Structural Kernel Learning
Optimization of highdimensional blackbox functions is an extremely cha...
read it

TimeSensitive Bayesian Information Aggregation for Crowdsourcing Systems
Crowdsourcing systems commonly face the problem of aggregating multiple ...
read it

Information Gathering in Networks via Active Exploration
How should we gather information in a network, where each node's visibil...
read it

Multiway Particle Swarm Fusion
This paper proposes a novel MAP inference framework for Markov Random Fi...
read it

Deep MultiModal Image Correspondence Learning
Inference of correspondences between images from different modalities is...
read it

Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
read it

Memoryaugmented Attention Modelling for Videos
We present a method to improve video description generation by modeling ...
read it

Deep disentangled representations for volumetric reconstruction
We introduce a convolutional neural network for inferring a compact dise...
read it

PartitionMerge: Distributed Inference and Modularity Optimization
This paper presents a novel meta algorithm, PartitionMerge (PM), which ...
read it

Multidimensional Parametric Mincuts for Constrained MAP Inference
In this paper, we propose novel algorithms for inferring the Maximum a P...
read it

Efficient Continuous Relaxations for Dense CRF
Dense conditional random fields (CRF) with Gaussian pairwise potentials ...
read it

Learning to Navigate the Energy Landscape
In this paper, we present a novel and efficient architecture for address...
read it

DeepContext: ContextEncoding Neural Pathways for 3D Holistic Scene Understanding
While deep neural networks have led to humanlevel performance on comput...
read it

Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts
Markov Networks are widely used through out computer vision and machine ...
read it

Efficient nongreedy optimization of decision trees
Decision trees and randomized forests are widely used in computer vision...
read it

CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits
We propose a novel algorithm for optimizing multivariate linear threshol...
read it

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
We propose a novel approach to reduce the computational cost of evaluati...
read it