Sebastian Litzinger
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  • Publications
    • Energy-Efficient Scheduling of Moldable Streaming Computations for the Edge-Cloud Continuum
    • Archive-based Covert Channel in Sensor Streaming Data
    • Packet-Type Aware Scheduling of Moldable Streaming Tasks on Multicore Systems with DVFS
    • Heuristic Scheduling of Streaming Applications for Energy Efficiency on Heterogeneous Multicores
    • Raising Energy Efficiency and Fault Tolerance with Parallel Streaming Application Scheduling on Multicore Systems
    • Reversible Network Covert Channel by Payload Modulation in Streams of Decimal Sensor Values
    • Packing Multiple Types of Cores for Energy-Optimized Heterogeneous Hardware-Software Co-Design of Moldable Streaming Computations
    • Network Steganography Through Redundancy in Higher-Radix Floating-Point Representations
    • Kleptography in Authentication Protocols: Why is It Still Possible?
    • Integrating Energy-Optimizing Scheduling of Moldable Streaming Tasks with Design Space Exploration for Multiple Core Types on Configurable Platforms
    • Energy-Efficient Execution of Streaming Task Graphs with Parallelizable Tasks on Multicore Platforms with Core Failures
    • Combining efficiency and scaling effects in activity analysis: towards an improved best practice criterion
    • Systematic search space design for energy-efficient static scheduling of moldable tasks
    • Effects of Continuous vs Discrete Frequency Scaling and Core Allocation on Energy Efficiency of Static Schedules for Moldable Tasks
    • Temperature-Aware Energy-Optimal Scheduling of Moldable Streaming Tasks onto 2D-Mesh-Based Many-Core CPUs with DVFS
    • Combining Design Space Exploration with Task Scheduling of Moldable Streaming Tasks on Reconfigurable Platforms
    • Analysing terrorist networks -- An entropy-driven method
    • Crown-scheduling of sets of parallelizable tasks for robustness and energy-elasticity on many-core systems with discrete dynamic voltage and frequency scaling
    • Static Scheduling of Moldable Streaming Tasks With Task Fusion for Parallel Systems With DVFS
    • Probabilistic Runtime Guarantees for Statically Scheduled Taskgraphs with Stochastic Task Runtimes
    • Reducing Energy Consumption of HMAC Applications on Heterogeneous Platforms
    • Influence of Incremental Constraints on Energy Consumption and Static Scheduling Time for Moldable Tasks with Deadline
    • Code generation for energy-efficient execution of dynamic streaming task graphs on parallel and heterogeneous platforms
    • Adaptive Crown Scheduling for Streaming Tasks on Many-Core Systems with Discrete DVFS
    • Maximizing Profit in Energy-Efficient Moldable Task Execution with Deadline
    • Robustness and Energy-elasticity of Crown Schedules for Sets of Parallelizable Tasks on Many-core Systems with DVFS
    • Influence of Discretization of Frequencies and Processor Allocation on Static Scheduling of Parallelizable Tasks with Deadlines
    • Generating Energy-Efficient Code for Parallel Applications Specified by Streaming Task Graphs with Dynamic Elements
    • Scheduling Moldable Parallel Streaming Tasks on Heterogeneous Platforms with Frequency Scaling
    • Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm
    • Bipartite Structures in Social Networks: Traditional versus Entropy-Driven Analyses
    • Weight Prediction on Missing Links in Social Networks -- a Cross-Entropy-Based Approach --
  • Talks
    • Fast Compression of Floating-Point Values with Exponent/Mantissa Shuffling
    • Heuristic Scheduling of Streaming Applications for Energy Efficiency on Heterogeneous Multicores
    • Reversible Network Covert Channel by Payload Modulation in Streams of Decimal Sensor Values
    • Network Steganography Through Redundancy in Higher-Radix Floating-Point Representations
    • Energy-Efficient Execution of Streaming Task Graphs with Parallelizable Tasks on Multicore Platforms with Core Failures
    • Temperature-Aware Energy-Optimal Scheduling of Moldable Streaming Tasks onto 2D-Mesh-Based Many-Core CPUs with DVFS
    • Energy-Efficient Execution of Streaming Task Graphs with Parallelizable Tasks on Multicore Platforms with Core Failures
    • Maximizing Profit in Energy-Efficient Moldable Task Execution with Deadline
    • Generating Energy-Efficient Code for Parallel Applications Specified by Streaming Task Graphs with Dynamic Elements
    • Influence of Incremental Constraints on Energy Consumption and Static Scheduling Time for Moldable Tasks with Deadline
    • Fault-Tolerant and Energy-Efficient Static Schedules for Multi-Variant Programs
    • Energy-efficient scheduling of stream processing applications with dynamic tasks
    • Adaptive crown scheduling for streaming tasks on many-core systems with discrete DVFS
    • Probabilistic Runtime Guarantees for Statically Scheduled Taskgraphs with Stochastic Task Runtimes
    • Reducing Energy Consumption of HMAC Applications on Heterogeneous Platforms
    • Influence of Discretization of Frequencies and Processor Allocation on Static Scheduling of Parallelizable Tasks with Deadlines
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  • Projects
    • Pandas
    • PyTorch
    • scikit-learn
  • Experience
  • Teaching
    • Learn JavaScript
    • Learn Python
  • Posters
    • Kleptography in Authentication Protocols: Why is it Still Possible?
    • Compute-efficient neural network architecture optimization by a genetic algorithm
    • Scheduling Moldable Parallel Streaming Tasks on Heterogeneous Platforms with Frequency Scaling

scikit-learn

Oct 26, 2023 · 1 min read
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scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Last updated on Oct 26, 2023
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Sebastian Litzinger
Authors
Sebastian Litzinger
Postdoc in Computer Science
← PyTorch Oct 26, 2023

© 2024 Sebastian Litzinger. This work is licensed under CC BY NC ND 4.0

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