
Combiner: Full Attention Transformer with Sparse Computation Cost
Transformers provide a class of expressive architectures that are extrem...
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SpreadsheetCoder: Formula Prediction from Semistructured Context
Spreadsheet formula prediction has been an important program synthesis p...
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Learning Discrete Energybased Models via Auxiliaryvariable Local Exploration
Discrete structures play an important role in applications like program ...
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Polymers for Extreme Conditions Designed Using SyntaxDirected Variational Autoencoders
The design/discovery of new materials is highly nontrivial owing to the...
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Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Retrosynthetic planning is a critical task in organic chemistry which id...
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Scalable Deep Generative Modeling for Sparse Graphs
Learning graph generative models is a challenging task for deep learning...
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Learning to Stop While Learning to Predict
There is a recent surge of interest in designing deep architectures base...
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EnergyBased Processes for Exchangeable Data
Recently there has been growing interest in modeling sets with exchangea...
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Differentiable Topk Operator with Optimal Transport
The topk operation, i.e., finding the k largest or smallest elements fr...
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Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry. ...
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Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
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Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification
We propose a new approach, called cooperative neural networks (CoNN), wh...
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Exponential Family Estimation via Adversarial Dynamics Embedding
We present an efficient algorithm for maximum likelihood estimation (MLE...
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Meta Particle Flow for Sequential Bayesian Inference
We present a particle flow realization of Bayes' rule, where an ODEbase...
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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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Kernel Exponential Family Estimation via Doubly Dual Embedding
We investigate penalized maximum loglikelihood estimation for exponenti...
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Adversarial Attack on Graph Structured Data
Deep learning on graph structures has shown exciting results in various ...
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KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question ...
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SyntaxDirected Variational Autoencoder for Structured Data
Deep generative models have been enjoying success in modeling continuous...
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Variational Reasoning for Question Answering with Knowledge Graph
Knowledge graph (KG) is known to be helpful for the task of question ans...
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KnowEvolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
The availability of large scale event data with time stamps has given ri...
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Learning Combinatorial Optimization Algorithms over Graphs
The design of good heuristics or approximation algorithms for NPhard co...
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Online Supervised Subspace Tracking
We present a framework for supervised subspace tracking, when there are ...
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Scan BStatistic for Kernel ChangePoint Detection
Detecting the emergence of an abrupt changepoint is a classic problem i...
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Provable Bayesian Inference via Particle Mirror Descent
Bayesian methods are appealing in their flexibility in modeling complex ...
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Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
Click prediction is one of the fundamental problems in sponsored search....
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Hanjun Dai
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