We are interested in image manipulation via natural language text – a ta...
Given a natural language instruction, and an input and an output scene, ...
There is a recent focus on designing architectures that have an Integer
...
Recently many neural models have been proposed to solve combinatorial pu...
Distantly supervised relation extraction (DS-RE) is generally framed as ...
We focus on the task of future frame prediction in video governed by
und...
Clustering single-cell RNA sequence (scRNA-seq) data poses statistical a...
Pre-trained language models (LMs) like BERT have shown to store factual
...
Our goal is to answer real-world tourism questions that seek
Points-of-I...
Recent research has proposed neural architectures for solving combinator...
Regularized Auto-Encoders (AE) form a rich class of methods within the
l...
The field of neural generative models is dominated by the highly success...
Real world question answering can be significantly more complex than wha...
We present CVC4-SymBreak, a derived SMT solver based on CVC4, and a
non-...
Many prediction tasks, especially in computer vision, are often inherent...
Combining logic and probability has been a long standing goal of AI. Mar...
Several lifted inference algorithms for probabilistic graphical models f...
Lifted inference reduces the complexity of inference in relational
proba...
We introduce the first system towards the novel task of answering comple...
Lifted inference algorithms commonly exploit symmetries in a probabilist...
There is a vast body of theoretical research on lifted inference in
prob...
Due to the intractable nature of exact lifted inference, research has
re...
An important approach for efficient inference in probabilistic graphical...