191 citations · 346 across the 7 of their papers we have counts for
8 papers
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
Giulia Zarpellon, Jason Jo, Andrea Lodi +1
Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs). Learning branching policies for MILP has become an…
Compositional generalization in a deep seq2seq model by separating syntax and semantics
Jake Russin, Jason Jo, Randall C. O'Reilly +1
Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of…
Modularity Matters: Learning Invariant Relational Reasoning Tasks
Jason Jo, Vikas Verma, Yoshua Bengio
We focus on two supervised visual reasoning tasks whose labels encode a semantic relational rule between two or more objects in an image: the MNIST Parity task and the colorized Pe…
Deep Neural Networks as 0-1 Mixed Integer Linear Programs: A Feasibility Study
Matteo Fischetti, Jason Jo
Deep Neural Networks (DNNs) are very popular these days, and are the subject of a very intense investigation. A DNN is made by layers of internal units (or neurons), each of which…
Measuring the tendency of CNNs to Learn Surface Statistical Regularities
Jason Jo, Yoshua Bengio
Deep CNNs are known to exhibit the following peculiarity: on the one hand they generalize extremely well to a test set, while on the other hand they are extremely sensitive to so-c…
The Calabi Conjecture
Rohit Jain, Jason Jo
In this essay we aim to explore the Geometric aspects of the Calabi Conjecture and highlight the techniques of nonlinear Elliptic PDE theory used by S.T. Yau [SY] in obtaining a so…
Learning Parameters for Weighted Matrix Completion via Empirical Estimation
Jason Jo
Recently theoretical guarantees have been obtained for matrix completion in the non-uniform sampling regime. In particular, if the sampling distribution aligns with the underlying…
Iterative Hard Thresholding for Weighted Sparse Approximation
Jason Jo
Recent work by Rauhut and Ward developed a notion of weighted sparsity and a corresponding notion of Restricted Isometry Property for the space of weighted sparse signals. Using th…