251 papers with code • 0 benchmarks • 0 datasets
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EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
In the literature, two types of features are typically used to perform this task: visual features and tool usage signals.
Determinantal thinning of point processes with network learning applications
A new type of dependent thinning for point processes in continuous space is proposed, which leverages the advantages of determinantal point processes defined on finite spaces and, as such, is particularly amenable to statistical, numerical, and simulation techniques.
Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks.
Key-Value Retrieval Networks for Task-Oriented Dialogue
Neural task-oriented dialogue systems often struggle to smoothly interface with a knowledge base.
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines.
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills.
Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System
Taskflow introduces an expressive task graph programming model to assist developers in the implementation of parallel and heterogeneous decomposition strategies on a heterogeneous computing platform.
A Reinforcement Learning Environment For Job-Shop Scheduling
Scheduling is a fundamental task occurring in various automated systems applications, e. g., optimal schedules for machines on a job shop allow for a reduction of production costs and waste.
BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes
We describe how the task of rescaling suitable DNN training tasks to fit dynamically changing holes can be formulated as a deterministic mixed integer linear programming (MILP)-based resource allocation algorithm, and show that this MILP problem can be solved efficiently at run time.