Search Results for author:
Found 11 papers, 2 papers with code
Automap: Towards Ergonomic Automated Parallelism for ML Models
The rapid rise in demand for training large neural network architectures has brought into focus the need for partitioning strategies, for example by using data, model, or pipeline parallelism.
Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with an assignment of values for the variables to be optimized, and iteratively improves it by searching a large neighborhood around the current assignment.
Solving Mixed Integer Programs Using Neural Networks
Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP.
Continuous Latent Search for Combinatorial Optimization
Combinatorial optimization problems are notoriously hard because they often require enumeration of the exponentially large solution space.
Neural Large Neighborhood Search
Results on several datasets show that it is possible to learn a neighbor selection policy that allows LNS to efficiently find good solutions.
Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving
We share this observation in the hope that it helps the SAT community better understand the hardness of random instances used in competitions and inspire other interesting ideas on SAT solving.
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler.
A Quantitative Evaluation Framework for Missing Value Imputation Algorithms
We consider the problem of quantitatively evaluating missing value imputation algorithms.
A Structured Prediction Approach for Missing Value Imputation
In this paper we propose a structured output approach for missing value imputation that also incorporates domain constraints.
3D Object Recognition with Deep Belief Nets
Our model achieves 6. 5% error on the test set, which is close to the best published result for NORB (5. 9%) using a convolutional neural net that has built-in knowledge of translation invariance.
Implicit Mixtures of Restricted Boltzmann Machines
We present a mixture model whose components are Restricted Boltzmann Machines (RBMs).
