We present Self-Driven Strategy Learning (sdsl), a lightweight
online le...
Piecewise-affine (PWA) systems are widely used for modeling and control ...
The softmax function is a ubiquitous component at the output of neural
n...
We present VeriX, a first step towards verified explainability of machin...
With the increasing availability of parallel computing power, there is a...
With the increasing application of deep learning in mission-critical sys...
Inspired by sum-of-infeasibilities methods in convex optimization, we pr...
Recently, Graph Neural Networks (GNNs) have been applied for scheduling ...
We introduce DeepCert, a tool-supported method for verifying the robustn...
Deep learning has emerged as an effective approach for creating modern
s...
Neural networks (NN) learn complex non-convex functions, making them
des...
Inspired by recent successes with parallel optimization techniques for
s...
The Boolean Satisfiability (SAT) problem is the canonical NP-complete pr...
Suppose we know that an object is in a sorted table and we want to deter...
In this project, we aimed to improve the runtime of Minisat, a
Conflict-...
We applied machine learning to predict whether a gene is involved in axo...