
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
There are two major classes of natural language grammars – the dependenc...
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Gradient Starvation: A Learning Proclivity in Neural Networks
We identify and formalize a fundamental gradient descent phenomenon resu...
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Unsupervised Learning of Dense Visual Representations
Contrastive selfsupervised learning has emerged as a promising approach...
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NUGAN: High resolution neural upsampling with GAN
In this paper, we propose NUGAN, a new method for resampling audio from...
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Neural Approximate Sufficient Statistics for Implicit Models
We consider the fundamental problem of how to automatically construct su...
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Recursive TopDown Production for Sentence Generation with Latent Trees
We model the recursive production property of contextfree grammars for ...
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Supervised Seeded Iterated Learning for Interactive Language Learning
Language drift has been one of the major obstacles to train language mod...
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Integrating Categorical Semantics into Unsupervised Domain Translation
While unsupervised domain translation (UDT) has seen a lot of success re...
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DataEfficient Reinforcement Learning with Momentum Predictive Representations
While deep reinforcement learning excels at solving tasks where large am...
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Generative Graph Perturbations for Scene Graph Prediction
Inferring objects and their relationships from an image is useful in man...
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ARDAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intracta...
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Graph DensityAware Losses for Novel Compositions in Scene Graph Generation
Scene graph generation (SGG) aims to predict graphstructured descriptio...
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A LargeScale, OpenDomain, MixedInterface DialogueBased ITS for STEM
We present Korbit, a largescale, opendomain, mixedinterface, dialogue...
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Countering Language Drift with Seeded Iterated Learning
Supervised learning methods excel at capturing statistical properties of...
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Pix2Shape – Towards Unsupervised Learning of 3D Scenes from Images using a Viewbased Representation
We infer and generate threedimensional (3D) scene information from a si...
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OutofDistribution Generalization via Risk Extrapolation (REx)
Generalizing outside of the training distribution is an open challenge f...
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Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
In this work, we propose a new family of generative flows on an augmente...
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CLOSURE: Assessing Systematic Generalization of CLEVR Models
The CLEVR dataset of naturallooking questions about 3Drendered scenes ...
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Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
Neural network pruning techniques have demonstrated it is possible to re...
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Ordered Memory
Stackaugmented recurrent neural networks (RNNs) have been of interest t...
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Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
We release the largest public ECG dataset of continuous raw signals for ...
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MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Previous works <cit.> have found that generating coherent raw audio wave...
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No Press Diplomacy: Modeling MultiAgent Gameplay
Diplomacy is a sevenplayer nonstochastic, noncooperative game, where ...
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VideoNavQA: Bridging the Gap between Visual and Embodied Question Answering
Embodied Question Answering (EQA) is a recently proposed task, where an ...
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Detecting semantic anomalies
We critically appraise the recent interest in outofdistribution (OOD) ...
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Benchmarking BonusBased Exploration Methods on the Arcade Learning Environment
This paper provides an empirical evaluation of recently developed explor...
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Adversarial Computation of Optimal Transport Maps
Computing optimal transport maps between highdimensional and continuous...
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Investigating Biases in Textual Entailment Datasets
The ability to understand logical relationships between sentences is an ...
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Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly ...
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Note on the bias and variance of variational inference
In this note, we study the relationship between the variational gap and ...
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Batch weight for domain adaptation with mass shift
Unsupervised domain transfer is the task of transferring or translating ...
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Hierarchical Importance Weighted Autoencoders
Importance weighted variational inference (Burda et al., 2015) uses mult...
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Improved Conditional VRNNs for Video Prediction
Predicting future frames for a video sequence is a challenging generativ...
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Counterpoint by Convolution
Machine learning models of music typically break up the task of composit...
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Maximum Entropy Generators for EnergyBased Models
Unsupervised learning is about capturing dependencies between variables ...
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Deep Generative Modeling of LiDAR Data
Building models capable of generating structured output is a key challen...
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Systematic Generalization: What Is Required and Can It Be Learned?
Numerous models for grounded language understanding have been recently p...
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Planning in Dynamic Environments with Conditional Autoregressive Models
We demonstrate the use of conditional autoregressive generative models (...
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Harmonic Recomposition using Conditional Autoregressive Modeling
We demonstrate a conditional autoregressive pipeline for efficient music...
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Representation Mixing for TTS Synthesis
Recent character and phonemebased parametric TTS systems using deep lea...
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Blindfold Baselines for Embodied QA
We explore blindfold (questiononly) baselines for Embodied Question Ans...
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Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Recurrent neural network (RNN) models are widely used for processing seq...
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On the Learning Dynamics of Deep Neural Networks
While a lot of progress has been made in recent years, the dynamics of l...
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Improving Explorability in Variational Inference with Annealed Variational Objectives
Despite the advances in the representational capacity of approximate dis...
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Approximate Exploration through State Abstraction
Although exploration in reinforcement learning is well understood from a...
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Visual Reasoning with Multihop Feature Modulation
Recent breakthroughs in computer vision and natural language processing ...
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On the Spectral Bias of Deep Neural Networks
It is well known that overparametrized deep neural networks (DNNs) are ...
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Learning Distributed Representations from Reviews for Collaborative Filtering
Recent work has shown that collaborative filterbased recommender system...
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Manifold Mixup: Encouraging Meaningful OnManifold Interpolation as a Regularizer
Deep networks often perform well on the data manifold on which they are ...
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Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
In this work, we propose a novel constituency parsing scheme. The model ...
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